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Whilst genetic and environmental risk factors for schizophrenia (SCZ) and major depressive disorder (MDD) have been established, it is unclear whether exposure to environmental risk factors is genetically confounded by passive, evocative or active gene-environment correlation (rGE).This study aims to investigate: (a) whether the genetic risk for SCZ/MDD in children is correlated with established environmental and psychosocial risk factors in two British community samples, the 1958 National Child Development Study (NCDS) and the Millennium Cohort Study (MCS), (b) whether these associations vary between both psychopathologies, and (c) whether findings differ across the two cohorts which were born 42 years apart.Polygenic risk scores (PRS) from existing large genome-wide associations studies (GWAS) were applied to test the correlation between the child genetic risk for SCZ/MDD and known environmental risk factors. In addition, parental and child genetic data from MCS were used to distinguish between passive and evocative rGE.The child polygenic risk for SCZ and MDD was correlated with single parenthood in MCS. Moreover, the lack of father's involvement in child care was associated with the genetic risk for SCZ in NCDS. However, we also found associations between several indicators of low socioeconomic status and heightened genetic risk for MDD in children in both cohorts. Further, the genetic risk for MDD was associated with parental lack of interest in the child's education in NCDS as well as more maternal smoking and less maternal alcohol consumption during childhood in MCS. According to sensitivity analyses in MCS (controlling for parental genotype), more than half of our significant correlations reflected passive rGE.Findings suggest that several established environmental and psychosocial risk factors for SCZ and MDD are at least partially associated with children's genetic risk for these psychiatric disorders.

Open Targets Genetics (https://genetics.opentargets.org) is an open-access integrative resource that aggregates human GWAS and functional genomics data including gene expression, protein abundance, chromatin interaction and conformation data from a wide range of cell types and tissues to make robust connections between GWAS-associated loci, variants and likely causal genes. This enables systematic identification and prioritisation of likely causal variants and genes across all published trait-associated loci. In this paper, we describe the public resources we aggregate, the technology and analyses we use, and the functionality that the portal offers. Open Targets Genetics can be searched by variant, gene or study/phenotype. It offers tools that enable users to prioritise causal variants and genes at disease-associated loci and access systematic cross-disease and disease-molecular trait colocalization analysis across 92 cell types and tissues including the eQTL Catalogue. Data visualizations such as Manhattan-like plots, regional plots, credible sets overlap between studies and PheWAS plots enable users to explore GWAS signals in depth. The integrated data is made available through the web portal, for bulk download and via a GraphQL API, and the software is open source. Applications of this integrated data include identification of novel targets for drug discovery and drug repurposing.

Research suggests that both genetic and environmental risk factors are involved in the aetiology of schizophrenia (SCZ) and major depressive disorder (MDD). Importantly, environmental and genetic risk factors are often related as evidenced in gene–environment correlation (rGE), which describes the observation that genetic and environmental factors are associated with each other. It is understood that rGE gets stronger over time as individuals select their environments more actively based on their genetic propensities. However, little is known whether rGEs remain stable over time or change across different development periods. Using data from three British longitudinal cohorts, we investigated whether rGE patterns of polygenic risk scores (PRS) for SCZ and MDD changed over time across childhood and adulthood, as well as across both from birth to age 55 and whether results differed between SCZ and MDD. Overall, the majority of rGEs remained stable across the investigated development periods. Furthermore, the few detected rGE changes which did differ between SCZ and MDD, could not be explained by the confounding of clinical cases and are therefore likely the result of actual changes in environmental and cultural risk factors with genetic susceptibility to SCZ and MDD likely playing a less significant role.

The Open Targets Platform (https://www.targetvalidation.org/) provides users with a queryable knowledgebase and user interface to aid systematic target identification and prioritisation for drug discovery based upon underlying evidence. It is publicly available and the underlying code is open source. Since our last update two years ago, we have had 10 releases to maintain and continuously improve evidence for target–disease relationships from 20 different data sources. In addition, we have integrated new evidence from key datasets, including prioritised targets identified from genome-wide CRISPR knockout screens in 300 cancer models (Project Score), and GWAS/UK BioBank statistical genetic analysis evidence from the Open Targets Genetics Portal. We have evolved our evidence scoring framework to improve target identification. To aid the prioritisation of targets and inform on the potential impact of modulating a given target, we have added evaluation of post-marketing adverse drug reactions and new curated information on target tractability and safety. We have also developed the user interface and backend technologies to improve performance and usability. In this article, we describe the latest enhancements to the Platform, to address the fundamental challenge that developing effective and safe drugs is difficult and expensive.

The current chapter aims to present a concise but comprehensive review of the current state of gene-by-environment interaction (GxE) research in the field of psychiatric genetics, with a special focus on research from molecular genetic studies. The chapter starts with a review of GxE studies of mental health disorders which includes both recent genome-wide GxE studies, as well as candidate gene studies, since the latter comprise the majority of such studies conducted in the field so far. Next, we critically evaluate the research conducted in the field so far, taking into account the methodological limitations of these studies, as well as recent theoretical concepts of Environmental Sensitivity, such as Differential Susceptibility, that challenge historic assumptions underlying GxE models. The chapter ends with suggestions and directions for future GxE research in mental health.