ArticleBehavior genetics2015
Nonparametric Estimates of Gene × Environment Interaction Using Local Structural Equation Modeling.
Article in Behavior genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- A decade of discovery: Leveraging genomic data in the ABCD study to illuminate adolescent neurodevelopment.Developmental cognitive neuroscience · 2026Review
- Genetic and environmental contributions to adult attachment styles: Evidence from the Minnesota Twin Registry.Journal of personality and social psychology · 2025Article
- Modelling nonlinear moderation effects with local structural equation modelling (LSEM): A non-technical introduction.International journal of psychology : Journal international de psychologie · 2025Article
- Estimating Local Structural Equation Models.Journal of Intelligence · 2023Article
- Two genetic analyses to elucidate causality between body mass index and personality.International journal of obesity (2005) · 2021Article
- Socioeconomic origin, future expectations, and educational achievement: A longitudinal three-generation study of the persistence of family advantage.Developmental psychology · 2021Article
- Genetic and Environmental Influences on Semantic Verbal Fluency Across Midlife and Later Life.Behavior genetics · 2021Article
- Adolescent Big Five personality and pubertal development: Pubertal hormone concentrations and self-reported pubertal status.Developmental psychology · 2021Article
- Kids becoming less alike: A behavioral genetic analysis of developmental increases in personality variance from childhood to adolescence.Journal of personality and social psychology · 2019Article
- Testing Cold and Hot Cognitive Control as Moderators of a Network of Comorbid Psychopathology Symptoms in Adolescence.Clinical psychological science : a journal of the Association for Psychological Science · 2019Article
- Genetic and Environmental Influences on Achievement Goal Orientations Shift with Age.European journal of personality · 2019Article
- Genetic and environmental influences on pubertal hormones in human hair across development.Psychoneuroendocrinology · 2018Article
- Does the heritability of cognitive abilities vary as a function of parental education? Evidence from a German twin sample.PloS one · 2018Article
- Genetic and environmental influences on household financial distress.Journal of economic behavior & organization · 2017Article
- Biometric Modeling of Gene-Environment Interplay: The Intersection of Theory and Method and Applications for Social Inequality.Journal of personality · 2017Review
- Ageing and brain white matter structure in 3,513 UK Biobank participants.Nature communications · 2016Article
- Estimating Modifying Effect of Age on Genetic and Environmental Variance Components in Twin Models.Genetics · 2016Article
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Authors and funding
4 authors.
Funding
Abstract
Gene × environment (G × E) interaction studies test the hypothesis that the strength of genetic influence varies across environmental contexts. Existing latent variable methods for estimating G × E interactions in twin and family data specify parametric (typically linear) functions for the interaction effect. An improper functional form may obscure the underlying shape of the interaction effect and may lead to failures to detect a significant interaction. In this article, we introduce a novel approach to the behavior genetic toolkit, local structural equation modeling (LOSEM). LOSEM is a highly flexible nonparametric approach for estimating latent interaction effects across the range of a measured moderator. This approach opens up the ability to detect and visualize new forms of G × E interaction. We illustrate the approach by using LOSEM to estimate gene × socioeconomic status interactions for six cognitive phenotypes. Rather than continuously and monotonically varying effects as has been assumed in conventional parametric approaches, LOSEM indicated substantial nonlinear shifts in genetic variance for several phenotypes. The operating characteristics of LOSEM were interrogated through simulation studies where the functional form of the interaction effect was known. LOSEM provides a conservative estimate of G × E interaction with sufficient power to detect statistically significant G × E signal with moderate sample size. We offer recommendations for the application of LOSEM and provide scripts for implementing these biometric models in Mplus and in OpenMx under R.
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