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Mixture modelling as an exploratory framework for genotype–trait associations
Author(s) -
Au Kinman,
Lin Rongheng,
Foulkes Andrea S.
Publication year - 2011
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2010.00750.x
Subject(s) - trait , covariance , mixed model , human immunodeficiency virus (hiv) , mixture model , random effects model , gaussian , computer science , econometrics , degrees of freedom (physics and chemistry) , genotype , statistics , computational biology , mathematics , biology , medicine , genetics , virology , meta analysis , physics , quantum mechanics , gene , programming language
Summary. We propose a mixture modelling framework for both identifying and exploring the nature of genotype–trait associations. This framework extends the classical mixed effects modelling approach for this setting by incorporating a Gaussian mixture distribution for random genotype effects. The primary advantages of this paradigm over existing approaches include that the mixture modelling framework addresses the degrees‐of‐freedom challenge that is inherent in application of the usual fixed effects analysis of covariance, relaxes the restrictive single normal distribution assumption of the classical mixed effects models and offers an exploratory framework for discovery of underlying structure across multiple genetic loci. An application to data arising from a study of antiretroviral‐associated dyslipidaemia in human immunodeficiency virus infection is presented. Extensive simulations studies are also implemented to investigate the performance of this approach.