A data-driven method for identifying rare variants with heterogeneous trait effects.

摘要

Collapsing multiple variants into one variable and testing their collective effect is a useful strategy for rare variant association analysis. Direct collapsing, however, is not valid or may significantly lose power when a group of variants to be collapsed have heterogeneous effects on target traits (i.e. some positive and some negative). This could be especially true for quantitative traits (such as blood pressure and body mass index), regardless of whether subjects are sampled randomly from a population or selectively from two extreme tails of the trait distribution. To deal with this problem, we propose a novel, data-driven method, the P-value Weighted Sum Test (PWST), which allows each variant to be individually weighted according to the evidence of association from the data itself. Specifically, both significance and direction of individual variant effects are used to calculate a single weighted sum score based on rescaled left-tail P-values from single-variant analysis, after which a permutation test of association is performed between the score and the trait. Our simulation under different sampling strategies shows that PWST significantly increases statistical power when there are heterogeneous variant effects. The appeal of the PWST approach is illustrated in an application to sequence data by detecting the collective effect of variants in the peroxisome proliferator-activated receptor alpha (PPARalpha) gene on triglycerides (TG) response to fenofibrate treatment from 300 subjects in the Genetics of Lipid Lowering and Diet Network study.