This document summarizes a seminar on association mapping in plants. It discusses how association mapping offers greater precision in locating quantitative trait loci (QTLs) than family-based linkage analysis by taking advantage of linkage disequilibrium across diverse populations. The key steps in association mapping are described, including population selection and structure analysis, high-throughput phenotyping and genotyping, measuring linkage disequilibrium, and association analysis to identify marker-trait links. Software for conducting association mapping and case studies in rice are also reviewed.

1. Doctoral seminarDoctoral seminar
onon
Presented By
Zuge Sopan Shivaji
Ph.D. Scholar
Dept. of Genetics and Plant Breeding,
CoA, Raipur.

3. Introduction
• Polygenic inheritance of agronomic traits- controlled by multiple genes
whose expression is affected by many factors. Hence phenotypic selection
becomes tedious job.
• Family mapping (Limitations-Biparental population, Low resolution,
Analysis of only 2 alleles, time consuming).
• Population or Association mapping (I) increased mapping resolution, (ii)
reduced research time, and (iii) greater allele number (Yu and Buckler,
2006).
• Association mapping identifies quantitative trait loci (QTLs) by examining
the marker-trait associations that can be attributed to the strength of linkage
disequilibrium between markers and phenotype across a set of diverse

4. • 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.
Offers greater precision in QTL location than family-based
linkage analysis.
Does not require family or pedigree information , can be
applied to a range of experimental and non-experimental
populations.
Association mapping (AM)

5. 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.

6. Direct and Indirect Allelic Association
D
*
Measure disease relevance (*)
directly, ignoring correlated
markers nearby
Direct Association
M1 M2 Mn
Assess trait effects on D via
correlated markers (Mi) rather than
susceptibility/etiologic variants.
D
Indirect Association & LD
•Allele of interest is itself
involved in phenotype
• Allele itself is not involved,
but a nearby correlated marker
changes phenotype

7. Linkage mapping
In 1913, the first individual to construct a
(very small) genetic map was Alfred
Sturtevant.
Genes/ markers in order, indicating the relative genetic distances
between them, and assigning them to their chromosome.
Distance = Recombination frequency=
No. of recombinants /Total progeny X
100
Suppose the recombination between loci
A and B is 6%, that between loci B and C
is 20%, and that between A and C 24%,
then we can order the loci along the
chromosome as…
(Hartal et al., 2010)

8. Mapping resolution
(Braulio et al., 2012)

9. Marker-trait associations in experimental and
natural populations
Experimental populations (e.g.
F2, RIL) 2-parental alleles; small
genetic variation; few meiotic
cycles; low resolution
Natural populations
many alleles; large genetic
variation; many meiotic cycles;
high resolution

10. Approaches to mapping genes
(Yu and Buckler, 2006)

11. Advantages of AM over linkage mapping
Linkage Mapping Association Mapping
 Structured Population
(e.g. Biparental population)
 Un-structured population
(e.g. Germplasm lines)
 Low resolution (few to several
centimorgans away from gene/QTL)
 High resolution (Much closer than
those by linkage mapping)
 Only few alleles can be detected  Many alleles can be detected
 Moderate marker density  High/moderate marker density
 Feasible in annual and biennial
species, not feasible in perennial
species
 Feasible in annual, biennial and
perennial species
 Narrow range  Wide range
 Time consuming  Less time required
(Yu et al., 2006)

12. 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.

13. (Zu et al., 2009)

14. Steps in association mapping

15. Mapping population 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
• Non randomly mated germplasm is used, population structure needs to
be controlled in the statistical analysis.
• Cluster analysis is done to know the variation in population and most
diverse individuals are selected from each cluster to represent the
individuals of that cluster.
(Yu et al., 2006)

16. Phenotyping
Phenotyping
• Success of AM depends on accuracy and throughput of genotyping
• Replications across multiple years in randomized plots and multiple
locations and environments.
• Field Design:- incomplete block design (Lattice), RBD (Eskridge,
2003).
Should be done on the basis of
• Diversity:- on the basis of phenotype and genotype
• Population structure:- Systematic difference in allele frequencies
btw. sub-populations…

17. Genotyping
• Mostly multiallelic, reproducible, PCR-based markers are used.
• Microsatellites or simple sequence repeats (SSRs), and SNPs are
more revealing than their dominant counterparts and, therefore, are
more powerful.
• Due to higher genome density, lower mutation rate and wide
distribution throughout the genome SNPs are rapidly becoming the
marker of choice for complex trait

18. Linkage Disequilibrium
Linkage disequilibrium means that we don’t need to genotype the
exact causal variant, but only a variant that is correlated with it.

19. Linkage Disequilibrium Map & Allelic Association
Primary Aim of LD maps: To identify the
relationship between marker and QTL or trait of
interest.
Marker 1 2 3 n
LD
D

20. Linkage disequilibrium (LD)
• LD refers to non random association of allels at different loci.
• LD follows the fact that closely located genes are transmitted as a block,
which only rarely breaks up in meiosis.
• Closely located genes often express linkage disequilibrium to each other: An
example: Consider two independently segregating genes A and B with two
alleles (A, a and B, b respectively)
• At equilibrium, the frequency of the AB should equal to the product of the
allele frequencies of A and B,
• PAB
Pab
=PAb
PaB
(1:1 ratio = no LD)
• Any deviation from these values implies LD.
A a Total
B AB aB B
b Ab ab b
Total A a

22. Cont.…..
(Zhu et al.,2009)

23. LD Decay with time for four different recombination
fractions (ϴ)
(Powell et al., 2006)

24. Factors affecting LD
LD increases due to population structure, relatedness (kinship), small
founder population size or genetic drift, selection (natural, artificial).
While factors like outcrossing, high recombination rate, high
mutation rate, gene conversion, etc., lead to a decrease/disruption in
LD. Thus, LD declines with 1) increase in genetic distance and
2) increase in number of generations.
(Huttley et al., 2005)

25. Evaluation of linkage disequilibrium and associating
genotype- phenotype
• 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
.
• Besides TASSEL there are many other softwares like DnaSP,
Arlequin etc. used to calculate D‘ and R2
.

26. Softwares used in AM
Sr. Software Focus Description
1. TASSEL Association analysis Free, LD statistics, sequence analysis, association mapping
2. Haploview
4.2
Haplotype analysis and LD LD and haplotype block analysis, haplotype population
frequency estimation, single SNP and haplotype
association tests.
3. 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
4. GenStat Stratification, LD and AM SSR markers, GLM and MLM-PCA methods
5. JMP
genomics
Stratification, LD and
structured AM
SNPs, CG and GWAS, analysis of common and rare
Variants
6. GenAMap Stratification, LD and
structured AM
SNPs, tree of functional branches, multiple visualization
tools
7 PLINK Stratification, LD and
structured AM
SNPs, multiple AM approaches, IBD and IBS Analyses
8. STRUCTU
RE
Population
structure
Compute a MCMC Bayesian analysis to estimate the
proportion of the genome of an individual originating from
the different inferred Populations
9. SPAGeDi Relative kinship genetic relationship analysis
(Braulio et al., 2012)

27. Advantages of AM
1. Saves time, effort, and cost needed for the development of specific
mapping populations.
2. The QTL-linked markers identified by AM can be directly used for MAS
3. AM has high resolution
4. AM would assess the entire range of diversity in the trait of interest
5. Associated markers identified during AM can be used for either selection
of parents for hybridization or for selection of desirable segregants

28. Disadvantages of AM
1. The results from AM are affected by several factors like selection
history, population structure, kinship, etc., may lead to false positive
association
2. Large number (hundreds of thousands or even millions) of markers
would be required to adequately cover the entire genome.
3. High quality phenotypic data required (Multiple environment with multi
location)
4. The rate of recombination is not uniform throughout the genome.

29. Need of Association mapping in Rice
• Rice (Oryza sativa) is a staple food that feeds 3 billion people.
• Largest variability among germplasm and genomic database as
compared to any other species.
• All the agronomic traits in rice (Grain yield, Days to maturity,
Height, etc.) have quantitative inheritance.
• Challenge and opportunity is to utilize this information to
understand and predict how genotypic variation gives rise to the
abundance of phenotypic variation and its utilization in MAS.

30. Association mapping studies in RiceAssociation mapping studies in Rice
Population Sample
Size
Markers
used
Trait Reference
Germplasm 523 5291 SNPs 12 agronomic traits (Qing et al., 2015)
Diverse accessions 203 154 SSRs Trait of Harvest Index (Li et al., 2012)
Diverse rice
accessions
383 44,000 SNPs Aluminum Tolerance (Famoso et al., 2011)
Diverse accessions 413 44K SNPs Agronomic traits (Zhao et al., 2011)
Diverse accessions 210 86 SSRs yield and grain
quality
(Borba et al., 2010)
Landraces 517 3,625,200
SNPs
14 agronomic traits (Xuehui et al., 2010)
Mini core
collection
90 108 SSR stigma and spikelet
characteristics
(Yan et al., 2009)
Diverse accessions 103 123 SSRs Yield and its
components
(Agrama et al., 2007)

31. 517 landraces were phenotyped and genotyped by sequencing 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.
Case Study

32. A total of 3,625,200 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
genes.
3,625 large-effect SNPs (representing mutations predicted to cause large
effects) were identified.
 Principal-component analysis
seperated rice germaplasm in two
groups i.e. indica and japonica.
 Further both indica and japonica had three subgroups.

33. 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.

35. • Ultimate aim of plant breeding is prediction of phenotype from
genotype
• Major agricultural economic traits are of complex nature
• It is desperate to dissect these complex traits and assign them function
• Advanced genomic tools like association mapping will be a valuable
option can be effectively and efficiently utilized to accelerate crop
improvement
• Association mapping is long term commitment, so have all the things
and then go for it
Conclusion