Grant: Fellowship Award - PA: Population Genetics and Genetic Epidemiology

While highly penetrant rare mutations causing monogenic disease are typically altering protein structure, common complex disorders such as type 2 diabetes, arthritis and cancers are thought to be caused by a complex combination of genetic and environmental factors, with each genetic variant modifying the risk of obtaining the disease. Only recently high-density genome wide association studies (GWAS) have provided a successful, systematic and unbiased approach for complex disease risk discovery. GWAS hits to date are providing evidence that regulatory variation accounts for a considerable proportion of risk alleles observed for chronic disease risk since coding variants have been ruled out in over half of the loci. Although the SNPs delineated by GWAS may be used to help identify people with an increased disease risk, full exploitation of the new risk genes require the identification of the causative SNPs to understand disease mechanisms allowing their targeting by novel treatment strategies. In this study, we propose to detect and map cis-variation in primary human fibroblasts, in regions identified in published GWAS, by measuring the relative expression of each gene allele. Allelic imbalance (AI) will be determined using the novel Illumina BeadArray system and cells will be genotyped using the Illumina Infinium chip. Disease associated regulatory haplotypes, defined by the AI, will then be further followed by us and our collaborators using re-sequencing, fine-mapping, bioinformatics predictions and functional validation of candidate regulatory variants. Ultimately, the identification of susceptibility genetic variants in common diseases will provide a functional basis of disease for pharmacogenetic association, will permit the development of genetic tests to confirm medical diagnostics and will, furthermore, greatly help in our understanding of disease pathogenesis.