Bayesian mixture models for the incorporation of prior knowledge to inform genetic association studies.
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.
Fridley
Brooke L
BL
Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota 55905, USA. fridley.brooke@mayo.edu
Serie
Daniel
D
Jenkins
Gregory
G
White
Kristin
K
Bamlet
William
W
Potter
John D
JD
Goode
Ellen L
EL
D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D016022:Case-Control Studies; D003111:Colonic Polyps / Q000235:genetics*; D015179:Colorectal Neoplasms / Q000235:genetics*; D005260:Female; D020022:Genetic Predisposition to Disease; D005838:Genotype; D006801:Humans; D008126:Lod Score; D008297:Male; D008875:Middle Aged; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide