Set-Based Tests for the Gene–Environment Interaction in Longitudinal Studies

Abstract

We propose a generalized score type test for set-based inference for gene-environment interaction with longitudinally measured quantitative traits. The test is robust to misspecification of within subject correlation structure and has enhanced power compared to existing alternatives. Unlike tests for marginal genetic association, set-based tests for gene-environment interaction face the challenges of a potentially misspecified and high-dimensional main effect model under the null hypothesis. We show that our proposed test is robust to main effect misspecification of environmental exposure and genetic factors under the gene-environment independence condition. When genetic and environmental factors are dependent, the method of sieves is further proposed to eliminate potential bias due to a misspecified main effect of a continuous environmental exposure. A weighted principal component analysis approach is developed to perform dimension reduction when the number of genetic variants in the set is large relative to the sample size. The methods are motivated by an example from the Multi-Ethnic Study of Atherosclerosis (MESA), investigating interaction between measures of neighborhood environment and genetic regions on longitudinal measures of blood pressure over a study period of about seven years with 4 exams.

Gene-by-Psychosocial Factor Interactions Influence Diastolic Blood Pressure in European and African Ancestry Populations: Meta-Analysis of Four Cohort Studies

“…In this study, we used longitudinal data from four large epidemiologic cohorts of European and/or African ancestry to investigate interactions between known socioeconomic/psychosocial and genetic risk factors on variation in BP. We used a novel genomic region-based method for repeated measures analysis, Longitudinal Gene-Environment-Wide Interaction Studies (LGEWIS) [ 28 ], to evaluate interactions rather than testing each single nucleotide polymorphism (SNP) individually. Region-based approaches such as LGEWIS may be advantageous for trans-ethnic analysis because they are able to detect interactions even in the presence of genetic heterogeneity in ancestrally diverse populations.…”

Interaction between Social/Psychosocial Factors and Genetic Variants on Body Mass Index: A Gene-Environment Interaction Analysis in a Longitudinal Setting

“…This limits the statistical power of the tests. LGEWIS [ 16 ] is a GEE-based dispersion test specifically designed for longitudinal studies to test the joint effect of genetic variants or gene-environment interactions in a gene/region on phenotypic variation. Briefly, Y ij denotes the outcome variable for i -th subject at time j , E ij represents the environmental exposures for the j -th observation on the i -th subject measured at time j ; X ij denotes covariates and are defined similarly as E ij .…”

“…This gene-level inference is an important property when comparing genetic determinants of disease across ethnicities due to the inherent population stratification and admixture that is present within ethnic groups. In order to effectively incorporate the rich longitudinal phenotype data collected in the HRS and reduce the multiple testing burden, we applied a novel set-based test for gene-environment interaction in longitudinal studies (LGEWIS) [ 16 ]. Following discovery analyses, we sought replication of significant findings in the Multi-Ethnic Study of Atherosclerosis (MESA).…”

Testing cross‐phenotype effects of rare variants in longitudinal studies of complex traits

“…A rare-variant statistical approach for cross-phenotype analysis of longitudinal outcomes requires a framework that can handle multiple phenotypes observed over multiple time points and furthermore can simultaneously handle information from multiple rare variants within a gene, because genebased testing can be more powerful than testing of individual variants (He et al, 2016;Kwee, Liu, Lin, Ghosh, & Epstein, 2008;Morris & Zeggini, 2010;Wu et al, 2011). Such an approach currently does not exist in the statistical or genetics literature.…”

“…However, such models cannot be applied to test the association of the longitudinal phenotype data with an entire gene or thousands of markers taken together in a flexible manner. A recent method based on the longitudinal genetic random field model allows longitudinal analysis of multiple genetic variants simultaneously (He et al, 2016), but no extension of the method is available for cross-phenotypic effects. Finally, there are other multimarker approaches such as sequence kernel association test (SKAT) (Wu et al, 2011) and similarity regression (SIMreg) (Tzeng et al, 2009) that are used for gene mapping, but such approaches do not directly apply to multiphenotype data or longitudinal data.…”