A Powerful Adaptive Cauchy-Variable Combination Method for Rare-Variant Association Analysis

Abstract

Set-based association analysis has emerged as a popular tool for testing the association of rare variants within a genomic region with complex diseases. However, when only a small proportion of variants are causal, combining the association signals of multiple markers within a genomic region may cause noise due to the inclusion of non-causal variants, which usually decreases the power of a test. Besides, the existing set-based methods are sensitive to the genetic architecture. Therefore, we extend the aggregated Cauchy association test (ACAT) and propose an adaptive Cauchy-variable combination method (AAC). The AAC method adaptively combines Cauchy-variables transformed from variant-level P-values by using the optimal number of P-values that is determined by the data; the AAC method can remove variants with larger P-values. Extensive simulation studies and Genetic Analysis Workshop 19 real data analysis show that AAC is more powerful than the other comparative methods when only a small proportion of variants are causal. And AAC is robust to the varied genetic architecture. In addition, the AAC method may use summary statistics, without requiring the original genotypic and phenotypic data.