the coevolutionary genetics of medicago truncatula … · the coevolutionary genetics of medicago...

The Coevolutionary Genetics of Medicago truncatula and its

Associated Rhizobia

by

Amanda Gorton

A thesis submitted in conformity with the requirements

For the degree of Masters of Science

Department of Ecology and Evolutionary Biology

University of Toronto

© Copyright by Amanda Gorton 2011

ii

The Coevolutionary Genetics of Medicago truncatula and its

Associated Rhizobia

Amanda Gorton

Masters of Science

Department of Ecology and Evolutionary Biology

University of Toronto

2011

Abstract

Contrary to the predictions of numerous theoretical models, variation in partner quality

continues to persist in mutualisms, including in the symbiosis between legumes and rhizobia.

One potential explanation for the maintenance of this genetic diversity is genotype × genotype

interactions, however it is unknown which genetic regions might underlie these interactions. To

investigate this question, I performed a quantitative trait loci mapping experiment with two

different rhizobium strains to locate potential regions of the genome influencing genotype ×

genotype interactions between the legume Medicago truncatula and its symbiont Sinorhizobium

meliloti. I found no evidence for genotype × genotype or QTL × rhizobium interactions, however

some of the QTLs colocalized with genes involved in the symbiosis signaling pathway,

suggesting variation in these genes could potentially affect plant performance and fitness traits.

These findings have important implications for the evolutionary interactions between legumes

and rhizobia, and the genetic architecture of Medicago truncatula.

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Acknowledgments

First and foremost, I want to thank my supervisor, John Stinchcombe, for his guidance,

support, and enthusiasm, without which my thesis would not exist. I have learned a great deal

from him, and I am truly grateful for the opportunities he has given me. I want to also thank the

rest of the Stinchcombe Lab. I was very fortunate to be part of a great lab, full of excellent

scientists. I want to thank Brandon Campitelli, Emily Josephs, Young Wha Lee, and Anna

Simonsen for their help, advice and friendship. In addition, a special thank you is needed for

Katy Heath, who not only performed much of the work upon which my thesis is based, but was

also an invaluable source of information and advice.

There are many people who helped me plant and harvest my experiment. In particular I

want to thank Theresa Chow, Leila Kent, and Nikki Scodras, who dried, weighed and counted

many hundreds of plants. I am very grateful for their assistance. None of my molecular work

would have been possible without the help of David Maj, who taught me everything I know

about the magic of PCR. Many thanks to David for his patience, endless troubleshooting, and

comic relief.

I want to thank Stephen Wright and Megan Frederickson, both of whom offered

invaluable advice and insight as members of my supervisory committee. I also want to thank

Asher Cutter for kindly agreeing to sit on my exam committee. Many thanks to Bruce Hall and

Andrew Petrie for their help and resourcefulness with greenhouse work.

During my time in the Department of Ecology and Evolutionary Biology, I was lucky to

meet and befriend a number of smart, kind and supportive graduate students. In particular, I want

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to thank Emily Austen, Lucia Kwan, Alison Parker, and Jane Ogilvie for their kindness and

encouragement.

I am incredibly grateful for the love and encouragement of my family and friends. Thank

you to my friends Leigha Dimitroff, Susannah Ireland, Christine Rentschler for always being

supportive and understanding. I want to thank my sister, Penelope Gorton, for our many chats,

and my brother, Andrew Gorton, for his invaluable computer skills. Thank you to my dad, Mike

Gorton, for spending many late hours in the greenhouse planting and picking fruits with me, and

to my mom, Pamela Gorton, for making sure I always had plenty of snacks and treats to keep me

going. Last but not least, I want to thank Mike le Riche, for keeping me focused, taking me out

for much needed breaks, and always cheering me up.

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Table of Contents

Acknowledgements……………………………….……………………………………………....iv

List of Tables……………………………………………………………………………………..ix

List of Figures……………………………………………………………………………………..x

List of Appendices………………………………………………………………………………..xi

Chapter 1: General Introduction……………………………………………………………….1

Mutualism stability and the maintenance of genetic variation……………………………………2

G × G interactions…………………………………………………………………………………6

Nodulation signaling pathway…………………………………………………………………….9

QTL mapping…………………………………………………………………………………….10

Study species……………………………………………………………………………………..13

Proposed experiment……………………………………………………………………………..14

Chapter 2: Resolving the genetic basis of genotype by genotype interactions between plants

and their mutualists…………………………………………………………………………….15

Materials and methods…………………………………………………………………………...19

Study species and mapping population…………………………………………………..19

Rhizobium strain isolation……………………………………………………………….20

Greenhouse experiment………………………………………………………………….20

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Planting design…………………………………………………………………..20

Rhizobium strain inoculation…………………………………………………….23

Trait measurement……………………………………………………………….24

Candidate gene sequencing………………………………………………………………25

Statistical analyses……………………………………………………………………….27

Quantitative genetic analysis…………………………………………………….27

Linkage map construction………………………………………………………..27

QTL analysis……………………………………………………………………..29

Results……………………………………………………………………………………………31

Survival and control plants………………………………………………………………31

Quantitative genetics……………………………………………………………………..32

QTL mapping…………………………………………………………………………….32

QTL by rhizobium strain interactions…………..………………………………………..40

Epistatic interactions……………………………………………………………………..40

QTL mapping across strain treatments….……………………………………………….41

Mapping of symbiotic signaling genes…………………………………………………..41

Branching orthologs in M. truncatula……………………………………………………45

Discussion……………………………………………………………………………………..…45

No evidence for a genetic basis of G × G interactions…………………………………..46

QTL architecture of phenotypic traits……………………………………………………48

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Epistatic interactions……………………………………………………………………..50

Colocalization of symbiotic signaling genes…………………………………………….53

Evidence for branching pattern orthologs………………………………………………..54

Conclusion……………………………………………………………………………….55

Chapter 3: Conclusions and future directions………………………………………………..57

Implications for G × G interactions……………………………………………………………...58

The QTL architecture of M. truncatula………………………………………………………….61

Genetic variation in Nod factor signaling genes…………………………………………………63

Future directions and final conclusions………………………………………………………….64

Literature cited…………………………………………………………………………………...68

Appendix…………………………………………………………………………………………81

viii

List of Tables

Table 1. Results of a mixed model ANOVA partitioning variation among RILs into genetic and

phenotypic variance……………………………………………………………………………...34

Table 2. QTL identified for plant traits collected on M. truncatula grown with the rhizobium

strain Naut………………………………………………………………………………………..36

Table 3. QTL identified for plant traits collected on M. truncatula grown with the rhizobium

strain Sals………………………………………………………………………………………...37

Table 4. QTL identified for plant traits collected on M. truncatula averaged over both rhizobium

strains…………………………………………………………………………………………….42

Table A-1. Information on the parental lines of the LR03 RIL mapping population……………83

Table A-2. Primer sequences used for sequencing Nod factor signaling genes in parental lines

and RILs………………………………………………………………………………………….84

Table A-3. PCR reagent concentration and volumes (in µL) used for sequencing Nod factor

signaling genes in parental lines and RILs………………………………………………………85

Table A-4. Correlations between RIL least-square means for all traits in the Naut and Sals

treatments………………………………………………………………………………………...86

Table A-5. Correlations between RIL least-square means for all traits in the across rhizobium

strains analysis…………………………………………………………………………………...87

Table A-6. Arabidopsis thaliana branching genes and description with the corresponding M.

truncatula orthologs……………………………………………………………………………...88

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List of Figures

Figure 1. Depiction of how negative frequency dependence can maintain genetic variation in

mutualisms………………………………………………………………………………………...8

Figure 2. Experimental set-up in greenhouse……………………………………………………22

Figure 3. Back-to-back histograms of trait least-square means of the M. truncatula RILs grown

with Naut and Sals……………………………………………………………………………….33

Figure 4. Genomic locations of significant QTL detected for the phenotypic traits of M.

truncatula when grown with rhizobium strains Naut and Sals…………………………………..38


truncatula averaged across rhizobium strains……………………………………………………43

Figure 6. Epistatic QTL detected between markers EM2265 and EM17407 in Naut…………...44

Figure A-1. Preliminary leaf count data collected on the parental lines of the LR03 RIL mapping

population grown with rhizobium strains Naut and Sals………………………………………...81

Figure A-2. Linkage map of the LR03 RIL mapping population………………………………..82

x

List of Appendices

Appendix…………………………………………………………………………………………81

Chapter 1: General introduction 1

Chapter 1:

General Introduction

Interspecific interactions are one of the central processes influencing evolution and

adaptation. Thompson (1988) expressed that just as variation within populations is required for

the evolution of species, variation in the outcome of interactions is required for the evolution of

species interactions. The outcome of interspecific interactions may be dependent on the

environment and can also vary with the age, size, and genotype of the interacting individuals

(e.g., Moran, 1981; Price, 1987; Pyke, 1987). For example, the effects of herbivory on grasses

can vary from reduced growth and reproduction to fatal depending on the age of the plant (Pyke,

1987).

Mutualisms – reciprocally beneficial interactions between species – are ubiquitous

throughout all organismal kingdoms and in all ecosystems (Boucher, 1985; Smith & Douglas,

1987). Organisms use mutualisms to obtain resources or services which they cannot obtain by

other means or produce themselves (Bronstein, 2001), and this exchange of goods is favoured

when the benefits obtained through the mutualism are greater than the cost of reciprocating (see

Leigh, 2010 for a review). As in other interspecific interactions, the outcome of mutualisms can

vary depending on the abiotic and biotic conditions in which the interactions take place

(Bronstein, 1994). For example, plants eliminate their connections with mycorrhizal fungi when

phosphorous is added to the soil because the benefits they receive from the symbiosis are no

longer needed in the presence of a cost-free alternative (Johnson, 2010).


My thesis focuses on the effect of genetic variation on the outcome of biotic interactions,

which in turn can affect the outcome of mutualisms. Using the model legume Medicago

truncatula and its associated rhizobia, I attempted to locate specific regions of the genome

influencing interactions between different partner genotypes. I conducted a quantitative trait loci

mapping experiment with two different rhizobium strains to identify loci affecting the outcome

of biotic interactions and plant fitness traits. In this chapter, I provide necessary background

information on a number of topics which are relevant to my thesis including: the mechanisms of

mutualism stability, an introduction to genotype by genotype interactions, a brief description of

the signaling process involved in the initiation of nodulation between legumes and rhizobia, and

an overview of the process of quantitative trait loci mapping. I have also included a brief

description of my study organism and a summary of my experiment.

Mutualism stability and the maintenance of genetic variation

The evolutionary dilemma of mutualisms is that while an individual can benefit from

cooperating, it can achieve higher fitness by exploiting the cooperative efforts of others (Axelrod

& Hamilton, 1981; Soberon Mainero & Martinez del Rio, 1985; Bull & Rice, 1991). Given this,

theoretical models predict that mutualisms should be highly susceptible to invasion by cheaters,

individuals that exploit the host but fail to reciprocate benefits, ultimately leading to mutualism

breakdown (Trivers, 1971; Bull & Rice, 1991; Denison, 2000; Sachs et al., 2004). However,

mutualisms continue to persist and although cheating individuals are present in most mutualisms,

complete shifts to parasitism appear to be rare (Sachs & Wilcox, 2006; Sachs & Simms, 2006),

therefore stabilizing mechanisms must exist. In particular, three mechanisms have been

proposed: partner fidelity feedback, partner choice, and host sanctions.


In partner fidelity feedback, the two partners are associated for extended series of

exchanges, ultimately leading to coupling of their fitness due to repeated interactions (Trivers,

1971; Axelrod & Hamilton, 1981; Bull & Rice, 1991; Sachs et al., 2004; Foster & Wenseleers,

2006). Therefore, if a partner – for example a microbial mutualist – fails to cooperate or cheats, it

negatively affects its own fitness because its host plant’s fitness loss feeds back as a fitness loss

to the microbial partner (Bull & Rice, 1991; Sachs et al., 2004). Partner fidelity is expected to

occur in vertically transmitted symbionts or horizontally transmitted symbionts where the

dispersal abilities of the partners are limited (Sachs et al., 2004; Foster & Wenseleers, 2006).

An alternative mechanism is host control over uncooperative individuals via partner

choice or host sanctions (Bull & Rice, 1991; Eshel & Cavalli-Sforza, 1982; Denison, 2000;Sachs

et al., 2004; Foster & Wenseleers, 2006). The host plant can assess the cooperative ability of

several individuals of the microbial mutualist and only select or reward those which are the most

cooperative. In contrast to partner fidelity, partners do not need to interact continuously for

mutualism to be maintained. Furthermore, this mechanism can operate either at the initiation of

the symbiosis (―partner choice‖, Eshel & Cavalli-Sforza, 1982; Noë, 1990; Noë & Hammerstein,

1994; Bull & Rice, 1991, e.g., Simms & Taylor, 2002; Mueller, 2004; Heath & Tiffin, 2009;

Gubry-Rangin et al., 2010) or during the symbiosis via punishment of uncooperative partners –

i.e., host sanctions (Denison, 2000; e.g., Kiers et al., 2003; Jander & Herre, 2010). Both

mechanisms are expected to occur in mutualisms with horizontal transmission of symbionts and

where the host has multiple partners from which to choose (Bull & Rice, 1991).

A recent model by Weyl et al. (2010) concluded that many of the examples of

punishment or host sanctions are more likely examples of partner fidelity feedback. Using a


principle-agent (host-symbiont) model, they found that some of the described examples of host

sanctions (e.g., Kiers et al., 2003; Jander & Herre, 2010) are more likely general adaptations of

hosts to environmental damage – i.e., a response of partner fidelity feedback. For example, in the

mutualism between yucca plants and yucca moths, the host plant aborts flowers with an excess

of moth eggs and low pollen loads (Pellmyr & Huth, 1994). If host sanctions are occurring,

punishment should be greater when moth eggs are present, whereas partner fidelity feedback

predicts equal levels of floral abortion whether the damage is due to cheaters or environmental

factors. Empirical evidence indicates that the latter is occurring: mechanical damage of the

ovules is sufficient to cause floral abortion, and the experimental addition of moth eggs does not

influence the host plant response (Richter & Weis, 1995; Marr & Pellmyr, 2003). I think the

model and logic presented by Weyl et al. (2010) is sound, and suggests that partner fidelity

feedback may be more common than previously thought. I also agree with Weyl et al. (2010)

that host sanctions are more likely in mutualisms where the symbionts have greater cognitive

power, as in the mutualism between cleaning fish and client fish (Bshary, 2002; Bshary &

Grutter, 2005).

Given these mechanisms of removing uncooperative individuals, it is expected that only

highly mutualistic partners will remain, and in turn there will be minimal intraspecific genetic

variation in partner quality (May, 1976; Bull & Rice, 1991; Denison, 2000). However, empirical

evidence for genetic variation in partner quality has been found in mutualisms, including the

symbiosis between legumes and rhizobia. In this symbiosis, soil bacteria fix nitrogen into a

plant-usable form in exchange for plant photosynthates and shelter inside specialized organs

known as root nodules. Studies have reported variation in plant fitness and performance traits


such as seed weight and plant biomass when grown with different rhizobium strains (e.g.,

Mytton et al., 1977; Parker, 1995; Burdon et al., 1999; Mhadhbi et al., 2005; Thrall et al., 2011),

i.e., there is genetic variation for the benefits the plants receive from the symbiosis. Similarly,

variation among rhizobium strains in the number of nodules and ability to nodulate has been

found in response to different legume cultivars and species (e.g., Lieven-Antoniou & Whittam,

1997; Robinson et al., 2000; Mhadhbi et al., 2005; Abi-Ghanem et al., 2010).

There are at least three non-exclusive hypotheses to explain the existence of such genetic

variation in partner quality in mutualistic interactions, as summarized by Heath & Tiffin (2007):

1) Selection processes, such as host sanctions (Denison, 2000; Kiers et al., 2003) may not be

completely effective at removing less beneficial partners, allowing them to remain in the

population, 2) The best partner is dependent on the abiotic or biotic environment (e.g., West et

al., 2002; Piculell et al., 2008; Heath et al., 2010; Heath & Lau, 2011) therefore it is expected to

change with varying conditions and/or 3) The fitness both the host and symbiont are dependent

on the genotype of its interacting partner – i.e., genotype by genotype interactions (G × G)

(Heath & Tiffin, 2007; Heath, 2010).

These three hypotheses have varying levels of evidence in the literature. Kiers and

Denison (2008) provided several suggestions for the continued persistence of less-effective

partners in plant-rhizosphere interactions given the existence of host sanctions. One explanation

is that some species may have weaker sanctions, although empirical evidence for variation in

mutualist quality being explicitly maintained due to ineffective host sanctions is lacking. In terms

of the influence of biotic and abiotic conditions on the outcome of mutualisms, a number of

studies have found the fitness of both plant hosts and microbial mutualists can vary in response


to a range of factors such as soil community composition, herbivory, nutrient availability, and

light availability (e.g., Heath & Tiffin, 2007; Piculell et al., 2008; Ehinger et al., 2009; Davitt et

al., 2010; Heath et al., 2010; Heath & Lau, 2011). However, there appears to be the most

evidence for G × G interactions: it has repeatedly been found that the fitness benefits both

partners obtain from the mutualism can vary in response to both the plant host genotype and the

genotype of the interacting symbiont (e.g., Mytton et al., 1977; Parker, 1995; Hoeksema &

Thompson, 2007; Heath & Tiffin, 2007; Heath, 2010). Nevertheless, the influence of G × G

interactions and environmental conditions are not mutually exclusive: selection mosaics or

genotype × genotype × environment interactions (Thompson, 2005) – where selection on species

interactions varies with the abiotic and biotic environment – exist in mutualisms (e.g., Hoeksema

& Thompson, 2007; Piculell et al., 2008; Heath et al., 2010). However, a greater understanding

of the influence of G × G interactions on the maintenance of genetic diversity in mutualist

quality should first be resolved prior to investigating the effect of increasing levels of complexity

such as environmental and trophic interactions.

G × G interactions

G × G interactions, also known as intergenomic epistasis (Wade, 2007), are epistatic

interactions between the genomes of two interacting partners, where the effects of a gene in one

partner can vary depending on the genetic background of the other partner (e.g., Moran, 1981;

Service, 1984; Hoeksema & Thompson, 2007; Vale & Little, 2009; Heath, 2010). In mutualisms,

the phenotype of an individual can change depending on the genome of its interacting partner: a

symbiont may be a cooperator with one individual, but it may be suboptimal partner or even a

cheater with another individual.


The presence of such G × G interactions can maintain genetic variation in mutualist

quality by frequency dependent selection, where the fitness of both the host and symbiont are

dependent on genotypes and frequencies of their interacting partners in the population (Bever,

1999; Molofsky et al., 2001; Heath & Tiffin, 2007). Bever (1999) generated a model with two

hosts and two symbionts to show that negative frequency dependent selection can maintain

genetic variation in mutualist quality. For example, if a particular host plant obtains the highest

fitness with one microbial mutualist, however the same microbial mutualist has higher fitness

with a different host genotype, eventually the frequency of the host will feed back negatively on

its own fitness through its symbiont (Figure 1). As such, no one host genotype or symbiont

genotype can dominate the population, leading to genetic variation in partner quality. In

contrast, in positive frequency dependent selection, the optimal partner matches for the host and

symbiont, therefore the frequency of the host feeds back positively on its own fitness (Bever,

1999; Molofsky et al., 2001). In well-mixed systems, positive frequency dependence is predicted

to lead to loss of species diversity (May, 1974), however it can maintain genetic variation in

partner populations if the populations are spatially structured (Molofsky et al., 2001). When

positive frequency dependent interactions occur over small spatial scales – such as interactions

between plants and soil communities – stable clusters of coexisting species are created, which

can be further maintained with increasing physical complexity of the environment (Molofsky et

al., 2001).

In the legume Medicago truncatula and its nitrogen-fixing bacteria Sinorhizobium

melliloti G × G interactions have been previously found (Heath & Tiffin, 2007; Heath, 2010). G

× G interactions are evidence that genome level variation is influencing the outcome of legume-


Host plant Host plant

genotype A genotype B

Microbial mutualist Microbial mutualist

genotype X genotype Y

Figure 1. Depiction of how negative-frequency dependence can maintain genetic variation in

mutualisms (Modified from Figure 1 in Bever, 1999). The direction of the arrows represents the

direction of fitness effects, and the width of the arrows indicates the relative fitness benefits

obtained from the interaction, with wider arrows meaning greater fitness. Black arrows indicate

fitness effects and benefits for host plants, white arrows indicate fitness effects and benefits for

the microbial mutualists.


rhizobium interactions, however the exact location of the loci and genes involved remain

unknown. The next step is to locate the regions of the genome causing G × G interactions.

Although the genes involved in the legume-rhizobium symbiosis pathway have been well

characterized (see Kouchi et al., 2010 for a review), little is known about the genomic regions

influencing natural variation in the fitness benefits both partners obtain from the mutualism –

i.e., G × G interactions. It is important to locate the genetic bases of this symbiotic variation, as it

is this genetic variation that is under coevolutionary selection. Ultimately, locating these regions

is an essential step towards identifying the genetic mechanisms of coevolution. Furthermore,

once this information is obtained, future studies can determine whether particular genomic

regions have similar or different effects on the outcome of the mutualism when exposed to

varying biotic or abiotic conditions. A promising place to look for potential functional genetic

variation is in the plant genes involved in the initiation of the signaling pathway between

legumes and rhizobia.

Nodulation signaling pathway

The initiation and subsequent formation of nodules in the legume-rhizobium symbiosis

involves a complex molecular signaling dialogue between the two partners (see Jones et al.,

2007; Ferguson et al., 2010 for reviews). The host plant releases flavonoids and isoflavonoids

into the rhizosphere, which stimulate the expression of nodulation (nod) genes in the rhizobia.

Nod genes encode proteins involved in the synthesis and secretion of nodulation factors (Nod

factors), lipochito-oligosaccharides which induce morphological changes in the host, leading to

rhizobial infection of the plant root. Nod factor structure varies between rhizobia species and

also determines host specificity. Nod factors are recognized by the root hairs of the host, leading


to root hair curling, calcium spiking, growth of infection threads, and ultimately root nodule

formation. Once inside the nodule, rhizobia differentiate into nitrogen-fixing bacteroids.

In the past decade, a number of host legume genes involved in Nod factor perception,

symbiotic signaling, bacterial infection and nodule formation have been identified (see Kouchi et

al., 2010 for list). Among these genes, five of them are involved in the earliest stages of the Nod

factor recognition and signaling pathway in M. truncatula: NFP, DMI1, DMI3, NORK and NIN

(see Jones et al., 2010, Figure 2 for pathway diagram). NFP is a lysine motif (LysM)-receptor-

like kinase and is the candidate for the Nod factor receptor gene (Amor et al., 2003; Arrighi et

al., 2006); DMI1 encodes a calcium gated ion channel located on the nuclear membrane (Ané et

al., 2004); DMI3 encodes a calcium-calmodulin-dependent kinase that can control gene

expression (Lévy et al., 2004); NORK or DMI2 encodes a receptor kinase that is also involved in

rhizobial Nod factor perception (Endre et al., 2002); and NIN is a putative transcription factor

and is required for the formation of the plant infection threads (Schauser et al., 1999). Loss of

function mutations in of any of these five genes results in no rhizobial infection or formation of

nodules (Schauser et al., 1999; Catoira et al., 2000; Amor et al., 2003), illustrating the

importance of these genes to the nodulation pathway. Given that these genes are essential to the

formation of the symbiosis, they are also promising candidate genes to investigate whether

sequence level variation affects the outcome of G × G interactions.

QTL mapping

Quantitative trait loci (QTL) mapping involves the construction of a genetic map

followed by subsequent searching for a statistical association between a genetic marker and a

phenotypic trait (Liu, 1998). A significant association between the trait and the markers indicates


the presence of a QTL, i.e., a region of the genome that explains variation in the quantitative trait

of interest (Doerge, 2002).

The first step in QTL mapping is to construct a mapping population from homozygous,

inbred parental lines (Mauricio, 2001; Doerge, 2002). The parental lines do not have to be

different in the mean phenotypic value of the trait, however they must display allelic variation

(Mauricio, 2001). These parental lines are then mated to produce F1 lines, which are

heterozygous at all markers. From here, a number of different crossing designs are used

(backcross, F2, recombinant inbred lines) to develop the population which will be used for QTL

mapping. Once the mapping population has been created, each line must be genotyped for

neutral markers spread evenly across the genome, and these marker genotypes are then used to

assemble the genetic map.

A range of techniques are used to perform QTL mapping: single-marker mapping

(Weller, 1986; Beckmann & Soller, 1988), interval mapping (Lander & Botstein, 1989),

composite interval mapping (Zeng, 1993; Zeng, 1994; Jansen, 1993), multiple interval mapping

(Kao et al., 1999; Zeng et al., 1999) and multiple trait mapping (Jiang & Zeng, 1995). All of

these techniques test for associations between the marker genotypes and the measured

phenotypic traits, ultimately leading to the detection of QTLs.

Single-marker mapping involves performing a t-test or an ANOVA at each marker to

detect QTL (Weller, 1986; Beckmann & Soller, 1988), however it provides no information about

the location of the loci and suffers from the problem of multiple testing. Interval mapping solves

this problem by using an estimated genetic map, which is searched in increments to determine if


a QTL is likely to be found within intervals defined by ordered pairs of markers (Lander &

Botstein, 1989). Composite interval mapping utilizes the same approach, however it also controls

for the influence of genetic background by including additional markers outside the interval as

cofactors in the model (Zeng, 1993; Janesen, 1993; Zeng, 1994). Multiple interval mapping uses

multiple marker intervals simultaneously to fit multiple QTL into one model, and also detects

epistasis between QTLs (Kao et al., 1999; Zeng et al., 1999). Finally multiple-trait mapping

extends the composite interval mapping method to multiple correlated traits (Jiang & Zeng,

1995).

Recombinant inbred lines (RILs) are generated either by taking the F1 generation through

multiple generations of selfing or brother-sister matings (Lynch & Walsh, 1998). The RILs

which are created are genetically identical within a line, but have significant between-line

genetic variance, as each line is a mixture of the two parental genomes (Lynch & Walsh, 1998;

Broman, 2005). RILs offer a number of advantages for QTL mapping for several reasons: lines

only need to be genotyped once; environmental, individual and measurement variability can be

reduced by phenotyping multiple individuals per line; they offer greater mapping resolution due

to increased recombination events in comparison to other crossing designs; and they are

excellent for measuring QTL × environment interactions because the same lines can be used in

different environments (Lynch & Walsh, 1998; Broman, 2005). For these reasons, I used RILs

for my experimental work.

Study species

Medicago truncatula (Fabaceae), commonly known as the barrel medic, is a small,

annual legume native to the Mediterranean region. It has trifoliate leaves, small inflorescences


with one to five yellow flowers and is found mainly in open areas (Bataillon & Ronfort, 2006). It

has recently been split into three subspecies, largely based on the characteristics of the fruit pod:

ssp. truncatula, ssp. tricycla, and ssp. longeaculata (Bataillon & Ronfort, 2006). It is also grown

in rotation with cereal crops in some areas of Australia to improve soil conditions (Barker et al.,

1990; Young et al., 2005; Bataillon & Ronfort, 2006). Populations of this legume display

substantial phenotypic variation for a range of traits, including flowering time, symbiont

specificity and disease resistance (Cook, 1999). Medicago truncatula forms indeterminate

nodules with the nitrogen-fixing bacteria Sinorhizobium medicae and Sinorhizobium meliloti

(Rhizobiaceae) (Bailly et al., 2006).

Medicago truncatula has been developed as the model legume for studying the genetics

of plant-mycorrhizal and plant-rhizobium symbioses due to its short generation time, high selfing

rate (~97.5%, Bonnin et al., 1996) and small diploid genome (2n = 16; 550 Mb);(Cook, 1999;

Ané et al., 2008; Young et al., 2005). To date, its genome has been 84% sequenced and

assembled, there is a HapMap in progress, and there are mutant collections, mapping

populations, cDNA libraries, a gene expression atlas and a genome browser which are available.

It is a member of the cool season legumes (Galegoid clade), and has high levels of synteny with

closely related crops like pea (Pisum sativum)(Choi et al., 2004; Aubert et al., 2006), lentils

(Lens culinaris)(Phan et al., 2007), and alfalfa (Medicago sativa)(Julier et al., 2003; Choi et al.,

2004).

Experiment

To investigate the impact of genetic architecture on the outcome of G × G interactions

between Medicago truncatula and Sinorhizobium meliloti, I performed a large quantitative trait


loci (QTL) mapping experiment with two genotypically distinct rhizobium strains (Chapter 2).

The goal of the experiment was to locate regions of the Medicago genome that influence G × G

interactions and plant fitness. As a second component, I was also interested in determining if

genetic variation in plant nodulation genes affects phenotypic traits. Specifically, I wanted to

address the following questions: (1) Which regions of the M. truncatula genome contribute to

variation in plant fitness? (2) Do the number and effect size of additive and epistatic QTL differ

in two genotypic distinct rhizobia treatments, i.e., are QTL differentially expressed depending on

the rhizobium strain? and (3) Does sequence level variation in known symbiosis genes have an

effect on plant fitness and the ecological outcome of the symbiosis?

I found numerous QTLs that appear to affect plant fitness traits, but no evidence for G ×

G interactions or differences in QTL detection between the rhizobium strain treatments. Some of

the plant fitness QTLs co-localized with the nodulation signaling genes, suggesting that variation

in these genes may potentially influence phenotypic plant traits. The implications of these results

for G × G interactions are included in Chapter 2, and Chapter 3 reviews the main findings of my

thesis and suggests avenues for future work.

Chapter 2: Resolving the genetic basis of genotype by genotype interactions 15

Chapter 2:

Resolving the Genetic Basis of Genotype by Genotype Interactions

Between Plants and their Mutualists

Introduction

Mutualisms, or interspecific cooperation, are reciprocally beneficial interactions between

species (Bronstein, 2001) that affect many ecological and evolutionary processes. Organisms

from every kingdom participate in mutualisms, from mitochondria within cells, to mycorrhizal

fungi and plants, to cleaner fish and their clients (Boucher, 1985; Smith & Douglas, 1987;

Bronstein, 1994). Despite their prevalence, theoretical models predict that mutualisms should be

characterized by low levels of genetic variation: either from the fixation of the best mutualists

(May, 1976) or from the invasion of cheaters (individuals who benefit from the goods exchange

but do not reciprocate; Trivers, 1971; Axelrod & Hamilton, 1981; Bull & Rice, 1991; Sachs et

al., 2004), or from the suite of mechanisms that can stabilize mutualisms against cheating

(Trivers, 1971; Bull & Rice, 1991; Denison, 2000; Simms & Taylor, 2002). In contrast to these

predictions from a diverse set of theoretical models, a persistent empirical observation is that

substantial genetic variation in mutualist partner quality exists (e.g., Heath & Tiffin, 2007;

Hoeksema & Thompson, 2007). One explanation for this genetic variation is genotype by

genotype interactions, where the fitness of both partners is dependent on the genotype of its

interacting partner (Bever, 1999; Heath & Tiffin, 2007; Heath, 2010). Although these

interactions have the potential to explain variation in mutualism quality and outcome, the

genomic regions influencing these interactions remain unknown. Here I used quantitative trait

loci mapping to investigate genomic regions that influence the outcome of possible genotype by


genotype interactions between the legume Medicago truncatula and its associated rhizobia, and

whether variation in known mutualism signaling genes affects plant fitness traits.

Models predict that genes underlying mutualisms should have minimal variation in

partner quality – either due to selection for the best partners, the invasion of cheating individuals,

or mechanisms preventing the spread of cheaters – typically assume the fitness of each partner is

constant regardless of the genotype of its interacting host or symbiont (e.g., Trivers, 1971; Bull

& Rice, 1991). In contrast, a number of experimental studies have demonstrated the opposite: the

fitness of individuals involved in coevolutionary interactions can be dependent on the genotype

of both partners – i.e., genotype × genotype interactions (G × G) for fitness (Moran, 1981;

Service, 1984; Salvaudon et al., 2005; Hoeksema & Thompson, 2007; Heath, 2010). G × G

interactions, also referred to as intergenomic epistasis (Wade, 2007), occur when the effects of a

gene in one partner has variable effects depending on the genetic background of the other

partner. Applied to mutualisms, this means the phenotype of an individual can change depending

on the genome of its interacting partner: a cheater with one individual may be a cooperator with

another partner (Heath & Tiffin, 2007; Heath, 2010). Such fluctuating interactions could

generate a form of frequency dependent selection, where the fitness of both the host and

symbiont are dependent on genotypes and frequencies of their interacting partners in the

population (Bever, 1999; Molofsky et al., 2001). In turn, these interactions could help maintain

genetic variation in partner quality and provide an explanation for the stability of cooperation

between species (Bever, 1999; Heath & Tiffin, 2007) .

One system where G × G interactions have been found is in the model legume Medicago

truncatula and its nitrogen-fixing bacteria Sinorhizobium melilioti (Heath & Tiffin, 2007; Heath,


2010). In this symbiosis, the rhizobia fix atmospheric nitrogen into a plant-usable form in

exchange for shelter inside specialized root nodules and plant photosynthates. For example,

Heath (2010) found that some rhizobium strains were highly beneficial mutualists with certain

plant genotypes, but provided little or no benefit when matched with different plant genotypes.

Despite the variety of genetic resources available in M. truncatula (e.g., genome ~85%

sequenced and assembled, HapMap in progress, gene expression atlas, available RIL mapping

populations), the genetic basis of these G × G interactions remains almost totally unknown.

In addition to the genomic resources which have been developed, several genes involved

in the signaling pathway between rhizobia and legumes have also been identified (Schauser et

al., 1999; Catoira et al., 2000; Endre et al., 2002; Ane et al., 2004; Lévy et al., 2004; Arrighi et

al., 2006). Briefly, nodule formation and host specificity are controlled by mutual signaling

between the two partners. Legumes release flavanoids into the soil, which trigger rhizobia to

produce Nod factors. Nod factors are lipochito-oligosaccharide compounds which in turn induce

multiple downstream responses in the host, ultimately leading to the formation of root nodules

(see Jones et al., 2007 for a review). Recently, five genes involved in the earliest stages of the

Nod factor recognition and signaling pathway have been characterized in M. truncatula: DMI1,

DMI3, NFP, NORK and NIN (see Figure 1 in De Mita et al., 2007 and Figure 2 in Jones et al.,

2007 for diagrams of the signaling pathway). DMI1 encodes an ion channel located on the

nuclear membrane (Ané et al., 2004); DMI3 encodes a calcium-calmodulin-dependent kinase

that can control gene expression (Lévy et al., 2004); NFP is a lysine motif (LysM)-receptor-like

kinase and is the candidate for the Nod factor receptor gene (Arrighi et al., 2006); DMI2 or

NORK encodes a receptor kinase and is involved in rhizobial Nod factor perception (Endre et al.,


2002); and NIN is required for the formation of the plant infection threads and has homologies to

transcription factors (Schauser et al., 1999). Mutants with non-functional copies of any of these

genes cannot be infected by rhizobia and therefore produce no nodules (Schauser et al., 1999;

Catoira et al., 2000; Amor et al., 2003). Given that these genes are essential to the formation of

the symbiosis, they are promising candidates to investigate whether sequence level variation

affects the outcome of G × G interactions.

Quantitative trait loci (QTL) mapping is an excellent method for locating regions of the

genome affecting ecologically and evolutionarily important traits. Previous QTL mapping

studies in M. truncatula have focused on agronomically important traits such as resistance to

pathogens (Djébali et al., 2009; Hamon et al., 2010), seed mineral concentrations (Sankaran et

al., 2009), seed germination (Dias et al., 2011) and flowering time (Pierre et al., 2008).

However, thus far, no one has examined QTL architecture of plant fitness traits in response to

two different rhizobium genotypes (mapping natural variation in G × G interactions), or the

contribution of known signaling genes to variation in phenotypic traits.

Using a recombinant inbred line (RIL) mapping population of M. truncatula, I examined

the QTL architecture of a range of plant fitness traits when grown with two genotypically distinct

S. meliloti strains. Specifically, I wanted to address the following questions: (1) Which regions of

the M. truncatula genome contribute to variation in plant fitness? (2) Do the number and effect

size of additive and epistatic QTL differ in two genotypic distinct rhizobia treatments, i.e., are

QTL differentially expressed depending on the rhizobium strain? and (3) Does sequence level

variation in known symbiosis genes have an effect on the ecological outcome of the symbiosis?


Materials and methods

Study species and mapping population

Medicago truncatula (Fabaceae), commonly known as the barrel medic, is a small,

annual legume native to the Mediterranean region. It is found in symbiosis with the nitrogen-

fixing bacteria Sinorhizobium medicae and S. meliloti (Rhizobiaceae) (Bailly et al., 2006). I

tested for G × G interations with S. meliloti. Medicago truncatula has trifoliate leaves, small

inflorescences with one to five yellow flowers and is found mainly in open areas (Bataillon &

Ronfort, 2006). It has been developed as the model legume for studying the genetics of plant-

mycorrhizal and plant-rhizobium symbioses due to its short generation time, high selfing rate

(~97.5%, Bonnin et al., 1996) and small diploid genome (2n = 16; 550 Mb);(Cook, 1999; Ané et

al., 2008; Young et al., 2005). Furthermore M. truncatula belongs to the cool season legumes

(Galegoid clade), and is closely related to important crops like pea (Pisum sativum), lentils (Lens

culinaris) and alfalfa (Medicago sativa)(Young & Udvardi, 2009).

Seeds from the M. truncatula recombinant inbred line (RIL) LR03 mapping population

were provided by L’Insitut National de Reserche Agronomique (INRA) in Montpellier, France.

The lines were created by manually crossing two M. truncatula inbred line accessions,

F803005.5 (530, female parent, origin: France) and DZA045.5 (735, male parent, origin:

Algeria) to produce an F1 generation, which was later selfed. The parental genotypes have been

previously found to be divergent in a number of traits (Appendix, Table A-1), and preliminary

results indicated that they exhibit evidence of G × G interactions with the two focal rhizobium

strains used in this experiment (ANOVA for leaf number, rhizobium strain × parental line, p =

0.02. See Appendix, Figure A-1). The RILs (n=177) were derived from the F2 generation by self-


fertilization and single seed descent for five generations, creating the F6 RIL mapping population

that I used.

Rhizobium strain isolation

Both S. meliloti strains used in this experiment (Sals b and Naut a, hereafter referred to as

Naut and Sals) were previously isolated and genotyped from soil samples collected on the

Mediterranean coast of southern France by Heath (Heath, 2010); full details are given there, and

only a brief summary is provided here. Strain Sals was isolated from the soil of the wild M.

truncatula Population Salses II, which is located near the town of Salses-le-Chateau and strain

Naut was isolated from Population Nautique, which is located just east of the town of La

Nautique (rhizobium strain locations approximately ~50 km apart). Wild M. truncatula seeds

taken from the two populations were grown in the greenhouse and inoculated with a mixture of

soil taken from that area and sterile water. After 2 months of growth, nodules from each plant

were crushed and streaked on plates with modified arabinose-gluconate (MAG) media. Once

rhizobia colonies were visble, one isolate from each plate was randomly selected and grown in 2

mL liquid MAG media for 2 days at 30°C. To ensure that the strains were not simply clonal

replicates, the strains were genotyped using rep-PCR.

Greenhouse experiment

Planting design

I grew 8 replicates of each RIL with the two rhizobium strain treatments. To minimize

contamination, the following design was used. One seedling per line was planted into Ray-Leach

SC10 UV-stabilized cone-tainers (Stuewe and Sons Inc, Tangent, OR); I then placed four racks

of cone-tainers into flow-trays (large plastic ―bins‖ that provide sub-irrigation) each of which


received its own rhizobium strain inoculation. Within each bin, there were 177 genotyped RILs,

4 replicates of each of the parental lines, and an additional 10 RILs that lacked genetic markers

(N=195 plants per bin). I used 8 replicate bins per rhizobium treatment for a total of 16 bins,

arranged in the greenhouse in a checkboard array of 8 blocks (one bin of each strain per block) to

eliminate any potential relationships between rhizobium treatment and spatial position. Within

rhizobium treatments, the design is a randomized blocked design; for the entire experiment, the

rhizobium treatment (which was applied to bin of 195 plants at a time) is a plot-level effect. An

additional 49 plants were not inoculated and were included as controls to test for contamination

within the greenhouse (see Figure 2 for greenhouse set-up).

All seeds were scarified with a razor blade then surface sterilized for 30 seconds in 95%

ethanol, followed by 7 minutes in full bleach. Following sterilization, seeds were imbibed in

ddH20 for 20 mins and plated on 0.08% agar. The plates were sealed with parafilm, wrapped in

aluminum foil and stratified at 4°C for 2 weeks. After 2 weeks, the seedlings were transplanted

into 164 mL cone-tainers containing a mixture of 1:1 Sunshine Mix 2 (Sun Gro Horticulture,

Bellvue, WA) and Turface (Profile Products, LLC Buffalo Grove, IL). I used Sunshine Mix 2

because it contains no added fertilizer, as it has been previously shown that nitrogen can affect

the outcome of the symbiosis (Heath & Tiffin, 2007; Heath et al., 2010). Prior to transplanting,

the soil mixture was steam sterilized at 121°C for 30 mins in small bags to kill any contaminants.

I planted one seed per line in a randomized design within each bin and conetainers were placed

in every other space in each 98 cell rack. Planting was completed on a bin by bin basis from Jan


Figure 2. Experimental set-up in the greenhouse. The numbers 1-16 represent the bins. The colour of the bin indicates the rhizobium

strain it was inoculated with (grey = Naut; white= Sals). The control bin in the bottom left corner was not inoculated with any strain in

order to assess greenhouse level contamination.


1-6 2010. Seeds that failed to establish were replaced with a second set of plants (when seeds for

that line were available) using the same methods described above on Jan 29-31 2010.

For two weeks following transplanting, I misted all seedlings twice daily with water to

prevent desiccation. Once plants were sufficiently large to have established roots, I bottom

watered the plants every second day. I fertilized the plants only once with N-free Fahreus

solution (Somasegaran & Hoben, 1994) and grew the plants under 16 hour days at an average

greenhouse temperature of 22°C.

Rhizobium strain inoculation

Three days before planting, I streaked agar petri plates containing tryptone yeast (TY)

media with thawed rhizobia culture stocks that were previously stored at -20°C. I incubated

plates at 30°C, and allowed the rhizobia cultures to grow for one week. Afterwards, I selected an

isolate from a single bacteria colony for each strain and inoculated sterile liquid TY growth

media. The liquid cultures were grown for three days at 30°C and were mixed continuously at a

rate of 200 rpm.

One week after planting, I inoculated all plants with the rhizobia cultures. Prior to

inoculation, I diluted both cultures to a density of 0.1 OD600. I inoculated each plant with 1 mL

of either Sals or Naut, which was determined based on bin number. The following day, I poured

25 mL of sterile water on the soil of each cone-tainer to help distribute the rhizobia cells

throughout the soil.


Trait measurement

I recorded a total of eight different traits on each plant as measures of plant growth, or

size, or fitness: leaf counts at 2 weeks, leaf counts at 6 weeks, date of first flower, number of

fruits, fruit weight, dry shoot weight, dry root weight and number of primary branches. Fruit

weight is positively correlated with seed number (r = 0.75, p < 0.0001, N = 100; Gorton, unpub.

data). For leaf counts, I counted the number of true leaves present on each plant at two and six

weeks. Early leaf count could be an important trait in the wild where seedlings are in competition

with their neighbours for access to light (Heath & Tiffin, 2007), and I used the second leaf count

as a measure of plant growth. I checked all plants daily for signs of flowering. Once the plants

began to set fruit, I collected mature fruit daily and kept them in separate coin envelopes for each

plant. The fruit fall off the plant easily once they are mature (personal observation), thus

occasionally a few fruit were missed. To estimate this loss, I collected all loose fruits in the

greenhouse (from the floor, drains, greenhouse benches, etc) to determine the percentage of fruits

successfully collected, which corresponded to 95.5% (Total number of fruits collected = 25,001;

total number of fruits lost = 1122).

The plants were harvested in sequential order based on planting date approximately 15

weeks after the first day of planting. Prior to harvesting, all fruits were removed for later

counting. At harvest, I separated above-ground and below-ground biomass. Once plants were

harvested, I weighed all the fruits per plant, and then calculated a new variable, ―average fruit

weight‖. Both the above-ground and below-ground biomass were dried at 55°C for 3 days prior

to weighing them. After harvesting, I counted the number of primary branches on each plant

based on the description in Moreau et al.(2006).


Unfortunately, it was not possible to record any traits for rhizobium fitness, such as

nodule counts, weight or branching, on the majority of the plants. Once I harvested all of the

plants, it was apparent that the roots were very dense and contained numerous nodules, which

would be impossible to process in a timely manner. Before eliminating collection of nodule data

altogether, I counted all nodules on every parental plant that was included in the experiment. I

also randomly selected a subset of 15-20 nodules from each parental plant and placed them in 1.5

mL tubes with Drierite to dry out the nodules for weighing. The analysis of the parental lines

indicated that there was no effect of rhizobium strain on nodule number (p = 0.53) or weight (p =

0.59), nor was there a strain × line interaction for either trait (p = 0.32, p = 0.31), therefore I

abandoned any further collection of rhizobia traits on the remaining plants.

Candidate gene sequencing

The parental lines were initially sequenced from polymerase chain reaction (PCR)

products for five genes involved in the Nod factor signaling pathway: DMI1, DMI3, NIN, NFP

and NORK. A separate set of F6 progeny and parental lines were grown exclusively for DNA

purposes. DNA was extracted from 100 mg of frozen leaf tissue according to DNAeasy Plant

Mini Kits (Qiagen), with one modification to the protocol: the final DNA elution used Tris HCl

in the place of Buffer AE and included a 30-min hot elution at 65°C. The PCR primer sequences

for DMI1 were taken from De Mita et al. (2007), and the remaining primer pairs were designed

from M. truncatula genomic DNA sequences for each gene, which are publicly available from

the National Centre for Biotechnology Information (www.ncbi.nlm.nih.gov). The primers were

designed using Integrated DNA Technologies (IDT) PrimerQuestSM

(www.idtdna.com/Scitools/Applications/Primerquest). Once a primer set was chosen, I used

http://www.ncbi.nlm.nih.gov/

http://www.idtdna.com/Scitools/Applications/Primerquest


NCBI’s BLASTn to ensure they only matched the gene of interest and would not amplify other

parts of the M. truncatula genome (See Appendix, Table A-2 for primer sequences).

The PCR reaction and protocol was optimized for each gene (modified from the

suggested protocol provided by Froggabio) and was performed using Froggabio I-TaqTM

DNA

polymerase and associated ingredients (see Appendix, Table A-3 for PCR mix details). All PCR

products were checked prior to sequencing using 1% agarose gel electrophoresis. I cleaned PCR

products by adding 0.2 µL Exonuclease I (Fermentas) and 2 µL Shrimp Alkaline Phosphatase

(New England BioLabs) to each sample. Samples were incubated at 37°C for 30 min then heated

to 80°C for 15 min. I sequenced the products with BigDye Terminator cycle sequencing kit

(v.3.1, Applied Biosystems) using the forward PCR primers, which were then run on an ABI

3730 DNA Analyzer by the Centre for the Analysis of Genome Evolution and Function

(University of Toronto). All RILs and parental lines were sequenced twice and I aligned the

DNA sequences for each gene using ClustalW in BioEdit (Hall, 1999).

Only DMI1, DMI3 and NFP contained one or more SNPs that differed between the

parents, therefore they were chosen to be fully sequenced in the RILs to develop markers. In

DMI1 (5815 bp), a fifth of the gene was successfully sequenced, and the fragment contained

three polymorphisms: one was a nonsynonymous change, resulting in the substitution of

isoleucine for threonine, and two were in introns. The entire NFP gene (1788 bp) was sequenced

and three polymorphisms were found, two of which were synonymous changes and one was a

nonsynonymous change, resulting in the substitution of tryptophan for serine in the protein

sequence. In DMI3 (6700 bp), a fifth of the gene was successfully sequenced, and this segment


contained five polymorphisms within introns. One SNP per gene was chosen to be scored across

all RILs and used as a molecular marker for QTL mapping.

Statistical analyses

Quantitative genetic analysis

I tested for genetic variation in plant traits in separate analyses for each rhizobium strain

treatment by using a mixed model ANOVA with restricted maximum likelihood (PROC

MIXED, SAS v. 9.2). For each trait as a response variable, I included block and planting set as

fixed effects and line as a random effect. The significance of the line effect was tested by running

the models with and without the line effect; the difference in the -2 log likelihoods of the models

was compared to chi-square distribution with 1 degree of freedom. To formally test for line ×

rhizobium genotype interactions, I also used mixed model ANOVA. For each trait as a response

variable, I included block, planting set, and rhizobium genotype as fixed effects, and line and

line × rhizobium genotype as random effects. As described above, I tested the significance of the

random effects with likelihood ratio tests by comparing the differences in -2 log likelihoods to a

chi-square distribution.

Linkage map construction

The RILs were genotyped at 204 markers, of which the majority are simple sequence

repeats (SSRs) and amplified fragment length polymorphisms (AFLPs). 201 of the marker

genotypes were provided by Marie-Laure Pilet and Alain Baranger from INRA. Some of the

markers have previously been published in Djébali et al. (2009) and Mun et al. (2006), and a

description of the remaining markers is in progress (Avia et al. in prep). A subset of the markers


from Mun et al. (2006) were designed from M. truncatula bacterial artificial chromosomes

(BAC) accessions, anchored to the integrated genetic-physical map

(http://www.medicago.org/genhome/map.php, University of Minnesota 2006). These markers are

indicated in bold on the linkage map. The remaining three markers (DMI1441, NFP1697 and

DMI3427) are single nucleotide polymorphisms which were designed from sequence data from

the symbiotic signaling pathway genes DMI1, NFP, and DMI3 using the method described

above.

I used JoinMap 4.0 (Van Ooijen, 2006) to determine the linkage map of the population

(N= 204 markers). Although I was provided with an ordered linkage map including 201 of the

markers (Avia et al. in prep, see Hamon et al., 2010 for partial map), I reordered all of them for

this experiment because I added the extra markers described above, and I did not have

phenotypic data for one of the RILs included in the original map ordering. I determined the

linkage groups using a LOD score between 4 and 8, and the marker order and distances between

markers were calculated using maximum likelihood mapping. All of the markers localized to the

same linkage groups where they were previously mapped, however in some groups the order of

the markers changed. Furthermore, for some of the linkage groups multiple marker orders were

generated. In these cases, I investigated the raw genotype data for evidence of improbable double

recombinants or transcript reading errors. I excluded a small number of problematic markers

from the analysis if they were located in regions of reasonable marker density (i.e., ~ 8-10 cM

between markers), and if removing them did not create a large gap in the linkage group.

http://www.medicago.org/genhome/map.php


QTL analysis

Although I found no evidence of significant line × rhizobium strain interactions for any

of the phenotypic traits described (see Results below), I still performed QTL mapping separately

for each strain treatment. The explanation for this approach is conditional neutrality: strain

specific QTL may exist, however they may exhibit conditional neutrality (sensu Mackay 2001)

where they affect phenotypes in one strain treatment, but have no effect in the other. If the QTL

which display conditional neutrality differ between the two rhizobium strains, it is possible for

different QTL to affect traits in the different strain treatments, even in the absence of a

significant line × rhizobium strain treatment interaction. To evaluate this possibility, I performed

QTL mapping in each rhizobium strain treatment separately.

For the QTL mapping, I estimated the least-square means of each plant trait using a

mixed model ANOVA (PROC MIXED, SAS v 9.2), which included block, plant set, and line as

independent variables. I did these estimations and all QTL mapping analyses described below

separately for each rhizobium strain treatment. I chose not to use BLUPs (best linear unbiased

predictors) or the random effects solutions of the mixed models described above because BLUPs

can have poor properties when used as data in regression-based analyses (Hadfield et al., 2010).

QTLs were detected using the composite interval mapping procedure (CIM; Zeng, 1993;

Jansen, 1993; Zeng, 1994) in QTL Cartographer v. 2.5 (Wang et al., 2011). The CIM procedure

tests the hypothesis that a QTL is located within an interval defined by two markers, while

accounting for genetic background (i.e., possible linked QTLs) by including additional markers

outside the interval as cofactors. The cofactors used in each CIM were selected using forward-


backward stepwise regression (p = 0.05) under the standard model (Model 6). All QTL analyses

were performed on the calculated least-square means of each RIL and tests were performed at 2

cM intervals, with a window size of 10 cM. For each trait, the genome-wide threshold values

(likelihood ratio test statistic (LR); p = 0.05) for declaring a significant QTL were determined

through 1000 permutations of the data set (Churchill & Doerge, 1994). For significant QTL, I

calculated 2-LOD support intervals as the nearest markers on either side of the QTL peak where

the LR dropped by 9.22 (Van Ooijen, 1992). All significant QTL were drawn on the linkage map

using MapChart (Voorrips, 2002).

To determine if QTLs were differentially expressed depending on the rhizobium

genotype (i.e., QTL × strain treatment or QTL × E), I performed a multi-way ANOVA for each

trait (PROC GLM, SAS v. 9.2). I selected the markers closest to the peak of each significant

QTL in the two strain treatments, and I included all of these markers as well as two-way marker

× rhizobium strain interactions as main effects in the model. A formal statistical test is needed

because it is possible for a QTL to have a significant phenotypic effect in one environment, but

have little or no effect in the other environment and thus remain undetected due to low statistical

power.

I also tested for epistatic interactions between markers (epistatic QTLs) for each trait in

both strain treatments using Epistacy (Holland, 1998). This program tests for the effect of all

possible pairwise combinations of the markers on each trait using SAS (PROC GLM). To

account for the problem of multiple testing and determine which results were truly significant, I

used a p-value threshold of 0.99 in Epistacy and implemented a false discovery approach using

QVALUE (Storey & Tibshirani, 2003) with the false discovery rate set at 0.05. The program


QVALUE assigns a q-value to each p-value, where the q-value indicates the expected percentage

that the result is a false discovery, given that test is interpreted as statistically significant. For

significant epistatic QTL, I also tested for QTL×QTL×E by including the two significant

markers, the two-way marker × marker interaction, and the three-way marker × marker ×

rhizobial strain interaction.

In addition to the strain-specific QTL mapping, I also mapped QTL for each plant trait on

the line least-square means estimated from a mixed model ANOVA that included both strain

treatments (i.e., QTL mapping on phenotypes averaged across both strain treatments). This

model included block, strain, plant set and line as fixed effects, and line × strain, strain × block

and line × strain × block as the random effects. The last two random effects were included

because the rhizobia strain treatments were applied half a block at a time, rather than to

individual plants.

Results

Survival and control plants

In total 2078 plants survived, with a mean and median of five plants per line in each

strain treatment. According to Keurentjes et al. (2007), in Arabidopsis thaliana, QTL detection

levels off at 4 replicates per line with 161 RILs. Therefore despite mortality, there was still

sufficient replication to perform QTL mapping in this experiment. Out of the 50 uninoculated

control plants, 30 survived and only one plant nodulated (indicative of contamination). The

remaining plants were small, pale green, did not flower or set seed, and had no nodules,

indicating that greenhouse level contamination was low.


Quantitative genetics

I detected significant genetic variation in all phenotypic traits in both rhizobium strain

treatments, with the exception of leaf number at two weeks in the Naut treatment (Table 1). I did

not find any significant line × rhizobium strain interaction (p = 0.5 for all traits), and a

comparison of the RIL trait least-square means when grown with Naut and Sals (Figure 2)

indicated that their distributions were very similar. All of the phenotypic traits displayed

evidence of transgressive segregation; i.e., the range of trait values of the RILs exceeded that of

the parental lines (Figure 3).

Broad sense heritability was in general always higher when estimated from RILs grown

in the Sals treatment (mean H2

= 0.29, range = 0.08-0.59), in comparison to RILs grown in the

Naut treatment (mean H2

= 0.19, range = 0.01-0.33) (Table 1). However, for the majority of the

phenotypic traits measured, the coefficient of genetic variation did not differ substantially

between the two rhizobium treatments (Table 1), suggesting that the differences in heritability

were due to differences in phenotypic variation or environmental effects. For example, the broad

sense heritability of average fruit weight was 0.14 higher in Sals, however the CVg is barely

larger; this can be attributable to the lower phenotypic variance in the Sals treatment. One

exception was shoot weight: both heritability and the coefficient of genetic variation were

substantially higher in the Sals treatment (Table 1).

QTL mapping

The final linkage map used for the QTL mapping analyses consisted of 184 markers

spanning over eight linkage groups and covering 1,215 cM (average of 6.6 cM/marker; for


Figure 3. Back-to-back histograms of trait least-square means of the M. truncatula RILs grown with rhizobium strains Naut (grey bars)

and Sals (white bars). Parental trait means are indicated by arrows (P1= female parent, line 530, P2= male parent, line 735).


Table 1. Mixed model ANOVA results partitioning variation among RILs into genetic (Vg) and phenotypic (Vp) variance. Broad sense

heritability (H2) and the coefficient of genetic variation (CVg) are also included.

Vg Vp H2 CVg Line significance

Trait Naut Sals Naut Sals Naut Sals Naut Sals Naut Sals

Leaf number – 2 weeks 0.006 0.05 0.66 0.68 0.01 0.08 4.36 13.10 0.75 <0.001

Leaf number – 6 weeks 8.21 10.52 51.31 55.43 0.10 0.19 15.82 17.84 <0.001 <0.001

Days to flowering 14.23 23.52 91.44 118.42 0.15 0.20 9.69 12.15 <0.001 <0.001

Fruit number 8.37 12.04 31.63 41.38 0.26 0.29 22.36 26.76 <0.001 <0.001

Average fruit weight 0.0003 0.0003 0.001 0.0008 0.33 0.47 19.01 19.25 <0.001 <0.001

Shoot weight 0.041 0.10 0.13 0.17 0.31 0.59 39.44 58.39 <0.001 <0.001

Root weight 0.014 0.02 0.08 0.06 0.17 0.30 22.23 25.78 <0.001 <0.001

Primary branch number 0.081 0.09 0.44 0.43 0.18 0.21 20.77 23.11 <0.001 <0.001


complete marker orders with genetic distances see Appendix, Figure A-2). All the significant

QTLs detected in the Sals and Naut rhizobium treatments are listed in Table 2 and 3. Due to the

lack of among-line variance for leaf number at two weeks (Table 1), I did not perform QTL

mapping on this trait. QTLs which had overlapping 2-LOD support intervals were considered to

be the same QTL, as indicated by the QTL number column in Table 2 and 3. In total, I identified

8 separate QTLs in the Naut rhizobium treatment, explaining between 3.5 and 10% of the total

genetic variation, depending on the trait examined (Table 2, Figure 4A). Three of these QTLs

affected multiple phenotypic traits: NQTL1-2 had a significant effect on leaf number, number of

fruits and shoot weight; NQTL3-2 had a significant effect on leaf number and number of fruits;

and NQTL8 had a significant effect on leaf number and average fruit weight. The direction of

NQTL3-2 was opposite for number of fruits and leaf number: the 530 allele increased the former

but decreased the latter (Table 2). All the other QTLs which affected multiple traits in Naut had

effects in the same direction. No QTLs were identified for days to flowering or root weight in

this treatment.

In the Sals rhizobium treatment, fewer QTLs were identified, with only 4 unique QTLs,

two of which affected multiple phenotypic traits: SQTL1-1 influenced leaf number, number of

fruit and root weight, and SQTL1-2 influenced number of fruit and shoot weight (Table 3, Figure

4B). These QTLs were of small effect, explaining between 6.1 and 12.7% of the total genetic

variation. In this treatment, no QTL were detected for days to flowering or primary branch

number.


Table 2. QTL identified for plant traits collected on M. truncatula grown with the Naut rhizobium strain. The significant QTL for each

trait are listed along with the chromosome, position, marker directly below the QTL peak, LOD score, 2-LOD support interval,

percent variance explained by each QTL (R2), and additive effect (a0, positive values indicate that 530 alleles increase trait means).

QTL with overlapping intervals share the same QTL number and indicate those which affected multiple traits.

QTL

Number

Chr

Position

(cM)

Marker

LOD

2-LOD

Interval (cM)

R2

a0 Trait

Leaf number - 6 weeks NQTL1-1 1 30.4 MTIC448 3.1 19.9- 43.8 5.8 1.114

NQTL1-2 1 113.7 MTIC064 4.9 91.5-119.7 10 1.375

NQTL3-2 3 151.8 MTB122 3.4 141.4-159.8 8.4 -1.362

NQTL8 8 52 DMI3427 3.7 39.9-74.9 7.4 1.197

Number of fruits NQTL1-2 1 94.3 EM4.333 3.3 75.3-103.6 7.2 1.052

NQTL3-2 3 158.3 MTIC044 4.1 141.8-159.8 8.8 1.268

Average fruit weight NQTL3-1 3 130.2 MTB6 5 124.6-134.8 8.9 -0.007

NQTL5 5 28.1 MTIC148 4.3 12-43.4 7.7 -0.007

NQTL8 8 84.9 MTB333 2.8 74.5-109.9 6.6 0.006

Shoot weight NQTL1-2 1 100.2 MTIC146 2.9 87.5-111.7 6.2 0.065

NQTL4-1 4 42.6 GO3.350 2.9 30.6-54.1 6.3 -0.065

Primary branch number NQTL4-2 4 122.3 MTIC186 4.8 112.1-133.4 3.5 0.048

Note: QTL were named as follows: rhizobium strain prefix (N = Naut), and the chromosome number as a suffix, with

an additional number depending if there were multiple QTL per chromosome.


Table 3. QTL identified for plant traits collected on M. truncatula grown with the Sals rhizobium strain. The significant QTLs for each

trait are listed along with the chromosome, position, marker directly below the QTL peak, LOD score, 2-LOD support interval,

percent variance explained by each QTL (R2), and additive effect (a0, positive values indicate that 530 alleles increase trait means).

QTL with overlapping intervals share the same QTL number and indicate those which affected multiple traits.

QTL

Number

Chr

Position

(cM)

Marker

LOD

2-LOD

Interval(cM)

R2

a0 Trait

Leaf number - 6 weeks SQTL1-1 1 30.4 MTIC448 2.9 17-43.6 6.1 1.302

Number of fruits SQTL1-1 1 26.3 MTB269 5.7 17.9-32.4 12.7 1.786

SQTL1-2 1 110.1 MTIC285 5 103.6-117.7 10.2 1.566

SQTL3 3 155.9 MTIC371 3.4 141.8-159.8 6.7 1.353

Average fruit weight SQTL5 5 12.2 MTIC078 4 2-28.1 7 -0.01

Shoot weight SQTL1-2 1 111.7 MTIC064 4.8 103.6-119.7 10.9 0.147

Root weight SQTL1-1 1 24.3 MTB269 3.1 13-34.4 6.9 0.049

Note: QTL were named as follows: rhizobium strain prefix (S = Sals), and the chromosome number as a suffix,

with an additional number depending if there were multiple QTL per chromosome.


A)


B)


truncatula when grown with rhizobium strains A) Naut and B) Sals. Chromosome number is

listed across the top of the linkage groups. The scale on the left indicates the genetic distance

between markers in centimorgans (cM). Each horizontal line represents the position of one

genetic marker. Phenotypic trait names are listed beside the estimated QTL positions, and the

length of the QTL represents the 2-LOD support interval. The vertical line in each QTL indicates

where the highest peak was located.


QTL by rhizobium strain interactions

A superficial comparison of the QTLs found in the two rhizobium strain treatments

indicated that four QTLs influenced the same traits in both treatments: NQTL1-1 and SQTL1-1

for leaf number, NQTL3-2 and SQTL3 for number of fruits, NQTL5 and SQTL5 for average

fruit weight, and NQTL1-2 and SQTL1-2 for shoot weight (Table 2, 3). However some of these

common QTLs differed in the number of phenotypic traits they affected: for example, NQTL1-1

only affected leaf number in Naut, but the same QTL influenced leaf number, fruit number, and

root weight in Sals. Furthermore, QTL for basal branch number were found only in Naut, and

QTL for root weight were found only in Sals. Despite these apparent differences in QTL between

the two strain treatments, no significant QTL×E (or QTL × rhizobium strain) interactions were

detected for any of the measured phenotypic traits (p = 0.23-0.97)

Epistatic interactions

In the genome-wide scan of epistatic interactions between markers, I detected one

significant epistatic QTL in Naut which influenced root weight. It was found on chromosome 6

between markers EM17.407 and EM2.265, and had a partial-r2 of 13% (Figure 6). These markers

are located 8.9 cM apart (see Appendix, Figure A-2 for genetic distances).The percentage of

each allelic class were as follows (where P1 = parental allele 530 and P2 = parental allele 735):

EM17.407-P1 × EM2.265-P1 = 36%, EM17.407-P1 × EM2.265-P2 = 6%, EM17.407-P2 ×

EM2.265-P2 = 53%, EM17.407-P2 × EM2.265-P1 = 5%. Neither marker was found within the

support intervals of the detected additive QTL, nor was there evidence of QTL×QTL×E (p =

0.38).


QTL mapping across strain treatments

In the QTL analyses performed using data averaged across the strain treatments, I found

10 main-effect QTLs, 4 of which affected multiple traits (Table 4, Figure 5). These QTLs

explained between 5 and 13.5% of the total genetic variation. No QTL were detected for days to

flowering. Many of the QTLs detected in this analysis were previously found in Naut and Sals

QTL mapping, however several new QTLs were found: QTL4-1 and QTL7 significantly affected

number of fruits, QTL3-1 affected average fruit weight, QTL5-2 affected plant weight and QTL6

influenced basal branch number.

Mapping of symbiotic signaling genes

The markers DMI1441, NFP1697 and DMI3427 corresponding to the symbiotic pathway

signaling genes DMI1, NFP and DMI3, respectively, all localized to where they are found on the

M. truncatula genome (http://www.medicagohapmap.org). DMI1 mapped at 17.6 cM on

chromosome 2, NFP mapped at 20.5 cM on chromosome 5, and DMI3 mapped at 46 cM on

chromosome 8 (See Appendix, Figure A-2 for positions).

A few of the QTLs colocalized with the symbiotic signaling genes DMI3 and NFP. In

Naut, DMI3427 was located directly below the highest point of one the QTL which affected leaf

number (NQTL8, Table 1) and NFP1660 was located within the interval of a QTL which

influenced average fruit weight (NQTL5, Table 1). The latter QTL was also detected in Sals (for

average fruit weight) and in the across strain analysis (average fruit weight and number of fruit).

The marker DMI1441 was not found to be significantly associated with any QTL.

http://www.medicagohapmap.org/


Table 4. QTL identified for plant traits collected on M. truncatula average over both rhizobium strains. The significant QTLs for each trait

are listed along with the chromosome, position, marker directly below the QTL peak, LOD score, 2-LOD support interval, percent

variance explained by each QTL (R2), and additive effect (a0, positive values indicate that 530 alleles increase trait means). QTL with

overlapping intervals share the same QTL number and indicate those which affected multiple traits.

Trait

QTL

number

Chr

Position

(cM)

Marker

2-LOD

Interval (cM)

R2

a0

Leaf number - 6 weeks QTL1-1 1 30.4 MTIC448 24.3-41.6 7.3 1.146

QTL1-2 1 110.1 MTIC285 100.2-119.7 7.9 1.123

Number of fruits QTL1-1 1 28.3 MTB269 17.9-34.4 10.7 1.268

QTL1-2 1 110.1 MTIC285 103.6-119.7 7.8 1.044

QTL3-2 3 156.9 MTIC237 143.8-159.7 6.7 1.043

QTL4-1 4 10.7 MTIC033 0-18.7 5.2 -0.86

QTL5-1 5 28.1 MTIC148 20.5-43.4 5.1 0.849

QTL7 7 14.9 MTIC147 0-70.3 6.3 0.945

Average fruit weight QTL3-1 3 41.3 MTIC124 29.5-52.2 5.3 -0.005

QTL3-2 3 158.3 MTIC044 147.8-159.7 5 -0.006

QTL5-1 5 26.5 NFP1660 12-36.8 8.7 -0.007

Shoot weight QTL1-2 1 100.2 MTIC146 98.3-103.6 11.9 0.135

QTL5-2 5 107.8 MTB310 93.4-129.8 7.7 -0.86

Root weight QTL1-1 1 21.9 MTB46 11.5-30.3 8.4 0.047

Primary branch number QTL4-2 4 124.3 MTIC186 110.1-139.4 13.5 0.134

QTL6 6 58.5 G06.1400 29.1-70.4 5.2 0.085

Note: QTL were named as follows: chromosome number as a suffix, with an additional number depending if there were

multiple QTL per chromosome.



truncatula averaged across rhizobium strains. Chromosome number is listed across the top of the

linkage groups. The scale on the left indicates the genetic distance between markers in

centimorgans (cM). Each horizontal line represents the position of one genetic marker.

Phenotypic trait names are listed beside the estimated QTL positions, and the length of the QTL

represents the 2-LOD support interval. The vertical line in each QTL indicates where the highest

peak was located.


Figure 6. Epistatic QTL detected between markers EM2265 and EM17407 in Naut. The least-

square means of each genotype combination are shown with + SE.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

530 735

Root

wei

ght

(g)

Marker allele at EM17407

EM2265-530

EM2265-735


Branching gene orthologs in M. truncatula

The genes involved in branching pattern have been well characterized in Arabidopsis

thaliana (Ehrenreich et al., 2007), and numerous candidate genes exist. I used BLASTn to

identify orthologs of 36 candidate genes from Arabidopsis that showed >200 bp sequence

identity in the M. truncatula genome. Significant sequence matches were further investigated

using the Medicago truncatula genome BLAST

(http://www.medicago.org/genome/cvit_blast.php ) to determine their location and if there were

any candidate genes in the region. Out of the 36 genes investigated (see Ehrenreich et al.,2007

for descriptions), three of the genes mapped to either chromosome 4 or 6 on the M. truncatula

genome, i.e., on the same chromosomes as the primary branch number QTLs. The genes MAX2

and PID mapped to two separate regions on the lower portion of chromosome 4, and were

located farther down the chromosome compared to the physically linked marker, MTIC089. Both

genes have orthologs which have been previously annotated: for MAX2 a cyclin-like Fbox is

located in the matching M. truncatula sequence and for PID there is a tyrosine protein kinase.

The gene PIN1 matched to sequence data on chromosome 6, and was also located farther down

the chromosome compared to the physically linked marker, MTIC153. PIN1 also had an

annotated ortholog, another auxin efflux carrier protein (Appendix, Table A-6)

Discussion

Genotype by genotype interactions between legumes and rhizobia have been previously

found to influence the benefits each partner receives from the symbiosis (Mhadhbi et al., 2005;

Laguerre et al., 2007; Heath & Tiffin, 2007; Rangin et al., 2008; Heath et al., 2010; Heath,

2010), and have been proposed to maintain genetic variation in mutualist quality (Bever, 1999;

http://www.medicago.org/genome/cvit_blast.php


Molofsky et al., 2001; Heath & Tiffin, 2007; Heath, 2010). To date, no one has attempted to

locate regions of the genome that influence G × G interactions. Here, I performed QTL mapping

in two genotypically distinct rhizobium treatments to map additive or epistatic QTL for plant

fitness traits and determine if they were differentially expressed depending on the rhizobium

genotype. Three major results emerged from the experiment: 1) I detected no evidence for G × G

interactions at the line × rhizobium strain level or at the QTL × rhizobium strain level 2) I

detected numerous QTL that appeared to affect multiple phenotypic traits, suggestive of

pleiotropy or tight linkage, and 3) QTL for plant fitness and growth traits co-localized with

previously described signaling genes. The implications of the results are discussed below.

No evidence for a genetic basis of G × G interactions

Despite preliminary data that suggested otherwise, I found no evidence for G × G

interactions for plant fitness traits in this population. It is possible to detect QTL × rhizobium

genotype interactions without a significant G × G interaction if multiple different QTLs exhibit

conditional neutrality. However no significant QTL × rhizobium strain interaction or RIL ×

rhizobium strain interaction were found in this experiment. One implication of this experiment is

that it illustrates the difficulties associated with trying to locate the genetic basis of context-

dependent traits. Both Naut and Sals have previously displayed varying partner quality when

grown with different plant genotypes (Heath, 2010) and a preliminary experiment found

evidence of G × G interactions when they were grown with the parental lines of the LR03 RIL

population (Appendix, Figure A-1). Nevertheless, in this experiment, there was no interaction

between plant line and rhizobium strain in the parents or in the RILs.


There are two potential hypotheses to explain the lack of G × G interaction detected in

this experiment: 1) The parental lines do not exhibit any form of G × G interactions, thus

accordingly neither do the RILs or 2) G × G interactions are small enough to only be detected

with multiple plant × rhizobium genotype combinations. Previous studies which have found

evidence of G × G interactions in the legume-rhizobia symbiosis investigated substantially more

unique genotype × genotype combinations in comparison to the number used in this experiment

(e.g., Heath & Tiffin, 2007; Laguerre et al., 2007; Rangin et al., 2008; Heath, 2010; Heath et al.,

2010) . Although each RIL differs from the others, they are essentially just an assortment of the

two parental genomes (Broman, 2005). Therefore, from a simplified view, my experiment was a

2 × 2 factorial design, represented by the two parental lines and the rhizobium genotypes Naut

and Sals. When RILs are used to detect G × G interactions, only large effect loci underlying such

interactions will be found because there are only a few genotype - genotype combinations. In

comparison, in experiments with multiple plant families and rhizobium genotypes, there are

considerably more genotypic combinations, and there is likely more statistical power to detect G

× G interactions. However, if these population-wide G × G interactions are due to multiple small

effect loci which are distributed throughout the plant and rhizobium genomes, the probability of

detecting such G × G interactions in traditional QTL mapping experiments using RILs is very

low.

An alternative approach to isolating the genomic regions influencing G × G interactions

would be to perform nested association mapping (NAM) (Yu et al., 2008). NAM combines the

advantages of using experimental crosses in traditional QTL mapping with the high resolution of

association mapping to resolve quantitative traits to their causal loci. In maize, 5000 RILs have


been created from separate crosses of a reference line with 25 genetically different inbred lines.

Each individual RIL is a mosaic of chromosome segments derived from either the reference line

or one of the founding inbred lines. In comparison to the design used in the present study, NAM

would allow significantly more legume × rhizobium genotypes to be tested and provide greater

statistical power to detect potential small effect loci influencing G × G interactions. The time and

cost of such a mapping design is considerable, however the potential outcomes would be

invaluable to the study of G × G interactions in the legume-rhizobium symbiosis.

QTL architecture of phenotypic traits

I detected numerous QTL in the two rhizobium strain treatments (Table 2 and 3). These

QTL were of small to moderate effect, explaining between 3.5 and 12.7% of the total genetic

variation, with additive effect sizes of 0.007-1.78. Despite significant genetic variation in

flowering time, no QTL were detected for this phenotypic trait, suggesting that the observed

genetic variation in days to flowering is due to many QTL of small effect rather than a few major

QTL. These small QTLs were probably undetectable with the mapping population size used in

this experiment (Lynch & Walsh, 1998).

All of the phenotypic traits displayed evidence of transgressive segregation (Figure 3),

suggesting that these traits are controlled by many genes of small effect. The most likely

explanation for the observed trangressive segregation is antagonistic QTLs, i.e., QTLs with

effects that are in the opposite direction to the parental differences for that trait (Rieseberg et al.,

2003). Antagonistic QTLs are commonly found in studies where transgressive phenotypes are

detected, and are more frequent in studies using plants, intraspecific crosses or investigating


morphological traits (Rieseberg et al., 1999; Rieseberg et al., 2003). Between the two strain

treatments, most of the measured traits had at least one antagonistic QTL (Figure 3, Table 2, 3),

with the exception of number of fruits, root weight and primary branch number. It is probable

that some QTL of small effect were not detected due to low power, which may explain the

missing antagonistic QTLs for those traits.

In all QTL analyses, there were QTL which influenced multiple traits: leaf number, fruit

number, average fruit weight, shoot weight and root weight all colocalized with at least one other

listed trait (Table 2, 3, 4; Figure 4 and 5). These regions could be indicative of pleiotropic genes

acting on multiple traits or numerous genes in tight linkage. For example, in Naut, plant weight,

fruit number and leaf number all colocalized to the end of chromosome 1. The QTL effects of

NQTL1-2 match the broadscale patterns evident in the line means: plant weight was positively

correlated with fruit number and fruit number is positively correlated with leaf number (see

Appendix, Table A-4 for correlation matrix), i.e., larger plants make more fruit. These regions

may indicate locations for QTL controlling a trait common to those phenotypes (e.g., growth rate

or plant size or plant fitness).

There were a few QTL which influenced multiple traits, however in opposite directions in

the two parental lines. NQTL3-2 in Naut increased number of fruit and decreased leaf number

(Table 2), which was surprising as these two traits are positively correlated, as mentioned above.

One possible explanation is that this region may contain multiple tightly linked QTL, which have

opposite effects on number of fruit and leaf number. Fine-mapping of QTLs often show that

single QTLs will break into multiple closely linked QTLs, which in turn frequently act in

opposite directions (e.g., Steinmetz et al., 2002; Kroymann & Mitchell-Olds, 2005). An


alternative explanation is that a single locus is influencing both fruit number and leaf number,

but in opposite directions, creating a genetic trade-off (i.e., antagonistic pleiotropy). Either tight

linkage between multiple QTLs or pleiotropy may account for the opposite fitness effects of

NQTL3-2, however additional generations of recombination or fine-mapping would be required

to distinguish between the two mechanisms.

Two additional QTLs had opposite effects in the across strain analysis: QTL3-2 and

QTL5-1 increased number of fruit but decreased average fruit weight (Table 3), which is

expected given their negative correlation (Appendix, Table A-5). The RILs appear to be adopting

two different life history strategies for producing fruit: either making fewer, larger fruit (quality),

or many, smaller fruit (quantity). It is possible that these differences in resource allocation

translate into variation in above or below ground biomass. For example, lines which produce the

small, lighter fruit may in turn have more resources to invest in vegetative growth, which could

be important for plants in natural habitats where competition with neighbours for access to light

is essential.

Epistatic interactions

To fully understand the genetic architecture of any trait, it is necessary to understand

epistasis, which is defined as the nonadditive interactions between alleles of different genes (see

Phillips (2008) for a review). Epistatic interactions influence a myriad of areas in evolutionary

genetics including the maintenance of genetic diversity, the evolution of sexual reproduction,

speciation, developmental canalization, and inbreeding depression (Fenster et al., 1997; Wolf et


al., 2000; Phillips, 2008). Given its importance to these evolutionary processes, accurate and

powerful tests for epistasis are essential.

In QTL mapping, ignoring epistatic interactions can lead to underestimating genetic

variance and overestimating individual QTL effects (Carlborg & Haley, 2004). Due to their

importance, QTL studies are increasingly investigating the presence of epistatic QTLs, i.e.,

marker by marker interactions which have significant effects on phenotypic traits (e.g., Leips &

Mackay, 2000; Weinig, et al., 2003; Malmberg et al., 2005; Brock et al., 2009). Epistatic QTLs

can be detected in one of two ways: either searching for epistatic interactions between pre-

existing QTLs which already have a significant main effect or performing all possible two-way

interactions between markers. The problem with the first approach is that it can miss epistatic

interactions where only one of the loci is involved in a main-effect QTL or additional epistatic

QTL between markers which have no main effect alone (Holland, 1998; Malmberg et al., 2005).

In this experiment, I tested all pairwise combinations of markers and found only one

epistatic QTL for root weight in Naut between markers EM2265 and EM17407 on chromosome

6 (Figure 6). In general, the low frequency of epistatic QTLs indicates that epistasis is relatively

rare in this mapping population, at least when plants are grown under greenhouse conditions.

There was no main-effect QTL detected in this region because the average effect of the EM2265

alleles over EM17407 was the same. However, the epistatic QTL explained a moderate amount

of the variation (13 %, in a model of root weight = EM2265 + EM17407 + EM2265 × EM17407

+ ε), therefore it is evident that variation at this genomic region has a significant effect on root

weight. Alternatively, it is possible that the effect is overestimated due to the small number of

lines in the recombinant allele class – i.e., the Beavis effect (Beavis, 1998). Nevertheless, the


QTL displayed an interesting crossing reaction norm between the two markers: RILs which have

alleles from the same parents for both markers have much lower root weight than those which

have alleles from alternate parents. Although this epistatic QTL displayed no evidence for QTL

× QTL × E, it would be interesting to see if the effect of the interaction changed with alternate

rhizobium genotypes, since root weight is likely to be correlated with rhizobia fitness traits such

as nodule number.

To account for the problem of multiple testing in Epistacy, Holland (1998) recommended

dividing the desired p-value by g(g-1)/2, where g is the number of linkage groups. Although this

approach is better than the naïve application of α = 0.05, it has two shortcomings. First, Epistacy

tests for epistatic interactions between all markers –i.e., within and between linkage groups, not

just between linkage groups–and second it does not account for the number of markers in the

analysis, which directly influences the number of statistical tests performed. In contrast, I suggest

that a better approach is to test all possible marker by marker interactions (number of tests = (n ×

n -1)/2, where n = number of markers) and then implement false discovery rate and q-value

testing to control for Type I errors in multiple testing (Benjamini & Hochberg, 1995; Storey,

2002).

As an example, using the correction suggested by Holland (1998), Epistacy detected 26

significant epistatic QTLs for leaf number in the Naut treatment. Analyzing the same data using

FDR, it seems likely that there are no significant epistatic QTLs – even interactions with p =

0.0002 had q-values of 0.75, suggesting a high chance of false discovery. Furthermore, plotting a

histogram of the p-values from the epistasis tests for leaf number suggests the null hypothesis:

there was a uniformly even distribution of p-values between zero and one, indicative of no


statistically significant interactions. Therefore, in future studies, I recommend implementing

FDR for detecting epistatic QTLs in Epistacy.

Colocalization of symbiotic signaling genes

Two of the symbiotic signaling genes, NFP and DMI3, colocalized with QTLs (Figure 4

and 5; see Appendix, Figure A-2 for positions). NFP was found in the interval of a QTL which

influenced average fruit weight on chromosome 5 in all treatments (NQTL, SQTL5, QTL5-1;

Table 2, 3, and 4). The effect sizes of these QTLs were small, however the QTL was consistently

detected across all analyses, suggesting that it indeed affects fitness. Similarly, DMI3 colocalized

with a QTL affecting leaf number in Naut only (NTQL8, Table 2); despite its small additive

effect, DMI3427 was the marker directly underlying the QTL peak, suggesting that DMI3 may

contribute to leaf number variation.

The colocalization of NFP and DMI3 with average fruit weight and leaf number indicates

that variation in nodulation signaling genes can potentially influence plant performance and

fitness traits. Legumes require rhizobia to survive and reproduce, and the formation of root

nodules is an essential component to the establishment of the symbiosis. NFP and DMI3 are both

among the genes which are required for the initiation of the nodulation pathway, and lab induced

mutations in either of these genes prevents the formation of nodules altogether (Catoira et al.,

2000; Ben Amor et al., 2003). Furthermore, NFP and DMI3 have both been found to be under

strong purifying selection in M. truncatula (De Mita et al., 2007). It has been hypothesized that

purifying selection is acting on these genes to maintain high specificity of recognition and

remove mutations which may be deleterious to the signaling molecules involved in establishment


of the symbiosis, and indeed this has been found in some populations of M. truncatula (De Mita

et al., 2006; De Mita et al., 2007). Given this information, it seems plausible that mutations in

Nod factor signaling genes will in turn affect plant fitness.

The colocalization of NFP and DMI3 with average fruit weight and leaf number QTLs

strongly supports this hypothesis: variation in signaling genes can affect plant traits. In addition,

these QTLs suggest that one of the parental lines is better able to attract and incorporate rhizobia

partners in root nodules, which in turn translates to obtaining higher fitness. The potential

mechanism leading to a better plant signaler is unknown, but the presence of the QTLs in these

regions suggests that variation in Nod factor signaling efficiency can exist between genetically

different individuals. Nevertheless, these results should be interpreted with caution without

further investigation of the loci underlying the QTL intervals. Colocalization of the QTLs with

NFP and DMI3 does not imply causality: QTL intervals often contain multiple genes and

millions of basepairs. NFP and DMI3 may simply be in tight linkage with the loci influencing

the plant fitness traits, therefore they both colocalized to the same QTL interval.

Evidence for branching pattern orthologs

The largest QTL was detected for primary branch number: in the across strain analysis,

QTL4-2 explained 13.5% of the total genetic variation (Table 4). This QTL was also detected in

the Naut treatment (NQTL4-2, Table 2), and an additional QTL was identified on chromosome 6

in the across strain analysis (QTL6). I found that primary branch number was positively

correlated with flowering date across all analyses (Appendix, Table A-4, A-5), confirming what

has been previously found in natural accessions (Julier et al., 2007).


The sequence comparison with Arabidopsis thaliana yielded three potential gene

orthologs of the branching pattern genes MAX2, PID and PIN1 in M. truncatula (Appendix,

Table A-5). MAX2, an Fbox protein, and PID, a serine/threonine kinase both matched to

segments of sequence data located on the lower end of chromosome 4, where the largest effect

QTL was detected for primary branch number. Located in these regions were two potential

orthologs: a cyclin-like Fbox and a tyrosine protein kinase. The other gene PIN1, an auxin efflux

carrier protein, mapped to sequence segments on chromosome 6, in the area of the other primary

branch QTL. In this region of the M. truncatula genome, there was an auxin efflux carrier

protein, an exact match for the gene function of PIN1.

It is difficult to determine the physical proximity of these orthologous genes to the branch

number QTLs as there is no concrete conversion of cM to kb in M. truncatula. Furthermore, one

potential problem is that the functions of these genes were likely annotated based on sequence

homology with Arabidopsis thaliana. Nevertheless, they are promising candidate genes for

branch morphology in M. truncatula, and future studies which anchor these genes into linkage

maps would help determine if they influence variation in branch morphology.

Conclusion

Mutualisms are predicted to have low genetic variation, yet empirical studies consistently

indicate that substantial genetic variation in partner quality exists. One potential mechanism

which may be maintaining this variation is genotype by genotype interactions between the host

and symbiont. I found no evidence for G × G interactions at the line or QTL level in this

population, but future studies should focus on using experimental designs which allow a greater


variety of genotype × genotype interactions to be investigated. In addition, I found a number of

QTLs influencing multiple traits, including some which colocalized with plant nodulation

signaling genes, suggesting these genes may potentially affect plant performance and fitness.

Chapter 3: Conclusions and future directions 57

Chapter 3:

Conclusions and Future Directions

The goal my thesis was to locate regions of the genome affecting the outcome of

interactions between legumes and rhizobia and to identify loci influencing variation in

phenotypic plant traits. Contrary to the predictions of numerous theoretical models (Trivers,

1971; May, 1976; Bull & Rice, 1991; West et al., 2002), variation in partner quality of both the

hosts and symbionts continues to persist in mutualisms (e.g., Bever et al., 1996; van der Heijden

et al., 1998; Mhadhbi et al., 2005). One potential explanation for the maintenance of this genetic

diversity is G × G interactions (Wade, 2007), where an individual’s optimal mutualist is

dependent on the genome of its interacting partner. Previous work on the legume-rhizobium

mutualism found that G × G interactions can affect the fitness benefits both partners receive from

the symbiosis (e.g., Heath & Tiffin, 2007; Rangin et al., 2008; Heath, 2010). However the

specific loci or genes underlying these interactions have yet to be identified. I conducted a QTL

mapping experiment with two different rhizobium strains to locate potential regions of the

genome influencing G × G interactions between M. truncatula and S. meliloti.

There were three major findings of my thesis. First, there were no G × G interactions

between the M. trunctula LR03 RIL population and the rhizobium strains Naut and Sals, nor

were there different QTL affecting plant traits in the two strain treatments, at least under the

experimental conditions I used. Second, there were numerous small to moderate affect QTLs

spread across the genome which affected multiple plant phenotypic traits, indicative of either

pleiotropy or tight linkage between genes. Lastly, two of 13 QTLs detected for plant fitness

components colocalized with genes involved in the nodulation pathway, suggesting that variation


in these genes can potentially affect plant performance and fitness traits. These findings have

important implications for the evolutionary interactions between legumes and rhizobia, and the

genetic architecture of Medicago truncatula.

Implications for G × G interactions

Genotype by genotype interactions between legumes and rhizobia are known to influence

the outcome of the mutualism (Mytton et al., 1977; Parker, 1995; Heath & Tiffin, 2007; Rangin

et al., 2008; Heath, 2010), therefore there must be genomic regions underlying these interactions.

In contrast, the results of my thesis suggest that there were no G × G interactions between the

RIL population and the strains Naut and Sals, and consequently no genomic regions influencing

the direction of partner genotype interactions were located. The generality of these conclusions

for future work on G × G interactions is potentially limited, as I only investigated one plant

population and two rhizobium strains. However, it does indicate that the presence of G × G

interactions is highly context-dependent since G × G interactions were previously found in Heath

(2010) using the same rhizobium strains (albeit with different plant lines) and preliminary data

suggested the parental lines of the RIL population I used displayed G × G for plant performance

traits (Appendix, Figure A-1).

One aspect of the experimental design was not discussed in Chapter 2: the parental lines

and rhizobium strains were from different locations, i.e., they were foreign partners. I omitted the

discussion of this topic from Chapter 2 as the effect of foreign vs native rhizobia on plant fitness

are mixed (e.g., Parker, 1995; Wilkinson & Parker, 1996; Burdon et al., 1999; Thrall et al.,

2000), and no firm conclusion can be drawn. Nevertheless, it has interesting implications which

are worth briefly addressing.


The rhizobium strains Naut and Sals were isolated from soil in south of France,

approximately 300 and 700 km away from the collection sites of the French and Algeria parents,

respectively (Appendix, Table A-1). Since they are geographically mismatched, there is no

known history of interaction between the partner genotypes. However, using foreign partners

instead of native partners could potentially affect the estimation of G × G interactions. If G × G

interactions are maintaining genetic variation in partner quality through frequency dependent

selection, plant genotypes with the highest fitness should change depending on the rhizobium

strain, resulting in negative correlations for plant fitness traits across rhizobium strains (Bever,

1999; Molofsky et al., 2001; Heath & Tiffin, 2007). In my thesis, fitness was positively

correlated across strain treatments, indicating no evidence of a genetic trade-off between

rhizobium genotypes. However, it is possible that there are a few loci with antagonistic effects in

Naut and Sals whose effects were masked by using a novel environment: some plant and

rhizobium genotypes may have initially high fitness in new environments due to chance alone,

which can lead to systematic bias towards positive correlation among life history traits and

fitness components (Service & Rose, 1985; Fry, 1993). The net effect of this might potentially

underestimate genetic trade-offs or small effect G × G interactions. Alternatively, foreign

individuals may bring new genetic variation to the population, thereby increasing the chance of

detecting G × G interactions. In future studies, to remove any effects of using novel genotype

combinations, it would be useful to grow the plant lines and rhizobium strains of interest together

for few generations in the greenhouse prior to beginning any experiments. Alternatively, this

issue could be avoided altogether by obtaining plant lines and rhizobium strains from the same

location.


One limitation of my thesis is that I was unable to record any rhizobium fitness traits.

Although G × G interactions did not influence any of the plant fitness traits in this population,

the same conclusion cannot be drawn concerning rhizobium fitness traits, such as nodule

number. As described in Chapter 2, a preliminary analysis of nodule traits on the parental plants

in the experiment yielded no support for plant line × rhizobium genotype interactions. Based on

this data, I abandoned any further collection of rhizobia traits and focused on recording other

phenotypic traits and developing molecular markers that seemed more promising. However,

given the results I obtained for the plant traits, and the results of previous studies, there are

several hypotheses and implications for rhizobium fitness traits.

Upon completion of the experiment, it was apparent that the RILs displayed transgressive

segregation for all traits, i.e., more extreme phenotypes than either of the parents (Figure 2).

Furthermore, even in cases where parental line means were close in value, at least one QTL was

detected for each trait, indicating that the parents displayed allelic variation in those traits. It is

entirely possible that similar distributions might have been found for rhizobium fitness traits

such as nodule number and weight, and in turn, significant QTL may have been detected. In

addition, nodule number has repeatedly been found to be influenced by G × G interactions

(Heath & Tiffin, 2007; Laguerre et al., 2007; Rangin et al., 2008; Heath, 2010; Heath et al.,

2010), whereas G × G for plant fitness traits such as fruit number and seed number appear to be

less often detected (Parker, 1995; Heath & Tiffin, 2007; Heath, 2010; Heath et al., 2010). These

two pieces of information together suggest that rhizobium fitness traits are promising avenues for

future studies trying to isolate the genomic regions influencing G × G interactions.


The QTL architecture of Medicago truncatula

QTL mapping is an excellent first step towards characterizing the genetic architecture of

a quantitative trait. I did not find any QTL × rhizobium strain interactions, however my thesis

nevertheless makes a significant contribution to the field as a novel approach and perspective to

QTL mapping. Specifically, it was novel because I mapped traits in response to changing

genotype interactions, not simply different environmental conditions. In addition, I identified

QTLs influencing the same traits in both strain treatments and found evidence for loci affecting

multiple traits, both of which can be used for future studies investigating the genetic basis of

phenotypic traits in the legume-rhizobium mutualism.

In comparison to previous QTL mapping studies which have investigated QTL ×

environment interactions (e.g., Fry et al., 1998; Juenger et al., 2005; Gardner & Latta, 2006;

Brock et al., 2010), my thesis presents a novel approach because I used separate genotypes of an

interacting species as different environments. I used the methodology for examining QTL ×

environment interactions, however I was testing for QTL × genotype interactions, a unique

analysis which has not been performed before. Although there are a number of QTL studies

which have mapped plant traits in response to interactions with other organisms, such as insects,

ectomycorrhizal fungi, and pathogens (e.g., Weinig et al., 2003; Tagu et al., 2005; Hamon et al.,

2010; Tetard-Jones et al., 2011), none of these investigate plant responses to more than one

genotype of the interacting species. I did not find any significant QTL × rhizobium genotype

interactions, but my approach could potentially be used in future experiments trying to locate the

genetic basis of G × G interactions between interacting species, such as herbivory, where G × G

interactions are known to exist (Moran, 1981; Service, 1984; Tetard-Jones et al., 2007).


As I described in Chapter 2, a number of the QTLs affected multiple traits, which is

indicative of either one locus acting pleiotropically or tight linkage between different loci acting

on each trait. Here I discuss the evolutionary implications of the two aforementioned

mechanisms. If variation in each trait is controlled by trait-specific loci, then each trait should be

able to evolve independently, unless they are in linkage disequilibrium. Alternatively, if

pleiotropy is occurring, it can have significant effects on the evolutionary trajectory of the traits

involved (Lande, 1979). For example, QTL1-2 in the across strain analysis influenced leaf

number, fruit weight and shoot weight in the same direction (Table 4), all of which are positively

correlated (Appendix, Table A-5). If this QTL is a true pleiotropic locus, these traits are expected

to evolve in the same direction, producing the common phenotype of larger plants having more

fruits and more leaves. However, pleiotropy can also create a situation where selection to

maintain one trait may constrain the evolvability of another – i.e., antagonistic pleiotropy

(Anderson et al., 2011). Such a situation could arise if the traits influenced by the pleiotropic

locus are either negatively correlated or influenced by the locus in opposite directions. As an

example, NQTL3-2 in Naut had opposite effects on leaf number and fruit weight (Table 2),

which could create a potential genetic trade-off between reproduction and vegetative growth.

However, as I mentioned in Chapter 2, ultimately determining if pleiotropy is truly occurring

requires fine-mapping of the QTLs or additional generations of recombination to break up any

potential linkage between genes.

Genetic variation at the Nod factor signaling genes

The signaling genes involved in the early stages of the Nod factor signaling pathway are

essential to the formation of the symbiosis between legumes and rhizobia (Jones et al., 2007;


Ferguson et al., 2010). Two of these genes, NFP and DMI3, were found within the intervals of

QTLs affecting leaf number and average fruit weight, suggesting that variation in these genes

may potentially affect plant performance and plant fitness. If NFP and DMI3 are the causal loci

of these QTLs, there are a number of interesting implications of these findings.

As described in Chapter 2, these results suggest that one of the parental lines must be

better signaler at some point in the nodulation pathway. The next step is to determine at both the

genomic and phenotypic level specifically which changes in the Nod factor signaling pathway

result in particular plant genotypes obtaining higher fitness. The nonsynonymous SNP in NFP is

a promising place to begin. The amino acid substitution may change the conformational structure

of the Nod factor receptor, allowing rhizobium Nod factors to be recognized and processed more

rapidly by the host plant. The functional changes in DMI3 are more difficult to conceive as all of

the SNPs I found are located in non-coding regions and this gene is involved later in the

signaling pathway (Catoira et al., 2000; Lévy et al., 2004). There are two possibilities: either

there is a mutation in the non-coding region upstream of DMI3 which results in differential gene

expression between the two parents, or there is a nonsynonymous mutation which is currently

undetected. I was only able to sequence a third of DMI3, therefore it is possible that some of the

genetic polymorphism may have been missed.

Under natural conditions, the parental allele which confers better signaling ability and

higher fitness should eventually spread to fixation. Alternatively, genetic variation in signaling

ability could be maintained if the allele or genotype which is the better signaler depends on the

rhizobial strain – i.e., NFP or DMI3 × rhizobium strain interaction, a form of G × G interaction.

Although I found no evidence that this occurring in my thesis, I think it is worthwhile


investigating further with additional rhizobium genotypes for two reasons: 1) The parental line

which had higher fitness depending on SNP at NFP and DMI3 is from a different country than

the rhizobium strains, suggesting some sort of foreign plant genotype advantage, and 2) There

may be an allele that results in an optimal plant signaler with all compatible rhizobium strains. In

other words, plant hosts with this allele would always obtain higher fitness in comparison to

those individuals with alternate alleles, regardless of the interacting rhizobium strain. Additional

investigations with more rhizobium strains from multiple locations would help determine if

either of these two observations are general patterns in the Medicago-Sinohizobium mutualism.

Future directions and final conclusions

There are a number of additional studies which would provide valuable insights into the

G × G interactions between legumes and rhizobia and their genetic basis. As I have mentioned

repeatedly throughout the discusson of my findings, I believe it is worthwhile to conduct similar

experiments with additional plant lines and rhizobium strains. By using a different experimental

design, such as nested association mapping (described in Chapter 2, pg. 47) or replicated F2

mapping, significantly more genotype combinations could be evaluated with greater statistical

power. Furthermore, precise strain selection could allow the impact of foreign vs native strains

on G × G interactions to be evaluated concurrently. Below I describe a few additional areas for

future studies to investigate based on the conclusions of my thesis.

The next goal in many QTL mapping studies is to perform fine-mapping in the regions

of interest either by increasing the number of markers or recombinants in the region of the

putative QTL. After an initial round of QTL mapping, the intervals of each QTL are often 10-30

cM wide, as can be seen in the QTLs I detected (Table 2, 3, 4). The problem is that these wide


intervals can in turn contain hundreds to thousands of genes and millions of basepairs. Fine

mapping can reduce the QTL interval to 1-5 cM which can be sufficiently small to locate

candidate genes of interest for additional functional studies (Abiola et al., 2003). However

putative QTLs should be confirmed in additional studies prior to performing fine-mapping, as it

is time consuming process. One method to validate QTLs is to create near-isogenic lines, which

differ from the parental lines only at the location of the targeted QTL (Borevitz & Chory, 2004).

As such, any phenotypic differences between the NILs and the parental lines are likely due to the

QTL. I think this is the next direction to proceed in to locate the specific loci affecting some of

traits I mapped in my thesis. In particular I think it would be worthwhile to confirm the QTLs

found for primary branching and the plant fitness QTLs which colocalized with the Nod factor

signaling genes. Once these QTLs have been validated, additional studies with more markers in

the region of the QTLs can help narrow down the interval and the potential causal loci.

Some of the genes involved in the Nod factor signaling pathway, such as NFP, are unique

to the legume-rhizobium symbiosis, while others, such as the DMI genes, also function in the

mycorrhizal infection process (Catoira et al., 2000). If DMI3 is the causal locus influencing plant

performance in my thesis, I think an interesting complementary experiment would be determine

if genomic variation in the DMI genes also influences plant phenotypes when grown with

mycorrhizal fungi. It would help determine if the influence of these genes is unique to affecting

the host plant in the legume-rhizobium interaction, or if it is a general pattern found in plant-

microbe symbioses.

One aspect that is missing from the investigation of G × G interactions between legumes

and rhizobia is an assessment of the frequency, distribution, and probability of association


between different genotypes. The underlying assumption of G × G interactions maintaining

genetic diversity in partner quality is that both the host and symbiont encounter different partner

genotypes often enough for frequency dependent selection to occur (Bever, 1999; Molofsky et

al., 2001; Heath & Tiffin, 2007). However, without explicit information on the frequency of

interactions, the actual – versus potential – importance of G × G interactions to coevolutionary

dynamics is unknown. It likely remains unanswered due to the difficulty of monitoring both

partners under natural conditions and a variety of other factors which can influenced the outcome

of G × G interactions such as herbivory, competitors and nutrient availability (e.g., Heath &

Tiffin, 2007; Heath et al., 2010; Heath & Lau, 2011).

A potential solution to help address these issues would be to perform a small, long-term

G × G controlled greenhouse experiment in a large soil flat. The experiment would start initially

with equal frequencies of two genotypes of each partner which have repeatedly shown evidence

of G × G interactions. In particular, it would be important to obtain pairings as described by

Bever (1999), where host plant genotype A obtains the highest fitness with microbial mutualist

X, but symbiont X obtains the highest fitness with host plant genotype B, and vice versa for host

plant genotype B and microbial mutualist Y. By using a large soil flat instead of individual pots,

the rhizobium strains would be able to move freely between host plants, and the outcome of G ×

G interactions within any one host would influence the frequencies of partner genotypes in the

entire experimental population. Once set up, the experiment would be left to run for two or three

plant generations, largely untouched. Inevitably, the density of plants would probably be

increasingly high after each generation, therefore some systematic thinning would be required or

the initial set-up could begin with plants at a very low density. Midway through the experiment,


half of the plants and their associated rhizobia would be randomly harvested and genotyped

(most likely only subset of nodules on each plant) to assess the frequencies and associations

between different partner genotypes. The remaining plants would be left to grow for an equal

number of generations prior to also being harvested and genotyped. If G × G interactions and

frequency dependent selection are truly occurring, there should be a marked difference in partner

genotype frequencies and associations at the two harvesting stages.

The results of such an experiment would not be directly comparable to field settings, as

there are undoubtedly many more plant and rhizobium genotypes which are interacting under

natural conditions. However, it would help narrow the gap in knowledge concerning the

fluctuating frequencies of partner genotypes across multiple generations. Furthermore,

depending on the results, this experiment could serve as a proof of concept for the ability of

frequency dependent selection to maintain partner quality diversity over coevolutionary time.

Although this experiment would involve a significant amount of work, and is also a long term

study (best suited as a collaborative experiment), the results could potentially contribute to

determining if G × G interactions and frequency dependent selection can truly maintain genetic

variation in mutualist quality over multiple generations.

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Appendix 81

Appendix

Figure A-1. Preliminary leaf count data collected on the parental lines of the LR03 RIL mapping

population grown with rhizobium strains Naut and Sals (Heath, unpublished). White = female

parent, line 530; black = male parent, line 735. Means are shown + SE (n= 20 for each plant

line). Rhizobium strain × parental line = 0.02.

Appendix 82

Figure A-2. Linkage map of the LR03 RIL mapping population, constructed in JoinMap 4.0.

Number of markers =184. Bolded markers are anchored to the M. truncatula genome. The

genetic distances (in cM) between markers are indicated on the left side of the linkage groups.

Appendix 83

Table A-1. Information on the parental lines of the LR03 RIL mapping population. From

www.montpellier.inra.fr/BRC-MTR

Parental lines

Information F833005-5 DZA045-5

Line number

Origin; altitude

L000530

France, Salernes; 261 m

L00735

Algeria, Annaba; 100 m

Latitude, longitude 43.5 N, 6.23 E 36.9 N, 7.7 E

Weight of 1000 seeds (mg)

Weight of 100 pods (mg)

4238

6104

4340

10574

Pod spines Short Long

Flowering time Later than A17 Later than A17

Frost Tolerant Sensitive

http://www.montpellier.inra.fr/BRC-MTR

Appendix 84

Table A-2. Primer sequences used for sequencing Nod factor signaling genes in parental lines and RILs All primers were initially used on

the parental lines of the LR03 RIL population to look for SNPs. Only those primers indicated with as asterisk were sequenced in the RILs.

Fragments Forward primer Reverse primer Source

DMI1

DMI1-MT1* AATACATACACATAAAAGGAATC CATCTACCATATAAGCAACTCT De Mita et al. 2007

DMI3

DMI3.3* TCTTGAGCTTTGTTCCGGTGGTGA AGATGTGAGCTACCGTGTTCCCAA This study

DMI3.4 CGTGATGGAACAGTTGACATGCGT TGTGTGCATTACCCTGAGCATGGA This study

NFP

NFP-1* TTACATGCCCTGTGGATTCTCCTC ATCTGCAGTCTCGGACGATGAAGT This study

NFP-2 AAGTACTTCATCGTCCGAGACTGC CTGCCAAAGAAGCCAAACTTAGAGC This study

NIN

NIN-1 TTGAGGAGCTGTTGGGAGAAGGTT TCCATTATCTCGTTCACCGCTGCT This study

NIN-2 GTGGTGCATCAGGTTGTGGTGTTT TACTGCTCTGATCATGCTGCTGCT This study

NIN-4 GTGAAGGCAACTTTCGCGGATGAA CTGCTGTTGCGGAAAGTGTTTGGA This study

NORK

NORK-1 GGTTGATATTGTCCGCGAGT TGACAAGGTTTGGGTTGTGA This study

NORK-2 ATATTGTCCGCGAGTTGGAG ATCTCGGTTGAGGGTGTGAC This study

NORK-3 CCCCTTTTGAATGCCTATGA TTTTCTTGGTTGTGCAGCAG This study

NORK-4 AACTCAGGGAACCCGAGAAT GTAACCCAGAAG AGGCACCA This study

Appendix 85

Table A-3. PCR reagent concentrations and volumes (in µL) used for sequencing Nod factor

signaling genes in parental lines and RILs. All reagents were obtained from Froggabio.

Reagent

DMI1

DMI3

NFP

NORK

NIN

ddH20 4.2 7.8 7.8 4.2 7.8

Betaine (5 M) 4 4 4 4 4

MgCl2 (25 mM) 2 2 2 2 2

10x PCR buffer 2 2 2 2 2

Forward primer 2 0.2 0.2 2 0.2

Reverse primer 2 0.2 0.2 2 0.2

dNTP (10 mM) 1.6 1.6 1.6 1.6 1.6

iTaq (5U/µL) 0.2 0.2 0.2 0.2 0.2

DNA template 2 2 2 2 2

Appendix 86

Table A-4. Correlations between RIL least-square means for all traits in Naut and Sals treatments. Data presented are the pearson

correlation coefficients between least-square line means calculated using PROC CORR in SAS (v.9.2). P< 0.0001 = ****, P< 0.001 =

***, P<0.01 = **, P<0.05 = *. Correlations above the diagonal are in Naut, correlations below are in Sals.

Leaf

number (6

weeks)

Days to

flowering

Fruit

number

Average

fruit

weight

Shoot

weight

Root

weight

Primary branch

number

Leaf number (6 weeks) -0.26 *** 0.36**** 0.29**** 0.07 0.14 0.29****

Days to flowering -0.36**** 0.08 -0.02 0.43**** 0.31**** 0.34****

Fruit number 0.35**** 0.14 -0.31**** 0.52**** 0.40**** 0.21 **

Average fruit weight 0.27** -0.08 -0.19** -0.11 -0.04 0.10

Shoot weight -0.08 0.59**** 0.49**** -0.15 0.66**** 0.35****

Root weight 0.11 0.50**** 0.51**** 0.05 0.70**** 0.38****

Primary branch number 0.33**** 0.24** 0.23** 0.09 0.18** 0.30****

Appendix 87

Table A-5. Correlations between RIL least-square means for all traits in the across rhizobium strains analysis. Data presented are the

pearson correlation coefficients between least-square line means calculated using PROC CORR in SAS (v.9.2). P< 0.0001 = ****, P<

0.001 = ***, P<0.01 = **, P<0.05 = *.

Leaf

number (6

weeks)

Days to

flowering

Fruit

number

Average

fruit

weight

Shoot

weight

Root

weight

Primary branch

number

Leaf number (6 weeks) -0.29**** 0.37**** 0.29**** -0.008 0.18 ** 0.33****

Days to flowering 0.16 * -0.04 0.57**** 0.46**** 0.39****

Fruit number -0.32**** 0.49**** 0.51**** 0.27 ***

Average fruit weight -0.13 0.02 0.09

Shoot weight 0.70**** 0.31****

Root weight 0.42****

Primary branch number

Appendix 88

Table A-6. Arabidopsis thaliana branching genes and descriptions with the matching Medicago

truncatula orthologs. DNA sequence alignment score obtained from BLASTn.

Gene Description Alignment score

with Medicago

Location in

Medicago genome

Gene

orthologs

MAX2

More

Axillary

Growth 2

F-box protein 320 Chr. 4 Cyclin-like

Fbox

PID

Pinoid

Serine/threonine

kinase

509 Chr. 4 Tyrosine

protein

kinase

PIN1

Pinformed1

Auxin efflux

carrier protein

549 Chr. 6 Auxin

efflux

carrier

the coevolutionary genetics of medicago truncatula … · the coevolutionary genetics of medicago...

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