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Premium A Joint Regression Analysis for Genetic Association Studies with Outcome Stratified Samples
Author(s)
Wu Colin O.,
Zheng Gang,
Kwak Minjung
Publication year2013
Publication title
biometrics
Resource typeJournals
PublisherInternational Biometric Society
Abstract 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).
Subject(s)association (psychology) , biology , computer science , gene , genetic association , genetics , genotype , mathematics , programming language , psychology , psychotherapist , quantitative trait locus , single nucleotide polymorphism , statistics , trait
Language(s)English
SCImago Journal Rank2.298
H-Index130
eISSN1541-0420
pISSN0006-341X
DOI10.1111/biom.12012

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