title: Set-based tests for genetic association in longitudinal studies.
abstract: Genetic association studies with longitudinal markers of chronic diseases (e.g., blood pressure, body mass index) provide a valuable opportunity to explore how genetic variants affect traits over time by utilizing the full trajectory of longitudinal outcomes. Since these traits are likely influenced by the joint effect of multiple variants in a gene, a joint analysis of these variants considering linkage disequilibrium (LD) may help to explain additional phenotypic variation. In this article, we propose a longitudinal genetic random field model (LGRF), to test the association between a phenotype measured repeatedly during the course of an observational study and a set of genetic variants. Generalized score type tests are developed, which we show are robust to misspecification of within-subject correlation, a feature that is desirable for longitudinal analysis. In addition, a joint test incorporating gene-time interaction is further proposed. Computational advancement is made for scalable implementation of the proposed methods in large-scale genome-wide association studies (GWAS). The proposed methods are evaluated through extensive simulation studies and illustrated using data from the Multi-Ethnic Study of Atherosclerosis (MESA). Our simulation results indicate substantial gain in power using LGRF when compared with two commonly used existing alternatives: (i) single marker tests using longitudinal outcome and (ii) existing gene-based tests using the average value of repeated measurements as the outcome.
authors: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A.
authors: Zihuai
authors: Z
authors: He
authors: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A.
authors: Min
authors: M
authors: Zhang
authors: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A.
authors: Seunggeun
authors: S
authors: Lee
authors: Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, U.S.A.
authors: Jennifer A
authors: JA
authors: Smith
authors: Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, California, U.S.A.
authors: Xiuqing
authors: X
authors: Guo
authors: Department of Medicine, Columbia University, New York, New York, U.S.A.
authors: Walter
authors: W
authors: Palmas
authors: Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, U.S.A.
authors: Sharon L R
authors: SL
authors: Kardia
authors: Department of Epidemiology, Drexel University, Philadelphia, U.S.A.
authors: Ana V
authors: AV
authors: Diez Roux
authors: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A.
authors: Bhramar
authors: B
authors: Mukherjee
mesh_terms: D050197:Atherosclerosis / Q000453:epidemiology* / Q000235:genetics*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D056726:Genetic Association Studies / Q000379:methods*; D005819:Genetic Markers / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000453:epidemiology / Q000235:genetics; D006801:Humans; D015994:Incidence; D008137:Longitudinal Studies; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D005819:Genetic Markers
keywords: Generalized estimating equations; Generalized score test; Genetic association; Longitudinal study; Multi-marker test; Random field