ORIGINAL PAPER

Population structure and association mapping of yieldcontributing agronomic traits in foxtail millet

Sarika Gupta • Kajal Kumari • Mehanathan Muthamilarasan •

Swarup Kumar Parida • Manoj Prasad

Received: 29 September 2013 / Revised: 6 December 2013 / Accepted: 31 December 2013

� Springer-Verlag Berlin Heidelberg 2014

Abstract

Key message Association analyses accounting for

population structure and relative kinship identified

eight SSR markers (p < 0.01) showing significant asso-

ciation (R2 = 18 %) with nine agronomic traits in fox-

tail millet.

Abstract Association mapping is an efficient tool for

identifying genes regulating complex traits. Although

association mapping using genomic simple sequence repeat

(SSR) markers has been successfully demonstrated in many

agronomically important crops, very few reports are

available on marker-trait association analysis in foxtail

millet. In the present study, 184 foxtail millet accessions

from diverse geographical locations were genotyped using

50 SSR markers representing the nine chromosomes of

foxtail millet. The genetic diversity within these accessions

was examined using a genetic distance-based and a general

model-based clustering method. The model-based analysis

using 50 SSR markers identified an underlying population

structure comprising five sub-populations which corre-

sponded well with distance-based groupings. The pheno-

typing of plants was carried out in the field for three

consecutive years for 20 yield contributing agronomic

traits. The linkage disequilibrium analysis considering

population structure and relative kinship identified eight

SSR markers (p \ 0.01) on different chromosomes show-

ing significant association (R2 = 18 %) with nine agro-

nomic traits. Four of these markers were associated with

multiple traits. The integration of genetic and physical map

information of eight SSR markers with their functional

annotation revealed strong association of two markers

encoding for phospholipid acyltransferase and ubiquitin

carboxyl-terminal hydrolase located on the same chromo-

some (5) with flag leaf width and grain yield, respectively.

Our findings on association mapping is the first report on

Indian foxtail millet germplasm and this could be effec-

tively applied in foxtail millet breeding to further uncover

marker-trait associations with a large number of markers.

Keywords Association mapping � Genetic diversity �Foxtail millet (Setaria italica) � Linkage disequilibrium �Marker-trait association � SSR markers � Population

structure

Introduction

Foxtail millet [Setaria italica (L.) Beauv.], because of its

small diploid genome (1C genome size = 515 Mb),

inbreeding nature, abiotic stress-tolerance along with the

release of its genome sequence by Beijing Genomics

Institute, China and US Department of Energy—Joint

Genome Initiative, is on a fast-track to transforming into a

model crop (Doust et al. 2009; Zhang et al. 2012; Ben-

netzen et al. 2012; Lata and Prasad 2013) for studying

functional genomics and to probe plant architecture, gen-

ome evolution, drought tolerance, and physiology in the

bioenergy grasses (Dekker 2003; Diao 2011; Lata et al.

2013). Foxtail millet is one of the important crops in the

Communicated by A. Dhingra.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s00299-014-1564-0) contains supplementarymaterial, which is available to authorized users.

S. Gupta � K. Kumari � M. Muthamilarasan �S. K. Parida � M. Prasad (&)

National Institute of Plant Genome Research (NIPGR), Aruna

Asaf Ali Marg, JNU Campus, New Delhi 110 067, India

e-mail: manoj_prasad@nipgr.ac.in

123

Plant Cell Rep

DOI 10.1007/s00299-014-1564-0

subfamily Panicoideae, distributed widely around warm

and temperate regions of the world including Asia, Europe,

America, Australia, and Africa, used as grain or forage

(Austin 2006). The recent archaeological discoveries

revealed that foxtail millet had contributed significantly to

human civilizations in Asia and Europe (Li and Wu 1996;

Hunt et al. 2008; Barton et al. 2009). The trace for centre of

origin was identified in China, where further domestication

for food grain took place gradually, 8,700 years ago

(Vavilov 1926; Lu et al. 2009). Though exact figures of

worldwide production of foxtail millet are not available,

taken together with other millet crops production it is

nearly 25 million tons (Mt) of grain in 2012 (FAO stat data

2013, http://faostat.fao.org/). Development of genomic

resources in foxtail millet and its breeding had made great

progress, resulting in significant improvement of yield

potential in recent years (Diao 2011; Pandey et al. 2013;

Kumari et al. 2013; Muthamilarasan et al. 2013).

A diversified germplasm collection plays a key role in

both breeding and genomic research for any crop species.

More than 30,000 germplasm of foxtail millet have been

conserved in different germplasm consortium all over the

world viz. Chinese National Gene Bank (CNGB), (26,670

accessions), International Crops Research Institute for the

Semi-Arid Tropics (ICRISAT), Patancheru (1,534 acces-

sions), National Institute of Agro-biological Sciences

(NIAS), Japan (1,279 accessions), and the Plant Genetic

Resources Conservation Unit (PGRCU), USDA (766

accessions) (Doust et al. 2009). The availability of large

germplasm collection facilitates the evaluation of popula-

tion diversity and genetic structure of germplasm acces-

sions which could further provide vital information for

resource management, association mapping, and allele

mining for novel variants and crop breeding. Interestingly,

genetic structure analysis of landraces has been carried out

in many plant species, including foxtail millet (Hirano

et al. 2011; Wang et al. 2012; Liu et al. 2011; Jia et al.

2013), rice (Zhang et al. 2009a), maize (Liu et al. 2003),

sorghum (Barro-Kondombo et al. 2010), soybean (Guo

et al. 2012), coffee (Razafinarivo et al. 2013) and wheat

(Liu et al. 2010; Zoric et al. 2012) which have contributed

greatly to genomic analysis and breeding of these crops.

Considerable efforts have been invested in tracing genes

responsible for evolutionarily and agronomically important

traits. In previous studies, a number of QTLs for agronomic

traits have been identified in foxtail millet by linkage

mapping approach, including basal branching (tillering)

and axillary branching (Doust et al. 2004), inflorescence

branching (Doust et al. 2005), plant height (PH), panicle

neck length, stem node number, panicle length (PcL) and

seed shattering (Mauro-Herrera et al. 2013). However, with

the progression of molecular biology and bio-statistical

tools, association mapping emerged as a new and powerful

tool which could be demonstrated to complement and

improve previous QTL information identified by linkage

analyses in marker-assisted selection (MAS) for different

crops.

Association mapping appears to be a promising

approach for plant breeders as it is based on linkage dis-

equilibrium (LD) and eliminates the main drawback of

classical linkage analysis such as, it does not involve the

prolonged, lingering and expensive generation of specific

genetic populations, rather it allows detection of associa-

tions between phenotypic variation and genetic polymor-

phisms in existing cultivars studied without further

development of new mapping populations. Furthermore,

this approach can detect larger number of alleles and

increase mapping resolution (Yu and Buckler 2006). It also

allows exploration of recombination events that had taken

place through multiple generations in natural populations

resulting in a tight linkage of causal polymorphisms with

nearby genomic regions, avoiding the large blocks of

linkage that are often obtained from two- or three-genera-

tion pedigrees and facilitating the identification of poly-

morphisms that are associated with quantitative traits.

Association mapping originated in human genetics, and

it has been widely used in various plant species in identi-

fying phenotype-associated marker and trait-associated

phenotypes. Reports on various crops including rice and

wheat (Crossa et al. 2007; Reimer et al. 2008; Yan et al.

2009) show the versatility of this approach in identifying

markers linked to genes and genomic regions associated

with desirable traits. Breseghello and Sorrells (2006) also

demonstrated the utility of association mapping in

enhancing the information derived from QTL studies

through execution of MAS in elite wheat germplasm.

Using genomic simple sequence repeat (SSR) markers,

association mapping has been successfully demonstrated in

rice (Jin et al. 2010), wheat (Liu et al. 2010; Maccaferri

et al. 2011; Zoric et al. 2012), barley (Mather et al. 2004),

sorghum (Upadhyaya et al. 2012) and chickpea (Kujur

et al. 2013). Recently, SNP marker-based genome-wide

association mapping for identifying genes controlling

agronomic traits using foxtail millet germplasm lines has

been reported (Jia et al. 2013). Considering the importance

of association mapping for dissecting the complex quanti-

tative and qualitative agronomic traits, we have attempted

to; (1) estimate genetic diversity and population structure

among a core collection of 184 foxtail germplasm acces-

sions, (2) estimate the extent of LD (linkage disequilib-

rium) and (3) analyse the association of SSR markers with

various yield component agronomic traits in foxtail millet.

Plant Cell Rep

123

Materials and methods

Materials and phenotyping

The foxtail millet accessions were collected from different

eco-regions representing ten countries (Fig. 1). Based on

the geographical distribution of these accessions, 184

accessions from the core collection (Lata et al. 2011) were

selected. The purpose of the sampling strategy was to

assemble a good representative set of accessions of foxtail

millet landraces. The detail of accessions from each

country, used in this study is listed in supplementary Table

S1.

The 184 foxtail millet accessions were phenotyped for

20 yield contributing agronomic traits precisely at experi-

mental field of National Institute of Plant Genome

Research (NIPGR, New Delhi) with three replications for

3 years (2009–2011) based on randomized complete block

design (RCBD) during warm growing season. The grain

yield (t ha-1) was determined after harvesting the plots at

full maturity. In addition to grain yield (GY), the following

important agronomic/morphological traits were also

recorded: thousand grain weight (GW) in gram (g) mea-

sured as the average of three samples of 1,000 grains per

plot, apical sterility (AS), days of flowering (DF), flag leaf

width (FLW) and length (FLL) measured in centimetres

(cm), fruit colour (FC) assumed from the seed coat colour,

grain shape (GSh), presence of inflorescence brittles (Inf

Br), inflorescence compactness (Inf Cp), presence of

inflorescence lobe (Inf Lb), inflorescence shape (Inf Sh),

leaf colour (LC), lobe compactness (LbCp), panicle length

(PcL), peduncle length (PdL), plant height (PH) measured

in centimetres (cm), plant pigmentation (Pl Pg), tiller

number (TN) and tillers maturity (TM) measured in terms

of occurrence of fruiting. The variations of morphological

traits among different accessions were observed in three

independent replicates for three consequent years

(2009–2011) in field condition. All the 20 agronomic traits

evaluated for 184 accessions in 3 years were analysed

using SPSS V14.0 (http://www.ibm.com/software/analy

tics/spss) to derive their summary statistics including mean,

range, standard deviation, variance and coefficient of var-

iation. The positive and negative correlation among 20

agronomic traits in 184 accessions was measured based on

Pearson correlation coefficient at 5 % level of significance

in SPSS. The heritability (h2 = r2g/r2p) for each trait was

estimated by measuring their genotypic (r2g) and pheno-

typic (r2p) variance in accessions.

SSR genotyping

Total genomic DNA of each accession was extracted using

a CTAB extraction procedure (Saghai-Maroof et al. 1994)

with modifications and subsequently quantified by agarose

gel electrophoresis. Genotyping studies were done with 50

genomic SSR markers distributed (physically mapped) on

nine chromosomes of foxtail millet (Jia et al. 2009; Pandey

et al. 2013) (supplementary Table S2). PCR amplification

was performed in a 25 ll total volume containing 1 U of

Taq DNA polymerase (Sigma, USA), 50 ng of genomic

DNA, 10 lmol of each forward and reverse primer,

0.5 mmol of each dNTPs, and 2.5 ll of 109 PCR reaction

Fig. 1 Geographical

distribution of 184 foxtail millet

accessions

Plant Cell Rep

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buffer [500 mM KCl, 200 mM Tris–HCl (pH 8.4), and

3 mM MgCl2] in iCycler thermal controller (Bio-Rad). The

PCR profile was: an initial denaturation of 3 min at 94 �C,

followed by 35 cycles of 60 s at 94 �C, 60 s at 50–55 �C,

and 2 min at 72 �C, and a final extension of 10 min at

72 �C. The amplified products were resolved on 3 %

metaphor agarose gel. Amplified PCR products which

could not give clear polymorphic pattern were further

tested on microchip-based electrophoresis system (Mul-

tiNA; Shimadzu Corporation, Kyoto, Japan). Results were

confirmed by three replicate assays. Null alleles and non-

specific fragments (errors due to stuttering) amplified by

SSR markers were analysed with Microchecker 2.2.3 (van

Oosterhout et al. 2004). The clear and reproducible alleles

amplified by each SSR marker among 184 accessions were

scored based on their fragment size (bp) resolved on a gel.

SSR diversity statistics and population structure

analysis

Polymorphism information content (PIC), frequency of

alleles, number of alleles per locus (NA) and expected

heterozygosity were calculated to estimate the genetic

variation within the 184 accessions from mini-core col-

lection. In addition, genetic differentiation among sub-

populations was assessed by Wright’s F-statistics (Weir

and Hill 2002). The pairwise significance of F-statistics

was obtained after sequential Bonferroni corrections (Rice

1989). Genetic distances were computed among these

genotypes and cluster analysis was performed on the

Rogers’s distances (Rogers 1972) using the neighbour-

joining method (NJ) in PowerMarker software ver2.5 (Liu

and Muse 2005). The association panel was analysed for

possible population structure with the model-based pro-

gram STRUCTURE 2.2 (Pritchard et al. 2000) for the 184

foxtail millet accessions using a length of burn-in period

100,000 and the number of iterations 100,000 and a model

allowing for admixture and correlated allele frequencies.

At least 10 runs of STRUCTURE were performed by set-

ting the number of sub-populations (K) from K = 2 to

K = 10.

Association analysis

Association tests between SSR marker alleles and traits

were performed using two approaches: a general linear

model (GLM) implemented in TASSEL v.2.0.1 (Bradbury

et al. 2007) software that do not consider control for pop-

ulation structure and a mixed linear model (MLM), that

controlled for both population structure and kinship

(Q ? K model) i.e. using the information on population

structure (Q, determined by running the program

STRUCTURE at optimized K = 5) to minimize false

positive associations. Kinship (K value) using SSR geno-

typic data of 184 accessions was estimated employing

SPAGeDi (Hardy and Vekemans 2002). The critical p val-

ues for assessing the significance of SSR markers were

calculated based on a false discovery rate (FDR) separately

for each trait (Benjamini and Hochberg 1995), which was

found to be highly stringent. A FDR cutoff of 0.05 was

used for determining significance of marker-trait associa-

tions (MTAs). The same approach was used to detect sig-

nificant associations between SSR markers and partial least

score (PLS) regression component scores which represent

the linear combinations of accession and traits. The p value

determines whether a trait is associated with a marker

significantly or not, the squared correlation coefficient (r2)

was used to estimate association of markers with traits

based on LD between each pair of marker loci (Pritchard

and Przeworski 2001) using TASSEL (Bradbury et al.

2007). Markers exhibiting a p value less than 0.05 were

considered significantly associated with a phenotypic trait.

Results

Agronomic characteristics

The phenotypic data used in this study were based on the

mean values recorded for 3 years (2009–2011). The sum-

mary statistics including mean, range, standard deviation,

variance and coefficient of variation for 20 agronomic traits

in 184 accessions were mentioned in Table 1. Coefficient

of variation was maximum for InfSh (0.974) followed by

InfLb (0.844) and minimum in TM (0.089). High and low

heritability of InfSh (85 %) and InfBr (65 %) was evident,

respectively (Table 2). Pearson correlation coefficients (at

p \ 0.05) of the agronomic traits in the 184 accessions

were also evaluated (Table 3). The correlation coefficient

among 184 accessions varied from -0.428 between GW

and FLW to 0.479 between LC and PiPg with an average of

0.25. A high positive correlation of grain yield with grain

weight (0.43), and TNs (0.22) and a negative correlation

with PH, flowering time and PcL were observed. These

wider phenotypic trait variations among 184 accessions

indicated that, the constituted association panel was suit-

able for association mapping for different yield traits.

Genetic diversity of core collection

A total of 214 alleles were obtained from the 50 SSR loci

scored for the 184 accessions, with an average of 4.3 alleles

per locus varying from two to eight. Major allele frequency

was in a range of 0.22–0.80 with an average 0.46. The

average gene diversity was 0.65 (range from 0.33 to 0.84)

and expected heterozygosity for individual loci ranged

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Table 1 Summary of genetic

diversity of 184 foxtail

accessions using 50 mapped

SSR microsatellite markers

S. no. Marker Allele frequency Allele no. Gene diversity Heterozygosity PIC

1 b165 0.33 6 0.78 0 0.75

2 p3 0.34 7 0.78 0.17 0.75

3 b260 0.22 7 0.84 0.26 0.81

4 b126 0.29 5 0.78 0 0.75

5 b112 0.39 5 0.75 0 0.72

6 b153 0.35 4 0.73 0.12 0.68

7 p87 0.53 4 0.64 0 0.59

8 SiGMS2303 0.65 3 0.51 0.58 0.45

9 b242 0.32 4 0.72 0 0.67

10 b233 0.37 4 0.71 0.04 0.65

11 b151 0.44 4 0.63 0 0.56

12 SiGMS4692 0.45 4 0.69 0.12 0.64

13 b163 0.64 3 0.52 0 0.46

14 p61 0.37 4 0.69 0 0.63

15 p85 0.63 3 0.51 0 0.44

16 b225 0.39 4 0.68 0 0.62

17 SiGMS6210 0.47 3 0.6 0.08 0.52

18 b109 0.22 8 0.83 0.44 0.81

19 b255 0.56 4 0.59 0.15 0.52

20 p100 0.53 4 0.63 0.11 0.58

21 b247 0.39 4 0.71 0.19 0.65

22 SiGMS7403 0.43 4 0.68 0.04 0.63

23 SiGMS7563 0.42 5 0.69 0.14 0.64

24 SiGMS7747 0.39 5 0.74 0.09 0.7

25 SiGMS7833 0.56 3 0.55 0 0.47

26 SiGMS7964 0.66 2 0.45 0.03 0.35

27 SiGMS8050 0.49 3 0.63 0.06 0.56

28 SiGMS8556 0.37 5 0.74 0.16 0.7

29 p17x 0.39 3 0.65 0 0.58

30 b196 0.49 4 0.64 0.01 0.58

31 p75 0.44 4 0.69 0.06 0.64

32 b129 0.62 3 0.54 0 0.48

33 SiGMS9034 0.31 6 0.77 0.13 0.73

34 SiGMS9645 0.47 4 0.57 0.09 0.47

35 p10 0.38 7 0.78 0.42 0.75

36 b190 0.29 4 0.75 0.99 0.7

37 SiGMS10379 0.8 3 0.33 0.14 0.31

38 b200 0.6 3 0.56 0.02 0.5

39 p59 0.4 5 0.71 0.17 0.66

40 b142 0.38 4 0.71 0 0.66

41 b202 0.7 3 0.46 0.06 0.41

42 SiGMS12222 0.59 4 0.57 0.06 0.51

43 SiGMS12819 0.49 6 0.67 0.31 0.63

44 b258 0.73 3 0.43 0 0.39

45 b185 0.35 4 0.69 0.43 0.63

46 p41 0.29 6 0.79 0.01 0.76

47 b171 0.47 3 0.61 0 0.53

48 b166 0.47 5 0.64 0.01 0.57

49 b241 0.45 3 0.64 0 0.57

50 b269 0.46 6 0.7 0 0.66

Mean 0.46 4.28 0.65 0.11 0.6

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from 0.00 to 0.99 with an average 0.11. Polymorphic

information content (PIC) values ranged from 0.31 to 0.81

with an average of 0.60 (Table 1).

Molecular diversity, population structure and genetic

relationships among 184 accessions

Two approaches were used in this study to analyse popu-

lation structure: unrooted NJ tree and Bayesian model-

based clustering. The NJ tree of accessions based on a

shared alleles distance matrix indicated a main sub-division

of accessions into at least five groups, namely I, II, III, IV

and V which included 15, 14, 24, 104 and 27 accessions,

respectively (Fig. 2). Distance-based methods often intro-

duce distortions and simplifications in the representation of

the relationships among members of larger clusters (Sneath

and Sokal 1973) and/or when accessions are related to two

or more distinct clusters, as is the case for many modern

cultivars obtained by crossing parents from different gene

pools (Maccaferri et al. 2005). For these reasons, accessions

were also grouped using a model-based Bayesian clustering

method (Pritchard et al. 2000; Falush et al. 2003). Admix-

ture model-based simulations were carried out by varying

K from 2 to 10 with 10 run for each K using all 184

accessions which showed evident knees at K = 5. The

average LnP(D) (log-likelihood) value increased continu-

ously with the increase in K from 1 to 10, however, its most

apparent inflection was obtained at one of the best replicates

of K = 5 (Fig. 3a). The results of population numbers

(K) were further confirmed using DK estimation (Evanno

et al. 2005). A sharp peak with a maximum value of DK was

obtained at K = 5 (Fig. 3b). This confirms the classification

of 184 foxtail millet accessions into five distinct population

groups with high resolution population structure. The esti-

mated population structure is presented in Fig. 4. Using this

approach, 184 accessions were assigned to the corre-

sponding A–E sub-populations, representing 14.6 % (27),

13.5 (25), 49.4 (91), 15.7 (29) and 6.5 % (12) of the

germplasm used for analysis. Genetic variation among five

identified sub-populations and among 184 accessions within

population was tested using AMOVA. The population dif-

ferentiation based on AMOVA revealed that 6.8, 3.2 and

1.7 % (p \ 0.05) of the total molecular variation in the five

sub-populations were attributed to genetic differentiation

among populations, within populations (among accessions)

and within accessions, respectively. The five sub-popula-

tions (A–E) had an Fst equal to 0.24, 0.19, 0.17, 0.18 and

0.25, respectively, supporting the existence of moderate

population structures. The overall Fst value estimated

within the sub-populations was 0.21 (Table 4). In addition,

the genetic distances among these four subgroups by pair-

wise Fst were measured. It showed variable level of genetic

differentiation between inferred populations. The pairwise

Fst value ranged from 0.41 to 0.98 with an average 0.54,

revealing smallest genetic distances between subpop B and

C the largest between subpop C and E (Table 5).

Table 2 Summary statistics of 20 agronomic traits evaluated in 184 foxtail millet accessions

Traits Range Mean Standard deviation Variance Coefficient of variation Heritability (%)

DF 36–65 51.41 5.42 29.43 0.11 78

PH 89.7–194 138.66 19.61 384.64 0.14 75

TN 1.4–8.0 4.31 1.16 1.36 0.27 82

FLL 16.6–56.3 31.92 6.96 48.42 0.22 79

FLW 1.1–6.6 1.87 0.56 0.31 0.30 80

PdL 6.9–31.7 18.80 4.40 19.34 0.23 71

PcL 6.6–32.5 14.64 3.92 15.37 0.27 77

TM 54–101 87.62 7.82 61.22 0.09 80

GY 4.0–42.3 12.91 7.57 57.26 0.59 84

GW 0.8–3.9 2.84 0.64 0.40 0.23 81

PlPg 0–1.0 0.57 0.44 0.20 0.77 67

LC 1.0–4.0 1.31 0.69 0.48 0.53 79

InfLb 0.0–3.0 1.48 1.25 1.55 0.84 78

InfBr 0.0–3.0 1.56 0.96 0.93 0.62 65

LbCp 0.0–3.0 1.67 1.11 1.23 0.66 83

InfSh 1.0–2.0 1.54 0.50 0.25 0.97 85

InfCp 0.0–3.0 1.48 0.92 0.85 0.62 79

FC 1.0–5.0 1.65 1.19 1.41 0.72 83

GSh 1.0–3.0 1.60 0.51 0.26 0.32 81

AS 0.0–1.0 0.59 0.49 0.24 0.83 80

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-0

.139

0.2

16

-0

.04

50

.154

-0

.026

0.1

66

0.0

36

0.0

35

-0

.344

0.0

78

1

InfS

h0

.143

-0

.208

-0

.03

80

.010

0.1

62

-0

.276

-0

.05

6-

0.1

93

0.1

35

-0

.189

-0

.33

0-

0.1

53

0.2

87

-0

.380

-0

.160

1

InfC

p-

0.0

10

0.1

07

-0

.11

1-

0.0

31

-0

.101

0.1

73

-0

.06

30

.243

-0

.168

0.1

80

0.0

04

-0

.13

3-

0.2

31

0.0

50

0.2

00

-0

.24

71

FC

0.0

27

-0

.019

-0

.08

7-

0.0

32

-0

.117

0.0

74

0.0

67

-0

.109

-0

.091

-0

.005

-0

.03

10

.01

90

.006

0.0

57

-0

.134

-0

.07

90

.119

1

GS

h0

.142

-0

.291

-0

.08

5-

0.0

52

0.1

20

-0

.223

-0

.18

8-

0.2

83

-0

.059

-0

.096

-0

.20

6-

0.0

21

0.1

96

-0

.190

-0

.175

0.4

54

-0

.147

0.0

77

1

AS

0.0

08

0.0

94

0.0

53

0.0

65

0.1

22

-0

.044

0.0

62

-0

.003

-0

.012

-0

.090

-0

.10

1-

0.0

28

0.1

41

-0

.185

-0

.109

0.0

74

-0

.033

0.0

18

0.1

35

1

Plant Cell Rep

123

Linkage disequilibrium (LD) and marker-trait

associations

The background LD (unlinked) in the genome created by

population structure was used to determine specific critical

value of LD. Extent of genome-wide LD was evaluated

through pairwise comparisons among the 50 marker loci

(Fig. 5). The LD pattern including a total 44 estimates

expressed by r2 averaged 0.26 ranging from 0.15 to 0.32.

The 95th percentile of the distribution of unlinked r2 esti-

mates was used as a population-specific threshold for this

parameter as an evidence of linkage (Breseghello and

Sorrells 2006), and the critical value is 0.28. Only 44 (from

GLM) estimates of LD parameter r2 were significant

(p \ 0.05) among the pairwise comparisons of all 50

markers among 184 accessions, indicative of support for

further analysis of MTAs.

In the present study, association mapping was used to

search for linked/responsive marker for the agronomic

traits. Using both GLM and MLM approaches, we identi-

fied eight SSR markers associated with nine different

agronomic traits at the p \ 0.05 probability level, and

contributing 6–25 % of the phenotypic variation. In com-

bination, all eight markers explained an average of 18.4 %

of the trait variation. A significant correlation was shown

by one SSR marker for DF, Inf Br, Inf Cp, Gr Sh, PcL, two

for GY and three for 1,000-GW, FLW and PdL. It was

observed that, P59, b129 and b185 were associated with

FLW; b129, b260 and p75 with grain weight (1,000); b129,

b225 and p75 with PdL; and p75 and b129 with grain yield

(Table 6). Of eight markers, three were associated with

more than one trait including SSR b129 which showed

significant R2 value for multiple traits like FLW (0.08),

PdL (0.15), GY (0.10), Inf Br (0.08), PcL (0.06) and 1,000

GW (0.19). SSR marker p75 was found to be associated

with 1,000 GW (0.13), Pd L (0.12) and GY (0.20) and b225

marker showed association with PdL (0.11) and Inf Cp

(0.08) and, therefore, these could be regarded as multi-trait

MTAs (Table 6).

Discussion

Over the years, SSRs have become the markers of choice

due to their abundance, co-dominance and locus-specific

nature. Consequently, these markers have been extensively

used for gene tagging, genetic mapping and genetic

diversity analysis in a number of crop plants including

I

II

III

IV

VFig. 2 Cluster tree derived by

Neighbour-joining (NJ) method

based on 50 SSR markers

between 184 accessions of

foxtail millet

Plant Cell Rep

123

foxtail millet (Jia et al. 2009; Liu et al. 2011; Wang et al.

2012; Gupta et al. 2012; Pandey et al. 2013). In the present

study, we used a set of 50 genomic SSR markers covering

all nine foxtail millet chromosomes to evaluate diversity,

population structure and trait association mapping in fox-

tail millet.

A total of 214 alleles were obtained from the 50 SSR

loci scored for the 184 accessions, with an average of 4.3

alleles per locus varying from two to eight which is com-

parable to Zoysiagrass (4.8, Tsuruta et al. 2005). Con-

versely, the average allele per locus observed in the present

study is higher than the earlier studies in foxtail millet (2.4,

Lin et al. 2011; 2.2, Gupta et al. 2012; 2.1, Pandey et al.

2013), sorghum (2.3, Hokanson et al. 1998). Further, the

average of 4.3 alleles per locus is lower than earlier reports

in foxtail millet (6.16, Jia et al. 2009; 14.04, Liu et al.

2011) and wheat (7.4, Prasad et al. 2000). The average

gene diversity was 0.65 (ranging from 0.33 to 0.84) and

expected heterozygosity for individual loci ranged from

0.00 to 0.99 with an average 0.11. PIC values ranged from

0.31 to 0.81 with an average of 0.60 which conforms to the

previous studies reported in foxtail millet (0.69, Jia et al.

2009; 0.72, Liu et al. 2011) and Zoysiagrass (0.69, Tsuruta

et al. 2005). The average PIC of 0.60 is higher than earlier

reports in foxtail millet (0.45, Gupta et al. 2012) and wheat

(0.31, Bohn et al. 1999). On contrary, it is lower than

earlier studies in maize (0.72, Pejic et al. 1998) and wheat

(0.79, Prasad et al. 2000).

Diversity using structure and cluster analysis data was

examined for their ecological distribution, but tight asso-

ciation could not be established between structure, traits

and ecological groups. On the whole, two separate analyses

done in this study basically agreed with each other with

minor inconsistency. Cluster I consists of 15 accessions,

out of which the major proportion belong to subpop A, II

mainly belongs to subpop D with 14 accessions, III consists

of 24 accessions of which 14 accessions belong to subpop

B, the major proportion of cluster IV (81 accessions)

mainly belongs to subpop C and cluster V belongs to

Fig. 3 Optimization of the number of populations (K value) varying

from K = 1–10 to determine best possible population number for 184

foxtail millet accessions using a STRUCTURE documented by

Pritchard et al. (2000) and b DK based on Evanno et al. (2005)

Fig. 4 Admixture bar plot

showing the five sub-

populations and membership

assignment

Table 4 Mean value of Fst within population

Mean value of Fst within population

PopA 0.2447

PopB 0.1903

PopC 0.1721

PopD 0.1866

PopE 0.2491

Table 5 Genetic distances between different groups from structure

analysis

PopA PopB PopC PopD PopE

PopA 0 0.67 0.69 0.47 0.77

PopB 0.71 0 0.42 0.52 0.92

PopC 0.72 0.41 0 0.48 0.88

PopD 0.52 0.46 0.49 0 0.84

PopE 0.81 0.91 0.98 0.91 0

Plant Cell Rep

123

Fig. 5 Linkage disequilibrium

patterns among 184 accessions

genotyped with 50 SSR

markers. The squared

correlation coefficients (r2) for

each pair of markers are

presented in the upper triangle

and their corresponding p values

in the lower triangle

Table 6 Marker-trait association with p value and marker-trait regression coefficient derived from 50 SSR markers and 184 accessions as a core

collection in foxtail millet

Traits Trait ID Markers Chromosome Adjusted p value R2

Days of flowering DF SiGMS9645 6 0.0009 0.15

Flag leaf width FLW b129 5 0.0009 0.08

Flag leaf width FLW p59 7 0.0009 0.25

Flag leaf width FLW b185 8 0.0009 0.13

Peduncle length PdL b225 3 0.0009 0.11

Peduncle length PdL b129 5 0.0009 0.15

Peduncle length PdL p75 5 0.008 0.12

Grain yield GY p75 5 0.0009 0.21

Grain yield GY b129 5 0.0009 0.10

1,000 grain wt GW b260 1 0.0009 0.25

1,000 grain wt GW p75 5 0.0009 0.13

1,000 grain wt GW b129 5 0.0009 0.20

Inflorescence bristles InfBr b129 5 0.0009 0.09

Grain shape GSh p61 3 0.01 0.06

Panicle length PcL b129 5 0.05 0.06

Inflorescence compactness InfCp b225 3 0.05 0.09

Plant Cell Rep

123

subpop E with 15 accessions out of 26 accessions. The

small discrepancy between groupings from the two meth-

ods observed was not unexpected, because cluster analysis

assigned a fixed branch position to each accession and

structure analysis resulted in a sub-population membership

percentage, and the highest percentage was used to assign

individuals to groups for easy interpretation. There are

mixed proportions of accessions with admixture with

parental genotypes in sub-populations defined by structure

which might have grouped in different defined clusters. In

subpop A, the percentage of mixed individuals was found

to be 18 %, likewise 13 % for B, 13 % for B and C, 20 %

for D and 12 % for E.

Association mapping is used to determine the degree to

which gene and trait or phenotype and genotype vary

together in sampled population on the basis of linkage

disequilibrium (Zondervan and Cardon 2004). It is

assumed that, if a marker is associated with a phenotypic

trait, it should associate with others that highly correlate

with this trait. In the present study, we identified eight SSR

markers associated (with overall 18 % association poten-

tial) with different yield contributing agronomic traits.

These trait-associated SSR markers once validated can be

used for identification of genes/QTLs regulating the agro-

nomic traits and eventually for marker-assisted genetic

enhancement of foxtail millet. In the present study, multi-

trait association has been shown by different markers with

significant r2 value like SSR b129 that is correlated with

traits like FLW (0.08), PdL (0.15), GY (0.10), Inf Br

(0.08), PcL (0.06) and GW (0.19), p75 with GY (0.20),

GW (0.13) and PdL (0.15). The SSR b129 along with SSR

p75 on chromosome 5 correlate with traits including grain

weight and yield suggesting significant association of traits

responsible for yield which could be further selected for

genotypic and phenotypic studies. Similarly, SSRs b225

and p61 showed association with Inf Cp and grain shape,

respectively, and these could also be considered as candi-

date markers to study grain quality and yield. SSR markers

b129 (which codes for Ubiquitin carboxyl-terminal

hydrolase) and p75 (codes for phospholipid acyltransfer-

ase) are located on the same chromosome (5) of foxtail

millet indicating the presence of a linkage block or QTL

region for the yield-associated genes (Fig. 6). Acyltrans-

ferase and Ubiquitin carboxyl-terminal hydrolase have

been reported in several crop plants for their function in

seed development and yield. For instance, Zhang et al.

(2009b) reported acyltransferases in Arabidopsis as an

essential element for normal pollen and seed development

and the expression of Ubiquitin carboxyl-terminal hydro-

lase in the panicle of rice (Wong et al. 2007) and during

flowering stages (Moon et al. 2009) have been reported.

However, there are many challenges with using signif-

icantly associated markers for marker-assisted breeding. In

the present study, we attempted to discover trait-associated

loci in foxtail millet and succeeded in identifying eight

markers which are associated with different agronomic

traits, though further confirmation is still required to cor-

relate with the gene alleles for better understanding and

utilization. The SNP marker-based genome-wide associa-

tion mapping mostly using the 916 Chinese germplasm

lines for identifying genes associated with agronomic traits

has been reported recently (Jia et al. 2013). In contrast our

findings on trait associations using SSR markers is the first

report on Indian foxtail millet germplasm to our knowledge

and would enhance QTL information and thus improve-

ment of foxtail millet. Further, the release of foxtail millet

genome sequences has generated large-scale genomic

resources such as SSRs, EST-derived SSRs and intron

length polymorphic markers for high-throughput genotyp-

ing applications. The availability of these resources in

Foxtail millet Marker Database (Suresh et al. 2013) pro-

vides an option to researchers and breeders in choosing

markers for downstream experimentation towards crop

improvement in millets and related grasses.

Acknowledgments Thanks are due to the Director, National Insti-

tute of Plant Genome Research (NIPGR), New Delhi, India for pro-

viding facilities. This work area was supported by the core grant of

NIPGR. Dr. Sarika Gupta and Mr. Mehanathan Muthamilarasan

acknowledge the award of DST-Young Scientist and Junior Research

Fellowship from the Dept. of Science and Technology (DST) and

University Grants Commission, New Delhi, respectively. We are also

thankful to the National Bureau of Plant Genetic Resources, New

Delhi/Hyderabad/Akola, India for providing the seed materials.

CM

Linkage map (Jia et al. 2009)

Start0.0

7.9

p75 (Phospholipid acyltransferase)30.5

End47.2

Chr5Mb

Physical map (Present study)

Start0.0

p75139.1

b129 End178.8

Chr5

b129 (Ubiquitin carboxy-terminal hydrolase)

Fig. 6 Chromosome location of markers (p75 and b129) linked to

important agronomic traits in foxtail millet. Physical positions of

markers indicated in Mb on physical map with reference linkage map

position in centiMorgan (cM)

Plant Cell Rep

123

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