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Author Notes:

Paul Rathouz, Dept. of Health Studies, 5841 S. Maryland Ave., MC 2007, Chicago, IL 60637, Ph: 773-834-1970, FAX: 773-702-1979, email: prathouz@uchicago.edu.

The authors thank Wendy Johnson and Robert F. Krueger for insightful comments on the first draft of this paper, which dramatically improved subsequent drafts.

Subjects:

Keywords:

  • Biometry
  • Data Interpretation, Statistical
  • Environment
  • Genetic Variation
  • Genetics
  • Humans
  • Models, Biological
  • Models, Genetic
  • Models, Statistical
  • Models, Theoretical
  • Reproducibility of Results
  • Research
  • Twin Studies as Topic

Specification, testing, and interpretation of gene-by-measured-environment interaction models in the presence of gene-environment correlation

Tools:

Journal Title:

Behavior Genetics

Volume:

Volume 38, Number 3

Publisher:

, Pages 301-315

Type of Work:

Article | Post-print: After Peer Review

Abstract:

Purcell (Twin Res 5:554-571, 2002) proposed a bivariate biometric model for testing and quantifying the interaction between latent genetic influences and measured environments in the presence of gene-environment correlation. Purcell's model extends the Cholesky model to include gene-environment interaction. We examine a number of closely related alternative models that do not involve gene-environment interaction but which may fit the data as well as Purcell's model. Because failure to consider these alternatives could lead to spurious detection of gene-environment interaction, we propose alternative models for testing gene-environment interaction in the presence of gene-environment correlation, including one based on the correlated factors model. In addition, we note mathematical errors in the calculation of effect size via variance components in Purcell's model. We propose a statistical method for deriving and interpreting variance decompositions that are true to the fitted model.

Copyright information:

© 2008 Springer Science+Business Media, LLC.

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