Retrospective Association Analysis of Longitudinal Binary Traits Identifies Important Loci and Pathways in Cocaine Use
Document Type
Article
Publication Date
12-1-2019
Publication Title
Genetics
Volume
213
Issue
4
First page number:
1225
Last page number:
1236
Abstract
Longitudinal phenotypes have been increasingly available in genome-wide association studies (GWAS) and electronic health record-based studies for identification of genetic variants that influence complex traits over time. For longitudinal binary data, there remain significant challenges in gene mapping, including misspecification of the model for phenotype distribution due to ascertainment. Here, we propose L-BRAT (Longitudinal Binary-trait Retrospective Association Test), a retrospective, generalized estimating equation-based method for genetic association analysis of longitudinal binary outcomes. We also develop RGMMAT, a retrospective, generalized linear mixed model-based association test. Both tests are retrospective score approaches in which genotypes are treated as random conditional on phenotype and covariates. They allow both static and time-varying covariates to be included in the analysis. Through simulations, we illustrated that retrospective association tests are robust to ascertainment and other types of phenotype model misspecification, and gain power over previous association methods. We applied L-BRAT and RGMMAT to a genome-wide association analysis of repeated measures of cocaine use in a longitudinal cohort. Pathway analysis implicated association with opioid signaling and axonal guidance signaling pathways. Lastly, we replicated important pathways in an independent cocaine dependence case-control GWAS. Our results illustrate that L-BRAT is able to detect important loci and pathways in a genome scan and to provide insights into genetic architecture of cocaine use.
Keywords
Ascertainment; Genome-wide association studies; Model misspecification; Robustness; Score test
Disciplines
Longitudinal Data Analysis and Time Series | Physical Sciences and Mathematics | Statistics and Probability
Language
English
Repository Citation
Wu, W.,
Wang, Z.,
Xu, K.,
Zhang, X.,
Amei, A.,
Gelernter, J.,
Zhao, H.,
Justice, A. C.,
Wang, Z.
(2019).
Retrospective Association Analysis of Longitudinal Binary Traits Identifies Important Loci and Pathways in Cocaine Use.
Genetics, 213(4),
1225-1236.
http://dx.doi.org/10.1534/genetics.119.302598