Using functional annotation to re-prioritize GWAS hits

Abstract

Genome-wide association studies (GWAS) often produce a few significant signals and many suggestive signals. The latter group represents an untapped resource of potentially great value, but separating true hits from spurious findings is a major challenge. One approach to take environmental/life style factors into account but no methods have been described that enable the use of genome-wide information to study gene-environment interactions in the development of complex diseases. We will summarize our current understanding of the problem and to provide new solutions in discovery of gene-gene and gene-environment interactions. Another approach to mining GWAS data is to integrate additional functional information. GWAS results have been made available to the public enabling researchers to characterize these SNPs in a manner that can inform follow up studies. Additional tools have been developed that will allow this information to be combined with p-values. These resources will be discussed in this session.