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Genome-wide Associations Lakshmi K Matukumalli Illumina SNP Genotyping Chemistry Genotype Data Fine Mapping with SNP Markers Advantages of SNPs as genetic markers as 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 Traditional Methods QTL LINKAGE MAPPING Where 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 MAPPING Predicting 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. Emergence of Variations Over Time Common Ancestor PENETRANCE The 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'. HERITABILITY The proportion of the variation in a given characteristic or state that can be attributed to (additive) genetic factors. time present Linkage Mapping Non-Parametric Linkage Transmission Disequilibrium Test (TDT) Genome Wide Association 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 Whole Genome Prediction Fit haplotype block into a statistical model: Effect Levels A B C D E F G H I J K L M 1 1 1 11 1 1 1 1 1 1 1 1 1 2 2 2 22 2 2 2 2 2 2 2 2 2 33 3 3 3 3 3 3 3 3 4 4 44 Genome Enhanced PBV Block Haplotype 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 Animal 2 11 12 22 13 0.01 +0.01 1.03 -0.74 0.98 +0.98 6.35 +0.05 22 11 22 24 0.06 +0.06 1.03 +1.03 0.98 +0.98 2.19 -8.59 GEPBV 8.67 -2.26 Genome Wide Association - Methods a. Gene centric approach b. Non-ascertained (Uniformly spaced / Tag SNPs) 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 region P=1.80 x 10(-14) WTCCC P=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 Clones Chromosome segments Genotyping A BC D E F G Neutral Evolution Versus Positive Natural Selection http://ai.stanford.edu/~serafim/CS374_2006/pre sentations/lecture5.ppt