Author:
Bouaziz Matthieu,Mullaert Jimmy,Bigio Benedetta,Seeleuthner Yoann,Casanova Jean-Laurent,Alcais Alexandre,Abel Laurent,Cobat Aurélie
Abstract
AbstractPopulation 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.
Funder
St. Giles Foundation
National Center for Research Resources
National Center for Advancing Translational Sciences
Rockefeller University
Agence Nationale de la Recherche
Publisher
Springer Science and Business Media LLC
Cited by
8 articles.
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