Controlling for human population stratification in rare variant association studies

Abstract Population stratification is a confounder of genetic association studies. In analyses of rare variants, corrections based on principal components (PCs) and linear mixed models (LMMs) yield conflicting conclusions. Studies evaluating these approaches generally focused on limited types of str...

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Autores principales:	Matthieu Bouaziz, Jimmy Mullaert, Benedetta Bigio, Yoann Seeleuthner, Jean-Laurent Casanova, Alexandre Alcais, Laurent Abel, Aurélie Cobat
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9db841d81fa14f6ea5b6aeb498a35477
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spelling	oai:doaj.org-article:9db841d81fa14f6ea5b6aeb498a354772021-12-02T17:27:19ZControlling for human population stratification in rare variant association studies10.1038/s41598-021-98370-52045-2322https://doaj.org/article/9db841d81fa14f6ea5b6aeb498a354772021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98370-5https://doaj.org/toc/2045-2322Abstract Population stratification is a confounder of genetic association studies. In analyses of rare variants, corrections based on principal components (PCs) and linear mixed models (LMMs) yield conflicting conclusions. Studies evaluating these approaches generally focused on limited types of structure and large sample sizes. We investigated the properties of several correction methods through a large simulation study using real exome data, and several within- and between-continent stratification scenarios. We considered different sample sizes, with situations including as few as 50 cases, to account for the analysis of rare disorders. Large samples showed that accounting for stratification was more difficult with a continental than with a worldwide structure. When considering a sample of 50 cases, an inflation of type-I-errors was observed with PCs for small numbers of controls (≤ 100), and with LMMs for large numbers of controls (≥ 1000). We also tested a novel local permutation method (LocPerm), which maintained a correct type-I-error in all situations. Powers were equivalent for all approaches pointing out that the key issue is to properly control type-I-errors. Finally, we found that power of analyses including small numbers of cases can be increased, by adding a large panel of external controls, provided an appropriate stratification correction was used.Matthieu BouazizJimmy MullaertBenedetta BigioYoann SeeleuthnerJean-Laurent CasanovaAlexandre AlcaisLaurent AbelAurélie CobatNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Matthieu Bouaziz Jimmy Mullaert Benedetta Bigio Yoann Seeleuthner Jean-Laurent Casanova Alexandre Alcais Laurent Abel Aurélie Cobat Controlling for human population stratification in rare variant association studies
description	Abstract Population stratification is a confounder of genetic association studies. In analyses of rare variants, corrections based on principal components (PCs) and linear mixed models (LMMs) yield conflicting conclusions. Studies evaluating these approaches generally focused on limited types of structure and large sample sizes. We investigated the properties of several correction methods through a large simulation study using real exome data, and several within- and between-continent stratification scenarios. We considered different sample sizes, with situations including as few as 50 cases, to account for the analysis of rare disorders. Large samples showed that accounting for stratification was more difficult with a continental than with a worldwide structure. When considering a sample of 50 cases, an inflation of type-I-errors was observed with PCs for small numbers of controls (≤ 100), and with LMMs for large numbers of controls (≥ 1000). We also tested a novel local permutation method (LocPerm), which maintained a correct type-I-error in all situations. Powers were equivalent for all approaches pointing out that the key issue is to properly control type-I-errors. Finally, we found that power of analyses including small numbers of cases can be increased, by adding a large panel of external controls, provided an appropriate stratification correction was used.
format	article
author	Matthieu Bouaziz Jimmy Mullaert Benedetta Bigio Yoann Seeleuthner Jean-Laurent Casanova Alexandre Alcais Laurent Abel Aurélie Cobat
author_facet	Matthieu Bouaziz Jimmy Mullaert Benedetta Bigio Yoann Seeleuthner Jean-Laurent Casanova Alexandre Alcais Laurent Abel Aurélie Cobat
author_sort	Matthieu Bouaziz
title	Controlling for human population stratification in rare variant association studies
title_short	Controlling for human population stratification in rare variant association studies
title_full	Controlling for human population stratification in rare variant association studies
title_fullStr	Controlling for human population stratification in rare variant association studies
title_full_unstemmed	Controlling for human population stratification in rare variant association studies
title_sort	controlling for human population stratification in rare variant association studies
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9db841d81fa14f6ea5b6aeb498a35477
work_keys_str_mv	AT matthieubouaziz controllingforhumanpopulationstratificationinrarevariantassociationstudies AT jimmymullaert controllingforhumanpopulationstratificationinrarevariantassociationstudies AT benedettabigio controllingforhumanpopulationstratificationinrarevariantassociationstudies AT yoannseeleuthner controllingforhumanpopulationstratificationinrarevariantassociationstudies AT jeanlaurentcasanova controllingforhumanpopulationstratificationinrarevariantassociationstudies AT alexandrealcais controllingforhumanpopulationstratificationinrarevariantassociationstudies AT laurentabel controllingforhumanpopulationstratificationinrarevariantassociationstudies AT aureliecobat controllingforhumanpopulationstratificationinrarevariantassociationstudies
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Controlling for human population stratification in rare variant association studies

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