P. K. GUPTAMOLECULAR BIOLOGY LABORATORYDEPTT. OF AGRICULTURAL BOTANY

CCS UNIVERSITYMEERUT

Quantitative Genetics: Markers for Conventional Breeding

Quantitative Genetics

Era 3 (1990s and 2000 onwards)QTL (Quantitative Trait Loci) AnalysisLD & Association Mapping: complex traits

•

Pre-Mendelian

Work•

Francis Galton

& Karl Pearson’s Work

•

Era 1 (1910-1950)•

Multi-Factor Hypothesis: simple traits

•

Era 2 (1960-1980)•

Biometrical Genetics: complex traits

Molecular Markers•

1st

Generation Markers (RFLP, RAPD)

•

2nd

Generation Markers (SSR, AFLP)•

3rd

Generation Markers (SNPs)

•

New Generation Microarray-based Markers (SFPs, DArT) Gupta et al. 2008: Heredity

Statistical Tools•

Maximum Likelihood

•

EM Algorithm•

Bayesian Approach & MCMC

•

Population Structure and TASSEL for Association Mapping

QTL AnalysisMethods:

•

1. Linkage Analysis (single marker analysis and interval mapping)

•

2. Linkage Disequilibrium (LD) and Association AnalysisRequirements:1. Normal distribution of the trait 2. No segregation distortion of marker genotypes

Methods of QTL Mapping

•

Single Marker Methods•

Interval Mapping (SIM, CIM, MIM)

•

Bayesian Approach & Markov Chain Monte Carlo (MCMC) Methods

Mapping Populations for QTL Mapping

•

Backcross (BC1

)•

F2

•

Doubled haploids (DH lines)•

Recombinant inbred lines (RI lines)

•

Advanced backcross (BCt

)•

Advanced intercross (Ft

)

Segregation Distortion

•

Can be caused due to selection •

It can bias the estimation in recombination

•

It can reduce the power to identify QTL•

It can bias the estimation of QTL position and effects

Statistical Issues Involed in QTL Mapping

•

Hypotheses•

Distribution of test Statistics (t, F, 2, LR/LOD)

•

Single QTL•

Multiple QTL

•

Significance level (Churchill & Doerge,’94)

Single Marker Analysis (SMA)

•

Model: yj

= + β1

xi + εji = Individuals 1……to …n;

yj

= trait value for individual i

= population mean;

β1 = trait-marker regression xi

= QTL genotype•

t-test for means of two marker allele classes

•

Marker-trait association through regression of trait on marker genotypes

t -

Test for QTL Analysis

•

Genotype the population using markers•

Classify the population in two groups for each marker (on the basis of marker alleles)

•

Work-out trait-means for each group•

Apply t-test for significance for difference between means for each marker

•

Significant difference means marker-trait association

QTL Analysis of Protein Content

SMA (Continued)

•

Likelihood Ratio Test (equivalent t-test): L(,β1

,2/y,X)

=

N{yi

– (

+ β1

Xi

), 2}yi

– (

+ β1

Xi

) = εj (normally distributed)LR = {L(,β1

,2)/ L(, 0

,2)}LOD = Log10

LR

Limitations of SMA•

Means of QTL genotype are confounded

•

QTL positions can not be precisely determined (can not distinguish between QTL with minor effect and close linkage from QTL with major effect and loose linkage)

•

Statistical power low

Interval Mapping: Three Steps

•

Recording of Data on Mapping Population-

1. Phenotype (Trait) & Genotype (Markers)

•

Construction of a molecular map: MAPMAKER

•

Estimation of position and effects of QTL

Simple Interval Mapping (SIM)

SIM involves scanning of each bin (1-2cM) for the presence of QTL: two approaches

•

Maximum Likelihood Approach (Lander and Botstein, 1989)

•

Regression Approach (Haley & Knott, 1992; Martinez & Curnow, 1992)

Maximum Likelihood Interval Mapping (MLIM) Approach

•

EM (Expectation-Maximization) algorithm•

Maximize the probability of the presence or absence of QTL

•

Work out LR ratio/LOD score•

Find threshold LOD score

•

Prepare a LOD plot and detect QTL

Interval Mapping by Regression

•

Haley & Knott; Martinez & Curnow, 1992•

Phenotypes are regressed on a variable ‘l’, based on (r, 1-r) for each position in a map interval & for flanking markers genotypes

•

Y = m + l (m = mean,

= QTL effect); can be calculated and its significance tested

Interval Mapping by Regression

•

A Q B

Marker P(QQ)AABB 0.99 AAbb 0.75aaBB 0.25Aabb 0.01

.......b

P(QQ)

Y .0 1

Substitute Probabilities for Missing Genotypes

MLIM vs RIM

•

MLIM is computationally demanding•

RIM is simpler and less demanding

•

In general, MLIM and RIM give similar results, and RIM is sometimes preferred

•

Under certain conditions, MLIM may be more precise and powerful

Limitations of SIM•

Number of QTLs can not be resolved (QTLs may be linked)

•

Exact positions of QTLs can not be determined (QTL interactions)

•

Statistical power low (limited information in the model)

Composite Interval Mapping (CIM)

•

A combination of SIM & multiple linear regression (Zeng, 1993, 1994): yj

= μ

+βi

xij +

Σ βk

xkj + εj βi = effect of QTL between markers i & i + 1; xij, xkj = dummy variables for markers i and k in individual j; βk = partial regression of trait on marker k

•

CIM can be conducted using MLIM or RIM•

QTL Cartographer can be used (all softwares do not have facility for CIM)

Advantages of CIM

•

The effects of other QTLs are removed•

QTL locations and effects unbiased

•

Resolution much higher than SMA/SIM•

More informative and efficient, since there are more variables in the model

Limitations of CIM

•

The test statistics in marker rich region and marker poor regions are not comparable

•

Only QTL main effects can be determined•

Joint contribution of multiple linked QTL can not be worked out (epistatic interactions can not be worked out)

•

Use of tightly linked markers as cofactors can reduce the statistical power

Multiple Interval Mapping (MIM)

•

Model: yj

= μ

+ Σ βir

xij + Σ

(βir

xis) + εj

Σ βir

xij

= main effects; Σ

(βir

xis

) = epistasis•

May start with CIM results to save time

•

Allows estimations of epistasis•

Allows estimation of Q x E interactions

QTLs with No Main Effects, But Only Epistatic Interactions

•

Many QTLs with no main effects, and having only epistatic interactions may be involved (Xing et al., 2002)

•

At the molecular level also protein ×

protein interactions may be involved in regulations of gene expression

Softwares for QTL Mapping

•

QTL Cartographer•

QTLmapper 1.0

•

MapMaker/QTL•

PLABQTL

•

QGENE•

Bmapqtl (Bayesian Interval Mapping)

Gene/QTL Tagging in Wheat

•

Two Markers for GPC•

Two Markers for PHST

•

One Marker for GW•

Validation of QTLs for GPC Using NILs

•

QTL Interval Mapping for GPC Using RILs•

QTL Interval Mapping for 14 Traits Using ITMImap and ITMIpop

QTL Analysis of Protein Content

Composite Interval Mapping (CIM) for days to maturity involving chromosome 2D

What is Association Mapping?

•

Population-study for marker-trait associations-

Also called Association analysis/LD mapping

-

Relies on historic sources of disequilibrium•

Two main approaches in humans: -

Case-control studies

-

Transmission disequilibrium tests

Association Mapping: Why?

–

More opportunities for recombination giving rare recombinants

–

Map distances more precise with relatively small sample sizes

–

If LD decays too fast in a region, many markers needed to scan target region

Contd

Association Mapping: Why?

•

Population survey: multiple alleles are represented, & inferences are broader than for a single mapping population

-

QTLs identified are useful in diverse genetic backgrounds

Association Mapping: Where?

•

Initially used for outbreds:

controlled crosses are difficult or impossible

•

In humans, association used to identify & clone several genes for diseases like:–

Cystic fibrosis

–

Diastrophic dysplasia–

One of the major Alzheimer’s factors

Association Mapping: How?

•

Two Main Approaches-

Candidate Gene Approach

-

Genome-Wide Association Mapping

Genome-Wide vs Candidate-Gene Association Mapping

Steps for Association Studies:

(1) Selection of population samples, (2) Study of population structure (3) Phenotyping

for traits of interest

(4) Genotyping candidate genes/genome(5) Testing marker-trait association

Types of Populations

•

Classic Association Populations

(diversity estimated and population of diverse unrelated individuals developed)

•

Family Based Association PopulationsJoint linkage & association analysis: TDT, family based association tests (FBATs); QTDT, Q Inbred Pedigree DT (QIPDT)

•

Special Association Populations

(e.g. NAM)

Population Structure (PS)

•

PS creates genome-wide LD between unlinked loci; effect of PS on Association is controlled by-Genomic Control (GC)-Structured Association (SA)-Unified mixed model approach (Q+K)(K = familial relatedness between individuals; Q = relatedness due to population structure are considered as independent variables)

•

The genetic makeup of the study population defines association tests to be used.

TASSEL for Association Studies

•

TASSEL = Trait Analysis by aSSociation. Evolution, Linkage (Yu et al. 2006)-LD calculated as D’

and r2 and P-values

-Diversity analysis tools also available-Sequence align, SNP/indel

extraction

-Uses SNPs/SSRs/sequences/phenotypes

Nested Association Mapping (NAM)

•

Combines QTL & Association Mapping-Select diverse founders & develop ~5000 RILs

from 25 families (~200 RILs/family)

-Densely genotyping the founders

Proposed by Ed Buckler-Yu et al. 2008, Genetics 178: 539

Genome reshuffling between 25 diverse founders

A pair of polymorphic CPS markers leading to fine mapping of NAM

CPS Markers = Common Parent Specific (B73) Markers

CAP markers

TDT & Admixture Mapping ‘Mapping by Admixture LD’

(MALD)

•

Transmission Disequilibrium Test (TDT) controls false positives by using two parents and single progeny

•

MALD detects linkage by testing ancestry of marker-trait association (when parent populations differ in allele frequencies)

Multi-trait Association Mapping (MTAP)

•

Stich

et al. (2008), TAG 117 (6): 947-954(Melchinger’

group on sugar beet)

•

Generally single traits are studied at a time in association mapping; same QTL may affect two or more correlated traits

•

MTAP distinguishes between pleiotropy

& close linkage

Association mapping in Multiple Segregating Populations (AMMSP)

•

Stich

et al. (TAG: 117, # 7, Nov. 2008)(Melchinger’s

group on sugar beet)

•

NAM needs populations by crossing many inbreds with a common genotype

•

AMMSP uses multiple related crosses from routine plant breeding programs

•

AMMSP Successfully used in sugar beet

Multiparent

Advanced Generation Inter-Cross (MAGIC) for LD

•

First prposed

and applied in mice•

Population captures majority of variation

•

Coarse mapping possible with low marker density in early generation

•

Fine mapping in advanced generations using higher marker density

•

Will exploit advances in genomics

Ideal Population for Association•

Formed from a few founding ancestors

•

Maintained in relative isolation•

Random mating has occurred over a number of generations, but not too many…–

LD decayed over fairly small map distances

–

Adequate LD still exists between marker & linked QTL

Linkage disequilibrium (LD) decay plot of shrunken 1 (sh1) in maize. Data from Remington et al., 2001

Association Analysis in Maize (Dwarf8 and sugary1)

•

Dwarf8 gene in maize: Thornsberry

et al., 2001–

Associated with early flowering & low pl ht

–

LD decayed rapidly –

no association between flowering time and tb1, located 1 cM

from d8

•

Sugary1 (su1) in sweet corn: Whitt et al., 2002–

Little recombination observed within the locus

More Genes for LD in Maize

•

Yellow1 (Y1)•

Teosinte Branched 1 (Tb1)

•

Lycopene

epsilon cyclase

(LcyE)-

Recommended for biofortification

of

maize for vitamin A using MAS (Science Jan 2008)

Extent of LD in Other Plants

•

Arabidopsis –

LD extends much farther (Ca maize)–

Self-pollinating

–

LD typically decays within ~250 kb (1 cM)•

Sugarcane –

LD extends ~10 cM

–

Due to bottleneck in breeding history (interspecific cross)

–

May also be an artifact due to polyploidy

Association Mapping of Kernel Size and Milling Quality in Wheat

•

Mark E Sorrells

(Cornell)•

Association Studies-

2005 Crop Sci

45: 1685-1695

-

2006 Genetics 172: 1165-1177•

QTL Interval Mapping-

2007 Field Crop Res

101: 172-179

Association Studies in Other Plants

•

Oryza sativa (Olsen and Purugganan

2002).•

Pinus taeda (Ersoz

2006)

•

Eucalyptus spp. (Thumma

et al. 2005)•

Lolium perenne (Sk

øt

2005)

•

Beta vulgaris (Hansen et al. 2001)(For details, see review by Ersoz

et al., 2007)

Germplasm

New Populations

New Synthetics, Lines, Varieties

Elite Synthetics, Lines, Varieties

Hybridization

Selection(Intermating)

Evaluation Trials

Genotypic & Phenotypic data

Parental Selection

Marker Assisted Selection

Novel & ValidatedQTL/MarkerAssociations

Integration of Association Analysis in a Breeding Program

Elite germplasmfeeds back intohybridization

nursery