understanding the genetic basis of symbiotic ...3374/...common bean (phaseolus vulgaris l.) is able...

UNDERSTANDING THE GENETIC BASIS OF SYMBIOTIC NITROGEN FIXATION IN COMMON BEAN (Phaseolus vulgaris L.) USING GENOMIC AND

TRANSCRIPTOMIC ANALYSES

By

Kelvin Kamfwa

A DISSERTATION

Submitted to Michigan State University

in partial fulfillment of the requirements for the degree of

Plant Breeding, Genetics and Biotechnology-Crop and Soil Sciences -Doctor of Philosophy

2015

ABSTRACT

UNDERSTANDING THE GENETIC BASIS OF SYMBIOTIC NITROGEN FIXATION IN COMMON BEAN (Phaseolus vulgaris L.) USING GENOMIC AND

TRANSCRIPTOMIC ANALYSES

By

Kelvin Kamfwa

Common bean (Phaseolus vulgaris L.) is able to fix atmospheric nitrogen (N2) through symbiotic

nitrogen fixation (SNF). SNF is a genetically complex trait controlled by several genes. Effective

utilization of existing SNF variability in common bean for genetic improvement requires an

understanding of its genetic architecture, which is poorly understood. To understand the molecular

genetic architecture of SNF variability three studies were conducted: (i) genome-wide association

study (GWAS), (ii) Quantitative Trait Loci (QTL) mapping study, and (iii) transcriptome profiling

study. GWAS was conducted using an Andean Diversity Panel (ADP) comprised of 259

genotypes. The ADP was evaluated for SNF in both greenhouse and field experiments, and

genotyped using an Illumina BARCBean6K_3 BeadChip with 5398 single nucleotide

polymorphism (SNP) markers. A mixed linear model was used to identify marker-trait

associations. The QTL mapping study was conducted using 188 F4:5 recombinant inbred lines

(RILs) derived from cross of Solwezi and AO-1012-29-3-3A. These 188 F4:5 RILs were evaluated

for SNF in greenhouse experiments, and genotyped using the same BARCBean6K_3 BeadChip.

Transcriptome profiling was conducted on RILs SA36 and SA118 contrasting for SNF that were

selected from the Solwezi x AO-1012-29-3-3A population used in the QTL mapping study. RNA

samples were collected from leaves, nodules and roots of SA36 and SA118 grown under N fixing

and non-fixing condition, and sequenced using Illumina technology. Using GWAS, significant

associations for nitrogen derived from atmosphere (Ndfa) were identified on chromosomes Pv03,

Pv07 and Pv09. QTL mapping identified QTL for Ndfa on Pv02, Pv04, Pv06, Pv07, Pv09, Pv10,

and Pv11. The GWAS peak identified on Pv09 for Ndfa overlapped with the QTL on Pv09 for

Ndfa identified in QTL mapping study. Previous studies have reported QTL for Ndfa on Pv04 and

Pv10. Genes encoding receptor kinases, transmembrane transporters, and transcription factors

(TFs) were among differentially expressed genes (DEGs) between SA36 and SA118 under N-

fixing condition, but not under non-fixing condition. Out of the 51 genes that were in 400 kb region

surrounding the GWAS peak on Pv07, only four including Phvul.007G048000 encoding a MADS

BOX transcription factor (TF) were identified as expression candidates for SNF in the

transcriptome profiling study. In the 400 kb region surrounding the GWAS peak on Pv09 there

were 44 genes, but only Phvul.009G137500 encoding a WRKY TF was identified as an expression

candidate gene in the RNA-seq study. Using GWAS, QTL mapping and transcriptome profiling,

genomic regions and expression candidate genes for SNF have been identified. Once validated,

these QTL and genes have potential to be used in marker-assisted breeding to circumvent

challenges of phenotypic selection for SNF, and accelerate genetic improvement of common bean

for symbiotic nitrogen fixation.

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To my sons Paul and Gabriel, and my daughter Precious

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ACKNOWLEDGEMENTS I wish to express my gratitude to my advisors Drs. Jim Kelly and Karen Cichy. Dr. Kelly gave

me an opportunity to work with him. This opportunity has forever changed my entire life. He

has given me unwavering support since the day I arrived in the US for my studies. Dr. Cichy

encouraged and supported me to do research that I could have just dreamed of doing.

I thank my committee members Drs. Robin Buell, Dechun Wang and Maren Friesen for their

willingness to serve on the committee. I have never taken their willingness for granted.

I thank the Legume Innovation Lab for the scholarship.

I thank the past and present members of Kelly and Cichy’s Labs for their support. In particular, I

thank Evan Wright, Halima Awale, Norm Blakley and Scott Shaw for their help with field,

greenhouse and lab experiments.

I thank my dad and mum for their love, encouragement and support. Dad told me that with hard

work, discipline and God’s grace, I had a lot of potential despite growing up in a remote village in

Zambia. Though mum never knew how to read and write, she understood the importance of

education, and always prepared me for school. I thank my wife Clara, my three kids Paul, Gabriel

and Precious. They are my precious gifts from God that inspired me to work hard. Their smiles

have been a source of my every day happiness and purpose. Finally, I thank God for his love and

grace.

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TABLE OF CONTENTS

LIST OF TABLES ....................................................................................................................... ix LIST OF FIGURES ..................................................................................................................... xi GENERAL INTRODUCTION .................................................................................................... 1 GENERAL INTRODUCTION .................................................................................................... 2

Problem Definition...................................................................................................................... 4 Objective ..................................................................................................................................... 5 Dissertation Outline .................................................................................................................... 5

CHAPTER 1 ................................................................................................................................ 12 GENOME-WIDE ASSOCIATION STUDY OF AGRONOMIC TRAITS IN COMMON BEAN ........................................................................................................................................... 12 Genome-Wide Association Study of Agronomic Traits in Common Bean ............................ 13

Abstract ..................................................................................................................................... 13 Introduction ............................................................................................................................... 14 Materials and Methods .............................................................................................................. 18

Plant Material ........................................................................................................................ 18 Field Phenotyping ................................................................................................................. 18 Genotyping ............................................................................................................................ 19 Phenotypic Data analyses ..................................................................................................... 19 Population Structure analysis and Marker-Trait Association Tests ...................................... 20

Results ....................................................................................................................................... 22 Phenotypic Traits .................................................................................................................. 22 Population Structure.............................................................................................................. 23 Trait-SNP Associations ......................................................................................................... 24

Phenological traits ............................................................................................................. 24 Plant Biomass at Maturity ................................................................................................. 24 Pod Number ...................................................................................................................... 24 Harvest Index and Pod Harvest Index .............................................................................. 25 Pod Weight........................................................................................................................ 25 Seed Number ..................................................................................................................... 25 Seed Yield ......................................................................................................................... 25

Discussion ................................................................................................................................. 27 Acknowledgements ................................................................................................................... 35 APPENDIX ............................................................................................................................... 37 LITERATURE CITED ............................................................................................................. 46

CHAPTER 2 ................................................................................................................................ 52 GENOME-WIDE ASSOCIATION ANALYSIS OF SYMBIOTIC NITROGEN FIXATION IN COMMON BEAN ................................................................................................................. 52 Genome-Wide Association Analysis of Symbiotic Nitrogen Fixation in Common Bean ..... 53

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Abstract ..................................................................................................................................... 53 Introduction ............................................................................................................................... 54 Materials and Methods .............................................................................................................. 58

Plant Materials ...................................................................................................................... 58 Greenhouse Experiments ...................................................................................................... 59 Field Experiments ................................................................................................................. 60 GH and Field Estimation of N fixed ..................................................................................... 62 Phenotypic Data Analyses .................................................................................................... 63 Genotyping ............................................................................................................................ 64 Population Structure Analysis and Marker-Trait Association Tests..................................... 65 Candidate Gene Identification .............................................................................................. 66

Results ....................................................................................................................................... 67 Population Structure.............................................................................................................. 67 Greenhouse Experiments ...................................................................................................... 67 Field Experiments ................................................................................................................. 68 Marker-Trait Associations .................................................................................................... 69

Chlorophyll Content.......................................................................................................... 69 Nodulation......................................................................................................................... 70 Shoot Biomass .................................................................................................................. 71 N Percentage in Biomass .................................................................................................. 71 N Percentage in Seed ........................................................................................................ 72 %Ndfa in Shoot Biomass at Flowering in Field Experiments .......................................... 72 Ndfa in Shoot Biomass at Flowering in GH and Field Experiments ................................ 73 Ndfa and %Ndfa in Seed for Field_2013 .......................................................................... 73

Allelic Effects of Significant SNPs on Ndfa_Shoot ............................................................. 74 Discussion ................................................................................................................................. 75

Marker-Trait Associations .................................................................................................... 79 Candidate Genes Associated With Significant SNPs ........................................................... 83 Conclusion ............................................................................................................................ 86

Acknowledgements ................................................................................................................... 86 APPENDIX ............................................................................................................................... 87 LITERATURE CITED ............................................................................................................. 98

CHAPTER 3 .............................................................................................................................. 105 TRANSCRIPTOME ANALYSIS OF TWO RECOMBINANT INBRED LINES OF COMMON BEAN CONTRASTING FOR SYMBIOTIC NITROGEN FIXATION ........ 105 Transcriptome analysis of two recombinant inbred lines of common bean contrasting for symbiotic nitrogen fixation....................................................................................................... 106

Abstract ................................................................................................................................... 106 Introduction ............................................................................................................................. 107 Methods................................................................................................................................... 110

Plant Materials .................................................................................................................... 110 Growing conditions ............................................................................................................. 111 Evaluation of SA36 and SA118 for SNF and related traits ................................................ 112 Total RNA isolation, cDNA library construction and sequencing ..................................... 112 Sequence reads analyses ..................................................................................................... 113

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Identification of DEGs and enriched molecular functions ................................................. 114 Results ..................................................................................................................................... 115

Responses of SA36 and SA118 to N fertilizer and rhizobium inoculation ........................ 115 Read mapping ..................................................................................................................... 116 Differentially expressed genes between leaves of SA36 and SA118 ................................. 116 DEGs between roots of SA36 and SA118 and enriched molecular functions .................... 117 Differentially expressed genes between nodules of SA118 and SA36 ............................... 119

Discussion ............................................................................................................................... 120 DEGs between leaves for SA36 and SA118 and enriched molecular functions ................ 122 DEGs between roots for SA36 and SA118 and enriched molecular functions .................. 123 DEGs between nodules of SA36 and SA118 and enriched molecular functions ............... 124 Conclusion .......................................................................................................................... 131

Acknowledgements ................................................................................................................. 131 APPENDIX ............................................................................................................................. 133 LITERATURE CITED ........................................................................................................... 148

CHAPTER 4 .............................................................................................................................. 155 IDENTIFICATION OF QUANTITATIVE TRAIT LOCI FOR SYMBIOTIC NITROGEN FIXATION IN COMMON BEAN .......................................................................................... 155 Identification of Quantitative Trait Loci for Symbiotic Nitrogen Fixation in Common Bean..................................................................................................................................................... 156

Abstract ................................................................................................................................... 156 Introduction ............................................................................................................................. 156 Materials and Methods ............................................................................................................ 158

Plant Materials .................................................................................................................... 158 Estimation of Amount of N fixed ....................................................................................... 160 Phenotypic Data Analysis ................................................................................................... 160 DNA Extraction and Genotyping........................................................................................ 161 Genetic Map Construction .................................................................................................. 161 QTL Analysis ...................................................................................................................... 162

Results ..................................................................................................................................... 163 Phenotypic Analyses ........................................................................................................... 163 Genetic Map Construction .................................................................................................. 164 QTL Analyses ..................................................................................................................... 165

Shoot Biomass ................................................................................................................ 165 Nitrogen Percentage in Shoot Biomass (%N) ................................................................. 166 Root Weight (RW) .......................................................................................................... 168 Nitrogen derived from atmosphere (Ndfa) ..................................................................... 168

Discussion ............................................................................................................................... 170 Conclusion .......................................................................................................................... 174

APPENDIX ............................................................................................................................. 176 LITERATURE CITED ........................................................................................................... 184

GENERAL CONCLUSIONS .................................................................................................. 189 GENERAL CONCLUSIONS .................................................................................................. 190

LITERATURE CITED ........................................................................................................... 195

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LIST OF TABLES Table 1.1: Means and ranges for ten agronomic traits for 237 common bean genotypes in Andean Diversity Panel (ADP) grown in 2012 and 2013 at Montcalm Research Farm, MI……..……….38 Table 1.2. Pearson Correlations coefficients among ten agronomic traits measured on 237 common bean genotypes grown at Montcalm Research Farm, MI in 2012 and 2013……………..……...39 Table 1.3: Chromosome, position, p-values, proportion of phenotypic variation explained (R2) and minor allele frequency of two most significant SNPs for ten agronomic traits measured on 237 genotypes grown in 2012 and 2013 at Montcalm Research Farm, MI…………………………. 40

Table 1.4: Geographic distributions of the alleles with larger positive effect on seed yield of two significant SNPs in a panel of 237 genotypes grown in 2012 and 2013 at Montcalm Research Farm, MI……………………………………………………………………………………...…………41

Table 2.1: Means and ranges for traits associated with Symbiotic Nitrogen Fixation in Andean Diversity Panel of 259 common bean genotypes grown in the GH in 2012 and 2014 at Michigan State University, East Lansing, MI and in the Field at Montcalm Research Farm, Entrican, MI in 2012 and 2013…………………………………………………………………………………....88

Table 2.2: Ten genotypes identified as superior in percentage of N derived from atmosphere (%Ndfa) and amounts of N in seed derived from atmosphere (Ndfa) from the Andean Diversity panel and two non-nodulating mutants grown in the Field at Montcalm Research Farm, Entrican, MI in 2013………………………………………………………………………………..………89

Table 2.3: Most significant SNP and candidate genes on relevant Phaseolus vulgaris chromosomes for SNF and related traits of the Andean Diversity Panel common bean genotypes evaluated in the GH at Michigan State University, East Lansing, MI in 2012 and 2014, and in the Field at Montcalm Research Farm, Entrican, MI in 2012 and 2013…………………………………………..……...90

Table 3.1. Statistics summary of read mapping to the common bean genome…………….…...134

Table 3.2: Number of differentially expressed genes in leaves, roots and nodules between SA36 and SA118. These numbers represent genes that were differentially expressed between SA36 and SA118 under N fixing condition, but not under non-fixing condition………………………....135 Table 3.3: List of differentially expressed transcription factors. These are transcription factors with significant differential expression between SA36 and SA118 in leaf, root and nodule under nitrogen fixing condition, but were not differentially expressed under non-fixing condition…………...136

Table 3.4: Enriched molecular functions of differentially expressed genes in leaves, roots and nodules between SA36 and SA118……………………………………………………………..139

x

Table 4.1. Means and ranges for shoot, root and SNF traits measured on 188 recombinant inbred lines and parents grown in greenhouse in 2014 and 2015 at Michigan State University MI......177

Table 4.2: Genetic Correlations coefficients among four traits measured on 188 recombinant inbred lines grown in greenhouse in 2014 and 2015 at Michigan State University, MI….……178

Table 4.3: Quantitative trait loci for shoot biomass, nitrogen percentage, nitrogen derived from atmosphere, and root weight identified in a population of 188 recombinant inbred lines grown in the greenhouse in 2014 and 2015 at Michigan State University MI……………………………179

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LIST OF FIGURES Figure 1.1. Principle Component Analysis (PCA) plot of PC1 against PC2 illustrating the population structure in the ADP. The cluster of blue triangles represents the 7 Middle American genotypes while the red represent the 237 Andean genotypes in 2 separate clusters……………42

Figure 1.2. Manhattan plots showing the same candidate SNP for both flowering in 2012 and maturity in 2013. The model of candidate gene Phvul.001G221100 associated with significant SNP on Pv01 is shown below……………………………………………………………………43

Figure 1.3. Manhattan Plots showing significant SNPs and their P-values from GWAS using MLM for Pod Harvest Index (PHI_13) on Pv03 in 2013, pod number (PN_13) on Pv05 and Pv07 in 2013, biomass (BM_12) on Pv02 and Pv08 in 2012 and pod weight (PW_12) on Pv08 in 2012 and number of pods per plant for 2013 season. Red line is the significance threshold of P=1.03 x 10-5 after Bonferonni correction of α = 0.05……………………………………………….……….…44

Figure 1.4. Manhattan Plots showing candidate SNPs and their P-values from GWAS using MLM for seed yield (Kg ha-1) on Pv03 and Pv09, and HI on Pv03 in 2012. Red line is the significance threshold of P=1.03 x 10-5 after Bonferonni correction of α = 0.05…………………….………..45

Figure 2.1. The quantile-quantile (QQ plots) plots for seed nitrogen percentage, comparing the effectiveness of using principal component analysis (PCA) and STRUCTURE software to control population structure in association tests using Mixed Linear Model……………….…………….92

Figure 2.2. Principle Component Analysis (PCA) plot of PC1 against PC2 illustrating the population structure comprised of two major sub-groups in the Andean Diversity Panel……….93

Figure 2.3. Frequency distribution graphs for Nitrogen derived from atmosphere in the seed (Ndfa_Seed) for Field_2013, and Nitrogen derived from atmosphere in the shoot at flowering (Ndfa_Shoot) of the Andean diversity panel genotypes evaluated in the Greenhouse (GH) and Field……………………………………………………………………………………………...94

Figure 2.4. Manhattan plots of association tests using MLM for N% in shoot biomass (GH_2014 and Field_2013) and N% in seed (Field_2013). A candidate gene for most significant SNP on Pv09 is also shown. The red solid horizontal line is the Bonferroni adjusted P-value (1.1 x 10-05). The dotted gray vertical lines are to show significant SNPs that were consistently significant for N% in shoot biomass and seed…………………………………………………………………..95

Figure 2.5. Manhattan plots of association tests using MLM and candidate genes for amount of N derived from atmosphere (Ndfa) using the ADP grown in greenhouse (GH) and field. The red solid horizontal line is the Bonferroni adjusted P-value (1.1 x 10-05). The dotted gray vertical lines are to show significant SNPs that were consistently identified in GH_2012, GH_2014 and Field_2013……………………………………………………………………………..…...……96

xii

Figure 2.6. Manhattan plots of association tests using MLM, and candidate gene for nodulation and amount of N in seed derived from atmosphere (Ndfa_Seed) identified using the ADP grown in the field in 2013. The red solid horizontal line is the Bonferroni adjusted P-value (1.1 x 10-05). The dotted gray vertical lines are to show SNPs that were consistently significant for nodulation and Ndfa_Seed in Field_2013…………………………………………………..……………….97

Figure 3.1. Growth characteristic of SA36 and SA118 under fixing and non-fixing condition…………………………………………………………………………..……………140

Figure 3.2. Differences in shoot dry weight (per plant) between SA36 and SA118 grown under nitrogen fixing and non-fixing conditions………………………………………………..…….141

Figure 3.3. Differences in total nitrogen in shoot biomass (per plant) between SA36 and SA118 grown under nitrogen fixing and non-fixing conditions…………………………..……………142

Figure 3.4. Difference in nodule fresh weight (per plant) between SA36 and SA118 grown under nitrogen fixing condition……………………………………………………………..…………143

Figure 3.5. Venn diagrams showing number of differentially expressed genes between SA36 and SA118 in leaf and root under fixing condition and non-fixing condition. In the upper Venn diagrams (A) 83 represents genes in the leaves that were differentially expressed between SA36 and SA118 under nitrogen fixing condition, but not under non-fixing condition. In the lower Venn diagram (B) 222 represent genes differentially expressed between SA36 and SA118 in roots under nitrogen fixing condition, but not under non-fixing condition…………………..………………144

Figure 3.6. Relative expression of Phvul.007G048000 (MADS BOX transcription factor) in leaves, roots and nodules of SA36 and SA118 grown under nitrogen fixing and non-fixing condition. Relative gene expression is presented using read count. Read count is number of reads (average of three replications) aligned to the gene after normalizing for total number of reads mapped for each library using HTSeq……………………………………………………………...…….….……145

Figure 3.7. Relative expression of Phvul.001G044500 (AP2 transcription factor) in leaves, roots and nodules of SA36 and SA118 grown under nitrogen fixing and non-fixing condition. Relative gene expression is presented using read count. Read count is number of reads (average of three replications) aligned to the gene after normalizing for total number of reads mapped for each library using HTSeq…………………………………………………………………………….146

Figure 4.1. Population distributions for shoot biomass, %N in shoot biomass and Ndfa. Blue arrow represents the mean for parent AO-1012-29-3-3A while red is for parent Solwezi………….…181

Figure 4.2. Genetic linkage map for Solwezi x AO-1012-29-3-3A, showing the locations of the identified QTL for shoot biomass (BM), percent of nitrogen in shoot (%N), root weight (RW) and nitrogen derived from atmosphere (Ndfa)……………………………………..………………..182

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GENERAL INTRODUCTION

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GENERAL INTRODUCTION

Nitrogen (N) is the most abundant element in the atmosphere. Yet, it is often the most limiting

element for plant growth and productivity, globally. Atmospheric nitrogen (N2) is inert, and

converting it to molecular forms that can be used by plants requires an energy intense process.

Plants belonging to family Fabacea (legumes), the third largest plant family, are able to convert

N2 into ammonia (NH3), for their use (de Bruijn 2015). N-fixation is achieved through a symbiotic

relationship between legumes and a special group of soil bacteria known as Rhizobium. This

symbiotic relationship is known as symbiotic nitrogen fixation (SNF), and takes place in a

specialized plant organ called nodule on the roots.

SNF begins with exchange of molecular signals between the legume and rhizobium in the soil.

The plant releases molecular signals mainly flavonoids from its roots into the rhizosphere (Hassan

and Mathesius 2012). When this signal is perceived by a compatible rhizobium, the Rhizobium

releases lipochitooligosaccharides, which are known as Nod factors (Wang et al. 2012). When

plant roots perceive Nod factors, biochemical, physiological, morphological and gene expression

changes in the root occurs (Long 2015; Oldroyd and Downie 2008). The major morphological

change that happens is the curling of the root hair, which entraps the Rhizobium (Esseling et al.

2003). This is followed by formation of an infection thread that grows inwardly towards the

dividing cortical cells that constitute the nodule primordial (Fournier et al. 2008). The infection

thread carries the Rhizobium, which is released into root cortex cells. The Rhizobium then

differentiates into bacteroid and is covered in a membrane called symbiosome that separates the

bacteroid from the rest of cell contents (Mohd Noor et al. 2015). The bacteroid multiply in the

infected cell, and make up the nodule as a specialized plant organ on the root (Oldroyd et al. 2011).

3

Once the nodules are fully formed and functional, the nitrogenase enzyme in the Rhizobium

catalyzes the reduction of atmospheric N2 to NH3, which is available for use by the plant (White

et al. 2007). The Rhizobium derives its nutrients from the plant for survival. Malate a downstream

photosynthetic product is the main source of energy for the rhizobium (Day and Copeland 1991;

Yurgel and Kahn 2004). The nodules remain functionally active until the plant goes into the

reproductive stage when the nodules begin to senescence (Bethlenfalvay and Phillips 1977; Lawn

and Brun 1974; Van de Velde et al. 2006).

Over the last two decades our understanding of genetic and molecular mechanisms involved in

SNF has expanded. This has mainly been through genetic studies, and recently genomic studies

using Medicago truncatula and Lotus japonicus, the two model plant species for legumes. Genetic

studies mainly using mutants with varying phenotypes for N fixation such as lack of nodulation,

hypernodulation, ineffective nodules among others, have been used to identify genes involved in

the establishment of SNF including formation and functioning of the nodules (Gresshoff 2003;

Oldroyd et al. 2011; Stacey et al. 2006). Some of the transcription factors (TFs) that regulate

expression of genes involved in SNF have also been identified (Libault et al. 2009; Sinharoy et al.

2015). In addition, key molecular mechanisms, biological processes, and pathways involved in

SNF including signal transduction, carbohydrate metabolism, and purine pathway have been

identified (Oldroyd and Downie 2004; Smith and Atkins 2002). Transcriptome analyses in M.

truncatula and L. japonicus have previously been used to gain insights into global gene expression

and molecular mechanisms involved in SNF, especially the early stages of nodulation (Chungopast

et al. 2014; Colebatch et al. 2004; El Yahyaoui et al. 2004; Hogslund et al. 2009; Kouchi et al.

2004; Lohar et al. 2006). These transcriptomic studies have revealed a complex molecular

4

architecture of SNF with several genes, molecular mechanisms and pathways involved. Though

genetic and transcriptomic studies have provided valuable knowledge on molecular genetics of

nodulation, our understanding of genes and molecular mechanisms that play significant role in

determining SNF variability in plants of economic value is still lacking.

Common bean (Phaseolus vulgaris L.) is a staple for millions of people in East Africa and South

America (Akibode and Maredia 2012). Common bean is considered weak in SNF in comparison

with other major seed legumes (Bliss 1993). Reasons attributed to this shortcoming include the

shorter growing season for most common bean genotypes that limits the supply of photo-

assimilates to nodules (Graham et al. 2003). Depending on the environment and genotype,

estimates of N fixed by common bean range from 0 kg ha-1 to 165 kg ha-1, which is considered

lower when compared with other major grain and pasture legumes (Giller 2001; Graham et al.

2003; Unkovich and Pate 2000). Genetic enhancement of the SNF process in common bean has

potential to improve its productivity.

Problem Definition

Adequate genetic variability for SNF and associated traits within common bean has been widely

reported (Buttery et al. 1997; Elizondo Barron et al. 1999; Graham and Rosas 1977; Graham 1981;

Herridge and Redden 1999; Pereira et al. 1993), suggesting that genetic improvement would be

feasible. Genetic improvement for SNF has been hampered by its genetic complexity. Several

plant traits including nodulation, photosynthesis, biomass accumulation, photo-assimilate

partitioning to the nodules that are involved in SNF are polygenic. The genetic basis of existing

variability for SNF common bean is poorly understood. Understanding the genetic architecture of

5

SNF in terms of the genomic regions and/or genes involved and their effects is critical to enhancing

our knowledge of its genetic control. This information should lead to the development of

molecular markers that can be used by breeders to indirectly select for SNF and circumvent the

challenges of direct selection. Relative to the importance of SNF, few studies to understand the

genetic architecture of SNF in common bean exist. Only four previous QTL mapping studies on

BNF and related traits in common bean have been published (Nodari et al. 1993; Ramaekers et al.

2013; Souza et al. 2000; Tsai et al. 1998) and many lack information on the specific genomic

regions and candidate genes controlling SNP.

Objective

To further the understanding of the genetic basis of variability for SNF and associated traits in

common bean, genome-wide association mapping, QTL mapping and transcriptome profiling

studies were conducted. These studies are described in more detail in the next chapters.

Dissertation Outline

Chapter 1 is a genome-wide association study aimed at understanding the genetic basis of

variability of agronomic traits in a diverse group of bean genotypes that comprise the Andean

diversity panel (ADP). The ADP was grown under low soil nitrogen conditions in Michigan.

Chapter 2 is a genome-wide association study aimed at understanding the genetic basis of N

derived from the atmosphere (Ndfa) variability in the ADP. The study was conducted under low

N conditions in field and greenhouse.

6

Chapter 3 is a study that explored utility of transcriptome profiling using RNA-sequencing to

identify genes and molecular mechanisms underlying contrasting SNF phenotypes of two

recombinant inbred lines SA36 and SA118 of common bean derived from a cross of Solwezi x

AO-1012-29-3-3A.

Chapter 4 is a QTL study aimed at understanding the genetic basis of variability for Ndfa in a

population of recombinant inbred lines derived from the cross of Solwezi x AO-1012-29-3-3A. .

General conclusion provides a summary of results for GWAS, QTL mapping and transcriptome

profiling with the major focus on corroborating results between studies.

7

LITERATURE CITED

8

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Bethlenfalvay GJ, Phillips DA (1977) Ontogenetic Interactions between Photosynthesis and Symbiotic Nitrogen-Fixation in Legumes. Plant Physiology 60:419-421

Bliss FA (1993) Breeding common bean for improved biological nitrogen fixation. Plant Soil 152:71-79

Buttery BR, Park SJ, Berkum Pv (1997) Effects of common bean (Phaseolus vulgaris L.) cultivar and rhizobium strain on plant growth, seed yield and nitrogen content. Canadian Journal of Plant Science 77:347-351

Chungopast S, Hirakawa H, Sato S, Handa Y, Saito K, Kawaguchi M, Tajima S, Nomura M (2014) Transcriptomic profiles of nodule senescence in Lotus japonicus and Mesorhizobium loti symbiosis. Plant Biotechnology 31:345-U115

Colebatch G, Desbrosses G, Ott T, Krusell L, Montanari O, Kloska S, Kopka J, Udvardi MK (2004) Global changes in transcription orchestrate metabolic differentiation during symbiotic nitrogen fixation in Lotus japonicus. Plant Journal 39:487-512

Day DA, Copeland L (1991) Carbon Metabolism and Compartmentation in Nitrogen-Fixing Legume Nodules. Plant Physiology and Biochemistry 29:185-201

de Bruijn FJ (2015) Biological nitrogen fixation. In: de Bruijn FJ (ed) Principles of Plant-Microbe Interactions. John Wiley & Sons, pp 215-224

El Yahyaoui F, Kuster H, Ben Amor B, Hohnjec N, Puhler A, Becker A, Gouzy J, Vernie T, Gough C, Niebel A, Godiard L, Gamas P (2004) Expression profiling in Medicago truncatula identifies more than 750 genes differentially expressed during nodulation, including many potential regulators of the symbiotic program(1[w]). Plant physiology 136:3159-3176

Elizondo Barron J, Pasini RJ, Davis DW, Stuthman DD, Graham PH (1999) Response to selection for seed yield and nitrogen (N2) fixation in common bean (Phaseolus vulgaris L.). Field Crops Research 62:119-128

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Esseling JJ, Lhuissier FGP, Emons AMC (2003) Nod factor-induced root hair curling: Continuous polar growth towards the point of nod factor application. Plant Physiology 132:1982-1988

Fournier J, Timmers AC, Sieberer BJ, Jauneau A, Chabaud M, Barker DG (2008) Mechanism of infection thread elongation in root hairs of Medicago truncatula and dynamic interplay with associated rhizobial colonization. Plant Physiology 148:1985-1995

Giller KE (2001) Nitrogen Fixation in Tropical Cropping Systems, 2 edn. CABI, New York, USA

Graham P, Rosas J (1977) Growth and development of indeterminate bush and climbing cultivars of Phaseolus vulgaris L. inoculated with Rhizobium. The Journal of Agricultural Science 88:503-508

Graham P, Rosas J, Estevez de Jensen C, Peralta E, Tlusty B, Acosta-Gallegos J, Arraes Pereira P (2003) Addressing edaphic constraints to bean production: the bean/cowpea CRSP project in perspective. Field Crops Research 82:179-192

Graham PH (1981) Some problems of nodulation and symbiotic nitrogen fixation in Phaseolus vulgaris L.: A review. Field Crops Research 4:93-112

Gresshoff PM (2003) Post-genomic insights into plant nodulation symbioses. Genome Biology 4:201

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12

CHAPTER 1

GENOME-WIDE ASSOCIATION STUDY OF AGRONOMIC TRAITS IN COMMON BEAN

[Published in: The Plant Genome 8 (2): 1-12]

13

Genome-Wide Association Study of Agronomic Traits in Common Bean

Kelvin Kamfwa, Karen A. Cichy, and James D. Kelly*

K. Kamfwa and J.D. Kelly, Dep. of Plant, Soil and Microbial Sciences, Michigan State Univ.,

1066 Bogue St., East Lansing, MI 48824; K.A Cichy, USDA-ARS, Sugarbeet and Bean

Research Unit, Michigan State Univ., 1066 Bogue St., East Lansing, MI 48824. *Corresponding

author ([email protected]).

Abstract

A genome-wide association study (GWAS) using a global Andean diversity panel (ADP) of 237

genotypes of common bean, Phaseolus vulgaris was conducted to gain insight into the genetic

architecture of phenology, biomass, yield components and seed yield. The panel was evaluated

for two years in field trials and genotyped with 5398 single nucleotide polymorphism (SNP)

markers. After correcting for population structure and cryptic relatedness, significant SNP markers

associated with several agronomic traits were identified. Positional candidate genes, including

Phvul.001G221100 on Phaseolus vulgaris (Pv) chromosome 01, associated with days to flowering

and maturity were identified. Significant SNPs for seed yield were identified on Pv03 and Pv09,

and co-localized with quantitative trait loci (QTL) for yield from previous studies conducted in

several environments and contrasting genetic backgrounds.

mailto:[email protected]

14

The majority of germplasm carrying the alleles with positive effects on seed yield was of African

origin, and largely underutilized in U. S. breeding programs. The study provided insights into the

genetic architecture of agronomic traits in Andean beans.

Key words: Phaseolus vulgaris, yield components, genome, phenological traits, seed yield,

linkage disequilibrium, genome-wide association study

Abbreviations: ADP, Andean diversity panel; BLAST, Basic local alignment search tool for

nucleotide; Bp, base pair; DTF, days to flowering; DTM, days to maturity; GWAS, Genome-wide

association studies; HI, harvest index; SW, 100 seed weight; Kbp, Kilo base pair; LD, linkage

disequilibrium; MAF, minor allele frequency; MLM, mixed linear model; PCA, principal

component analysis; PHI, pod harvest index; PN, pod number; Pv, Phaseolus vulgaris

chromosome; PW, pod weight; QTL, quantitative trait loci; RIL, recombinant inbred line; SN,

seed number per plant; SNP, single nucleotide polymorphism.

Introduction

By 2050, the projected 9.6 billion people will require 70% more food than the current demand,

(FAO 2009)(FAO 2009)(FAO 2009)and most of this increased demand will be from developing

countries mainly in Africa (Alexandratos and Bruinsma, 2012; FAO, 2009). Climate change will

also likely exacerbate food security challenges especially in parts of Africa (Sassi 2013). To meet

this increased global food demand, the productivity of most food crops must increase especially in

Africa where the yields are far below their potential (Beebe 2012; Mueller et al. 2012). Common

bean, an inexpensive and major source of protein in many African and Latin American countries,

15

is a key commodity for improving food security because it is widely grown and fits well in the low

input agricultural systems practiced in these two regions where most farmers cannot afford inputs

such as fertilizers and irrigation (Beebe et al. 2012; Broughton et al. 2003).

Improving seed yield is a major objective of bean breeding programs (Beaver and Osorno 2009;

Kelly et al. 1998; Vandemark et al. 2014). Steady yield gains have been made over the last decades

resulting from both genetic and improved crop management (Singh et al. 2007; Vandemark et al.

2014). Seed yield is a quantitative trait and in common bean, it is determined primarily by three

yield components: number of pods per plant, number of seeds per pod and seed weight (Adams

1967). All three yield components are quantitative in nature and are based on the interaction of

physiological and morphological features of the plant (Wallace et al. 1993). The number of pods

per plant and seeds per pod exhibit low heritability (Coyne 1968). Understanding the genetic

architecture of yield and its components is a basis for the genetic improvement of common bean

for seed yield. Identifying genomic regions contributing to yield and its components is a basis for

marker-assisted breeding that could accelerate gains in breeding for yield.

Numerous mapping studies in common bean have reported QTL for yield and yield components

on several chromosomes. Koinange et al. (1996) reported QTL for pods per plant on Pv01 and

Pv08 in a population of 65 F8 RILs from inter gene pool cross of Midas x G12873. Tar’an et al.

(2002) reported QTL for seed yield on Pv05, Pv09 and Pv11, for pod number per plant on Pv02 in

145 F4:5 RILs from OAC Seaforth x OAC 95-4 navy bean cross. Beattie et al. (2003) reported QTL

for seed yield on Pv03 and Pv05 in a population of 110 F5:7 RILs from a cross WO3391 × OAC

Speedvale. They also reported QTL for pod number per plant on Pv02, Pv03 and Pv05 (Beattie et

16

al. 2003). Blair et al. (2006) reported QTL for seed yield on Pv02, Pv03, Pv04 and Pv09 in an

inbred backcross population of 157 BC2F3:5 from a cross between ICA Cerinza (cultivated

recurrent parent) and G24404 (wild donor parent). In the same population, QTL for pods per plant

were identified on Pv07, Pv09 and Pv11 (Blair et al. 2006). Wright and Kelly (2011) reported QTL

for yield on Pv03, Pv05, Pv10 and Pv11 in a population of 96 F4:5 RILs from a black bean cross

between Jaguar and 115M. Checa and Blair (2012) identified QTL for seed yield on Pv03, Pv04

and Pv10 in F5:8 RILs from an inter gene pool cross of G2333 and G19839. Recently,

Mukeshimana et al. (2014) reported QTL for seed yield on Pv03 and Pv09 in a population of 125

F5:7 RILs from inter gene pool cross of SEA5 x CAL96. The limited number of markers and small

population sizes that were used resulted in QTL with low resolution. As a result inferences on

positional candidate genes associated with the identified QTL were difficult to make.

Advances in common bean genomics such as the sequenced genome (Schmutz et al. 2014) have

resulted in the development of high throughput and efficient genotyping platforms including the

BARCBean6K_3 Beadchip with nearly 6000 SNP markers (Hyten et al. 2010). The availability of

SNP Beadchip has created an opportunity to conduct GWAS to dissect the genetic architecture of

yield and yield components. The analysis allow for the identification of QTL with more enhanced

resolution because of the smaller linkage disequilibrium (LD) blocks in an association panel than

in bi-parental mapping populations (Nordborg and Weigel 2008). Enhanced resolution is critical

for making inferences on positional candidate genes. The smaller LD blocks result from historical

recombinations of genotypes from a genetically diverse panel as opposed to bi-parental mapping

populations where the LD blocks are longer because of short-lived recombinations resulting from

the few generations of recombination (Myles et al. 2009; Zhu et al. 2008). At each locus there are

17

potentially several alleles being studied in GWAS (Yu and Buckler 2006) whereas in bi-parental

mapping only two alleles from parents that are segregating will be captured. From an applied

perspective, GWAS is more efficient to investigate, simultaneously, the genomic potential and

genetic variability in a large collection of germplasm for potential use in a breeding program (Zhao

et al. 2011).

Two gene pools the Andean and Middle American have been described in common bean (Gepts

1998; Koenig and Gepts 1989). Greater genetic variability exists in the Middle American than the

Andean gene pool (Bitocchi et al. 2013). As a result more progress in the genetic improvement of

several traits including yield has been documented in the Middle American gene pool than the

Andean gene pool (Beebe 2012; Beebe et al. 2001; Kornegay et al. 1992; White et al. 1992).

However, moving favorable genes for several agronomic traits from the Mesoamerican into the

Andean gene pool has been challenging especially due to incompatibility and linkage drag (Gepts

and Bliss 1985; Singh and Gutiérrez 1984). The Andean beans are the most popular beans in Africa

(Beebe 2012; Wortmann 1998) but their yields are lower than Middle American beans. In this

study a global diversity panel of 237 Andean genotypes from several regions where common bean

is grown including Africa, North America, Central America and South America was studied.

Genome-wide association study was conducted to enhance our understanding of the genetic

architecture of agronomic traits including phenological traits, yield components and seed yield in

common bean using the diversity present in the ADP.

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Materials and Methods

Plant Material

The ADP comprised of 237 genotypes from mainly Africa, North America, Central America,

South America and a few from Europe and Asia was assembled (Cichy et al. 2014). The panel

contains varieties from public and private breeding programs, elite lines and land races. These

materials were collected from dry bean repositories in the U.S., from CIAT collection and some

were collected during country visits to African countries. The panel represents the major Andean

seed types and varieties important in Africa and North America.

Field Phenotyping

The ADP was field planted at the Montcalm Research Farm near Entrican, MI, USA in 2012 and

2013 growing seasons. The farm is located in central Michigan where Andean beans are

commercially produced. The soil type is a combination of Eutric Glossoboralfs (coarse-loamy,

mixed) and Alfic Fragiorthods (coarse-loamy, mixed, frigid) and rainfall was supplemented with

overhead irrigation as needed. No fertilizer was applied to the plots and recommended practices

were followed for weed and insect control. Soil samples collected from the trial site before planting

showed that in 2012 season the nitrate level in the soil was on average 36 ppm whereas in 2013 it

was 2.4 ppm. Before planting, seed was inoculated with commercial Rhizobium ‘Nodulator’

(Becker Underwood, Ames IA) with an undisclosed strain at the rate suggested on the package.

However, common bean has been grown on this site for many years and there is also adequate

native Rhizobium. In both seasons, the panel was planted in a randomized complete block design

with two replications. Each genotype was planted in two row plots of 4.75 M long each and inter-

row spacing of 0.50 M. Phenological traits for days to flowering (DTF) and days to maturity

19

(DTM) were collected on all entries in both years. In 2012, three plants were sampled per plot at

maturity and in 2013 six plants were sampled per plot at maturity. The aboveground biomass (BM)

of these plants was recorded and all pods were removed, counted, weighed and threshed. Total

seed weight and 100-seed weight (SW) was measured on threshed seed. Biomass (BM), pod

number (PN), pod weight (PW), seed number (SN) and seed yield per plant were an average of

three (2012 season) or six (2013 season) plants. Pod harvest index (PHI) was calculated by dividing

seed weight by weight of pods that possessed seed (Beebe et al. 2008). Harvest index (HI) was

computed as the ratio of seed weight to total biomass. In both years, seed yield per hectare was

calculated from yield measured for each plot and seed weight was adjusted to 16% moisture

content.

Genotyping

DNA was collected using CTAB extraction protocol (Doyle 1987) with some modifications, from

young leaf tissue of a single plant of each genotype. DNA was quantified using a using a

spectrophotometer and its quality checked on an agarose gel. DNA samples were genotyped using

an Illumina BARCBEAN6K_3 with 5398 SNPs (Hyten et al., 2010).

Phenotypic Data analyses

Statistical analyses for field data were conducted using mixed models in SAS 9.3 (SAS Institute

2011). Assumption for normally distributed data required for analysis of variance (ANOVA) and

SNP-trait association test was checked for all traits measured. This was done on the combined

residuals of all treatments for each trait using the normality tests in PROC UNIVARIATE. Based

on normality test results that showed non-normal data for all traits measured in this study, data for

20

all traits were transformed. All the trait means are reported in their original values. An ANOVA

using PROC MIXED was conducted on all the traits based on the following statistical model:

𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖 = 𝜇𝜇 + 𝛼𝛼𝑖𝑖 + 𝛽𝛽𝑖𝑖 + (𝛼𝛼𝛽𝛽)𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑖𝑖(𝑖𝑖) + ℰ𝑖𝑖𝑖𝑖𝑖𝑖

Where: Yijk is the response variable (such as yield), with genotype i in the environment j, repetition

k; αi is the fixed effect of the genotype i; βj is the random effect of the year j; αβ is the random

effect of the interaction between genotype i and year j; γ is the random effect of a replication with

year j; ε is the random error term, which is assumed to be normally distributed with mean =0 and

variance δ2e. Pearson correlation analysis using PROC CORR was conducted on the average values

for 2012 and 2013 growing seasons.

Population Structure analysis and Marker-Trait Association Tests

To assess the population genetic structure in the panel, the software program STRUCTURE

(Pritchard et al., 2000) and Principal Component Analysis (PCA) was implemented in the software

program EIGENSTRAT (Price et al. 2006). A subset of 89 SNPs not in LD and distributed across

11 chromosomes were employed for analysis with STRUCTURE. Length of Burnin periods was

set to 50000 while number of Markov Chain Monte Carlo (MCMC) repetitions after Burnin was

also set to 50000. An assumption of the presence of admixtures in the population was made. The

K range was set to 1-10 and the number of reps for each simulation to five. The ideal number of

sub-populations was determined using the Delta K (∆K) method (Evanno et al. 2005) implemented

in the software STRUCTURE HARVESTER (Earl and vonHoldt 2012).

After filtering for low quality and monomorphic SNPs, 5326 SNPs were retained. These were

filtered further for minor allele frequency (MAF>0.02) (Stanton-Geddes et al. 2013) and a final

21

total of 4850 SNPs were used in PCA and association analyses. To correct for cryptic relatedness

in the panel the Kinship matrix (K) was included in our association analyses. The kinship matrix

was calculated using Scaled Identity by Descent method in TASSEL 5.0 (Bradbury et. al., 2007).

To determine the SNP-trait associations, a Mixed Linear Model (MLM) (Yu et al., 2005; Zhang et

al., 2010) was implemented in software program TASSEL. The following MLM equation was

used:

𝑌𝑌 = 𝑋𝑋𝛼𝛼 + 𝑃𝑃𝛽𝛽 + 𝐾𝐾𝜇𝜇 + ℰ

Where: Y the phenotype of a genotype; X is the fixed effect of the SNP; P is the fixed effect of

population structure (from PCA matrix); K is the random effect of relative kinship i.e., cryptic

relatedness among genotypes (from kinship matrix); ε is the error term, which is assumed to be

normally distributed with mean = 0 and variance δ2e. Bonferonni corrected p=1.0 x 10-5 (for α =

0.05 and 4850 SNPs) (which is the most conservative) was used to determine the significance

threshold for SNPs. This was used for all traits except DTF and DTM, which was set to p=1.0 x

10-4 to retain SNPs associated with candidate genes.

To gain insights into the positional candidate genes associated with significant SNPs, Jbrowse on

Phytozome v10 (Goodstein et al. 2012) was used to browse the common bean genome version 1.0

(Schmutz et al. 2014). Positional candidate genes where identified by conducting LD analysis in

TASSEL 5.0 for the genomic region surrounding significant SNPs. A gene was considered a

positional candidate if: (i) the gene contained a significant SNP or (ii) the gene contained a SNP

that was in LD with a significant SNP. The functional annotation on Phytozome v10 (Goodstein

et al. 2012) for the gene was then checked to make inferences about the plausible role of the gene

22

in the control of a trait. For the gene with inadequate functional annotation data, genomic sequence

data from Phytozome v10 was used in a search against NCBI and TAIR (Rhee et al. 2003)

databases using BLASTN (Zhang et al. 2000).

Results

Phenotypic Traits

Highly significant (P<0.0001) differences existed among the 237 genotypes for all the traits

measured in both 2012 and 2013. The means and ranges for the traits measured are presented in

Table 1.1. The means for BM, PW and SN were higher in 2012 than 2013. As expected, there were

several significant correlations among traits measured (Table 1.2). Seed weight was negatively

correlated with PN and SN (Table 1.2). Yield per plant was negatively correlated with DTF and

DTM and was positively correlated with all other traits. About 26 genotypes out of 237 genotypes

in the ADP flowered after 50 days after planting and were considered photoperiod sensitive. Of

these 23 were from Africa, two from South America and one was from North America. The

negative correlation between DTF and seed yield could be attributed to the presence of these

photoperiod sensitive and late maturing genotypes in the panel whose seed filling duration was

reduced because of the short growing season in Michigan. Falling temperatures towards end of

summer could have reduced photo-assimilates produced before the end of seed filling. However,

these genotypes did reach harvest maturity and samples were collected and plots harvested for data

analysis.

23

Population Structure

The STRUCTURE (Pritchard et al., 2000) analysis and Evano test (∆K) indicated a two sub-

population structure within the 237 ADP genotypes. These two sub-populations are consistent with

the Andean or Middle American gene pools. Among the 237 genotypes, 228 were from the

Andean genepool. The remaining 9 genotypes were from Middle American gene pool. There were

sixteen Andean lines that had between 10-40% of their genomes as introgressions from Middle

American gene pool.

Analysis of population structure with PCA, revealed that the first, second and third principal

component (PC) accounted for 36.3%, 12.1% and 5.0% of the genotypic variability in the ADP,

respectively. A plot of PC1 against PC2 clearly showed three clusters of genotypes (Figure 1.1).

One of these clusters was comprised of seven genotypes that were comprised of Middle American

genotypes in the STRUCTURE analysis. The results of PCA and STRUCTURE are comparable

though the bigger sub-population of Andean genotypes in STRUCTURE analysis was split into

two clusters in PCA. The smallest cluster of these two comprised of 19 Andean genotypes of which

14 were landraces from East Africa, four were varieties from North America and two were from

varieties from the Caribbean. The other bigger Andean cluster comprised of genotypes from many

geographic regions. The preliminary GWAS analyses showed comparable results when

STRUCTURE or PCA results were used as a covariate to account for population structure in the

panel. The first three PC’s that together explained 53.4% of the genotypic variability in the ADP

were used as covariates to correct for population structure.

24

Trait-SNP Associations

Phenological traits Significant (P<1.0 x 10-4) SNPs were identified for DTF on Pv01 and Pv08 in 2012 (Figure 1.2).

The most significant (P=6.9 x 10-6) SNP for DTF in 2012 that explained 9% of the variability in

DTF was located on Pv08 (Table 1.3). One of the SNPs identified in 2012, ss715646578 on Pv01,

was just below (P=5.6 x 10-4) the significance threshold in 2013.

One significant (P=7.4 x 10-5) SNP was identified on Pv01 in 2013 for DTM. This SNP also

explained about 9% of the variation in DTM and was the same SNP associated with DTF (Table

1.3). No significant associations for DTM were identified in 2012.

Plant Biomass at Maturity Significant (P<1.0 x 10-5) SNPs for BM were identified in 2012 season. SNPs were detected on

Pv02 and Pv08 (Figure 1.3) with the most significant (P=5.2 x 10-7) SNP on Pv08 that explained

12% of the variation in BM (Table 1.3). No significant associations for BM were identified in

2013.

Pod Number Significant (P<1.0 x 10-5) SNPs for PN were identified in 2013 on Pv05 and Pv07 (Figure 1.3).

The most significant (P=2.2 x 10-6) SNP on Pv05 explained about 10% of variation in PN (Table

1.3). No significant associations for PN were identified in 2012.

25

Harvest Index and Pod Harvest Index Significant SNPs for HI were identified in 2012. The most significant (P=2.9 x 10-6) SNP was on

Pv03 and explained 12% of variability for HI in the ADP in 20112. No significant associations

were identified in 2013.

Significant association was identified in 2013 for PHI on Pv04 (Figure 1.3). The most significant

SNP (P=4.5 x 10-6) was on Pv04 and accounted for 10% of the variability for PHI. No significant

associations were detected for PHI in 2012.

Pod Weight Significant SNPs for PW were identified on Pv08 in 2012 (Figure 1.3). The most significant SNP

(P=4.3 x 10-8) accounted for about 14% of the variability in PW (Table 1.3). In 2013 season,

significant associations for PW were identified on Pv08. The most significant (P=8.8 x 10-6) SNP

explained about 9% of the variability in PW in 2013 (Table 1.3).

Seed Number Significant SNPs for SN were identified in 2013 on Pv03 and Pv05 (Figure 1.4). The most

significant SNP (P=6.7 x 10-7) was located on Pv03 and accounted for about 13% of the phenotypic

variation in SN (Table 1.3). No significant SNPs for SN were identified in 2012.

Seed Yield Significant (P<1.0 x 10-5) SNP for seed yield were identified on both per hectare and per plant

basis in 2012. Several significant associations were identified for yield on a per plant basis on Pv08

26

in 2012. The most significant SNP (P=1.0 x 10-7) explained about 13% variation in seed yield per

plant in the panel (Table 1.3). SNPs significantly associated with seed yield per hectare were

identified on Pv03 and Pv09 (Figure 1.4) in 2012. The most significant (P=4.5 x 10-7) SNP was

located on Pv03 and accounted for 14% variability in seed yield per hectare (Table 1.3). No

significant associations were identified for SW, yield on both per plant and hectare basis in 2013

season.

The larger positive effect on seed yield for significant SNP ss715646178 with alleles G and T on

Pv09 came from minor allele G (MAF=0.09). The average yield for genotypic class GG on

ss715646178 was 1690 Kg ha-1 while for TT it was 1561 Kg ha-1. For SNP ss715649410 on Pv03

with alleles A and G, G being the minor allele (MAF=0.12), the larger positive effect on seed yield

was from G allele. On SNP ss715649410, the averages for seed yield of genotypic classes GG and

AA were 1672 Kg ha-1 and 1559 Kg ha-1, respectively. Among 237 genotypes in the ADP, only

28 and 21 genotypes carried the minor allele for ss715649410 and ss715646178, respectively

(Table 1.4). The geographic distributions of genotypes that carried these alleles with larger positive

effect are presented in Table 1.4. Twenty-one genotypes carried alleles with larger effect at both

ss715646178 and ss715649410. The average yield for these 21 genotypes was 1824 kg ha-1. A

group of 216 genotypes that did not carry the larger effect allele at both ss715646178 and

ss715649410 averaged about 1627 kg ha-1. Clearly, there is a beneficial yield effect of having both

alleles with larger effect in a single genotype. Of these 21 lines carrying the larger effect allele at

both ss715646178 and ss715649410, 12 were from Africa, eight from North America and one

from South America. All the 12 genotypes from Africa were not photoperiod sensitive in

27

Michigan. These materials could serve as sources of germplasm in breeding for yield in North

American bean breeding programs.

Discussion

Most agronomic traits in common beans including seed yield are genetically complex. Previous

QTL studies using bi-parental populations have provided some insights into the genetic

architecture of a number of agronomic traits in common beans. In this study we used a genome-

wide association study approach to investigate the genetic architecture of phenology, biomass,

yield components and seed yield in the Andean gene pool of common beans.

Means for BM, PN and seed yield per plant were higher in 2012 than 2013. This could be attributed

to higher soil nitrogen available at the 2012 site (Nitrate=36 ppm) than the 2013 site (Nitrate=2.4

ppm) at the time of planting. This higher soil nitrogen could have benefited the plants in 2012

especially in early growth stages when there was little nitrogen fixation by the plant. Significant

correlations of most of the traits measured with seed yield were observed among the 237 genotypes

in the ADP. This was expected as most of these traits are inter-related and are determinants of seed

yield. All the traits measured in this study can essentially be categorized into three groups: aerial

biomass (BM, PW, and PN), phenology (DTF and DTM) and seed yield (seeds per plant, yield per

hectare) and HI and PHI are computed based on these factors. Seed weight was negatively

correlated with PN and SN. This could indicate compensation among yield components, which

has been previously reported (Adams 1967). Significant correlations between phenological traits,

yield components, aerial biomass at flowering and seed yield have been reported previously

(Scully et al. 1991). Both DTF and DTM were negatively correlated with yield (Table 1.2). This

could be attributed to the photoperiod sensitivity of a significant number of genotypes in the ADP,

28

due to the long day length in Michigan during the growing season. Photoperiod sensitive genotypes

flowered and matured later. Therefore, they had an extended vegetative growth stage and

accumulated more biomass than the photoperiod insensitive genotypes. In addition, many of these

genotypes were inefficient in partitioning to the seeds resulting in lower yields. It is probable that

if the panel was to be evaluated in a tropical environment in East Africa where most of the

photoperiod sensitive materials are adapted and grown, the correlation between yield and the two

time traits (days to flowering and maturity) would be positive.

Flowering is an important agronomic trait that is strongly influenced by the environment and is

key in the adaptation of common bean genotypes to different geographic locations (Wallace et al.

1993). In this study, we identified SNPs significantly associated with DTF on Pv01 and PV08. The

QTL on Pv08 was reported previously (Koinange et al. 1996; Pérez-Vega et al. 2010) and the QTL

on Pv01 has been widely reported (Blair et al. 2006; Koinange et al. 1996; Mukeshimana et al.

2014; Pérez-Vega et al. 2010). Since previous studies have consistently reported QTL for

flowering on Pv01, it is likely to be stable across several environment and genetic backgrounds.

Potential positional candidate genes for flowering in the region around significant SNP

ss715646578 on Pv01 were investigated. There were four genes in LD with ss715646578. Among

these genes was Phvul.001G221100 (Figure 1.2) that was about 4.5 Kbp downstream of

ss715646578 and in LD. The functional annotation on Phytozome indicated that

Phvul.001G221100 is a two-component sensor histidine kinase. BASTN search of

Phvul.001G221100 genomic sequence against TAIR database resulted in the best hit to the

Arabidopsis thaliana gene phyA that codes for phytochrome A. Phytochrome A is a photoreceptor

pigment reported to control photoperiod sensitivity in Arabidopsis (Reed et al. 1994). A BLASTN

29

search against of Phvul.001G221100 genomic sequence against NCBI data resulted in a best hit to

a gene GmPhyA3 in Glycine max. GmPhyA3 has been cloned and characterized as contributing to

the complex flowering response and maturity systems in soybean (Watanabe et al. 2009).

Apparently, this gene is conserved in P. vulgaris, G. max and A. thaliana and appears to retain

similar functions in photoperiod sensitivity, flowering and maturity in these three species. Based

on GWAS results and comparative genomics, Phvul.001G221100 is a strong candidate as the gene

on Pv01 controlling photoperiod sensitivity and flowering in common bean.

In P. vulgaris the locus for photoperiod sensitivity (Ppd) was previously mapped to Pv01 (Gu et

al. 1998). Due to differences in the marker technologies used and the large confidence intervals

for the QTL in previous studies, it is difficult to ascertain whether previously identified Ppd QTL

co-localize with candidate gene Phvul.001G221100. Photoperiod sensitive genotypes flower late

in extended day light environments and the phenomenon is more common in the Andean gene pool

(Kornegay et al. 1993). A significant number of genotypes (26 out of 237 genotypes) in the ADP

were photoperiod sensitive in Michigan where there is extended daylight and high temperatures

during critical periods of the growing season.

Days to maturity is critical for the adaption to geographic areas with shorter growing seasons and

short rainy seasons in tropical regions. We identified significant SNPs for maturity on Pv01.

Previous studies have also reported a QTL for maturity on Pv01 (Koinange et al. 1996;

Mukeshimana et al. 2014; Pérez-Vega et al. 2010). In this study the significant SNP ss715646578

on Pv01 for DTF in 2012 was the same significant SNP for maturity in 2013 (Figure 1.3). Co-

localization of DTF and DTM QTL in common bean has been reported previously (Koinange et

30

al. 1996). This may suggest that SNP ss715646578 is associated with a gene that has a pleiotropic

effect on flowering and maturity. This may also suggest that this SNP may be in LD with two

different genes controlling these two traits.

To gain insights into how selection for flowering and maturity in different geographic regions has

affected the allele frequencies of SNP ss715646578 that is in LD with Phvul.001G221100, we

investigated allele frequencies of all significant SNPs. The MAF for SNP ss48340819 that is

significantly associated with flowering and maturity was the highest (MAF=0.36) among all

significant SNPs for all traits measured (Table 1.3). There are two plausible reasons the higher

MAF for flowering and maturity than for other traits measured in this study including seed yield.

First, more materials from Africa flowered and matured later than materials from the North

America. This could be a reflection of emphasis placed on breeding for earliness in North America

because of the shorter growing season and to a lesser extent in Africa were the growing season is

longer. This could have resulted in spatial variation in flowering fitness optimum and the frequency

of alleles carried on SNP ss48340819. Because of the significant representation of both late and

early flowering genotypes carrying contrasting alleles at SNP ss48340819, the MAF is expected

to be larger. Second, during selection for maturity breeders rarely select for extreme maturity

phenotypes, which is in contrast in selecting for yield where extreme high yield phenotype are

sought. Extreme phenotypes are always few and are caused by rare alleles. This means frequency

of minor alleles at loci for yield would be lower compared to DTF and DTM loci. The MAF of the

flowering and maturity SNP (ss48340819; MAF=0.36) is in contrast to the SNPs for seed yield

(MAF>0.13) where directional mode of selection is practiced in which the highest yielding

genotypes are selected. Though the QTL for flowering on Pv01 has been widely reported, this is

31

the first report where a QTL for flowering was resolved to a much smaller genomic region that

could facilitate the identification of candidate gene(s). A candidate gene for flowering and maturity

was identified through GWAS and comparative genomics enabled by the newly released genome

for common bean. We have demonstrated how useful the sequenced P. vulgaris genome will be in

advancing the knowledge of the candidate genes underlying important QTL.

In 2012, highly significant SNPs on Pv08 were identified that were associated BM, PW and yield

per plant. Plant biomass was significantly correlated with PW and yield per plant in the correlation

analyses (Table 1.2). These SNPs associated with more than one trait could be due to pleiotropy

or due to linked genes that fall in the same LD block and are tagged by the same SNPs. Since pods

were part of BM in our measurements, pleiotropy between BM and PW, cannot be considered.

However, pleiotropy is plausible between yield and the two aerial biomass components (BM and

PW). Whereas linkage can be proven if a population can be used that captures more recombinations

in the genomic region where significant SNPs for more than two traits are, pleiotropy is difficult

to prove. From a plant breeding perspective whether pleiotropy or linkage is the underlying basis

for same SNPs to be associated with BM, PW and yield per plant does not matter much because

of the positive effects of these SNPs on BM, PW and yield per plant. Looking at significant

associations for BM and yield on Pv08 helps to reinforce prior research that selecting for three

major physiological components of yield i.e., BM, HI and DTF (in adapted genotypes) should

result in an increase in seed yield in common bean (Wallace et al. 1993).

Significant SNPs for HI were identified on Pv03 in 2012 (Figure 1.4). The two most significant

SNPs ss715639243 and ss715648538 for HI and SY (Table 1.3), respectively, on Pv03 were in

strong LD (r2=1; D’=1). This may suggest that these SNPs were in LD with a pleiotropic gene for

32

HI and seed yield. The other possible scenario was that ss715639243 and ss715648538 could have

been in LD with linked genes for HI and seed yield.

Pod number is a major yield component with a significant contribution to seed yield per plant

(Adams, 1967). In this study, significant SNPs were identified for PN on Pv05 and Pv07 in 2013

seasons. QTL for PN have been reported previously on Pv05 (Beattie et al. 2003) and Pv07 (Blair

et al. 2006). Two significant SNPs ss715649615 and ss715650235 in 2013 for PN and SN,

respectively, on Pv05 were in LD (r2=0.2; D’=1). This may suggest that these SNPs could be in LD

with a pleiotropic gene or genes in linkage for these two traits.

Significant SNPs for SN were identified on Pv03 and Pv05 (Table 1.3). Significant SNPs for both

SN in 2013 and seed yield in 2012 were identified on Pv03. Results of LD analysis for the entire

Pv03 indicated that the two most significant SNPs ss715639901 and ss715648538 for SN and seed

yield (Table 1.3), respectively, were in strong LD (r2=1; D’=1). Numbers of seeds per plant and

seed yield are closely inter-related and as noted earlier they could be collapsed into a single

category of yield. This could explain the significant associations on the same chromosome and the

strong LD of significant SNPs for these two traits.

Several significant SNPs were identified on Pv03 and Pv09 for seed yield per hectare and on Pv08

for yield per plant in 2012 season. There are several reports of QTL for seed yield and some of

these are consistent with our results. Seed yield QTL were identified on Pv03 (Blair et al. 2006;

Checa and Blair 2012; Mukeshimana et al. 2014; Wright and Kelly 2011) and on Pv09 (Blair et

al. 2006; Mukeshimana et al. 2014; Tar'an et al. 2002). The QTL, SY3.3SC for seed yield identified

33

by Mukeshimana et al. (2014) had a marker interval of ss715640477-ss715649325 that contained

three SNPs. LD analysis between these SNPs and the significant (7.8 x 10-6) SNP ss715649410

for seed yield in the current study, indicated two of three SNPs were in LD (r2>0.6; D’>0.9) with

ss715649410. Also, one of these three SNPs in SY3.3SC interval was in strong LD (r2=0.9; D’=1)

with the most significant (4.5 x 10-7) SNP ss715648538 for seed yield in the current study. Another

QTL for seed yield on Pv03 that was identified by Mukeshimana et al. (2014) was in the marker

interval ss715646941-ss715648035, which had 19 SNPs. Eight of these 19 SNPs were in LD

(r2>0.5; D’>0.8) with the significant SNP ss715649410 in the current study. These results suggest

that the gene or genes underlying the QTL for seed yield identified by Mukeshimana et al. (2014)

are the same one in LD with significant SNP ss715649410 in the current study. Five different

studies with very diverse populations including the current study have consistently reported seed

yield QTL on Pv03 and four studies have reported seed yield QTL on Pv09. If these QTL are stable

and expressed in diverse genetic backgrounds they could be used as potential candidates for

marker-assisted breeding for seed yield. The geographic distributions of minor alleles with a larger

positive effect on seed yield for two significant SNPs ss715949410 (P=7.6 x 10-6) and

ss715646178 (P=1.9 x 10-6) on Pv03 and Pv09 was widespread (Figure 5). This may indicate the

potential of this ADP as a source of germplasm with favorable rare alleles from different countries

to breed for increased seed yield. Genotypes from other countries carrying alleles with positive

effect on seed yield could potentially be used to introduce new genetic variability in the breeding

programs. This could play a significant role in increasing gains in breeding for yield in Andean

beans where gains have only been modest for some market classes because of lack of depth in

genetic variability. Since yield is a cumulative and complex trait (Kelly et al. 1998), many genes

each with small but cumulative effects that are strongly influenced by environmental factors

34

including weather and management contribute to yield. The fact that we only identified a few SNPs

associated with yield does not mean that these were the only genetic determinants of yield in

respective years but this indicates that we may have missed several loci with smaller contributions

to yield. The current study had only sufficient power to identify polymorphic loci with large effects

on seed yield due to the limited size of the ADP. Based on simulations to identify genes with

effects as low as 5% in GWAS, over 1000 genotypes would be needed and even a greater number

for genes with smaller effects would be needed (Yan et al. 2011).

Most of the traits measured in this study had few significant SNPs. In addition, most SNPs were

significant in one year only. There are two plausible reasons for this. First, the stringent

significance level used following the conservative Bonferonni correction cut-off several SNPs that

could be significant if the significance threshold was to be lowered. Second, most of the agronomic

traits measured in this study tend to be significantly affected by the environment, resulting in a

significant genotype by environment interaction that could have confounded the identification of

same significant SNPs in both years. Given the genetic complexity of seed yield and its strong

interaction with the environment, further evaluation of the ADP in several environments would

help in validating the QTL identified in the current study and their stability across environment.

The proportion of the phenotypic variation explained by our significant SNPs is lower than

previously reported values. It is plausible that in some previously reported QTL, the R2 values for

yield and yield components were inflated because of the small population sizes and limited marker

density (Bernardo 2008). The R2 values reported in this study that ranged from 9% to 14% are

consistent with genetic complexity of traits such as yield that are controlled by several genes with

small but cumulative effect.

35

This study has demonstrated the effectiveness of GWAS to identify QTL with more enhanced

resolution for important agronomic traits of common bean, which resulted in the identification of

candidate genes for days to flowering and maturity. A substantial number of QTL for the

agronomic traits that were identified in this study are consistent with the QTL identified in previous

studies that used diverse populations for bi-parental linkage mapping with low marker resolution.

Furthermore, we identified novel QTL for several agronomic traits. Given the size of the panel this

study is insufficient to identify QTL with smaller effect for the traits measured. We identified QTL

with large effect and some are potential candidates for marker-assisted breeding to accelerate gains

in breeding for seed yield. Future studies, using segregating populations at the significant SNP

loci may be necessary to validate the identified yield QTL and determine their usefulness in

breeding. Also, it would be interesting to see what would happen to yield gain if a population

comprised of ADP genotypes with large effect alleles at significant SNPs is to be assembled,

intermate these genotypes and then select for yield directly from the resulting progeny population.

Our study provides more insights into the genetic architecture of important agronomic traits

contributing to yield of common bean.

Acknowledgements

Research was supported by the Borlaug LEAP program, USDA-ARS and was also made possible

through support provided by the Feed the Future Innovation Lab for Collaborative Research on

Grain Legumes by the Bureau for Economic Growth, Agriculture, and Trade, U.S. Agency for

International Development, under the terms of Cooperative Agreement No. EDH-A-00-07-00005-

00, and this work was supported in part by funding from the Norman Borlaug Commemorative

36

Research Initiative (US Agency for International Development). The opinions expressed in this

publication are those of the authors and do not necessarily reflect the views of the U.S. Agency for

International Development or the U.S. Government. We also thank Dr. Zixang Wen for his helpful

comments on some aspects of data analyses and Mr. Jose L.C Velasco who extracted DNA.

37

APPENDIX

38

Table 1.1. Means and ranges for ten agronomic traits for 237 common bean genotypes in Andean

Diversity Panel (ADP) grown in 2012 and 2013 at Montcalm Research Farm, MI.

ADP (n=237 genotypes) Trait Year Mean † Min. ‡ Max. ‡ Days to Flowering 2012 43.4±0.3 28.0 69.0 2013 44.7±0.3 34.0 60.0 Days to Maturity 2012 91.1±0.4 75.0 115.0 2013 89.2±0.3 73.0 113.0 Biomass per Plant (g) 2012 32.8±0.6 10.8 96.7 2013 25.5±0.3 12.3 48.1 Hundred Seed Weight (g) 2012 44.2±0.4 17.4 68.8 2013 45.2±0.5 16.1 70.3 Pod Number per Plant 2012 11.0±0.2 3.3 28.0 2013 9.2±0.1 4.0 20.7 Harvest Index 2012 0.45±0 0.18 0.65 2013 0.50±0 0.26 0.76 Pod Harvest Index 2012 0.70±0 0.23 0.84 2013 0.73±0 0.40 0.83 Pod Weight per Plant (g) 2012 21.1±0.5 5.0 59.8 2013 17.8±0.2 4.9 64.3 Seeds per Plant 2012 32.8±0.2 9.5 92.0 2013 29.4±0.4 11.5 69.2 Seed Yield per Plant (g) 2012 14.8±0.3 3.7 38.5 2013 12.9±0.2. 3.4 25.8 Seed Yield (Kg ha-1) 2012 1599±26.0 485 3689 2013 1647±31.5 136 3845

† Mean ± Standard Error of the Mean; ‡Max and Min represent the maximum and minimum range

for a trait

39

Table 1.2. Pearson Correlations coefficients among ten agronomic traits measured on 237 common bean genotypes grown at Montcalm

Research Farm, MI in 2012 and 2013.

Traits Pod

Weight Pod

Number Seed

Number Seed

Weight

Seed Yield/ Plant

Pod Harvest Index

Harvest Index

Days to Flowering

Days to Maturity

Seed Yield

Biomass 0.87*** 0.68*** 0.62*** 0.24*** 0.87*** 0.12** -0.26*** 0.17** 0.19*** 0.25*** Pod Weight 0.72*** 0.61*** 0.39*** 0.96*** 0.07ns 0.61*** -0.27*** -0.22*** 0.37*** Pod Number 0.81*** -0.17** 0.62*** 0.15** 0.39*** -0.1* -0.13** 0.17** Seed Number -0.38*** 0.65*** 0.29*** 0.36*** 0.14** 0.04ns 0.07ns Seed Weight 0.34*** -0.13** 0.32*** -0.44*** -0.27*** 0.36*** Seed Yield/Plant 0.31*** 0.68*** -0.21*** -0.12* 0.36*** Pod Harvest Index 0.41*** 0.15** 0.18** 0.06ns Harvest Index -0.37*** -0.39*** 0.46*** Days to Flowering 0.70*** -0.33*** Days to Maturity -0.37***

* Significant at α=0.05; **Significant at α=0.01; ***Significant at α=0.001

40

Table 1.3. Chromosome, position, p-values, proportion of phenotypic variation explained (R2) and minor allele frequency of two most

significant SNPs for ten agronomic traits measured on 237 genotypes grown in 2012 and 2013 at Montcalm Research Farm, MI.

Trait

Year SNP† Chr. SNP Position P-value‡ R2§

Minor Allele Frequency

Days to Flowering 2012 ss715646088 Pv08 57734680 6.9E-06 0.09 0.15 2012 ss715646578 Pv01 48340819 1.1E-05 0.10 0.37

Days to Maturity 2013 ss715646578 Pv01 48340819 7.4E-05 0.09 0.37 Biomass 2012 ss715639408 Pv08 5150618 5.2E-07 0.12 0.13

2012 ss715647433 Pv02 38769141 2.1E-06 0.10 0.10 Harvest Index 2012 ss715639243 Pv03 45577363 2.9E-06 0.12 0.13 2012 ss715641141 Pv03 46054672 2.9E-06 0.12 0.13 Pod Harvest Index 2013 ss715648677 Pv04 297638 4.5E-06 0.10 0.29 Number of Pods 2013 ss715649615 Pv05 27957387 2.2E-06 0.10 0.03 2013 ss715647649 Pv07 40059490 3.8E-06 0.11 0.03 Pod Weight 2012 ss715639408 Pv08 5150618 4.3E-08 0.14 0.13 2012 ss715649359 Pv08 4743573 1.9E-07 0.14 0.13

2013 ss715647392 Pv08 59337110 8.8E-06 0.09 0.13 Seed Number 2013 ss715639901 Pv03 25241093 6.7E-07 0.13 0.09

2013 ss715650235 Pv05 27277193 4.5E-06 0.10 0.13 Yield per Plant 2012 ss715639408 Pv08 5150618 1.0E-07 0.13 0.13

2012 ss715649359 Pv08 4743573 2.8E-07 0.14 0.13 2013 ss715647002 Pv09 20618286 8.0E-06 0.09 0.12

Seed Yield 2012 ss715648538 Pv03 38268568 4.5E-07 0.14 0.09 2012 ss715646178 Pv09 10005643 1.9E-06 0.11 0.09

†SNP=Single Nucleotide Polymorphic code; ‡P=significance level and E=exponential; § R2 is phenotypic variation explained by the

SNP

41

Table 1.4. Geographic distributions of the alleles with larger positive effect on seed yield of two significant SNPs in a panel of 237

genotypes grown in 2012 and 2013 at Montcalm Research Farm, MI.

Allele and SNP G (ss715649410) † G (ss715646178) † Country Number of Genotypes Angola 2 1 Canada 1 1 Georgia 1 0 Kenya 2 2 Malawi 1 1 Puerto Rico 5 2 Tanzania 10 8 Uganda 1 1 USA 5 5

† G was the minor allele with a frequency of 0.12 and 0.09 for both ss715649410 (on Pv03) and ss715646178 (on Pv09), respectively.

42

Figure 1.1. Principle Component Analysis (PCA) plot of PC1 against PC2 illustrating the

population structure in the ADP. The cluster of blue triangles represents the 7 Middle

American genotypes while the red represent the 237 Andean genotypes in 2 separate

clusters

43

Figure 1.2. Manhattan plots showing the same candidate SNP for both flowering in 2012

and maturity in 2013. The model of candidate gene Phvul.001G221100 associated with

significant SNP on Pv01 is shown below.

44

Figure 1.3. Manhattan Plots showing significant SNPs and their P-values from GWAS

using MLM for Pod Harvest Index (PHI_13) on Pv03 in 2013, pod number (PN_13) on

Pv05 and Pv07 in 2013, biomass (BM_12) on Pv02 and Pv08 in 2012 and pod weight

(PW_12) on Pv08 in 2012 and number of pods per plant for 2013 season. Red line is the

significance threshold of P=1.03 x 10-5 after Bonferonni correction of α = 0.05

45

Figure 1.4. Manhattan Plots showing candidate SNPs and their P-values from GWAS

using MLM for seed yield (Kg ha-1) on Pv03 and Pv09, and HI on Pv03 in 2012. Red line

is the significance threshold of P=1.03 x 10-5 after Bonferonni correction of α = 0.05

46

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CHAPTER 2

GENOME-WIDE ASSOCIATION ANALYSIS OF SYMBIOTIC NITROGEN FIXATION IN COMMON BEAN

[Published in: Theoretical and Applied Genetics 128 (10): 1999-2017.]

53

Genome-Wide Association Analysis of Symbiotic Nitrogen Fixation in Common Bean Kelvin Kamfwa · Karen A. Cichy · James D. Kelly*

K. Kamfwa and J.D. Kelly, Dep. of Plant, Soil and Microbial Sciences, Michigan State

Univ., 1066 Bogue St., East Lansing, MI 48824; K.A Cichy, USDA-ARS, Sugarbeet and

Bean Research Unit, Michigan State Univ., 1066 Bogue St., East Lansing, MI 48824.

*Corresponding author ([email protected]).

Abstract

A genome-wide association study (GWAS) was conducted to explore the genetic basis of

variation for symbiotic nitrogen fixation (SNF) and related traits in the Andean diversity

panel (ADP) comprised of 259 common bean (Phaseolus vulgaris) genotypes. The ADP

was evaluated for SNF and related traits in both greenhouse and field experiments. After

accounting for population structure and cryptic relatedness significant SNPs were

identified on chromosomes Pv03, Pv07 and Pv09 for nitrogen derived from atmosphere

(Ndfa) in the shoot at flowering, and for Ndfa in seed. The SNPs for Ndfa in shoot and

Ndfa in seed co-localized on Pv03 and Pv09. Two genes Phvul.007G050500 and

Phvul.009G136200 that code for leucine-rich repeat receptor-like protein kinases (LRR-

RLK) were identified as candidate genes for Ndfa. LRR-RLK genes play a key role in

signal transduction required for nodule formation. Significant SNPs identified in this study

could potentially be used in marker-assisted breeding to accelerate genetic improvement

of common bean for SNF.


54

Key words: Phaseolus vulgaris, symbiotic nitrogen fixation, genome-wide association

study

Abbreviations: ADP, Andean Diversity Panel; BLASTn, Basic local alignment search

tool for nucleotide; GWAS, Genome-wide association study; Ndfa, Nitrogen derived from

the atmosphere; GH, Greenhouse; MLM, mixed linear model; LD, Linkage disequilibrium;

LRR-RLK, Leucine-rich repeat receptor-like protein kinase; N, Nitrogen; Pv, Phaseolus

vulgaris chromosome; SNF, Symbiotic nitrogen fixation; SNP, Single Nucleotide

Polymorphism

Introduction

Nitrogen (N) is frequently the most limiting nutrient for crop productivity (Boddey et al.

2009; Vance 2001). The two major sources of N for crop production are synthetic fertilizers

and symbiotic nitrogen fixation (SNF) by legumes (Peoples et al. 2009a). SNF is the result

of a symbiotic relationship between legumes and a diverse group of bacteria called

Rhizobium (Graham 2009). This process begins with exchange of molecular signals

between the legume root system and Rhizobium in the soil. The legume releases

metabolites, usually flavonoids from its roots into the soil. This triggers the release of Nod-

factors (lipochitooligosaccharides) from the Rhizobium which when perceived by the plant

induces the formation of an infection thread and subsequently a specialized organs on the

roots called nodules, which contain the Rhizobium (Gage 2009). When nodules are fully

developed and functional, Rhizobium reduces atmospheric N2 to ammonia, which is

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assimilated into forms of N that the plant can use (Strodtman and Emerich 2009). In return

for the fixed N, the plant supplies the bacteria with photo-assimilates. SNF plays a

significant role in crop productivity by providing N that is needed for plant growth and

seed yield. In addition, SNF plays an important role in maintaining and enhancing soil

fertility in a sustainable manner (Jensen and Hauggaard-Nielsen 2003). In low input

agricultural systems such as those in Africa and Latin America, SNF makes it possible to

successfully grow grain legume crops with minimal N fertilizers (Mafongoya et al. 2009).

This is important because most farmers in these regions do not have access or cannot afford

fertilizers (Mafongoya et al. 2009). In countries where farmers can afford fertilizer and it

is easily accessible, SNF still plays an important role because it reduces the amount of

fertilizer applied thereby reducing the cost of producing the crop and potential ground

water pollution (Vance 2001). In addition, SNF supports crop productivity in organic

farming systems where artificial fertilizers cannot be applied.

Common bean (Phaseolus vulgaris) is the most important legume for direct consumption

and a staple for millions of people in East Africa and South America (Akibode and Maredia

2012; Broughton et al. 2003). Common bean is considered weak in SNF in comparison

with other major grain legumes (Bliss 1993). Reasons attributed to this shortcoming

include the short growing season for most common bean genotypes that limits the supply

of photo-assimilates to nodules (Graham et al. 2003). Depending on the environment and

genotype, estimates of N fixed by common bean range from 0 kg ha-1 to 165 kg ha-1 with

an average of 55 kg ha-1, which is considered low when compared with other major grain

and pasture legumes (Giller 2001; Graham et al. 2003; Unkovich and Pate 2000). Whereas

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the amounts of N fixed by soybean (Glycine max) are adequate for successful production

without synthetic N fertilizer, common bean requires that fixed N be supplemented with N

fertilizer (Giller 2001). Indeed, common bean seed yield response to N fertilizer application

tends to be significant (Giller 2001; Herridge and Redden 1999). Enhancing the SNF

process in common bean has potential to improve its overall productivity. Genetic

variability for SNF and associated traits within common bean has been widely reported

(Buttery et al. 1997; Elizondo Barron et al. 1999; Graham and Rosas 1977; Graham 1981;

Herridge and Redden 1999; Pereira et al. 1993), suggesting that genetic improvement

would be feasible. Efforts to improve SNF in common bean from the Middle American

gene pool have been successful and resulted in release of cultivars with enhanced SNF

(Bliss et al. 1989). However, sustained success in developing Andean cultivars with

enhanced SNF has been elusive. Most bean breeding programs do not routinely select for

enhanced SNF because phenotyping for SNF is laborious and expensive especially when

15N-isotope method (Shearer and Kohl 1986) is used. Genetic improvement for SNF has

been hampered by its genetic complexity. Several plant traits including nodulation,

photosynthesis, biomass accumulation, photo-assimilate partitioning to the nodules are

involved in SNF. Many genes control these traits, and the environment significantly affects

their expression, which limits the genetic enhancement of SNF. Understanding the genetic

architecture of SNF in terms of the genomic regions and/or genes involved and their effects

is critical to enhancing our knowledge of its genetic control.

Developing molecular markers that can be used by breeders to indirectly select for SNF

would circumvent the challenges of direct selection for SNF, and accelerate the

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development of common bean cultivars with enhanced SNF. Given the importance of SNF,

few studies to understand the genetic architecture of SNF in common bean and other

economically important legumes exist. Only four previous QTL mapping studies on SNF

and related traits in common bean have been published (Nodari et al. 1993; Ramaekers et

al. 2013; Souza et al. 2000; Tsai et al. 1998). Three of these studies used a population of

recombinant inbred lines (RILs) from a cross of BAT 93 x Jalo EEP558 grown in the

greenhouse (GH) to identify QTL for nodule number on Pv01, Pv02, Pv03, Pv05, Pv06,

Pv07, Pv09, Pv10 and Pv11 (Nodari et al. 1993; Souza et al. 2000; Tsai et al. 1998). A

common theme among these studies is the use of nodule number to indirectly assay for

SNF. SNF was directly assayed using 15N natural abundance method (Shearer and Kohl

1986) to map QTL for SNF in only one study (Ramaekers et al. 2013). QTL for percentage

of N derived from atmosphere (%Ndfa) were identified on Pv01, Pv04, and Pv10, and

nodule dry weight on Pv03 in a population of 83 F5:8 RILs from a G2333 x G19839 cross

grown in the field (Ramaekers et al. 2013). When the same population was evaluated in

the GH, QTL for total N were identified on Pv04 and Pv10 (Ramaekers et al. 2013).

Enhancing SNF in Andean beans could be one way of improving their productivity as the

yields of Andean beans continue to lag behind those of Middle American beans

(Vandemark et al. 2014). Andean beans are the most widely grown bean types in Africa

(Beebe 2012), mostly by small-scale farmers who cannot afford fertilizer and are reliant on

SNF as a source of N. Though, Middle American genotypes with enhanced SNF have been

identified, introgressing SNF genes from Middle American germplasm is normally

constrained by genetic incompatibilities and difficulties in recovering the large Andean

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seed size in progenies (Singh and Gutiérrez 1984). Identifying superior genotypes for SNF

from within the Andean gene pool could circumvent these challenges. In this study, we

used a genome wide association study (GWAS) to explore the genetic architecture of SNF

in a panel comprised of 259 Andean bean genotypes with the goal to identify traits and

genomic regions associated with improved SNF in Andean beans.


Plant Materials

A subset of the Andean Diversity Panel (ADP) comprised of 259 Andean bean genotypes

from Africa, South America, North America, Central America, Caribbean, Asia and

Europe was used in this study (Cichy et al. 2015). The genotypes in the ADP included

varieties, elite lines and landraces. The ADP was evaluated in replicated greenhouse (GH)

and field experiments in Michigan, USA. More details about the makeup of the diversity

panel can be found in Cichy et al. (2015). This panel was used recently to identify QTL for

agronomic traits and a candidate gene for days to flowering and maturity in common bean

(Kamfwa et al. 2015). Two Andean non-nodulating mutants (no-nods), G51396A and

G51493A were included as checks in both greenhouse and field experiments. Both

G51396A and G51493A have determinate growth habit. In addition, the no-nods were used

to calculate nitrogen derived from atmosphere (Ndfa) in field experiments.

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Greenhouse Experiments

The ADP was evaluated for SNF and related traits in the greenhouse at Michigan State

University, East Lansing, Michigan (MI), USA in 2012 and 2014 hereafter referred to as

GH_2012 and GH_2014, respectively. In both GH_2012 and GH_2014, 259 Andean

genotypes including two no-nods genotypes were planted in a randomized complete block

design with two replications. Before planting, seeds were sterilized in sodium hypochlorite

and then rinsed in distilled water. Then eight seeds for two replications of each genotype

were inoculated with Rhizobium tropici strain CIAT 899 by submerging seeds for two

minutes in a broth culture of Rhizobium made from yeast extract manitol media (Vincent

1970). Four seeds of each genotype were planted in a 4-liter plastic pot filled with perlite

and vermiculite in a 2:1 (v/v) ratio. Ten days after planting, thinning was done to leave two

plants in each pot. A second inoculation was done by applying 1 ml of CIAT 899 broth.

The N-free nutrient solution (Broughton and Dilworth 1970), was applied once per day

through drip irrigation until flowering, when plants were harvested. Throughout the

experiment, 13 hours of supplemental light per day was provided, and temperature was

maintained at 24oC in the GH. At 32 days after planting, chlorophyll content was measured

using a Soil and Plant Analysis Development (SPAD) chlorophyll meter (SPAD-502Plus)

on one fully developed leaf of each of the two plants in the pot (Uddling et al. 2007). An

average of these two values was computed. SPAD meter measures the absorbance of the

leaf in the red and near-infra red regions. Based on these two absorbance values, the meter

calculates a numerical value proportional to chlorophyll content in the leaf (Uddling et al.

2007). At flowering, plants were harvested by carefully shaking off the perlite/vermiculite

media. Roots were carefully washed in water to avoid losing the nodules. The plant was

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then separated into roots, nodules and shoot. After this nodules and the shoot were dried

in the oven at 60oC for 72 hours. The nodule dry weight and shoot biomass of the two

plants for each genotype were recorded. The shoot was then ground with a Christy-Turner

Lab Mill to pass through a 1 mm screen. About 5 mg of ground tissue were prepared and

shipped to University of California, Davis Stable Isotope Facility for 15N natural abundance

and total N analyses. Both GH_2012 and GH_2014 experiments were handled similarly.

Field Experiments

The ADP was evaluated for SNF and related traits in the field at the Montcalm Research

Farm near Entrican, MI, USA in 2012 and 2013 growing seasons, hereafter referred to as

Field_2012 and Field_2013, respectively. In Field_2012, 259 ADP genotypes were

evaluated, whereas the number was reduced to 237 in Field_2013. The reduction resulted

from the elimination of genotypes that showed lack of adaptation to temperate field

conditions in Michigan in Field_2012 and also due to limited seed quantities for some

genotypes. The farm is located in central Michigan where Andean beans are produced on

course textured sandy soils. The soil type is a combination of Eutric Glossoboralfs (coarse-

loamy, mixed) and Alfic Fragiorthods (coarse-loamy, mixed, frigid) and rainfall was

supplemented with overhead irrigation as needed. No fertilizer was applied to plots and

recommended practices were followed for weed and insect control. Soil samples collected

from the trial site before planting showed that in Field_2012 nitrate level in the soil was on

average 36 mg kg-1, whereas in Field_2013 it was 2.4 mg kg-1. Before planting, seed was

inoculated with commercial Rhizobium ‘Nodulator’ (Becker Underwood, Ames IA) with

an undisclosed strain at the rate suggested on the package. Common bean has been grown

61

on this site for many years and there is adequate native Rhizobium. In both seasons, the

ADP was planted in a randomized complete block design with two replications. Each

genotype was planted in two row plots of 4.75 m long each and inter-row spacing of 0.50

m. The two Andean no-nod mutants were included in the planting as checks and also for

computations of amounts of N fixed. At 35 days after planting, chlorophyll content was

measured on three plants in each plot using SPAD meter and then the average value was

calculated. Days to flowering were recorded on all entries in both years. In Field_2012 at

flowering, two plants were sampled from each plot by digging with a shovel and carefully

removed the soil. These two plants were separated into shoot and roots. A visual nodulation

score of 0-6 based on the number of nodules was recorded for each genotype in both years.

A nodule score of 0 represented no nodules while 6 was for high nodulation. Roots were

discarded while the shoots were oven-dried at 60oC for 72 hours and then weighed. Shoot

was ground using Christy-Turner Lab Mill to pass through a 1 mm screen. About 5 mg of

ground tissue was shipped to University of California, Davis Stable Isotope Facility for 15N

natural abundance and total N analyses.

In the current study the primary method used to evaluate SNF was estimating %Ndfa and

Ndfa in shoot biomass at flowering in both GH and field experiments. However, from the

plant breeding standpoint of enhancing SNF, an ideal genotype is one that not only fixes

adequate N, and stores it in leaves and stems but one that is also efficient in partitioning or

remobilizing fixed N from the stems, leaves, and pod walls to the seed, which is the

economic yield. To identify genotypes with enhanced N fixation, and efficiency in

partitioning and remobilizing the fixed N to the seed, we measured total N and 15N natural

62

abundance in the seed, which were used to estimate %Ndfa and Ndfa. Seed harvested from

Field_2013 was ground to powder and sent to UC Davis Stable Isotope Facility for total N

and 15N natural abundance analyses. We focused on Field_2013 experiment because our

interest was to determine N in the plant derived from fixation under low soil N levels. The

data on %Ndfa and Ndfa in seed was also used in association analyses to determine whether

genomic regions associated with %Ndfa and Ndfa in shoot biomass at flowering would co-

localize with %Ndfa and Ndfa estimated using seed.

GH and Field Estimation of N fixed

The amount of N fixed or Ndfa by a single plant grown in GH experiments was estimated

as total shoot biomass at flowering multiplied by the %N in shoot biomass, minus the total

N of a non-fixing mutant. Although we used N-free nutrient solution to grow plants, we

could not assume that the entire N in the plants came from fixation as the seed contains

approximately 4% N that sustains the plant prior to fixing N. It was on this premise that

we subtracted the N of non-fixing mutants from that of fixing genotypes to have a more

accurate estimate of N fixed under GH conditions.

In field experiments the amount of Ndfa in the shoot biomass at flowering and in seed were

estimated using the 15N natural abundance method (Giller 2001; Shearer and Kohl 1986).

This isotopic method has been reported to give more accurate estimates of Ndfa under field

conditions (Peoples et al. 2009b). In this method the proportion of total N in the plant that

was derived from fixation (%Ndfa) and N from fixation (Ndfa) were estimated using the

following two equations from Giller (2001):

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%𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 = �𝛿𝛿15𝑁𝑁𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑝𝑝𝑝𝑝𝑝𝑝𝑟𝑟𝑝𝑝 − 𝛿𝛿15𝑁𝑁𝑟𝑟𝑖𝑖𝑓𝑓𝑖𝑖𝑟𝑟𝑓𝑓 𝑝𝑝𝑝𝑝𝑝𝑝𝑟𝑟𝑝𝑝� (𝛿𝛿15𝑁𝑁𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑝𝑝𝑝𝑝𝑝𝑝𝑟𝑟𝑝𝑝 − 𝐵𝐵)�

𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 = 𝑁𝑁𝑝𝑝𝑡𝑡𝑝𝑝𝑝𝑝𝑝𝑝 ∗ %𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁

For Equation 1: %Ndfa is percentage of N in the shoot biomass at flowering or in seed that

is derived from atmosphere i.e., N fixed, hereafter referred to as %Ndfa_Shoot or

%Ndfa_Seed, respectively; δ15Nreference plant is the 15N in the no-nod, non-fixing plant (we

used an average of two no-nods); δ15Nfixing plant is the 15N in the fixing plant; ‘B’ is the δ15N

of the same N fixing plant when grown in N-free GH conditions. For estimating %Ndfa

using shoot biomass at flowering each genotype had its own ‘B’ value derived from GH

evaluation of the ADP. This ‘B’ was also used to estimate %Ndfa using seed. For equation

2: Ndfa is the N amount in the shoot biomass at flowering or N amount in the seed that is

derived from atmosphere i.e., fixed N, hereafter referred to as Ndfa_Shoot and Ndfa_Seed,

respectively; Ntotal is the total N in the fixing plant that includes both fixed N and mineral

N from the soil; %Ndfa is the N percent derived from atmosphere computed in equation 1.

In the case of Ndfa_Seed, Ntotal refers to the total N in the seed yield per hectare that was

computed from plot seed yield in Field_2013.

Phenotypic Data Analyses

Statistical analyses for field data were conducted using mixed models in SAS 9.3 (SAS

Institute 2011). Assumption for normally distributed residuals required for analysis of

variance (ANOVA) and SNP-trait association test was checked for all traits measured.

Normality tests were conducted on the combined residuals of all treatments for each trait

using PROC UNIVARIATE and traits that were not normally distributed were

transformed. Normality test results indicated that all traits except seed N percentage were

64

not normally distributed. Therefore, all traits except seed N percentage were transformed

using natural logarithmic transformation for use in ANOVA and GWAS analyses. All the

trait means are reported in their original values. An ANOVA using PROC MIXED was

conducted on all the traits based on the following statistical model:


Where: Yijk is the response variable e.g., Ndfa, with genotype i in the environment j,

replication k within environment; αi was the fixed effect of the genotype i; βj was the

random effect of the environment j; αβ was the random effect of the interaction between

genotype i and environment j; γ was the random effect of a replication with environment j;

ε was the random error term, which was assumed to be normally distributed with mean =0.

Genetic correlation analyses were conducted on selected traits using multivariate restricted

maximum likelihood estimation with SAS PROC MIXED as described in Holland (2006).

Genotyping

DNA was collected as described in Cichy et al. (2015). DNA samples were genotyped

using an Illumina BARCBean6K_3 BeadChip with 5398 SNPs (Hyten et al., 2010) in the

Soybean Genomics and Improvement USDA Laboratory (USDA–ARS, Beltsville Agricultural

Research Center) in Maryland, US. The SNP genotyping was conducted on the Illumina

platform by following the Infinium HD Assay Ultra Protocol (Illumina Inc.). The Infinium

II assay protocol includes the procedures to make, incubate, and fragment amplified DNA,

prepare the bead assay, hybridize samples to the BARCBean6K_3 BeadChip, extend and

stain samples, and image the bead assay. The SNP alleles were called using the

65

GenomeStudio Genotyping Module v1.8.4 (Illumina, Inc.). The data were manually

adjusted for allele calls.

Population Structure Analysis and Marker-Trait Association Tests

After filtering for low quality and monomorphic SNPs, 5326 SNPs were retained. These

were filtered further for minor allele frequency (MAF≥0.05) and a final total of 4623 SNPs

were used in population structure, kinship, and association analyses. GWAS results can

be confounded by population stratification. The two popular methods for detecting

population stratification in association panels are Principal Component Analysis (PCA)

(Price et al. 2006) and subpopulation clustering using STRUCTURE (Pritchard et al. 2000).

To decide on the best method to use in our study, we compared the effectiveness of PCA

and STRUCTURE based on quantile-quantile (QQ) plots from association tests for all traits

using a Mixed Linear Model implemented in TASSEL 5.0 (Bradbury et al. 2007). Principal

Component Analysis (PCA) was implemented in software program EIGENSTRAT (Price

et al. 2006). As illustrated in the QQ plots for seed N percentage (Figure 2.1), both PCA

and STRUCTURE were effective in controlling for population structure. In both methods,

there was a near-agreement of plots of expected and observed p-values with the X=Y line

until a sharp curve towards the end representing what may be true associations. The trend

was similar for all traits. Based on these results we chose PCA for assessing the population

structure in the panel and account for it in association tests for all traits reported in this

study. To correct for cryptic relatedness in the panel, Kinship matrix (K) was included in

our association analyses. The kinship matrix was calculated using Scaled Identity by

Descent method implemented in TASSEL 5.0 (Bradbury et al. 2007). To determine the

66

SNP-trait associations, we used a Mixed Linear Model (MLM) (Zhang et al. 2010)

implemented in software program TASSEL 5.0. The following MLM equation was used:

𝑌𝑌 = 𝑋𝑋𝛼𝛼 + 𝑃𝑃𝛽𝛽 + 𝐾𝐾𝜇𝜇 + ℰ

Where: Y the phenotype of a genotype; X was the fixed effect of the SNP; P was the fixed

effect of population structure (from PCA matrix); K was the random effect of relative

kinship i.e., cryptic relatedness among genotypes (from kinship matrix); ε was the error

term, which was assumed to be normally distributed with mean = 0. To estimate the

proportion of phenotypic variation accounted for by a significant SNP we used the R2

computed in TASSEL. We used the conservative Bonferroni correction to control for error

(false positives) associated with multiple tests. The Bonferroni corrected threshold P-value

of 1.1 x 10-5 was calculated for 4623 SNP-trait association tests for each trait at α = 0.05.

Candidate Gene Identification

We used Jbrowse on Phytozome v10 (Goodstein et al. 2012) to browse the common bean

genome version 1.0 (Schmutz et al. 2014), to gain insights into positional candidate genes

associated with significant SNPs. A gene was considered a candidate gene if it contained a

significant SNP (Bonferroni corrected P=1.1 x 10-5) or if there was a significant SNP

within the immediate genomic region (±20 kb). The focus was on the most significant SNPs

(peak SNPs with strongest signal) on each chromosome. We also conducted LD analyses

in TASSEL 5.0 (Bradbury et al. 2007) to determine the strength of LD between the most

significant SNP and its immediate surrounding significant SNPs to be more confident that

they were tagging the same candidate gene. In addition, the gene was considered a

candidate gene if it coded for a protein whose role or possible role in SNF or related traits

67

had been established or proposed. If there was no functional annotation on Phytozome v10,

we did a BLAST search (Zhang et al. 2000) using the genomic sequence as a query against

the Arabidopsis thaliana genome on TAIR (Rhee et al. 2003) and soybean genome on

NCBI.

Results

Population Structure

The PCA indicated the presence of a population structure among genotypes in the ADP.

The first, second, third and fourth principal components accounted for 41.3%, 7.1%, 4.3%

and 2.6% of the genotypic variability in the ADP, respectively. A plot of PC1 against PC2

revealed the existence of two clusters (Figure 2.2). The smaller cluster comprised of 22

genotypes. Fourteen of these were landraces from East Africa, five were cultivars from

North America, two were cultivars from the Caribbean and one was a cultivar from South

America. The larger cluster had 239 genotypes comprised of landraces, elite lines and

cultivars from several geographic regions. To account for this population structure, we used

the first four PC’s that together explained over 55.3% of the genotypic variation in the ADP

in MLM for association tests. The variation explained by subsequent PC’s after the fourth

PC only had marginal incremental values hence the decision to use only the first four PC’s.

Greenhouse Experiments

Highly significant differences (P<0.001) were observed among ADP genotypes in

GH_2012 and GH_2014 for chlorophyll content, nodule dry weight, shoot biomass, N% in

68

shoot biomass and Ndfa (Table 2.1). The means of these five traits were slightly higher in

GH_2014 than GH_2012. The frequency distribution graphs for Ndfa_Shoot in GH_2012

and GH_2014 showed a continuous distribution that is typical of a quantitative trait (Figure

2.3). There were several significant genetic correlations among traits measured in the GH.

In GH_2012, Ndfa significantly correlated with chlorophyll content (r=0.49; P<0.001),

shoot biomass (r=0.98; P<0.001), and nodule dry weight (r=0.8; P<0.001).

Field Experiments

There were highly significant differences (P<0.001) among ADP genotypes evaluated in

both Field_2012 and Field_2013 for chlorophyll content, nodule score, shoot biomass at

flowering, and N% in shoot biomass. Genotype differences in N% in the seed were also

significant in Field_2013. The means for chlorophyll content and shoot biomass were

higher in Field_2012 than Field_2013 while the mean for nodule score was higher, in

Field_2013 than Field_2012 (Table 2.1). In both Field_2012 and Field_2013, highly

significant differences among genotypes were observed for %Ndfa_Shoot and

Ndfa_Shoot. In Field_2012, %Ndfa_Shoot ranged from 0.8% to 41.4% with an average of

12.4%, and exhibited a narrower range and smaller average than in Field_2013 where

%Ndfa_Shoot ranged from 1.7% to 88.5% with an average of 37.3% (Table 2.1). This

represented a nearly four-fold increase in Ndfa_Shoot average between Field_2012 and

Field_2013. In Field_2013, there was a strong correlation (r=0.9; P<0.001) between

Ndfa_Shoot and shoot biomass but in Field_2012 this correlation was not significant.

Correlation between Ndfa_Shoot and chlorophyll content was significant in Field_2013

but not in Field_2012. Genotype by year interactions were highly significant (P<0.001) for

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all six traits recorded in Field_2012 and Field_2013. The best performing genotype for

Ndfa_Shoot in Field_2013 was ADP631, a Canadian light red kidney cultivar, OAC

Inferno whose %Ndfa_Shoot estimate was 88.5%. However in Field_2012 the

%Ndfa_Shoot for ADP631 was estimated at 7%.

Significant differences for %Ndfa_Seed and Ndfa_Seed were observed among genotypes

in Field_2013. The %Ndfa_Seed ranged from 3.6% to 98.2% with an average of 45.5%.

The Ndfa_Seed ranged from 1.4 to 98.6 kg ha-1 with an average of 29.5 kg ha-1. The

Ndfa_Seed frequency distribution graph follows a pattern consistent with that for a

quantitative trait (Figure3). There was a significant (r=0.30; P<0.001) correlation between

%Ndfa_Shoot and %Ndfa_Seed. The 10 genotypes that performed better in both

%Ndfa_Seed and Ndfa_Seed and %Ndfa_Shoot are shown in Table 2.2. These 10

genotypes had %Ndfa_Seed and Ndfa_Seed greater than 70% and 55 kg ha-1, respectively,

and could be considered superior in both N fixation and partitioning of fixed N to the seed.

Marker-Trait Associations

Chlorophyll Content In GH_2012, two SNPs both on chromosome Pv09 were significantly associated with

chlorophyll content. The most significant SNP (ss715647747; P=6.1 x 10-06) explained

about 7% of the variation in chlorophyll content (Table 2.3). In GH_2014 three SNPs, all

on Pv09 were significantly associated with chlorophyll content, and the most significant

SNP (ss715648916; P=3.1 x 10-6) explained about 8% of the genetic variability. The two

most significant SNPs in GH_2012 and GH_2014 (ss715647747 and ss715648916) were

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in strong linkage disequilibrium (LD) (r2=0.98; D’=1). The most significant SNP

(ss715647747) on Pv09 in GH_2012 was also significant in GH_2014.

In Field_2012, only one significant SNP for chlorophyll content was identified on Pv01

that explained about 9% of variation (Table 2.3). Significant SNPs for chlorophyll content

were identified in Field_2013 on Pv09. In Field_2013, the most significant SNP was

ss715648916 and explained about 7% the chlorophyll content variation (Table 2.3). This

SNP was also the most significant for chlorophyll content in GH_2014. Some of the

significant SNPs for chlorophyll content on Pv09 were also significant for other traits in

GH and field experiments. Significant SNP ss715648916 for chlorophyll content in

GH_2014 was also significant for shoot biomass in GH_2014. Another SNP

(ss715647747) significant for chlorophyll content in GH_2013 was also significant for

shoot biomass in GH_2013. Significant SNPs for chlorophyll content on Pv09 were

consistently identified in two GH experiments and in Field_2013. In some cases the

significant SNPs for chlorophyll content in GH_2014 were same as those in Field_2013

(Table 2.3).

Nodulation Nodulation was evaluated as nodule dry weight in the GH, and as nodule score in the field

experiments. Two SNPs were significant for nodulation in Field_2013, and both were

located on Pv09 (Table 2.3). The most significant SNP (ss715648787; P=1.1 x 10-6)

explained about 12% of the variability in nodule scores in Field_2013. This SNP was also

significant for chlorophyll content (GH_2014 and Field_2013), shoot biomass (GH_2014),

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N% in shoot biomass (GH_2014 and Field_2013), N% in seed (Field_2013), %Ndfa_Shoot

(Field_2013), Ndfa_Shoot (Field_2013 and GH_2014), and Ndfa_Seed (Table 2.3). No

significant SNPs for nodulation were identified in Field_2012 or the two GH experiments.

Shoot Biomass Significant SNPs for shoot biomass were identified in both GH and field experiments. In

GH_2012, eleven SNPs on Pv01, Pv03, Pv07, and Pv09 were significant. The most

significant SNP was on Pv09, and explained about 11% of variation in shoot biomass

(Table 2.3). In GH_2014, four SNPs on Pv09 were significant. The most significant (P=1.3

x 10-08) SNP was ss715648916, and explained about 13% of variability in shoot biomass

in GH_2014. This SNP was also significant for chlorophyll content, nodulation, N% in

shoot biomass, %Ndfa and Ndfa in GH, and field experiments (Table 2.3). In Field_2012,

five SNPs on Pv07 and Pv08 were significant, the most significant being on Pv07 that

explained about 11% of the variation in shoot biomass. In Field_2013, three significant

SNPs, on Pv04 were significant. The most significant (P=6.4 x 10-08) SNP in Field_2013

explained about 17% of variation shoot biomass.

N Percentage in Biomass Significant SNPs for N% in shoot biomass were identified in GH_2014 and Field_2013.

In GH_2014, three SNPs on Pv03 and ten SNPs on Pv09 were significant. The most

significant (3.7 x 10-09) SNP in GH_2014 was ss715648916 on Pv09 and explained about

15% of variation (Table 2.3, Figure 2.4). In Field_2013, two SNPs on Pv09 were

significant. The most significant SNP was the same one detected in GH_2014 but with a

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lower R2 value of 10%. This SNP (ss715648916) on Pv09 that was consistently significant

in both GH and field experiments for N% in shoot biomass was also significant for N% in

the seed (Figure 2.4). In addition, ss715648916 was significant for chlorophyll content,

nodulation, shoot biomass, %Ndfa_Shoot, Ndfa_Shoot, Ndfa_Seed in GH and field

experiments (Table 2.3). No significant SNPs were identified for N% in shoot biomass in

GH_2012 and Field_2012.

N Percentage in Seed A total of seventeen SNPs were significantly associated with N percentage in the seed.

Sixteen SNPs were on Pv09 and one SNP was on Pv03. The most significant SNP

(ss715648916; P=1.1 x 10-9) was on Pv09 that explained about 17% of variation in N

percentage in the seed (Table 2.3). This SNP was also significant for N percentage in the

shoot biomass at flowering in (GH_2012, GH_2013 and Field_2013), Ndfa (GH_2012),

Ndfa_Shoot (Field_2013), and Ndfa_Seed (Field_2013).

%Ndfa in Shoot Biomass at Flowering in Field Experiments In Field_2013, significant SNPs for %Ndfa_Shoot were identified on Pv02, Pv03, Pv07,

Pv09, Pv10 and Pv11 (Table 2.3). The most significant (ss715646392; P=2.9 x 10-13) SNP

was on Pv03 and explained about 22% of variation in Field_2013. The most significant for

%Ndfa_Shoot on Pv09 (ss715648916) that explained about 19% of variation was also

significant for chlorophyll content, shoot biomass, N% in shoot biomass and Ndfa in GH

and field experiments (Table 2.3). There were no significant SNPs for %Ndfa_Shoot in

Field_2012.

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Ndfa in Shoot Biomass at Flowering in GH and Field Experiments Significant SNPs for Ndfa were identified in both GH and field experiments (Figure 2.5).

In GH_2012, a total of 12 SNPs on Pv03, Pv07 and Pv09 were significant for Ndfa. The

highest number (nine) of significant SNPs was on Pv07. The most significant SNP was on

Pv09, and explained about 13% of variability in Ndfa (Table 2.3). Most of the significant

SNPs for Ndfa in GH_2012 were also significant for shoot biomass in GH_2012. In

GH_2014, one SNP on Pv02, ten SNPs on Pv03 and 12 SNPs on Pv09 were significant for

Ndfa. The most significant SNP (ss715648916; P=3.4 x 10-13) was on Pv09, and explained

about 20% of variation in Ndfa in GH.

In Field_2013, a total of 25 SNPs on Pv02, Pv03, Pv07, Pv09, Pv10 and Pv11 were

significant for Ndfa_Shoot. The most significant SNP was on Pv03 and explained about

23% of Ndfa_Shoot variation in Field_2013 (Table 2.3). Most significant SNPs for

Ndfa_Shoot in Field_2013 were consistently significant for Ndfa in GH_2012 and

GH_2014 (Figure 2.5). There were no significant SNPs for Ndfa_Shoot identified in

Field_2012.

Ndfa and %Ndfa in Seed for Field_2013 A total of eleven SNPs, five on Pv03 and six on Pv09 were significant for Ndfa_Seed in

Field_2013. The most significant SNPs on Pv03 (ss715646392) and Pv09 (ss715648916)

explained about 9% and 11%, respectively, of Ndfa_Seed variability in Field_2013. These

two most significant SNPs for Ndfa_Seed on Pv03 and Pv09 were also the most significant

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for Ndfa_Shoot on Pv03 and Pv09 in GH_2014 and Field_2013 (Table 2.3). In addition,

ss715648916 on Pv09 was also significant for nodulation (Figure 2.6), chlorophyll content,

N percentage in shoot biomass at flowering and N percentage in seed in Field_2013. No

significant SNPs for %Ndfa_Seed in were identified.

Allelic Effects of Significant SNPs on Ndfa_Shoot

Using Ndfa_Shoot data from Field_2013, we assessed the allelic effects on Ndfa_Shoot of

significant SNPs located on Pv03, Pv07, and Pv09. The most significant SNP on Pv03

ss715646392 had C as its minor allele (0.05; Table 2.3), and T as the major allele, which

had a major effect on Ndfa_Shoot. The Ndfa_Shoot for homozygous TT and CC were 125

and 94 mg N per plant, respectively. The most significant SNP on Pv07 was ss715646473.

The minor allele for this SNP was G (MAF=0.06; Table 2.3) while A was the major allele

and was the allele that had a major effect on Ndfa_Shoot in Field_2013. At this SNP, the

homozygous AA and GG genotypes for Ndfa_Shoot were 120 and 92 mg N per plant,

respectively. The most significant SNP on Pv09 was ss715648916. The minor allele at this

SNP was C (MAF=0.09; Table 2.3), and the major allele was T. The minor allele C had a

major effect on Ndfa_Shoot. At this SNP, homozygous CC genotypes fixed about 163 mg

per plant of N compared to 112 mg N per plant for TT genotypes. Genotypes possessing

alleles with major effects did not come from a single geographic region or market class.

We assessed the effect on Ndfa_Shoot of having all the three major effect alleles of

ss715646392 (Pv03), ss715646473 (Pv07) and ss715648916 (Pv09) occurring

simultaneously in a single genotype. The OAC Inferno cultivar (ADP631) was the only

genotype in the ADP that had major effect alleles at all three most significant SNP loci for

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Ndfa_Shoot. In addition, this genotype also carried major effect allele at ss715647197,

which was the most significant SNP in GH_2012.

Discussion

The genetic enhancement of SNF in common bean requires adequate genetic variability for

the trait, and an understanding of the genetic basis of this variability would also foster

breeding strategies that deploy marker technology. In this study, we investigated the

variability of SNF and related traits in the Andean Diversity Panel of common bean. We

explored the genetic basis of this variability, using a genome-wide association approach.

We observed significant differences among genotypes, and wide phenotypic ranges for

Ndfa_Shoot and Ndfa_Seed measured in GH and field experiments. The high averages for

%Ndfa_Shoot (37.3%) and %Ndfa_Seed (45.5%) in Field_2013 are comparable to

estimates from previous studies (Graham et al. 2003; Hardarson et al. 1993; Unkovich and

Pate 2000). In Field_2013, ten genotypes in the panel had both %Ndfa_Shoot and

%Ndfa_Seed values higher than 50% and 70%, respectively (Table 2.2), which was higher

than most previous estimates (Giller 2001; Hardarson et al. 1993; Tsai et al. 1993;

Unkovich and Pate 2000; van Kessel and Hartley 2000). Common bean has been

considered poor in SNF when compared to other grain legumes such as soybean. Reports

indicate that soybean can be grown without supplemental N fertilizer and still produce

competitive seed yields as most genotypes fix over 70 %Ndfa (Giller 2001). Results of the

current study show that there are common bean genotypes within the Andean gene pool

with competitive %Ndfa values that rival those of soybean, and could be grown without

supplemental N fertilizer. In addition, this study has provided evidence of adequate genetic

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variability within the Andean gene pool to support genetic improvement of Andean beans

for enhanced N fixation. The ten genotypes identified in Table 2.2 were not only superior

in %Ndfa_Shoot and %Ndfa_Seed, but also in partitioning and remobilizing fixed N to the

economic yield (i.e. seed). These ten genotypes were from different geographic regions of

Africa, North America, and Europe and could potentially be used as germplasm in breeding

for enhanced SNF. In addition different market classes were represented in this class of

genotypes with enhanced SNF. This is advantageous from a plant breeding perspective as

breeding programs from Africa or the Americas can choose genotypes adapted to local

growing environments. Breeding for enhanced SNF within local market classes and

maturity classes will increase the prospects of recovering progenies with desirable

agronomic traits, provided selection is practiced in low-N soils. Although Ndfa_Seed does

not capture all the variability for SNF, focusing selection on high Ndfa_Seed would be

easier to integrate into most breeding programs as seed is generally harvested and does not

necessitate additional measurement of traits at flowering.

A significant correlation (r=0.7, P<0.001) between flowering and Ndfa_Shoot was

observed in Field_2013. In general, genotypes that flowered later had higher Ndfa_Shoot

values than those that flowered earlier. This result was expected because an early maturing

genotype does not have sufficient time to accumulate sufficient above ground biomass that

could serve as an adequate source and sink for photo-assimilates and fixed N, respectively.

In addition, the period of active N fixation before the on-set of nodule senescence in early

maturing genotypes is shorter resulting in lower amounts of N fixed. Delayed flowering

has long been known to lead to a significant amount of N fixed in legumes (Graham 1981).

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One study in soybean suggested that a delay in flowering of 9 days would double seasonal

N fixation (Hardy and Havelka 1976). This association complicates breeding for enhanced

SNF in environments where growing seasons are short, and emphasis must be placed on

earliness.

Chlorophyll content and shoot biomass were higher in Field_2012 than Field_2013,

whereas nodule score, %Ndfa_Shoot and Ndfa_Shoot were higher in Field_2013 than

Field_2012 (Table 2.1). This anomaly could be attributed to differences in soil N at the

time of planting in these two years. In Field_2012, mineral N (nitrate) in the soil was 36

mg kg-1 compared to 2.4 mg kg-1 in Field_2013. Higher soil N suppresses nodulation while

lower soil N enhances nodulation and SNF. Differences in the significance of correlations

between shoot biomass and Ndfa_Shoot in Field_2012 and Field_2013 could also be due

to differences in soil N in these two years. Under high soil N, most of the N required for

shoot biomass production would be coming from soil N while in low soil N SNF would be

the major source of N. The correlation between shoot biomass and Ndfa_Shoot is weakened

when evaluations for SNF are conducted on high N soils, which has implications from a

breeding perspective. Because of the expensive nature of SNF tests, routine selection for

SNF is rarely conducted with common bean. Identification of traits indirectly related to

SNF that can be measured cost effectively would be valuable. Shoot biomass fits this

requirement, and has been used in previous studies to indirectly select for SNF, and as a

proxy trait to identify QTL for SNF in soybean (Santos et al. 2013). However, indirect

selections for SNF using shoot biomass would only be effective when conducted under low

soil N. This also applies to using chlorophyll content since field measurements with a

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SPAD meter are fast and inexpensive, making it desirable as a phenotyping tool in breeding

for enhanced SNF. Likewise, its effectiveness for use as an indirect trait for selecting for

SNF would only be effective if plants are evaluated in a low-N site.

The total amount of N fixed by the plant is a product of biomass and N%. To maximize on

the amount of N fixed by the plant both factors should be high. Genotypes that derive most

of their N from the atmosphere (high %Ndfa) but have lower amount of shoot biomass

would result in lower total N fixed. In this study, we observed genotypes that had higher

%Ndfa_Shoot but only had modest amounts of Ndfa_Shoot because they produced less

biomass. Genotypes with both high %Ndfa_Shoot and shoot biomass had the highest total

N fixed. This relationship is consistent with prior knowledge that in general bush types fix

lower amount of N than the climbing beans despite the %Ndfa being higher in some bush

types than in climbing beans (Graham and Rosas 1977; Graham 1981). From a breeding

perspective, however, a preferred genotype would be one that not only fixes adequate N,

but also partitions and remobilizes the fixed N to the seed.

In Field_2013, the average %Ndfa_Shoot was 37%, which was lower than 45% for

Ndfa_Seed (Table 2.1). The 15N natural abundance method used in this study measures

%Ndfa and Ndfa in a time-integrated manner. Therefore, this difference was expected, and

it represents the amount of N that was fixed from flowering up to the time when the nodules

senesced. The magnitude of this difference would depend on how long the nodules can

continue actively fixing N after the on-set of plant reproductive phase given the competing

needs for photo-assimilates by the nodules and seed filling. There are suggestions in

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literature that selecting genotypes whose nodules senescence late can be an avenue for

maximizing the amount of Ndfa between flowering and physiological maturity (Giller

2001).

Marker-Trait Associations

In the current study significant SNPs for nodulation were identified on Pv09 in Field_2013.

The most significant SNP on Pv09 was also consistently associated with Ndfa_Shoot and

related traits in both GH and field experiments. Previous studies in common bean that used

bi-parental mapping populations have reported QTL for nodulation. Tsai et al. (1998)

reported QTL for nodule number on Pv02, Pv04, Pv05 and Pv09 using BAT93 x Jalo

EEP558 population of RILs evaluated under high N. Nodule number and nodule dry weight

have previously been used as proxies for SNF in studies aimed at identifying QTL for SNF

in common bean and soybean (Santos et al. 2013). We did not identify QTL for nodule dry

weight in the two GH experiments. However, in the Field_2013, where we used a nodule

score as a quick and less labor-intensive method than nodule dry weight, we identified

significant SNPs for nodulation. In addition, significant SNPs for nodulation (nodule score)

co-localized with significant SNPs for Ndfa_Shoot and Ndfa_Seed in Field_2013. These

results demonstrated that nodule dry weight may not be a useful proxy trait to identify QTL

for SNF in GH studies, but in field experiments a less labor-intensive nodule score is an

effective proxy trait.

In the current study, several SNPs on Pv03, Pv07, and Pv09 were consistently significant

for Ndfa_Shoot in both GH and field experiments. We explored the effects of alleles at the

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most significant loci for Ndfa_Shoot i.e. ss715646392, ss715646473 and ss715648916 on

Pv03, Pv07 and Pv09, respectively. We were particularly interested in genotypes that

carried major effect alleles at all these three loci. We identified OAC Inferno (ADP631) as

the only genotype in the panel that carried beneficial alleles at all three SNP loci.

Interestingly, OAC Inferno had the highest Ndfa_Shoot (88.5%) and second highest

Ndfa_Seed (98.2%) in Field_2013. This result though involving a single genotype may

suggest the additive effects of these major alleles at significant SNP loci for Ndfa_Shoot.

Combining these major alleles in the same background during breeding could provide

phenotypes with enhanced SNF. However, combining these alleles through conventional

selection would be challenging. The most effective and efficient way would be through

marker-assisted selection using markers that could tag these alleles.

We also explored differences or similarities of association tests results for Ndfa measured

at flowering using entire shoot biomass (Ndfa_Shoot) and Ndfa measured using seed

(Ndfa_Seed). There were more significant SNPs, on more chromosomes that were

associated with Ndfa_Shoot than Ndfa_Seed. In addition, when R2 values of consistently

significant SNPs for Ndfa_Shoot and Ndfa_Seed in Field_2013 were compared, R2 for

Ndfa_Shoot were larger than Ndfa_Seed. This trend is best illustrated by ss715646392, the

most significant SNP for both Ndfa_Shoot and Ndfa_Seed in Field_2013 on Pv09, where

R2 was reduced from 23% for Ndfa_Shoot to 11% for Ndfa_Seed. In the case of the most

significant SNP ss715648916 for Ndfa_Shoot and Ndfa_Seed on Pv09, the R2 value

decreased from 14% for Ndfa_Shoot to 9% for Ndfa_Seed. The reduction in the number

of significant markers and the R2 values when Ndfa_Shoot is compared to Ndfa_Seed may

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be attributed to the confounding effect of genotypic differences in remobilization and

partitioning efficiency of fixed N to the seed (Ndfa_Seed). The Ndfa_Shoot biomass would

not be confounded by genotypes differences in remobilization or partitioning since the

entire above ground shoot biomass was used to estimate Ndfa_Shoot. Therefore, a

correlation between Ndfa_Shoot and genotype would be expected to be stronger than

correlation between Ndfa_Seed and genotype. This association could possibly have

resulted in the identification of more significant SNPs with larger effects on Ndfa_Shoot

than Ndfa_Seed.

Co-localization of significant SNPs for Ndfa_Shoot and Ndfa_Seed was observed on Pv03

and Pv09 for Field_2013. In addition, a significant correlation between Ndfa_Shoot and

Ndfa_Seed was detected. Ndfa in seed only accounts for fixed N in seed, which is underlain

by several physiological processes controlling partitioning and remobilization of N to the

seed, and does not account for N in the rest of plant biomass. Therefore, we were intrigued

by the co-localization of significant SNPs for Ndfa in shoot biomass at flowering, which

includes the entire above ground biomass. This co-localization of significant SNPs for

Ndfa_Shoot and Ndfa_Seed that were derived from different tissues and growth stages

provided further support for important roles of genomic regions on Pv03 and Pv09 in

controlling Ndfa. This co-localization suggests that at the time of physiological maturity

the seed is the major sink of fixed N and most of the N in other plant parts i.e. leaves, stems

and pod walls is remobilized to the seed.

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In this study we identified a significant genetic correlation between days to flowering and

Ndfa_Shoot (r=0.7, P<0.001). We explored whether the QTL for Ndfa_Shoot and

Ndfa_Seed identified in this study co-localized with the flowering QTL on Pv01,

previously identified by Kamfwa et al. (2015). None of the QTL for Ndfa identified in the

current study co-localized with the flowering QTL on Pv01. Similarly, none of QTL for

Ndfa identified in the current study co-localized with genomic region on Pv01 were the fin

(PvTFL1y) gene that controls determinacy is located (Kwak et al. 2008; Repinski et al.

2012) and recently validated in the ADP (Cichy et al. 2015). In addition, correlation

between Ndfa and determinacy was weak (r=0.2). These results suggest that the genetic

basis of N fixation in the ADP was not influenced by flowering or determinacy despite the

strong correlations between Ndfa and flowering. The QTL for Ndfa_Shoot and Ndfa_Seed

co-localized with QTL for seed yield on Pv03 identified by Kamfwa et al. (2015). Since

most of the N in the seed produced under low N is derived from SNF, genotypes superior

in SNF are likely to produce higher seed yield than genotypes with low SNF potential on

a low N soil. This result provides further support for recommendations by Bliss et al. (1993)

that selection based on high seed yield produced under low N is effective for genetic

enhancement of SNF in common bean.

Previous studies on the genetic architecture of SNF in common bean are scarce. Ramaekers

et al. (2013) is the only published study in common bean that identified QTL for Ndfa. In

that study QTL were identified for Ndfa on Pv04 and Pv10 using an intergene pool RIL

population of G2333 x G19839 evaluated in the GH. When this population was evaluated

in the field, QTL for Ndfa were identified on Pv01 and Pv10. Differences in marker

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platforms make it difficult to determine whether significant SNPs on Pv01 and Pv10 in the

current study co-localize with the QTL identified by Ramaekers et al. (2013). We identified

more QTL for Ndfa_Shoot since more alleles for Ndfa likely exist in the diverse ADP than

the number of alleles segregating in the bi-parental population used by Ramaekers et al.

(2013). The level of N available in field studies clearly effects the detection of QTL that

control SNF based on lack of results from Field_2012 when N levels were high. The

relatively high soil N levels (90 mg kg-1 N) available in the field where Ramaekers et al.

(2013) evaluated the RIL population could have had a confounding effect on the expression

of genes for Ndfa, resulting in fewer QTLs identified. In this study we identified several

significant SNPs for Ndfa_Shoot in GH_2012, GH_2014 and Field_2013 on seven

chromosomes, and the variation explained by individual significant SNPs ranged 8% to

23%. Given the limitation of the size of the association panel, our study was underpowered

to identify QTL with smaller effects. Therefore, we could have missed QTL with smaller

effects. Larger association panels with greater marker density would help identify these

QTL with smaller effects. Knowing whether the QTL identified in the Andean germplasm

in the current study are the same in the Middle-American gene pool would be useful for

breeders.

Candidate Genes Associated With Significant SNPs

One of the advantages of GWAS over QTL mapping that uses bi-parental mapping

populations is the ability to identify positional candidate genes, which results from

enhanced mapping resolution. In this study, we identified three candidate genes for BNF

and related traits. The first candidate gene was Phvul.009G136200 on Pv09 that codes for

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leucine-rich repeat receptor-like protein kinase (LRR-RLK). This gene was 12.7 kb

downstream of ss715648916, which was consistently significant for Ndfa_Shoot

(GH_2014 and Field_2013) and Ndfa_Seed (Field_2013) (Figs. 4, 5, 6). In addition,

ss715648916 was significant for nodulation (Field_2013), chlorophyll content

(Field_2013), shoot biomass (GH_2014), N percentage in shoot biomass (Field_2013 and

GH_2014), and N% in the seed (Field_2013). LRR-RLK’s have been reported to play a

critical role in signal transduction required for nodule formation (Sanchez-Lopez et al.

2012; Stracke et al. 2002). The Rhizobium releases the lipochitooligosaccharides (Nod

factors) that are perceived by the LRR domain of the LRR-RLK. This results in the

formation of signaling complex and subsequent downstream responses that include the

formation of infection thread and nodules (Stracke et al. 2002). A second candidate gene

Phvul.007G050500 on Pv07 also encodes a LRR-RLK. The SNP (ss715646473) associated

with this gene was consistently significant for Ndfa_Shoot in GH_2012 and Field_2013

(Table 2.3, Fig 5). This SNP was located in the exon of Phvul.007G050500 and is part of

the LRR domain for signal perception. Three genes in the immediate upstream and two

genes in the downstream region of Phvul.007G050500 were identified also as LRR-RLK.

Sanchez-Lopez et al. (2011) demonstrated the role of LRR-RLK’s in nodule development

in common bean. For example, the knockdown expression through RNAi of an LRR-RLK

gene called PvSymRK in common bean resulted in the formation of scarce and defective

nodules (Sanchez-Lopez et al. 2011). It is plausible that the three LRR-RLK candidate

genes we have identified in the current study are among many other genes with a role in

nodule development and nitrogen fixation in common bean as SNF is an integrated process

occurring over a longer time period. The identification of four genes encoding LRR-RLK

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as candidate genes for Ndfa demonstrates that early events in the infection process may

play a key role in determining the amount of N fixed by the plant.

The other candidate gene identified on Pv09 was Phvul.009G231000 (Table 2.3) that was

associated with ss715647197, the most significant SNP for Ndfa in GH_2012.

Phvul.009G231000 was 1.1 kb upstream of ss715647197. Because there was no functional

annotation for this gene on Phytozome, a BLAST search revealed the highest hits in A.

thaliana (TAIR) and Medicago truncatula (NCBI) were for genes that code for calmodulin,

which are calcium-transporting proteins (Mitra et al. 2004). Following the perception of

Nod factors by the legumes, there is a spike in the levels of free calcium in the cytoplasm

of cells for roots hairs (Riely et al. 2004). Calcium spiking is reported to be an essential

component of the signaling cascade required in nodule development (Levy et al. 2004).

The nodulation signaling pathway has been reported to contain calcium-activated kinases.

Alfalfa mutants defective in calcium-spike response do not nodulate (Ehrhardt et al. 1996).

The influx of calcium to the root hair is reported to cause depolarization and subsequent

curling of the root hair that precedes the formation of an infection thread and nodules. The

flux of calcium in the root hair cells is mediated by calcium binding proteins called

calmodulin (Riely et al. 2004; Stacey et al. 2006). It is plausible that the candidate gene

Phvul.009G231000 that has high sequence similarity to calmodulin genes in A. thaliana

played a significant role in calcium spikes and subsequent root hair morphological changes

required for nodule formation. Further functional genomics studies are required to confirm

the roles of the identified candidate genes in SNF.

86

Conclusion

In this study we explored the genetic architecture of SNF and related traits in common

bean. The enhanced mapping resolution from GWAS resulted in the identification of

several significant SNPs and candidate genes for SNF and related traits. Once the identified

QTL in this study are validated in different populations and genetic backgrounds, they

could potentially be used in marker-assisted breeding to accelerate the genetic

improvement of SNF in common bean.

Acknowledgements

Research was supported by the Feed the Future Innovation Lab for Collaborative

Research on Grain Legumes by the Bureau for Economic Growth, Agriculture, and

Trade, U.S. Agency for International Development, under the terms of Cooperative

Agreement No. EDH-A-00-07-00005-00; and the U.S. Department of Agriculture,

Agricultural Research Service. The opinions expressed in this publication are those of the

authors and do not necessarily reflect the views of the U.S. Agency for International

Development or the U.S. Government. We also thank Dr. Zixang Wen for his helpful

comments on some aspects of data analyses.

87

APPENDIX

88

Table 2.1. Means and ranges for traits associated with Symbiotic Nitrogen Fixation in

Andean Diversity Panel of 259 common bean genotypes grown in the GH in 2012 and

2014 at Michigan State University, East Lansing, MI and in the Field at Montcalm

Research Farm, Entrican, MI in 2012 and 2013.

Trait Experiment Mean Min. Max. Chlorophyll Content (SPAD) GH_2012 29.8±0.2 20.6 39.8 GH_2014 35.1±0.2 25.1 45.8 Field_2012 35.5±0.1 27.0 44.9 Field_2013 32.2±0.1 22.2 40.8 Nodule Dry Wt./Plant (mg) GH_2012 117±2.0 37 272 GH_2014 140±3.0 49 285 Nodule Score (0-6 scale) Field_2012 2.9±0.1 0.5 6.0 Field_2013 4.1±0.1 1.0 6.0 Shoot Biomass /Plant (g) GH_2012 3.1±0 1.3 6.8 GH_2014 3.6±0.1 1.1 8.3 Field_2012 20.0±0.2 8.7 38.4 Field_2013 10.1±0.1 5.1 17.0 N% in Shoot Biomass GH_2012 3.2±0 2.2 4.5 GH_2014 2.8±0 2.1 3.7 Field_2012 3.1±0 2.3 4.1 Field_2013 3.0±0 1.7 3.9 N% in Seed Field_2013 3.9±0 3.1 4.7 %Ndfa_Shoot Field_2012 12.4±0.6 0.8 41.4 Field_2013 37.3±0.8 1.7 88.5 %Ndfa_Seed Field_2013 45.5±1.2 3.6 98.7 Ndfa_Shoot /Plant (mg) GH_2012 59±1 10 112 GH_2014 62±1 9 130 Field_2012 71±4 2 273 Field_2013 123±4 6 523 Ndfa_Seed (kg ha-1) Field_2013 29.5±2.7 2.9 92.3

Ndfa= Nitrogen derived from atmosphere; GH_2012=evaluations in the GH in 2012;

GH_2014=evaluations in the GH in 2014; Field_2012=evaluations in the field in 2012;

Field_2013=evaluations in the field in 2013; ± S.E the Mean; Max and Min represent the

range a trait

89

Table 2.2. Ten genotypes identified as superior in percentage of N derived from atmosphere (%Ndfa) and amounts of N in seed derived

from atmosphere (Ndfa) from the Andean Diversity panel and two non-nodulating mutants grown in the Field at Montcalm Research

Farm, Entrican, MI in 2013.

ID Cultivar Country (Region) Seed Color DTM %Ndfa- Shoot

%Ndfa- Seed

Ndfa-Seed (kg ha-1)

Seed Yield (kg ha-1)

Ten ADP Genotypes ADP001 Rozi Koko Tanzania (Africa) Red Mottled 94 78.2 84.1 89.5 2094 ADP280 G14440 Spain (Europe) White 95 77.5 86.8 57.0 1904 ADP303 G17913 Hungary (Europe) Biege 87 67.2 81.9 69.2 1594 ADP437 PC-50 Dominican (Caribbean) Red Mottled 93 80.9 84.9 64.9 1958 ADP483 PI209815 Kenya (Africa) Yellow 93 61.2 76.7 60.0 1980 ADP601 Camelot U.S. (N. America) DRK 86 51.0 94.2 61.3 1679 ADP631 OAC Inferno Canada (N. America) LRK 95 88.5 98.2 72.4 2253 ADP644 Fox Fire U.S. (N. America) LRK 77 73.4 98.7 92.3 2570 ADP680 Clouseau U.S. (N. America) LRK 82 65.4 84.2 89.6 2938 ADP684 Majesty Canada (N. America) DRK 85 68.2 78.1 56.4 1814

Experimental Checks (Non-Nodulating Mutants) G51493A NA - Yellow 57 0 0 0 450 G51396A NA - DRK 56 0 0 0 424 LSD0.05 (for ADP genotypes ) 4.4 32 33 29 893

ADP=Andean Diversity Panel Identity; DTM=days to maturity; %Ndfa=Percentage of N derived from atmosphere in the shoot biomass

at flowering; %Ndfa_Seed= Percentage of N in the seed derived from atmosphere; Ndfa_Seed=Amount of N (kg ha-1) in the seed derived

from the atmosphere; LRL=Light Red Kidney; DRK=Dark Red Kidney; LSD=Least Significant Difference

90

Table 2.3. Most significant SNP and candidate genes on relevant Phaseolus vulgaris chromosomes for SNF and related traits of the

Andean Diversity Panel common bean genotypes evaluated in the GH at Michigan State University, East Lansing, MI in 2012 and 2014,

and in the Field at Montcalm Research Farm, Entrican, MI in 2012 and 2013.

Trait Exp Chr. SNP Position MAF P-value R2 Candidate Gene/Annotation Chlorophyll GH_12 Pv09 ss715647747 26619346 0.10 6.1E-06 0.07 -

GH_14 Pv09 ss715648916 20055067 0.09 3.1E-06 0.08 Phvul.009G136200-LRR-RLK Field_12 Pv01 ss715639380 36919960 0.12 7.8E-06 0.09 - Field_13 Pv01 ss715641865 14396817 0.06 1.4E-10 0.15 - Field_13 Pv09 ss715648916 20055067 0.09 1.0E-06 0.07 Phvul.009G136200-LRR-RLK

Nodule Score Field_13 Pv09 ss715648787 20055067 0.09 1.1E-06 0.12 Phvul.009G136200-LRR-RLK Shoot Biomass GH_12 Pv01 ss715646315 48116724 0.17 1.0E-05 0.08 -

GH_12 Pv03 ss715645580 50004386 0.05 1.1E-05 0.10 - GH_12 Pv07 ss715646458 4252888 0.09 8.2E-06 0.11 - GH_12 Pv09 ss715647197 34101880 0.11 6.4E-06 0.11 Phvul.009G231000-Calmodulin GH_14 Pv09 ss715648916 20055067 0.09 1.3E-08 0.13 Phvul.009G136200-LRR-RLK Field_12 Pv07 ss715639237 42895691 0.12 2.3E-06 0.11 - Field_12 Pv08 ss715647448 4201160 0.05 5.8E-08 0.15 - Field_13 Pv04 ss715647346 45251507 0.14 6.4E-08 0.17 -

N% in Shoot GH_14 Pv03 ss715639320 47948032 0.12 1.8E-06 0.11 - GH_14 Pv09 ss715648916 20055067 0.09 3.7E-09 0.15 Phvul.009G136200-LRR-RLK Field_13 Pv09 ss715648916 20055067 0.09 3.2E-06 0.10 Phvul.009G136200-LRR-RLK

N% in Seed Field_13 Pv02 ss715639746 49033652 0.30 1.4E-06 0.12 - Field_13 Pv03 ss715646392 1178905 0.05 1.0E-05 0.09 - Field_13 Pv09 ss715648916 20055067 0.09 1.1E-09 0.17 Phvul.009G136200-LRR-RLK

%Ndfa_Shoot Field_13 Pv02 ss715649646 39149364 0.10 1.6E-06 0.09 - Field_13 Pv03 ss715646392 1178905 0.05 2.9E-13 0.22 -

91

Table 2.3 (cont’d) Field_13 Pv07 ss715646473 4048349 0.06 1.5E-07 0.17 - Field_13 Pv09 ss715648916 20055067 0.09 6.4E-10 0.19 Phvul.009G136200-LRR-RLK Field_13 Pv10 ss715650111 25088744 0.10 1.2E-06 0.11 - Field_13 Pv11 ss715649573 42485910 0.20 1.6E-06 0.12 -

Ndfa GH_12 Pv03 ss715645580 50004386 0.05 3.1E-06 0.11 - GH_12 Pv07 ss715646473 4048349 0.06 1.8E-06 0.12 Phvul.007G050500-LRR-RLK GH_12 Pv09 ss715647197 34101880 0.11 8.4E-07 0.13 Phvul.009G231000-Calmodulin GH_14 Pv02 ss715643723 25332620 0.16 7.2E-06 0.09 GH_14 Pv03 ss715639320 47948032 0.12 2.1E-08 0.12 - GH_14 Pv09 ss715648916 20055067 0.09 3.4E-13 0.20 Phvul.009G136200-LRR-RLK

Ndfa_Shoot Field_13 Pv02 ss715649646 39149364 0.09 1.0E-05 0.08 - Field_13 Pv03 ss715646392 1178905 0.05 5.2E-13 0.23 - Field_13 Pv07 ss715646473 4048349 0.06 1.4E-06 0.12 - Field_13 Pv09 ss715648916 20055067 0.09 1.4E-09 0.14 Phvul.009G136200-LRR-RLK Field_13 Pv10 ss715650111 25088744 0.10 6.2E-06 0.09 - Field_13 Pv11 ss715649610 48038510 0.09 1.1E-05 0.08 -

Ndfa_Seed Field_13 Pv03 ss715646392 1178905 0.05 5.2E-05 0.11 - Field_13 Pv09 ss715648916 20055067 0.09 8.9E-06 0.09 Phvul.009G136200-LRR-RLK

MAF=minor allele frequency; Ndfa=N derived from atmosphere; GH_2012=evaluations in the GH in 2012; GH_2014=evaluations in

the GH in 2014; Field_2012=evaluations in the field in 2012; Field_2013=evaluations in the field in 2013; SNP=SNP code; E=exponent

of the P-value; R2 is phenotypic variation explained by the SNP; LRR-RLK=Leucine Rich Repeat Receptor-like Kinase

92

Figure 2.1. The quantile-quantile (QQ plots) plots for seed nitrogen percentage, comparing

the effectiveness of using principal component analysis (PCA) and STRUCTURE software

to control population structure in association tests using Mixed Linear Model.

93

Figure 2.2. Principle Component Analysis (PCA) plot of PC1 against PC2 illustrating the

population structure comprised of two major sub-groups in the Andean Diversity Panel.

94

Figure 2.3. Frequency distribution graphs for Nitrogen derived from atmosphere in the

seed (Ndfa_Seed) for Field_2013, and Nitrogen derived from atmosphere in the shoot at

flowering (Ndfa_Shoot) of the Andean diversity panel genotypes evaluated in the

Greenhouse (GH) and Field.

95

Figure 2.4. Manhattan plots of association tests using MLM for N% in shoot biomass

(GH_2014 and Field_2013) and N% in seed (Field_2013). A candidate gene for most

significant SNP on Pv09 is also shown. The red solid horizontal line is the Bonferroni

adjusted P-value (1.1 x 10-05). The dotted gray vertical lines are to show significant

SNPs that were consistently significant for N% in shoot biomass and seed.

96

Figure 2.5. Manhattan plots of association tests using MLM and candidate genes for

amount of N derived from atmosphere (Ndfa) using the ADP grown in greenhouse (GH)

and field. The red solid horizontal line is the Bonferroni adjusted P-value (1.1 x 10-05).

The dotted gray vertical lines are to show significant SNPs that were consistently

identified in GH_2012, GH_2014 and Field_2013.

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Figure 2.6. Manhattan plots of association tests using MLM, and candidate gene for

nodulation and amount of N in seed derived from atmosphere (Ndfa_Seed) identified

using the ADP grown in the field in 2013. The red solid horizontal line is the Bonferroni

adjusted P-value (1.1 x 10-05). The dotted gray vertical lines are to show SNPs that were

consistently significant for nodulation and Ndfa_Seed in Field_2013.

98

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CHAPTER 3

TRANSCRIPTOME ANALYSIS OF TWO RECOMBINANT INBRED LINES OF COMMON BEAN CONTRASTING FOR SYMBIOTIC NITROGEN FIXATION

[Submitted for publication in BMC Genomics]

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Transcriptome analysis of two recombinant inbred lines of common bean

contrasting for symbiotic nitrogen fixation

Kelvin Kamfwa1, Dongyan Zhao2, James D. Kelly1 and Karen A. Cichy3*

1Dep. of Plant, Soil and Microbial Sciences, Michigan State Univ., 1066 Bogue St., East

Lansing, MI 48824; 2Dep. of Plant Biology, Michigan State Univ., Plant Biology

Building, East Lansing, MI 48824; 3USDA-ARS, Sugarbeet and Bean Research Unit,

Michigan State Univ., 1066 Bogue St., East Lansing, MI 48824.

*Corresponding author: Karen A. Cichy (Email: [email protected])

Abstract

Common bean (Phaseolus vulgaris L.) is able to fix atmospheric nitrogen (N2) through

symbiotic nitrogen fixation (SNF). Effective utilization of existing variability for SNF in

common bean for genetic improvement requires an understanding of underlying genes and

molecular mechanisms. The utility of transcriptome profiling using RNA-sequencing was

explored to identify genes and molecular mechanisms underlying contrasting SNF

phenotypes of two recombinant inbred lines SA36 and SA118 of common bean.

A total of 30 RNA samples were collected from leaves, nodules and roots of SA36 and

SA118 grown under N fixing and non-fixing condition, and sequenced using Illumina

technology. Differential gene expression and functional enrichment analyses were

conducted.


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Genes encoding receptor kinases, transmembrane transporters, and transcription factors

(TFs) were among differentially expressed genes (DEGs) between SA36 and SA118 under

N-fixing condition, but not under non-fixing condition. Enriched molecular functions of

DEGs up-regulated in SA36 included purine nucleoside binding, oxidoreductase and

transmembrane receptor activities in nodules, and transport activity in roots. TFs identified

in this study are strong candidates for future studies aimed at enhancing our understanding

of functional roles of these factors in SNF. Information generated in this study could

support development of gene-based markers to accelerate genetic improvement of common

bean for SNF.

Key words: Common bean, Phaseolus vulgaris, RNA-seq, symbiotic nitrogen fixation,

transcriptome, transcription factor.

Introduction

Nitrogen (N) is the most abundant element in the atmosphere. Yet, it is often the most

limiting element to plant growth and crop productivity globally. Plants belonging to family

Fabaceae (legumes), the third largest plant family, are able to convert atmospheric N (N2)

into ammonia (NH3) through a symbiotic relationship with soil bacteria known as

Rhizobium (de Bruijn 2015). This relationship is known as symbiotic nitrogen fixation

(SNF), and takes place in a specialized plant organ called nodules on the roots.

SNF begins with exchange of molecular signals between the legume and rhizobium in the

soil. This exchange is followed by formation of an infection thread and nodule primordial

108

that contains rhizobium (Long 2015; Oldroyd and Downie 2008). When nodules are fully

formed, the nitrogenase in Rhizobium catalyzes reduction of N2 to NH3, which is available

for plant use (White et al. 2007). The Rhizobium derives its nutrients from the plant for

survival. Malate a downstream photosynthetic product is the main source of energy for the

rhizobium (Day and Copeland 1991).

Over the last two decades our understanding of genetic and molecular mechanisms

involved in SNF has advanced. This has been through genetic studies, and recently

genomic studies using Medicago truncatula and Lotus japonicus, the two model legume

species. Genetic studies mainly using mutants with varying phenotypes for N fixation such

as lack of nodulation, hypernodulation, ineffective nodules among others, have been used

to identify genes involved in the establishment of SNF including formation and functioning

of the nodules (Gresshoff 2003; Oldroyd et al. 2011; Stacey et al. 2006). Some of the

transcription factors (TFs) that regulate expression of genes involved in SNF have also

been identified (Libault et al. 2009; Sinharoy et al. 2015). In addition, key molecular

mechanisms, biological processes, and pathways involved in SNF including signal

transduction, carbohydrate metabolism, and purine pathway have been identified (Oldroyd

and Downie 2004; Smith and Atkins 2002). Transcriptome analyses in M. truncatula and

L. japonicus have previously been used to gain insights into global gene expression and

molecular mechanisms involved in SNF, especially the early stages of nodulation

(Chungopast et al. 2014; Colebatch et al. 2004; El Yahyaoui et al. 2004; Hogslund et al.

2009; Kouchi et al. 2004; Lohar et al. 2006). These transcriptomic studies have revealed

a complex molecular architecture of SNF involving several genes, molecular mechanisms

109

and pathways. Though genetic and transcriptomic studies have provided valuable

information on molecular genetics of nodulation, our understanding of genes and molecular

mechanisms that play significant role in determining SNF variability in plants with mature

functioning nodules is still lacking.

Common bean (Phaseolus vulgaris L.) is a staple for millions of people in East Africa and

Latin America (Akibode and Maredia 2012). Although common bean is considered poor

in SNF when compared to other economic legumes such as soybeans, significant genetic

variability for SNF exists within common bean (Kamfwa et al. 2015). Effective

exploitation of this variability for genetic improvement of SNF requires an understanding

of underlying genes and molecular mechanisms. Within common bean, studies aimed at

understanding the molecular and genetic basis of SNF variability are limited, and have

mainly been quantitative trait loci mapping studies and recently genome-wide association

studies (GWAS). In this study we explored the utility of RNA-seq transcriptome analysis

to understand gene expression differences and possible molecular mechanisms

underpinning contrasting SNF phenotypes of two common bean recombinant inbred lines

(RILs), SA36 and SA118. Our study was focused on identifying genes not only important

to SNF, but also important in explaining differences in SNF abilities between these two

RILs. Studies focused on identifying genes important in explaining contrasting SNF

phenotypes of genotypes with breeding value have potential to bridge the gap between

basic and applied research aimed at developing breeding tools to enhance SNF in common

bean.

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Methods

Plant Materials

Two F4:5 recombinant inbred lines (RILs), SA36 and SA118 of common bean were used in

the current study. SA36 and SA118 were chosen from a bi-parental mapping population of

213 RILs derived from a cross of Solwezi and AO-1012-29-3-3A, two Andean parents with

contrasting SNF phenotypes. Solwezi is a landrace that is widely grown in Zambia with

indeterminate growth and large round red mottled seed type. AO-1012-29-3-3A is

determinate, red kidney breeding line developed at University of Puerto Rico with

resistance to seed weevils (Acanthoscelides obtectus) (Kusolwa et al. 2015). Evaluation for

SNF in the greenhouse (GH) at Michigan State University (MSU) of five genotypes grown

in Zambia and AO-1012-29-3-3A, showed Solwezi to be more superior to AO-1012-29-3-

3A in SNF. A population of 213 F4:5 RILs was developed from a cross of Solwezi and AO-

1012-29-3-3A using single seed descent method, and evaluated for SNF in the GH at

Michigan State University. Among these 213 RILs, SA36 and SA118 showed contrasting

SNF phenotypes, but had similar seed type (red kidneys), growth habit (determinate) and

number of days to flower. In GH evaluations, SA36 fixed more N and had higher nodule

dry weight than SA118. Similarities in seed type, seed weight, growth habit and days to

flowering suggested SA36 and SA118 had similar genetic backgrounds despite contrasting

SNF phenotypes. Similarities in genetic background achieved by using RILs were needed

to minimize the confounding effect of genetic backgrounds on gene expression differences

between SA36 and SA118. These attributes made SA36 and SA118 ideal for our study

objective.

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Growing conditions

SA36 and SA118 were grown under N fixing and non-fixing conditions in 4-liter plastic

pots filled with perlite and vermiculite in a 2:1 (v/v) ratio in the GH at Michigan State

University, East Lansing, Michigan, USA in 2015. Under the non-fixing condition, 20 g of

‘Osmocot’ fertilizer (14% nitrogen, 14%phosphorus, 14% potassium) was applied to pots

and thoroughly mixed with perlite and vermiculite before planting. Another 40 g of

‘Osmocot’ fertilizer (5.6 g of N) was applied to the two seedlings at trifoliate stage in each

pot. High rates of N fertilizer application suppress nodulation and N fixation (Streeter and

Wong 1988). In addition, a nutrient solution of micronutrients was applied to ensure normal

growth. SA36 and SA118 grown under non-fixing condition served as controls to the N

fixing genotypes for identifying differentially expressed genes (DEGs) between SA36 and

SA118 whose differential expression status were restricted to SNF for a respective tissue.

Before planting, seeds were sterilized in sodium hypochlorite and then rinsed in distilled

water. For plantings under fixing condition, rinsed seeds were inoculated with Rhizobium

tropici strain CIAT899 (Graham et al. 1994) by submerging them for two minutes in a broth

culture of Rhizobium made from yeast extract manitol media (Vincent 1970). Inoculated

and un-inoculated seeds were planted at a rate of two seeds per pot. All pots were watered

with water until seeds germinated (eight days after planting), that was when N-free nutrient

solution (Broughton and Dilworth 1970) was applied to plants under N-fixing condition,

but continued with water application to plants growing under non-fixing condition.

Nutrient and water applications continued up to flowering (38 days) when samples for

RNA extraction and nodule dry weight, shoot dry weight and total N fixed were collected.

Throughout the experiment, 13 hours of supplemental light per day was provided, and

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temperature was maintained between 23oC to 25oC in the GH. We chose to collect samples

at flowering stage because at this stage the nodules are fully developed. The rate of SNF

peaks at flowering and declines afterwards because the pods that begin to form become a

major sink for photo-assimilates, which reduces assimilates partitioned to nodules.

Evaluation of SA36 and SA118 for SNF and related traits

To assess the SNF phenotypes of SA36 and SA118, plants in additional pots in each

replication were harvested and separated into roots, shoot and nodules for plants grown

under N-fixing condition, and into roots and shoot for plants grown under non-fixing

conditions. These samples were oven-dried at 60oC for 72 h, and weighed to obtain shoot

and nodule dry weights. The shoot was ground and sent for N concentration analysis to A

& L Great Lakes Laboratories, Fort Wayne, Indiana, USA. The amount of N fixed per plant

for plants growing under N-fixing condition was computed as a product of N concentration

in the shoot and shoot dry weight.

Total RNA isolation, cDNA library construction and sequencing

At flowering, leaf, nodule and root tissues were collected from N fixing plants while from

the non-fixing plants only leaf and root tissues were collected since both SA36 and SA118

did not form nodules under these conditions. In total 30 samples were collected,

immediately put in liquid N, and then stored under -80oC prior to total RNA extraction.

Total RNA was extracted using the TRIzol kit (Invitrogen, Carlsbad, CA, USA) following

the manufactures protocol. A DNAase Quigen kit was used to remove any DNA. A

spectrophotometer NanoDrop 2000 (Thermo Fisher Scientific, Waltham, MA, USA) was

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used to measure total RNA concentration and purity. To check the integrity of the total

RNA, we used the Biological analyzer Agilent 2100 (Agilent, Santa Clara, CA, USA).

Thirty mRNA-seq libraries were prepared at the RTSF Genomics core at Michigan State

University using the Illumina TruSeq Stranded mRNA Library preparation kit (Illumina,

San Diego, CA, USA) following manufacturer’s instructions. Libraries were pooled for

multiplexed sequencing at RTSF Genomics core at Michigan State University using

Illumina HiSeq 2500 to generate single end (SE) reads of 50 bp.

Sequence reads analyses

The quality of reads was checked using FastQC (Andrews 2010). Adapters were removed

using Cutadapt version 1.8.1 (Martin 2011), and only reads with greater than 30 bp were

retained. The P. vulgaris v1.0 reference genome (Schmutz et al. 2014) was indexed using

Bowtie2 version 2.2.3 (Langmead and Salzberg 2012). After this cleaned reads were

mapped to the P. vulgaris v1.0 genome using TopHat2 version 2.0.14 (Kim et al. 2013).

TopHat was set to allow a maximum of two base pair mismatches. The minimum and

maximum intron size was set to 4 and 11 kbp, respectively. All other parameters for TopHat

were used at default settings. To determine the expression status of a gene, we used

Cufflinks version 2.2.1 (Trapnell et al. 2012). Cufflinks was used to calculate normalized

gene expression levels reported as fragments per kilobase pair of exon model per million

fragments mapped (FPKM). A gene was considered expressed if its FPKM 95% confidence

interval lower boundary was greater than zero.

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Identification of DEGs and enriched molecular functions

The number of reads that mapped to a gene were counted using htseq-count from the

HTSeq.py python package (Anders et al. 2015). Gene pair-wise differential expression

analysis was done using DESeq2 R package on read count values normalized to the

effective library size (Anders and Huber 2010). A gene was identified as differently

expressed based on false discovery rate (FDR) < 0.01 (Benjamini–Hochberg correction)

(Anders & Huber, 2010). The lists of DEGs were filtered further for fold expression

change, and only genes with absolute Log2 fold-change (|Log2FC|) ≥ 2 were retained for

downstream analyses. In this study we were focused on genes whose differential expression

status was restricted to SNF fixing condition. We assumed that genes with differential

expression status restricted to fixing condition formed the molecular genetic basis of the

contrasting SNF phenotypes between SA36 and SA118. To identify genes in leaves or roots

whose expression status was restricted to SNF we followed two steps. First, we identified

genes differentially expressed in the same tissue type between SA36 and SA118 under

fixing condition and then under non-fixing condition. Second, we subtracted the genes that

were differentially expressed under both fixing and non-fixing conditions from the list of

genes differentially expressed under fixing condition for the same tissue type. The final list

from the second step represented genes whose differential expression status was considered

to be associated with SNF for a particular tissue type. For the nodules, all genes that were

differentially expressed between SA36 and SA118 were assumed to be associated with SNF

considering that no nodules formed under non-fixing condition and sole purpose for

nodules is nitrogen fixation.

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To gain insights into possible molecular mechanisms underlying the contrasting SNF

phenotypes of SA36 and SA118, gene ontology (GO) term (Harris et al. 2004) enrichment

analysis of DEGs (with |Log2FC| ≥ 2) was conducted. Singular enrichment analysis tool

from AgriGO (Du et al. 2010) was used based on GO annotations from P. vulgaris v1.0

reference genome. The singular enrichment analysis was done using fisher’s test and

significance threshold of FDR<0.05.

To demonstrate the usefulness of the transcriptome data generated in the current study for

developing gene-based makers that can be used to indirectly select for improved SNF in

common bean, we called SNPs in the coding sequence of genes that were differentially

expressed in leaf, root and nodules between SA36 and SA118 using SAMtools version 1.2

(Li et al. 2009) and BCFtools version 1.2 (Li 2011).

Results

Responses of SA36 and SA118 to N fertilizer and rhizobium inoculation

At flowering, the growth stage samples were collected, both SA36 and SA118 had fully

developed nodules under N-fixing condition, but under the non-fixing condition neither

formed nodules. Major differences in shoot dry weight between SA36 and SA118 were

observed under the fixing condition but not under non-fixing condition (Figure 3.1). Under

N-fixing condition, the shoot dry weight for SA36 was 5.6 g plant-1 compared to 1.6 g plant-

1 for SA118 (Figure 3.2). Under non-fixing condition, SA36 and SA118 weighed 9.4 g plant-

1 and 8.5 g plant-1, respectively (Figure 3.2). In terms of total N fixed per plant, which was

computed as a product of shoot dry weight and N% in the shoot, SA36 was superior to

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SA118. SA36 fixed 179 mg plant-1 N, which was significantly higher than 46 mg plant-1 N

for SA118 (Figure 3.3). However, under non-fixing condition the total N in shoot dry

biomass for SA36 and SA118 were similar, with 385 mg plant-1 N for SA36 and 365 mg

plant -1 N for SA118 (Figure 3.3). SA36 was also superior to SA118 in nodule fresh weight.

The nodule fresh weight for SA36 was 1136 mg plant-1 compared to 615 mg plant-1 for

SA118 (Figure 3.4).

Read mapping

A total of 861 M 50 bp SE reads were generated from 30 RNA-seq libraries of leaf, root

and nodule tissues of SA36 and SA118 grown under N-fixing and non-fixing conditions

with three replications. Number of reads per library ranged from 19.8 M to 41.7 M with an

average of 28.7 M (Table 3.1). Per base Phred value for all the libraries was greater than

25. After removing adapters, and discarding reads with less than 30bp, reads per library

ranged from 19.7 M to 41.4 M with an average of 28.4 M (Table 3.1). The average

percentage of mapped reads ranged for 30 libraries was 97.1% (Table 3.1). Of the number

of reads that mapped, the number of uniquely mapped reads ranged from 17.5 M to 38.7

M. The average percentage of uniquely mapped reads of the total mapped reads was 94.6%

(Table 3.1).

Differentially expressed genes between leaves of SA36 and SA118

Under N-fixing condition, 22,715 genes were expressed in leaves of SA36 and SA118,

representing 83.5% of the estimated 27,197 genes in P. vulgaris. The number of expressed

genes under non-fixing condition was 22,811. Between leaves of SA36 and SA118, there

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were 177 DEGs under fixing condition compared to 3415 under non-fixing condition. Out

of the 177 DEGs, 83 were differentially expressed only under fixing condition while the

remaining 94 were differentially expressed under both fixing and non-fixing conditions

(Figure 3.5A). We assumed that the differential expression status of these 83 genes was

related to SNF. Of these 83 DEGs, 59 had |Log2FC| ≥ 2 (Additional file 1: Table S1).

Fifteen of these 59 genes did not have functional annotations on Phytozome 10.3. Among

the 59 DEGs, 38 were up-regulated in SA36 while 21 were up-regulated in SA118 (Table

3.2). Among the DEGs upregulated in SA36, genes encoding xyloglucan:xyloglucosyl

transferase involved in carbohydrate metabolism were the most represented (five out of 38

DEGs). Three genes encoding leucine rich repeat receptor-like kinases (LRR-RLK) were

up-regulated in SA118 compared to one in SA36. Three genes encoding AP2, Homeobox

and GT-2 TFs were up-regulated in SA36 whereas two genes encoding WRKY and MYB

TFs were up-regulated in SA118 (Table 3.3). GO enrichment analysis identified transferase

activity, (transferring hexosyl groups) as the only significantly enriched molecular function

of DEGs up-regulated in SA36 (Table 3.4). There were no enriched molecular functions of

DEGs up-regulated in leaves of SA118.

DEGs between roots of SA36 and SA118 and enriched molecular functions

A total of 23,313 genes were expressed in roots of SA36 and SA118 under fixing condition,

representing 86% of the estimated genes in P. vulgaris. Under non-fixing condition, 23,289

genes were expressed in roots of SA36 and SA118. Between roots of SA36 and SA118, there

were 471 DEGs under N-fixing condition compared to 2528 under non-fixing condition

(Figure 3.5B). Out of these 471 DEGs, 222 were differentially expressed under fixing

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condition while the remaining 249 were differentially expressed under both fixing and non-

fixing conditions (Figure 3.5). These 222 represent genes in the roots important to SNF,

and possibly contributing to SNF phenotypic differences between SA36 and SA118. Out of

the 222 DEGs, 121 had |Log2FC| ≥ 2 (Additional file 2: Table S2). Among the 121 DEGs,

35 did not have functional annotation on Phytozome 10.3. Of the 121 DEGs, 86 were up-

regulated in SA36 compared to 35 up-regulated in SA118 (Table 3.2 and Additional file 2:

Table S2). Among the 86 DEGs up-regulated in SA36, eight encode transporter proteins,

and this was the most represented group. The transporters encoded by these eight genes

include MFS transporter (Phvul.008G011700), aquaporin (Phvul.011G067200), ABC

transporters (Phvul.002G176600, Phvul.007G078000, Phvul.003G283900), zinc/iron

transporters (Phvul.006G001000 and Phvul.006G003300) and sugar transporter

(Phvul.009G030800). In contrast, there were no genes encoding transporter proteins

among the 36 genes up-regulated in SA118. Four genes (Phvul.011G068300,

Phvul.007G238100, Phvul.007G238200, and Phvul.008G018700) encoding nucleoporins

were up-regulated in SA36. This was in contrast to SA118 where no nucleoporins were up-

regulated. Three genes, all encoding MYB TFs were up-regulated in SA36 while two genes

encoding NAM and AP2 TFs were up-regulated in SA118 (Table 3.3). The GO term

enrichment analysis identified transporter activity and iron ion binding as enriched

molecular functions of DEGs up-regulated in SA36 (Table 3.4). For DEGs up-regulated in

SA118, oxidoreductase activity was the only enriched molecular function observed (Table

3.4).

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Differentially expressed genes between nodules of SA118 and SA36

A total of 22,066 genes were expressed in nodules of SA36 and SA118, representing 81.1%

of the estimated genes in P. vulgaris. A total of 5,131 (18.9%) genes were not expressed

in both SA36 and SA118 nodules in all three replications. Of the expressed 22,066

expressed genes, 1,127 (5.1% of expressed genes) showed significant differential

expression between nodules of SA118 and SA36. Out of these 1,127 DEGs, 558 had

|Log2FC| ≥ 2 (Additional file 3: Table S3). A total of 131 out of these 558 did not have

functional annotation on Phytozome 10.3. Of these 558 DEGs, 147 were up-regulated in

SA36 while 411 were up-regulated in SA118 (Additional file 3: Table S3). Genes that

encode transporter proteins, LRR-RLKs and TFs were among the 147 DEGs up-regulated

in SA36 (Additional file 3: Table S3). Several genes with no annotated function were also

among DEGs.

Some of the transporter genes up-regulated in SA36 include Phvul.011G196900 (EamA-

like transporter), Phvul.001G028700 (xanthine-uracil permease), Phvul.007G025900

(malate transporter), Phvul.007G244600 (Nodulin-like monocarboxylate transporter), and

several other transmembrane transporters. In contrast, a fewer number of transporter genes

were up-regulated in SA118 nodules. Phvul.002G214100 encoding glutamine synthetase

involved in fixed N assimilation was among DEGs up-regulated in SA36.

A total of 36 genes encoding TFs were differentially expressed in nodules between SA36

and SA118 (Table 3.3). SA118 exhibited a stronger transcriptional response in nodules than

SA36, and is consistent with higher number of DEGs observed in SA118 than SA36. Of the

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36 TFs genes, five genes encoding bHLH, MBF1, MADS-box and homeobox TFs were

up-regulated in SA36. Among these five, Phvul.007G048000 encoding MAD BOX was

only expressed in nodules and roots (Figure 3.6). In the roots Phvul.007G04800 was

weakly expressed in both SA36 and SA118 (Figure 3.6). In SA118, 31 genes encoding AP2

(10), MYB (8), WRKY (6), bHLH (3), NAM (2), PLATZ (1), Dof (1) and GRAS (1) TFs

were up-regulated. Among the AP2 encoding genes up-regulated in SA118,

Phvul.001G044500 was only expressed in nodules and roots under fixing condition (Figure

3.7).

The GO term enrichment analysis identified purine ribonucleotide binding, transmembrane

receptor activity and oxidoreductase activity as significantly enriched molecular functions

of genes up-regulated in SA36 (Table 3.4). Significantly enriched molecular functions of

DEGs up-regulated in SA118 included fatty-acid synthase activity and hydrolase activity.

Several SNPs were called in DEGs. A total of 113 SNPs were called in 32 of the 59 DEGs

in leaf tissue (Additional file 4: Table S4). Out of 121 DEGs in the root, 60 contained 287

SNPs (Additional file 5: Table S5). A total of 1123 SNPs were called in 271 out of 558

DEGs in nodules (Additional file 6: Table S6)

Discussion

Effective utilization of existing genetic variability for SNF in common bean for genetic

improvement requires an understanding of underlying genes and molecular mechanisms.

This study explored the utility of transcriptome profiling to develop an understanding of

molecular genetic differences underlying contrasting SNF phenotypes of two RILs SB38

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and SA118. Though transcriptome profiling for SNF has been conducted in the two model

legume plants, M. truncatula and L. japonicus using wild type and mutants that differ in

N-fixation, the potential use of basic knowledge from these studies to improve SNF of

economic food legumes has been limited. By using RILs with breeding value, our study

has potential to bridge the gap between basic studies and applied use of knowledge

generated from basic studies to enhance SNF of common bean a staple for millions of

people in Africa and Latin America.

We compared the phenotypic performance of SA36 and SA118 under fixing condition and

non-fixing condition in the GH. GH evaluation showed SA36 to be superior to SA118 in

shoot dry weight, nodule dry weight, and total amount of N fixed under N fixing condition.

However, shoot dry weight, and total N in shoot biomass under non-fixing condition were

similar between SA36 and SA118. These results demonstrate that observed differences in

shoot and root dry weights between SA36 and SA118 under fixing condition resulted from

differences in SNF rates, and that under non-fixing condition with optimal source of soil

N, SA36 and SA118 have similar capacity to accumulate shoot biomass and N. These

phenotypic results of similar biomass and N accumulation under non-fixing condition but

drastically different values under N-fixing condition provide strong support for their use to

identify genes that control SNF genetic variability in common bean.

The highest number of DEGs between SA36 and SA118 was found in nodules, followed by

roots, and leaves. These results suggest that among leaves, roots and nodules, gene

expression differences in nodules had the largest contribution to explaining molecular

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genetic basis of contrasting SNF phenotype between SA36 and SA118. The higher number

of DEGs in nodules is consistent with the specialized nature of the nodule as an organ

purposely developed by the plant for SNF.

DEGs between leaves for SA36 and SA118 and enriched molecular functions

This study identified several DEGs in leaves between SA36 and SA118 whose differential

expression status was associated with SNF. Genes encoding proteins involved in

carbohydrate metabolism were among DEGs, and the majority of these were up-regulated

in SA36. In addition, the enriched molecular function of DEGs up-regulated in SA36 was

transferase activity (transferring of hexosyl groups), which is associated with carbohydrate

metabolism. Leaves are the primary source of carbon for nodule metabolism. A genotype

with high SNF ability is expected to have high carbohydrate metabolism activities, which

is consistent with the higher expression of carbohydrate metabolism genes in the leaves of

SA36 than SA118.

Among DEGs, one and three genes encoding LRR-RLK were up-regulated in SA36 and

SA118, respectively. Receptor kinases have been implicated in local and long distance

regulation of nodule development (Oldroyd and Downie 2004). It is plausible that receptor

kinases identified in the current research as differentially expressed in the leaves could be

involved in long distance regulation of nodule number, nodule development, or nodule

functioning. Apart from the role of leaves as being a source and sink for carbon and fixed

N, respectively, and in long distance signaling to regulate nodulation through the auto-

regulation of nodulation (Krusell et al. 2002), other contributions of leaves to SNF are still

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not well understood. Genes identified in this study as differentially expressed, and

important to SNF represent candidates for future studies aimed at expanding our

understanding of the additional contribution of leaves to SNF.

DEGs between roots for SA36 and SA118 and enriched molecular functions

Carbon and N fluxes between nodules and the rest of the plant rely on transporter proteins

in the roots. Consistent with this, several genes encoding transporter proteins were among

347 DEGs in roots between SA36 and SA118. The majority of these transporter genes were

up-regulated in SA36. Additionally, transporter activity was one of the enriched molecular

functions of DEGs up-regulated in SA36. The transporter genes up-regulated in SA36

encode two ABC transporters, two sugar transporters, two iron transporters and an

aquaporin transporter. Some of the genes for these transporters were also up-regulated in

the nodules of SA36. These results suggest higher fluxes of carbon and other elements from

the shoot to nodules, and may be N compounds from nodules to the rest of the plant in

SA36 than SA118. This implies more available carbon and other elements for nodule

metabolism and corresponding increases in SNF in SA36 than in SA118. Four genes

encoding nucleoporins were up-regulated in SA36. In contrast, no genes encoding

nucleoporins were up-regulated in SA118. Nucleoporins are constituents of the nuclear

pore complex that mediates macromolecular transport such as mRNA and protein across

the nuclear envelope (Saito et al. 2007). Nucleoporins have been implicated in calcium

spiking in roots associated with early events of nodulation. The L. japonicus mutant

(nup85) with defective expression of a nucleoporin in the roots was defective in root nodule

symbiosis and nod-factor induced calcium spiking (Saito et al. 2007). Iron binding was the

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second molecular function enriched in DEGs up-regulated in SA36. Genes encoding

hemopexin and hemerythrin, which binds iron were up-regulated in SA36. In addition,

genes encoding iron dehydrogenase that is involved in iron metabolism were up-regulated

in SA36. Iron is required for synthesis of iron-containing compounds essential to SNF in

both the plant and rhizobium. In rhizobium, iron is required for synthesis of nitrogenase

complex and is part of the FeMo co-factor required for reducing N2 to NH3. In the plant,

iron is a component of the heme moiety of leghemoglobin that facilitates oxygen diffusion

to respiring rhizobium under low oxygen environment needed for functioning of the

Rhizobium (Appleby 1984).

DEGs between nodules of SA36 and SA118 and enriched molecular functions

Metabolic cooperation between Rhizobium and the legume plant is the basis of SNF. The

plant supplies reduced carbon to Rhizobia in exchange for reduced nitrogen from the

Rhizobium. These exchanges happen in the nodule. Therefore, metabolism and transport

of carbon and N are key physiological processes of the nodule. The purine pathway plays

a dominant role in N metabolism of tropical legumes such as common bean and soybean

(Smith and Atkins 2002). In these legumes, fixed N (NH+4) is first assimilated into

glutamine. Through the purine pathway, the assimilated N is converted into inosine

monophosphate (IMP), and after a series of oxidation and enzymatic steps, IMP is

converted into ureides that are transported from the nodule into xylem vessels of roots for

distribution to the rest of the plant (Smith and Atkins 2002; Zrenner et al. 2006). In this

study, genes encoding proteins involved in the purine pathway and assimilation of N were

up-regulated in SA36. In addition, purine nucleoside binding was among the enriched

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molecular functions of DEGs that were up-regulated in SA36. Phvul.002G214100 that

encodes glutamine synthatase (GS) was strongly up-regulated (log2FC=3.4) in SA36. GS

is the enzyme required for assimilation of fixed NH4 into glutamine (Lam et al. 1996). The

higher oxidoreductase enzyme activity in SA36 than SA118 could have been crucial to

meeting the increased oxidation reactions of converting IMP to ureides in SA36. These

results are consistent with the observed higher SNF rates for SA36 than SA118.

Transport system is a key component of the P. vulgaris-Rhizobium symbiosis that handles

carbon and nitrogen fluxes in the nodule. The symbiosome membrane is a critical interface

of fluxes between the plant and Rhizobium (Mohd Noor et al. 2015). In addition to

transport across symbiosome membrane, transport across plasma membranes plays an

important role in carbon and N metabolism in the nodule (Udvardi and Poole 2013). In this

study, several genes encoding transporter proteins were differentially expressed between

SA36 and SA118. The majority of genes involved in the transportation of carbon and N

compounds were up-regulated in SA36. In addition, transmembrane transport activity was

among significantly enriched molecular functions of DEGs up-regulated in SA36.

Phvul.011G196900 encoding an EamA-like transporter was strongly up-regulated

(log2FC=3.2) in SA36. Phvul.011G196900 is a homologue of Medtr8g041390

(MtN21/EamA-like gene) in M. truncatula and Glyma.13G189700 in soybean (Glycine

max). In M. truncatula, MtN21/EamA-like was initially described as a nodulin induced

during M. truncatula-R. meliloti symbiosis (Gamas et al. 1996). MtN21/EamA-like contains

a metabolite transporter domain characteristic of proteins that transport amino acids such

as glutamine asparagine (Denance et al. 2014). Glutamine and asparagine play are

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important to assimilation of fixed N (Lam et al. 1996). The strong up-regulation of EamA-

like transporter may suggest a higher flux of glutamine in SA36 than SA118, and is

consistent with the observed up-regulation of glutamine synthatase in SA36 nodules.

The upstream compounds for synthesis of ureides include xanthine and uric acid. Xanthine-

uracil permeases are proteins that transport xanthine (Udvardi and Poole 2013).

Phvul.001G028700 that encodes xanthine-uracil permeases was up-regulated in SA36,

suggesting higher synthesis of ureides in SA36 than SA118. Malate supplied by the plant is

the source of reduced carbon for bacteroid metabolism (Yurgel and Kahn 2004). A malate

transporter gene Phvul.007G025900 was strongly up-regulated (log2FC=3.9) in SA36

compared to SA118, suggesting higher influx of malic acid to the bacteroids in SA36 than

SA118. Overall, more transporter genes were up-regulated in nodules of SA36 than SA118,

suggesting higher fluxes of carbon and N in the nodules of SA36 than SA118.

Signal transduction is an important molecular process within the Rhizobium-legume

symbiosis. Receptor kinases are a key component of signal transduction, and have been

implicated in local and long distance regulation of nodule development (Ferguson et al.

2010; Oldroyd and Downie 2004). Whereas the role of receptor kinases in the early stages

of symbiosis has been proposed, the role of receptor kinases in the functioning of mature

nodules is not well understood. In the current study, transmembrane receptor kinase

activity was among molecular functions significantly enriched in DEGs upregulated in

SA36. A total of 21 genes encoding LRR-RLK’s were up-regulated in SA118 compared to

three up-regulated in SA36. The role of LRR-RLK’s in the functioning of common bean

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nodule has been demonstrated using RNAi. Knockdown of PvSymRK, which encodes a

LRR-RLK in common bean resulted in the formation of ineffective nodules (Sanchez-

Lopez et al. 2011). The differentially expressed LRR-RLK genes identified in the current

study are strong candidates for future studies aimed at characterizing the functional role of

more LRR-RLK genes in mature nodule functioning.

The functional role of most TFs in legumes, particularly in SNF, a signature biological

process of legumes remains unknown (Libault et al. 2009). In a developmentally complex

process such as SNF that involve expression of several genes in many pathways, TFs are

expected to play a leading role in coordinating expression of these genes. Some of the TFs

involved in the early stage of symbiosis have been identified in previous studies (Sinharoy

et al. 2015; Smit et al. 2005; Vernie et al. 2008). However, knowledge of TFs involved in

the functioning of mature nodules that explains contrasting SNF phenotypes of common

bean is limited. In this study genes encoding TFs that may be important to functioning of

mature nodules, and possibly contributing to molecular genetic differences underlying the

contrasting SNF phenotypes of SA118 and SA36 were identified. Among the 558 DEGs in

the nodules, 36 encode TFs. Genes in M. truncatula, L. japonicus and G. max belonging to

some of the TF families identified as having differentially expressed in the current study

have previously been implicated in nodule development and functioning. Among the 36

TF genes differentially expressed between nodules of SA36 and SA118, Phvul.007G048000

and Phvul.001G044500 were particularly interesting because of their tissue specific

expression patterns. Phvul.007G048000 encodes a MADS box TF, and showed a 2.8 fold

increase in expression in SA36 over SA118. Interestingly, Phvul.007G048000 showed no

128

evidence of expression in leaves, and was weakly expressed in roots under both fixing and

non-fixing conditions (Figure 3.6). This restricted expression pattern of

Phvul.007G048000 is consistent with a previous study, which reported that among seven

diverse tissue types, Phvul.007G048000 was only expressed in nodule tissue (O'Rourke et

al. 2014). The current study provides further support to restricted tissue expression of

Phvul.007G048000, but more importantly it has shown that increased expression levels of

Phvul.007G048000 a MADS box TF is associated with enhanced SNF rate. The genomic

location of Phvul.007G048000 (3,876,555 bp – 3,877,440 bp) is within the region

(3,466,123 bp – 4,742,067 bp) where a recent GWAS identified significant SNPs for SNF

in a common bean Andean diversity panel evaluated under GH and field conditions

(Kamfwa et al. 2015). Based on results of the current study and the previous GWAS,

Phvul.007G048000 a MADS box TF is an excellent candidate for genetic manipulation to

improve the P. vulgaris-Rhizobium symbiosis. Being a TF with nodule specific expression

makes Phvul.007G048000 a better target for genetic manipulation because it may be

responsible for coordinated expression of several genes only in the nodule. Among TFs up-

regulated in SA118 nodules, Phvul.001G044500 that encodes a AP2 was strongly up-

regulated in SA118 than in SA36 (log2FC=4.1). Also, Phvul.001G044500 showed

significantly higher expression levels in the roots of SA118 than SA36 under fixing

condition. However, Phvul.001G044500 showed no evidence of expression in roots under

non-fixing condition, and in leaves under both fixing and non-fixing condition (Figure 3.7).

Results of this study suggest that increased expression of Phvul.001G044500 an AP2 TF

is associated with reduced SNF rates. In addition to Phvul.001G044500, nine other AP2

encoding genes were up-regulated in SA118. In contrast, there was no AP2 encoding gene

129

up-regulated in SA36 (Table 3.3). This result provides further support for the relationship

between increased AP2 TFs expression and low SNF rates. Relationship between increased

expression of AP2 TF and ineffective nodule functioning has been demonstrated

previously. Recent work on P. vulgaris-R. etli symbiosis showed that high mRNA levels

of an AP2 TF following a drastic decrease by the targeting micro-RNA (miR172C) was

associated with ineffective nodules (Nova-Franco et al. 2015). In addition, AP2 TFs in

common bean have been postulated to regulate genes related to nodule senescence (Nova-

Franco et al. 2015). Five genes encoding bHLH TFs were differentially expressed in

nodules between SA36 and SA118, with two and three up-regulated in SA36 and SA118,

respectively. Of the two up-regulated in SA36, Phvul.002G216700 is homologous to

Glyma.15G061400 (GmbHLHm1) in soybean, and Medtr2g010450 (MtbHLH1) in M.

truncatula (http://www.phytozome.org). Interestingly, Phvul.002G216700 and

Medtr2g010450 (MtbHLH1) seem to have some similarities in tissue expression patterns.

In the current study, Phvul.002G216700 was not expressed in leaves under both N-fixing

and non-fixing condition, but was expressed in nodules and roots, which is similar to

reported restricted expression of its homolog Medtr2g010450 (MtbHLH1) to roots and

nodules (Godiard et al. 2011). Recent functional studies demonstrated the importance of

GmbHLHm1 and MtbHLH1 in nodule development and functioning. Soybean plants that

lost GmbHLHm1 activity showed a significant reduction in nodule number, nodule fitness

and development (Chiasson et al. 2014). In M. truncatula, a transgenic plant with impaired

MtbHLH1 expression produced nodules with vascular defects and exhibited poor nutrient

exchanges between nodules and roots (Godiard et al. 2011). In addition, MtbHLH1 was

postulated to regulate asparagine synthase gene (Godiard et al. 2011), an enzyme required

130

for assimilation of fixed N. TF families were identified in the current study whose role in

nodule development and functioning has been documented previously. In addition TF

families with no previously reported role in mature nodule functioning have also been

identified.

One of the DEGs in root nodules, Phvul.009G231000 was recently identified as a candidate

gene for SNF using GWAS on an Andean bean diversity panel (Kamfwa et al. 2015).

Currently, there is no functional annotation for Phvul.009G231000 on Phytozome 10.3.

However, Phvul.009G231000 has high sequence similarity to AT2G26190 in Arabidopsis

thaliana, which encodes a calmoduline-binding protein. Calmoduline proteins are

associated with calcium fluxes. The nodulation-signaling pathway has been reported to

contain calcium-activated kinases (Oldroyd and Downie 2004). The identification of

Phvul.009G231000 as a candidate gene for SNF in two studies with different approaches

and genetic backgrounds provides further support for the role of Phvul.009G231000 in

SNF in common bean.

Phenotypic selection for SNF is expensive and sometimes ineffective because of

environment effects on SNF. Development of gene-based markers can circumvent these

challenges. The SNPs in DEGs identified in this study can be used to develop gene-based

markers to indirectly select for enhanced SNF. These markers would be more informative

since they are derived from genes not only important to SNF, but also contribute to genetic

variability in SNF in common bean.

131

Conclusion

Genes that are differentially expressed between SA36 and SA118 under N fixing condition,

but not under non-fixing condition were identified. These DEGs encode various proteins

including receptor kinases, TFs and transporters. Additionally, genes that currently have

no functional annotation were among DEGs. Significantly enriched molecular functions in

DEGs upregulated in SA36 include purine nucleoside binding, oxidoreductase and receptor

kinase activities in nodules, transport activity in roots, and glycosyl transferase activity in

leaves. The identified DEGs and their enriched molecular functions form the molecular

genetic basis of the contrasting SNF phenotypes between SA36 and SA118. Genes encoding

TFs identified in the current study are strong candidates for future functional studies aimed

at characterizing the role of TFs in SNF to develop further our understanding of the gene

regulatory network of SNF. In addition, the DEGs identified and data generated in the

current study provide a valuable resource for developing a set of gene-based markers

specific to SNF that can be used to accelerate the genetic improvement of common bean

for SNF.

Acknowledgements

Research was supported by the Borlaug LEAP program, USDA-ARS and was also made

possible through support provided by the Feed the Future Innovation Lab for Collaborative

Research on Grain Legumes by the Bureau for Economic Growth, Agriculture, and Trade,

U.S. Agency for International Development, under the terms of Cooperative Agreement

No. EDH-A-00-07-00005-00, and this work was supported in part by funding from the

Norman Borlaug Commemorative Research Initiative (US Agency for International

132

Development). The opinions expressed in this publication are those of the authors and do

not necessarily reflect the views of the U.S. Agency for International Development or the

U.S. Government. We thank Jack Colicchio for his helpful comments on some aspects of

data analyses.

133

APPENDIX

134

Table 3.1. Statistics summary of read mapping to the common bean genome

Tissue (Replication) Total Reads High-quality Mapped (%)

Uniquely Mapped (% of mapped)

Fixing Condition SA118 Nodule (R1) 29 746 570 29 297 934 28 193 790 (96.2) 25 161 438 (89.2) SA118 Nodule (R2) 39 369 353 38 614 671 37 304 756 (96.6) 34 695 243 (93.0) SA118 Nodule (R3) 30 084 472 28 834 719 27 658 997 (95.9) 23 950 737 (86.6) SA36 Nodule (R1) 35 885 970 35 340 122 33 760 366 (95.5) 30 790 251 (91.2) SA36 Nodule (R2) 19 758 854 19 675 059 18 975 163 (96.4) 17 506 439 (92.3) SA36 Nodule (R3) 23 291 342 23 186 169 22 400 771 (96.6) 21 056 138 (94.0) SA36 Leaf (R1) 22 924 623 22 868 500 21 995 986 (96.2) 20 873 291 (94.9) SA36 Leaf (R2) 24 586 264 24 470 190 23 902 260 (97.7) 22 578 284 (94.5) SA36 Leaf (R3) 24 208 366 24 160 247 23 602 246 (97.7) 22 332 576 (94.6) SA118 Leaf (R1) 31 287 828 30 731 986 29 211 106 (95.1) 27 982 601 (95.8) SA118 Leaf (R2) 32 367 954 31 948 215 31 266 813 (97.9) 28 818 909 (92.2) SA118 Leaf (R3) 20 133 267 20 084 301 19 626 510 (97.7) 18 061 453 (92.0) SA36 Root (R1) 32 132 215 31 702 339 31 066 482 (98.0) 30 127 895 (97.0) SA36 Root (R2) 26 133 451 25 915 534 25 388 570 (98.0) 24 660 032 (97.1) SA36 Root (R3) 26 740 990 26 626 277 26 098 242 (98.0) 25 365 596 (97.2) SA118 Root (R1) 27 800 225 27 579 786 26 400 450 (95.7) 25 705 966 (97.4) SA118 Root (R2) 22 374 226 22 338 255 21 825 866 (97.7) 21 076 211 (96.6) SA118 Root (R3) 24 904 597 24 744 936 24 106 901 (97.4) 24 336 187 (96.8) Non-Fixing Condition SA36 Leaf (R1) 26 609 927 26 113 636 25 223 395 (96.6) 23 567 101 (93.4) SA36 Leaf (R2) 32 387 528 32 364 064 31 572 228 (97.6) 29 658 298 (93.4) SA36 Leaf (R3) 21 454 037 21 408 563 20 975 741 (98.0) 19 677 080 (93.8) SA118 Leaf (R1) 36 376 779 36 268 748 35 522 625 (97.9) 33 116 973 (93.2) SA118 Leaf (R2) 41 744 529 41 392 746 40 496 211 (97.8) 38 727 429 (95.6) SA118 Leaf (R3) 21 454 037 21 408 563 20 975 741 (98.0) 19 677 080 (93.8) SA36 Root (R1) 34 378 160 34 169 430 33 383 738 (97.7) 32 459 446 (97.2) SA36 Root (R2) 31 779 328 31 498 002 30 637 386 (97.3) 29 557 415 (96.5) SA36 Root (R3) 36 758 293 36 138 880 35 410 085 (98.0) 34 370 648 (97.1) SA118 Root (R1) 29 185 232 28 904 129 27 928 868 (96.6) 27 012 105 (96.7) SA118 Root (R2) 30 319 711 29 450 519 28 723 193 (97.5) 27 922 661 (97.2) SA118 Root (R3) 25 067 610 24 914 856 23 812 750 (95.6) 23 110 723 (97.1)

135

Table 3.2. Number of differentially expressed genes in leaves, roots and nodules between

SA36 and SA118. These numbers represent genes that were differentially expressed

between SA36 and SA118 under N fixing condition, but not under non-fixing condition.

Up-regulated (|Log2

(FC)| ≥ 2)

Comparison DEGs DEGs (|Log2

(FC)| ≥ 2) SA36 SA118 SA36 Leaf vs. SA118 Leaf 83 59 38 21 SA36 Root vs. SA118 Root 222 121 86 35 SA36 Nodule vs. SA118 Nodule 1127 558 147 411

DEGs, differentially expressed genes: |Log2 (FC)| ≥ 2, absolute logarithmic fold change in expression greater or equal to

136

Table 3.3. List of differentially expressed transcription factors. These are transcription factors with significant differential expression

between SA36 and SA118 in leaf, root and nodule under nitrogen fixing condition, but were not differentially expressed under non-

fixing condition.

Gene Identifier Chr. (Position in bp) Transcription

Factor Read count for SA36

Read count for SA118 Log2FC Adj. P

Leaf: Up-regulated in SA36

Phvul.004G122000 Pv04 (39326716-39327951) AP2 1967.3 232.5 2.5 0.0070 Phvul.001G187000 Pv01 (45258083-45261720) GT-2 392.3 72.7 2.2 0.0056 Phvul.010G148700 Pv10 (41934612-41940511) Homeobox 341.9 81.5 2.0 1.2E-08 Leaf: Up-regulated in SA118 Phvul.005G018500 Pv05 (1604423-1605864) MYB 221.2 1068.3 -2.2 1.1E-06 Phvul.006G074600 Pv06 (19393601-19396850) WRKY 2.2 167.9 -3.2 0.0068 Root: Up-regulated in SA36 Phvul.002G292600 Pv02 (45587489-45590225) MYB 142.6 19.0 2.6 3.2E-08 Phvul.007G208400 Pv07 (44697797-44699909) MYB 234.2 66.8 2.1 0.0010 Phvul.004G171200 Pv04 (45277672-45279263) MYB 153.4 28.8 2.0 0.0045 Root: Up-regulated in SA118 Phvul.006G188900 Pv06 (29705815-29707591) NAM 7.9 65.2 -2.3 0.0044 Phvul.006G106100 Pv06 (22259920-22260531) AP2 0 19.2 -2.6 0.0032 Nodule: Up-regulated in SA36 Phvul.003G094700 Pv03 (19512352-19514272) bHLH 85.5 1.0 5.0 1.2E-12 Phvul.010G148700 Pv10 (41934612-41940511) Homeobox 105.8 17.8 2.3 3.9E-04 Phvul.011G005800 Pv11 (430648-437018) MADS BOX 347.1 32.8 3.3 1.9E-17 Phvul.007G048000 Pv07 (3876555-3877440) MADS BOX 31.7 1.0 2.8 0.0093

137

Table 3.3 (cont’d) Phvul.004G162100 Pv04 (44426684-44427426) MBF1 94.0 3.9 3.5 5.9E-05 Nodule: Up-regulated in SA118 Phvul.004G169800 Pv04 (45126736-45127899) AP2 2.7 161.5 -5.0 1.5E-15 Phvul.010G050500 Pv10 (8020695-8021348) AP2 1.1 157.2 -5.7 3.1E-16 Phvul.001G044500 Pv01 (4680371-4681060) AP2 0 35.3 -4.1 4.4E-05 Phvul.009G196900 Pv09 (29159605-29160767) AP2 5.5 40.3 -2.4 0.0066 Phvul.002G036000 Pv02 (3561530-3562521) AP2 1.6 31.6 -2.8 0.0088 Phvul.010G050800 Pv10 (8082893-8083593) AP2 130.5 1190.9 -3.0 1.4E-08 Phvul.003G102500 Pv03 (25181566-25183062) AP2 2.4 38.1 -3.2 0.0001 Phvul.003G212800 Pv03 (42804542-42805711) AP2 5.6 213.9 -3.9 9.5E-06 Phvul.003G292400 Pv03 (51831261-51832171) AP2 15.5 466.8 -4.0 4.0E-07 Phvul.007G273000 Pv07 (51127595-51128470) AP2 7.1 95.1 -2.9 0.0008 Phvul.002G007500 Pv02 (860605-862788) bHLH 47.3 256.5 -2.1 0.0056 Phvul.003G231200 Pv03 (45237056-45239851) bHLH 143.6 998.3 -2.5 0.0005 Phvul.003G231100 Pv03 (45216543-45218543) bHLH 2.4 60.3 -3.4 0.0003 Phvul.011G024700 Pv11 (2054940-2056988) NAM 95.6 698.4 -2.8 2.8E-14 Phvul.009G152900 Pv09 (22214660-22216369) NAM 211.2 1729.4 -2.7 4.4E-05 Phvul.003G248500 Pv03 (47458824-47459525) Dof 15.3 90.0 -2.4 5.9E-06 Phvul.003G212200 Pv03 (42719744-42722190) GRAS 8.4 61.2 -2.5 0.0015 Phvul.011G109600 Pv11 (13902942-13904399) MYB 9.2 854.2 -3.7 0.0003 Phvul.007G108500 Pv07 (13461806-13464239) MYB 124.3 544.8 -2.1 3.0E-11 Phvul.003G232300 Pv03 (45418410-45419954) MYB 24.0 137.1 -2.2 0.0083 Phvul.001G215100 Pv01 (47821425-47822714) MYB 6.2 125.6 -3.3 0.0001 Phvul.007G242300 Pv07 (48190783-48192713) MYB 2.4 151.9 -4.6 8.3E-09 Phvul.004G053600 Pv04 (6865813-6867929) MYB 10.6 69.4 -2.5 0.0001 Phvul.009G062700 Pv09 (10947123-10947797) MYB 124.7 1119.1 -2.7 0.0001 Phvul.007G211800 Pv07 (45045204-45046968) MYB 7.2 114.0 -2.9 0.0026 Phvul.003G173300 Pv03 (38424473-38426629) PLATZ 11.4 77.2 -2.4 0.0021

138

Table 3.3 (cont’d) Phvul.002G265400 Pv02 (43085670-43087004) WRKY 191.2 843.3 -2.0 1.1E-07 Phvul.006G111700 Pv06 (22762481-22764805) WRKY 85.0 389.9 -2.1 8.8E-07 Phvul.005G181800 Pv05 (40322573-40324669) WRKY 852.5 4369.3 -2.1 0.0009 Phvul.002G297100 Pv02 (46023368-46025419) WRKY 57.0 771.9 -3.5 1.6E-13 Phvul.009G137500 Pv09 (20185631-20187441) WRKY 7.7 191.1 -4.0 9.8E-10 Phvul.010G111900 Pv10 (37576223-37578860) WRKY 47.7 1204.9 -4.4 3.3E-25

Chr., chromosome; Position, is the physical position in base pair (bp); Log2FC, Log2 fold change in expression of SA36 over SA118; Adj. P, is the corrected P-value for FDR=0.01. Read count for SA36 and SA118 is number of reads (average of three replications) aligned to the gene after normalizing for total number of reads mapped for each library using HTSeq

139

Table 3.4. Enriched molecular functions of differentially expressed genes in leaves, roots and nodules between SA36 and SA118.

GO Identifier Molecular Function # (Input List) # (Ref) P-value FDR Leaf: Molecular functions of DEGs Up-regulated in SA36 GO:0016758 Transferase activity, transferring hexosyl groups 5 387 0.0001 0.0017 Root: Molecular functions of DEGs Up-regulated in SA36 GO:0005215 Transporter activity 9 820 0.0005 0.0250 GO:0005506 Iron Ion binding activity 6 642 0.0022 0.0420 Root: Molecular functions of DEGs Up-regulated in SA118 GO:0016491 Oxidoreductase activity 8 1621 0.0012 0.0069 Nodule: Molecular functions of DEGs Up-regulated in SA36 GO:0004888 Transmembrane receptor activity 7 129 9.4E-06 0.0005 GO:0001883 Purine nucleoside binding 25 2587 0.0027 0.0400 GO:0016491 Oxidoreductase activity 18 1626 0.0030 0.0400 Nodule: Molecular functions of DEGs Up-regulated in SA118 GO:0004312 Fatty-acid synthase activity 5 15 1.5E-05 0.0025 GO:0016798 Hydrolase activity, acting on glycosyl bonds 18 420 0.0003 0.0200

GO is Gene Ontology; # (Input List) is number of genes in the input list of differentially expressed genes with this molecular function; # (Ref) is number of genes in the reference genome with this molecular function; GO categories were identified using the AgriGO Singular Enrichment Analysis; FDR, false discovery rate.

140

Figure 3.1. Growth characteristic of SA36 and SA118 under fixing and non-fixing

condition

141

Figure 3.2. Differences in shoot dry weight (per plant) between SA36 and SA118 grown

under nitrogen fixing and non-fixing conditions.

142

Figure 3.3. Differences in total nitrogen in shoot biomass (per plant) between SA36 and

SA118 grown under nitrogen fixing and non-fixing conditions.

143

Figure 3.4. Nodule fresh weight (per plant) difference between SA36 and SA118 grown

under nitrogen fixing condition.

0

200

400

600

800

1000

1200

1400

SA36 SA118

Nod

ule

Fres

h W

t. (g

)

144

Figure 3.5. Venn diagrams showing number of differentially expressed genes between

SA36 and SA118 in leaf and root under fixing condition and non-fixing condition. In the

upper Venn diagrams (A) 83 represents genes in the leaves that were differentially

expressed between SA36 and SA118 under nitrogen fixing condition, but not under non-

fixing condition. In the lower Venn diagram (B) 222 represent genes differentially

expressed between SA36 and SA118 in roots under nitrogen fixing condition, but not

under non-fixing condition.

145

Figure 3.6. Relative expression of Phvul.007G048000 (MADS BOX transcription factor)

in leaves, roots and nodules of SA36 and SA118 grown under nitrogen fixing and non-

fixing condition. Relative gene expression is presented using read count. Read count is

number of reads (average of three replications) aligned to the gene after normalizing for

total number of reads mapped for each library using HTSeq.

05

10152025303540

Nor

mal

ized

read

cou

nt

146

Figure 3.7. Relative expression of Phvul.001G044500 (AP2 transcription factor) in

leaves, roots and nodules of SA36 and SA118 grown under nitrogen fixing and non-fixing

condition. Relative gene expression is presented using read count. Read count is number

of reads (average of three replications) aligned to the gene after normalizing for total

number of reads mapped for each library using HTSeq.

0

50

100

150

200

250

Nor

mal

ized

read

cou

nt

147

Additional Files

Additional file 1: Table S1. Genes differentially expressed in leaves between SA36 and

SA118 under fixing condition but were not differentially expressed under non-fixing

condition. Format: XLS, (Excel Spreadsheet).

Additional file 2: Table S2. Genes differentially expressed in roots between SA36 and

SA118 under fixing condition but were not differentially expressed under non-fixing

condition. Format: XLS, (Excel Spreadsheet).

Additional file 3: Table S3. Genes differentially expressed in nodules between SA36

and SA118. Format: XLS, (Excel Spreadsheet).

Additional file 4: Table S4. Table S5 List of single nucleotide polymorphism (SNPs)

and their physical positions in genes that were differentially expressed in leaves between

SA36 and SA118. Format: XLS, (Excel Spreadsheet).


and their physical positions in genes that were differentially expressed in roots between

SA36 and SA118. Format: XLS, (Excel Spreadsheet).


and their physical positions in genes that were differentially expressed in nodules

between SA36 and SA118. Format: XLS, (Excel Spreadsheet).

148

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CHAPTER 4 IDENTIFICATION OF QUANTITATIVE TRAIT LOCI FOR SYMBIOTIC NITROGEN

FIXATION IN COMMON BEAN

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Identification of Quantitative Trait Loci for Symbiotic Nitrogen Fixation in Common Bean

Abstract

Productivity of common bean (Phaseolus vulgaris L.) can be enhanced through genetic

improvements in the complex genetic architecture that regulates symbiotic nitrogen fixation

(SNF). This study was aimed at understanding the genetic architecture of SNF through QTL

analysis. A total of 188 F4:5 recombinant inbred lines (RILs) derived cross of Solwezi and AO-

1012-29-3-3A were evaluated for SNF in the greenhouse and genotyped with 5398 SNPs. Using

composite interval mapping, QTL for nitrogen derived from atmosphere (Ndfa) were identified on

chromosomes Pv02, Pv04, Pv06, Pv07, Pv09, Pv10, and Pv11. QTL for shoot biomass, root

weight, percentage of nitrogen in shoot, and Ndfa co-localized on Pv07. Some of the QTL

identified in the current study co-localized with previously reported QTL, indicating the stability

of these traits across genetic backgrounds and environments. The QTL for nitrogen percentage in

shoot on Pv03 and QTL for Ndfa on Pv09 overlapped with previously identified QTL. Once the

QTL with large effects on Ndfa identified in the current study are validated in multiple genetic

backgrounds and environments, they could potentially be deployed in marker-assisted breeding to

accelerate development of common bean germplasm with enhanced SNF.

Introduction

Common bean (Phaseolus vulgaris L.) is a staple crop for millions of people in East Africa and

Latin America (Akibode and Maredia 2012). Like other legumes, common bean is able to fix

atmospheric nitrogen (N2) into NH3 for its use through a process known as symbiotic nitrogen

fixation (SNF). SNF results from a symbiotic relationship between common bean and soil bacteria

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known as Rhizobium. In countries where farmers can afford fertilizers, SNF reduces the amount

of N fertilizer for crop production thereby reducing production costs and potential ground water

pollution (Vance 2001). When compared to other grain legumes such as soybeans and cowpeas,

common bean is considered weak in N fixation (Bliss 1993). The average amount of N fixed by

common bean is 55 kg ha-1, which is lower than the average for soybean or cowpea (Graham et al.

2003). However, numerous studies have reported adequate genetic variability for SNF within

common bean (Buttery et al. 1997; Elizondo Barron et al. 1999; Graham and Rosas 1977; Pereira

et al. 1993). The amount of N fixed by common bean varies from 0 kg ha-1 to 150 kg ha-1,

depending on the environment and genotype (Graham et al. 2003; Unkovich and Pate 2000). This

genetic variability can support genetic improvement of common bean for SNF. Previous studies

that have reported significant seed yield increases following application of N fertilizer suggesting

that genetic enhancement of SNF in common bean has potential to improve productivity (Herridge

and Redden 1999). There have been past efforts to improve SNF in common bean through

breeding. In some cases these efforts have resulted in the development of varieties with enhanced

SNF (Bliss et al. 1989). However, sustained success in developing germplasm with enhanced SNF

has been limited. This limited success can be attributed to the complex genetic architecture of SNF.

Several genes with varying effects control SNF. Expression of these genes is strongly influenced

by the environment. Knowledge of the genetic architecture of SNF can support development of

strategy and molecular tools for genetic enhancement of SNF. To date, studies aimed at

understanding genetic architecture of SNF or its associated traits have been limited to four QTL

mapping studies (Nodari et al. 1993; Ramaekers et al. 2013; Souza et al. 2000; Tsai et al. 1998),

and recently a genome-wide association study (Kamfwa et al. 2015). Two studies used the same

BAT93 x Jalo EEP558 population to identify QTL for nodule weight and nodule number under

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high and low soil N (Souza et al. 2000; Tsai et al. 1998), but they did not measure SNF directly.

Ramaekers et al. (2013) used a population of F4:5 recombinant inbred lines (RILs) derived from

G2333 X G19839 to identify QTL for Ndfa on Pv04 and Pv10 under greenhouse (GH) and field

conditions. In GWAS by Kamfwa et al. (2015) significant SNP markers for Ndfa were identified

on Pv02, Pv03, Pv07 and Pv10 an Andean diversity panel evaluated in the GH and field. The

objective of this study was to identify QTL for SNF and its related traits in a bi-parental mapping

population derived from two Andean parents and identify recombinant lines for use in breeding.

Knowledge of the QTL for SNF can be used to develop molecular markers to indirectly select for

Ndfa, and circumvent challenges of direct selection of traits associated with improved SNF in

common bean.


Plant Materials

A total of 188 F4:5 RILs derived from a cross of Solwezi and AO-1012-29-3-3A were used in the

current study. The RILs were developed using single seed descent method and advanced to the F5

generation. AO-1012-29-3-3A is an Andean breeding line developed at University of Puerto Rico

(Kusolwa et al. 2015). It has a determinate growth habit and a red kidney seed type combined with

resistance to seed weevils (Acanthoscelides obtectus Say). Solwezi is a landrace that is widely

grown in Zambia with indeterminate growth habit (type IV), and large round red mottled seed

type. In Zambia, small-scale farmers grow common bean without fertilizer application on soils

with low nutrient level. Despite these marginal soil-growing conditions, farmers successfully grow

Solwezi without fertilizer application. In part, this can be attributed to its superior SNF ability.

Previous evaluation for SNF in the greenhouse at Michigan State University (MSU) of four

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genotypes grown in Zambia and AO-1012-29-3-3A, showed Solwezi to be superior to AO-1012-

29-3-3A in SNF. An Andean non-nodulating mutant G51493A was used as a reference check for

N stress and for calculating the amount of N fixed in this study.

The 188 F4:5 RILs and the parents were evaluated for SNF and related traits in two greenhouse

(GH) experiments at MSU. The first experiment was planted in December 2014 (here after referred

to as GH_14), and the second experiment was planted in February 2015 (here after referred to as

GH_15). A randomized complete block design with three replications was used in both GH_14

and GH_15. The 188 RILs, the parents and G51493A check were planted in 4-liter plastic pots

filled with vermiculite and perlite in 1:2 (v/v) ratio. For each replication, four seeds of each RIL,

parents and no-nod were sterilized in sodium hypochlorite and then rinsed in distilled water. After

rinsing seeds were inoculated with CIAT899, a strain of Rhizobium tropici (Graham et al. 1994).

This was done by submerging seeds for two minutes in a broth culture containing CIAT899 made

from yeast extract manitol media (Vincent 1970). Immediately after this, the four seeds were

planted in a pot and watered with tap water until seedlings emerged. After emergence seedlings

were thinned to two per pot. To ensure adequate inoculum for nodulation, a second inoculation

was done when plants were trifoliate stage by applying 1 ml of the CIAT899 broth to each pot.

About 500 ml of N-free nutrient solution was applied to each pot every day from seedling

emergence up to flowering (Broughton and Dilworth 1970). During the experiment, the

temperature in the GH ranged from 24oC to 26oC, and 13 hours of light was provided. At flowering,

the above ground shoots of two plants in each pot were harvested and dried in the oven at 120oC

for three days. After drying shoot dry weight was measured. The shoot was then ground with a

Christy-Turner Lab Mill to pass through a 1 mm screen. Ground samples from GH_14 were

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shipped to the Isotope facility at UC Davis, California for total N and 15N analyses. Ground

samples from the GH_15 were sent A&L Laboratories in Indianapolis, US for analysis of N

percentage in shoot biomass.

Estimation of Amount of N fixed

The amount of N fixed from the atmosphere (Ndfa) per genotype was calculated by multiplying

shoot biomass (BM) with %N in shoot biomass, minus the total N in shoot biomass of a non-

nodulating mutant. The subtraction of the total N in shoot biomass of a non-nodulating mutant

from the total of a fixing genotype was to account for the N that did not come from N fixation.

The Ndfa values reported in the current study are per single plant.

Phenotypic Data Analysis

Statistical analyses for field data were conducted using mixed models in SAS 9.3 (SAS Institute

2011). PROC UNIVARIATE in SAS was used to conduct normality tests on the combined

residuals of all treatments for each trait measured in GH_14 and GH_15. Normality tests results

showed shoot biomass, %N and Ndfa to be normally distributed. An analysis of variance for each

trait was conducted using PROC MIXED based on the following statistical model:


Where: Yijk is the response variable e.g., Ndfa, with genotype i in the environment j, replication k

within environment; αi was the fixed effect of the genotype i; βj was the random effect of the

environment j; αβ was the random effect of the interaction between genotype i and environment

j; γ was the random effect of a replication with environment j; ε was the random error term, which

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was assumed to be normally distributed with mean =0. Genetic correlation analyses were

conducted between root weight, shoot biomass, %N and Ndfa were conducted using multivariate

restricted maximum likelihood estimation using PROC MIXED (Holland 2006).

DNA Extraction and Genotyping

DNA was extracted from leaves of the 188 RILs and parents grown in the GH at MSU using a

previously described protocol (Cichy et al. 2015). DNA samples were genotyped using an Illumina

BARCBean6K_3 BeadChip with 5398 SNPs (Song et al. 2015) in the Soybean Genomics and

Improvement USDA Laboratory (USDA-ARS, Beltsville Agricultural Research Center) in MD,

USA. The SNP genotyping was conducted on the Illumina platform by following the Infinium HD

Assay Ultra Protocol (Illumina Inc.). SNP alleles were called using GenomeStudio Software from

Illumina, Inc.

Genetic Map Construction

The 5398 SNP markers were filtered for polymorphism. The remaining 760 polymorphic markers

were used to build genetic linkage maps using software JoinMap version 4.1 (Van Ooijen 2011).

In JoinMap additional filtering was done to remove markers with severe segregation distortion

from the expected 1:1 ratio from further analyses. Additional filtering was also done to leave one

marker on a mapped position if more than one mapped to the same position. In JoinMap markers

were grouped into linkage groups using a logarithm of odds (LOD) score threshold score of 5. A

regression mapping procedure was used to order markers within linkage groups, and map distances

between markers were estimated from recombination frequencies using Kosambi mapping

function implemented in JoinMap. Linkage maps were displayed using MapChart (Voorrips 2002).

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QTL Analysis

QTL analysis was conducted using composite interval mapping (CIM) method implemented in the

software Win QTL Cartographer version 2.5-011 (Wang et al. 2012). In composite interval

mapping the following control parameters were used: (i) model 6 (Standard model), (ii) 5

control/background markers, (iii) 10 cM window size, and (iv) forward and backward multiple

regression model, (V) 1 cM walk speed (genome scan interval). A permutation test (Doerge and

Churchill 1996) for each trait was conducted in QTL Cartographer (1000 permutations) to

determine a genome-wide LOD threshold at P=0.05 for declaring a QTL significant. The position

with the highest LOD score for a given testing region was considered as the position of the QTL.

The amount of phenotypic variance explained by a QTL at a given test position was determined

using the coefficient of determination (R2) from the QTL cartographer software program. The QTL

were named based on guidelines provided by the Genetics Committee of Bean Improvement

Cooperative (Miklas and Porch 2010). Briefly, the letters at the beginning of the name represents

the trait abbreviation, the number that immediately follows the abbreviation, but preceding the

period represents the linkage group (which is also the chromosome number), the number after the

period represents the number of this QTL in the order of discovery. Since Ramaekers et al. (2013)

is the only published paper to have reported QTL for Ndfa their QTL were used as the basis for

ordering the discovery of QTL. QTL consistently identified in GH_14 and GH_15 were given the

same name.

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Results

Phenotypic Analyses

The t-test showed that the parents (Solwezi and AO-1012-29-3-3A) were significantly different

for shoot biomass, Ndfa, but not for %N in shoot biomass. Solwezi was superior to AO-1012-29-

3-3A in shoot biomass, root weight and Ndfa (Table 4.1). In GH_14 Ndfa for Solwezi was 156.2

g plant-1 compared to 82.2 g plant -1 for AO-1012-29-3-3A.

Normal frequency distributions for shoot biomass, root weight, N% and Ndfa measured on 188

RILs were observed (Figure 4.1). These traits showed continuous distribution in both GH_14 and

GH_15, which confirmed their quantitative inheritance. Transgressive segregation was observed

in the mapping population. In both GH_14 and GH_15, there were several RILs with means for

shoot biomass, %N, root weight and Ndfa that were outside the range of the parents Solwezi and

AO-1012-29-3-3A.

In both GH_14 and GH_15, there was significant genetic variability among RILs (p<0.01) for

shoot biomass, %N, root weight, and NDFA. In GH_14, the population mean for shoot biomass

was 2.9 g plant-1, and the range was 0.8 to 5.5 g plant-1 (Table 4.1). In GH_15, the population mean

for shoot biomass was 4.2 g plant-1, and the range was 1.4 to 7.5 g plant-1. In GH_14 and GH_15,

population means for %N were 3.0% and 2.9%, respectively. In GH_14, the population mean for

Ndfa was 92.3 mg N plant-1, and the range was from 33.5 to 181.4 mg N plant-1 (Table 4.1). In

GH_15, population mean for Ndfa increased to 104 mg N plant-1.

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Significant positive genetic correlations between traits measured in this study were observed

(Table 4.2). In both GH_14 and GH_15, the most significant positive correlation (r=0.88 in

GH_14, and r=0.91 in GH_15) was between Ndfa and shoot biomass (Table 4.2).

Genetic Map Construction

A total of 5398 SNP markers on the BARCBean6K_3 Beadchip were assayed on the parents and

the 188 RILs. A total of 760 were polymorphic between parents. After filtering to remove SNPs

mapping to same position, and markers with severe segregation distortions, 518 SNPs remained to

build a genetic linkage map. A total of eleven linkage groups named Pv01-Pv11, representing the

11 chromosomes in common bean were constructed. The total genetic distance of these 11 linkage

groups was 613.6 cM. The average genetic distance per linkage group was 55.8 cM. The smallest

linkage group was Pv06 with 41.4 cM, and the largest was Pv04 with 83.7 cM. The average

number of markers per linkage group was 47. The average genetic distance between markers was

0.3 cM. The number of markers per linkage group ranged from 16 on Pv01 to 84 on Pv04. In

general, the linkage group orientation and order of most SNPs within a linkage map was in

agreement with the Stampede x Red Hawk population linkage map, in which the SNPs were

initially mapped (Song et al. 2015). An exception was observed for orientation of linkage group

Pv03, where the map was inverted when compared to the Stampede x Red Hawk linkage map

orientation. However, the order of markers within Pv03 linkage group constructed in the current

study and the corresponding Stampede x Red Hawk were in agreement.

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QTL Analyses

A total of 24 QTL were identified for shoot biomass, %N in shoot biomass, root weight, and Ndfa

on nine linkage groups in GH_14 and GH_15 (Table 4.3 and Figure 4.1). To avoid redundancy of

QTL names, QTL found in the same genomic location for a given trait in GH_14 and GH_15 were

considered one and given the same name. This reduced the total number of identified QTL from

24 to 16. In general, more QTL were identified in GH_14 than in GH_15.

Shoot Biomass A total of 8 QTL for shoot biomass were identified in GH_14 and GH_15 (Table 4.3; Figure 4.2).

The LOD score threshold for GH_14 and GH_15 were 3.2 and 3.0, respectively. In GH_14, four

QTL were identified on Pv06, Pv07, Pv10 and Pv11. The QTL identified on Pv06, was designated

BM6.1, spanned a region of 9.7 -16 cM and was flanked by SNPs ss715646389 and ss715647706.

The peak for BM6.1 was at 12.3 cM, and the nearest marker was ss715650286, with physical

position at 22.149902 Mb. BM6.1 had a LOD score of 7.7 and explained about 12.4% of shoot

biomass variation in GH_14. The allele with favorable effect on shoot biomass at BM6.1 came

from AO-1012-29-3-3A. The second QTL identified in GH_14 for shoot biomass was located on

Pv07, and was designated BM7.2. The peak for BM7.2 was at 53.3 cM, and the nearest marker

was ss715639206 at 49.871844 Mb. BM7.2 spanned 42.2-62.7 cM, and was flanked by SNPs

ss715649067 and ss715645231. The LOD score for BM7.2 was 13.3, and explained 23.6% shoot

biomass variation in GH_14. Alleles at this QTL with positive effect on shoot biomass came from

Solwezi. The third QTL in GH_14 was identified on Pv10, and was designated BM10.2. The peak

position for BM10.2 was at 20.6 cM, and the nearest marker was ss715646324 at 39.660637 Mb.

BM10.2 spanned 19.0-32.9 cM, and was flanked by SNPs ss715647915 and ss715646967. The

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LOD score for BM10.2 was 5.2, and explained 9% of shoot biomass variation in GH_14. Alleles

with favorable effect on shoot biomass at BM10.2 came from Solwezi. The fourth QTL identified

in GH_14 was located on Pv11, and was designated BM11.1. The peak for this QTL was at 47.6

cM, and the nearest marker was ss715640672, located at 8.211733 Mb. BM11.1 spanned 44.7-

54.0 cM, and was flanked by ss715640672 and ss715650232. The LOD score for BM11.1 was 4.5

and explained 7.8% shoot biomass variation. In GH_15 a QTL was identified on Pv06, with a peak

at 15.3 cM. This QTL overlapped with BM6.1, and was considered to be the same QTL detected

in GH_14. The LOD score and R2 for BM6.1 were less in GH_15 than in GH_14. The second QTL

identified in GH_15 was on Pv07, with a peak on 52.7 cM. This QTL overlapped with BM7.2,

previously detected in GH_14. In GH_15, BM7.2 explained 18.4% of shoot biomass variation

compared to 23.6% in GH_14. The third QTL identified in GH_15 was on Pv09, and was

designated BM9.1. The peak for BM9.1 was at 63.5 cM, and the nearest marker was ss715647170

located at 34.212155 Mb. BM9.1 spanned 53.6-63.4 cM, and was flanked by ss715640300 and

ss715647170. The LOD score for BM9.1 was 5.9, and explained 10.4% of shoot biomass variation

in GH_15. Favorable allele at this QTL came from Solwezi. The fourth QTL identified in GH_15

was on Pv11, with a peak at 52.2 cM. This QTL overlapped with BM11.1, and was given this same

designation. In GH_15, BM11.1 explained 6.5% of shoot biomass, compared to 7.8% in GH_14.

In this study QTL for shoot biomass were consistently identified on Pv06, Pv07 and Pv11. The

LOD scores and R2 for all three QTL were higher in GH_14 than GH_15.

Nitrogen Percentage in Shoot Biomass (%N) A total of six QTL for %N were identified in GH_14 and GH_15 (Table 4.3; Figure 4.2). In both

GH_14 and GH_15, the LOD score threshold was 3.0. The first QTL identified in GH_14 was on

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Pv01, and was designated %N1.2. The peak for this QTL was at 32.2 cM, and nearest marker was

ss715650418 located at 36.23558 Mb. The LOD score for %N1.2 was 3.8, and explained 5.8% of

the variation in %N in GH_14. The allele with positive effect on %N at %N1.2 came from Solwezi.

The second QTL in GH_14 was identified on Pv03, and was designated %N3.2. The peak for this

QTL was at 2.0 cM, and the nearest marker was ss715646441 located at 47.811352 Mb. The LOD

score for %N3.2 was 6.2, and it explained 13.1% of the variation in %N in GH_14. The allele with

positive effect on %N at %N3.2 came from Solwezi. The third QTL identified in GH_14 was on

Pv04, and was designated %N4.2. The peak for this QTL was at 44.1 cM, and the nearest marker

was ss715647337 located at 37.280582 Mb. The LOD score for %N4.2 was 7.2, and explained

14.2% of variation in %N in GH_14. The allele at %N4.2 with positive effect on %N came from

Solwezi. The fourth QTL identified in GH_14 was on Pv07, and hereafter designated %N7.1. The

peak for this QTL was at 56.8 cM, and the nearest marker was ss715639341 located at 45.324468

Mb. The LOD for %N7.1 was 3.3, and explained 5.1% of variation in %N in GH_14. The allele

with positive effect on %N at %N7.1 came from Solwezi.

In GH_15, the first QTL was identified on Pv03, with a peak at 13 cM. This QTL overlapped with

%N3.2 identified earlier in GH_14 so these two QTL were considered the same and given same

name. The LOD score and R2 for %N7.1 in GH_14 and GH_15 were also similar. Favorable allele

at this QTL came from Solwezi. The second QTL identified in GH_15 was on Pv10, and was

designated %N10.1. The peak for this QTL was at 13.5 cM, with the nearest marker being

ss715645510 at 40.992209 Mb. The LOD score for %N10.1 was 3.2, and explained 8.6% of the

variation in %N in GH_15. Favorable allele at this %N10.1 came from Solwezi.

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Root Weight (RW) Root weight was only measured in GH_14. A single QTL for root weight was identified in GH_14

with LOD threshold score of 2.9. This QTL was located on Pv07, and was designated as RW7.2

(Table 4.3; Figure 4.2). The peak for RW7.2 was at 54.3 cM, and the nearest marker was

ss715645239 located at 50.132528 Mb. This QTL spanned 53.3-54.3 cM, and was flanked by

SNPs ss715645236 and ss715645239. The LOD score for RW7.2 was 3.5, and explained 6.6% of

root weight variation in the population in GH_14. The allele with positive additive effects on root

weight at RW7.2 came from Solwezi.

Nitrogen derived from atmosphere (Ndfa) A total of nine QTL for Ndfa were identified in GH_14 and GH_15 (Table 4.3; Figure 4.2). The

LOD thresholds for Ndfa in GH_14 and GH_15 were 3.1 and 3.0, respectively. The first QTL was

identified in GH_14 on Pv02, and was designated NDFA2.1. The peak for this QTL was at 19.5

cM, and the nearest marker to this peak was ss715639728 located at 41.666960 Mb. The LOD

score for NDFA2.1 was 6.5 and explained 12.9% of Ndfa variation in GH_14. The allele with

positive effect on Ndfa at NDFA2.1 came from Solwezi. The second QTL was identified in GH_14

on Pv04, and was designated NDFA4.2. The peak for this QTL was at 33 cM, and the nearest

marker was ss715639216 located at 39.341516 Mb. The LOD score for NDFA4.2 was 3.3, and

explained 4.2% of Ndfa variation in GH_14. Alleles with positive effect on Ndfa at NDFA4.2

came from Solwezi. The third QTL identified in GH_14 was on Pv06, and was designated

NDFA6.1. The peak for this QTL was at 12.3 cM, and the nearest marker was ss715650286 located

at 22.149902 Mb. The LOD score for NDFA6.1 was 9.0, and explained 12.1% of Ndfa variation

in GH_14. The allele with positive effect on Ndfa at NDFA6.1 came from AO-1012-29-3-3A. The

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fourth QTL identified in GH_14 was on Pv07, and was designated NDFA7.1. The peak for this

QTL was at 53.6 cM, and the nearest marker was ss715639206 located at 49.871844 Mb. The

LOD score for NDFA7.1 was 8.5, and explained 11.5% NDFA variation in GH_14. The allele

with positive effect on Ndfa at NDFA7.1 came from Solwezi. The fifth QTL identified in GH_14

was on Pv10, and was designated NDFA10.2. The peak for this QTL was at 15.6 cM, and the

nearest marker was ss715647919 located 40.638544 Mb. The LOD score for NDFA10.2 was 7.2,

and explained 13.7% of Ndfa variation in GH_14. The allele with positive effect on Ndfa at

NDFA10.2 came from Solwezi. The sixth QTL identified in GH_14 was located on Pv11, and was

designated NDFA11.1. The peak for this QTL was at 56.1 cM, and the nearest marker was

ss715647555 located at 44.938496 Mb. The LOD score for NDFA11.1 was 3.3, and explained

4.4% of Ndfa variation in GH_14. The allele with positive effect on Ndfa at NDFA11.1 came from

Solwezi.

In GH_15, three QTL for Ndfa were identified (Table 4.3; Figure 4.2). The first QTL identified in

GH_15 was on Pv07. The peak for this QTL was at 55.3 cM, and overlapped with NDFA7.1

identified in GH_14. These two QTL were considered as the same and given same designation

NDFA7.1. The percentage of variation in Ndfa explained by NDFA7.1 increased from 11.5% in

GH_14 to 14.9% in GH_15. The second QTL identified in GH_15 was on Pv09, and was

designated NDFA9.1. The peak for this QTL was at 63.4 cM, and the nearest marker was

ss715647170 located at 34.212155 Mb. The LOD score for NDFA9.1 was 5.7, and explained 8.3%

of NDFA variation in GH_15. The allele with positive effect on Ndfa at NDFA9.1 came from

Solwezi. The third QTL identified in GH_15 was on Pv11, and its peak was at 56.1 cM. This QTL

overlapped with NDFA11.1 identified in GH_14 and was given same designation NDFA11.1. In

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GH_14 NDFA11.1 explained 4.4% of the variation in Ndfa compared to 5.3% in GH_15 (Table

4.3).

Discussion

SNF has a complex genetic architecture involving several genes and genomic regions. Adequate

genetic variability for SNF exists within common bean. However, the genetic basis of this

variability is poorly understood. In this study, QTL analyses were conducted to identify genomic

regions controlling SNF variability in a population of 188 RILs derived from two Andean parents

Solwezi and AO-1012-29-3-3A that differed in ability to fix N.

Transgressive segregations for shoot biomass, %N, root weight and Ndfa were observed in the

population suggesting that favorable and unfavorable alleles for these traits were present in both

parents (Figure 4.1). Although the majority of the QTL for shoot biomass and Ndfa and alleles

with favorable effect came from Solwezi parent, there were exceptions. Among the five QTL for

shoot biomass, BM6.1 that was consistently identified in GH_14 and GH_15 received its favorable

allele from AO-1012-29-3-3A. Similarly, NDFA6.1 that was consistently identified in GH_14 and

GH_15, and was among the seven QTL identified for Ndfa that received favorable allele from AO-

1012-29-3-3A. These results demonstrate the presence of favorable alleles for SNF in both parents

that could have contributed to the transgressive segregation observed in the population.

The most significant positive genetic correlations among traits measured in the current study was

between shoot biomass and Ndfa, and most of the QTL for Ndfa and shoot biomass co-localized.

This suggests that in a growing environment with low soil N, shoot biomass can be used to reliably

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predict SNF ability of a given genotype and to identify genomic regions controlling SNF. It is

faster and cheaper to collect data on shoot biomass than Ndfa.

In this study multiple QTL for shoot biomass, %N and Ndfa were identified on several

chromosomes. This confirms the polygenic genetic architecture of SNF, and is consistent with

previous genetic studies that reported multiple QTL for SNF. QTL for shoot biomass were

identified on Pv06, Pv07, Pv09, Pv10 and Pv11. Ramaekers et al. (2013) reported shoot biomass

QTL on Pv07 and Pv10 in G2333 x G19839 mapping population. Because of differences in

genotyping platforms used in the two studies, it is difficult to ascertain if QTL previously identified

on Pv07 and Pv10 overlap. In a recent GWAS that used an Andean diversity panel, significant

SNPs for shoot biomass were identified on Pv07 under field conditions. The QTL for shoot

biomass BM7.2 that was consistently identified in GH_14 and GH_15 overlapped with

ss715650286 (49.87144 Mb) reported to be significantly associated with shoot biomass in GWAS

(Kamfwa et al. 2015).

QTL for %N in the shoot were identified on Pv01, Pv03, Pv04, Pv07 and Pv10. Previous studies

have reported QTL for %N in shoot biomass on similar chromosomes. The QTL for %N in shoot

biomass on Pv01, Pv03 and Pv04 were reported first by Ramaekers et al. (2013). Kamfwa et al.

(2015) used GWAS to identify SNPs on Pv03 significantly associated with shoot biomass. The

QTL %N3.2 identified in the current study overlapped with the genomic region that contained

significant SNPs for shoot biomass in GWAS. The nearest marker to the peak of %N3.2 was

ss715646441 located at 47.811352 Mb on Pv03 while the most significant SNP on Pv03 for %N

in shoot in GWAS was ss715639320 located at 47.948032 Mb (Kamfwa et al. 2015). Based on the

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overlapping genomic regions and the extensive linkage disequilibrium in self-pollinated crops such

as common bean, it is plausible that the gene/s underlying the QTL identified in the current study

and the previous GWAS are the same, and have stable expression in different environments and

genetic backgrounds.

QTL for Ndfa were identified on Pv02, Pv04, Pv06, Pv07, Pv09, Pv10 and Pv11 in the current

study. QTL for Ndfa have been previously reported on six of these seven chromosomes with the

exception of Pv06. Ramaekers et al. (2013) reported QTL for Ndfa on Pv04 and Pv10. Genomic

regions on Pv02, Pv07, Pv09, Pv10 and Pv11 were significantly associated with Ndfa in previous

GWAS. Some of these regions identified in GWAS overlap or are in close proximity to the QTL

identified in the current study. NDFA9.1 identified in the current study on Pv09 overlapped with

the genomic region that contained SNPs significantly associated with Ndfa (Kamfwa et al. 2015).

In the current study, the nearest marker to the peak of NDFA9.1 was ss715647170 located at

34.212155 Mb while the most significant SNP associated with Ndfa on Pv09 in GWAS was

ss715647197 located at 34.101880 Mb. The two SNPs flanking NDFA9.1 span a physical distance

31.690110 - 34.212155 Mb on Phaseolus vulgaris genome (Schmutz et al. 2014) where 144 genes

are located. Transcriptome data of two RILs selected from the mapping population that are highly

contrasting for SNF, showed that only nine out of these 144 genes were differentially expressed

between these two RILs (chapter 3). These nine genes are their functional annotations are

presented in Table 4.3. The nine genes could be part of the genetic basis of the contrasting SNF

phenotype between these two RILs. Based on the GWAS, QTL mapping and transcriptome

analyses these nine genes are both positional and expression candidate genes for Ndfa on Pv09.

The nine genes include Phvul.009G231000 that encodes a calmodulin, and was reported as the

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candidate gene for genomic region on Pv09 associated with Ndfa (Kamfwa et al. 2015).

Calmodulin are proteins associated with calcium spiking a major biochemical event involved in

nodulation (Levy et al. 2004). Three of the nine candidate genes encode leucine-rich receptor

kinases (LRR-RLK), one encodes cytokinin dehydrogenase, and the other one encode a tubby

protein. LRR-RLKs are reported to play signaling role in nodulation (Oldroyd and Downie 2004).

Using three different, but complementary approaches, and populations with different genetic

architecture, the genomic region on Pv09 has consistently been associated with Ndfa. This suggests

that gene(s) underlying this genomic region have a stable expression of Ndfa in different genetic

backgrounds.

QTL for shoot biomass (BM7.2), %N in shoot (%N7.1), root weight (RW7.2) and NDFA

(NDFA7.1) co-localized on Pv07. BM7.2 and NDFA7.1 were consistently identified in GH_14

and GH_15. The co-localization of QTL for Ndfa and shoot biomass is expected, given that Ndfa

was computed as a product of shoot biomass and %N. However, the co-localization of root weight,

%N, and shoot biomass that are not computationally related can be attributed to two possible

scenarios. The first scenario is pleiotropy where the same gene controls different traits, and

physiological relationships among shoot biomass, root weight, %N and Ndfa. Shoot biomass is a

major contributor to Ndfa. It is the source of photo-assimilates that drives SNF, and is also a sink

for the fixed N. Any gene that is involved in plant growth, measured as biomass accumulation per

unit time is likely to contribute to shoot biomass and ultimately to Ndfa. The genes involved in

plant growth are likely to pleiotropically control both shoot weight and root weight. The second

possible scenario for co-localization of traits on Pv07 could be that linked genes that are

functionally different controlled these traits. This scenario could be tested using larger population

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sizes with more recombination and enhanced mapping resolution. The region on Pv07 (50-60 cM)

that contained peaks for NDFA7.1 and BM7.2 was investigated for candidate genes. The two SNPs

flanking NDFA7.1, %N7.2, BM7.2 are ss7156480044 and ss715645231 that span 48.296112-

50.656251 Mb. This genomic region contained 369 genes. Out of these 369 only 18 were

differentially expressed in the nodules between the two contrasting RILs that were selected from

the mapping population (chapter 3). These 18 genes are both positional and expression candidate

genes for NDFA7.1, BM7.2, and %N7.1. Among these 18 candidate genes, three encode

transporter proteins. These included Phvul.007G244600 that encodes a nodulin-like

monocarboxylate transmembrane transporter, Phvul.007G247200 encoding ATP-binding cassette

transporter, and Phvul.007G273200 a sugar transmembrane transporter. These three transporter

genes were more highly expressed in SA36 that fixes more N than SA118. A fourth gene among

the 18 included Phvul.007G273000 that encodes an AP2 transcription factor. A previous study

demonstrated relationship between expression levels of AP2 TF and nodule functioning. Nova-

Franco et al. (2015) showed that a high mRNA level of an AP2 TF following a drastic decrease by

the targeting micro-RNA (miR172C) was associated with ineffective nodules in P. vulgaris-

Rhizobium etli symbiosis. AP2 TFs in common bean have also been postulated to regulate genes

related to nodule senescence (Nova-Franco et al. 2015).

Conclusion

In this study multiple QTL for Ndfa and related traits were identified that provided insights into

the genetic basis of SNF variability in a population of 188 RILs derived from Andean parents

Solwezi and AO-1012-29-3-3A. Some of the QTL identified overlap with previously identified

QTL, while other QTL were novel. The QTL identified on Pv09 for Ndfa (NDFA9.1) overlapped

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with the genomic region that contained significant SNPs for Ndfa identified in a previous GWAS.

Both the QTL overlapping with previously identified QTL and novel QTL should be extensively

validated in multiple genetic backgrounds and environments. Once validated, these QTL have

potential to be used in marker-assisted breeding to circumvent challenges of phenotypic selection

for SNF, and accelerate genetic improvement of common bean for symbiotic nitrogen fixation.

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APPENDIX

177

Table 4.1. Means and ranges for shoot, root and SNF traits measured on 188 recombinant inbred lines and parents grown in the

greenhouse in 2014 and 2015 at Michigan State University MI.

Trait Experiment Parental means RILs (n=188) Solwezi AO-1012-29-3-3A t-test Mean Range Anova P

Shoot Biomass (g plant-1) GH_14 6.2±0.1 3.1±0.1 ** 2.9±0.1 0.8-5.5 ** Shoot Biomass (g plant-1) GH_15 5.6±0.1 3.3±0.1 ** 4.2±0.1 1.4-7.5 ** %N in Shoot GH_14 2.8±0 2.6±0 ns 3.0±0 1.9-4.0 ** %N in Shoot GH_15 2.6±0 2.7±0 ns 2.9±0 2.2-3.6 ** Ndfa (mg plant-1) GH_14 156.2±2.1 82.2±1.9 ** 92.3±1.6 33.5-181.4 ** Ndfa (mg plant-1) GH_15 160.0±3.2 83.7±2.1 ** 104.4±2.3 38.4-262.9 ** Root Weight (g plant-1) GH_14 6.5±0.1 3.8±0.1 ** 4.8±0.1 1.0-13.1 **

RILs =recombinant inbred lines; GH_2014=evaluations in the GH in 2012; GH_2015=evaluations in the GH in 2014; ± S.E the Mean;

t-test represent the level of significance for the p-value of a t-test between parental means; ANOVA P represent the level of significance

for the p-value of analysis of variance on the means of RILs

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Table 4.2. Genetic Correlations coefficients among four traits measured on 188

recombinant inbred lines grown in the greenhouse in 2014 and 2015 at Michigan State

University, MI.

Traits Shoot Biomass %N Root Weight Ndfa Shoot Biomass 1 0.58*(0.78*) 0.72* 0.88*(0.91*) %N 1 0.2* 0.7*(0.52*) Root Weight 1 0.59*(0.37*) Ndfa 1

* Significant at α=0.05; the numbers outside the parenthesis in the table are coefficients

for GH_2014 while numbers in parenthesis are coefficients in GH_2015

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Table 4.3. Quantitative trait loci for shoot biomass, nitrogen percentage, nitrogen derived from atmosphere, and root weight identified

in a population of 188 recombinant inbred lines grown in the greenhouse in 2014 and 2015 at Michigan State University MI.

Trait Env QTL LG Peak (cM)

Nearest marker (Physical position in Mb) Marker interval for QTL

LOD score R2 Add

Shoot Biomass (BM) BM GH_14 BM6.1 Pv06 12.3 ss715650286 (22.149902) ss715646389- ss715647706 7.7 12.4 -0.4 BM GH_14 BM7.2 Pv07 53.3 ss715639206 (49.871844) ss715649067-ss715645231 13.3 23.6 0.7 BM GH_14 BM10.2 Pv10 20.6 ss715646324 (39.660637) ss715647915- ss715646967 5.2 9.0 0.5 BM GH_14 BM11.1 Pv11 47.6 ss715647462 (45.129824) ss715640672- ss715650232 4.5 7.8 0.5 BM GH_15 BM6.1 Pv06 15.3 ss715647706 (19.617959) ss715647706- ss715650286 3.3 5.4 -0.2 BM GH_15 BM7.2 Pv07 52.7 ss715645236 (50.054146) ss715649067-ss715645231 8.6 18.4 0.3 BM GH_15 BM9.1 Pv09 63.5 ss715647170 (34.212155) ss715640300-ss715647170 5.9 10.4 0.2 BM GH_15 BM11.1 Pv11 52.2 ss715647462 (45.129824) ss715647608- ss715647462 3.6 6.5 0.2 %N in Shoot %N GH_14 %N1.2 Pv01 32.2 ss715650418 (36.235568) ss715646837- ss715640825 3.8 5.8 0.1 %N GH_14 %N3.2 Pv03 2.0 ss715646441 (47.811352) ss715646441- ss715648039 6.2 13.1 0.1 %N GH_14 %N4.2 Pv04 44.1 ss715647337 (37.280582) ss715640495- ss715647337 7.2 14.2 0.1 %N GH_14 %N7.1 Pv07 56.8 ss715639341 (45.324468) ss715646774- ss715639341 3.3 5.1 0.1 %N GH_15 %N3.2 Pv03 13 ss715646441 (47.811352) ss715646441- ss715646440 6.2 13.8 0.1 %N GH_15 %N10.1 Pv10 13.5 ss715645510 (40.992209) ss715645503- ss715645510 3.2 8.6 0.1 Root Weight RW GH_14 RW7.2 Pv07 54.3 ss715645239 (50.132528) ss715645236-ss715645239 3.5 6.6 0.7 Ndfa Ndfa GH_14 NDFA2.1 Pv02 19.5 ss715639728 (41.666960) ss715648913-ss715647802 6.5 12.9 17.5 Ndfa GH_14 NDFA4.2 Pv04 33.0 ss715639216 (39.341516) ss715639216-ss715639215 3.3 4.2 7.4 Ndfa GH_14 NDFA6.1 Pv06 12.3 ss715650286 (22.149902) ss715646389-ss715647706 9.0 12.1 -13.4 Ndfa GH_14 NDFA7.1 Pv07 53.6 ss715639206 (49.871844) ss715649067-ss715645231 8.5 11.5 15.2 Ndfa GH_14 NDFA10.2 Pv10 15.6 ss715647919 (40.638544) ss715647919- ss715646974 7.2 13.7 13.4 Ndfa GH_14 NDFA11.1 Pv11 56.1 ss715647555 (44.938496) ss715647462-ss715647558 3.3 4.4 7.5

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Table 4.3 (cont’d) Ndfa GH_15 NDFA7.1 Pv07 55.3 ss715646609 (48.634959) ss715648044- ss715645231 7.8 14.9 9.8 Ndfa GH_15 NDFA9.1 Pv09 63.4 ss715647170 (34212155) ss715640300-ss715647170 5.7 8.3 6.9 Ndfa GH_15 NFFA11.1 Pv01 56.1 ss715647555 (44.938496) ss715647462-ss715647558 3.5 5.3 5.8

Env = environment; LOD is the logarithm of odds; LG is linkage group; R2 = proportion of phenotypic variance explained by the QTL; Add. = Additive effects of the QTL. A positive value means that the allele with positive effect on the trait at that QTL came from Solwezi while a negative number means that it came from AO-1012-29-3-3A.

181

Figure 4.1. Population distributions for shoot biomass, %N in shoot biomass and Ndfa.

Blue arrow represents the mean for parent AO-1012-29-3-3A while red is for parent

Solwezi.

182

Figure 4.2. Genetic linkage map for Solwezi x AO-1012-29-3-3A, showing the locations

of the identified QTL for shoot biomass (BM), percent of nitrogen in shoot (%N), root

weight (RW) and nitrogen derived from atmosphere (Ndfa).

183

Figure 4.2 (cont’d)

184

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GENERAL CONCLUSIONS

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GENERAL CONCLUSIONS Symbiotic Nitrogen fixation (SNF) in common bean (Phaseolus vulgaris L.) is a

genetically complex trait controlled by several genes. Genetic variability for SNF exists

within common bean, and has previously been used to develop varieties with enhanced

SNF. The genetic basis of this SNF variability is poorly understood. Effective utilization

of existing genetic variability for SNF for crop improvement requires an understanding of

its genetic basis. To understand the genetic basis of variability for SNF in common bean

three studies were conducted: (i) genome-wide association study, (ii) QTL mapping study,

and (iii) transcriptomic study. In GWAS, an Andean diversity panel (ADP) comprised of

259 Andean genotypes were evaluated for Ndfa in greenhouse and field experiments

(Chapter 2). The ADP was genotyped using an Illumina BARCBean6K_3 BeadChip with

5398 SNP markers. A mixed linear model was used to identify marker-trait associations.

Genomic regions identified using GWAS were validated in different genetic background

using QTL mapping conducted on 188 F4:5 RILs derived from Solwezi x AO-1012-29-3-

3A population (Chapter 4). The 188 F4:5 RILs were evaluated for Ndfa in greenhouse

experiments, and genotyped using the same BARCBean6K_3 BeadChip. Composite

interval mapping was used to identify QTL. To identify expression candidate genes

associated with the genomic regions identified in GWAS and QTL mapping, transcriptome

analysis was conducted on RILs SA36 and SA118 that were selected from the Solwezi x

AO-1012-29-3-3A population used in the QTL mapping study in chapter 4. These two

RILs were highly contrasting for SNF, but had similar genetic background as suggested by

similarities in their seed type, growth habit and days to flowering (Chapter 3). RNA was

collected from nodules, roots and leaves of SA36 and SA118, and sequenced using

191

Illumina. Sequenced data was analyzed in the context of differentially expressed genes

between SA36 and SA118. A summary of corroborating results from these three studies is

presented below.

A GWAS peak for Ndfa was consistently identified on Pv07 in GH and field experiments

(Chapter 2). The most significant SNP on this peak for Ndfa was ss715646473 located at

4.048349 Mb. When the genomic region ±200 kb of 4.048349 Mb surrounding the GWAS

peak on Pv07 was investigated for expression candidate genes using transcriptome data

(chapter 4), 51 genes were detected. Out of these 51, only four were identified as expression

candidate genes (Chapter 3). These four included Phvul.007G048000 (a MADS Box TF),

Phvul.007G048600 (protein kinase), Phvul.007G048700 (protein kinase) and

Phvul.007G049400 (protein kinase). Among these four expression candidate genes,

Phvul.007G048000 encoding a MADS BOX TF had a particularly interesting tissue

expression pattern only in nodules (Chapter 3). Phvul.007G048000 was up-regulated in

SA36 the RIL that fixes more N than SA118. Phvul.007G048000 was not differentially

expressed in the roots, and there was no evidence of its expression in the leaf tissue. This

expression pattern suggested that enhanced expression of this MADS BOX TF is

associated with enhanced SNF.

A QTL (NDFA7.2) for Ndfa was identified on Pv07 in the Solwezi x AO-1012-29-3-3A

mapping population (Chapter 4). The closest SNP to NDFA7.2 was ss715646609 located

at 48.634959 Mb. This QTL were considered as different because of the long physical

distance between this closest SNP and the most significant SNP that identified in GWAS

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on Pv07. The region on Pv07 spanning 50-60 cM that contained peaks for NDFA7.1 was

investigated for candidate genes. The two SNPs flanking NDFA7.1 were ss7156480044

and ss715645231 that span 48.296112- 50.656251 Mb. This genomic region contained 369

genes. Out of these 369 genes, only 18 were identified as expression candidates for SNF in

the transcriptome profiling study (Chapter 3). These 18 genes are both positional and

expression candidate genes for NDFA7.1, BM7.2, and %N7.1. Among these 18 candidate

genes, three encode transporter proteins. These included Phvul.007G244600 that encodes

a nodulin-like monocarboxylate transmembrane transporter, Phvul.007G247200 encoding

ATP-binding cassette transporter, and Phvul.007G273200 a sugar transmembrane

transporter. These three transporter genes were more expressed in SA36 that fixes more N

than SA118. The basis of symbiotic relationship between the plant and Rhizobium is

metabolic cooperation. In this mutualism cooperation the plant supplies the rhizobium with

nutrients including carbon in the form of sugars. In return, the rhizobium fixes and supplies

N to the plant. In a genotype with high N fixation rate, the sugar demand by the rhizobia is

expected to be high. The higher expression in SA36 than SA118 of the three transporter

genes, may be necessary to meet the high sugar requirements of SA36 that has higher N

fixation rate than SA118. A fourth gene among the 18 included Phvul.007G273000 that

encode an AP2 transcription factor. A previous study demonstrated relationship between

expression levels of AP2 TF and nodule functioning. Nova-Franco et al. (2015) showed

that a high mRNA level of an AP2 TF following a drastic decrease by the targeting micro-

RNA (miR172C) was associated with ineffective nodules in P. vulgaris-Rhizobium etli

symbiosis. AP2 TFs in common bean have also been postulated to regulate genes related

to nodule senescence (Nova-Franco et al. 2015).

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GWAS identified two peaks on Pv09 for Ndfa (Chapter 2). The SNP associated with the

first peak was ss715648916 located at 20.055067 Mb. This SNP was consistently identified

as significant in both field and greenhouse experiments, and in both shoot and seed.

Therefore, the gene associated with this SNP had a stable contribution to Ndfa across

environments and tissue types. This first peak on Pv09 was also associated with N

concentration in the shoot and seed. To identify candidate genes underlying the GWAS

peak for Ndfa on Pv09, the genomic region of ±200 kb around the most significant SNP

ss715648916 located at 20.055067Mb was investigated. Out of the 44 genes in this 400 kb

genomic region, only Phvul.009G137500 that encodes a WRKY transcription factor was

identified as an expression candidate gene for SNF in the transcriptome profiling study

(Chapter 3). This WRKY TF was significantly up-regulated (4-fold change) in SA118 (RIL

low in SNF) than SA36 (RIL high in SNF). The second peak on Pv09 was identified at

34.101880 Mb. The most significant SNP for Ndfa at this peak was ss715647197. Given

the long physical distance between these two peaks (over 13 Mb), it is unlikely they were

tagging the same gene(s) for Ndfa. In addition, the LD between the most significant SNPs

at the two peaks (ss715648916 and ss715647197) was weak. The genomic region of the

second peak on Pv09 overlapped with the QTL for Ndfa (NDFA9.1) identified using

Solwezi x AO-1012-29-3-3A population of RILs (Chapter 4). The most significant SNP at

the second peak (ss715647197) was located 34.101880 Mb while the closest SNP to the

QTL NDFA9.1 was ss715647170 located at 34.212155 Mb. Consistent identification of

this genomic region in GWAS and QTL mapping studies suggests it is stable in different

genetic backgrounds. The 200 kb genomic region of 34.212155 Mb surrounding the

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GWAS peak, and overlaps with the peak for QTL NDFA9.1 was investigated for candidate

genes. Out of the 29 genes in this 200 kb genomic region, only four were identified as

expression candidate genes for SNF in the transcript (Chapter 3). These four expression

candidates included Phvul.009G231000 (calmoduline-binding protein),

Phvul.009G231600 (sterol regulatory element-binding protein), Phvul.009G231700

(cytokinin dehydrogenase), and Phvul.009G233700 (leucine-rich repeat-containing

protein). In roots, calmodulin-binding proteins are associated with calcium fluxes (Riely et

al. 2004; Stacey et al. 2006). One of the important functions of calcium ions is in the

nodulation-signaling pathway, which has been reported to contain calcium-activated

kinases (Oldroyd and Downie 2004). Cytokinin dehydrogenase have been implicated in

nodulation (Held et al. 2008).

GWAS and QTL mapping identified QTL for Ndfa on Pv03, Pv07 and Pv07. The QTL on

Pv09 was consistently identified in GWAS and QTL mapping. Using transcriptome data

of the two contrasting RILs, expression candidate genes underlying the QTL identified in

GWAS and QTL mapping have been identified. Once the effects on Ndfa of the identified

QTL and genes are validated, they can potentially be used in marker-assisted breeding to

develop common bean germplasm with enhanced SNF.

195

LITERATURE CITED

196

LITERATURE CITED Held M, Pepper AN, Bozdarov J, Smith MD, Emery RN, Guinel FC (2008) The pea nodulation mutant R50 (sym16) displays altered activity and expression profiles for cytokinin dehydrogenase. Journal of Plant Growth Regulation 27:170-180

Nova-Franco B, Íñiguez LP, Valdés-López O, Alvarado-Affantranger X, Leija A, Fuentes SI, Ramírez M, Paul S, Reyes JL, Girard L (2015) The Micro-RNA72c-APETALA2-1 Node as a Key Regulator of the Common Bean-Rhizobium etli Nitrogen Fixation Symbiosis. Plant Physiology 168:273-291

Oldroyd GED, Downie JA (2004) Calcium, kinases and nodulation signalling in legumes. Nature Reviews Molecular Cell Biology 5:566-576

Riely BK, Ane JM, Penmetsa RV, Cook DR (2004) Genetic and genomic analysis in model legumes bring Nod-factor signaling to center stage. Current Opinion in Plant Biology 7:408-413


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