Association Mapping for

improvement of agronomic traits

in Rice• Hifzur Rahman

Methods in Crop Improvement

• To meet the food needs of the human population, plant breeders

select for agronomically important trais like yield.

• Determining the genetic basis of economically important complex

traits is a major goal.

• Linkage mapping has been a key tool for identifying the genetic

basis of quantitative traits in plants.

• Identification of QTLs or genes associated to particular trait

accelerated the pace of crop improvement either by introgressing

the identified QTLs/genes in desired genotype by MAB or by

transgenic technology.

QTL approach

Uses standard bi-parental mapping populations

F2 or RILs

These have a limited number of recombination events.

Resulting in low resolution of map i.e. the QTL covers many cM.

Additional steps required to narrow QTL or clone gene.

Difficult to discover closely linked markers for the causative gene

• Association mapping, also known as "linkage disequilibrium

mapping", is a method of mapping quantitative trait loci (QTLs) that

takes advantage of linkage disequilibrium to link phenotypes to

genotypes.

• Uses the diverse lines from the natural populations or germplasm

collections.

• Discovers linked markers associated (=linked) to gene controlling

the trait.

Association mapping (AM)

Association mapping (AM): How it works?

• Association studies are based on the assumption that a marker locus

is ‘sufficiently close’ to a trait locus so that some marker allele

would be ‘travelling’ along with the trait allele through many

generations during recombination. Murillo and Greenberg, 2008.

Major goal

• To identify inter-individual genetic variants, mostly single

nucleotide polymorphisms (SNPs), which show the strongest

association with the phenotype of interest, either because they are

causal or, more likely, statistically correlated or in linkage

disequilibrium (LD) with an unobserved causal variant(s).

Advantages of AM over linkage mapping

1. Much higher mapping

resolution,

2. Greater allele number and

broader reference

population

3. Possibility of exploiting historically measured trait data

4. Less research time in establishing an association

(Flint-Garcia et al., 2003)

(Yu and Buckler, 2006)

Association analysis

Two approaches:-

On the basis of distance between two loci

By analyzing linkage disequilibrium between marker and target

gene in natural population.

• LD refers to nonrandom association of alleles at different loci.

• LD can occur between more distant sites or sites located in

different chromosomes

LD Quantification

• LD is difference between the observed gametic frequencies of

haplotypes and the expected gametic haplotype frequencies under

linkage equilibrium .

• D = PAB − PAPB = (PABPab − PAbPaB)

• D is informative for comparisons of different allele frequencies across

loci and strongly inflated in a small sample size and low-allele

frequencies

• Verified with the r2 (0 to 1) before using for quantification of extent of

LD in case of low allele frequency.

Calculation and visualization of LD:

• LD can be calculated using available haplotyping algorithms

• Maximum likelihood estimate (MLE).

• Pairwise LD can be depicted as a color-code triangle plot based on

significant pairwise LD level (r2, and D)

Computer softwares:

• “Graphical Overview of Linkage Disequilibrium” (GOLD )

• “Trait Analysis by aSSociation, Evolution and Linkage”

(TASSEL)

• PowerMarker

Factors affecting LD

LD increases due to mating system (self-pollination), genetic

isolation, population structure, relatedness (kinship), small

founder population size or genetic drift, admixture, selection

(natural, artificial, and balancing), epistasis, and genomic

rearrangements.

While factors like outcrossing, high recombination rate, high

mutation rate, gene conversion, etc., lead to a decrease/disruption

in LD.

LD Decay:

LD will tend to decay with genetic

distance between the loci under

consideration.

Loci attains linkage equilibrium (LE), i.e.

alleles are not preferentially paired

anymore.

LD decays by one-half with each

generation of random mating.

Thus, LD declines as the number of

generations increases, so that in old

populations LD is limited to small

distances. Raveendran et. al., 2008

Types of association mapping

1. Genome wide association mapping: search whole genome for

causal genetic variation. A large number of markers are tested for

association with various complex traits and it doesn’t require any

prior information on the candidate genes.

2. Candidate gene association mapping: dissect out the genetic

control of complex traits, based on the available results from

genetic, biochemical, or physiology studies in model and non-

model plant species (Mackay, 2001). Requires identification of

SNPs between lines within specific genes.

Zhu et al., 2008

Steps in Association Mapping

Abdurakhmonov & Abdukarimov, 2008

Power to detect associations depends on

Sample size and experimental design

accurate phenotypic evaluations.

genotyping,

genetic architecture.

Phenotyping and Germplasm selection

Phenotyping

• Replications across multiple years in randomized plots and multiple

locations and environments

• influence of flowering time on other correlated traits, photoperiod

sensitivity, lodging, and susceptibility to prevalent pathogens because

these traits affect the measurement of other morphological or agronomic

traits at field condition. (Raveendran et al. 2008)

• Field Design:- incomplete block design (Lattice) (Eskridge, 2003).

Should be done on the basis of

• Diversity:- on the basis of phenotype and genotype

• Population structure

Germplasm selection and Population structure

• Randomly or non-randomly mated germplasm

• Randomly mated populations represent a rather narrow group of

germplasm, likely to lower resolution and harbor only a narrow

range of alleles

• Nonrandomly mated germplasm is used, population structure needs

to be controlled in the statistical analyses

(Yu et al., 2006)

• A set of unlinked, selectively neutral background markers are used

to achieve genome-wide coverage to broadly characterize the

genetic composition of individuals.

• Cluster analysis and boot strapping is done.

• On the basis of cluster analysis most diverse individuals are

selected from each cluster to represent the individuals of that

cluster.

• Helps in preventing spurious associations if population structure

and relatedness exist.

Rafalski et al 2010

Estimation of population structure

Low- dimensional projection

PCA based methods (Patterson et al., 2006)

Clustering

Distance- based (Bowcock et al., 1994)

Model- based

STRUCTURE (Pritchard et al., 2000)

mStruct (Shringarpure & Xing, 2008)

Evaluation of linkage disequilibrium and associating genotype- phenotype

• Structure of linkage disequilibrium (LD) for a specific locus will,

reveal the association resolution possible at that locus.

• TASSEL (http://www.maizegenetics.net) is used to measure the

extent of LD as squared allele frequency correlation estimates (R2,

Weir, 1996) and measure the significance of R2.

• Eg. if LD decays within 1000 bp, then 1 or 2 markers per 1000 bp

will be needed to identify associations.

• Besides TASSEL there are many other softwares like DnaSP,

Arlequin etc. used to calculate D’ and R2.

Softwares used in AMSoftware Focus Description

Haploview 4.2 Haplotypeanalysis andLD

LD and haplotype block analysis, haplotype population frequency estimation, single SNP and haplotype association tests, permutation testing for association significance

SVS 7 Stratification,LD and AM

Estimate stratification, LD, haplotypes blocks and multiple AM approaches for up to 1.8 million SNPs and 10,000 sample

TASSEL Stratification, LD and AM SSR markers, GLM and MLM methods

GenStat Stratification, LD and AM SSR markers, GLM and MLM-PCA methods

JMP genomics Stratification, LD and structured AM

SNPs, CG and GWAS, analysis of common and rare Variants

GenAMap Stratification, LD and structured AM

SNPs, tree of functional branches, multiple visualization tools

PLINK Stratification, LD and structured AM

SNPs, multiple AM approaches, IBD and IBS Analyses

STRUCTURE Populationstructure

Compute a MCMC Bayesian analysis to estimate the proportion of the genome ofan individual originating from the different inferred Populations

SPAGeDi Relative kinship genetic relationship analysis

BAPS 5.0 Populationstructure

Compute Bayesian analysis to estimate the proportion of the genome of an individualand assign individuals to genetic clusters by either considering them as immigrants or as descendents from immigrants

mStruct Population Structure Detection of population structure in the presence of admixing and mutations from multi-locus genotype data. It is an admixture model which incorporates a mutation process on the observed genetic markers

LDheatmap LD LD estimation (r2) displayed as heatmap plots using SNPs

Arlequin 3.5 Genetic analysis and LD Hierarchical analysis of genetic structure (AMOVA), LD for D′ and r2. Version 3.5incorporate s a R function to parse XML output files to produce publication quality Graphics

Examples of association mapping studies

• 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.

• First attempt on candidate-gene association mapping study in plants

(maize) resulted in the identification of DNA sequence polymorphisms

within the D8 locus associated with flowering time (Thornsberry et al.,

2001).

• Using same population, Whitt et al., 2002 associated the candidate gene

su1 with sweetness taste , bt2, sh1 and sh2 with kernel composition,

and Wilson et al., 2004 ae1 and sh2 with starch pasting properties.

Association mapping studies in plant species.

Association mapping studies in RicePopulation Sampl

e SizeBG markers Trait Reference

Diverse land races 577 577 Starch quality (Bao et al., 2006)

Diverse accessions 103 123 SSRs Yield and its components (Agrama et al., 2007)

Landraces SSRs Heading date, plant height and panicle length

Wen et al. (2009)

Landraces SNPs Multiple agronomic traits Huang et al. (2010)

Diverse accessions 203 154 SSRs,1indel

Trait of Harvest Index Li et al. (2012)

Diverse accessions 210 86 SSRs yield and grain quality Borba et al. (2010)

diverse rice accessions

383 44,000SNPs Aluminum Tolerance Famoso et al (2011)

Mini core collection

90 108 SSR+indel stigma and spikelet characteristics

Yan et al. (2009)

Diverse accessions 950 Sequence based Flowering time and grain yield Huang et al. (2011)

Diverse accessions 127 Sequence based Aroma Singh et al. (2010)

Diverse accessions 413 44K SNP chip Agronoical traits Zhao et al. (2011)

• Out of 18,000 accession of global origin, a USDA rice mini core collection of 203 accession were used for phenotyping 14 agronomic traits.

• Out of 14 agronomic trait 5 traits were correlated with grain yield per plant: plant height, plant weight, tillers, panicle length, and kernels/ branch.

• Genotyped with 155 SSRs and Model based clustering using STRUCTURE seperated the accessions into 5 main clusters namely in ARO, AUS, TRJ,TEJ, IND.

4 main groups (AUS, IND, TEJ and TRJ) were separately analyzed for the LD

measured by R2

mean R2 ranged from 0.04 for IND to 0.10 for TEJ and TRJ.

IND had the most linked marker pairs with significant LD (9.53%), while TRJ

had the least (5.57%).

LD decay in distances was about 20 cM within both AUS and IND, while it

decayed about 30 and 40 cM within TRJ and TEJ

Association analysis on candidate genes

Association study employs techniques from molecular biology, field

sampling/breeding, bioinformatics and statistics.

1. Select candidate genes using existing QTL and positional cloning

2. Choose diverse germplasm for the trait.

3. Score phenotypic traits in replicated trials.

4. Amplify and sequence candidate genes.

5. Manipulate sequence into valid alignments and identify.

6. Obtain diversity estimates and evaluate patterns of selection

7. Statistically evaluate associations between genotypes and

phenotypes taking population structure into account.

BADH gene was isolated from all 16 varieties and sequenced

Sequence trace files from each variety were assembled into contigs

using combined Phred/Pharp/Consed software.

Polymorphism tags were generated automatically by Polyphred

software integrated with the Consed.

High quality SNPs from transcribed region were then identified

manually and screen shots of the SNP trace files for the two alleles.

MassARRAY Assay Design 3.1software was further used to

detect more SNPs

127 diverse rice varieties and landraces were used to analyse

polymorphism for the identified SNPs

Phylogenetic tree of the BADH1 gene sequence obtained by

resequencing of 16 rice varieties and Nipponbare reference gene

sequence was constructed using MEGA 4.0.

Analysis of the BADH1 sequence variation among 127 rice

varieties was done based on the scores of 15 validated SNPs

identified by resequencing of the BADH1 gene from 16 varieties

and Nipponbare using the Sequenom MassARRAY assays.

• Two common BADH1 protein haplotypes (corresponding to four

BADH1 SNP haplotypes) were analyzed in all 127 rice varieties

and also separately in the aromatic and salt-tolerant subgroups of

varieties

• 54 SNPs giving more than 95%success rates were used for the

population structure analysis using STRUCTURE software .

• Two haplotypes of the BADH1 protein, PH1 and PH2 were

modeled and docked.

• The three exonic SNPs were

• (1) S6 in exon 4 with a T/A polymorphism resulting in asparagine to

lysine substitution at amino acid position 144;

• (2) S18in exon 11 with a C/A polymorphism resulting in glutamine to

lysine substitution at amino acid position 345, and

• (3) S19 in exon 11 with T/C polymorphism resulting in isolucine to

threonine substitution at amino acid position 347.

• PH1 has 15 active GABald binding site where as PH2 has 8.

• 517 landraces were phenotyped and genotyped by sequencing upto

one fold coverage using Illumina Genome Analyzer II

• Aligned sequence reads to the rice reference genome for SNP

identification

• Discrepancies with rice reference genome were called as candidate

SNPs.

• A total of 3,625,200 nonredundant SNPs were identified, resulting

in an average of 9.32 SNPs per kb, with 87.9% of the SNPs located

within 0.2 kb of the nearest SNP

• A total of 167,514 SNPs were found in the coding regions of

25,409 annotated genes.

• 3,625 large-effect SNPs (representing mutations predicted to cause

large effects) were identified.• Neighbor-joining tree as well as the

principal-component analysis

seperated rice germaplasm in two

groups i.e. indica and japonica.

• Further both indica and japonica had three subgroups.

Because of strong population differentiation between the two

subspecies of cultivated rice GWAS was conducted only for 373

indica lines using mixed linear model (MLM)

80 associations for the 14 agronomic traits were identified.

Heading date strongly correlated with both population structure

and geographic distribution.

Genome-wide LD decay rates of indica and

japonica were estimated at ~123 kb and ~167

kb, where the r2 drops to 0.25 and 0.28,

• 413 diverse accessions of O. sativa were phenotyped for 34 traits

and genotyped using 44K SNP array.

• Probe was prepared from DNA, labelled and hybridized against

array.

• Genotype calling was done using ALCHEMY program• 36,901 high-performing SNPs (call rate > 70 %) were used for all

analyses.

• PCA analysis was done to determine population structure and

separated all the accessions into 5 clusters.

• mixed model approach was implemented to correct population

structure

• SNP LD among the 44K common SNPs were detected using r2

using PLINK software.

• LD decay was observed at ~ 100 kb in indica ,

200 kb in aus and temperate japonica , and 300

kb in tropical japonica giving and average

marker distance of about 10kb

GWAS for various traitsPlant

heightPanicle length

Flowering time

Photo

peri

od

sensi

tivit

y

Comparison

Candidate gene approach

Genome wide association Mapping

GWA using Markers SNP genotyping using Microarray

Whole genome sequencing

• Choice of candidate gene and marker within them often involves some guess work so chances are there many earlier unreported genes will go undetected.

• Discovery of large number of markers.

• In crops like A. thaliana (125Mb) ~140,000 and in maize (475Mb)~10-15 million markers will be required to give complete coverage.

• Good and robust can process large number of sample and identify large no. of SNPs in one shot.

• But if polymorphism is not present in initial discovery panel remains undetected in large sample.

• Detects all polymorphisms in the population thus avoids the erosion of power due to ascertainment bias.