A data-adaptive sum test for disease association with multiple common or rare variants

Abstract

Since associations between complex diseases and common variants are typically weak, and approaches to genotyping rare variants (e.g. by next-generation resequencing) multiply, there is an urgent demand to develop powerful association tests that are able to detect disease associations with both common and rare variants. In this article we present such a test. It is based on data-adaptive modifications to a so-called Sum test originally proposed for common variants, which aims to strike a balance between utilizing information on multiple markers in linkage disequilibrium and reducing the cost of large degrees of freedom or of multiple testing adjustment. When applied to multiple common or rare variants in a candidate region, the proposed test is easy to use with 1 degree of freedom and without the need for multiple testing adjustment. We show that the proposed test has high power across a wide range of scenarios with either common or rare variants, or both. In particular, in some situations the proposed test performs better than several commonly used methods.

Fig. 1

Distributions of the naive score test statistics U/√V (a), the adjusted score statistics (U − U₀)/√V₀ (c) and (aSum b)/a (e), and their observed values against the quantiles from N(0.3, 1) (b), N(0, 1) (d) and χ²₁ (f), respectively. All are based on n = 500 and 1,000 replicates of the simulated data with a compound symmetry covariance structure.