Enrichment analysis of genetic association in genes and pathways by aggregating signals from both rare and common variants

Abstract

New high-throughput sequencing technologies have brought forth opportunities for unbiased analysis of thousands of rare genomic variants in genome-wide association studies of complex diseases. Because it is hard to detect single rare variants with appreciable effect sizes at the population level, existing methods mostly aggregate effects of multiple markers by collapsing the rare variants in genes (or genomic regions). We hypothesize that a higher level of aggregation can further improve association signal strength. Using the Genetic Analysis Workshop 17 simulated data, we test a two-step strategy that first applies a collapsing method in a gene-level analysis and then aggregates the gene-level test results by performing an enrichment analysis in gene sets. We find that the gene set approach which combines signals across multiple genes outperforms testing individual genes separately and that the power of the gene set enrichment test is further improved by proper adjustment of statistics to account for gene-wise differences.