Leveraging pleiotropy for the improved treatment of psychiatric disorders.

Over 90% of drug candidates fail in clinical trials, while it takes 10-15 years and one billion US dollars to develop a single successful drug. Drug development is more challenging for psychiatric disorders, where disease comorbidity and complex symptom profiles obscure the identification of causal mechanisms for therapeutic intervention. One promising approach for determining more suitable drug candidates in clinical trials is integrating human genetic data into the selection process. Genome-wide association studies have identified thousands of replicable risk loci for psychiatric disorders, and sophisticated statistical tools are increasingly effective at using these data to pinpoint likely causal genes. These studies have also uncovered shared or pleiotropic genetic risk factors underlying comorbid psychiatric disorders. In this article, we argue that leveraging pleiotropic effects will provide opportunities to discover novel drug targets and identify more effective treatments for psychiatric disorders by targeting a common mechanism rather than treating each disease separately.

The genetic landscape of substance use disorders.

Substance use disorders represent a significant public health concern with considerable socioeconomic implications worldwide. Twin and family-based studies have long established a heritable component underlying these disorders. In recent years, genome-wide association studies of large, broadly phenotyped samples have identified regions of the genome that harbour genetic risk variants associated with substance use disorders. These regions have enabled the discovery of putative causal genes and improved our understanding of genetic relationships among substance use disorders and other traits. Furthermore, the integration of these data with clinical information has yielded promising insights into how individuals respond to medications, allowing for the development of personalized treatment approaches based on an individual's genetic profile. This review article provides an overview of recent advances in the genetics of substance use disorders and demonstrates how genetic data may be used to reduce the burden of disease and improve public health outcomes.

Phenome-wide screening of the putative causal determinants of depression using genetic data.

Depression is one of the most common mental health disorders and one of the top causes of disability throughout the world. The present study sought to identify putative causal associations between depression and hundreds of complex human traits through a genome-wide screening of genetic data and a hypothesis-free approach. We leveraged genome-wide association studies summary statistics for depression and 1504 complex traits and investigated potential causal relationships using the latent causal variable method. We identified 559 traits genetically correlated with depression risk at FDR < 5%. Of these, 46 were putative causal genetic determinants of depression, including lifestyle factors, diseases of the nervous system, respiratory disorders, diseases of the musculoskeletal system, traits related to the health of the gastrointestinal system, obesity, vitamin D levels and the use of prescription medications, among others. No phenotypes were identified as potential outcomes of depression. Our results suggest that genetic liability to multiple complex traits may contribute to a higher risk for depression. In particular, we show a putative causal genetic effect of pain, obesity and inflammation on depression. These findings provide novel insights into the potential causal determinants of depression and should be interpreted as testable hypotheses for future studies to confirm, which may facilitate the design of new prevention strategies to reduce depression's burden.

Genome-wide meta-analysis of ascertainment and symptom structures of major depression in case-enriched and community cohorts.

BACKGROUND: Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and etiological subtypes. There are several challenges to integrating symptom data from genetically informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data. METHODS: We conducted genome-wide association studies of major depressive symptoms in three cohorts that were enriched for participants with a diagnosis of depression (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts who were not recruited on the basis of diagnosis (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors. RESULTS: The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for the skip-structure in community cohorts (use of Depression and Anhedonia as gating symptoms). CONCLUSION: The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analyzing genetic association data.

Genetic heterogeneity in self-reported depressive symptoms identified through genetic analyses of the PHQ-9.

BACKGROUND: Depression is a clinically heterogeneous disorder. Previous large-scale genetic studies of depression have explored genetic risk factors of depression case-control status or aggregated sums of depressive symptoms, ignoring possible clinical or genetic heterogeneity. METHODS: We analyse data from 148 752 subjects of white British ancestry in the UK Biobank who completed nine items of a self-rated measure of current depressive symptoms: the Patient Health Questionnaire (PHQ-9). Genome-Wide Association analyses were conducted for nine symptoms and two composite measures. LD Score Regression was used to calculate SNP-based heritability (h2SNP) and genetic correlations (rg) across symptoms and to investigate genetic correlations with 25 external phenotypes. Genomic structural equation modelling was used to test the genetic factor structure across the nine symptoms. RESULTS: We identified nine genome-wide significant genomic loci (8 novel), with no overlap in loci across symptoms. h2SNP ranged from 6% (concentration problems) to 9% (appetite changes). Genetic correlations ranged from 0.54 to 0.96 (all p < 1.39 × 10-3) with 30 of 36 correlations being significantly smaller than one. A two-factor model provided the best fit to the genetic covariance matrix, with factors representing 'psychological' and 'somatic' symptoms. The genetic correlations with external phenotypes showed large variation across the nine symptoms. CONCLUSIONS: Patterns of SNP associations and genetic correlations differ across the nine symptoms, suggesting that current depressive symptoms are genetically heterogeneous. Our study highlights the value of symptom-level analyses in understanding the genetic architecture of a psychiatric trait. Future studies should investigate whether genetic heterogeneity is recapitulated in clinical symptoms of major depression.

Exploring Phenotypic and Genetic Overlap Between Cannabis Use and Schizotypy.

There is a well-established relationship between cannabis use and psychosis, although the exact nature of this relationship is not fully understood. Recent studies have observed significant genetic overlap between a diagnosis of schizophrenia and lifetime cannabis use. Expanding on this work, the current study aimed to examine whether genetic overlap also occurs for subclinical psychosis (schizotypy) and cannabis use, as well as examining the phenotypic association between the traits. Phenotypic correlations were calculated for a variety of schizotypy and cannabis phenotypes in the UK Biobank (UKB), and single nucleotide polymorphism (SNP)-based heritability estimates and genetic correlations were calculated for these UKB phenotypes as well as for several other variables taken from recent genomewide association studies. Positive phenotypic correlations were observed between 11 out of 12 pairs of the cannabis use and schizotypy phenotypes (correlation range .05-.18), indicating a robust association between increased symptoms of schizotypy and cannabis use. SNP-based heritability estimates for two schizotypy phenotypes remained significant after multiple testing correction: social anhedonia (h2SNP = .08, SE = .02, N = 4025) and ever seen an unreal vision (h2SNP = .35, SE = .10, N = 150,717). Finally, one significant genetic correlation was observed between schizotypy and cannabis use, a negative correlation between social anhedonia and number of times used cannabis (rg = -.30, p = .012). The current study suggests the relationship between cannabis use and psychosis is also seen in subclinical symptoms of psychosis, but further research with larger samples is needed to determine the biological mechanisms underlying this association.

The genetic architecture of substance use and its diverse correlations with mental health traits.

Although harmful substance use is common and represented by shared symptom features and high genetic correlations, the underlying genetic relationships between substance use traits have not been fully explored. We have investigated the genetic architecture of substance use traits through exploratory and confirmatory factor analyses using genomic structural equation modeling (Genomic SEM), and explored genetic correlations between different aspects of substance use and mental health-related traits. Genomic SEM was used to identify latent factors representing the relationships between 14 substance use traits (alcohol, nicotine, cannabis and opioid use), and to confirm or modify existing latent factors for 38 mental health-related traits. A bi-factor model best explained the genetic overlap between substance use traits, including a general substance use factor and two sub-factors representing genetic liability specific to alcohol use or smoking. The SNP-based heritability of these factors ranged from 2 to 7 % and each factor had 10 or more independent significant SNPs identified. Bivariate correlations revealed patterns of genetic overlap with other mental health-related factors unique to each substance use factor. Variations in the genetic overlap between psychiatric traits and different aspects of substance use can be used to further investigate the pleiotropy present between these traits, and explore commonalities in etiology.

Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries.

Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. Here we performed genome-wide association studies meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signaling and brain aging-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson's disease and attention-deficit/hyperactivity disorder. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases.

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.

Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries.

Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. We performed GWAS meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and, for the first time, the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signalling and brain ageing-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson's disease and ADHD. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases.

Identification of drug repurposing candidates for the treatment of anxiety: A genetic approach.

Anxiety disorders are a group of prevalent and heritable neuropsychiatric diseases. We previously conducted a genome-wide association study (GWAS) which identified genomic loci associated with anxiety; however, the biological consequences underlying the genetic associations are largely unknown. Integrating GWAS and functional genomic data may improve our understanding of the genetic effects on intermediate molecular phenotypes such as gene expression. This can provide an opportunity for the discovery of drug targets for anxiety via drug repurposing. We used the GWAS summary statistics to determine putative causal genes for anxiety using MAGMA and colocalization analyses. A transcriptome-wide association study was conducted to identify genes with differential genetically regulated levels of gene expression in human brain tissue. The genes were integrated with a large drug-gene expression database (Connectivity Map), discovering compounds that are predicted to "normalise" anxiety-associated expression changes. The study identified 64 putative causal genes associated with anxiety (35 genes upregulated; 29 genes downregulated). Drug mechanisms adrenergic receptor agonists, sigma receptor agonists, and glutamate receptor agonists gene targets were enriched in anxiety-associated genetic signal and exhibited an opposing effect on the anxiety-associated gene expression signature. The significance of the project demonstrated genetic links for novel drug candidates to potentially advance anxiety therapeutics.

The association between trauma exposure, polygenic risk and individual depression symptoms.

BACKGROUND: Traumatic experiences are associated with increased risk for major depressive disorder (MDD). This study sought to determine the extent that trauma exposure, depression polygenic risk scores (PRS), and their interaction are associated with MDD and individual depression symptoms. METHODS: Data from 102,182 individuals from the large-scale UK Biobank population cohort was analysed. A series of regression analyses were conducted to estimate the association between trauma, depression PRS and 1) current depression, 2) lifetime MDD case-control status, 3) nine individual current depressive symptoms, and 4) thirteen individual symptoms experienced during a major depressive episode. Additive and multiplicative PRS-by-trauma interactions were also assessed. RESULTS: Trauma and depression PRS were significantly associated with both current depression and lifetime MDD. A positive, additive interaction effect was observed on depression, but multiplicative interactions were not significant. Trauma exposure and depression PRS were associated with specific patterns of depression symptoms; Trauma was associated with low self-esteem, suicidal ideation, and atypical (but not typical) neurovegetative symptoms. Additive interaction effects were observed on six out of nine current depressive symptoms. CONCLUSIONS: Trauma exposure and genetic predisposition to depression may lead to particular symptomatology, which may contribute to the extreme clinical heterogeneity observed in individuals with major depression.

Genetic structure of major depression symptoms across clinical and community cohorts.

Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and aetiological subtypes. There are several challenges to integrating symptom data from genetically-informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data. We conducted genome-wide association studies of major depressive symptoms in three clinical cohorts that were enriched for affected participants (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors. The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for missing data patterns in the community cohorts (use of Depression and Anhedonia as gating symptoms). The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analysing genetic association data.

Ten challenges for clinical translation in psychiatric genetics.

Genome-wide association studies have identified hundreds of robust genetic associations underlying psychiatric disorders and provided important biological insights into disease onset and progression. There is optimism that genetic findings will pave the way to precision psychiatry by facilitating the development of more effective treatments and the identification of groups of patients that these treatments should be targeted toward. However, there are several challenges that must be addressed before genetic findings can be translated into the clinic. In this Perspective, we highlight ten challenges for the field of psychiatric genetics, focused on the robust and generalizable detection of genetic risk factors, improved definition and assessment of psychopathology and achieving better clinical indicators. We discuss recent advancements in the field that will improve the explanatory and predictive power of genetic data and ultimately contribute to improving the management and treatment of patients with a psychiatric disorder.

Influences on the Genetic Relationship Between Cannabis Use and Schizophrenia: The Role of the Externalizing Spectrum.

BACKGROUND AND HYPOTHESIS: The nature of the robust association between cannabis use and schizophrenia remains undetermined. Plausible hypotheses explaining this relationship include the premise that cannabis use causes schizophrenia, increased liability for schizophrenia increases the risk of cannabis use initiation (eg, self-medication), or the bidirectional causal hypothesis where both factors play a role in the development of the other. Alternatively, factors that confound the relationship between schizophrenia and cannabis use may explain their association. Externalizing behaviors are related to both schizophrenia and cannabis use and may influence their relationship. STUDY DESIGN: This study aimed to evaluate whether externalizing behaviors influence the genetic relationship between cannabis use and schizophrenia. We conducted a multivariate genome-wide association analysis of 6 externalizing behaviors in order to construct a genetic latent factor of the externalizing spectrum. Genomic structural equation modeling was used to evaluate the influence of externalizing behaviors on the genetic relationship between cannabis use and schizophrenia. RESULTS: We found that externalizing behaviors partially explained the association between cannabis use and schizophrenia by up to 42%. CONCLUSIONS: This partial explanation of the association by externalizing behaviors suggests that there may be other unidentified confounding factors, alongside a possible direct association between schizophrenia and cannabis use. Future studies should aim to identify further confounding factors to accurately explain the relationship between cannabis use and schizophrenia.

An analysis of genetically regulated gene expression and the role of co-expression networks across 16 psychiatric and substance use phenotypes.

Genome-wide association studies (GWASs) have identified thousands of risk loci for psychiatric and substance use phenotypes, however the biological consequences of these loci remain largely unknown. We performed a transcriptome-wide association study of 10 psychiatric disorders and 6 substance use phenotypes (GWAS sample size range, N = 9725-807,553) using expression quantitative trait loci data from 532 prefrontal cortex samples. We estimated the correlation of genetically regulated expression between phenotype pairs, and compared the results with the genetic correlations. We identified 393 genes with at least one significant phenotype association, comprising 458 significant associations across 16 phenotypes. Overall, the transcriptomic correlations for phenotype pairs were significantly higher than the respective genetic correlations. For example, attention deficit hyperactivity disorder and autism spectrum disorder, both childhood developmental disorders, had significantly higher transcriptomic correlation (r = 0.84) than genetic correlation (r = 0.35). Finally, we tested the enrichment of phenotype-associated genes in gene co-expression networks built from human prefrontal cortex samples. Phenotype-associated genes were enriched in multiple gene co-expression modules and the implicated modules contained genes involved in mRNA splicing and glutamatergic receptors, among others. Together, our results highlight the utility of gene expression data in the understanding of functional gene mechanisms underlying psychiatric disorders and substance use phenotypes.

A Local Genetic Correlation Analysis Provides Biological Insights Into the Shared Genetic Architecture of Psychiatric and Substance Use Phenotypes.

BACKGROUND: Global genetic correlation analysis has provided valuable insight into the shared genetic basis between psychiatric and substance use disorders. However, little is known about which regions disproportionately contribute to the global correlation. METHODS: We used Local Analysis of [co]Variant Annotation to calculate bivariate local genetic correlations across 2495 approximately equal-sized, semi-independent genomic regions for 20 psychiatric and substance use phenotypes. We performed a transcriptome-wide association study using expression weights from the prefrontal cortex to identify risk genes for each phenotype, followed by probabilistic fine-mapping to prioritize credible causal genes within each bivariate locus. RESULTS: We detected 80 significant (p < 2.08 × 10-6) bivariate local genetic correlations across 61 loci. The expression effect directions for risk genes within each bivariate locus were largely consistent with the local correlation coefficients, suggesting that genetically regulated gene expression may be used in the functional interpretation of local genetic correlations. Probabilistic fine-mapping identified several genes that may drive pleiotropic mechanisms for genetically correlated phenotypes. For example, we confirmed a local genetic correlation between schizophrenia and smoking behavior at 15q25 and prioritized PSMA4 as the most credible gene candidate underlying both phenotypes. CONCLUSIONS: Our study reveals previously unreported local bivariate genetic correlations between psychiatric and substance use phenotypes, which we fine-mapped to identify shared credible causal genes underlying genetically correlated phenotypes.

Genetic evidence that the causal association of educational attainment with reduced risk of Alzheimer's disease is driven by intelligence.

Alzheimer's disease (AD) is predicted to affect 132 million people by 2050. Targeting modifiable lifestyle risk factors that are associated with an increased risk of AD could prevent a large proportion of dementia cases, allowing people to reach the end of their life dementia free. However, evidence obtained from the observational studies does not take into account how risk factors are correlated with one another, and whether they causally contribute to increased AD risk. In this study, we determine whether the relationship between previously speculated AD risk factors and AD susceptibility is consistent with causality using large-scale genetic data. We focus on educational attainment (EA), intelligence and household income which have been previously shown to be causally associated with AD. Using GWAS-by-subtraction and Multivariable Mendelian Randomization we show that of these, only the cognitive component of EA (intelligence) is independently causally associated with AD. This work has ramifications for the modifiability of lifestyle risk factors for AD.

Understanding genetic risk factors for common side effects of antidepressant medications.

Background: Major depression is one of the most disabling health conditions internationally. In recent years, new generation antidepressant medicines have become very widely prescribed. While these medicines are efficacious, side effects are common and frequently result in discontinuation of treatment. Compared with specific pharmacological properties of the different medications, the relevance of individual vulnerability is understudied. Methods: We used data from the Australian Genetics of Depression Study to gain insights into the aetiology and genetic risk factors to antidepressant side effects. To this end, we employed structural equation modelling, polygenic risk scoring and regressions. Results: Here we show that participants reporting a specific side effect for one antidepressant are more likely to report the same side effect for other antidepressants, suggesting the presence of shared individual or pharmacological factors. Polygenic risk scores (PRS) for depression associated with side effects that overlapped with depressive symptoms, including suicidality and anxiety. Body Mass Index PRS are strongly associated with weight gain from all medications. PRS for headaches are associated with headaches from sertraline. Insomnia PRS show some evidence of predicting insomnia from amitriptyline and escitalopram. Conclusions: Our results suggest a set of common factors underlying the risk for antidepressant side effects. These factors seem to be partly explained by genetic liability related to depression severity and the nature of the side effect. Future studies on the genetic aetiology of side effects will enable insights into their underlying mechanisms and the possibility of risk stratification and prophylaxis strategies.