ArticleJournal of the American Statistical Association2017
Set-Based Tests for the Gene-Environment Interaction in Longitudinal Studies.
Article in Journal of the American Statistical Association, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Gene-by-Psychosocial Factor Interactions Influence Diastolic Blood Pressure in European and African Ancestry Populations: Meta-Analysis of Four Cohort Studies.International journal of environmental research and public health · 2017Pooled it
- Marginal interaction test for detecting interactions between genetic marker sets and environment in genome-wide studies.G3 (Bethesda, Md.) · 2025Article
- Gene-environment interactions in human health.Nature reviews. Genetics · 2024Review
- Structural and social determinants of health: The multi-ethnic study of atherosclerosis.PloS one · 2024Article
- An optimal kernel-based multivariate U-statistic to test for associations with multiple phenotypes.Biostatistics (Oxford, England) · 2022Article
- A Cross-validated Ensemble Approach to Robust Hypothesis Testing of Continuous Nonlinear Interactions: Application to Nutrition-Environment Studies.Journal of the American Statistical Association · 2022Article
- A hierarchical integrative group least absolute shrinkage and selection operator for analyzing environmental mixtures.Environmetrics · 2021Article
- Gene-Environment Interaction: A Variable Selection Perspective.Methods in molecular biology (Clifton, N.J.) · 2021Review
- Scalable and Robust Regression Methods for Phenome-Wide Association Analysis on Large-Scale Biobank Data.Frontiers in genetics · 2021Review
- A Fast and Accurate Method for Genome-wide Scale Phenome-wide G × E Analysis and Its Application to UK Biobank.American journal of human genetics · 2019Article
- A powerful and data-adaptive test for rare-variant-based gene-environment interaction analysis.Statistics in medicine · 2019Article
- Testing cross-phenotype effects of rare variants in longitudinal studies of complex traits.Genetic epidemiology · 2018Article
- Interaction between Social/Psychosocial Factors and Genetic Variants on Body Mass Index: A Gene-Environment Interaction Analysis in a Longitudinal Setting.International journal of environmental research and public health · 2017Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.