Abstract
A key component to understanding etiology of complex diseases, such as cancer, diabetes, alcohol dependence, is to investigate gene-environment interactions. This work is motivated by the following two concerns in the analysis of gene-environment interactions. First, multiple genetic markers in moderate linkage disequilibrium may be involved in susceptibility to a complex disease. Second, environmental factors may be subject to misclassification. We develop a genotype based Bayesian pseudolikelihood approach that accommodates linkage disequilibrium in genetic markers and misclassification in environmental factors. Since our approach is genotype based, it allows the observed genetic information to enter the model directly thus eliminating the need to infer haplotype phase and simplifying computations. Bayesian approach allows shrinking parameter estimates towards prior distribution to improve estimation and inference when environmental factors are subject to misclassification. Simulation experiments demonstrated that our method produced parameter estimates that are nearly unbiased even for small sample sizes. An application of our method is illustrated using a case-control study of interaction between early onset of drinking and genes involved in dopamine pathway.
Highlights
A key component to prevention and control of complex diseases, such as cancer, hypertension, diabetes, and alcoholism, is to study the independent, cumulative, and interactive effects of genetic and environmental factors
We develop a genotype based Bayesian pseudolikelihood approach that accommodates linkage disequilibrium in genetic markers and misclassification in environmental factors
This analysis has the potential to impact the Journal of Probability and Statistics understanding of the role of genetic influences under various environmental exposures, providing valuable information to 1 better understand the biological pathways involved in the disease and its progression, providing major clues to the underlying causes of alcohol dependence; 2 design personalized interventions targeted to individuals with enhanced vulnerability to the disease the risk genes may help identify patients at higher risk long before any symptoms occur ; 3 gain critical understanding for drug discovery
Summary
A key component to prevention and control of complex diseases, such as cancer, hypertension, diabetes, and alcoholism, is to study the independent, cumulative, and interactive effects of genetic and environmental factors. Most of the results of published genomewide association studies are based on single nucleotide polymorphism SNP analysis 2 This approach may suffer from low power due to a large number of tests and small effect sizes of individual SNPs. the true causal genetic marker is often not genotyped, rather is captured through linkage disequilibrium LD with the typed markers. The definition of early age of getting drunk is subject to misclassification due to uncertainty associated with the recall In light of these concerns, we develop a Bayesian methodology for analysis of geneenvironment interactions in case-controls studies. Our Bayesian approach has the ability to shrink the parameter estimates towards prior and reduce variability in parameter estimates This property is essential when environmental exposure is subject to misclassification, especially in studies with smaller sample sizes, for example, of subtypes of complex disease.
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