SIB course 4-8 Feb 2008 Part 2: Statistical analysis applied to genome Whole Genome Association and proteome analyses Sven Bergmann Department of Medical Genetics University of Lausanne Rue de Bugnon 27 - DGM 328 CH-1005 Lausanne Switzerland work: ++41-21-692-5452 cell: ++41-78-663-4980 http://serverdgm.unil.ch/bergmann Overview Genetic variation: SNPs ATTGCAATCCGTGG...ATCGAGCCA…TACG ATTGCACGCCG… • Basics • What is association? ATTGCAAGCCGTGG...ATCTAGCCA…TACG ATTGCAAGCCG… • Whole genome association ATTGCAAGCCGTGG...ATCTAGCCA…TACG ATTGCAAGCCG… • CoLaus Study ATTGCAATCCGTGG...ATCGAGCCA…TACG ATTGCACGCCG… • Challenges ATTGCAAGCCGTGG...ATCTAGCCA…TACG ATTGCAAGCCG… Phenotypic variation: What is association? SNPs trait variant chromosome Genetic variation yields phenotypic variation 1.2 1 0.8 Population with ‘ ’ allele Population with ‘ ’ allele 0.6 0.4 0.2 0 -6 -4 -2 0 2 4 6 Distributions of “trait” 1
Quantifying Significance T-test t -value (significance) can be translated into p -value (probability) Whole Genome Association Regression analysis “residuals” “intercept” “coefficients” Y “response” X “feature(s)” Whole Genome Association Whole Genome Association High Scan Entire Genome * * significance - 500,000s SNPs * * * Identify local regions of interest, examine Low genes, SNP density significance gegulatory regions, etc Similar approach, but looking at the entire genome! Replicate the finding That is: 500.000 SNPs! 2
Building Haplotype Maps for Gene-finding Linkage Disequilibrium 1. Human Genome Project D 1 2 3 n Marker � Good for consensus, not good for individual LD differences Sept 01 Feb 02 April 04 Oct 04 2. Assay genetic variants � Verify polymorphisms, catalogue correlations amongst sites Markers close together on chromosomes � Anonymous with respect to Oct 2002 – 2007… are often transmitted together, yielding a traits non-zero correlation between the alleles. GWA: >20 publications in 2006/2007 Imputing SNPs Massive! CoLaus = Cohort Lausanne Analysis of Genotypes only individuals 6’189 Phenotypes Genotypes 159 measurement 500.000 SNPs 144 questions Principle Component Analysis reveals SNP-vectors explaining largest variation in the data Collaboration with: Peter Vollenweider & Gerard Waeber(CHUV) 3
WGA with different covariates Ethic groups cluster according to geographic distances indicate importance of population stratification Genomic Control Origin of grandparents PC2 Both Principal Components PC2 PC1 PC1 Prospects: Module analysis Challenges • Multiple Hypothesis testing: BPS=Systolic Blood Pressure -6 a significant result Is one SNP with p=10 when testing 500.000 SNPs? • Covariates & Interactions For what do we have to correct the phenotypes? (Age, sex, treatments, other SNPs …) • Data Integration How to validate finding? (Replication Studies, Meta-Analyses, Re-sequencing, Function Studies, …) Modular Approach for Integrative Personalized Medicine Analysis of CoLaus Data Measurements Tests Module Individuals links … a dream or Questions the future? Questionaire Or SNPs 4
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