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Link to original content: https://dx.doi.org/10.1007/s10519-014-9698-y
Replication of a Gene–Environment Interaction Via Multimodel Inference: Additive-Genetic Variance in Adolescents’ General Cognitive Ability Increases with Family-of-Origin Socioeconomic Status | Behavior Genetics Skip to main content
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Replication of a Gene–Environment Interaction Via Multimodel Inference: Additive-Genetic Variance in Adolescents’ General Cognitive Ability Increases with Family-of-Origin Socioeconomic Status

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Abstract

The present study of general cognitive ability attempts to replicate and extend previous investigations of a biometric moderator, family-of-origin socioeconomic status (SES), in a sample of 2,494 pairs of adolescent twins, non-twin biological siblings, and adoptive siblings assessed with individually administered IQ tests. We hypothesized that SES would covary positively with additive-genetic variance and negatively with shared-environmental variance. Important potential confounds unaddressed in some past studies, such as twin-specific effects, assortative mating, and differential heritability by trait level, were found to be negligible. In our main analysis, we compared models by their sample-size corrected AIC, and base our statistical inference on model-averaged point estimates and standard errors. Additive-genetic variance increased with SES—an effect that was statistically significant and robust to model specification. We found no evidence that SES moderated shared-environmental influence. We attempt to explain the inconsistent replication record of these effects, and provide suggestions for future research.

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Notes

  1. We are grateful to two anonymous referees for calling to our attention the points made in this paragraph concerning stability of SES.

  2. See (Tucker-Drob et al. 2009) and McCallum and Mar (1995) for discussion of how quadratic trends may be mistaken for multiplicative interactions.

  3. We consider effect sizes and their interval estimates to be more scientifically interesting and informative than hypothesis tests. However, our confidence intervals only have a marginal 95 % coverage probability; their joint coverage probability is presumably smaller. Also, not every free parameter we estimated is an easily interpretable effect size, and further, the null hypothesis is indeed of interest and somewhat plausible for certain parameters. We therefore report p-values as well, and when making decisions about null hypotheses, compare them to the conventional significance level of \(\alpha =0.05\). P values are also easier than confidence intervals for the reader to adjust for “multiple testing.” We report 17 of them altogether. A Bonferroni correction would certainly be conservative, but skeptical readers are free to hold our results to its standard of \(\alpha =0.0029\).

  4. Readers certainly can think of models we could have fitted, but did not. Some readers may be interested in Table S3 (Online Resource), which, for the sake of completeness, reports point estimates and standard errors from a post hoc, “full” model in which all parameters under consideration were freely estimated.

  5. Unfortunately, several important primary sources by Akaike are inaccessible to us, due to being conference presentations or being written in Japanese. We do not cite sources we cannot read. Here, we rely on secondary sources by Burnham and Anderson (2001, 2002, 2004) and Pawitan (2013).

  6. It may be objected that basing inference about a parameter only upon those models in which it is freely estimated ignores evidence about the parameter conveyed by those models in which it is fixed. If one’s objective is regression prediction rather than inference, Burnham and Anderson (2002) do recommend calculating the model-averaged regression coefficient from models in which it is fixed, as well as those in which it is free. However, as Bartels (1997, footnote 11) points out, a model-averaged estimate computed in this way will not have a normal sampling distribution, which complicates its use for statistical inference.

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Acknowledgments

This research was supported in part by USPHS Grants from the National Institute on Alcohol Abuse and Alcoholism (AA09367 and AA11886), the National Institute on Drug Abuse (DA05147, DA13240, and DA024417), and the National Institute on Mental Health (MH066140). The first author (RMK) was supported by a Doctoral Dissertation Fellowship from the University of Minnesota Graduate School and by grant DA026119 from the National Institute on Drug Abuse. The authors acknowledge the assistance of Niels G. Waller and Saonli Basu, who provided helpful comments on an early draft of this paper. The first author gives his special thanks to Scott I. Vrieze and Joshua D. Isen for thought-provoking discussion of model-selection and of the main effects of SES, respectively.

Conflict of interest

Robert M. Kirkpatrick, Matt McGue, and William G. Iacono declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

The MTFS and SIBS studies were reviewed and approved by the Institutional Review Board at the University of Minnesota. Written informed assent or consent was obtained from all participants, with parents providing written consent for their minor children.

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  1. Robert M. Kirkpatrick

    Present address: Virginia Institute for Psychiatric & Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, 23298-0126, USA

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  1. Department of Psychology, University of Minnesota, 75 E. River Rd, Minneapolis, MN, 55455, USA

    Robert M. Kirkpatrick, Matt McGue & William G. Iacono

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Correspondence to Robert M. Kirkpatrick.

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Kirkpatrick, R.M., McGue, M. & Iacono, W.G. Replication of a Gene–Environment Interaction Via Multimodel Inference: Additive-Genetic Variance in Adolescents’ General Cognitive Ability Increases with Family-of-Origin Socioeconomic Status. Behav Genet 45, 200–214 (2015). https://doi.org/10.1007/s10519-014-9698-y

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