Reuse of this article is permitted in accordance with the terms and conditions set out at http://author services.wiley.com/bauthor/onlineopen.asp.
Mixture modelling as an exploratory framework for genotype–trait associations
Version of Record online: 8 FEB 2011
Journal compilation © 2011 Royal Statistical Society
Journal of the Royal Statistical Society: Series C (Applied Statistics)
Volume 60, Issue 3, pages 355–375, May 2011
How to Cite
Au, K., Lin, R. and Foulkes, A. S. (2011), Mixture modelling as an exploratory framework for genotype–trait associations. Journal of the Royal Statistical Society: Series C (Applied Statistics), 60: 355–375. doi: 10.1111/j.1467-9876.2010.00750.x
- Issue online: 12 APR 2011
- Version of Record online: 8 FEB 2011
- [Received May 2009. Final revision October 2010]
- Genetic associations;
- Latent class;
- Mixture models
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.