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A unified linear mixed model for familial relatedness and population structure in genetic association studies.
DeVogel, Nicholas; Auer, Paul L; Manansala, Regina; Rau, Andrea; Wang, Tao.
  • DeVogel N; Division of Biostatistics, Institute for Health and Equity, Milwaukee, Wisconsin, USA.
  • Auer PL; Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA.
  • Manansala R; Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA.
  • Rau A; Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA.
  • Wang T; INRAE, AgroParisTech, GABI, Université Paris-Saclay, Jouy-en-Josas, France.
Genet Epidemiol ; 45(3): 305-315, 2021 04.
Article en En | MEDLINE | ID: mdl-33175443
ABSTRACT
Familial relatedness (FR) and population structure (PS) are two major sources for genetic correlation. In the human population, both FR and PS can further break down into additive and dominant components to account for potential additive and dominant genetic effects. In this study, besides the classical additive genomic relationship matrix, a dominant genomic relationship matrix is introduced. A link between the additive/dominant genomic relationship matrices and the coancestry (or kinship)/double coancestry coefficients is also established. In addition, a way to separate the FR and PS correlations based on the estimates of coancestry and double coancestry coefficients from the genomic relationship matrices is proposed. A unified linear mixed model is also developed, which can account for both the additive and dominance effects of FR and PS correlations as well as their possible random interactions. Finally, this unified linear mixed model is applied to analyze two study cohorts from UK Biobank.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Genoma / Modelos Genéticos Tipo de estudio: Risk_factors_studies Límite: Humans Idioma: En Año: 2021 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Genoma / Modelos Genéticos Tipo de estudio: Risk_factors_studies Límite: Humans Idioma: En Año: 2021 Tipo del documento: Article