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. 2017 Feb:13:136-141.
doi: 10.1016/j.copsyc.2016.08.002. Epub 2016 Aug 21.

Gene-Environment Interplay in the Context of Romantic Relationships

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Gene-Environment Interplay in the Context of Romantic Relationships

Mark A Whisman et al. Curr Opin Psychol. 2017 Feb.

Abstract

A growing body of research supports an important role for genetic factors on intimate, romantic relationships. In this article, we review research that has examined the interplay between genetic and environmental influences on romantic relationships and the associations between relationship outcomes and important individual differences related to relationships. We first elaborate on how behavioral genetic and molecular genetic methods can be used to understand the etiology of relationship outcomes. We then review empirical studies that have examined gene-environment correlations and gene-by-environment interactions in predicting romantic relationship outcomes (e.g., relationship formation, relationship quality and functioning, relationship dissolution) and their association with the physical health, mental health, and well-being of relationship partners.

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Figures

Figure 1
Figure 1
Possible examples of the pattern of genetic and environmental influences on a hypothetical phenotype resulting from quantGxE. Heritability (A) and proportions of variance due to shared environmental (C) and nonshared environmental (E) influence are plotted as a function of relationship quality, shown on the X axis at five different levels: −2, −1, 0, 1, and 2 Standard Deviations from the Mean. Example A follows the Diathesis-stress model, in which genetic influences are highest in the “riskiest” environment, here extremely low levels of relationship quality. Example B follows the Bioecological model, in which genetic influences on the phenotype are highest in the least risky environment of high relationship quality. Example C follows the Differential susceptibility model, in that genetic influences are highest in both the high risk and low risk environments. In these examples, we have artificially constrained C to 0, but several studies of quantGxE have found that C is substantially greater than 0, particularly in extreme ends of the environmental context.

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