ArticleBehavior genetics2022
Modeling Interaction and Dispersion Effects in the Analysis of Gene-by-Environment Interaction.
Article in Behavior genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
14 citing papers in PubMed, 26 citations in OpenAlex.
- COVID-19-related school closures and students' achievement and motivation: Did teacher support make a difference.NPJ science of learning · 2026Article
- Genetic risk for high body mass index before and amidst the obesity epidemic: Cross-cohort analysis of four british birth cohort studies.PLoS genetics · 2026Article
- The genetic lottery goes to school: Better schools compensate for the effects of students' genetic differences.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Polygenic Associations With Educational Attainment in East Versus West Germany: Differences Emerge After Reunification.Psychological science · 2025Article
- Analysis of Sex-Specific Gene-by-Cohort and Genetic Correlation-by-Cohort Interaction in Educational and Reproductive Outcomes Using the UK Biobank Data.Journal of health and social behavior · 2024Article
- Interaction of family SES with children's genetic propensity for cognitive and noncognitive skills: No evidence of the Scarr-Rowe hypothesis for educational outcomes.Research in social stratification and mobility · 2024Article
- Gene‒Environment Interaction Between CAST Gene and Eye-Rubbing in the Chinese Keratoconus Cohort Study: A Case-Only Study.Investigative ophthalmology & visual science · 2024Article
- Genotype × environment interactions in gene regulation and complex traits.Nature genetics · 2024Review
- Exploring the Fetal Origins Hypothesis Using Genetic Data.Social forces; a scientific medium of social study and interpretation · 2024Article
- Many roads to a gene-environment interaction.American journal of human genetics · 2024Review
- Modeling genotype-protein interaction and correlation for Alzheimer's disease: a multi-omics imaging genetics study.Briefings in bioinformatics · 2024Article
- Trends in the ability of socioeconomic position to predict individual body mass index: an analysis of repeated cross-sectional data, 1991-2019.BMC medicine · 2023Article
- Factorizing polygenic epistasis improves prediction and uncovers biological pathways in complex traits.American journal of human genetics · 2023Article
- Amplification is the primary mode of gene-by-sex interaction in complex human traits.Cell genomics · 2023Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
Genotype-by-environment interaction (GxE) studies probe heterogeneity in response to risk factors or interventions. Popular methods for estimation of GxE examine multiplicative interactions between individual genetic and environmental measures. However, risk factors and interventions may modulate the total variance of an epidemiological outcome that itself represents the aggregation of many other etiological components. We expand the traditional GxE model to directly model genetic and environmental moderation of the dispersion of the outcome. We derive a test statistic, [Formula: see text], for inferring whether an interaction identified between individual genetic and environmental measures represents a more general pattern of moderation of the total variance in the phenotype by either the genetic or the environmental measure. We validate our method via extensive simulation, and apply it to investigate genotype-by-birth year interactions for Body Mass Index (BMI) with polygenic scores in the Health and Retirement Study (N = 11,586) and individual genetic variants in the UK Biobank (N = 380,605). We find that changes in the penetrance of a genome-wide polygenic score for BMI across birth year are partly representative of a more general pattern of expanding BMI variation across generations. Three individual variants found to be more strongly associated with BMI among later born individuals, were also associated with the magnitude of variability in BMI itself within any given birth year, suggesting that they may confer general sensitivity of BMI to a range of unmeasured factors beyond those captured by birth year. We introduce an expanded GxE regression model that explicitly models genetic and environmental moderation of the dispersion of the outcome under study. This approach can determine whether GxE interactions identified are specific to the measured predictors or represent a more general pattern of moderation of the total variance in the outcome by the genetic and environmental measures.
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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 OpenQuestion graph.