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Bayesian mixture models for the incorporation of prior knowledge to inform genetic association studies
Author(s) -
Fridley Brooke L.,
Serie Daniel,
Jenkins Gregory,
White Kristin,
Bamlet William,
Potter John D.,
Goode Ellen L.
Publication year - 2010
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.20494
Subject(s) - frequentist inference , genetic association , bayesian probability , linkage (software) , genome wide association study , computer science , association (psychology) , flexibility (engineering) , genetic architecture , bayesian inference , computational biology , machine learning , data mining , biology , artificial intelligence , statistics , genetics , quantitative trait locus , mathematics , single nucleotide polymorphism , psychology , genotype , psychotherapist , gene
In the last decade, numerous genome‐wide linkage and association studies of complex diseases have been completed. The critical question remains of how to best use this potentially valuable information to improve study design and statistical analysis in current and future genetic association studies. With genetic effect size for complex diseases being relatively small, the use of all available information is essential to untangle the genetic architecture of complex diseases. One promising approach to incorporating prior knowledge from linkage scans, or other information, is to up‐ or down‐weight P ‐values resulting from genetic association study in either a frequentist or Bayesian manner. As an alternative to these methods, we propose a fully Bayesian mixture model to incorporate previous knowledge into on‐going association analysis. In this approach, both the data and previous information collectively inform the association analysis, in contrast to modifying the association results ( P ‐values) to conform to the prior knowledge. By using a Bayesian framework, one has flexibility in modeling, and is able to comprehensively assess the impact of model specification on posterior inferences. We illustrate the use of this method through a genome‐wide linkage study of colorectal cancer, and a genome‐wide association study of colorectal polyps. Genet. Epidemiol . 34:418–426, 2010. © 2010 Wiley‐Liss, Inc.