Permutation in genetic association studies with covariates: controlling the familywise error rate with score tests in generalized linear models

TL;DR

This paper discusses permutation methods for controlling the familywise error rate in genome-wide association studies, proposing an approximate solution suitable for large datasets and comparing it with simulations.

Contribution

It introduces an approximate permutation testing approach for regression models in GWA studies, addressing computational challenges and lack of standard methods.

Findings

Proposed an efficient approximate permutation method.

Compared methods using simulated GWA data.

Demonstrated control of error rate with the new approach.

Abstract

In genome-wide association (GWA) studies the goal is to detect associations between genetic markers and a given phenotype. The number of genetic markers can be large and effective methods for control of the overall error rate is a central topic when analyzing GWA data. The Bonferroni method is known to be conservative when the tests are dependent. Permutation methods give exact control of the overall error rate when the assumption of exchangeability is satisfied, but are computationally intensive for large datasets. For regression models the exchangeability assumption is in general not satisfied and there is no standard solution on how to do permutation testing, except some approximate methods. In this paper we will discuss permutation methods for control of the familywise error rate in genetic association studies and present an approximate solution. These methods will be compared using simulated data.