The Interplay of Genes and Environment across Multiple Studies (IGEMS) is a consortium of 21 twin studies from 5 countries (Australia, Denmark, Finland, Sweden, and United States) established to explore the nature of gene–environment interplay in cognitive, physical, and emotional health across the adult lifespan. The combined data from over 145,000 participants (aged 18 to 108 years at intake) has supported multiple research projects over the three phases of development since its inception in 2010. Phases 1 and 2 focused on launching and growing the consortium and supported important developments in data harmonization, analyses of data pooled across multiple studies, incorporation of linkages to national registries and conscription data, and integration of molecular genetic and classical twin designs. IGEMS Phase 3 focuses on developing appropriate infrastructure to maximize utilization of this large twin consortium for aging research.

Many empirical systems contain complex interactions of arbitrary size, representing, for example, chemical reactions, social groups, co-authorship relationships, and ecological dependencies. These interactions are known as higher-order interactions, and the collection of these interactions comprise a higher-order network, or hypergraph. Hypergraphs have established themselves as a popular and versatile mathematical representation of such systems, and a number of software packages written in various programming languages have been designed to analyze these networks. However, the ecosystem of higher-order network analysis software is fragmented due to specialization of each software’s programming interface and compatible data representations. To enable seamless data exchange between higher-order network analysis software packages, we introduce the Hypergraph Interchange Format (HIF), a standardized format for storing higher-order network data. HIF supports multiple types of higher-order networks, including undirected hypergraphs, directed hypergraphs, and abstract simplicial complexes, while actively exploring extensions to represent multiplex hypergraphs, temporal hypergraphs, and ordered hypergraphs. To accommodate the wide variety of metadata used in different contexts, HIF also includes support for attributes associated with nodes, edges, and incidences. This initiative is a collaborative effort involving authors, maintainers, and contributors from prominent hypergraph software packages. This project introduces a JSON schema with corresponding documentation and unit tests, example HIF-compliant datasets, and tutorials demonstrating the use of HIF with several popular higher-order network analysis software packages.

Depression, a leading cause of global disability, arises from a multifaceted combination of genetic and environmental components. This study explores the relationship between major depressive disorder (MDD) polygenic scores (PGS), characteristics and symptoms of depression, and community-shared socioeconomic factors derived from postal code data in a cohort of 12,646 individuals from the Australian Genetics of Depression Study (AGDS). Our findings reveal that people living in areas with relatively higher socioeconomic advantages and education/occupation scores are more likely to report experiencing fewer depressive symptoms during their worst depressive period, as well as fewer number of lifetime episodes. Additionally, participants who reported depression onset later in life tend to currently reside in wealthier areas. Interestingly, no significant interaction between genetic and socioeconomic factors was observed, suggesting their independent contribution to depression outcomes. This research underscores the importance of integrating socioeconomic factors into psychiatric evaluation and care, and points to the critical role of public policy in addressing mental health disparities driven by socioeconomic factors. Future research should aim to further elucidate the causal relationships within these associations and explore the potential for integrated genetic and socioeconomic approaches in mental health interventions.