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

ABSTRACT: 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. Sp

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC3201701 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature