A Joint Regression Analysis for Genetic Association Studies with Outcome Stratified Samples

Genetic association studies in practice often involve multiple traits resulting from a common disease mechanism, and samples for such studies are often stratified based on some trait outcomes. In such situations, statistical methods using only one of these traits may be inadequate and lead to under-...

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Bibliographic Details
Published in:Biometrics 2013-06, Vol.69 (2), p.417-426
Main Authors: Wu, Colin O., Zheng, Gang, Kwak, Minjung
Format: Article
Language:English
Subjects:
Arthritis, Rheumatoid - genetics
Arthritis, Rheumatoid - immunology
Autoantibodies - blood
Autoantibodies - genetics
BIOMETRIC METHODOLOGY
Biometry - methods
Biostatistics
Genetic Association Studies - statistics & numerical data
Genetic association study
Genetic research
Humans
Joint regression model
Likelihood Functions
Models, Genetic
Models, Statistical
Peptides, Cyclic - immunology
Pleiotropic analysis
Polymorphism, Single Nucleotide
Qualitative trait
Quantitative trait
Quantitative Trait Loci
Regression Analysis
Stratified sample
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Summary:Genetic association studies in practice often involve multiple traits resulting from a common disease mechanism, and samples for such studies are often stratified based on some trait outcomes. In such situations, statistical methods using only one of these traits may be inadequate and lead to under-powered tests for detecting genetic associations. We propose in this article an estimation and testing procedure for evaluating the shared-association of a genetic marker on the joint distribution of multiple traits of a common disease. Specifically, we assume that the disease mechanism involves both quantitative and qualitative traits, and our samples could be stratified based on the qualitative trait. Through a joint likelihood function, we derive a class of estimators and test statistics for evaluating the shared genetic association on both the quantitative and qualitative traits. Our simulation study shows that the joint likelihood test procedure is potentially more powerful than association tests based on separate traits. Application of our proposed procedure is demonstrated through the rheumatoid arthritis data provided by the Genetic Analysis Workshop 16 (GAW16).
ISSN:0006-341X
1541-0420
DOI:10.1111/biom.12012