Advancing Diversity in Behavior Genetics: Strategies for Incorporating Undergraduates into Student-Driven Research.

Undergraduate research experiences are crucial for fostering the next generation of behavior genetics researchers. However, incorporating undergraduates into research can be challenging for faculty mentors. In this article, we provide strategies for successfully integrating undergraduates into behavior genetics research based on our experiences mentoring undergraduates in our lab. These strategies include: (1) Practicing reflexivity, specifically an ongoing self-examination and critical self-awareness of personal biases, beliefs, and practices; (2) Implementing an Inclusion, Diversity, Equity, and Access (IDEA) centered approach; (3) empowering students through clear expectations; (4) Providing focused training and mentorship; (5) Aligning research projects with student interests; (6) Assigning meaningful tasks; and (7) Facilitating professional development opportunities. By following these strategies, faculty mentors can cultivate a supportive and inclusive research environment that empowers undergraduates for successful careers in behavior genetics research.

Effects of Genetic Relatedness of Kin Pairs on Univariate ACE Model Performance.

The current study explored the impact of genetic relatedness differences (ΔH) and sample size on the performance of nonclassical ACE models, with a focus on same-sex and opposite-sex twin groups. The ACE model is a statistical model that posits that additive genetic factors (A), common environmental factors (C), and specific (or nonshared) environmental factors plus measurement error (E) account for individual differences in a phenotype. By extending Visscher's (2004) least squares paradigm and conducting simulations, we illustrated how genetic relatedness of same-sex twins (HSS) influences the statistical power of additive genetic estimates (A), AIC-based model performance, and the frequency of negative estimates. We found that larger HSS and increased sample sizes were positively associated with increased power to detect additive genetic components and improved model performance, and reduction of negative estimates. We also found that the common solution of fixing the common environment correlation for sex-limited effects to .95 caused slightly worse model performance under most circumstances. Further, negative estimates were shown to be possible and were not always indicative of a failed model, but rather, they sometimes pointed to low power or model misspecification. Researchers using kin pairs with ΔH less than .5 should carefully consider performance implications and conduct comprehensive power analyses. Our findings provide valuable insights and practical guidelines for those working with nontwin kin pairs or situations where zygosity is unavailable, as well as areas for future research.

The Analytic Identification of Variance Component Models Common to Behavior Genetics.

Many behavior genetics models follow the same general structure. We describe this general structure and analytically derive simple criteria for its identification. In particular, we find that variance components can be uniquely estimated whenever the relatedness matrices that define the components are linearly independent (i.e., not confounded). Thus, we emphasize determining which variance components can be identified given a set of genetic and environmental relationships, rather than the estimation procedures. We validate the identification criteria with several well-known models, and further apply them to several less common models. The first model distinguishes child-rearing environment from extended family environment. The second model adds a gene-by-common-environment interaction term in sets of twins reared apart and together. The third model separates measured-genomic relatedness from the scanner site variation in a hypothetical functional magnetic resonance imaging study. The computationally easy analytic identification criteria allow researchers to quickly address model identification issues and define novel variance components, facilitating the development of new research questions.

Responding to a 100-Year-Old Challenge from Fisher: A Biometrical Analysis of Adult Height in the NLSY Data Using Only Cousin Pairs.

In 1918, Fisher suggested that his research team had consistently found inflated cousin correlations. He also commented that because a cousin sample with minimal selection bias was not available the cause of the inflation could not be addressed, leaving this inflation as a challenge still to be solved. In the National Longitudinal Survey of Youth (the NLSY79, the NLSY97, and the NLSY-Children/Young Adult datasets), there are thousands of available cousin pairs. Those in the NLSYC/YA are obtained approximately without selection. In this paper, we address Fisher's challenge using these data. Further, we also evaluate the possibility of fitting ACE models using only cousin pairs, including full cousins, half-cousins, and quarter-cousins. To have any chance at success in such a restricted kinship domain requires an available and highly-reliable phenotype; we use adult height in our analysis. Results provide a possible answer to Fisher's challenge, and demonstrate the potential for using cousin pairs in a stand-alone analysis (as well as in combination with other biometrical designs).

Your friends know how long you will live: a 75-year study of peer-rated personality traits.

Although self-rated personality traits predict mortality risk, no study has examined whether one's friends can perceive personality characteristics that predict one's mortality risk. Moreover, it is unclear whether observers' reports (compared with self-reports) provide better or unique information concerning the personal characteristics that result in longer and healthier lives. To test whether friends' reports of personality predict mortality risk, we used data from a 75-year longitudinal study (the Kelly/Connolly Longitudinal Study on Personality and Aging). In that study, 600 participants were observed beginning in 1935 through 1938, when they were in their mid-20s, and continuing through 2013. Male participants seen by their friends as more conscientious and open lived longer, whereas friend-rated emotional stability and agreeableness were protective for women. Friends' ratings were better predictors of longevity than were self-reports of personality, in part because friends' ratings could be aggregated to provide a more reliable assessment. Our findings demonstrate the utility of observers' reports in the study of health and provide insights concerning the pathways by which personality traits influence health.

Decomposing the causes of the socioeconomic status-health gradient with biometrical modeling.

The consistent relationship between socioeconomic status (SES) and health has been widely covered in the media and scientific journals, which typically argue that physical-health inequalities are caused by material disadvantage directly or indirectly (e.g., chronic environmental-stress, health care resources, etc.). Such explanations do not explain the finely stratified health differences across the entire range of SES. Recent theories have helped address such limitations, but implicate multiple different explanatory pathways. For example, differential epidemiology articles have argued that individual differences are the "fundamental cause" of the gradient (Gottfredson, 2004). Alternatively, variants of allostatic load theory (McEwen & Stellar, 1993), such as the Risky Families model (Repetti, Taylor, & Seeman, 2002) implicate the early home-environment. These theory-driven pathways align with interpretations associated with biometrical models; yet, little research has applied biometrical modeling to understanding the sources of the gradient. Our study presents several innovations and new research findings. First, we use kinship information from a large national family dataset, the NLSY79, whose respondents are approximately representative of United States adolescents in 1979. Second, we present the first biometrical analysis of the relationships between SES and health that uses an overall SES measure. Third, we separate physical and mental health, using excellent measurement of each construct. Fourth, we use a bivariate biometrical model to study overlap between health and SES. Results suggest divergent findings for physical and mental health. Biometrical models indicate a primarily genetic etiology for the link between SES and physical health, and a primarily environmental etiology for the link between SES and mental health. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The Psychological Science Accelerator: Advancing Psychology through a Distributed Collaborative Network.

Concerns have been growing about the veracity of psychological research. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions, or attempt to replicate prior research, in large, diverse samples. The PSA's mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time-limited), efficient (in terms of re-using structures and principles for different projects), decentralized, diverse (in terms of participants and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside of the network). The PSA and other approaches to crowdsourced psychological science will advance our understanding of mental processes and behaviors by enabling rigorous research and systematically examining its generalizability.