Psychiatric Genomics: An Update and an Agenda

Identification of gene co-expression modules from zebrafish brain data: Applications in psychiatry illustrated through alcohol-related traits

Cumulative evidence suggests that zebrafish is a useful model in psychiatric research. Weighted Gene Co-expression Network Analysis (WGCNA) enables the reduction of genome-wide expression data to modules of highly co-expressed genes, which are hypothesized to interact within molecular networks. In this study, we first applied WGCNA to zebrafish brain expression data across different experimental conditions. Then, we characterized the different co-expression modules by gene-set enrichment analysis and hub gene-phenotype association. Finally, we analyzed association of polygenic risk scores (PRSs) based on genes of some interesting co-expression modules with alcohol dependence in 524 patients and 729 controls from Galicia, using competitive tests. Our approach revealed 34 co-expression modules in the zebrafish brain, with some showing enrichment in human synaptic genes, brain tissues, or brain developmental stages. Moreover, certain co-expression modules were enriched in psychiatry-related GWAS and comprised hub genes associated with psychiatry-related traits in both human GWAS and zebrafish models. Expression patterns of some co-expression modules were associated with the tested experimental conditions, mainly with substance withdrawal and cold stress. Notably, a PRS based on genes from co-expression modules exclusively associated with substance withdrawal in zebrafish showed a stronger association with human alcohol dependence than PRSs based on randomly selected brain-expressed genes. In conclusion, our analysis led to the identification of co-expressed gene modules that may model human brain gene networks involved in psychiatry-related traits. Specifically, we detected a cluster of co-expressed genes whose expression was exclusively associated with substance withdrawal in zebrafish, which significantly contributed to alcohol dependence susceptibility in humans.

Gray matter volumetric correlates of the polygenic risk of depression: A study of the Human Connectome Project data

Genetic factors confer risks for depression. Understanding the neural endophenotypes, including brain morphometrics, of genetic predisposition to depression would help in unraveling the pathophysiology of depression. We employed voxel-based morphometry (VBM) to examine how gray matter volumes (GMVs) were correlated with the polygenic risk score (PRS) for depression in 993 young adults of the Human Connectome Project. The phenotype of depression was quantified with a DSM-oriented scale of the Achenbach Adult Self-Report. The PRS for depression was computed for each subject using the Psychiatric Genomics Association Study as the base sample. In multiple regression with age, sex, race, drinking severity, and total intracranial volume as covariates, regional GMVs in positive correlation with the PRS were observed in bilateral hippocampi and right gyrus rectus. Regional GMVs in negative correlation with the PRS were observed in a wide swath of brain regions, including bilateral frontal and temporal lobes, anterior cingulate cortex, thalamus, lingual gyri, cerebellum, and the left postcentral gyrus, cuneus, and parahippocampal gyrus. We also found sex difference in anterior cingulate volumes in manifesting the genetic risk of depression. In addition, the GMV of the right cerebellum crus I partially mediated the link from PRS to depression severity. These findings add to the literature by highlighting 1) a more diverse pattern of the volumetric markers of depression, with most regions showing lower but others higher GMVs in association with the genetic risks of depression, and 2) the cerebellar GMV as a genetically informed neural phenotype of depression, in neurotypical individuals.

Cross-site reproducibility of human cortical organoids reveals consistent cell type composition and architecture

While guided human cortical organoid (hCO) protocols reproducibly generate cortical cell types at one site, variability in hCO phenotypes across sites using a harmonized protocol has not yet been evaluated. To determine the cross-site reproducibility of hCO differentiation, three independent research groups assayed hCOs in multiple differentiation replicates from one induced pluripotent stem cell (iPSC) line using a harmonized miniaturized spinning bioreactor protocol across 3 months. hCOs were mostly cortical progenitor and neuronal cell types in reproducible proportions that were consistently organized in cortical wall-like buds. Cross-site differences were detected in hCO size and expression of metabolism and cellular stress genes. Variability in hCO phenotypes correlated with stem cell gene expression prior to differentiation and technical factors associated with seeding, suggesting iPSC quality and treatment are important for differentiation outcomes. Cross-site reproducibility of hCO cell type proportions and organization encourages future prospective meta-analytic studies modeling neurodevelopmental disorders in hCOs.

Meta-analysis of set-based multiple phenotype association test based on GWAS summary statistics from different cohorts

Genome-wide association studies (GWAS) have emerged as popular tools for identifying genetic variants that are associated with complex diseases. Standard analysis of a GWAS involves assessing the association between each variant and a disease. However, this approach suffers from limited reproducibility and difficulties in detecting multi-variant and pleiotropic effects. Although joint analysis of multiple phenotypes for GWAS can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits, most of the multiple phenotype association tests are designed for a single variant, resulting in much lower power, especially when their effect sizes are small and only their cumulative effect is associated with multiple phenotypes. To overcome these limitations, set-based multiple phenotype association tests have been developed to enhance statistical power and facilitate the identification and interpretation of pleiotropic regions. In this research, we propose a new method, named Meta-TOW-S, which conducts joint association tests between multiple phenotypes and a set of variants (such as variants in a gene) utilizing GWAS summary statistics from different cohorts. Our approach applies the set-based method that Tests for the effect of an Optimal Weighted combination of variants in a gene (TOW) and accounts for sample size differences across GWAS cohorts by employing the Cauchy combination method. Meta-TOW-S combines the advantages of set-based tests and multi-phenotype association tests, exhibiting computational efficiency and enabling analysis across multiple phenotypes while accommodating overlapping samples from different GWAS cohorts. To assess the performance of Meta-TOW-S, we develop a phenotype simulator package that encompasses a comprehensive simulation scheme capable of modeling multiple phenotypes and multiple variants, including noise structures and diverse correlation patterns among phenotypes. Simulation studies validate that Meta-TOW-S maintains a desirable Type I error rate. Further simulation under different scenarios shows that Meta-TOW-S can improve power compared with other existing meta-analysis methods. When applied to four psychiatric disorders summary data, Meta-TOW-S detects a greater number of significant genes.

Psychiatric polygenic risk scores: Experience, hope for utility, and concerns among child and adolescent psychiatrists

Recent advances in psychiatric genetics have enabled the use of polygenic risk scores (PRS) to estimate genetic risk for psychiatric disorders. However, the potential use of PRS in child and adolescent psychiatry has raised concerns. This study provides an in-depth examination of attitudes among child and adolescent psychiatrists (CAP) regarding the use of PRS in psychiatry. We conducted semi-structured interviews with U.S.-based CAP (n = 29) who possess expertise in genetics. The majority of CAP indicated that PRS have limited clinical utility in their current form and are not ready for clinical implementation. Most clinicians stated that nothing would motivate them to generate PRS at present; however, some exceptions were noted (e.g., parent/family request). Clinicians spoke to challenges related to ordering, interpreting, and explaining PRS to patients and families. CAP raised concerns regarding the potential for this information to be misinterpreted or misused by patients, families, clinicians, and outside entities such as insurance companies. Finally, some CAP noted that PRS may lead to increased stigmatization of psychiatric disorders, and at the extreme, could be used to support eugenics. As PRS testing increases, it will be critical to examine CAP and other stakeholders' views to ensure responsible implementation of this technology.

Associations of prenatal exposure to PM2.5 and its components with offsprings' neurodevelopmental and behavioral problems: A prospective cohort study from China

Prenatal exposure to fine particulate matter (PM2.5) has been linked with increased neurodevelopmental disorders. However, the most detrimental component of PM2.5 and the most vulnerable exposure time windows remain undetermined, especially in areas with high PM2.5 levels. In a prospective cohort study involving 4494 mother-child dyads, we examined the associations of prenatal exposure to PM2.5 and its four main components with children's neurodevelopmental and behavioral problems (NBPs), separately in three pregnancy trimesters. Poisson regression and generalized additive models were used to depict the linear and nonlinear associations, respectively. Weighted quantile sum and Bayesian kernel machine regression models were applied to examine the effects of exposure to both mixed and individual components. Results showed that exposure to PM2.5 and its components throughout the three trimesters increased the risk of children's NBPs (Risk ratio for PM2.5: 1.16, 95 % confidence interval 1.14-1.18 per μg/m3 in the first trimester; 1.15, 1.12-1.17 in the second trimester; 1.06, 1.04-1.08 in the third trimester), with associations gradually diminishing as pregnancy progressed (P values for trends < 0.05). Among the four main components of PM2.5, exposure to SO42- posed the highest risks on children's NBPs, while organic matter contributed the largest proportion to the overall impacts of PM2.5 exposure. These results underscore the significance of mitigating PM2.5 exposure in pregnant women to reduce the risk of neurodevelopmental disorders in offspring. Our findings would inform risk assessment of PM2.5 exposure and facilitate the development of precision preventive strategies targeting specific components of PM2.5 in similar areas with high levels of exposure.

Multitrait Genetic Analysis Identifies Novel Pleiotropic Loci for Depression and Schizophrenia in East Asians

BACKGROUND AND HYPOTHESIS

While genetic correlations, pleiotropic loci, and shared genetic mechanisms of psychiatric disorders have been extensively studied in European populations, the investigation of these factors in East Asian populations has been relatively limited.

STUDY DESIGN

To identify novel pleiotropic risk loci for depression and schizophrenia (SCZ) in East Asians. We utilized the most comprehensive dataset available for East Asians and quantified the genetic overlap between depression, SCZ, and their related traits via a multitrait genome-wide association study. Global and local genetic correlations were estimated by LDSC and ρ-HESS. Pleiotropic loci were identified by the multitrait analysis of GWAS (MTAG).

STUDY RESULTS

Besides the significant correlation between depression and SCZ, our analysis revealed genetic correlations between depression and obesity-related traits, such as weight, BMI, T2D, and HDL. In SCZ, significant correlations were detected with HDL, heart diseases and use of various medications. Conventional meta-analysis of depression and SCZ identified a novel locus at 1q25.2 in East Asians. Further multitrait analysis of depression, SCZ and related traits identified ten novel pleiotropic loci for depression, and four for SCZ.

CONCLUSIONS

Our findings demonstrate shared genetic underpinnings between depression and SCZ in East Asians, as well as their associated traits, providing novel candidate genes for the identification and prioritization of therapeutic targets specific to this population.

'Almost nothing is firmly established': A History of Heredity and Genetics in Mental Health Science

Background

For more than a century, scientists have tried to find the key to causation of mental ill health in heredity and genetics. The difficulty of finding clear and actionable answers in our genes has not stopped them looking. This history offers important context to understanding mental health science today.

Methods

This article explores the main themes in research on genetics and inheritance in psychiatry from the second half of the nineteenth century to the present day, to address the question: what is the history of genetics as a causative explanation in mental health science? We take a critical historical approach to the literature, interrogating primary and secondary material for the light it brings to the research question, while considering the social and historical context.

Results

We begin with the statistics gathered in asylums and used to 'prove' the importance of heredity in mental ill health. We then move through early twentieth century Mendelian models of mental inheritance, the eugenics movement, the influence of social psychiatry, new classifications and techniques of the postwar era, the Human Genome Project and Genome Wide Association Studies (GWAS) and epigenetics. Setting these themes in historical context shows that this research was often popular because of wider social, political and cultural issues, which impacted the views of scientists just as they did those of policymakers, journalists and the general public.

Conclusions

We argue that attempting to unpick this complex history is essential to the modern ethics of mental health and genetics, as well as helping to focus our efforts to better understand causation in mental ill-health.For a succinct timeline of the history of psychiatric genetics, alongside the history of other proposed causes for mental ill-health, visit: https://historyofcauses.co.uk/.

Estimating the direct effects of the genetic liabilities to bipolar disorder, schizophrenia, and behavioral traits on suicide attempt using a multivariable Mendelian randomization approach

Bipolar disorder (BD) and schizophrenia (SZ) are associated with higher odds of suicide attempt (SA). In this study, we aimed to explore the effect of BD and SZ genetic liabilities on SA, also considering the contribution of behavioral traits, socioeconomic factors, and substance use disorders. Leveraging large-scale genome-wide association data from the Psychiatric Genomics Consortium (PGC) and the UK Biobank (UKB), we conducted a two-sample Mendelian randomization (MR) analysis to evaluate the putative causal effect of BD (41,917 cases, 371,549 controls) and SZ (53,386 cases, 77,258 controls) on SA (26,590 cases, 492,022 controls). Then, we assessed the putative causal effect of BD and SZ on behavioral traits, socioeconomic factors, and substance use disorders. Considering the associations identified, we evaluated the direct causal effect of behavioral traits, socioeconomic factors, and substance use disorders on SA using a multivariable MR approach. The genetic liabilities to BD and SZ were associated with higher odds of SA (BD odds ratio (OR) = 1.24, p = 3.88 × 10-12; SZ OR = 1.09, p = 2.44 × 10-20). However, while the effect of mental distress (OR = 1.17, p = 1.02 × 10-4) and risk-taking (OR = 1.52, p = 0.028) on SA was independent of SZ genetic liability, the BD-SA relationship appeared to account for the effect of these risk factors. Similarly, the association with loneliness on SA was null after accounting for the effect of SZ genetic liability. These findings highlight the complex interplay between genetic risk of psychiatric disorders and behavioral traits in the context of SA, suggesting the need for a comprehensive mental health assessment for high-risk individuals.

Epilepsy and childhood psychiatric disorders: a two-sample bidirectional Mendelian randomization study

BACKGROUND

Observational studies have indicated that psychiatric disorders are the most common comorbidities in pediatric epilepsy. However, the existence and direction of a causal relationship between the two remains controversial. This study aims to investigate the association between common childhood psychiatric disorders and epilepsy using a two-sample, bidirectional Mendelian randomization (MR) approach.

METHODS

Genetic instruments were obtained from the most recent and largest genome-wide association studies (GWAS), including datasets for epilepsy (N_case = 29,994, N_control = 52,538), attention deficit hyperactivity disorder (ADHD) (N_case = 38,691, N_control = 186,843), autism spectrum disorder (ASD) (N_case = 18,381, N_control = 27,969), and Tourette syndrome (TS) (N_case = 4,819, N_control = 9488). MR analyses were conducted using the inverse variance weighted (IVW) method, weighted median method, and MR-Egger regression.

RESULTS

No reliable evidence was found to suggest a causal effect of ADHD, ASD, or TS on epilepsy, nor was there any reliable evidence indicating that epilepsy increases the risk of these three psychiatric disorders. These findings remained consistent across various sensitivity analyses.

CONCLUSION

Although observational studies have highlighted a high comorbidity rate between pediatric epilepsy and psychiatric disorders like ADHD and ASD, the MR analysis did not confirm a causal relationship between them. This suggests that previous studies might have been influenced by confounding biases or other biases, potentially overestimating the true relationship. A deeper understanding of the mechanisms underlying these comorbidities is crucial for refining the treatment of pediatric epilepsy.

Seven psychiatric traits and the risk of increased carotid intima-media thickness: a Mendelian randomization study

Background

Numerous observational studies have suggested an association between psychiatric traits and carotid intima-media thickness (cIMT). However, whether these associations have a causal relationship remains unknown, largely due to issues of reverse causality and potential confounders. This study aims to elucidate the potential causal role of psychiatric traits in the risk of arterial injury as measured by cIMT.

Methods

We utilized instrumental variables for attention deficit/hyperactivity disorder (ADHD, n = 226,534), bipolar disorder (n = 353,899), major depressive disorder (n = 142,646), post-traumatic stress disorder (n = 174,494), obsessive-compulsive disorder (n = 9,725), autism spectrum disorder (n = 173,773), and anxiety disease (n = 17,310), derived from the largest corresponding genome-wide association studies (GWAS). Summary statistics for cIMT associations were obtained from a meta-analysis combining GWAS data from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortia (n = 71,128) and the UK Biobank study (n = 45,185). The inverse-variance weighted method served as the primary analytical tool, supplemented by additional statistical methods in the secondary analyses to corroborate the findings. Adjustments were made according to the Bonferroni correction threshold.

Results

The Mendelian randomization analyses indicated a suggestive causal link between genetically predicted ADHD and cIMT (beta = 0.05; 95% confidence interval, 0.01-0.09; p = 0.018). Sensitivity analyses largely concurred with this finding. However, no significant associations were found between other psychiatric traits and cIMT.

Conclusions

This study provides insights into the risk effect of ADHD on cIMT, suggesting that arteriopathy and potential associated complications should be considered during the treatment and monitoring of patients with ADHD.

Relationship of Genetic Polymorphisms and Microbial Composition with Binge Eating Disorder: A Systematic Review

Humans are the result of an evolutionary process, and because of this, many biological processes are interconnected with each other. The intestine-brain axis consists of an intricately connected neuronal-neuroendocrine circuit that regulates the sensation of hunger and satiety. Genetic variations and the consumption of unnatural diets (ultra-processed foods, high contents of sugars, etc.) can override this circuit and cause addiction to certain foods and/or the inability to feel satiety in certain situations. The patients who come to consultations (mainly psychology or nutrition) in an attempt to resolve this problem sometimes fail, which leads to them looking for new strategies based on biological predisposition. This investigation aims to evaluate the genetic studies regarding the microbiota carried out in the last 12 years in humans to try to determine which genes and microbes that have been recently studied are related to patients diagnosed with binge eating disorder or compulsive eating (presenting obesity or not). The protocol followed the PRISMA statement, and the following databases were searched from 2012 until the present day: PubMed, PsycINFO, SCOPUS, and Web of Science. Twenty-four international articles were analyzed, including cross-sectional or exploratory studies; five of them referred to the microbial composition, and in nineteen, the existence of genetic polymorphisms present in binge eating disorder or in compulsive eating could be observed: DRD2, OPRM1, COMT, MC4R, BNDF, FTO, SLC6A3, GHRL, CARTPT, MCHR2, and LRP11. Even though there is still much to investigate on the subject, it must be highlighted that, in the last 4 years, a two-fold increase has been observed in potential markers and in studies related to the matter, also highlighting the importance of different analyses in relation to psychosocial factors and their interaction with the genetic and microbial factors, for which research on the matter must be continued.

Application of GWAS summary data and drug-induced gene expression profiles of neural progenitor cells in psychiatric drug prioritization analysis

Common psychiatric disorders constitute one of the most substantial healthcare burdens worldwide. However, drug development in psychiatry remains hampered partially due to the lack of approaches to estimating drugs that can simultaneously modulate the expression of a nontrivial fraction of disease susceptibility genes. We proposed a new drug prioritization strategy under the framework of our previously proposed phenotype-associated tissues estimation approach (DESE) by investigating the drugs' selective perturbation effect on disease susceptibility genes. Based on the genome-wide association study summary data and drug-induced gene expression profiles of neural progenitor cells, we applied this strategy to prioritize candidate drugs for schizophrenia, depression and bipolar I disorder and identified several known therapeutic drugs among the top-ranked drug candidates. Also, our results revealed that the disease susceptibility genes involved in the selective gene perturbation analysis were enriched with many biologically sensible function terms and interacted with known therapeutic drugs. Our results suggested that selective gene perturbation analysis could be a promising starting point to prioritize biologically sensible drug candidates under the "one drug, multiple targets" paradigm for the drug development of common psychiatric disorders.

Building Confidence in Discussing Genetics With Patients With Eating Disorders and Their Families

Many individuals with eating disorders and their family members are well-informed about advances in science that could affect the treatment and outcome of these illnesses. They appropriately apply this knowledge to evaluate available treatments and advocate for the best possible evidence-based care. They ask hard questions that many clinicians are often ill-prepared to answer. Genetics has advanced our understanding of eating disorders and provides a novel lens through which to understand these pernicious illnesses. Clinicians can now update their understanding of the etiology of eating disorders and abandon outdated etiological theories, some of which have done harm to patients and their families. Without becoming expert in psychiatric genetics, psychiatrists and other mental health care professionals can develop a general overview of the science, understand what it can and cannot offer, incorporate genetic factors into their case conceptualizations, and boost their confidence in discussing these topics with patients and families.

Editorial Perspective: The paradox of precision health in early development - building large samples to yield individual-level measures

Precision health refers to the use of individualised biomarkers or predictive models to provide more tailored information about an individual's likely prognosis. For child psychiatry and psychology, we argue that this approach requires a focus on neurocognitive measures collected in early life and at large scale. However, the large sample sizes necessary to uncover individual-level predictors are currently rare in studies of neurodevelopmental conditions in early childhood. We recommend two strategies going forward: first, including neurocognitive measures in new national cohort studies, and second, synergising measures and data across currently funded longitudinal studies.

Pleiotropy and genetically inferred causality linking multisite chronic pain to substance use disorders

Individuals suffering from chronic pain develop substance use disorders (SUDs) more often than others. Understanding the shared genetic influences underlying the comorbidity between chronic pain and SUDs will lead to a greater understanding of their biology. Genome-wide association statistics were obtained from the UK Biobank for multisite chronic pain (MCP, Neffective = 387,649) and from the Million Veteran Program and the Psychiatric Genomics Consortium meta-analyses for alcohol use disorder (AUD, Neffective = 296,974), cannabis use disorder (CanUD, Neffective = 161,053), opioid use disorder (OUD, Neffective = 57,120), and problematic tobacco use (PTU, Neffective = 270,120). SNP-based heritability was estimated for each of the traits and genetic correlation (rg) analyses were performed to assess MCP-SUD pleiotropy. Bidirectional Mendelian Randomization analyses evaluated possible causal relationships. Finally, to identify and characterize individual loci, we performed a genome-wide pleiotropy analysis and a brain-wide analysis using imaging phenotypes available from the UK Biobank. MCP was positively genetically correlated with AUD (rg = 0.26, p = 7.55 × 10-18), CanUD (rg = 0.37, p = 8.21 × 10-37), OUD (rg = 0.20, p = 1.50 × 10-3), and PTU (rg = 0.29, p = 8.53 × 10-12). Although the MR analyses supported bi-directional relationships, MCP had larger effects on AUD (pain-exposure: beta = 0.18, p = 8.21 × 10-4; pain-outcome: beta = 0.07, p = 0.018), CanUD (pain-exposure: beta = 0.58, p = 2.70 × 10-6; pain-outcome: beta = 0.05, p = 0.014) and PTU (pain-exposure: beta = 0.43, p = 4.16 × 10-8; pain-outcome: beta = 0.09, p = 3.05 × 10-6) than the reverse. The genome-wide analysis identified two SNPs pleiotropic between MCP and all SUD investigated: IHO1 rs7652746 (ppleiotropy = 2.69 × 10-8), and CADM2 rs1248857 (ppleiotropy = 1.98 × 10-5). In the brain-wide analysis, rs7652746 was associated with multiple cerebellum and amygdala imaging phenotypes. When analyzing MCP pleiotropy with each SUD separately, we found 25, 22, and 4 pleiotropic variants for AUD, CanUD, and OUD, respectively. To our knowledge, this is the first large-scale study to provide evidence of potential causal relationships and shared genetic mechanisms underlying MCP-SUD comorbidity.

Micronutrient-Associated Single Nucleotide Polymorphism and Mental Health: A Mendelian Randomization Study

PURPOSE

Previous studies have demonstrated the link between micronutrients and mental health. However, it remains uncertain whether this connection is causal. We aim to investigate the potential causal effects of micronutrients on mental health based on linkage disequilibrium score (LDSC) regression and Mendelian randomization (MR) analysis.

METHODS

Utilizing publicly available genome-wide association study (GWAS) summary datasets, we performed LDSC and MR analysis to identify candidate micronutrients with potential causal effects on mental health. Single nucleotide polymorphisms (SNPs) significantly linked with candidate micronutrients with a genome-wide significance level (p < 5 × 10-8) were selected as instrumental variables (IVs). To estimate the causal effect of candidate micronutrients on mental health, we employed inverse variance weighted (IVW) regression. Additionally, two sensitivity analyses, MR-Egger and weighted median, were performed to validate our results.

RESULTS

We found evidence supporting significant causal associations between micronutrients and mental health. LDSC detected several candidate micronutrients, including serum iron (genetic correlation = -0.134, p = 0.032) and vitamin C (genetic correlation = -0.335, p < 0.001) for attention-deficit/hyperactivity disorder (ADHD), iron-binding capacity (genetic correlation = 0.210, p = 0.037) for Alzheimer's disease (AD), and vitamin B12 (genetic correlation = -0.178, p = 0.044) for major depressive disorder (MDD). Further MR analysis suggested a potential causal relationship between vitamin B12 and MDD (b = -0.139, p = 0.009). There was no significant heterogeneity or pleiotropy, indicating the validity of the findings.

CONCLUSION

In this study, we identified underlying causal relationships between micronutrients and mental health. Notably, more research is necessary to clarify the underlying biological mechanisms by which micronutrients affect mental health.

Schizophrenia and risk preference: a bidirectional two-sample mendelian randomization study

Increasing evidence shows that risk preference is associated with schizophrenia. However, the causality and direction of this association are not clear; Therefore, we used Mendelian randomization (MR) to examine the potential bidirectional relationship between risk preference and schizophrenia. Genome-wide association studies (GWAS) summary data on risk preference of 939,908 participants from the UK Biobank and 23andMe were used to identify general risk preference. Data from 320,404 subjects (76,755 cases and 243,649 controls) from The Psychiatric Genomics Consortium were used to identify schizophrenia. The weighted median (WM), the inverse variance weighted (IVW), and the Mendelian randomization-Egger (MR-Egger) methods were used for the MR analysis to estimate the causal effect and detect the directional pleiotropy. The GWAS summary data were respectively from two combined samples, containing 939,908 and 320,404 subjects of European ancestry. Mendelian randomization evidence suggested that risk preference was associated with increased onset of schizophrenia (OR = 2.84, 95CI%: 1.77-4.56, P = 1.58*10 - 5) and that schizophrenia was also associated with raised risk preference (OR = 1.11, 95CI%: 1.07-1.15, P = 7.98*10 - 8). With the use of large-scale GWAS data, robust evidence suggests an interaction between risk preference and schizophrenia. This also indicates that early identification of and intervention for increased risk preference may improve the prognosis of schizophrenia.

Generation Scotland: an update on Scotland's longitudinal family health study

PURPOSE

Generation Scotland (GS) is a large family-based cohort study established as a longitudinal resource for research into the genetic, lifestyle and environmental determinants of physical and mental health. It comprises extensive genetic, sociodemographic and clinical data from volunteers in Scotland.

PARTICIPANTS

A total of 24 084 adult participants, including 5501 families, were recruited between 2006 and 2011. Within the cohort, 59% (approximately 14 209) are women, with an average age at recruitment of 49 years. Participants completed a health questionnaire and attended an in-person clinic visit, where detailed baseline data were collected on lifestyle information, cognitive function, personality traits and mental and physical health. Genotype array data are available for 20 026 (83%) participants, and blood-based DNA methylation (DNAm) data for 18 869 (78%) participants. Linkage to routine National Health Service datasets has been possible for 93% (n=22 402) of the cohort, creating a longitudinal resource that includes primary care, hospital attendance, prescription and mortality records. Multimodal brain imaging is available in 1069 individuals.

FINDINGS TO DATE

GS has been widely used by researchers across the world to study the genetic and environmental basis of common complex diseases. Over 350 peer-reviewed papers have been published using GS data, contributing to research areas such as ageing, cancer, cardiovascular disease and mental health. Recontact studies have built on the GS cohort to collect additional prospective data to study chronic pain, major depressive disorder and COVID-19.

FUTURE PLANS

To create a larger, richer, longitudinal resource, 'Next Generation Scotland' launched in May 2022 to expand the existing cohort by a target of 20 000 additional volunteers, now including anyone aged 12+ years. New participants complete online consent and questionnaires and provide postal saliva samples, from which genotype and salivary DNAm array data will be generated. The latest cohort information and how to access data can be found on the GS website (www.generationscotland.org).

Quantifying the relative importance of genetics and environment on the comorbidity between mental and cardiometabolic disorders using 17 million Scandinavians

Mental disorders are leading causes of disability and premature death worldwide, partly due to high comorbidity with cardiometabolic disorders. Reasons for this comorbidity are still poorly understood. We leverage nation-wide health records and near-complete genealogies of Denmark and Sweden (n = 17 million) to reveal the genetic and environmental contributions underlying the observed comorbidity between six mental disorders and 15 cardiometabolic disorders. Genetic factors contributed about 50% to the comorbidity of schizophrenia, affective disorders, and autism spectrum disorder with cardiometabolic disorders, whereas the comorbidity of attention-deficit/hyperactivity disorder and anorexia with cardiometabolic disorders was mainly or fully driven by environmental factors. In this work we provide causal insight to guide clinical and scientific initiatives directed at achieving mechanistic understanding as well as preventing and alleviating the consequences of these disorders.

Exploring biologically oriented precision mental health initiatives for the care of patients with eating disorders: A narrative review

OBJECTIVE

Eating disorders (EDs) represent a major public health burden. Increasingly, studies suggest mental health (MH) fields are failing to improve the effectiveness of treatments and that alternative models of care must be considered. Precision mental health (PMH) seeks to tailor treatment to individual needs and relies on a comprehensive understanding of the neurobiological and physiological underpinnings of mental illness.

METHODS

In this narrative review, published literature with focus on biological application of PMH strategies for EDs is reviewed and summarised.

RESULTS

A total of 39 articles were retained for the review covering a variety of themes with relevance to PMH. Many studies of biological markers with PMH applicability focused on anorexia nervosa. Although a variety of potential PMH research applications were identified, the review failed to identify any evidence of implementation into routine ED practice.

CONCLUSIONS

Despite the theoretical merit of biological application of PMH in ED treatment, clinical applications for standard practice are lacking. There is a need to invest further in studies that seek to identify biological markers and investigate neurobiological underpinnings of disease in hopes of targeting and developing treatments that can be better tailored to the individualised needs of patients.

Genetically Informed Study Highlights Income-Independent Effect of Schizophrenia Liability on Mental and Physical Health

BACKGROUND AND HYPOTHESIS

Individuals with schizophrenia (SCZ) suffer from comorbidities that substantially reduce their life expectancy. Socioeconomic inequalities could contribute to many of the negative health outcomes associated with SCZ.

STUDY DESIGN

We investigated genome-wide datasets related to SCZ (52 017 cases and 75 889 controls) from the Psychiatric Genomics Consortium, household income (HI; N = 361 687) from UK Biobank, and 2202 medical endpoints assessed in up to 342 499 FinnGen participants. A phenome-wide genetic correlation analysis of SCZ and HI was performed, also assessing whether SCZ genetic correlations were influenced by the HI effect on SCZ. Additionally, SCZ and HI direct effects on medical endpoints were estimated using multivariable Mendelian randomization (MR).

STUDY RESULTS

SCZ and HI showed overlapping genetic correlations with 70 traits (P < 2.89 × 10-5), including mental health, substance use, gastrointestinal illnesses, reproductive outcomes, liver diseases, respiratory problems, and musculoskeletal phenotypes. SCZ genetic correlations with these traits were not affected by the HI effect on SCZ. Considering Bonferroni multiple testing correction (P < 7.14 × 10-4), MR analysis indicated that SCZ and HI may affect medical abortion (SCZ OR = 1.07; HI OR = 0.78), panic disorder (SCZ OR = 1.20; HI OR = 0.60), personality disorders (SCZ OR = 1.31; HI OR = 0.67), substance use (SCZ OR = 1.2; HI OR = 0.68), and adjustment disorders (SCZ OR = 1.18; HI OR = 0.78). Multivariable MR analysis confirmed that SCZ effects on these outcomes were independent of HI.

CONCLUSIONS

The effect of SCZ genetic liability on mental and physical health may not be strongly affected by socioeconomic differences. This suggests that SCZ-specific strategies are needed to reduce negative health outcomes affecting patients and high-risk individuals.

Factor analysis of lifetime psychopathology and its brain morphometric and genetic correlates in a transdiagnostic sample

There is a lack of knowledge regarding the relationship between proneness to dimensional psychopathological syndromes and the underlying pathogenesis across major psychiatric disorders, i.e., Major Depressive Disorder (MDD), Bipolar Disorder (BD), Schizoaffective Disorder (SZA), and Schizophrenia (SZ). Lifetime psychopathology was assessed using the OPerational CRITeria (OPCRIT) system in 1,038 patients meeting DSM-IV-TR criteria for MDD, BD, SZ, or SZA. The cohort was split into two samples for exploratory and confirmatory factor analyses. All patients were scanned with 3-T MRI, and data was analyzed with the CAT-12 toolbox in SPM12. Psychopathological factor scores were correlated with gray matter volume (GMV) and cortical thickness (CT). Finally, factor scores were used for exploratory genetic analyses including genome-wide association studies (GWAS) and polygenic risk score (PRS) association analyses. Three factors (paranoid-hallucinatory syndrome, PHS; mania, MA; depression, DEP) were identified and cross-validated. PHS was negatively correlated with four GMV clusters comprising parts of the hippocampus, amygdala, angular, middle occipital, and middle frontal gyri. PHS was also negatively associated with the bilateral superior temporal, left parietal operculum, and right angular gyrus CT. No significant brain correlates were observed for the two other psychopathological factors. We identified genome-wide significant associations for MA and DEP. PRS for MDD and SZ showed a positive effect on PHS, while PRS for BD showed a positive effect on all three factors. This study investigated the relationship of lifetime psychopathological factors and brain morphometric and genetic markers. Results highlight the need for dimensional approaches, overcoming the limitations of the current psychiatric nosology.

Omics Approaches to Investigate the Pathogenesis of Suicide

Suicide is the second leading cause of death in U.S. adolescents and young adults and is generally associated with a psychiatric disorder. Suicidal behavior has a complex etiology and pathogenesis. Moderate heritability suggests genetic causes. Associations between childhood and recent life adversity indicate contributions from epigenetic factors. Genomic contributions to suicide pathogenesis remain largely unknown. This article is based on a workshop held to design strategies to identify molecular drivers of suicide neurobiology that would be putative new treatment targets. The panel determined that while bulk tissue studies provide comprehensive information, single-nucleus approaches that identify cell type-specific changes are needed. While single-nuclei techniques lack information on cytoplasm, processes, spines, and synapses, spatial multiomic technologies on intact tissue detect cell alterations specific to brain tissue layers and subregions. Because suicide has genetic and environmental drivers, multiomic approaches that combine cell type-specific epigenome, transcriptome, and proteome provide a more complete picture of pathogenesis. To determine the direction of effect of suicide risk gene variants on RNA and protein expression and how these interact with epigenetic marks, single-nuclei and spatial multiomics quantitative trait loci maps should be integrated with whole-genome sequencing and genome-wide association databases. The workshop concluded with a recommendation for the formation of an international suicide biology consortium that will bring together brain banks and investigators with expertise in cutting-edge omics technologies to delineate the biology of suicide and identify novel potential treatment targets to be tested in cellular and animal models for drug and biomarker discovery to guide suicide prevention.

The Importance of Large-Scale Genomic Studies to Unravel Genetic Risk Factors for Autism

Autism spectrum disorder (ASD) is a common and highly heritable neurodevelopmental disorder. During the last 15 years, advances in genomic technologies and the availability of increasingly large patient cohorts have greatly expanded our knowledge of the genetic architecture of ASD and its neurobiological mechanisms. Over two hundred risk regions and genes carrying rare de novo and transmitted high-impact variants have been identified. Additionally, common variants with small individual effect size are also important, and a number of loci are now being uncovered. At the same time, these new insights have highlighted ongoing challenges. In this perspective article, we summarize developments in ASD genetic research and address the enormous impact of large-scale genomic initiatives on ASD gene discovery.

The impact of assortative mating, participation bias and socioeconomic status on the polygenic risk of behavioural and psychiatric traits

To investigate assortative mating (AM), participation bias and socioeconomic status (SES) with respect to the genetics of behavioural and psychiatric traits, we estimated AM signatures using gametic phase disequilibrium and within-spouses and within-siblings polygenic risk score correlation analyses, also performing a SES conditional analysis. The cross-method meta-analysis identified AM genetic signatures for multiple alcohol-related phenotypes, bipolar disorder, major depressive disorder, schizophrenia and Tourette syndrome. Here, after SES conditioning, we observed changes in the AM genetic signatures for maximum habitual alcohol intake, frequency of drinking alcohol and Tourette syndrome. We also observed significant gametic phase disequilibrium differences between UK Biobank mental health questionnaire responders versus non-responders for major depressive disorder and alcohol use disorder. These results highlight the impact of AM, participation bias and SES on the polygenic risk of behavioural and psychiatric traits, particularly in alcohol-related traits.

The United Kingdom Eating Disorders Genetics Initiative

OBJECTIVE

The United Kingdom Eating Disorders Genetics Initiative (EDGI UK), part of the National Institute for Health and Care Research (NIHR) Mental Health BioResource, aims to deepen our understanding of the environmental and genetic etiology of eating disorders. EDGI UK launched in February 2020 and is partnered with the UK eating disorders charity, Beat. Multiple EDGI branches exist worldwide. This article serves the dual function of providing an in-depth description of our study protocol and of describing our initial sample including demographics, diagnoses, and physical and psychiatric comorbidities.

METHOD

EDGI UK recruits via media and clinical services. Anyone living in England, at least 16 years old, with a lifetime probable or clinical eating disorder is eligible to sign up online: edgiuk.org. Participants complete online questionnaires, donate a saliva sample for genetic analysis, and consent to medical record linkage and recontact for future studies.

RESULTS

As of September 2022, EDGI UK recruited 7435 survey participants: 98% female, 93.1% white, 97.8% cisgender, 65.9% heterosexual, and 52.1% have a university degree. Over half (57.8%) of these participants have returned their saliva DNA kit. The most common diagnoses are anorexia nervosa (48.3%), purging disorder (37.8%), bulimia nervosa (37.5%), binge-eating disorder (15.8%), and atypical anorexia nervosa (7.8%).

CONCLUSION

EDGI UK is the largest UK eating disorders study and efforts to increase its diversity are underway. It offers a unique opportunity to accelerate eating disorder research. Researchers and participants with lived experience can collaborate on projects with unparalleled sample size.

PUBLIC SIGNIFICANCE STATEMENT

Eating disorders are debilitating and costly for society but are under-researched due to underfunding. EDGI UK is one of the largest eating disorder studies worldwide with ongoing recruitment. The collected data constitute a resource for secondary analysis. We will combine data from all international EDGI branches and the NIHR BioResource to facilitate research that improves our understanding of eating disorders and their comorbidities.

Assessing the Role of Cortisol in Anxiety, Major Depression, and Neuroticism: A Mendelian Randomization Study Using SERPINA6/SERPINA1 Variants

Background

Previous evidence informed by the toxic stress model suggests that higher cortisol causes anxiety and major depression, but clinical success is lacking. To clarify the role of cortisol, we used Mendelian randomization to estimate its associations with anxiety, major depression, and neuroticism, leveraging the largest available genome-wide association studies including from the Psychiatric Genomics Consortium, the UK Biobank, and FinnGen.

Methods

After meta-analyzing 2 genome-wide association studies on morning plasma cortisol (n = 32,981), we selected single nucleotide polymorphisms (SNPs) at p < 5 × 10-8 and r2 < 0.3 in the SERPINA6/SERPINA1 gene region encoding proteins that influence cortisol bioavailability. We applied these SNPs to summary genetic associations with the outcomes considered (n = 17,310-449,484), and systolic blood pressure as a positive outcome, using inverse-variance weighted meta-analysis accounting for correlation. Sensitivity analyses addressing SNP correlation and confounding by childhood maltreatment and follow-up analyses using only SNPs that colocalized with SERPINA6 expression were conducted.

Results

Cortisol was associated with anxiety (pooled odds ratio [OR] 1.16 per cortisol z score; 95% CI, 1.04 to 1.31), but not major depression (pooled OR 1.02, 95% CI, 0.95 to 1.10) or neuroticism (β -0.025; 95% CI, -0.071 to 0.022). Sensitivity analyses yielded similar estimates. Cortisol was positively associated with systolic blood pressure, as expected. Using rs9989237 and rs2736898, selected using colocalization, cortisol was associated with anxiety in the UK Biobank (OR 1.32; 95% CI, 1.01 to 1.74) but not with major depression in FinnGen (OR 1.14; 95% CI, 0.95 to 1.37).

Conclusions

Cortisol was associated with anxiety and may be a potential target for prevention. Other targets may be more relevant to major depression and neuroticism.

A novel multi-omics mendelian randomization method for gene set enrichment and its application to psychiatric disorders

Genome-wide association studies (GWAS) of psychiatric disorders (PD) yield numerous loci with significant signals, but often do not implicate specific genes. Because GWAS risk loci are enriched in expression/protein/methylation quantitative loci (e/p/mQTL, hereafter xQTL), transcriptome/proteome/methylome-wide association studies (T/P/MWAS, hereafter XWAS) that integrate xQTL and GWAS information, can link GWAS signals to effects on specific genes. To further increase detection power, gene signals are aggregated within relevant gene sets (GS) by performing gene set enrichment (GSE) analyses. Often GSE methods test for enrichment of "signal" genes in curated GS while overlooking their linkage disequilibrium (LD) structure, allowing for the possibility of increased false positive rates. Moreover, no GSE tool uses xQTL information to perform mendelian randomization (MR) analysis. To make causal inference on association between PD and GS, we develop a novel MR GSE (MR-GSE) procedure. First, we generate a "synthetic" GWAS for each MSigDB GS by aggregating summary statistics for x-level (mRNA, protein or DNA methylation (DNAm) levels) from the largest xQTL studies available) of genes in a GS. Second, we use synthetic GS GWAS as exposure in a generalized summary-data-based-MR analysis of complex trait outcomes. We applied MR-GSE to GWAS of nine important PD. When applied to the underpowered opioid use disorder GWAS, none of the four analyses yielded any signals, which suggests a good control of false positive rates. For other PD, MR-GSE greatly increased the detection of GO terms signals (2,594) when compared to the commonly used (non-MR) GSE method (286). Some of the findings might be easier to adapt for treatment, e.g., our analyses suggest modest positive effects for supplementation with certain vitamins and/or omega-3 for schizophrenia, bipolar and major depression disorder patients. Similar to other MR methods, when applying MR-GSE researchers should be mindful of the confounding effects of horizontal pleiotropy on statistical inference.

Unravelling the genetic basis of Schizophrenia

Neuronal development is a highly regulated mechanism that is central to organismal function in animals. In humans, disruptions to this process can lead to a range of neurodevelopmental phenotypes, including Schizophrenia (SCZ). SCZ has a significant genetic component, whereby an individual with an SCZ affected family member is eight times more likely to develop the disease than someone with no family history of SCZ. By examining a combination of genomic, transcriptomic and epigenomic datasets, large-scale 'omics' studies aim to delineate the relationship between genetic variation and abnormal cellular activity in the SCZ brain. Herein, we provide a brief overview of some of the key omics methods currently being used in SCZ research, including RNA-seq, the assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and high-throughput chromosome conformation capture (3C) approaches (e.g., Hi-C), as well as single-cell/nuclei iterations of these methods. We also discuss how these techniques are being employed to further our understanding of the genetic basis of SCZ, and to identify associated molecular pathways, biomarkers, and candidate drug targets.

Population Neuroscience: Principles and Advances

In population neuroscience, three disciplines come together to advance our knowledge of factors that shape the human brain: neuroscience, genetics, and epidemiology (Paus, Human Brain Mapping 31:891-903, 2010). Here, I will come back to some of the background material reviewed in more detail in our previous book (Paus, Population Neuroscience, 2013), followed by a brief overview of current advances and challenges faced by this integrative approach.

A Unifying Hypothesis for the Genome Dynamics Proposed to Underlie Neuropsychiatric Phenotypes

The sheer number of gene variants and the extent of the observed clinical and molecular heterogeneity recorded in neuropsychiatric disorders (NPDs) could be due to the magnified downstream effects initiated by a smaller group of genomic higher-order alterations in response to endogenous or environmental stress. Chromosomal common fragile sites (CFS) are functionally linked with microRNAs, gene copy number variants (CNVs), sub-microscopic deletions and duplications of DNA, rare single-nucleotide variants (SNVs/SNPs), and small insertions/deletions (indels), as well as chromosomal translocations, gene duplications, altered methylation, microRNA and L1 transposon activity, and 3-D chromosomal topology characteristics. These genomic structural features have been linked with various NPDs in mostly isolated reports and have usually only been viewed as areas harboring potential candidate genes of interest. The suggestion to use a higher level entry point (the 'fragilome' and associated features) activated by a central mechanism ('stress') for studying NPD genetics has the potential to unify the existing vast number of different observations in this field. This approach may explain the continuum of gene findings distributed between affected and unaffected individuals, the clustering of NPD phenotypes and overlapping comorbidities, the extensive clinical and molecular heterogeneity, and the association with certain other medical disorders.

Dissecting the biology of feeding and eating disorders

Feeding and eating disorders (FEDs) are heterogenous and characterized by varying patterns of dysregulated eating and weight. Genome-wide association studies (GWASs) are clarifying their underlying biology and their genetic relationship to other psychiatric and metabolic/anthropometric traits. Genetic research on anorexia nervosa (AN) has identified eight significant loci and uncovered genetic correlations implicating both psychiatric and metabolic/anthropometric risk factors. Careful explication of these metabolic contributors may be key to developing effective and enduring treatments for devastating, life-altering, and frequently lethal illnesses. We discuss clinical phenomenology, genomics, phenomics, intestinal microbiota, and functional genomics and propose a path that translates variants to genes, genes to pathways, and pathways to metabolic outcomes to advance the science and eventually treatment of FEDs.

Large-Scale Neuroimaging of Mental Illness

Neuroimaging has provided important insights into the brain variations related to mental illness. Inconsistencies in prior studies, however, call for methods that lead to more replicable and generalizable brain markers that can reliably predict illness severity, treatment course, and prognosis. A paradigm shift is underway with large-scale international research teams actively pooling data and resources to drive consensus findings and test emerging methods aimed at achieving the goals of precision psychiatry. In parallel with large-scale psychiatric genomics studies, international consortia combining neuroimaging data are mapping the transdiagnostic brain signatures of mental illness on an unprecedented scale. This chapter discusses the major challenges, recent findings, and a roadmap for developing better neuroimaging-based tools and markers for mental illness.

Genome-wide association study identifies 30 obsessive-compulsive disorder associated loci

Obsessive-compulsive disorder (OCD) affects ~1% of the population and exhibits a high SNP-heritability, yet previous genome-wide association studies (GWAS) have provided limited information on the genetic etiology and underlying biological mechanisms of the disorder. We conducted a GWAS meta-analysis combining 53,660 OCD cases and 2,044,417 controls from 28 European-ancestry cohorts revealing 30 independent genome-wide significant SNPs and a SNP-based heritability of 6.7%. Separate GWAS for clinical, biobank, comorbid, and self-report sub-groups found no evidence of sample ascertainment impacting our results. Functional and positional QTL gene-based approaches identified 249 significant candidate risk genes for OCD, of which 25 were identified as putatively causal, highlighting WDR6, DALRD3, CTNND1 and genes in the MHC region. Tissue and single-cell enrichment analyses highlighted hippocampal and cortical excitatory neurons, along with D1- and D2-type dopamine receptor-containing medium spiny neurons, as playing a role in OCD risk. OCD displayed significant genetic correlations with 65 out of 112 examined phenotypes. Notably, it showed positive genetic correlations with all included psychiatric phenotypes, in particular anxiety, depression, anorexia nervosa, and Tourette syndrome, and negative correlations with a subset of the included autoimmune disorders, educational attainment, and body mass index.. This study marks a significant step toward unraveling its genetic landscape and advances understanding of OCD genetics, providing a foundation for future interventions to address this debilitating disorder.

[Genetic studies on forensic-psychiatric inpatients? : Clinical, ethical and legal considerations]

BACKGROUND

Research on people deprived of liberty raises serious questions, especially concerning behavioral genetic studies.

QUESTION

Does including criminally detained patients with mental disorders in genetic studies lead to a gain of new knowledge and can this be ethically and legally justified?

METHOD

Evaluation of existing literature and interdisciplinary reflection.

RESULTS

After a review of research ethics and legal norms, we consider the benefits and risks of behavioral genetic research, taking the unique situation of test persons deprived of their liberty into account. The fundamental right to freedom of research also justifies foundational research in forensic psychiatry and psychotherapy. The possible future benefits of improving treatment plans must be weighed against the risks resulting from potential data leaks and inappropriate public reception of research results. Then we analyze possible threats to voluntary and informed consent to study participation in more detail by the ethical concept of vulnerability. Alongside problems with grasping complex issues, above all dependencies and power dynamics in the correctional system play a pivotal role. Recommendations on the ethical and legal inclusion of this study population are given.

CONCLUSION

Including criminally detained study participants can be ethically and legally justified when autonomous consent is supported by specific organizational and legal procedures and measures, for example via a clear professional and organizational separation of correction and research.

Quantifying the Relative Importance of Genetics and Environment on the Comorbidity between Mental- and Cardiometabolic Disorders: A Comprehensive Analysis of National Register Data from 17 million Scandinavians

Mental disorders (MDs) are leading causes of disability and premature death worldwide, partly due to high comorbidity with cardiometabolic disorders (CMDs). Reasons for this comorbidity are still poorly understood. We leverage nation-wide health records and complete genealogies of Denmark and Sweden (n=17 million) to reveal the genetic and environmental contributions underlying the observed comorbidity between six MDs and 14 CMDs. Genetic factors contributed about 50% to the comorbidity of schizophrenia, affective disorders, and autism spectrum disorder with CMDs, whereas the comorbidity of attention-deficit/hyperactivity disorder and anorexia with CMDs was mainly or fully driven by environmental factors. These findings provide causal insight to guide clinical and scientific initiatives directed at achieving mechanistic understanding as well as preventing and alleviating the consequences of these disorders.

Leveraging big data for causal understanding in mental health: a research framework

Over the past 30 years there have been numerous large-scale and longitudinal psychiatric research efforts to improve our understanding and treatment of mental health conditions. However, despite the huge effort by the research community and considerable funding, we still lack a causal understanding of most mental health disorders. Consequently, the majority of psychiatric diagnosis and treatment still operates at the level of symptomatic experience, rather than measuring or addressing root causes. This results in a trial-and-error approach that is a poor fit to underlying causality with poor clinical outcomes. Here we discuss how a research framework that originates from exploration of causal factors, rather than symptom groupings, applied to large scale multi-dimensional data can help address some of the current challenges facing mental health research and, in turn, clinical outcomes. Firstly, we describe some of the challenges and complexities underpinning the search for causal drivers of mental health conditions, focusing on current approaches to the assessment and diagnosis of psychiatric disorders, the many-to-many mappings between symptoms and causes, the search for biomarkers of heterogeneous symptom groups, and the multiple, dynamically interacting variables that influence our psychology. Secondly, we put forward a causal-orientated framework in the context of two large-scale datasets arising from the Adolescent Brain Cognitive Development (ABCD) study, the largest long-term study of brain development and child health in the United States, and the Global Mind Project which is the largest database in the world of mental health profiles along with life context information from 1.4 million people across the globe. Finally, we describe how analytical and machine learning approaches such as clustering and causal inference can be used on datasets such as these to help elucidate a more causal understanding of mental health conditions to enable diagnostic approaches and preventative solutions that tackle mental health challenges at their root cause.

Evaluation of bidirectional relationships between risk preference and mood disorders: A 2-sample Mendelian randomization study

BACKGROUND

Risk preference is often defined as the tendency to engage in risky activities. Increasing evidence shows that risk preference is associated with mood disorders. However, the causality and direction of this association are not clear.

METHODS

Genome-wide association study summary data of risk preference in 939,908 participants from UK Biobank and 23andMe were used to identify general risk preference. Data for 413,466 individuals taken from The Psychiatric Genomics Consortium were used to identify bipolar disorder (BP). Data for 807,553 individuals taken from The Psychiatric Genomics Consortium were used to identify major depressive disorder (MDD). The weighted median, inverse-variance weighting, and Mendelian randomization-Egger methods were used for the Mendelian randomization analysis to estimate a causal effect and detect directional pleiotropy.

RESULTS

GWAS summary data were obtained from three combined samples, containing 939,908, 413,466 and 807,553 individuals of European ancestry. Mendelian randomization evidence suggested that risk preference increased the onset of BP, and BP also increased risk preference (P < 0.001). In contrast, there were no reliable results to describe the relationship of risk preference with MDD (P > 0.05). Furthermore, there was no significant relationship between MDD and risk preference.

CONCLUSION

Using large-scale GWAS data, robust evidence supports a mutual relationship between risk preference and BP, but no relationship between risk preference and MDD was observed. This study indicates a potential marker for the early identification of MDD and BP. Additionally, it shows that reducing risk preferences for patients with BP may be a valuable intervention for treating BP.

Topologically associating domains define the impact of de novo promoter variants on autism spectrum disorder risk

Whole-genome sequencing (WGS) studies of autism spectrum disorder (ASD) have demonstrated the roles of rare promoter de novo variants (DNVs). However, most promoter DNVs in ASD are not located immediately upstream of known ASD genes. In this study analyzing WGS data of 5,044 ASD probands, 4,095 unaffected siblings, and their parents, we show that promoter DNVs within topologically associating domains (TADs) containing ASD genes are significantly and specifically associated with ASD. An analysis considering TADs as functional units identified specific TADs enriched for promoter DNVs in ASD and indicated that common variants in these regions also confer ASD heritability. Experimental validation using human induced pluripotent stem cells (iPSCs) showed that likely deleterious promoter DNVs in ASD can influence multiple genes within the same TAD, resulting in overall dysregulation of ASD-associated genes. These results highlight the importance of TADs and gene-regulatory mechanisms in better understanding the genetic architecture of ASD.

Polygenic profiles define aspects of clinical heterogeneity in attention deficit hyperactivity disorder

Attention deficit hyperactivity disorder (ADHD) is a complex disorder that manifests variability in long-term outcomes and clinical presentations. The genetic contributions to such heterogeneity are not well understood. Here we show several genetic links to clinical heterogeneity in ADHD in a case-only study of 14,084 diagnosed individuals. First, we identify one genome-wide significant locus by comparing cases with ADHD and autism spectrum disorder (ASD) to cases with ADHD but not ASD. Second, we show that cases with ASD and ADHD, substance use disorder and ADHD, or first diagnosed with ADHD in adulthood have unique polygenic score (PGS) profiles that distinguish them from complementary case subgroups and controls. Finally, a PGS for an ASD diagnosis in ADHD cases predicted cognitive performance in an independent developmental cohort. Our approach uncovered evidence of genetic heterogeneity in ADHD, helping us to understand its etiology and providing a model for studies of other disorders.

Pharmacogenetics of selective serotonin reuptake inhibitors (SSRI): A serotonin reuptake transporter (SERT)-based approach

Neuropsychiatric disorders are considered to be the most common cause of disability worldwide. Serotonin and its transporter is a prominent paradigm in mood disorders. Response to selective serotonin reuptake inhibitors (SSRI) is altered due to heterogeneity in the serotonin transporter gene, SLC6A4 (solute carrier family 6 member 4). The reported polymorphisms are found to be in different regions of the transporter gene: promoter region (5-HTTLPR and various single nucleotide polymorphisms within it), intron (STin2), and exon 9 (I425V). The long and short alleles of the 5-HTTLPR gene, which are prevalent among variations, may mediate differential effects. In long allelic variant carriers, an increased response to SSRI and timely recovery is due to increased availability of SERT. Whereas, SERT availability is significantly decreased in short allelic carriers, necessitating a reduction in SSRI dosage due to the increased risk of adverse drug reactions. Thus, pharmacogenetic investigations are required to understand the impact of functional variations on the efficacy and tolerability of SSRI. Identifying the carrier variants may aid in clear-decision making of the treatment regimen, aiding the approach of personalized medication.

A primer on the use of machine learning to distil knowledge from data in biological psychiatry

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

Identifying genetic differences between bipolar disorder and major depression through multiple GWAS

Background

Accurate diagnosis of bipolar disorder (BD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A key reason is that the first manic episode is often preceded by a depressive one, making it difficult to distinguish BD from unipolar major depressive disorder (MDD).

Aims

Here, we use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores that may aid early differential diagnosis.

Methods

Based on individual genotypes from case-control cohorts of BD and MDD shared through the Psychiatric Genomics Consortium, we compile case-case-control cohorts, applying a careful merging and quality control procedure. In a resulting cohort of 51,149 individuals (15,532 BD cases, 12,920 MDD cases and 22,697 controls), we perform a variety of GWAS and polygenic risk scores (PRS) analyses.

Results

While our GWAS is not well-powered to identify genome-wide significant loci, we find significant SNP-heritability and demonstrate the ability of the resulting PRS to distinguish BD from MDD, including BD cases with depressive onset. We replicate our PRS findings, but not signals of individual loci in an independent Danish cohort (iPSYCH 2015 case-cohort study, N=25,966). We observe strong genetic correlation between our case-case GWAS and that of case-control BD.

Conclusions

We find that MDD and BD, including BD with a depressive onset, are genetically distinct. Further, our findings support the hypothesis that Controls - MDD - BD primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BD and, importantly, BD with depressive onset from MDD.

Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks

Background

Gene co-expression networks (GCNs) describe relationships among expressed genes key to maintaining cellular identity and homeostasis. However, the small sample size of typical RNA-seq experiments which is several orders of magnitude fewer than the number of genes is too low to infer GCNs reliably. recount3, a publicly available dataset comprised of 316,443 uniformly processed human RNA-seq samples, provides an opportunity to improve power for accurate network reconstruction and obtain biological insight from the resulting networks.

Results

We compared alternate aggregation strategies to identify an optimal workflow for GCN inference by data aggregation and inferred three consensus networks: a universal network, a non-cancer network, and a cancer network in addition to 27 tissue context-specific networks. Central network genes from our consensus networks were enriched for evolutionarily constrained genes and ubiquitous biological pathways, whereas central context-specific network genes included tissue-specific transcription factors and factorization based on the hubs led to clustering of related tissue contexts. We discovered that annotations corresponding to context-specific networks inferred from aggregated data were enriched for trait heritability beyond known functional genomic annotations and were significantly more enriched when we aggregated over a larger number of samples.

Conclusion

This study outlines best practices for network GCN inference and evaluation by data aggregation. We recommend estimating and regressing confounders in each data set before aggregation and prioritizing large sample size studies for GCN reconstruction. Increased statistical power in inferring context-specific networks enabled the derivation of variant annotations that were enriched for concordant trait heritability independent of functional genomic annotations that are context-agnostic. While we observed strictly increasing held-out log-likelihood with data aggregation, we noted diminishing marginal improvements. Future directions aimed at alternate methods for estimating confounders and integrating orthogonal information from modalities such as Hi-C and ChIP-seq can further improve GCN inference.

Dynamic fusion of genomics and functional network connectivity in UK biobank reveals static and time-varying SNP manifolds

Many psychiatric and neurological disorders show significant heritability, indicating strong genetic influence. In parallel, dynamic functional network connectivity (dFNC) measures functional temporal coupling between brain networks in a time-varying manner and has proven to identify disease-related changes in the brain. However, it remains largely unclear how genetic risk contributes to brain dysconnectivity that further manifests into clinical symptoms. The current work aimed to address this gap by proposing a novel joint ICA (jICA)-based "dynamic fusion" framework to identify dynamically tuned SNP manifolds by linking static SNPs to dynamic functional information of the brain. The sliding window approach was utilized to estimate four dFNC states and compute subject-level state-specific dFNC features. Each state of dFNC features were then combined with 12946 SZ risk SNPs for jICA decomposition, resulting in four parallel fusions in 32861 European ancestry individuals within the UK Biobank cohort. The identified joint SNP-dFNC components were further validated for SZ relevance in an aggregated SZ cohort, and compared for across-state similarity to indicate level of dynamism. The results supported that dynamic fusion yielded "static" and "dynamic" components (i.e., high and low across-state similarity, respectively) for SNP and dFNC modalities. As expected, the SNP components presented a mixture of static and dynamic manifolds, with the latter largely driven by fusion with dFNC. We also showed that some of the dynamic SNP manifolds uniquely elicited by fusion with state-specific dFNC features complemented each other in terms of biological interpretation. This dynamic fusion framework thus allows expanding the SNP modality to manifolds in the time dimension, which provides a unique lens to elicit unique SNP correlates of dFNC otherwise unseen, promising additional insights on how genetic risk links to disease-related dysconnectivity.

Differentiating Biomarker Features and Familial Characteristics of B-SNIP Psychosis Biotypes

Idiopathic psychosis shows considerable biological heterogeneity across cases. B-SNIP used psychosis-relevant biomarkers to identity psychosis Biotypes, which will aid etiological and targeted treatment investigations. Psychosis probands from the B-SNIP consortium (n = 1907), their first-degree biological relatives (n = 705), and healthy participants (n = 895) completed a biomarker battery composed of cognition, saccades, and auditory EEG measurements. ERP quantifications were substantially modified from previous iterations of this approach. Multivariate integration reduced multiple biomarker outcomes to 11 "bio-factors". Twenty-four different approaches indicated bio-factor data among probands were best distributed as three subgroups. Numerical taxonomy with k-means constructed psychosis Biotypes, and rand indices evaluated consistency of Biotype assignments. Psychosis subgroups, their non-psychotic first-degree relatives, and healthy individuals were compared across bio-factors. The three psychosis Biotypes differed significantly on all 11 bio-factors, especially prominent for general cognition, antisaccades, ERP magnitude, and intrinsic neural activity. Rand indices showed excellent consistency of clustering membership when samples included at least 1100 subjects. Canonical discriminant analysis described composite bio-factors that simplified group comparisons and captured neural dysregulation, neural vigor, and stimulus salience variates. Neural dysregulation captured Biotype-2, low neural vigor captured Biotype-1, and deviations of stimulus salience captured Biotype-3. First-degree relatives showed similar patterns as their Biotyped proband relatives on general cognition, antisaccades, ERP magnitudes, and intrinsic brain activity. Results extend previous efforts by the B-SNIP consortium to characterize biologically distinct psychosis Biotypes. They also show that at least 1100 observations are necessary to achieve consistent outcomes. First-degree relative data implicate specific bio-factor deviations to the subtype of their proband and may inform studies of genetic risk.

COLOCdb: a comprehensive resource for multi-model colocalization of complex traits

Large-scale genome-wide association studies (GWAS) have provided profound insights into complex traits and diseases. Yet, deciphering the fine-scale molecular mechanisms of how genetic variants manifest to cause the phenotypes remains a daunting task. Here, we present COLOCdb (https://ngdc.cncb.ac.cn/colocdb), a comprehensive genetic colocalization database by integrating more than 3000 GWAS summary statistics and 13 types of xQTL to date. By employing two representative approaches for the colocalization analysis, COLOCdb deposits results from three key components: (i) GWAS-xQTL, pair-wise colocalization between GWAS loci and different types of xQTL, (ii) GWAS-GWAS, pair-wise colocalization between the trait-associated genetic loci from GWASs and (iii) xQTL-xQTL, pair-wise colocalization between the genetic loci associated with molecular phenotypes in xQTLs. These results together represent the most comprehensive colocalization analysis, which also greatly expands the list of shared variants with genetic pleiotropy. We expect that COLOCdb can serve as a unique and useful resource in advancing the discovery of new biological mechanisms and benefit future functional studies.

An evolutionary perspective on complex neuropsychiatric disease

The forces of evolution-mutation, selection, migration, and genetic drift-shape the genetic architecture of human traits, including the genetic architecture of complex neuropsychiatric illnesses. Studying these illnesses in populations that are diverse in genetic ancestry, historical demography, and cultural history can reveal how evolutionary forces have guided adaptation over time and place. A fundamental truth of shared human biology is that an allele responsible for a disease in anyone, anywhere, reveals a gene critical to the normal biology underlying that condition in everyone, everywhere. Understanding the genetic causes of neuropsychiatric disease in the widest possible range of human populations thus yields the greatest possible range of insight into genes critical to human brain development. In this perspective, we explore some of the relationships between genes, adaptation, and history that can be illuminated by an evolutionary perspective on studies of complex neuropsychiatric disease in diverse populations.

[Genetic Diagnostics in Everyday Clinical Practice in Child and Adolescent Psychiatry: Indications, Framework Conditions, Hurdles, and Proposed Solutions]

Genetic Diagnostics in Everyday Clinical Practice in Child and Adolescent Psychiatry: Indications, Framework Conditions, Hurdles, and Proposed Solutions Abstract: Health insurance covers medically necessary genetic testing in Germany. Diagnostic genetic testing has become increasingly important for child and adolescent psychiatry (CAP), reflected by the rising number of national guidelines relevant to CAP, including genetic testing in the recommended diagnostic work-up. However, implementation of theses guidelines in routine clinical care is lacking. This article provides a concise overview of the relevance of genetic testing in CAP-related national guidelines. It outlines the legal and financial framework for genetic testing in Germany. Furthermore, it points out barriers to implementation and offers potential solutions. It then provides examples from clinical practice highlighting the potential benefits patients and their family members might have from receiving a genetic diagnosis. The article closes by outlining future CAP-relevant areas in which genetic testing may become clinically relevant.