An Evolutionary Framework for Association Testing in Resequencing Studies

Sequencing technologies are becoming cheap enough to apply to large numbers of study participants and promise to provide new insights into human phenotypes by bringing to light rare and previously unknown genetic variants. We develop a new framework for the analysis of sequence data that incorporates all of the major features of previously proposed approaches, including those focused on allele counts and allele burden, but is both more general and more powerful. We harness population genetic theory to provide prior information on effect sizes and to create a pooling strategy for information from rare variants. Our method, EMMPAT (Evolutionary Mixed Model for Pooled Association Testing), generates a single test per gene (substantially reducing multiple testing concerns), facilitates graphical summaries, and improves the interpretation of results by allowing calculation of attributable variance. Simulations show that, relative to previously used approaches, our method increases the power to detect genes that affect phenotype when natural selection has kept alleles with large effect sizes rare. We demonstrate our approach on a population-based re-sequencing study of association between serum triglycerides and variation in ANGPTL4.Author Summary: Studies correlating genetic variation to disease and other human traits have examined mostly common mutations, partly because of technological restrictions. However, recent advances have resulted in dramatically declining costs of obtaining genomic sequence data, which provides the opportunity to detect rare genetic variation. Existing methods of analysis designed for an earlier era of technology are not optimal for discovering links to rare mutations. We take advantage of 1) the advanced theoretical understanding of evolutionary mechanics and 2) genome-wide evidence about evolutionary forces on the human genome to suggest a framework for understanding observed correlations between rare genetic variation and modern traits. The model leads to a powerful test for genetic association and to an improved interpretation of results. We demonstrate the new method on previously confirmed results in a gene related to high blood cholesterol levels.