Rare-variant association studies: When are aggregation tests more powerful than single-variant tests?

Abstract

Because single-variant tests are not as powerful for identifying associations with rare variants as for common variants, aggregation tests pooling information from multiple rare variants within genes or other genomic regions were developed. While single-variant tests generally have yielded more associations, recent large-scale biobank studies have uncovered numerous significant findings through aggregation tests. We investigate the range of genetic models for which aggregation tests are expected to be more powerful than single-variant tests for rare-variant association studies. We consider a normally distributed trait following an additive genetic model with c causal out of v total rare variants in an autosomal gene/region with region heritability h², measured in n independent study participants. Analytic calculations assuming independent variants, for which we developed a user-friendly online tool, show that power depends on nh²,c, and v. These analytic calculations and simulations based on 378,215 unrelated UK Biobank participants revealed that aggregation tests are more powerful than single-variant tests only when a substantial proportion of variants are causal and that power is strongly dependent on the underlying genetic model and set of rare variants aggregated. For example, if we aggregate all rare protein-truncating variants (PTVs) and deleterious missense variants, aggregation tests are more powerful than single-variant tests for >55% of genes when PTVs, deleterious missense variants, and other missense variants have 80%, 50%, and 1% probabilities of being causal, with n=100,000 and h²=0.1%. With continued use of single-variant and aggregation tests in rapidly growing studies, our investigation sheds light on the situations favoring each test.