Genome-wide Associations

Lakshmi K Matukumalli

Illumina SNP Genotyping

Chemistry

Genotype Data

Fine Mapping with SNP Markers

Advantages of SNPs as genetic markersas compared to microsatellites.

•High abundance

•Distribution throughout the genome

•Ease of genotyping

•Improved accuracy

•Availability of high throughput

multiplex genotyping platforms

Objectives for GWA

Create Cures for Diseases (Humans) Localize diseases to narrow chromosomal regions Identify causative mutations for disease Genetic predisposition to drugs / diseases Personalized medicine

Selection Decisions (Live stock & Plants) Increased productivity, disease resistance, composition

(Fat, protein, tenderness) Identification of QTL regions Application of Marker assisted selection Genome Selection

QTL

LINKAGE MAPPINGWhere genes are mapped by typing genetic markers in families to identify regions that are associated with disease or trait values within pedigrees more often than are expected by chance. Such linked regions are more likely to contain a causal genetic variant.

ADMIXTURE MAPPINGPredicting the recent ancestry of chromosomal segments across the genome to identify regions for which recent ancestry in a particular population correlates with disease or trait values. Such regions are more likely to contain causal variants that are more common in the ancestral population.

PENETRANCEThe proportion of individuals with a specific genotype who manifest the genotype at the phenotypic level. For example, if all individuals with a specific disease genotype show the disease phenotype, then the genotype is said to be 'completely penetrant'.

HERITABILITYThe proportion of the variation in a given characteristic or state that can be attributed to (additive) genetic factors.

Traditional Methods

Common

Ancestor

Emergence of Variations Over Time

time present

Linkage Mapping

Non-Parametric Linkage

Transmission Disequilibrium Test (TDT)

Principles

• Fisher’s theory of additive effects of common alleles

* Human heterozygosity is attributed to common ancestral variants (CDCV Common disease common variant hypothesis)

* Variants influencing common late onset diseases of modernity may not have been subject to purifying selection

Genome Wide Association

Whole Genome Prediction

Fit haplotype block into a statistical model:

Effect A B C D E F G H I J K L M

Levels1

3

4

3

1

3

4

1

3

4

1

3

1

3

4

1 1

3 3

1

3

1

2

3

1 1 1

2 2 2 2 22 2 2 22 2 2

1 1

2

Genome Enhanced PBVBlock

GEPBVHaplotype A B C D

1 +0.01 +1.03 -1.23 +6.35

2 +0.06 -0.74 +0.98 +2.19

3 +0.05

4 -8.59

Animal 1

1 1 1 2 2 2 1 3

0.01

+0.01

1.03

-0.74

0.98

+0.98

6.35

+0.05 8.67

Animal 2

2 2 1 1 2 2 2 4

0.06

+0.06

1.03

+1.03

0.98

+0.98

2.19

-8.59 -2.26

a. Gene centric approachb. Non-ascertained (Uniformly spaced / Tag SNPs)

Genome Wide Association - Methods

Age-Related Macular Degeneration

Complement Factor H Polymorphism

Samples

1,464 Patients T2D

1,464 Case Controls

Traits

Glucose metabolism

Lipids

Obesity

Blood pressure

Follow-up

107 SNPs on extreme p-values genotyped on 10, 850 additional populations

Type 2 diabetes and triglyceride levels

T2D

•non-coding region near CDKN2A and CDKN2B

•Intron of IGF2BP2

•Intron of CDKAL1

Triglycerides

Intron of glucokinase regulatory protein

Coronary Heart Disease

375,00 SNPs

WTCCC

1926 Case

2938 Controls

German GI Family

875 Case

1644 Control

Genotyping by candidate gene approach

Significant Associations

9p21.3 regionP=1.80 x 10(-14) WTCCCP=3.40 x 10(-6), German MI Family.

The WTCCC study revealed nine loci that were strongly associated with coronary artery disease (P<1.2 x 10(-5)) and less than a 50% chance of being falsely positive). Two additional loci at 6q25.1 and 2q36.3 were also successfully replicated in the German study:

The combined analysis of the two studies identified four additional loci significantly associated with coronary artery disease (P<1.3 x 10(-6))) and a high probability (>80%) of a true association: chromosomes 1p13.3, 1q41, 10q11.21, and 15q22.33.

GWA of seven Common Diseases

14,000 Cases (2,000 each)

3,000 shared controls

Determining Marker Order

Chromosome segments

Clones

Genotyping

A BC D E F G

Neutral Evolution Versus

Positive Natural Selectionhttp://ai.stanford.edu/~serafim/CS374_2006/

presentations/lecture5.ppt