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Taking population stratification into account by local permutations in rare-variant association studies on small samples.


ABSTRACT: Many methods for rare variant association studies require permutations to assess the significance of tests. Standard permutations assume that all individuals are exchangeable and do not take population stratification (PS), a known confounding factor in genetic studies, into account. We propose a novel strategy, LocPerm, in which individual phenotypes are permuted only with their closest ancestry-based neighbors. We performed a simulation study, focusing on small samples, to evaluate and compare LocPerm with standard permutations and classical adjustment on first principal components. Under the null hypothesis, LocPerm was the only method providing an acceptable type I error, regardless of sample size and level of stratification. The power of LocPerm was similar to that of standard permutation in the absence of PS, and remained stable in different PS scenarios. We conclude that LocPerm is a method of choice for taking PS and/or small sample size into account in rare variant association studies.

SUBMITTER: Mullaert J 

PROVIDER: S-EPMC8604757 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Taking population stratification into account by local permutations in rare-variant association studies on small samples.

Mullaert Jimmy J   Bouaziz Matthieu M   Seeleuthner Yoann Y   Bigio Benedetta B   Casanova Jean-Laurent JL   Alcaïs Alexandre A   Abel Laurent L   Cobat Aurélie A  

Genetic epidemiology 20210817 8


Many methods for rare variant association studies require permutations to assess the significance of tests. Standard permutations assume that all individuals are exchangeable and do not take population stratification (PS), a known confounding factor in genetic studies, into account. We propose a novel strategy, LocPerm, in which individual phenotypes are permuted only with their closest ancestry-based neighbors. We performed a simulation study, focusing on small samples, to evaluate and compare  ...[more]

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