ArticleBehavior genetics2021
The Analytic Identification of Variance Component Models Common to Behavior Genetics.
Article in Behavior genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Tracing the Right Path: Determination of Large Pedigree Segmentation and Relatedness.Behavior genetics · 2026Article
- ADHD and Student Grades in Children from the ABCD StudyBehavior genetics · 2026Article
- ggpedigree: Visualizing Pedigrees with 'ggplot2' and 'plotly'.Journal of open source software · 2026Article
- Detecting mtDNA Effects with an Extended Pedigree Model: An Analysis of Statistical Power and Estimation Bias.Behavior genetics · 2025Article
- Measuring the Associations Between Brain Morphometry and Polygenic Risk Scores for Substance use Disorders in Drug-Naive Adolescents.Behavior genetics · 2025Article
- Show Me Some ID: A Universal Identification Program for Structural Equation Models.Psychometrika · 2025Article
- BGmisc: An R Package for Extended Behavior Genetics Analysis.Journal of open source software · 2024Article
- Effects of Genetic Relatedness of Kin Pairs on Univariate ACE Model Performance.Twin research and human genetics : the official journal of the International Society for Twin Studies · 2023Article
- Simulated nonlinear genetic and environmental dynamics of complex traits.Development and psychopathology · 2023Article
- MR-DoC2: Bidirectional Causal Modeling with Instrumental Variables and Data from Relatives.Behavior genetics · 2023Article
- Bullying Perpetration and Depressive Symptoms: A Causal Investigation of TwinLife Data.Clinical psychology & psychotherapyArticle
- Using Instrumental Variables to Measure Causation over Time in Cross-Lagged Panel Models.Multivariate behavioral researchArticle
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Authors and funding
4 authors.
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Abstract
Many behavior genetics models follow the same general structure. We describe this general structure and analytically derive simple criteria for its identification. In particular, we find that variance components can be uniquely estimated whenever the relatedness matrices that define the components are linearly independent (i.e., not confounded). Thus, we emphasize determining which variance components can be identified given a set of genetic and environmental relationships, rather than the estimation procedures. We validate the identification criteria with several well-known models, and further apply them to several less common models. The first model distinguishes child-rearing environment from extended family environment. The second model adds a gene-by-common-environment interaction term in sets of twins reared apart and together. The third model separates measured-genomic relatedness from the scanner site variation in a hypothetical functional magnetic resonance imaging study. The computationally easy analytic identification criteria allow researchers to quickly address model identification issues and define novel variance components, facilitating the development of new research questions.
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