Partitioning the Genomic Components of Behavioral Disinhibition and Substance Use (Disorder) Using Genomic Structural Equation Modeling.

Externalizing behaviors encompass manifestations of risk-taking, self-regulation, aggression, sensation-/reward-seeking, and impulsivity. Externalizing research often includes substance use (SUB), substance use disorder (SUD), and other (non-SUB/SUD) "behavioral disinhibition" (BD) traits. Genome-wide and twin research have pointed to overlapping genetic architecture within and across SUB, SUD, and BD. We created single-factor measurement models-each describing SUB, SUD, or BD traits-based on mutually exclusive sets of European ancestry genome-wide association study (GWAS) statistics exploring externalizing variables. We then assessed the partitioning of genetic covariance among the three facets using correlated factors models and Cholesky decomposition. Even when the residuals for indicators relating to the same substance were correlated across the SUB and SUD factors, the two factors yielded a large correlation (rg = 0.803). BD correlated strongly with the SUD (rg = 0.774) and SUB (rg = 0.778) factors. In our initial decompositions, 33% of total BD variance remained after partialing out SUD and SUB. The majority of covariance between BD and SUB and between BD and SUD was shared across all factors, and, within these models, only a small fraction of the total variation in BD operated via an independent pathway with SUD or SUB outside of the other factor. When only nicotine/tobacco, cannabis, and alcohol were included for the SUB/SUD factors, their correlation increased to rg = 0.861; in corresponding decompositions, BD-specific variance decreased to 27%. Further research can better elucidate the properties of BD-specific variation by exploring its genetic/molecular correlates.

Partitioning the Genomic Components of Behavioral Disinhibition and Substance Use (Disorder) Using Genomic Structural Equation Modeling.

Externalizing behaviors encompass manifestations of risk-taking, self-regulation, aggression, sensation-/reward-seeking, and impulsivity. Externalizing research often includes substance use (SU), substance use disorder (SUD), and other (non-SU/SUD) "behavioral disinhibition" (BD) traits. Genome-wide and twin research have pointed to overlapping genetic architecture within and across SUB, SUD, and BD. We created single-factor measurement models-each describing SUB, SUD, or BD traits--based on mutually exclusive sets of European ancestry genome-wide association study (GWAS) statistics exploring externalizing variables. We then applied trivariate Cholesky decomposition to these factors in order to identify BD-specific genomic variation and assess the partitioning of BD's genetic covariance with each of the other facets. Even when the residuals for indicators relating to the same substance were correlated across the SUB and SUD factors, the two factors yielded a large zero-order correlation (rg=.803). BD correlated strongly with the SUD (rg=.774) and SUB factors (rg=.778). In our initial decompositions, 33% of total BD variance remained after removing variance associated with SUD and SUB. The majority of covariance between BD and SU and between BD and SUD was shared across all factors. When only nicotine/tobacco, cannabis, and alcohol were included for the SUB/SUD factors, their zero-order correlation increased to rg=.861; in corresponding decompositions, BD-specific variance decreased to 27%. In summary, BD, SU, and SUD were highly genetically correlated at the latent factor level, and a significant minority of genomic BD variation was not shared with SU and/or SUD. Further research can better elucidate the properties of BD-specific variation by exploring its genetic/molecular correlates.

Reinvigoration of a Diversity, Equity, and Inclusion Committee at the Institute for Behavioral Genetics: Student-driven Progress.

Extremist far-right ideologies, including scientifically inaccurate beliefs about race, are on the rise (Mierina and Koroleva 2015; Youngblood 2020); individuals perpetuating such ideologies occasionally cite genetics research, including behavioral genetics research. This highlights the need for behavioral geneticists to actively confront extremist ideology and promote anti-racism. We emphasize the need for Diversity, Equity and Inclusion (DEI) committees within behavioral genetics institutions. DEI committees can lead to: greater awareness of ways in which behavioral genetics has been misused (historically and currently) to harm minoritized communities, increased discussions on conducting ethical behavioral genetics research, and increased collaboration for conducting more diverse behavioral genetics research. We discuss the activities and goals of the student-driven DEI committee at the Institute for Behavior Genetics (IBG). At the same time, we acknowledge we have a long way to go, both as a committee and as a field. Our committee is still in its early stages; we discuss challenges to increasing DEI in the field and present future goals for both IBG and the behavioral genetics community as we explore the process of implementing DEI work.

South Asia: The Missing Diverse in Diversity.

South Asia, making up around 25% of the world's population, encompasses a wide range of individuals with tremendous genetic and environmental diversity. This region, which spans eight countries, is home to over 4500 anthropologically defined groups that speak numerous languages and have an array of religious beliefs and cultures, making it one of the most diverse places in the world. Much of the region's rich genetic diversity and structure is the result of a complex combination of population history, migration patterns, and endogamous practices. Despite the overwhelming size and diversity, South Asians have often been underrepresented in genetic research, making up less than 2% of the participants in genetic studies. This has led to a lack of population specific understanding of genetic disease risks. We aim to raise awareness about underlying genetic diversity in this ancestry group, call attention to the lack of representation of the group, and to highlight strategies for future studies in South Asians.

Evidence of correlations between human partners based on systematic reviews and meta-analyses of 22 traits and UK Biobank analysis of 133 traits.

Positive correlations between mates can increase trait variation and prevalence, as well as bias estimates from genetically informed study designs. While past studies of similarity between human mating partners have largely found evidence of positive correlations, to our knowledge, no formal meta-analysis has examined human partner correlations across multiple categories of traits. Thus, we conducted systematic reviews and random-effects meta-analyses of human male-female partner correlations across 22 traits commonly studied by psychologists, economists, sociologists, anthropologists, epidemiologists and geneticists. Using ScienceDirect, PubMed and Google Scholar, we incorporated 480 partner correlations from 199 peer-reviewed studies of co-parents, engaged pairs, married pairs and/or cohabitating pairs that were published on or before 16 August 2022. We also calculated 133 trait correlations using up to 79,074 male-female couples in the UK Biobank (UKB). Estimates of the 22 mean meta-analysed correlations ranged from rmeta = 0.08 (adjusted 95% CI = 0.03, 0.13) for extraversion to rmeta = 0.58 (adjusted 95% CI = 0.50, 0.64) for political values, with funnel plots showing little evidence of publication bias across traits. The 133 UKB correlations ranged from rUKB = -0.18 (adjusted 95% CI = -0.20, -0.16) for chronotype (being a 'morning' or 'evening' person) to rUKB = 0.87 (adjusted 95% CI = 0.86, 0.87) for birth year. Across analyses, political and religious attitudes, educational attainment and some substance use traits showed the highest correlations, while psychological (that is, psychiatric/personality) and anthropometric traits generally yielded lower but positive correlations. We observed high levels of between-sample heterogeneity for most meta-analysed traits, probably because of both systematic differences between samples and true differences in partner correlations across populations.