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Taking population stratification into account by local permutations in rare-variant association studies on small samples.
Mullaert, Jimmy; Bouaziz, Matthieu; Seeleuthner, Yoann; Bigio, Benedetta; Casanova, Jean-Laurent; Alcaïs, Alexandre; Abel, Laurent; Cobat, Aurélie.
Afiliación
  • Mullaert J; Université de Paris, IAME, INSERM, Paris, France.
  • Bouaziz M; AP-HP, Hôpital Bichat, DEBRC, Paris, France.
  • Seeleuthner Y; Laboratory of Human Genetics of Infectious Diseases, Paris, EU, France.
  • Bigio B; Laboratory of Human Genetics of Infectious Diseases, Paris, EU, France.
  • Casanova JL; Université de Paris, Imagine Institute, Paris, EU, France.
  • Alcaïs A; Laboratory of Human Genetics of Infectious Diseases, Paris, EU, France.
  • Abel L; Université de Paris, Imagine Institute, Paris, EU, France.
  • Cobat A; St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, New York, USA.
Genet Epidemiol ; 45(8): 821-829, 2021 12.
Article en En | MEDLINE | ID: mdl-34402542
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.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Genética de Población / Modelos Genéticos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Genet Epidemiol Asunto de la revista: EPIDEMIOLOGIA / GENETICA MEDICA Año: 2021 Tipo del documento: Article País de afiliación: Francia

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Genética de Población / Modelos Genéticos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Genet Epidemiol Asunto de la revista: EPIDEMIOLOGIA / GENETICA MEDICA Año: 2021 Tipo del documento: Article País de afiliación: Francia