Document Detail

Bivariate association analyses for the mixture of continuous and binary traits with the use of extended generalized estimating equations.
MedLine Citation:
PMID:  18924135     Owner:  NLM     Status:  MEDLINE    
Genome-wide association (GWA) study is becoming a powerful tool in deciphering genetic basis of complex human diseases/traits. Currently, the univariate analysis is the most commonly used method to identify genes associated with a certain disease/phenotype under study. A major limitation with the univariate analysis is that it may not make use of the information of multiple correlated phenotypes, which are usually measured and collected in practical studies. The multivariate analysis has proven to be a powerful approach in linkage studies of complex diseases/traits, but it has received little attention in GWA. In this study, we aim to develop a bivariate analytical method for GWA study, which can be used for a complex situation in which continuous trait and a binary trait are measured under study. Based on the modified extended generalized estimating equation (EGEE) method we proposed herein, we assessed the performance of our bivariate analyses through extensive simulations as well as real data analyses. In the study, to develop an EGEE approach for bivariate genetic analyses, we combined two different generalized linear models corresponding to phenotypic variables using a seemingly unrelated regression model. The simulation results demonstrated that our EGEE-based bivariate analytical method outperforms univariate analyses in increasing statistical power under a variety of simulation scenarios. Notably, EGEE-based bivariate analyses have consistent advantages over univariate analyses whether or not there exists a phenotypic correlation between the two traits. Our study has practical importance, as one can always use multivariate analyses as a screening tool when multiple phenotypes are available, without extra costs of statistical power and false-positive rate. Analyses on empirical GWA data further affirm the advantages of our bivariate analytical method.
Jianfeng Liu; Yufang Pei; Chris J Papasian; Hong-Wen Deng
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Genetic epidemiology     Volume:  33     ISSN:  1098-2272     ISO Abbreviation:  Genet. Epidemiol.     Publication Date:  2009 Apr 
Date Detail:
Created Date:  2009-03-18     Completed Date:  2009-07-09     Revised Date:  2014-09-11    
Medline Journal Info:
Nlm Unique ID:  8411723     Medline TA:  Genet Epidemiol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  217-27     Citation Subset:  IM    
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MeSH Terms
Genetic Predisposition to Disease
Genome-Wide Association Study / methods*
Models, Genetic
Grant Support
P50 AR055081/AR/NIAMS NIH HHS; P50 AR055081/AR/NIAMS NIH HHS; P50 AR055081-01/AR/NIAMS NIH HHS; P50 AR055081-019002/AR/NIAMS NIH HHS; R01 AG026564/AG/NIA NIH HHS; R01 AG026564/AG/NIA NIH HHS; R01 AG026564-03/AG/NIA NIH HHS; R01 AR050496/AR/NIAMS NIH HHS; R01 AR050496-01/AR/NIAMS NIH HHS; R01 AR050496-01A1/AR/NIAMS NIH HHS; R21 AG027110/AG/NIA NIH HHS

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