Fast permutation tests and related methods, for association between rare variants and binary outcomes

TL;DR

This paper introduces permutation and approximate unconditional tests tailored for rare genetic variants in case-control studies, improving Type I error control and power over standard methods.

Contribution

It develops novel analytical methods to efficiently approximate Type I error rates for permutation-based tests in genetic association studies involving rare variants.

Findings

Proposed tests better control Type I error in unbalanced case-control studies.

Numerical studies show improved power over standard tests.

Application to real data demonstrates practical utility.

Abstract

In large scale genetic association studies, a primary aim is to test for association between genetic variants and a disease outcome. The variants of interest are often rare, and appear with low frequency among subjects. In this situation, statistical tests based on standard asymptotic results do not adequately control the Type I error rate, especially if the case:control ratio is unbalanced. In this paper, we propose the use of permutation and approximate unconditional tests for testing association with rare variants. We use novel analytical calculations to efficiently approximate the true Type I error rate under common study designs, and in numerical studies show that the proposed classes of tests significantly improve upon standard testing methods. We also illustrate our methods in data from a recent case-control study, for genetic causes of a severe side-effect of a common drug treatment.