QTL-IM, AM & GS FOR MOLECULAR

BREEDING: AN APPRAISAL

P K Gupta

CCS University, Meerut, India

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please contact the author

OUTLINE OF LECTURE

• MTA Studies Conducted at CCSU Meerut

• Some Interval Mapping (IM) Results

• Issues Involved in Interval Mapping

• Some GWAS Results

• Issues Involved in GWAS

• Issues involved in Genomic Selection (GS)

MARKER-TRAIT ASSOCIATION (MTA)

STUDIES IN WHEAT AT CCSU, MEERUT

BSA; Chi-square test; t-test, etc. (1999-2003)

QTL Interval Mapping (2003-Contd.)

Association Mapping (2004-Contd)

During this process, lessons were learnt; experimental

designs and methods of analysis were modified to

improve precision of detecting MTAs.

MAS studies were also conducted for PHST and GPC

& published (Plant Breeding, and Field Crops Res)

Composite Interval Mapping (CIM) for days to maturity involving

chromosome 2D

Theor. Appl. Genet. 99: 341-45 (1999)

Theor. Appl. Genet. 99: 336-40 (1999)

Theor. Appl. Genet. 100: 1290-94 (2000)

Plant Breeding 120: 273-278 (2003)

Plant Science 164: 267-277 (2003)

Theor. Appl Genet. 111: 1052-59 (2005)

Func Integ Genom. 5: 254-59 (2005)

Euphytica 151: 135-144 (2006)

Mol. Breeding 19: 163–177 (2007)

Euphytica 168: 319–329 (2009)

Plant Sci 177: 114-122 (2009)

Mol Breeding 29: 963–972 (2012)

EPISTATIC QTL FOR GRAIN PROTEIN

CONTENT (GPC) IN TWO POPULATIONS

Kulwal et al. (2005) Funct. & Integr. Genomics 5: 254-259.

HIGHER ORDER INTERACTIONS

Higher Order Interactions can be

studied to understand the

networks involved in complex QTs

Multitrait Composite Interval Mapping (MCIM) involving three correlated

traits. Trait 1= early growth habit; Trait 2= days to heading, and Trait 3=

days to maturity involving chromosome 1D

Conditional QTL genetic effects (Zhu 1995;

Wang et al. 2012, GPC vs Yield)

SOME ISSUES WITH INTERVAL MAPPING

Type of mapping populations: Multiple Biparental vs Multiparental (e.g., MAGIC; Two Wheat MAGIC Populations in Australia; PBJ 10: Sept 2012; MAGIC populations also in rice at IRRI)

Size of mapping population: 100s vs 1000s (Beavis Effect; Xu, S. Genetics, 2003)

Genotyping: WGS, GBS & Sliding Window; Haplotypes

Statistical models: mixed models preferred - QQ (+higher order) & QE interactions - Conditional QTLs for correlated traits (Zhu 1995) - Epigenetic modifications

NIL, ILs, AI-RILs, BCRILs, SSSLs,

CSSLs & MAGIC POPULATIONS

Improved Recombination;

Can also be used for AM

CSSLs

(ILs) &

SSSLs

in Rice

9 founders to give

36 families

BCRILs

SUBSTITUTION MAPPING OF BLAST RESISTANCE

QTL USING RICE SSSLs (ZHANG ET AL. 2012)

with non-idealized CSSLs, the

method becomes more complex

SEQUENCING BASED HT GENOTYPING OF

RICE RILS (SLIDING WINDOW APPROACH)

Huang et al.2009

(Genome Res);

Xie et al. 2010

(PNAS);

Wang et al. 2011

TAG (Mapping

49 QTL);

Zong et al.2012;

JGG (pyramiding

of 8 QTL using

MAPS) (Marker

Assisted

Phenotypic

Selection )

Sliding Window

Approach

Genotyping Using NGS

(QTL Interval & Assoc Mapping)

Recombination Bins as Markers Rice 150 RILs Sequenced (bar-coding &

multiplexing)using Illumina: Recombination Bins

Identified using Sliding Window Approach were used as

Markers: Huang et al, 2009; Xie et al., 2010; Wang et

al., 2011)

Genotyping by Sequencing (GBS) Bar-coded multiplexing for genotyping (GBS)

4 Haplotypes in 34 Inbreds for

Maize Stearoyl-ACP Gene:

(Picture from A. Rafalski)

Haplotypes and Intragenic

Epistasis for MAS

Individual SNPs may Interact

within a Gene

Population-Based Methods

- Case-Control Studies (Binary QTs)

- Genome Wide Association Studies (GWAS) and

Candidate Gene Approach (continuous QTs)

Family-Based Methods

- Transmission Disequilibrium Test (TDT)

- Quantitative Transmission Disequilibrium Test

(QTDT) & Quantitative Inbred Pedigree

Disequilibrium Test (QIPDT)

- Henderson Mixed Model Approach (BLUP)

Population Structure in Wheat

LnP(D) = mean posterior probability

Interval mapping = 10 MTAs

GWAS = 25 MTAs

(11 New)

Common = 4 MTAs

30 MTAs for PHST; 8 located

in regions earlier reported; only

3 MTAs with FDR correction

GWAS STUDIES BY CCSU STUDENTS IN

OTHER LABS (MINNESOTA AND CORNELL)

MAJOR ISSUES IN

ASSOCIATION MAPPING

Experimental Design (a shift towards CG &

family-based approaches)

Statistical Models (model search hardly done)

Population Structure/Stratification

Multiple Testing (GWER & FDR)

Rare Alleles & Missing Heritability

Single locus vs multi-locus models &

p large n small problem

Single vs multi-trait & conditional effects

EXPERIMENTAL DESIGN IN LINKAGE AND

ASSOCIATION MAPPING

Association Mapping: Critical Considerations Shift

From Genotyping to Experimental design

(Ed Buckler’s Group, 2009: Plant Cell)

Shift:

-Linkage Analysis to Joint Linkage Analysis

-Population-Based to Family-Based AM (FBAM)

(use MAGIC, NAM)

-Joint Linkage Analysis + AM

-Simple GWAS to JLAM and CG approaches

STATISTICAL MODELS

Simple Linear Models (only fixed effects)

-Generalized Linear Models (GLM): Simple

Regression, ANOVA

-Step-Wise Linear Models (SWLM)

Mixed Linear Models (fixed and random effects)

-Mixed Linear Models (MLM): 10 Different models

-Multi-locus Mixed Linear Model (MLMM)

-Multi-trait Mixed Linear Model (MTMM)

-LASSO-LMM Model

DIFFERENT MIXED LINEAR MODELS

(WITH AND WITHOUT STRUCTURE)

QG and G Methods Involving Genome Wide Selection (Bernardo, 2013);

look after the effect of genetic background (not addressed by QK & K)

T is probability that a variant

from one parent each of

inbreds I and j are alike in

state, given they are not IBD.

T = 0.70

QK0.70 and PK0.70

seem to be the best

approaches, since

they give observed

values of P close to

expected values

(Stich et al.

2008;

Genetics)

POPULATION STRUCTURE IN AM

Population Structure causes false positives

LnP(D) versus Delta K method

Conditional analysis approaches to deal with

population structure::

-Single locus: 1. GC, 2. SA, 3. PCA, step-wise

regression, linear mixed model;

-Multilocus mixed model (Nature Genet July’12)

K-Matrix: Yu et al. (2006); Zhao et al. (2007);

Stich et al. (2008; REML)

REML = Restricted Maximum Likelihood

NUMBER OF SUBPOPULATIONS

(K ANDΔK; Evanno et al. 2005)

Removing Population

Structure is not the Solution

Nature Reviews Genetics, January 2013

Adaptation to environment and differences in genetic background may

cause serious problem leading to confounding of results; depending upon

trait, environment may or may not be important, but genetic background is

certainly important as shown in the following recent report:

PROBLEM OF COMPUTATIONAL DEMAND

Computing time in MLM increases as n3 so that with

increase in number of genotypes, which is desirable,

computation demands becomes a bottleneck

Efficient mixed model association (EMMA) improves

computation speed; but this method also becomes

impractical with increased number of data points

A number of other improved methods include

GRAMMAR, EMMAX, P3D, CMLM & GEMMA

Compressed MLM (CMLM) clusters individuals into

fewer groups on the basis of kinship, thus reducing

the computation work

Multiple Testing Problem (Power 80%)

~40% <7%

Bonferroni Corrections

False Discovery Rate

(FDR) Method

Bayesian Approach

Markers Positives Negatives FDR GWER

used (NS/S) False True True False

100/10 5 8 95 2 5/13 7/110

1000/100 50 80 950 20 50/130 70/1100

10,000/1000 500 800 9500 200 500/1300 700/11000

Bonferroni correction

Bonferroni step-down

(Holms) correction

Westfall & Young Permutation

Genome Wide

Error Rate

(GWER)

Benjamini & Hochberg FDR

Storey & Tibshirani q-value

False Discovery

Rate (FDR)

• False negative

• Stringency

• False positive

• Statistical power

Multiple testing

correction

Figure: Comparison of various methods used for multiple testing correction

Different Methods of Multiple Testing Corrections

Can Bayesian approach be used for more stringent test?

PROBLEM OF LOW FREQUENCY ALLELES

(RARE ALLELE ISSUE)

Association Mapping has low power to

detect QTL with rare alleles; a large

population is needed to detect such QTL,

which can be important

Issue of markers with low MAF is difficult

to address due to statistical issues, but

efforts are being made

MLMM, LASSO-MLM AND MTMM

NATURE GENETICS (2012)

Single locus & single trait studies give biased estimates

Therefore, several multi-locus mixed models have been

suggested, the latest being MLMM (2012) & LMM-

Lasso (Bioinformatics, 2013)

This leads to ‘p large n small’ problem;

For correlated traits, MTMM suggested (results are

biased and GWAS power is low, if correlations not

considered); conditional QTL genetic effects estimated

for correlated traits.

“LARGE P SMALL N” PROBLEM IN

MULTI LOCUS ANALYSIS

In model space, no. of polymorphisms fitted must be < no.

of individuals; apply correction and/or use a better model.

A simple, stepwise mixed-model regression with forward

inclusion and backward elimination suggested in

MLMM, which performs well in terms of FDR and power.

LASSO-LMM (Rakitsch et al. 2013; published on-line on

Nov. 22, 2012, Bioinformatics) is better than MLMM

GWAS IN TOMATO USING MLM & MLMM

4TH ICQG (EDINBURGH; 17 - 22 JUN 2012), P355

FAMILY BASED ASSOCIATION MAPPING

• Power and Accuracy of QTL detection depend on

(i) Population size;

(ii) Phenotype Intensity

(iii) Marker density

• FBAM using multiple segregating families (having

balanced allele frequencies) is becoming

popular due to its advantages over PBAM

DIFFERENT TYPES OF FAMILY BASED

POPULATIONS FOR AM

ASSOCIATIVE TRANSCRIPTOMICS

IN POLYPLOID BRASSICA NAPUS

Generate mRNA-Seq on diverse AM panel

Identify SNPs using Unigenes as reference

Record data on transcript abundance for

eQTL analysis and conduct GWAS with

SNPs & Gene Expression Markers (GEMs)

July 2012

Testing Reliability of GWAS Results

Enrichment ratio using known candidate genes

(Atwell et al., 2010)

Candidate gene (CG) AM

- Dwarf8 and flowering time in maize

(Thornsberry et al., 2001)

- >50 genes for flowering time in Arabidopsis

(Ehrenreich et al., 2009)

- Candidate genes for drought tolerance (Xu et al.

Feb 5, 2013; JEB)

-

GENOMIC SELECTION (GS)

No MTAs needed; all

markers used for selection

Alleles are assigned

breeding values

Predicting breeding

value is crucial

No GS work in India so far

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QTL effect

Fre

quen

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Criteria for Genomic Selection

Genome-wide genotypic data used to predict the trait

with a high level of accuracy to allow selection.

Accuracy of prediction is crucial, and models are being

developed to improve prediction

Select markers that are most predictive of genomic

breeding values

Compute adjusted genotype means across environments

of a target region and then use these means in GS

Single stage and two stage GS

SUMMARY &

CONCLUSIONS

Commonly used methods of genetic dissection of

QTs suffer with limitations and are still evolving

Limited genetic variation and low resolution in

interval mapping, problems of false positives,

multiple testing, and rare alleles in AM and that of

prediction models in GS need to be addressed

Geneticists and plant breeders need to equip

themselves with skill of a statistician to be able to

make the best use of available tools