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Modeling Interaction and Dispersion Effects in the Analysis of Gene-by-Environment Interaction

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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, \(\xi\), 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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Data Availability

Data is available as indicated in manuscript.

Code Availability

Code is available at https://github.com/ben-domingue/scalingGxE.

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Acknowledgements

The authors would like to thank Dan Benjamin, Dalton Conley, Michel Nivard, Paul Rathouz, Mijke Rhemtulla, Subu Subramanian, and Patrick Turley for helpful comments on an early version of this manuscript. This research was conducted using the UK Biobank Resource (Application No. 36046).

Funding

This work was supported in part by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1656518 (ST), by the Institute of Education Sciences under Grant No. R305B140009 (ST), by NIH grants R01MH120219, R01AG054628, and R01HD083613 (EMTD), and by the Jacobs Foundation (EMTD). Any opinions expressed are those of the authors alone and should not be construed as representing the opinions of funding agencies. EMTD is a member of the Population Research Center and the Center for Aging and Population Sciences at the University of Texas at Austin, which are funded by NIH center grants P2CHD042849 and P30AG066614, respectively.

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Authors and Affiliations

  1. Graduate School of Education, Stanford University and Center for Population Health Sciences, Stanford Medicine, Stanford, USA

    Benjamin W. Domingue

  2. Graduate School of Education, Stanford University, Stanford, USA

    Klint Kanopka

  3. Department of Psychology, University of Texas at Austin, Austin, USA

    Travis T. Mallard

  4. Department of Sociology and Office of Population Research, Princeton University, Princeton, USA

    Sam Trejo

  5. Department of Psychology and Population Research Center, University of Texas at Austin, Austin, USA

    Elliot M. Tucker-Drob

Authors
  1. Benjamin W. Domingue
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  2. Klint Kanopka
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  3. Travis T. Mallard
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  4. Sam Trejo
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  5. Elliot M. Tucker-Drob
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Contributions

BWD and ETMD designed the study with additional input from KK. BWD, TMT, and EMTD contributed to analysis. All authors were involved in drafting the manuscript.

Corresponding authors

Correspondence to Benjamin W. Domingue or Elliot M. Tucker-Drob.

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Conflict of interest

Benjamin W. Domingue, Klint Kanopka, Travis T. Mallard, Sam Trejo, and Elliot M. Tucker-Drob declare that they have no conflict of interest.

Human and Animal Rights and Informed consent

De-identified data from the Health and Retirement study and the UK Biobank were analyzed for this article. As none of the authors of this article was involved in the original data collection and none has access to participant identifiers from either project, this research reported here does not constitute human subject research. This article does not report results of any studies with non-human animals performed by any of the authors.

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Domingue, B.W., Kanopka, K., Mallard, T.T. et al. Modeling Interaction and Dispersion Effects in the Analysis of Gene-by-Environment Interaction. Behav Genet 52, 56–64 (2022). https://doi.org/10.1007/s10519-021-10090-8

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  • DOI: https://doi.org/10.1007/s10519-021-10090-8

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