assocn mapg for class

8/13/2019 Assocn Mapg for Class

1/35

ASSOCIATION MAPPING

IN PLANTS


2/35

Linkage analysis (QTL mapping)Association mapping (Linkage Disequilibrium based)

Candidate gene studies through genomic

approaches

Approaches to dissect complex traits


3/35

Disadvantages of linkage mapping

Amount of genetic variation between any two parents is limited.

Genetic backgrounds in which QTL mapping is done is not

always a representative of the crop genetic background.

Only a few generations of effective recombination taking place

leading to longer segments in LD, the consequence being reduced

resolution.

i.e homozygosity is reached in a faster pace making

dissection of a genomic region difficult -(fine mapping not

possible)


4/35

Linkage mapping - counts recombination between

markers and the trait of interest (linkage) in a

biparental population.

Association mapping- measures correlation between

marker alleles and trait allele in a population (linkage

Disequilibrium)

Association analysis, also known as LD mapping or

association mapping, is apopulation-basedsurveyused to identify trait-marker relationships based on

linkage disequilibrium (Flint-Garcia et al. 2003)


5/35

How does one proceed?

Haplotype- a set of closely linked genetic markers on

a chromosome that tend to be inherited together


6/35

Linkage vs Association

Linkage

Family-based

Few markers for genomecoverage (300-400 SSRs)

Good for initial detection; poor

for fine-mapping

Powerful for rare variants

Association (LD mapping)

Families or unrelated

Many markers for genomecoverage (105106SNPs)

Poor for initial detection; good

for fine-mapping

Powerful for common variants;

rare variants generally

impossible

Complementary

idea is to understandmolecular genetic basis of phenotypic variation


7/35

Gene and genotype frequencies

Locus 1: allele A freq = pA

allele a freq = pa

Locus 2: allele B freq = pB

allele b freq = pB

pA + pa = 1

pAA + pAa + paa = 1

What is pAB (gamete) when there is linkage disequilibrium?

And when there is no linkage?

You restore HWE after a single generation of random mating in a population

only when the individual loci are considered singly/ one at a time.

pB + pb = 1

pBB + pBb + pbb = 1


8/35

Linkage Disequilibrium

PABPAPB

PAbPAPb= PA(1-PB)

PaBPaPB= (1-PA)PB

PabPaPb= (1-PA) (1-PB)

8

B b Total

A PAB

PAb

PA

a PaB

Pab

Pa

Total PB

Pb

1.0

SNP 1

SNP 2

Linkage Disequilibrium (LD) is a

measure of the non-random association

of alleles at two different loci.


9/35

Whatever we measure as LD is in fact gametic

phase disequilibriumand thus will remain true only

when it is due to linkage. (corollary?)

D = ru-st


10/35

Factors affecting LD

Mutation

Self pollination

Genetic isolation

Population admixture

Small founder population / genetic drift

Selection

Epistasis

Factors Increasing LD Factors Decreasing LD

High recombination

Recurrent mutation

Outcrossing

Gene conversion


11/35

Linkage DisequilibriumExampleHow does LD arise?

There are only three haplotypes: AG, CG, and CC.

There is no AC haplotype, i.e., PAC= 0.

However, PAPC=1/9, thus PAPC PAC .

These two SNPs are in linkage disequilibrium

11

-- A -- -- -- G -- -- --

-- C -- -- -- G -- -- --

-- A -- -- -- G -- -- --

-- C -- -- -- G -- -- --

-- C -- -- -- C-- -- --

Before mutation After mutation

PA=1/2PC=1/2

PG=1

PA=1/3

PC=2/3

PG=2/3

PC=1/3


12/35

Linkage EquilibriumExampleHow does LD disappear?

After recombination,

PAG= PAPG = 1/4,

PCG

= PC

PG

= 1/4,

PCC= PCPC= 1/4, and

PAC= PAPC= 1/4.

Thus, these two SNPs are linkage equilibrium.

12

-- A -- -- -- G -- -- --

-- C -- -- -- G -- -- --

-- C -- -- -- C -- -- --

-- A-- -- -- C-- -- --

-- A -- -- -- G -- -- --

-- C -- -- -- G -- -- --

-- C -- -- -- C -- -- --

Before recombination After recombination

PA=1/2

PC=1/2

PG=1/2

PC=1/2


13/35

Measure of LD: D Coefficient

The measure the non-randomness of two loci is represented by adeviation Das follows:

D= PABPab PAbPaB

PAB= PAPB + D

PAb= PA(1-PB) - D

PaB= (1-PA)PB - D

Pab= (1-PA) (1-PB) + D

D= 0 when the two loci are in linkage equilibrium.

13


14/35

Standardization of DCoefficient

Dcoefficient is normalized (Lewontin, 1964) since range of D is always

determined by the allele frequency.

D = D/Dmax, where Dmax stands for the absolute maximalpossible value of D.

BaaBBa

bAAbbA

baabba

BAABBA

BabA

baBA

PPDPDPP

PPDPDPP

PPDPDPP

PPDPDPP

DPPPP

D

DPPPP

D

D

0

0

0

0

.0if,),min(

;0if,),min('

14

0

-PAPB PaPB

D D


15/35

Interpretation of D

Dis constrained between -1 and +1.

D= 1 (perfect positive LD between SNP alleles)

D= 0 (linkage equilibrium between SNP alleles)

D= -1 (perfect negative LD between SNP alleles)

D= 0.87 (strong positive LD between SNP alleles)

D= 0.12 (weak positive LD between SNP alleles)

15


16/35

Measure of LD: r2(Hill and Robertson, 1968)

r2

= (PAB

pA

pB)2

/pA

pa

pB

pb

0 r2 1.

Most relevant LD measurement.

0.1 to 0.2 r2refers to LD decay

r2is the square of correlation coefficient when alleles are binary

coded.

If r2= LD value of SNP with another and h2q= total trait

variation, then,

r2* h2q= the trait variation that can be explained by these SNPs

r2=2/K, where Kis the number of chromosomes.


17/35

Decay of LD over Time

The chromosome recombination decreases LD so that

equilibrium is attained at the end.

17


18/35

3/6

2/43/2

6/23/5

2/6

3/6 5/6

Allele 6is associated with trait of interest

4/62/6

6/6

6/6

3/4

5/2

Controls Cases

Allelic Association


19/35

Direct Association

Mutant or susceptible polymorphism

Allele of interest is itself involved in phenotype

Indirect AssociationAllele itself is not involved, but a nearby correlated

marker changes phenotype

Spurious association

Apparent association not related to genetic causes(most common outcome)

Linkage Disequilibrium:correlation between (any) markers in population

Allelic Association: correlation between marker allele and trait

Allelic Association

Three Common Forms


20/35

Indirect and Direct Allelic Association

D

*

Measure trait relevance

(*) directly, ignoring

correlated markers nearby

Direct Association

M1 M2 Mn

Assess trait effects on QTL

via correlated markers (Mi)

D

Indirect Association & LD


21/35

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.02 0.04 0.06 0.08 0.1

Recombination fraction

D

10 gens

20 gens

50 gens

250 gens

How far apart can markers be to detect association?

Dt= (1

)tD0

Expected decay of linkage disequilibrium


22/35

Association Mapping for crop

improvement(AM versus QTL Mapping)

Since Association Mapping can be conducted directly on the

breeding material:

1. Direct inference from research to breeding is possible.

2. Phenotypic variation is observed for most traits of interest.

3. Marker polymorphism is higher than in biparentalpopulations.

4. No need of pedigrees or structured mapping populations.

5. Routine evaluations provide phenotypic data.

6. Higher resolution possible because of recombination over

very large number of generation studiedthrough extent ofhaplotype sharing.

7. Association Mapping provides other useful information

about:

Organization of genetic variation and

Polymorphism across the genome


23/35

Types of Populations

1. Germplasm Bank Collection

A collection of genetic resources including landraces, exotic material

and wild relatives.

2. Synthetic Populations

Outcrossing populations synthesized from inbred lines (segregating

generations). May be used for recurrent selection.

3. Elite Lines

Inbred lines (and checks) manipulated with the objective of releasing

new varieties in the short term.


24/35

Characteristics Related to Association Mapping

S. No Aspects of AM GP Bank Synthetic

population

Elite GP

1 Sample Core collection Segregating

progenies

Elite lines

2 Sample turnover Static Ephemeral Gradually

substituting

3 Source of Pdata Screenings Progeny tests Yield trials

4 Types of traits High h2 and

domestication

traits

Depends on the

evaluation

scheme

Low h2 traits

5 Type of Marker SNP SNP/SSR SSR

6 LD Low Intermediate and

fast decaying

High


25/35

S. No Aspects of AM GP Bank Synthetic

population

Elite GP

7 Population

structure

Medium Low High

8 Allele diversity

among samples

High Intermediate Low

9 Allele diversity

within samples

Variable 1 or 2 alleles 1 allele

10 Power Low Intermediate and

decreasing

High; could

allow genomic

scan

11 Resolution High; could allow

fine mapping

Intermediate and

increasing

Low

12 Use of informative

markers

Transfer of new

alleles by marker

assisted BC

Incorporation in

selection index

MAS in

progenies

(after

validation)


26/35

Germplasm bank core-collections - for allele-mining of

candidate genes and fine-mapped QTLs

Elite lines - to detect genomic regions associated with

traits of interest

Synthetic populations might represent a balance between

power and precision, and have the major advantage of being

unstructured

Summary (of characteristics)


27/35

Pearson Chi square test

Yates correction

Fishers Exact test


28/35

Structured Association

To tackle highly structured populations

Looks for closely related clusters and develops Q by use of a set of

random unlinked markers

Corrects the false associations

STRUCTURE (Pritchard et al. 2000) estimates population structure

and shared coancestry coefficients for all markers

Not good enough when some degree of relationship (Kinship) is also

present.


29/35

Accounts for multiple levels of relatedness (Yu et al., 2006)

Uses Q matrix (from STRUCTURE) to account for population

subdivision

Uses K matrix to account for relatedness within populationsusing Spagedisoftware

Superior to other methods (Structured Association, Genomic

Control and Quantitative Transmission Disequilibrium Test) in

Type I error control and statistical power

Implemented in the software TASSEL

Replacement of Q matrix with P matrix makes it more robust

(Price et al., 2006 and Zhou et al., 2007)

Mixed Linear Model (MLM)


30/35

Population Stratification

A population under study may have sub-populations, which may lead to

Spurious association.

Loss of power to detect real association.

EIGENSTRAT (Price et al. 2006 Nat. Genet.) uses principal components to

extract information on stratification and adjust for the stratification in

association analysis.

Mixed Population = Sub-population 1 + Sub-population 2

A a A a A a

Case 70 80=

10 40+

60 40

Control 50 100 20 80 30 20


31/35

A method for joint QTL linkage and association mapping in a set of

RIL populations derived from matings to a common parent

Dense genotyping of SNPs is performed only in the parents

Only common parent-specific SNPs are genotyped in the RILs and

are used to identify the parental origin of chromosomal segments,

allowing projection of sequence information from parents to RILs

LD information from ancient recombination is thus captured,

allowing for high resolution mapping with far less genotyping effort

Nested Association Mapping


32/35

Power of Association Mapping

Decisive Factors

Extent and evolution of LD in the population (mode of pollination)

Complexity and mode of gene action for the trait of interest

Sample size

Preselecting a priori known QTLs or candidate genes

Samples with longer LD blocks

Availability of pedigree and genomic information and resourcesQuality of phenotypic data

Association mapping panel constructed

Efficiency of targeted gene sequencing (genotypic data)


33/35

LD and AM studies in various plant species

S.

No.

Crop Population LD Extent Traits

1 Rice Diverse landraces andaccessions

5- 500 kb; 20-30 cM; 50-225cM

Glutinous phenotype, Starchquality, yield and itscomponents

2 Wheat Diverse

cultivars


34/35

Common Statistical Software

Packages for Association Mapping

S. No. Software Package Focus Remarks

1 TASSEL Association analysis Free, LD stat, sequenceanalysis, AM by GLM andMLM

2 STRUCTURE Population structure Free, widely used for PSanalysis

3 SPAGeDi Relative kinship Free, Genetic relationshipanalysis

4 EINGENSTART PCA, Association

analysis

Free, PCA was proposed

as an alternative forpopulation for PSA

5 MTDFREML Mixed model Free, can be used forplant data

6 ASREML Mixed model Commercial, can be usedfor plant data


35/35

Two major typesGenome-wide screen and candidate gene

Genome-wide screen

Hypothesis-free

High-cost: large genotyping

requirements

Multiple-testing issues

Possible many false positives,

fewer misses

Candidate gene

Hypothesis-driven

Low-cost: small genotyping

requirements

Multiple-testing less important

Possible many misses, fewer

false positives

assocn mapg for class

Documents