The general public and scientific community alike are abuzz over the release of ChatGPT and GPT-4. Among many concerns being raised about the emergence and widespread use of tools based on large language models (LLMs) is the potential for them to propagate biases and inequities. We hope to open a conversation within the environmental data science community to encourage the circumspect and responsible use of LLMs. Here, we pose a series of questions aimed at fostering discussion and initiating a larger dialogue. To improve literacy on these tools, we provide background information on the LLMs that underpin tools like ChatGPT. We identify key areas in research and teaching in environmental data science where these tools may be applied, and discuss limitations to their use and points of concern. We also discuss ethical considerations surrounding the use of LLMs to ensure that as environmental data scientists, researchers, and instructors, we can make well-considered and informed choices about engagement with these tools. Our goal is to spark forward-looking discussion and research on how as a community we can responsibly integrate generative AI technologies into our work.

Previous research has shown that self-reports of the amount of social support are heritable. Using the Kessler perceived social support (KPSS) measure, we explored sex differences in the genetic and environmental contributions to individual differences. We did this separately for subscales that captured the perceived support from different members of the network (spouse, twin, children, parents, relatives, friends and confidant). Our sample comprised 7059 male, female and opposite-sex twin pairs aged 18−95 years from the Australian Twin Registry. We found tentative support for different genetic mechanisms in males and females for support from friends and the average KPSS score of all subscales, but otherwise, there are no sex differences. For each subscale alone, the additive genetic (A) and unique environment (E) effects were significant. By contrast, the covariation among the subscales was explained — in roughly equal parts — by A, E and the common environment, with effects of different support constellations plausibly accounting for the latter. A single genetic and common environment factor accounted for between half and three-quarters of the variance across the subscales in both males and females, suggesting little heterogeneity in the genetic and environmental etiology of the different support sources.

The ‘16Up’ study conducted at the QIMR Berghofer Medical Research Institute from January 2014 to December 2018 aimed to examine the physical and mental health of young Australian twins aged 16−18 years (N = 876; 371 twin pairs and 18 triplet sets). Measurements included online questionnaires covering physical and mental health as well as information and communication technology (ICT) use, actigraphy, sleep diaries and hair samples to determine cortisol concentrations. Study participants generally rated themselves as being in good physical (79%) and mental (73%) health and reported lower rates of psychological distress and exposure to alcohol, tobacco products or other substances than previously reported for this age group in the Australian population. Daily or near-daily online activity was almost universal among study participants, with no differences noted between males and females in terms of frequency or duration of internet access. Patterns of ICT use in this sample indicated that the respondents were more likely to use online information sources for researching physical health issues than for mental health or substance use issues, and that they generally reported partial levels of satisfaction with the mental health information they found online. This suggests that internet-based mental health resources can be readily accessed by adolescent Australians, and their computer literacy augurs well for future access to online health resources. In combination with other data collected as part of the ongoing Brisbane Longitudinal Twin Study, the 16Up project provides a valuable resource for the longitudinal investigation of genetic and environmental contributions to phenotypic variation in a variety of human traits.

This article describes Dr Nathan Gillespie’s PhD training and supervision under Professor Nick Martin and their ongoing collaborations. Drs Gillespie and Martin have collaborated on numerous biometrical genetic analyses applied to cross-sectional and longitudinal twin data, combined molecular and phenotypic modeling, as well as genomewide meta-analyses of psychoactive substance use and misuse. Dr Gillespie remains an active collaborator with Professor Martin, including ongoing data collection, analysis and publications related to the Brisbane Longitudinal Twin Study.

The International Cannabis Consortium (ICC) was founded in 2013 by Jacqueline Vink, Nathan Gillespie, Karin Verweij and Eske Derks. The largest contribution to the first meta-analysis was made by Prof. Nick Martin. The ICC has published two primary publications, in Translational Psychiatry and Nature Neuroscience, and many secondary publications. The study’s principal investigators will always be grateful for Nick’s contribution to science as they would not have been able to do any of this work without the contributions of Nick and others who collected samples. Nick has made unique contributions to the careers of many junior researchers by supporting their development and growth into senior positions.

The aim of the 25 and Up (25Up) study was to assess a wide range of psychological and behavioral risk factors behind mental illness in a large cohort of Australian twins and their non-twin siblings. Participants had already been studied longitudinally from the age of 12 and most recently in the 19Up study (mean age = 26.1 years, SD = 4.1, range = 20–39). This subsequent wave follows up these twins several years later in life (mean age = 29.7 years, SD = 2.2, range =  22–44). The resulting data set enables additional detailed investigations of genetic pathways underlying psychiatric illnesses in the Brisbane Longitudinal Twin Study (BLTS). Data were collected between 2016 and 2018 from 2540 twins and their non-twin siblings (59% female, including 341 monozygotic complete twin-pairs, 415 dizygotic complete pairs and 1028 non-twin siblings and singletons). Participants were from South-East Queensland, Australia, and the sample was of predominantly European ancestry. The 25Up study collected information on 20 different mental disorders, including depression, anxiety, substance use, psychosis, bipolar and attention-deficit hyper-activity disorder, as well as general demographic information such as occupation, education level, number of children, self-perceived IQ and household environment. In this article, we describe the prevalence, comorbidities and age of onset for all 20 examined disorders. The 25Up study also assessed general and physical health, including physical activity, sleep patterns, eating behaviors, baldness, acne, migraines and allergies, as well as psychosocial items such as suicidality, perceived stress, loneliness, aggression, sleep–wake cycle, sexual identity and preferences, technology and internet use, traumatic life events, gambling and cyberbullying. In addition, 25Up assessed female health traits such as morning sickness, breastfeeding and endometriosis. Furthermore, given that the 25Up study is an extension of previous BLTS studies, 86% of participants have already been genotyped. This rich resource will enable the assessment of epidemiological risk factors, as well as the heritability and genetic correlations of mental conditions.

While snus has been the focus of increasing public health interest, twin studies have examined neither sources of individual variation for its use nor the sources of resemblance between snus and cigarette use. Twins from the Norwegian Institute of Public Health Panel were assessed by self-report questionnaire for the initiation of regular use and maximal quantity used for snus and cigarettes. Twin modeling was performed using OpenMx on data from 2767 twins including 856 complete pairs. Fitting univariate twin models produced similar results for cigarette initiation and quantity with estimates of additive genetic, shared environmental and unique environmental effects of approximately 77%, 0% and 23%, respectively. Estimates of snus initiation and quantity were, respectively, approximately 53%, 26% and 21%. Joint analyses suggested that the genetic, shared environmental and unique environmental correlations between cigarette and snus initiation and quantity were +.82, 0 and +.42, respectively. However, these results could not be statistically distinguished from a model which postulated that resemblance between cigarette initiation and quantity resulted from genetic and unique environmental correlations of +.47 and +.43. Compared with cigarette initiation and quantity of use in Norwegian twins, the role of genes was less prominent and shared environment more prominent for initiation and quantity of use of snus. Joint analyses of both tobacco phenotypes suggested, but did not confirm definitively, that genetic risk factors for cigarette and snus use were similar but not identical, while shared environmental factors existed that were specific to snus use.

We recently reported an association of offspring educational attainment with polygenic risk scores (PRS) computed on parent’s non-transmitted alleles for educational attainment using the second GWAS meta-analysis article on educational attainment published by the Social Science Genetic Association Consortium. Here we test the replication of these findings using a more powerful PRS from the third GWAS meta-analysis article by the Consortium. Each of the key findings of our previous paper is replicated using this improved PRS (N = 2335 adolescent twins and their genotyped parents). The association of children’s attainment with their own PRS increased substantially with the standardized effect size, moving from β = 0.134, 95% CI = 0.079, 0.188 for EA2, to β = 0.223, 95% CI = 0.169, 0.278, p < .001, for EA3. Parent’s PRS again predicted the socioeconomic status (SES) they provided to their offspring and increased from β = 0.201, 95% CI = 0.147, 0.256 to β = 0.286, 95% CI = 0.239, 0.333. Importantly, the PRS for alleles not transmitted to their offspring — therefore acting via the parenting environment — was increased in effect size from β = 0.058, 95% CI = 0.003, 0.114 to β = 0.067, 95% CI = 0.012, 0.122, p = .016. As previously found, this non-transmitted genetic effect was fully accounted for by parental SES. The findings reinforce the conclusion that genetic effects of parenting are substantial, explain approximately one-third the magnitude of an individual’s own genetic inheritance and are mediated by parental socioeconomic competence.

Psychological distress (PSYCH), somatic distress (SOMA), affective disorders (AD), and substance use (SU) frequently co-occur. The genetic relationship between PSYCH and SOMA, however, remains understudied. We examined the genetic and environmental influences on these two disorders and their comorbid AD and SU using structural equation modeling. Self-reported PSYCH and SOMA were measured in 1,548 twins using the two subscales of a 12-item questionnaire, the Somatic and Psychological Health Report. Its reliability and psychometric properties were examined. Six ADs, involvement of licit and illicit substance, and two SU disorders were obtained from 1,663–2,132 twins using the World Mental Health Composite International Diagnostic Interview and/or from an online adaption of the same. SU phenotypes (heritability: 49–79%) were found to be more heritable than the affective disorder phenotypes (heritability: 32–42%), SOMA (heritability: 25%), and PSYCH (heritability: 23%). We fit separate non-parametric item response theory models for PSYCH, SOMA, AD, and SU. The IRT scores were used as the refined phenotypes for fitting multivariate genetic models. The best-fitting model showed the similar amount of genetic overlap between PSYCH–AD (genetic correlation rG = 0.49) and SOMA–AD (rG =0.53), as well as between PSYCH–SU (rG = 0.23) and SOMA–SU (rG = 0.25). Unique environmental factors explained 53% to 76% of the variance in each of these four phenotypes, whereas additive genetic factors explained 17% to 46% of the variance. The covariance between the four phenotypes was largely explained by unique environmental factors. Common genetic factor had a significant influence on all the four phenotypes, but they explained a moderate portion of the covariance.

Research on environmental and genetic pathways to complex traits such as educational attainment (EA) is confounded by uncertainty over whether correlations reflect effects of transmitted parental genes, causal family environments, or some, possibly interactive, mixture of both. Thus, an aggregate of thousands of alleles associated with EA (a polygenic risk score; PRS) may tap parental behaviors and home environments promoting EA in the offspring. New methods for unpicking and determining these causal pathways are required. Here, we utilize the fact that parents pass, at random, 50% of their genome to a given offspring to create independent scores for the transmitted alleles (conventional EA PRS) and a parental score based on alleles not transmitted to the offspring (EA VP_PRS). The formal effect of non-transmitted alleles on offspring attainment was tested in 2,333 genotyped twins for whom high-quality measures of EA, assessed at age 17 years, were available, and whose parents were also genotyped. Four key findings were observed. First, the EA PRS and EA VP_PRS were empirically independent, validating the virtual-parent design. Second, in this family-based design, children's own EA PRS significantly predicted their EA (β = 0.15), ruling out stratification confounds as a cause of the association of attainment with the EA PRS. Third, parental EA PRS predicted the SES environment parents provided to offspring (β = 0.20), and parental SES and offspring EA were significantly associated (β = 0.33). This would suggest that the EA PRS is at least as strongly linked to social competence as it is to EA, leading to higher attained SES in parents and, therefore, a higher experienced SES for children. In a full structural equation model taking account of family genetic relatedness across multiple siblings the non-transmitted allele effects were estimated at similar values; but, in this more complex model, confidence intervals included zero. A test using the forthcoming EA3 PRS may clarify this outcome. The virtual-parent method may be applied to clarify causality in other phenotypes where observational evidence suggests parenting may moderate expression of other outcomes, for instance in psychiatry.