Prioritizing genetic variants for causality on the basis of preferential linkage disequilibrium

Abstract

To date, the widely used genome-wide association studies (GWASs) of the human genome have reported thousands of variants that are significantly associated with various human traits. However, in the vast majority of these cases, the causal variants responsible for the observed associations remain unknown. In order to facilitate the identification of causal variants, we designed a simple computational method called the "preferential linkage disequilibrium (LD)" approach, which follows the variants discovered by GWASs to pinpoint the causal variants, even if they are rare compared with the discovery variants. The approach is based on the hypothesis that the GWAS-discovered variant is better at tagging the causal variants than are most other variants evaluated in the original GWAS. Applying the preferential LD approach to the GWAS signals of five human traits for which the causal variants are already known, we successfully placed the known causal variants among the top ten candidates in the majority of these cases. Application of this method to additional GWASs, including those of hepatitis C virus treatment response, plasma levels of clotting factors, and late-onset Alzheimer disease, has led to the identification of a number of promising candidate causal variants. This method represents a useful tool for delineating causal variants by bringing together GWAS signals and the rapidly accumulating variant data from next-generation sequencing.