Weighting sequence variants based on their annotation increases power of whole-genome association studies.

Abstract

The consensus approach to genome-wide association studies (GWAS) has been to assign equal prior probability of association to all sequence variants tested. However, some sequence variants, such as loss-of-function and missense variants, are more likely than others to affect protein function and are therefore more likely to be causative. Using data from whole-genome sequencing of 2,636 Icelanders and the association results for 96 quantitative and 123 binary phenotypes, we estimated the enrichment of association signals by sequence annotation. We propose a weighted Bonferroni adjustment that controls for the family-wise error rate (FWER), using as weights the enrichment of sequence annotations among association signals. We show that this weighted adjustment increases the power to detect association over the standard Bonferroni correction. We use the enrichment of associations by sequence annotation we have estimated in Iceland to derive significance thresholds for other populations with different numbers and combinations of sequence variants.