Abstract
A wealth of literature suggests that normative and heavy alcohol consumption continue to follow a historical pattern of greater prevalence among males as compared to females. Some prior research suggested that sex-specific factors might explain some of this gender gap. Generally speaking, though, more recent studies have indicated that the sources of differences for most complex traits, both genetic and environmental, are similar for males and females. To the best of our knowledge, however, no studies have tested whether genetic and environmental factors common to both sexes are more often expressed in males, on average, thereby accounting for some of the mean sex difference in alcohol use. The current study used nationally representative data from American twin respondents and a multiple group genetic factor model with a mean structure to address this gap in the literature. Results provide no evidence of sex differences in covariance structure and suggest that genetic and nonshared environmental influences common to both sexes largely explain why male alcohol use is more frequent and severe, on average, than is female use. In contrast, shared environmental influences seem to play a less important role. We discuss our findings in the context of the existing literature and chart out directions for future research.
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Notes
Selection bias is one additional potential source of apparent sex differences in frequency and severity of alcohol use. More males than females, for instance, might select to treatment and receive an AUD diagnosis. Here we do not consider this possibility further because we are attending to differences detected in nationally representative data.
Researchers can test still higher levels of invariance (e.g., invariance of factor variances and residuals) but typically only configural, metric, and scalar invariance are evaluated. This is partly because invariance at these levels ensures most inferences of interest are not compromised by bias, and partly because higher levels are often more difficult to achieve (for further discussion, see Wang et al. 2018).
Mplus fixes the first loading on each factor to one by default. We freed these loadings so they could be estimated and constrained them equal across the groups. Mplus constrains intercepts to zero by default when the WLSMV estimator is used and information about them is captured by thresholds. Observed variable residuals are not estimated under delta parameterization.
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George B. Richardson and Brian B. Boutwell declare that they have no conflict of interest.
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This study involved analysis of a publicly available and de-identified dataset, the Institutional Review Board at the University of Cincinnati determined that it did not meet the regulatory criteria for research involving human subjects.
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Richardson, G.B., Boutwell, B.B. Decomposition of Mean Sex Differences in Alcohol Use Within a Genetic Factor Model. Behav Genet 50, 320–331 (2020). https://doi.org/10.1007/s10519-020-10004-0
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DOI: https://doi.org/10.1007/s10519-020-10004-0