Comparison of mixed model based approaches for correcting for population substructure with application to extreme phenotype sampling.

Onifade, Maryam; Roy-Gagnon, Marie-Hélène; Parent, Marie-Élise; Burkett, Kelly M

Onifade, Maryam; Roy-Gagnon, Marie-Hélène; Parent, Marie-Élise; Burkett, Kelly M.

Affiliation

Onifade M; Department of Mathematics and Statistics, University of Ottawa, Ottawa, Canada.
Roy-Gagnon MH; School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada.
Parent MÉ; Centre Armand-Frappier Santé Biotechnologie, Institut national de la recherche scientifique, Université du Québec, Laval, Canada.
Burkett KM; Department of Mathematics and Statistics, University of Ottawa, Ottawa, Canada. kburkett@uottawa.ca.

BMC Genomics ; 23(1): 98, 2022 Feb 04.

Article in En | MEDLINE | ID: mdl-35120458

Subject(s)

Genome-Wide Association Study; Models, Genetic; Case-Control Studies; Computer Simulation; Linear Models; Phenotype; Polymorphism, Single Nucleotide

Key words

Extreme phenotype sampling; Generalized linear mixed models; Genome-wide association study; Population stratification; Type 1 error

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Genome-Wide Association Study / Models, Genetic Type of study: Observational_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: BMC Genomics Journal subject: GENETICA Year: 2022 Type: Article Affiliation country: Canada

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