Can a brief intervention alter genetic and environmental influences on psychological traits? An experimental behavioral genetics approach

Measurement Invariance Testing Works

Psychometricians have argued that measurement invariance (MI) testing is needed to know if the same psychological constructs are measured in different groups. Data from five experiments allowed that position to be tested. In the first, participants answered questionnaires on belief in free will and either the meaning of life or the meaning of a nonsense concept called "gavagai." Since the meaning of life and the meaning of gavagai conceptually differ, MI should have been violated when groups were treated like their measurements were identical. MI was severely violated, indicating the questionnaires were interpreted differently. In the second and third experiments, participants were randomized to watch treatment videos explaining figural matrices rules or task-irrelevant control videos. Participants then took intelligence and figural matrices tests. The intervention worked and the experimental group had an additional influence on figural matrix performance in the form of knowing matrix rules, so their performance on the matrices tests violated MI and was anomalously high for their intelligence levels. In both experiments, MI was severely violated. In the fourth and fifth experiments, individuals were exposed to growth mindset interventions that a twin study revealed changed the amount of genetic variance in the target mindset measure without affecting other variables. When comparing treatment and control groups, MI was attainable before but not after treatment. Moreover, the control group showed longitudinal invariance, but the same was untrue for the treatment group. MI testing is likely able to show if the same things are measured in different groups.

Joint Consideration of Means and Variances Might Change the Understanding of Etiology

Twin and adoption studies compare the similarities of people with differing degrees of relatedness to estimate genetic and environmental contributions to trait population variance. The analytic workhorse of these kinds of variance-focused designs is the intraclass correlation, which estimates similarity between pairs of individuals. Group means, by contrast, play no overt role in estimating genetic and environmental influences. Although this focus on variance has made very important contributions to understanding psychological characteristics, we contend that the exclusion of mean effects from behavioral genetic designs may have obscured key environmental influences and impeded full appreciation of the ubiquity and nature of gene-environment interplay in human outcomes. We provide empirical examples already in the literature and a theoretical framework for thinking through the incorporation of mean effects using largely forgotten, non-Mendelian theory regarding how genes influence human outcomes. We conclude that the field needs to develop models capable of fully incorporating mean effects into twin and adoption studies.

Genetic determinism, essentialism and reductionism: semantic clarity for contested science

Research linking genetic differences with human social and behavioural phenotypes has long been controversial. Frequently, debates about the ethical, social and legal implications of this area of research centre on questions about whether studies overtly or covertly perpetuate genetic determinism, genetic essentialism and/or genetic reductionism. Given the prominent role of the '-isms' in scientific discourse and criticism, it is important for there to be consensus and clarity about the meaning of these terms. Here, the author integrates scholarship from psychology, genetics and philosophy of science to provide accessible definitions of genetic determinism, genetic reductionism and genetic essentialism. The author provides linguistic and visual examples of determinism, reductionism and essentialism in science and popular culture, discusses common misconceptions and concludes with recommendations for science communication.

Developmental behavioral genetics research on school achievement is missing vulnerable children, to our detriment

Gene-environment processes tell us how genetic predispositions and environments work together to influence children in schools. One type of gene-environment process that has been extensively studied using behavioral genetics methods is a gene-by-environment interaction. A gene-by-environment interaction shows us when the effect of your context on a phenotype differs depending on your genetic predispositions, or vice versa, when the effect of your genetic predispositions on a phenotype differs depending on your context. Developmental behavioral geneticists interested in children's school achievement have examined many different contexts within the gene-by-environment interaction model, including contexts measured from within children's home and school environments. However, this work has been overwhelmingly focused on WEIRD samples children, leaving us with non-inclusive scientific evidence. This can lead to detrimental outcomes when we overgeneralize this non-inclusive scientific evidence to racialized groups. We conclude with a call to include racialized children in more research samples.

Genetic and environmental influences on quality of life: The COVID-19 pandemic as a natural experiment

By treating the coronavirus disease 2019 (COVID-19) pandemic as a natural experiment, we examine the influence of substantial environmental change (i.e., lockdown measures) on individual differences in quality of life (QoL) in the Netherlands. We compare QoL scores before the pandemic (N = 25,772) to QoL scores during the pandemic (N = 17,222) in a sample of twins and their family members. On a 10-point scale, we find a significant decrease in mean QoL from 7.73 (SD = 1.06) before the pandemic to 7.02 (SD = 1.36) during the pandemic (Cohen's d = 0.49). Additionally, variance decomposition shows an increase in unique environmental variance during the pandemic (0.30-1.08), and a decrease in the heritability estimate from 30.9% to 15.5%. We hypothesize that the increased environmental variance is the result of lockdown measures not impacting everybody equally. Whether these effects persist over longer periods and how they impact health inequalities remain topics for future investigation.