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Available online at https://www.docsj.com/doc/0514952057.html,

Association genetics in crop improvement

J Antoni Rafalski

Increased availability of high throughput genotyping

technology together with advances in DNA sequencing and in

the development of statistical methodology appropriate for

genome-wide association scan mapping in presence of

considerable population structure contributed to the increased

interest association mapping in plants.While most published

studies in crop species are candidate gene-based,genome-

wide studies are on the increase.New types of populations

providing for increased resolution and power of detection of

modest-size effects and for the analysis of epistatic

interactions have been developed.Classical biparental

mapping remains the method of choice for mapping the effects

of alleles rare in germplasm collections,such as some disease

resistance genes or alleles introgressed from exotic

germplasm.

Address

DuPont Agricultural Biotechnology Group and Pioneer Hi-Bred

International,Wilmington,DE,USA

Corresponding author:Rafalski,J Antoni

(j-antoni.rafalski@https://www.docsj.com/doc/0514952057.html,)

Current Opinion in Plant Biology2010,13:174–180

This review comes from a themed issue on

Genome studies and molecular genetics–Plant biotechnology

Edited by Rajeev K.Varshney and Douglas R.Cook

Available online19th January2010

1369-5266/$–see front matter

#2010Elsevier Ltd.All rights reserved.

DOI10.1016/j.pbi.2009.12.004

Introduction

Rapid progress in the development of genomic tools,

including genome sequencing[1]and high-density

single nucleotide polymorphism(SNP)genotyping

[2,3]enabled development of new powerful approaches

to the mapping of complex traits and to the subsequent

identi?cation of causal genes.While these methods

have been?rst applied in human genetics[4],their

applications in crop genetics and crop improvement

are becoming popular.In plants,the ability to create

germplasm collections and large experimental popu-

lations consisting of homozygous individuals at will is

a signi?cant practical advantage.Here I am going to

focus on genetic association mapping,especially whole

genome scan methodology,and highlight both the

bene?ts of this method as well as signi?cant challenges

encountered during several years of practicing this

approach.Other more detailed reviews are available

[5–7,8 ,9,10].

Overview of association mapping

methodology

Association mapping,also called linkage disequilibrium

(LD)mapping,refers to the analysis of statistical associ-

ations between genotypes,usually individual SNPs or

SNP haplotypes,determined in a collection of individ-

uals,and the traits(phenotypes)of the same individuals

(Figure1).As this de?nition implies,association mapping

is closely related to well established genetic methods,

such as quantitative trait loci(QTL)mapping[11].Until

recently genetic mapping was usually done in purpose-

created populations,such as a progeny of parents chosen

on the basis of the difference between them for the

trait(s)of interest,or in de?ned pedigrees(families).

By contrast,genetic association mapping involves using

a collection of individuals,such as those derived from wild

populations,germplasm collections or subsets of breeding

germplasm.Consequently,at each locus,several alleles

may be simultaneously evaluated for association in a

diverse population,while only two alleles segregate in

any biparental population.

Two association mapping methodologies are in use:Can-

didate gene association and Whole Genome Scan,also

called Genome-Wide Association Study.In the candidate

gene approach,one tests the hypothesis‘is there a cor-

relation between DNA polymorphisms in gene A and the

trait of interest’.For example,one can ask if in a diverse

maize germplasm collection there is a correlation be-

tween DNA sequence alleles of phytoene synthase(or

any other gene involved in carotenoid biosynthesis)and

carotenoid content of seeds[12,13,14 ].This approach

assumes good understanding of the biochemistry and

genetics of the trait,and many genes may escape atten-

tion.Therefore,in absence of detailed knowledge of the

biochemical pathway of interest,including regulatory

genes,whole genome scan,described below,is a better

choice.

Genome scan involves testing for association most of the

segments of the genome,by genotyping densely distrib-

uted genetic marker loci covering all chromosomes

(Figure1).The hypothesis under consideration is simple:

‘one(or more)of the genetic loci being considered is

either causal for the trait or in linkage disequilibrium with

the causal locus’.Candidate gene association,which

assumes some understanding of the genetics of the trait,

could be considered a subset of a more general genome

scan approach.

Design of genome scan experiments

Choice of populations for association mapping and of the appropriate marker density are crucial decisions.One of the sources of false positives in association mapping is population structure,which is a division of the population into distinct subgroups related by kinship (Figure 2).Complex population structure could be expected in crop species that were subject to a severe domestication bot-tleneck followed by breeders’selection.Pronounced differences in the germplasm used in different regions of the world and maturity-related clines of allele frequen-cies for many genes may also be expected.Examples include the division of maize germplasm into heterotic groups [15]and a severe post-domestication bottleneck associated with popularization of soybean in North Amer-ica [16].

Different methods and software tools have been devel-oped to correct the results for population structure [17,18 ,19,20 ,21],usually by dividing the germplasm collections into subgroups or adjusting the probability of the null hypothesis (p value),but these methods are not perfect and in certain cases may result in the increase of false negatives,therefore the populations should be best chosen so that the genetic distances between mem-bers are not clustered,thus minimizing structure.Popu-lation structure may be evaluated by using a modest number of genetic markers (50–100),and the outcome of this analysis could be helpful in avoiding type I errors [17].

Size of the experimental population is an important https://www.docsj.com/doc/0514952057.html,rger germplasm collections obviously

provide more power,and in practice at least 100–500individuals are needed.The interaction between number of individuals,distribution of allele frequencies,and precision of phenotyping is complex [22,23],therefore more work on rigorous power analysis is needed before better guidance could be provided.

Association genetics in crop improvement Rafalski 175

Figure

1

Principle of association analysis.(a )A collection of genetically diverse individuals is genotyped at densely spaced loci distributed throughout the genome;(b )the genotypes are divided into groups sharing SNP haplotypes (shown)or individual SNPs,at each locus in turn;(c )the distributions of phenotypic values for each of the haplotypes (or alleles)are compared and probability of null hypothesis (the distributions are equivalent)is evaluated statistically.

Figure

2

Confounding of association mapping by populations structure.The dendrogram represents population structure in a subset of maize

breeding lines.If a certain trait,such as disease resistance (red dots),is common in subgroup A but rare in subgroup B,any markers with

significantly uneven allele distribution between the two subgroups will be positive in an association test,irrespective of their genomic locations.

Before choosing the appropriate number of genetic mar-kers(usually SNPs)for genome scan,it is necessary to have some understanding of the linkage disequilibrium in the population selected for the study.Linkage disequili-brium,de?ned as association between genetic loci,in general decreases with distance between marker loci, more slowly in inbreds(soybean),faster in outbred species(maize),although breeding practices have a large impact[24,25].LD is however very non-uniform across the genome,with both general trends(more LD in centromeric regions)and pronounced local?uctuations [26].If the genome could be divided into distinct linkage blocks[27],with recombination hotspots between them, it would be appropriate to choose at least a few diagnostic markers per haplotype block,in order to be able to distinguish at least the most common haplotypes[28].

A fully rational choice of marker density can only be made after at least a subset of the germplasm collection has been already genotyped with very high marker density (>106SNPs in humans).At present this is not practical in most crop species,and a more pragmatic although inexact approach may be taken.For example,if the LD decreases to approximately r2=0.5in,on average,2cM,in a 1000cM genome one would have about1000/2=500 blocks of linkage,requiring perhaps2500SNP markers

or preferably much more,to distinguish common haplo-types and account for large variation in rate of LD decay.

In maize elite breeding germplasm,we have been suc-cessful in genome scan mapping with10000single copy loci,each represented by haplotypes consisting of several SNPs,de?ning2–10haplotypes per locus[29 ].

The dif?culties with population structure and LD have been recently addressed by mouse genetics community by developing specialized populations from multiple inter-crosses between a modest number of very diverse parental individuals.This approach reduced LD(increasing genetic resolution)and avoided population structure.A similar approach has been developed in maize and Arabidopsis [30 ].In maize,Ed Buckler’s group at Cornell created nested association mapping(NAM)populations,consisting of5000recombinant inbred lines(RILs)from25families, with200RILs per family.The families were generated by crossing25diverse maize inbredlineswithB73asa common parent,essentially combining several high resolution bipar-entalpopulationintoa largeexperiment,affording veryhigh resolution and power to detect associations including epi-staticinteractions[22,31].Withveryhigh-densitygenotypic data available,mapping a new trait in these populations is immediately accessible by acquiring new phenotype measurements[32 ].A high-density haplotype map of the NAM parental lines has been published recently[33]. Genetic resolution

Genetic resolution of any mapping methodology ulti-mately depends on the amount of recombination available in the experimental population,as measured by the rate of decay of LD(Figure3).In collections of distantly related individuals many generations have passed and much recombination occurred since the last common ancestor, therefore resolution of association mapping will,in general, be considerably higher than in simple biparental popu-lations.However,in biparental populations additional rounds of intercrossing or use of very large progeny set increases resolution at the expense of additional time and labor[34].In maize,biparental populations of3000indi-viduals have in some cases resolved linkages down to single gene resolution[35].Similar results may be achievable by association mapping in a couple hundred of individuals, although wide variations in LD decay across the genome make any generalizations very dif?cult.The tradeoff is that populations affording high resolution require correspond-ingly higher marker density to assure high genome cover-age.In the next few years it will be feasible to interrogate plant genomes at every gene,at>106SNP loci,as it is now possible in humans[36].In the near future,genotyping of germplasm collections by genome sequencing will be practically possible,as it is already happening in Arabi-dopsis(https://www.docsj.com/doc/0514952057.html,/AtG1001/)[37]. Phenotyping challenge

The power of association mapping is strongly dependent upon the quality of phenotypic data.The design of?eld experiments is beyond the scope of this review,however, it is important to stress that in most cases it is necessary to use well-controlled environmental conditions,including, when possible,use of growth chambers,especially for the collection of samples for metabolomic or biochemical

176Genome studies and molecular genetics–Plant biotechnology

Figure

3

Example of linkage disequilibrium decline around locus MZA4812

(arrow)on chromosome6(23.8cM)in maize non-stiff stalk breeding

germplasm.Each point of the graph represents the value of LD measure

r2between MZA4812and another locus,plotted at that locus’

appropriate position on chromosome6(Y axis,cM).The LD initially

declines rapidly,but r2values around0.5are found within5cM of

MZA4812.

phenotypes.Relevance of such phenotypes for ?eld per-formance will have to be separately established.High throughput methods to precision phenotyping,frequently referred to as phenomics [38]are developing rapidly and automated facilities for high precision phenotyping are being established [39].

Interpreting and using genetic association data

Association analysis could be applied to individual mar-kers,usually SNPs,or haplotypes composed of several linked SNP markers.In the presence of LD,haplotype association is likely to be more powerful [40],but argu-ments have been made that ancestral causal polymorph-isms could be better detected by individual SNP analysis.Methods to identify the best haplotype-based approach have been developed (e.g.,Ref.[41]).Both approaches may be tried,although this will increase the number of tests signi?cantly,relative to haplotype only approach.If the complete haplotype block of the population under investigation was known,markers could be targeted to each of the blocks [42],but such resolution is not cur-rently available in crop species.

Genome scan analysis consists comparing the distribution of phenotypes among the individuals carrying a particular SNP or haplotype and those lacking it.The probability of

the differences between these two distributions arising at random (p value)may be obtained from linear regression,ANOVA or one of several non-parametric statistical methods.The results are reported as a graph of prob-abilities of association (p values)on Y axis as a function marker genetic map position represented on X axis (Figure 1in Ref.[29 ]).The p value has to be corrected for multiple tests.For a more detailed discussion of statistical methodologies the reader should turn to more specialized reviews [43,44].Software tools,including TASSEL,popular among plant geneticists,have been developed to facilitate association mapping [45 ].The discussion of the statistical methodologies most appro-priate for structured plant populations continues [19,20 ].The outcome of this analysis is a list of putative associations at a chosen corrected p value cutoff.These hypotheses should be independently validated.

Epistatic interactions

Epistasis,de?ned by the effect of the allelic state at one locus on the phenotypic expression of an allele at another locus,could in principle be detected by association map-ping.In practice,the power to detect epistasis in mod-erate-size populations is low for two locus systems,and extremely low for higher order interactions (three or more loci).In association populations allele frequencies at the

Association genetics in crop improvement Rafalski 177

Figure

4

Frequencies of the eight most common haplotypes,averaged across 1000genetic loci in a collection of maize breeding lines.Error bars indicate ?one standard deviation.Association mapping is appropriate for the simultaneous evaluation of the effects of three or four alleles in a population.Rare alleles should be evaluated in biparental populations.

loci of interest rarely approach the1:1ratios reducing power to detect epistasis relative to biparental popu-lations.

Validation and applications

Validation of the hypotheses generated by association mapping constitutes an integral part of the experiment. In one approach,near isogenic lines(NILs)differing in the alleles at the candidate locus are constructed by repeated backcrossing into a reference genetic back-ground.Resulting NILs are then phenotyped side by side,and the amount of phenotypic variation ascribed to the presence of introgressed segment is estimated.Bipar-ental populations segregating for the relevant alleles at the associated locus may also be used[29 ]. Alternatively,the association experiment could be expanded by inclusion of additional individuals,in the expectation that the strength of the association should improve,if the association hypothesis is correct. Association mapping is usually performed with the objec-tive of applying the results for genotype-based selection of superior individuals in plant breeding,or as a step toward positional cloning.

In marker assisted recurrent selection,breeders identify desirable alleles at one or more loci,basing on the out-come of a mapping experiment,and then use closely linked genetic markers for selecting individuals in breed-ing populations[46–48].This approach results in?xing the desirable allele(s)in the population(s)of interest. An alternative approach,genome selection aims at increasing the frequency of desirable alleles across the whole genome.To this end,selection indices are con-structed for all available genetic markers across the gen-ome,proportional to the strength of association for the trait(s)of interest[49,50].Additional biasing factors could be introduced to favor certain trait loci and alleles on the basis of their desirability or commercial value. Limitations

The detection power of association mapping greatly depends not only on the magnitude of the effect that can be ascribed to a locus,relative to other loci present in the population,but also on the allele frequency distri-bution(Figure4).Rare alleles cannot be detected with good con?dence,unless their effect is very large.There-fore,segregating biparental populations are more appro-priate for the mapping of alleles rare in the germplasm pool of interest.

Examples

Much of the association mapping in crop plants is just emerging from the research phase and is beginning to be applied,especially in commercial breeding setting.A few selected representative examples are listed in Table1. Conclusions

Genetic association mapping enriches the repertoire of tools available for the dissection of trait architecture in crop plants and model species.As high-density genotyp-ing becomes increasingly accessible this approach will gain power to identify with high resolution genetic loci and in some cases causal polymorphism affecting agro-nomic and end-use traits in crop plants,as long as relevant alleles are present at high frequency.Mapping in de?ned biparental populations will remain the method of choice for rare alleles,especially those with moderate effects, and for the study of epistatic interactions.Independent validation of the associations found by either approach and evaluation of their effects in different genetic back-grounds remains an essential,even though sometimes neglected,aspect of a genetic experiment.Precision phenotyping remains a major challenge for mapping many agronomically important traits such as nitrogen use ef?-ciency of drought tolerance.In the future,associations

178Genome studies and molecular genetics–Plant biotechnology

Table1

Examples of association mapping studies in plants

Species Topic Reference Arabidopsis Association of variation in bif2with multiple traits[51] Barley Flowering time association mapping[52] Maize Candidate gene mapping in structured populations[53] Maize Analysis of population structure[54,55] Maize Candidate association mapping of starch-related traits[56] Maize Maize population suitable for association mapping[57] Maize Candidate gene association mapping of anthocyanin biosynthesis[58] Maize Whole genome scan of oleic acid content[29 ] Maize Carotenoid content candidate gene association mapping[14 ]

n/a Mixed-model analysis of associations in presence of population structure[18 ]

n/a Software for associations analysis—TASSEL[45 ] Pearl millet Flowering time association analysis[59 ] Teosinte Association mapping of multiple traits[60]

between epigenetic pro?les and phenotypes will be sub-jected to analysis.

Acknowledgements

I appreciate frequent stimulating discussions that I had with Scott Tingey, Stan Luck and members of my research group.

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