Epistasis Blog

From the Computational Genetics Laboratory at the University of Pennsylvania (www.epistasis.org)

Saturday, February 26, 2005

Mixed effects models

A new paper by Foulkes et al. ("Mixed modelling to characterize genotype-phenotype associations", Stat Med. 2005 Mar 15;24(5):775-89) [PubMed] explores the use of mixed models to detect gene-gene and gene-environment interactions. Here is the abstract:

We propose using mixed effects models to characterize the association between multiple gene polymorphisms, environmental factors and measures of disease progression. Characterizing high-order gene-gene and gene-environment interactions presents an analytic challenge due to the large number of candidate genes and the complex, undescribed interactions among them. Several approaches have been proposed recently to reduce the number of candidate genes and post hoc approaches to identify gene-gene interactions are described. However, these approaches may be inadequate for identifying high-order interactions in the absence of main effects and generally do not permit us to control for potential confounders. We describe how mixed effects models and related testing procedures overcome these limitations and apply this approach to data from a cohort of subjects at risk for cardiovascular disease. Four (4) genetic polymorphisms in three genes of the same gene family are considered. The proposed modelling approach allows us first to test whether there is a significant genetic contribution to the variability observed in our disease outcome. This contribution may be through main effects of multi-locus genotypes or through an interaction between genotype and environmental factors. This approach also enables us to identify specific multi-locus gentoypes that interact with environmental factors in predicting the outcome. Mixed effects models provide a flexible statistical framework for controlling for potential confounders and identifying interactions among multiple genes and environmental factors that explain the variability in measures of disease progression.


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