UCSF

The role of rare variants in complex disease is hotly debated, but the design of genetic association studies to statistically associate rare variants is not well understood. Here, we simulate rare variant association studies across different case/control panel sampling strategies, sequencing methods, and genetic architecture models based on evolutionary forces to determine the statistical performance of RVATs widely in use. We find that the highest statistical power of RVATs is achieved by sampling case/control individuals from the extremes of an underlying quantitative trait distribution. We also demonstrate that the use of genotyping arrays, in conjunction with imputation from a whole genome sequenced (WGS) reference panel, recovers the vast majority (90%) of the power that could be achieved by sequencing the case/control panel using current tools. Finally, we show that the statistical performance of RVATs decreases as rare variants become more important in the trait architecture. Our work shows that RVATs are not yet well-powered enough to make generalizable conclusions about the role of rare variants in complex trait architectures.