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Abstract:
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Genome-wide association study (GWAS) has been a great success in the past decade. However, significant challenges still remain in both identifying new risk loci and interpreting results. Complex structure of linkage disequilibrium also makes it challenging to separate causal variants from nonfunctional ones in large haplotype blocks. In this presentation, I will describe our recent efforts to integrate genomic functional annotations from computational predictions (e.g. genomic conservation) and high-throughput experiments (e.g. the ENCODE and Roadmap Epigenomics Projects) with GWAS summary statistics. The usefulness of our methods will be demonstrated through their applications to several large GWASs. At the single nucleotide polymorphism level, top ranked SNPs after prioritization have both higher replication rates and consistently stronger enrichment of eQTLs. Within each risk locus, our methods may be able to distinguish functional sites from groups of correlated SNPs. Tissue and cell specific annotations allow us to infer relevant tissue/cell types at each risk locus. I will also discuss the improvement of genetic risk prediction using annotation data.
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