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Whole Genome Association Study: What can be learned from a large clinical cohort?

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If you have a question about this talk, please contact M. Madan Babu.

The Cohorte Lausannoise (CoLaus) is a random population sample of more than 6’000 individuals who were genotyped using Affymetrix 500k SNP -arrays and for whom a large number of clinically relevant parameters have been measured.

About 1/3 of the CoLaus individuals had grandparents who immigrated to Switzerland – mainly from other European countries. This allowed for comparing the country of origin of these individual with the projection of their genotypic profile onto the principal components of the entire genotypic dataset. We found an astonishingly close correspondence between genetic and geographic distances; indeed, a geographical map of Europe arises naturally as an efficient two-dimensional summary of genetic variation in Europeans.

Next I will present results from several whole-genome association studies that employed CoLaus data that included height, body-mass-index, serum lipid concentrations and blood pressure as phenotypes, and used classical scans testing one SNP at a time. These studies (like many others) elucidated many loci with highly significant associations, which are promising candidates towards unraveling mechanisms of actions. Yet, together these variants only explain a small fraction of the phenotypic variance, indicating that we still miss a comprehensive picture of: (a) what are the causal variants, (b) what effects are attributed by rare variants and/or copy number variations, (c) what fraction of the variance can be explained by SNP -SNP or SNP -environment interactions, and (d) what are the intrinsic limitations of currently used algorithms in dealing with very large sets of genotypic and phenotypic data, which are partially incomplete or noisy.

I will conclude by outlining our research dealing with these challenges.

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