Status and prospects of association mapping in crop plants

DEPARTMENT OF AGRICULTURAL BIOTECHNOLOGY
COLLEGE OF AGRICULTURE
ORISSA UNIVERSITY OF AGRICULTURE AND TECHNOLOGY
BHUBANESWAR - 751003
DOCTORAL SEMINAR - I
STATUS AND PROSPECTS OF ASSOCIATION MAPPING
IN CROP PLANTS
Presented By : -
Jyoti Prakash Sahoo
01ABT/Ph.D./17
12-12-2019 JYOTI PRAKASH SAHOO, DEPT. OF AGRIL. BIOTECH. , OUAT, BBSR - 751003 1
Advisor : -
Dr. K. C. Samal (Professor)
Dept. of Agril. Biotech.
CA, OUAT, BBSR

Complex Traits and Mapping
Association
Mapping
Linkage Mapping
• 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).

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)

Association Mapping (AM)
• 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.
• Does not require family or pedigree information , can be applied to a range of
experimental and non-experimental populations.
• 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.
.

Mapping Resolution
(Braulio et al., 2012)

Association Mapping (AM): How It Works?
 Detects and locates QTL based on the strength of the correlation between mapped
genetic markers and traits.
 It exploits historical recombinations.
 It relies on decay of LD at a rate determined by the genetic distance between loci and the
number of generations since it arose.
Traditional
QTL Mapping Association
Mapping
(Braulio et al., 2012)
Meiosis has been elapsed as
recombination will have removed
the association between QTL and
markers not tightly linked.

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

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). It
requires identification of SNPs between lines within specific genes.
(Yu et al., 2006)
Association
Mapping pannel

Experimental Designs and Models for
Association Mapping
Designs Features
Structured association
Designed to minimize the effects of population structure; one version
is the general linear model (GLM)
Mixed linear model (MLM)
Designed to minimize the effects of population structure and
kinship; markers and Q treated as fixed effects, while background
QTLs are treated as random effects
Multilocus mixed model
(MLMM)
Multiple loci used as cofactors in the model; uses stepwise mixed
model regression for the selection of loci and an approximate version of
mixed model of correction for population structure
Multitrait mixed model
(MTMM)
Simultaneous analysis of two or more correlated traits using the
mixed model; separates genetic and environmental correlations and
corrects for population Structure
Joint linkage association
mapping (JLAM)
Analysis of a sample drawn from a natural population and the open-
pollinated progeny from this sample
Nested association mapping
(NAM)
LD and linkage mapping in NAM populations
Source: Marker Assisted Plant Breeding: Principles and Practices B.D.Singh and A.K.Singh

Steps in Association Mapping
Abdurakhmonov et al., 2010)

Mapping Population and Population Structure
 The population may be based on a natural/breeding population or it may be a
family-based population.
 AM can also be performed in biparental and multiparent populations.
 Generally, doubled haploid, F3, etc., families derived from several biparental
crosses generated by mating a group of inbreds in diallel scheme or in a random
manner are used for AM.
 In case of multiparent populations, two populations, namely, multiparent
advanced generation intercrosses (MAGIC) and nested association mapping
(NAM) populations, have become very popular.

Phenotyping
 Success of AM depends on accuracy and throughput of phenotyping
 Replications across multiple years in randomized plots and multiple locations
and environments.
 Field Design:- incomplete block design (Lattice), RBD (Eskridge, 2003).
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

 Genetic linkage: Non- random association of alleles as a result of their proximity on
the same chromosome.
 LD: Non-random association of alleles at two or more loci, not necessarily on the
same chromosome.
 Linkage Disequilibrium – could be because of linkage and non-linkage.
 LD between linked loci is because of physical presence on the same chromosome.
 LD between the non-linked loci is because of epistatic selection.
 Linkage is resulted from recombination events in the last 2-3 generations, Linkage-
Disequilibrium is resulted from much earlier, ancestral recombination events.
 Linkage measures co-segregation in a pedigree, Linkage-Disequilibrium measures co-
segregation in a population (essentially a huge pedigree).
LD, PAB ≠ PA× PB
D = (pAB × pab) – (pAb × paB)
Concept of Linkage Disequilibrium (LD)

LD decay
 LD decay is the decline in the magnitude of LD between two loci due to recombination between them.
 Rate of LD decay = rd
r = frequency of recombination between the two loci
d = disequilibrium
 LD between two loci decays both
Temporally – as the generation advances
Spatially – with increasing distance between two loci
Increasing LD
 Mating system (self-pollination)
 Population structure and
relatedness (kinship)
 Small population size
 Admixture
 Selection
 Genomic rearrangements
Decreasing LD
 Out-crossing
 High recombination rate
 High mutation rate
 Gene conversion
Factors Affecting LD and Association Mapping

LD estimate Formula Remarks
D D = pAB.pab − pAb.paB  Basic estimate of LD.
 Difference between the product of frequency of coupling phase
and repulsion phase gamete.
 Depend on allele frequency; not in common use.
Dʹ D′ = D/Dmax
Dmax = min(pA. pb , pa.pB) ifD>O
Dmax = min(pA.pB , pa.pb) ifD<O
 Minimizes the effect of low allele frequencies.
 Ranges between 0-1.
 D' =1 , indicates complete LD.
 D' <1, indicates recombination.
 D' measures only recombination differences.
 More reliable estimate of physical distance between loci since it
is independent on allele frequencies.
 Strongly influenced by small sample size.
r2 r2 = D2 /(pA.pa.pB.pb)  Ranges from 0-1.
 r2 = 0, alleles are segregating independently.
 r2 = 1, when two loci have identical allele frequencies;
absence of recombination.
 Most appropriate measure of LD for AM, r2 values above 1/3
considered useful for LD mapping.
 More reliable under low allele frequencies.
 Reflect both mutation and recombination histories.
Estimates of Linkage Disequilibrium
LD
D
Dʹ
r2
Jennings (1917) – Concept of LD
Lewontin(1964) - Quantification of LD

Graphic Representation of LD
LD triangle and decay plots
Linkage disequilibrium (LD) decay plot
 LD Decay is considered below r2 = 0.1 threshold
 AM, a higher threshold value of LD (r2 ≥ 0.2) is used as
cutoff point.
• LD values put in both x-axis and y-axis
• The triangle plot represents a specific region
of the genome or a single gene, and the
significant values between pairs of several
markers covering the region along with their
p-values are dipicted as coloured cells above
and below respectively of the diagonal.
Software – TASSEL (Trait Analysis by Association, Evolution and Linkage)
Abdurakhmonov et al., 2010)

Analysis for Population Structure and Kinship
 Population structure signifies that individuals in a population do not form a single
homogeneous group, but they are distributed in few to several distinct subgroups
that show different gene frequencies.
 Kinship refers to. relatedness between different pairs of individuals/lines of the
sample
 Population structure arises due to geographical isolation, and natural and
artificial selections.
 Population structure of the sample can be estimated by using the STRUCTURE
program.
 The GLM, MLM, EMMA etc. models for AM minimize the effects of population
structure.

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 andAM
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 IBSAnalyses
8. STRUCTURE Populatin
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
Software for Association Mapping Studies

Status of Association Mapping in Plants
Plant
species
Populations Sample
size
Background
markers
Traits Reference
Maize Diverse inbred lines 92 141 Flowering time (Thornsberry et al., 2001)
Elite inbred lines 71 55 Flowering time (Andersen et al., 2005)
Diverse inbred lines and
landraces
375 + 275 55 Flowering time (Camus-Kulandaivelu et
al., 2006)
Diverse inbred lines 95 192 Flowering time (Salvi, 2007)
Diverse inbred lines 102 47 Kernel composition
Starch pasting
properties
(Wilson et al., 2004)
Diverse inbred lines 86 141 Maysin synthesis (Szalma et al., 2005)
Elite inbred lines 75 151 Kernel color (Palaisa et al., 2004)
Diverse inbred lines 57 120 Sweet taste (Tracy et al., 2006)
Elite inbred lines 553 8950 Oleic acid content (Belo et al., 2008)
Diverse inbred lines 282 553 Carotenoid content (Harjes et al., 2008)
Sorghum Diverse inbred lines 377 47 Community resource
report
(Casa et al., 2018)
Wheat Diverse cultivars 95 93 Kernel size, milling
quality
(Breseghello and Sorrells,
2016)

Status of Association Mapping in Plants
Plant species Populations Sample
size
Background
markers
Traits Reference
Arabidopsis Diverse ecotypes 95 104 Flowering time (Olsen et al., 2004)
Diverse ecotypes 95 2553 Disease resistance
Flowering time
(Aranzana et al., 2005)
(Zhao et al., 2007)
Diverse accessions 96 90 Shoot branching (Ehrenreich et al., 2007)
Barley Diverse cultivars 148 139 Days to heading, leaf
rust, yellow dwarf
virus,
(Kraakman et al., 2017)
Potato Diverse cultivars 123 49 Late blight resistance (Malosetti et al., 2007)
Rice Diverse land races 105 124 Glutinous phenotype (Olsen and Purugganan,
2002)
Diverse land races 577 577 Starch quality (Bao et al., 2006)
Diverse accessions 103 123 Yield and its
components
(Agrama et al., 2018)
Sugarcane Diverse clones 154 2209 Disease resistance (Wei et al., 2006)
Chickpea Diverse accessions 300 1872 Drought tolerance (Thudi et al., 2014)
Soybean Diverse accessions 305 37573 Salt tolerance (Tuyen et al., 2019)
Contd ..

Case Study
Nobel et al., 2018
• Assess the genetic diversity, population structure, LD and mapping
capabilities of a large and diverse mungbean germplasm panel using a
high-throughput SNP genotyping platform
Aim : -
Trait - Seed coat color

Plant Materials
16 wild accessions
originating from
Australia
 seed coat color
 seed size and weight
 days to flower
 days to maturity
 plant habit
 plant height
 reaction foliar diseases
used for comparison to the
diversty panel of cultivated
mungbeans
466 accessions
representing the cultivated
mungbean in Australia by the
National Mungbean
Improvement Program
(Queensland, DAF)
phenotypic traits observed and characterized

Genotyping
total genomic DNA extracted
genotyping-by-sequencing (GBS)
methodology DArT P/L (DArT1)
complexity reduction of the genomic DNA to
remove repetitive
sequences using methylation sensitive
restrictive enzymes prior to sequencing on next
generation sequencing platforms
sequence data generated, aligned to
the mungbean reference genome sequence
Vradi_ver6, to identify single nucleotide
polymorphisms (SNPs)
markers
all 482 mungbean accessions
were planted at Hermitage
Research Facility, Warwick,
QLD, Australia (28120 S, 15250
E), over the summer of 2015
Phenotyping
Seed coat color was qualitatively
field trial design was
unreplicated single field
plots for each accession,
4.5 m2 in size containing
an average of 130 plants.
five categories
(green, black, brown,
yellow, and speckled)

Analysis of Germplasm Diversity
• total of 22,230 SNP markers were identified and 16,462 were physically mapped across the 11
chromosomes
• an average of 1,497 SNPs were identified per chromosome with an average marker density of
57.81 SNPs/Mb
• 7,675 SNPs segregated within the cultivated population, with an average PIC value of 0.174
• 6,174 SNPs segregated within the wild population with an average PIC value of 0.305

Analysis of Germplasm Diversity
1 2 3 4 5 6 7 8 9 10 11
 Botstein et al. (1980) suggested that, PIC values for bi-allelic SNP markers range from 0 to 0.5. So
they have reported PIC values greater than or equal to 0.25 as highly informative.
 Within the cultivated population 34% of the SNPs had a PIC value greater than or equal to
0.25 compared to the wild population, which had 56%.
 So they concluded that, the high PIC value derived from the wild population is consistent with
the expectations that a greater proportion of highly polymorphic markers in the wild
population due to the selective breeding seen in the cultivated population.
Contd ..

Estimation of Linkage Disequilibrium
calculated based on the allele frequency correlations (r2) using the TASSEL program (v5.1.0)
 The number and density of markers required for
an association mapping analysis is determined
by the distance over which LD decays. Here, the
LD patterns of mungbean reflect its long
history of domestication.
 The squared correlations of allele frequencies r2
of the cultivated mungbean population
decreased to half of its maximum value at
approximately 100 kb physical distance
compared to the wild mungbean population
which had largely decayed by 60 kb.
 Wild mungbean has retained a higher degree
of allelic diversity providing an important
source of material for increasing the genetic
diversity of the cultivated gene pool.
cultivated
wild

Analysis of Population Structure of 466
Cultivated Mungbean Accessions
Sub-population 1 (color-coded orange)
Sub-population 2 (color-coded pink)
Sub-population 3 (color-coded green)
Sub-population 4 (color-coded red)
No. of accessions Seed Coat Colour
Sub-population 1 25 Green
Sub-population 2 40 Green, Speckled, Yellow and Brown
Sub-population 3 59 Yellow and Green
Sub-population 4 22 Green, Speckled

Principal Coordinate Analysis 466 Cultivated
Mungbean Accessions
 Principal coordinate analysis was also
used to visualize the relationships
amongst the cultivated accessions in the
panel.
 When the four sub-populations were
plotted, they clustered toward the
extremities of the plot based on their
genetic differences.
 The first two principal coordinates
accounted for approximately 34.04% of
the genotypic variance with coordinates
one (x-axis) and two (y-axis) explaining
18.18 and 15.86%, respectively.
Color-coded according to membership (based on >90% identity) to sub-populations identified from structure analysis; sub-population 1
(color-coded red), sub-population 2 (color-coded yellow), sub population 3 (color-coded green), and sub-population 4 (color-coded purple).

Genetic Diversity Between the Sub - Populations
 The genome-wide genetic differentiation between the four contrasting mungbean sub-
populations identified in structure were calculated using fixation index (FST) using
PopGenome.
 Sub-populations 1 and 3 were the most closely related with an overall FST value of
0.42, while sub-populations 1 and 2 show the highest degree of differentiation, with
an FST value of 0.57.
 Sub-population 1 had uniform green seed coats, in contrast to sub-population 2
which had a wide variation of seed coat colors.

Genome-wide Association Study of
Seed Coat Colour QTL
TASSEL v5.1.0
• 9 SNPs were identified as significantly associated with seed color located in five distinct genomic
regions distributed across chromosomes 3, 4, 5, and 7
• VrMYB113 is the homolog of the Arabidopsis gene MYB113 involved in anthocyanin
biosynthesis.
• Vrsf30h1 is the homolog of a previously identified gene (sf30h1) controlling seed coat color
through flavonoid 30- hydroxylase in soybean.
• They claimed that, this data set can provide high resolution mapping opportunities.
Chromosome 4
Chromosome 5
VrMYB113 Vrsf30h1

Summary
• This study aimed to characterize a mungbean diversity panel consisting of 466 cultivated accessions and
demonstrate its utility by conducting a pilot genome-wide association study of seed coat color.
• In addition 16 wild accessions were genotyped for comparison and in total over 22,000 polymorphic
genome-wide SNPs were identified and used to analyze the genetic diversity, population structure, linkage
disequilibrium (LD) of mungbean.
• Polymorphism was lower in the cultivated accessions in comparison to the wild accessions, with average
polymorphism information content values 0.174, versus 0.305 in wild mungbean. LD decayed in _100 kb
in cultivated lines, a distance higher than the linkage decay of _60 kb estimated in wild mungbean.
• 4 distinct subgroups were identified within the cultivated lines, which broadly corresponded to
geographic origin and seed characteristics. In a pilot genome-wide association mapping study of seed coat
color, five genomic regions associated were identified, two of which were close to seed coat color genes in
other species.
Nobel et al., 2018

Challenges and Future Perspectives
in Association Mapping
 Missing heritability.
 Refinements in experimental design and statistical analyses.
 Development of new algorithms for efficient detection of epistasis.
 Population structure.
 Accurate phenotyping.
 Discovery of rare alleles of genes/ QTLs associated with the rare marker alleles.
 Association studies and MAS
 Mapping of QTLs by jointly using linkage and LD
 Linkage disequilibrium maps in plants
 Appropriate statistical models
 Identification of QTNs (Quantitative trait nucleotides)

References
1. Abdurakhmonov, I. Y., & Abdukarimov, A. (2008). Application of association mapping to understanding the genetic diversity
of plant germplasm resources. International journal of plant genomics, 2008.
2. Noble, T. J., Tao, Y., Mace, E. S., Williams, B., Jordan, D. R., Douglas, C. A., & Mundree, S. G. (2018). Characterization of
linkage disequilibrium and population structure in a mungbean diversity panel. Frontiers in plant science, 8, 2102.
3. Pachchigar, K. P., Khunt, A., & Nilesh, P. ASSOCIATION MAPPING.
4. Stich, B., & Melchinger, A. E. (2010). An introduction to association mapping in plants. CAB Rev, 5, 1-9.
5. Sahoo, J. P., Sharma, V., Verma, R. K., Chetia, S. K., Baruah, A. R., Modi, M. K., & Yadav, V. K. (2019). Linkage analysis for
drought tolerance in kharif rice of Assam using microsatellite markers.
6. Sahoo, J. P., & Sharma, V. (2018). Impact of LOD Score and Recombination Frequencies on the Microsatellite Marker Based
Linkage Map for Drought Tolerance in Kharif Rice of Assam. Int. J. Curr. Microbiol. App. Sci, 7(8), 3299-3304.
7. Sahoo, J. P., Singh, S. K., & Saha, D. (2018). A review on linkage mapping for drought stress tolerance in rice. Journal of
Pharmacognosy and Phytochemistry, 7(4), 2149-2157.
8. SAHOO, J. P., & MOHARANA, A. 75. status and Prospects of Association Mapping in Crop Plants.

Status and prospects of association mapping in crop plants

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