Charting the Landscape of Genetic Overlap Between Mental Disorders and Related Traits Beyond Genetic Correlation

Childhood trajectories of emotional and behavioral difficulties are related to polygenic liability for mood and anxiety disorders

BACKGROUND

Symptoms related to mood and anxiety disorders (emotional disorders) often present in childhood and adolescence. Some of the genetic liability for mental disorders, and emotional and behavioral difficulties seems to be shared. Yet, it is unclear how genetic liability for emotional disorders and related traits influence trajectories of childhood behavioral and emotional difficulties, and if specific developmental patterns are associated with higher genetic liability for these disorders.

METHODS

This study uses data from a genotyped sample of children (n = 54,839) from the Norwegian Mother, Father, and Child Cohort Study (MoBa). We use latent growth models (1.5-5 years) and latent profile analyses (1.5-8 years) to quantify childhood trajectories and profiles of emotional and behavioral difficulties and diagnoses. We examine associations between these trajectories and profiles with polygenic scores for bipolar disorder (PGSBD), anxiety (PGSANX), depression (PGSDEP), and neuroticism (PGSNEUR).

RESULTS

Associations between PGSDEP, PGSANX, and PGSNEUR, and emotional and behavioral difficulties in childhood were more persistent than age-specific across early childhood (1.5-5 years). Higher PGSANX and PGSDEP were associated with steeper increases in behavioral difficulties across early childhood. Latent profile analyses identified five profiles with different associations with emotional disorder diagnosis. All PGS were associated with the probability of classification into profiles characterized by some form of difficulties (vs. a normative reference profile), but only PGSBD was uniquely associated with a single developmental profile.

CONCLUSIONS

Genetic risk for mood disorders and related traits contribute to both a higher baseline level of, and a more rapid increase in, emotional and behavioral difficulties across early and middle childhood, with some indications for disorder-specific profiles. Our findings may inform research on developmental pathways to emotional disorders and the improvement of initiatives for early identification and targeted intervention.

Unveiling the shared genetic architecture between testosterone and polycystic ovary syndrome

Testosterone (T) is a critical predictor of polycystic ovary syndrome (PCOS) but the genetic overlap between T and PCOS has not been established. Here by leveraging genetic datasets from large-scale genome-wide association studies, we assessed the genetic correlation and polygenic overlap between PCOS and three T-related traits using linkage disequilibrium score regression and the bivariate causal mixture model methods. The conjunctional false discovery rate (conjFDR) method was employed to identify shared causal variants. Functional annotation of variants was conducted using FUMA. Total T and bioavailable T exhibited positive correlations with PCOS, while sex hormone-binding globulin (SHBG) showed a negative correlation. All three traits demonstrated extensive genetic overlap with PCOS, with a minimum of 68% of T-related variants influencing PCOS. The conjFDR revealed 4 to 6 causal variants within joint genomic loci shared between PCOS and T-related traits. Functional annotations suggested that these variants might impact PCOS by modulating nearby genes, such as FSHB. Our findings support the hypothesis that PCOS is significantly influenced by androgen abnormalities. Additionally, this study identified several causal variants potentially involved in shared biological mechanisms between PCOS and T regulation.

Identification of novel genomic loci for anxiety symptoms and extensive genetic overlap with psychiatric disorders

AIMS

Anxiety disorders are prevalent and anxiety symptoms (ANX) co-occur with many psychiatric disorders. We aimed to identify genomic loci associated with ANX, characterize its genetic architecture, and genetic overlap with psychiatric disorders.

METHODS

We included a genome-wide association study of ANX (meta-analysis of UK Biobank and Million Veterans Program, n = 301,732), schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD), and validated the findings in the Norwegian Mother, Father, and Child Cohort (n = 95,841). We employed the bivariate causal mixture model and local analysis of covariant association to characterize the genetic architecture including overlap between the phenotypes. Conditional and conjunctional false discovery rate analyses were performed to boost the identification of loci associated with anxiety and shared with psychiatric disorders.

RESULTS

Anxiety was polygenic with 12.9k genetic variants and overlapped extensively with psychiatric disorders (4.1k-11.4k variants) with predominantly positive genetic correlations between anxiety and psychiatric disorders. We identified 119 novel loci for anxiety by conditioning on the psychiatric disorders, and loci shared between anxiety and MDn = 47 $$ \left(n=47\right) $$ , BIPn = 33 $$ \left(n=33\right) $$ , SCZn = 71 $$ \left(n=71\right) $$ , ADHDn = 20 $$ \left(n=20\right) $$ , and ASDn = 5 $$ \left(n=5\right) $$ . Genes annotated to anxiety loci exhibit enrichment for a broader range of biological pathways including cell adhesion and neurofibrillary tangle compared with genes annotated to the shared loci.

CONCLUSIONS

Anxiety is highly polygenic phenotype with extensive genetic overlap with psychiatric disorders, and we identified novel loci for anxiety implicating new molecular pathways. The shared genetic architecture may underlie the extensive cross-disorder comorbidity of anxiety, and the identified molecular underpinnings may lead to potential drug targets.

Investigating the shared genetic architecture between depression and subcortical volumes

Depression, a widespread and highly heritable mental health condition, profoundly affects millions of individuals worldwide. Neuroimaging studies have consistently revealed volumetric abnormalities in subcortical structures associated with depression. However, the genetic underpinnings shared between depression and subcortical volumes remain inadequately understood. Here, we investigate the extent of polygenic overlap using the bivariate causal mixture model (MiXeR), leveraging summary statistics from the largest genome-wide association studies for depression (N = 674,452) and 14 subcortical volumetric phenotypes (N = 33,224). Additionally, we identify shared genomic loci through conditional/conjunctional FDR analyses. MiXeR shows that subcortical volumetric traits share a substantial proportion of genetic variants with depression, with 44 distinct shared loci identified by subsequent conjunctional FDR analysis. These shared loci are predominantly located in intronic regions (58.7%) and non-coding RNA intronic regions (25.4%). The 269 protein-coding genes mapped by these shared loci exhibit specific developmental trajectories, with the expression level of 55 genes linked to both depression and subcortical volumes, and 30 genes linked to cognitive abilities and behavioral symptoms. These findings highlight a shared genetic architecture between depression and subcortical volumetric phenotypes, enriching our understanding of the neurobiological underpinnings of depression.

Genetic neurodevelopmental clustering and dyslexia

Dyslexia is a learning difficulty with neurodevelopmental origins, manifesting as reduced accuracy and speed in reading and spelling. It is substantially heritable and frequently co-occurs with other neurodevelopmental conditions, particularly attention deficit-hyperactivity disorder (ADHD). Here, we investigate the genetic structure underlying dyslexia and a range of psychiatric traits using results from genome-wide association studies of dyslexia, ADHD, autism, anorexia nervosa, anxiety, bipolar disorder, major depressive disorder, obsessive compulsive disorder, schizophrenia, and Tourette syndrome. Genomic Structural Equation Modelling (GenomicSEM) showed heightened support for a model consisting of five correlated latent genomic factors described as: F1) compulsive disorders (including obsessive-compulsive disorder, anorexia nervosa, Tourette syndrome), F2) psychotic disorders (including bipolar disorder, schizophrenia), F3) internalising disorders (including anxiety disorder, major depressive disorder), F4) neurodevelopmental traits (including autism, ADHD), and F5) attention and learning difficulties (including ADHD, dyslexia). ADHD loaded more strongly on the attention and learning difficulties latent factor (F5) than on the neurodevelopmental traits latent factor (F4). The attention and learning difficulties latent factor (F5) was positively correlated with internalising disorders (.40), neurodevelopmental traits (.25) and psychotic disorders (.17) latent factors, and negatively correlated with the compulsive disorders (-.16) latent factor. These factor correlations are mirrored in genetic correlations observed between the attention and learning difficulties latent factor and other cognitive, psychological and wellbeing traits. We further investigated genetic variants underlying both dyslexia and ADHD, which implicated 49 loci (40 not previously found in GWAS of the individual traits) mapping to 174 genes (121 not found in GWAS of individual traits) as potential pleiotropic variants. Our study confirms the increased genetic relation between dyslexia and ADHD versus other psychiatric traits and uncovers novel pleiotropic variants affecting both traits. In future, analyses including additional co-occurring traits such as dyscalculia and dyspraxia will allow a clearer definition of the attention and learning difficulties latent factor, yielding further insights into factor structure and pleiotropic effects.

The risks for major psychiatric disorders in the siblings of probands with major depressive disorder

Using a case-controlled study including siblings of major depression (MD) and control probands, born 1970-1990 and followed through 2018, we sought to clarify the degree to which the familial liability to MD is reflected in its clinical features, and the pattern of psychiatric disorders at elevated risk in the siblings of MD probands. The study population included full-siblings of 197,309 MD and matched 197,309 control probands. The proband-sibling tetrachoric correlation of for MD was +0.20. Both linear and quadratic effects of younger AAO and number of episodes significantly increased the risk of MD in siblings. Male sex, anxiety disorder, alcohol use disorder (AUD), inpatient treatment, psychotic symptoms, severity, and antidepressant prescription in MD probands increased the risk of MD in siblings. Cox proportional hazard models (hazard ratios, 95% CI) revealed a significantly increased risk of attention deficit hyperactivity disorder (1.82, 1.76-1.88), generalized anxiety disorder (1.79, 1.74-1.85), bipolar disorder (1.78, 1.70-1.85), MD (1.74, 1.72-1.76), obsessive-compulsive disorder (1.72, 1.65-1.80), phobic anxiety disorder (1.71, 1.65-1.76), and panic disorder (1.68, 1.64-1.72) in MD co-siblings. The HRs for AUD (1.64, 1.60-1.68), post-traumatic stress disorder (1.62, 1.59-1.66) were modestly lower, and the lowest was seen for schizophrenia (1.42, 1.30-1.54). The overall pattern of increased risk of these disorders was similar in reared-apart half-siblings and cousins of MD probands. Our findings suggest that MD is familial, and a range of important clinical factors predict its familial liability. The familial liability to MD, mostly due to genetic factors, is shared with a broad range of psychiatric disorders.

Decoding frontotemporal and cell-type-specific vulnerabilities to neuropsychiatric disorders and psychoactive drugs

Frontotemporal lobe abnormalities are linked to neuropsychiatric disorders and cognition, but the role of cellular heterogeneity between temporal lobe (TL) and frontal lobe (FL) in the vulnerability to genetic risk factors remains to be elucidated. We integrated single-nucleus transcriptome analysis in 'fresh' human FL and TL with genetic susceptibility, gene dysregulation in neuropsychiatric disease and psychoactive drug response data. We show how intrinsic differences between TL and FL contribute to the vulnerability of specific cell types to both genetic risk factors and psychoactive drugs. Neuronal populations, specifically PVALB neurons, were most highly vulnerable to genetic risk factors for psychiatric disease. These psychiatric disease-associated genes were mostly upregulated in the TL, and dysregulated in the brain of patients with obsessive-compulsive disorder, bipolar disorder and schizophrenia. Among these genes, GRIN2A and SLC12A5, implicated in schizophrenia and bipolar disorder, were significantly upregulated in TL PVALB neurons and in psychiatric disease patients' brain. PVALB neurons from the TL were twofold more vulnerable to psychoactive drugs than to genetic risk factors, showing the influence and specificity of frontotemporal lobe differences on cell vulnerabilities. These studies provide a cell type resolved map of the impact of brain regional differences on cell type vulnerabilities in neuropsychiatric disorders.

Distinct biological signature and modifiable risk factors underlie the comorbidity between major depressive disorder and cardiovascular disease

Major depressive disorder (MDD) and cardiovascular disease (CVD) are often comorbid, resulting in excess morbidity and mortality. Here we show that CVDs share most of their genetic risk factors with MDD. Multivariate genome-wide association analysis of shared genetic liability between MDD and atherosclerotic CVD revealed seven loci and distinct patterns of tissue and brain cell-type enrichments, suggesting the involvement of the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic and psychosocial or lifestyle risk factors. Our data indicated causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and showed that the causal effects were partly explained by metabolic and psychosocial or lifestyle factors. The distinct signature of MDD-atherosclerotic CVD comorbidity suggests an immunometabolic subtype of MDD that is more strongly associated with CVD than overall MDD. In summary, we identified biological mechanisms underlying MDD-CVD comorbidity and modifiable risk factors for prevention of CVD in individuals with MDD.

Alcohol use disorder and body mass index show genetic pleiotropy and shared neural associations

Background

The prevalence of co-occurring heavy alcohol consumption and obesity is increasing in the United States. Despite neurobiological overlap in the regulation of alcohol consumption and eating behavior, alcohol- and body mass index (BMI)-related phenotypes show no or minimal genetic correlation. We hypothesized that the lack of genetic correlation is due to mixed effect directions of variants shared by AUD and BMI.

Methods

We applied MiXeR, to investigate shared genetic architecture between AUD and BMI in individuals of European ancestry. We used conjunctional false discovery rate (conjFDR) analysis to detect loci associated with both phenotypes and their directional effect, Functional Mapping and Annotation (FUMA) to identify lead single nucleotide polymorphisms (SNPs), Genotype-Tissue Expression (GTEx) samples to examine gene expression enrichment across tissue types, and BrainXcan to evaluate the shared associations of AUD and BMI with brain image-derived phenotypes.

Results

MiXeR analysis indicated polygenic overlap of 80.9% between AUD and BMI, despite a genetic correlation (r g ) of -.03. ConjFDR analysis yielded 56 lead SNPs with the same effect direction and 76 with the opposite direction. Of the 132 shared lead SNPs, 53 were novel for both AUD and BMI. GTEx analyses identified significant overexpression in the frontal cortex (BA9), hypothalamus, cortex, anterior cingulate cortex (BA24), hippocampus, and amygdala. Amygdala and caudate nucleus gray matter volumes were significantly associated with both AUD and BMI in BrainXcan analyses.

Conclusions

More than half of variants significantly associated with AUD and BMI had opposite directions of effect for the traits, supporting our hypothesis that this is the basis for their lack of genetic correlation. Follow-up analyses identified brain regions implicated in executive functioning, reward, homeostasis, and food intake regulation. Together, these findings clarify the extensive polygenic overlap between AUD and BMI and elucidate several overlapping neurobiological mechanisms.

Identification of Novel Genomic Loci for Anxiety and Extensive Genetic Overlap with Psychiatric Disorders

Background

Anxiety disorders are prevalent and anxiety symptoms co-occur with many psychiatric disorders. We aimed to identify genomic risk loci associated with anxiety, characterize its genetic architecture, and genetic overlap with psychiatric disorders.

Methods

We used the GWAS of anxiety symptoms, schizophrenia, bipolar disorder, major depression, and attention deficit hyperactivity disorder (ADHD). We employed MiXeR and LAVA to characterize the genetic architecture and genetic overlap between the phenotypes. Conditional and conjunctional false discovery rate analyses were performed to boost the identification of genomic loci associated with anxiety and those shared with psychiatric disorders. Gene annotation and gene set analyses were conducted using OpenTargets and FUMA, respectively.

Results

Anxiety was polygenic with 12.9k estimated genetic risk variants and overlapped extensively with psychiatric disorders (4.1-11.4k variants). MiXeR and LAVA revealed predominantly positive genetic correlations between anxiety and psychiatric disorders. We identified 114 novel loci for anxiety by conditioning on the psychiatric disorders. We also identified loci shared between anxiety and major depression (n = 47), bipolar disorder (n = 33), schizophrenia (n = 71), and ADHD (n = 20). Genes annotated to anxiety loci exhibit enrichment for a broader range of biological pathways and differential tissue expression in more diverse tissues than those annotated to the shared loci.

Conclusions

Anxiety is a highly polygenic phenotype with extensive genetic overlap with psychiatric disorders. These genetic overlaps enabled the identification of novel loci for anxiety. The shared genetic architecture may underlie the extensive cross-disorder comorbidity of anxiety, and the identified genetic loci implicate molecular pathways that may lead to potential drug targets.

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.

Local patterns of genetic sharing challenge the boundaries between neuropsychiatric and insulin resistance-related conditions

The co-occurrence of insulin resistance (IR)-related metabolic conditions with neuropsychiatric disorders is a complex public health challenge. Evidence of the genetic links between these phenotypes is emerging, but little is currently known about the genomic regions and biological functions that are involved. To address this, we performed Local Analysis of [co]Variant Association (LAVA) using large-scale (N=9,725-933,970) genome-wide association studies (GWASs) results for three IR-related conditions (type 2 diabetes mellitus, obesity, and metabolic syndrome) and nine neuropsychiatric disorders. Subsequently, positional and expression quantitative trait locus (eQTL)-based gene mapping and downstream functional genomic analyses were performed on the significant loci. Patterns of negative and positive local genetic correlations (|rg|=0.21-1, pFDR<0.05) were identified at 109 unique genomic regions across all phenotype pairs. Local correlations emerged even in the absence of global genetic correlations between IR-related conditions and Alzheimer's disease, bipolar disorder, and Tourette's syndrome. Genes mapped to the correlated regions showed enrichment in biological pathways integral to immune-inflammatory function, vesicle trafficking, insulin signalling, oxygen transport, and lipid metabolism. Colocalisation analyses further prioritised 10 genetically correlated regions for likely harbouring shared causal variants, displaying high deleterious or regulatory potential. These variants were found within or in close proximity to genes, such as SLC39A8 and HLA-DRB1, that can be targeted by supplements and already known drugs, including omega-3/6 fatty acids, immunomodulatory, antihypertensive, and cholesterol-lowering drugs. Overall, our findings underscore the complex genetic landscape of IR-neuropsychiatric multimorbidity, advocating for an integrated disease model and offering novel insights for research and treatment strategies in this domain.

Genome-wide association analyses using machine learning-based phenotyping reveal genetic architecture of occupational creativity and overlap with psychiatric disorders

Creativity is known to be heritable and exhibits familial aggregation with psychiatric disorders; however, the complex nature of their relationship has not been well-established. In the present study, we demonstrate that using an expanded and validated machine learning (ML)-based phenotyping of occupational creativity (OC) can allow us to further understand the trait of creativity, which was previously difficult to define and study. We conducted the largest genome-wide association study (GWAS) on OC with 241,736 participants from the UK Biobank and identified 25 lead variants that have not yet been reported and three candidate causal genes that were previously associated with educational attainment and psychiatric disorders. We found extensive genetic overlap between OC and psychiatric disorders with mixed effect direction through various post-GWAS analyses, including the bivariate causal mixture model. In addition, we discovered a strongly genetic correlation between our original GWAS and the GWAS adjusted for education years (rg = 0.95). Our GWAS analysis via ML-based phenotyping contributes to the understanding of the genetic architecture of creativity, which may inform genetic discovery and genetic prediction in human cognition and psychiatric disorders.

Genome-wide analyses reveal shared genetic architecture and novel risk loci between opioid use disorder and general cognitive ability

BACKGROUND

Opioid use disorder (OUD), a serious health burden worldwide, is associated with lower cognitive function. Recent studies have demonstrated a negative genetic correlation between OUD and general cognitive ability (COG), indicating a shared genetic basis. However, the specific genetic variants involved, and the underlying molecular mechanisms remain poorly understood. Here, we aimed to quantify and identify the genetic basis underlying OUD and COG.

METHODS

We quantified the extent of genetic overlap between OUD and COG using a bivariate causal mixture model (MiXeR) and identified specific genetic loci applying conditional/conjunctional FDR. Finally, we investigated biological function and expression of implicated genes using available resources.

RESULTS

We estimated that ~94% of OUD variants (4.8k out of 5.1k variants) also influence COG. We identified three novel OUD risk loci and one locus shared between OUD and COG. Loci identified implicated biological substrates in the basal ganglia.

CONCLUSION

We provide new insights into the complex genetic risk architecture of OUD and its genetic relationship with COG.

Dissecting the genetic overlap between three complex phenotypes with trivariate MiXeR

Comorbidities are an increasing global health challenge. Accumulating evidence suggests overlapping genetic architectures underlying comorbid complex human traits and disorders. The bivariate causal mixture model (MiXeR) can quantify the polygenic overlap between complex phenotypes beyond global genetic correlation. Still, the pattern of genetic overlap between three distinct phenotypes, which is important to better characterize multimorbidities, has previously not been possible to quantify. Here, we present and validate the trivariate MiXeR tool, which disentangles the pattern of genetic overlap between three phenotypes using summary statistics from genome-wide association studies (GWAS). Our simulations show that the trivariate MiXeR can reliably reconstruct different patterns of genetic overlap. We further demonstrate how the tool can be used to estimate the proportions of genetic overlap between three phenotypes using real GWAS data, providing examples of complex patterns of genetic overlap between diverse human traits and diseases that could not be deduced from bivariate analyses. This contributes to a better understanding of the etiology of complex phenotypes and the nature of their relationship, which may aid in dissecting comorbidity patterns and their biological underpinnings.

The schizophrenia syndrome, circa 2024: What we know and how that informs its nature

With new data about different aspects of schizophrenia being continually generated, it becomes necessary to periodically revisit exactly what we know. Along with a need to review what we currently know about schizophrenia, there is an equal imperative to evaluate the construct itself. With these objectives, we undertook an iterative, multi-phase process involving fifty international experts in the field, with each step building on learnings from the prior one. This review assembles currently established findings about schizophrenia (construct, etiology, pathophysiology, clinical expression, treatment) and posits what they reveal about its nature. Schizophrenia is a heritable, complex, multi-dimensional syndrome with varying degrees of psychotic, negative, cognitive, mood, and motor manifestations. The illness exhibits a remitting and relapsing course, with varying degrees of recovery among affected individuals with most experiencing significant social and functional impairment. Genetic risk factors likely include thousands of common genetic variants that each have a small impact on an individual's risk and a plethora of rare gene variants that have a larger individual impact on risk. Their biological effects are concentrated in the brain and many of the same variants also increase the risk of other psychiatric disorders such as bipolar disorder, autism, and other neurodevelopmental conditions. Environmental risk factors include but are not limited to urban residence in childhood, migration, older paternal age at birth, cannabis use, childhood trauma, antenatal maternal infection, and perinatal hypoxia. Structural, functional, and neurochemical brain alterations implicate multiple regions and functional circuits. Dopamine D-2 receptor antagonists and partial agonists improve psychotic symptoms and reduce risk of relapse. Certain psychological and psychosocial interventions are beneficial. Early intervention can reduce treatment delay and improve outcomes. Schizophrenia is increasingly considered to be a heterogeneous syndrome and not a singular disease entity. There is no necessary or sufficient etiology, pathology, set of clinical features, or treatment that fully circumscribes this syndrome. A single, common pathophysiological pathway appears unlikely. The boundaries of schizophrenia remain fuzzy, suggesting the absence of a categorical fit and need to reconceptualize it as a broader, multi-dimensional and/or spectrum construct.

Conduct disorder - a comprehensive exploration of comorbidity patterns, genetic and environmental risk factors

Conduct disorder (CD), a common mental disorder in children and adolescents, is characterized by antisocial behavior. Despite similarities with antisocial personality disorder (ASPD) and possible diagnostic continuity, CD has been shown to precede a range of adult-onset mental disorders. Additionally, little is known about the putative shared genetic liability between CD and adult-onset mental disorders and the underlying gene-environment interplay. Here, we interrogated comorbidity between CD and other mental disorders from the Norwegian Mother, Father and Child Cohort Study (n = 114 500) and investigated how polygenic risk scores (PRS) for mental health traits were associated with CD/CD traits in childhood and adolescence. Gene-environment interplay patterns for CD was explored with data on bullying and parental education. We found CD to be comorbid with several child and adult-onset mental disorders. This phenotypic overlap corresponded with associations between PRS for mental disorders and CD. Additionally, our findings support an additive gene-environment model. Previously conceptualized as a precursor of ASPD, we found that CD was associated with polygenic risk for several child- and adult-onset mental disorders. High comorbidity of CD with other psychiatric disorders reflected on the genetic level should inform research studies, diagnostic assessments and clinical follow-up of this heterogenous group.

The Nature of the Familial Risk for Psychosis in Bipolar Disorder

BACKGROUND AND HYPOTHESIS

To clarify whether the familial liability to psychosis associated with bipolar disorder (BD) is nonspecific or has a greater effect on risk for psychosis in cases with prominent mood symptoms and/or a remitting course.

STUDY DESIGN

We examined, in 984 809 offspring raised in intact families in Sweden, born 1980-1996 and followed-up through 2018, by multivariable Cox proportional hazards regression, risk in offspring of parents with BD for 7 psychotic disorders: Psychotic MD (PMD), psychotic BD (PBD), schizoaffective disorder (SAD), acute psychoses, psychosis NOS, delusional disorder (DD) and schizophrenia (SZ). Diagnoses were obtained from national registers.

STUDY RESULTS

In the offspring of BD parents, the hazard ratios (HR) for these 7 disorders formed an inverted U-shaped curve, rising from 2.98 for PMD, to peak at 4.49 for PBD and 5.25 for SAD, and then declining to a HR of 3.48 for acute psychoses and 3.22 for psychosis NOS, to a low of 2.19 for DD and 2.33 for SZ. A similar pattern of risks was seen in offspring of mothers and fathers affected with BD and in offspring predicted from age at onset in their BD parent.

CONCLUSIONS

The BD-associated risk for psychosis impacts most strongly on mood disorders, moderately on episodic psychotic syndromes, and least on chronic psychotic disorders. These results support prior clinical studies suggesting a qualitative difference in the familial substrate for psychosis occurring in BD and SZ.

Associations Between Attention-Deficit/Hyperactivity Disorder (ADHD), ADHD Medication, and Shorter Height: A Quasi-Experimental and Family-Based Study

OBJECTIVE

The association between attention-deficit/hyperactivity disorder (ADHD) and shorter height is unclear. This study examined the risk of shorter height in individuals with ADHD, and the influence of prenatal factors, ADHD medication, psychiatric comorbidity, socioeconomic factors, and familial liability.

METHOD

We drew on Swedish National Registers for 2 different study designs. First, height data for 14,268 individuals with ADHD and 71,339 controls were stratified into 2 groups: (1) before stimulant treatment was introduced in Sweden, and (2) after stimulant treatment was introduced in Sweden. Second, we used a family-based design including 833,172 relatives without ADHD with different levels of relatedness to the individuals with ADHD and matched controls.

RESULTS

ADHD was associated with shorter height both before (below-average height: OR = 1.31, 95% CI = 1.22-1.41) and after (below-average height: OR = 1.21, 95% CI = 1.13-1.31) stimulants for ADHD were introduced in Sweden, and was of similar magnitude in both cohorts. The association between ADHD and shorter height attenuated after adjustment for prenatal factors, psychiatric disorders, and socioeconomic status. Relatives of individuals with ADHD had an increased risk of shorter height (below-average height in full siblings: OR = 1.14, 95% CI = 1.09-1.19; maternal half siblings: OR = 1.10, 95% CI = 1.01-1.20; paternal half siblings: OR = 1.15, 95% CI = 1.07-1.24, first full cousins: OR = 1.10, 95% CI = 1.08-1.12).

CONCLUSION

Our findings suggest that ADHD is associated with shorter height. On a population level, this association was present both before and after ADHD medications were available in Sweden. The association between ADHD and height was partly explained by prenatal factors, psychiatric comorbidity, low socioeconomic status, and a shared familial liability for ADHD.

Genome-wide Association Analysis of Schizophrenia and Vitamin D Levels Shows Shared Genetic Architecture and Identifies Novel Risk Loci

Low vitamin D (vitD) levels have been consistently reported in schizophrenia (SCZ) suggesting a role in the etiopathology. However, little is known about the role of underlying shared genetic mechanisms. We applied a conditional/conjunctional false discovery rate approach (FDR) on large, nonoverlapping genome-wide association studies for SCZ (N cases = 53 386, N controls = 77 258) and vitD serum concentration (N = 417 580) to evaluate shared common genetic variants. The identified genomic loci were characterized using functional analyses and biological repositories. We observed cross-trait SNP enrichment in SCZ conditioned on vitD and vice versa, demonstrating shared genetic architecture. Applying the conjunctional FDR approach, we identified 72 loci jointly associated with SCZ and vitD at conjunctional FDR < 0.05. Among the 72 shared loci, 40 loci have not previously been reported for vitD, and 9 were novel for SCZ. Further, 64% had discordant effects on SCZ-risk and vitD levels. A mixture of shared variants with concordant and discordant effects with a predominance of discordant effects was in line with weak negative genetic correlation (rg = -0.085). Our results displayed shared genetic architecture between SCZ and vitD with mixed effect directions, suggesting overlapping biological pathways. Shared genetic variants with complex overlapping mechanisms may contribute to the coexistence of SCZ and vitD deficiency and influence the clinical picture.

Psychiatric disorders and brain white matter exhibit genetic overlap implicating developmental and neural cell biology

Improved understanding of the shared genetic architecture between psychiatric disorders and brain white matter may provide mechanistic insights for observed phenotypic associations. Our objective is to characterize the shared genetic architecture of bipolar disorder (BD), major depression (MD), and schizophrenia (SZ) with white matter fractional anisotropy (FA) and identify shared genetic loci to uncover biological underpinnings. We used genome-wide association study (GWAS) summary statistics for BD (n = 413,466), MD (n = 420,359), SZ (n = 320,404), and white matter FA (n = 33,292) to uncover the genetic architecture (i.e., polygenicity and discoverability) of each phenotype and their genetic overlap (i.e., genetic correlations, overlapping trait-influencing variants, and shared loci). This revealed that BD, MD, and SZ are at least 7-times more polygenic and less genetically discoverable than average FA. Even in the presence of weak genetic correlations (range = -0.05 to -0.09), average FA shared an estimated 42.5%, 43.0%, and 90.7% of trait-influencing variants as well as 12, 4, and 28 shared loci with BD, MD, and SZ, respectively. Shared variants were mapped to genes and tested for enrichment among gene-sets which implicated neurodevelopmental expression, neural cell types, myelin, and cell adhesion molecules. For BD and SZ, case vs control tract-level differences in FA associated with genetic correlations between those same tracts and the respective disorder (rBD = 0.83, p = 4.99e-7 and rSZ = 0.65, p = 5.79e-4). Genetic overlap at the tract-level was consistent with average FA results. Overall, these findings suggest a genetic basis for the involvement of brain white matter aberrations in the pathophysiology of psychiatric disorders.

The shared genetic risk architecture of neurological and psychiatric disorders: a genome-wide analysis

While neurological and psychiatric disorders have historically been considered to reflect distinct pathogenic entities, recent findings suggest shared pathobiological mechanisms. However, the extent to which these heritable disorders share genetic influences remains unclear. Here, we performed a comprehensive analysis of GWAS data, involving nearly 1 million cases across ten neurological diseases and ten psychiatric disorders, to compare their common genetic risk and biological underpinnings. Using complementary statistical tools, we demonstrate widespread genetic overlap across the disorders, even in the absence of genetic correlations. This indicates that a large set of common variants impact risk of multiple neurological and psychiatric disorders, but with divergent effect sizes. Furthermore, biological interrogation revealed a range of biological processes associated with neurological diseases, while psychiatric disorders consistently implicated neuronal biology. Altogether, the study indicates that neurological and psychiatric disorders share key etiological aspects, which has important implications for disease classification, precision medicine, and clinical practice.

Dissociation of Cognitive Effort-Based Decision Making and Its Associations With Symptoms, Cognition, and Everyday Life Function Across Schizophrenia, Bipolar Disorder, and Depression

BACKGROUND

Anhedonia and amotivation are symptoms of many different mental health disorders that are frequently associated with functional disability, but it is not clear whether the same processes contribute to motivational impairments across disorders. This study focused on one possible factor, the willingness to exert cognitive effort, referred to as cognitive effort-cost decision making.

METHODS

We examined performance on the deck choice task as a measure of cognitive effort-cost decision making, in which people choose to complete an easy task for a small monetary reward or a harder task for larger rewards, in 5 groups: healthy control (n = 80), schizophrenia/schizoaffective disorder (n = 50), bipolar disorder with psychosis (n = 58), current major depression (n = 60), and past major depression (n = 51). We examined cognitive effort-cost decision making in relation to clinician and self-reported motivation symptoms, working memory and cognitive control performance, and life function measured by ecological momentary assessment and passive sensing.

RESULTS

We found a significant diagnostic group × reward interaction (F8,588 = 4.37, p < .001, ηp2 = 0.056). Compared with the healthy control group, the schizophrenia/schizoaffective and bipolar disorder groups, but not the current or past major depressive disorder groups, showed a reduced willingness to exert effort at the higher reward values. In the schizophrenia/schizoaffective and bipolar disorder groups, but not the major depressive disorder groups, reduced willingness to exert cognitive effort for higher rewards was associated with greater clinician-rated motivation impairments, worse working memory and cognitive control performance, and less engagement in goal-directed activities measured by ecological momentary assessment.

CONCLUSIONS

These findings suggest that the mechanisms contributing to motivational impairments differ among individuals with psychosis spectrum disorders versus depression.

Shared Genetic Loci Between Schizophrenia and White Blood Cell Counts Suggest Genetically Determined Systemic Immune Abnormalities

BACKGROUND

Immune mechanisms are indicated in schizophrenia (SCZ). Recent genome-wide association studies (GWAS) have identified genetic variants associated with SCZ and immune-related phenotypes. Here, we use cutting edge statistical tools to identify shared genetic variants between SCZ and white blood cell (WBC) counts and further understand the role of the immune system in SCZ.

STUDY DESIGN

GWAS results from SCZ (patients, n = 53 386; controls, n = 77 258) and WBC counts (n = 56 3085) were analyzed. We applied linkage disequilibrium score regression, the conditional false discovery rate method and the bivariate causal mixture model for analyses of genetic associations and overlap, and 2 sample Mendelian randomization to estimate causal effects.

STUDY RESULTS

The polygenicity for SCZ was 7.5 times higher than for WBC count and constituted 32%-59% of WBC count genetic loci. While there was a significant but weak positive genetic correlation between SCZ and lymphocytes (rg = 0.05), the conditional false discovery rate method identified 383 shared genetic loci (53% concordant effect directions), with shared variants encompassing all investigated WBC subtypes: lymphocytes, n = 215 (56% concordant); neutrophils, n = 158 (49% concordant); monocytes, n = 146 (47% concordant); eosinophils, n = 135 (56% concordant); and basophils, n = 64 (53% concordant). A few causal effects were suggested, but consensus was lacking across different Mendelian randomization methods. Functional analyses indicated cellular functioning and regulation of translation as overlapping mechanisms.

CONCLUSIONS

Our results suggest that genetic factors involved in WBC counts are associated with the risk of SCZ, indicating a role of immune mechanisms in subgroups of SCZ with potential for stratification of patients for immune targeted treatment.

Genomic findings in schizophrenia and their implications

There has been substantial progress in understanding the genetics of schizophrenia over the past 15 years. This has revealed a highly polygenic condition with the majority of the currently explained heritability coming from common alleles of small effect but with additional contributions from rare copy number and coding variants. Many specific genes and loci have been implicated that provide a firm basis upon which mechanistic research can proceed. These point to disturbances in neuronal, and particularly synaptic, functions that are not confined to a small number of brain regions and circuits. Genetic findings have also revealed the nature of schizophrenia's close relationship to other conditions, particularly bipolar disorder and childhood neurodevelopmental disorders, and provided an explanation for how common risk alleles persist in the population in the face of reduced fecundity. Current genomic approaches only potentially explain around 40% of heritability, but only a small proportion of this is attributable to robustly identified loci. The extreme polygenicity poses challenges for understanding biological mechanisms. The high degree of pleiotropy points to the need for more transdiagnostic research and the shortcomings of current diagnostic criteria as means of delineating biologically distinct strata. It also poses challenges for inferring causality in observational and experimental studies in both humans and model systems. Finally, the Eurocentric bias of genomic studies needs to be rectified to maximise benefits and ensure these are felt across diverse communities. Further advances are likely to come through the application of new and emerging technologies, such as whole-genome and long-read sequencing, to large and diverse samples. Substantive progress in biological understanding will require parallel advances in functional genomics and proteomics applied to the brain across developmental stages. For these efforts to succeed in identifying disease mechanisms and defining novel strata they will need to be combined with sufficiently granular phenotypic data.

Multivariate genetic analysis of personality and cognitive traits reveals abundant pleiotropy

Personality and cognitive function are heritable mental traits whose genetic foundations may be distributed across interconnected brain functions. Previous studies have typically treated these complex mental traits as distinct constructs. We applied the 'pleiotropy-informed' multivariate omnibus statistical test to genome-wide association studies of 35 measures of neuroticism and cognitive function from the UK Biobank (n = 336,993). We identified 431 significantly associated genetic loci with evidence of abundant shared genetic associations, across personality and cognitive function domains. Functional characterization implicated genes with significant tissue-specific expression in all tested brain tissues and brain-specific gene sets. We conditioned independent genome-wide association studies of the Big 5 personality traits and cognitive function on our multivariate findings, boosting genetic discovery in other personality traits and improving polygenic prediction. These findings advance our understanding of the polygenic architecture of these complex mental traits, indicating a prominence of pleiotropic genetic effects across higher order domains of mental function such as personality and cognitive function.

Delineating disorder-general and disorder-specific dimensions of psychopathology from functional brain networks in a developmental clinical sample

The interplay between functional brain network maturation and psychopathology during development remains elusive. To establish the structure of psychopathology and its neurobiological mechanisms, mapping of both shared and unique functional connectivity patterns across developmental clinical populations is needed. We investigated shared associations between resting-state functional connectivity and psychopathology in children and adolescents aged 5-21 (n = 1689). Specifically, we used partial least squares (PLS) to identify latent variables (LV) between connectivity and both symptom scores and diagnostic information. We also investigated associations between connectivity and each diagnosis specifically, controlling for other diagnosis categories. PLS identified five significant LVs between connectivity and symptoms, mapping onto the psychopathology hierarchy. The first LV resembled a general psychopathology factor, followed by dimensions of internalising- externalising, neurodevelopment, somatic complaints, and thought problems. Another PLS with diagnostic data revealed one significant LV, resembling a cross-diagnostic case-control pattern. The diagnosis-specific PLS identified a unique connectivity pattern for autism spectrum disorder (ASD). All LVs were associated with distinct patterns of functional connectivity. These dimensions largely replicated in an independent sample (n = 420) from the same dataset, as well as to an independent cohort (n = 3504). This suggests that covariance in developmental functional brain networks supports transdiagnostic dimensions of psychopathology.

Shared genetic architecture between irritable bowel syndrome and psychiatric disorders reveals molecular pathways of the gut-brain axis

BACKGROUND

Irritable bowel syndrome (IBS) often co-occurs with psychiatric and gastrointestinal disorders. A recent genome-wide association study (GWAS) identified several genetic risk variants for IBS. However, most of the heritability remains unidentified, and the genetic overlap with psychiatric and somatic disorders is not quantified beyond genome-wide genetic correlations. Here, we characterize the genetic architecture of IBS, further, investigate its genetic overlap with psychiatric and gastrointestinal phenotypes, and identify novel genomic risk loci.

METHODS

Using GWAS summary statistics of IBS (53,400 cases and 433,201 controls), and psychiatric and gastrointestinal phenotypes, we performed bivariate casual mixture model analysis to characterize the genetic architecture and genetic overlap between these phenotypes. We leveraged identified genetic overlap to boost the discovery of genomic loci associated with IBS, and to identify specific shared loci associated with both IBS and psychiatric and gastrointestinal phenotypes, using the conditional/conjunctional false discovery rate (condFDR/conjFDR) framework. We used functional mapping and gene annotation (FUMA) for functional analyses.

RESULTS

IBS was highly polygenic with 12k trait-influencing variants. We found extensive polygenic overlap between IBS and psychiatric disorders and to a lesser extent with gastrointestinal diseases. We identified 132 independent IBS-associated loci (condFDR < 0.05) by conditioning on psychiatric disorders (n = 127) and gastrointestinal diseases (n = 24). Using conjFDR, 70 unique loci were shared between IBS and psychiatric disorders. Functional analyses of shared loci revealed enrichment for biological pathways of the nervous and immune systems. Genetic correlations and shared loci between psychiatric disorders and IBS subtypes were different.

CONCLUSIONS

We found extensive polygenic overlap of IBS and psychiatric and gastrointestinal phenotypes beyond what was revealed with genetic correlations. Leveraging the overlap, we discovered genetic loci associated with IBS which implicate a wide range of biological pathways beyond the gut-brain axis. Genetic differences may underlie the clinical subtype of IBS. These results increase our understanding of the pathophysiology of IBS which may form the basis for the development of individualized interventions.

Dimensional clinical phenotyping using post-mortem brain donor medical records: post-mortem RDoC profiling is associated with Alzheimer's disease neuropathology

Introduction

Transdiagnostic dimensional phenotypes are essential to investigate the relationship between continuous symptom dimensions and pathological changes. This is a fundamental challenge to post-mortem work, as assessments of phenotypic concepts need to rely on existing records.

Methods

We adapted well-validated methodologies to compute National Institute of Mental Health Research Domain Criteria (RDoC) scores using natural language processing (NLP) from electronic health records (EHRs) obtained from post-mortem brain donors and tested whether cognitive domain scores were associated with Alzheimer's disease neuropathological measures.

Results

Our results confirm an association of EHR-derived cognitive scores with neuropathological findings. Notably, higher neuropathological load, particularly neuritic plaques, was associated with higher cognitive burden scores in the frontal (ß = 0.38, P = 0.0004), parietal (ß = 0.35, P = 0.0008), temporal (ß = 0.37, P = 0.0004) and occipital (ß = 0.37, P = 0.0003) lobes.

Discussion

This proof-of-concept study supports the validity of NLP-based methodologies to obtain quantitative measures of RDoC clinical domains from post-mortem EHR. The associations may accelerate post-mortem brain research beyond classical case-control designs.

Shared genetic loci between Alzheimer's disease and multiple sclerosis: Crossroads between neurodegeneration and immune system

BACKGROUND

Neuroinflammation is involved in the pathophysiology of Alzheimer's disease (AD), including immune-linked genetic variants and molecular pathways, microglia and astrocytes. Multiple Sclerosis (MS) is a chronic, immune-mediated disease with genetic and environmental risk factors and neuropathological features. There are clinical and pathobiological similarities between AD and MS. Here, we investigated shared genetic susceptibility between AD and MS to identify putative pathological mechanisms shared between neurodegeneration and the immune system.

METHODS

We analysed GWAS data for late-onset AD (N cases = 64,549, N controls = 634,442) and MS (N cases = 14,802, N controls = 26,703). Gaussian causal mixture modelling (MiXeR) was applied to characterise the genetic architecture and overlap between AD and MS. Local genetic correlation was investigated with Local Analysis of [co]Variant Association (LAVA). The conjunctional false discovery rate (conjFDR) framework was used to identify the specific shared genetic loci, for which functional annotation was conducted with FUMA and Open Targets.

RESULTS

MiXeR analysis showed comparable polygenicities for AD and MS (approximately 1800 trait-influencing variants) and genetic overlap with 20% of shared trait-influencing variants despite negligible genetic correlation (rg = 0.03), suggesting mixed directions of genetic effects across shared variants. conjFDR analysis identified 16 shared genetic loci, with 8 having concordant direction of effects in AD and MS. Annotated genes in shared loci were enriched in molecular signalling pathways involved in inflammation and the structural organisation of neurons.

CONCLUSIONS

Despite low global genetic correlation, the current results provide evidence for polygenic overlap between AD and MS. The shared loci between AD and MS were enriched in pathways involved in inflammation and neurodegeneration, highlighting new opportunities for future investigation.

The relationship between cannabis use, schizophrenia, and bipolar disorder: a genetically informed study

BACKGROUND

The relationship between psychotic disorders and cannabis use is heavily debated. Shared underlying genetic risk is one potential explanation. We investigated the genetic association between psychotic disorders (schizophrenia and bipolar disorder) and cannabis phenotypes (lifetime cannabis use and cannabis use disorder).

METHODS

We used genome-wide association summary statistics from individuals with European ancestry from the Psychiatric Genomics Consortium, UK Biobank, and International Cannabis Consortium. We estimated heritability, polygenicity, and discoverability of each phenotype. We performed genome-wide and local genetic correlations. Shared loci were identified and mapped to genes, which were tested for functional enrichment. Shared genetic liabilities to psychotic disorders and cannabis phenotypes were explored using causal analyses and polygenic scores, using the Norwegian Thematically Organized Psychosis cohort.

FINDINGS

Psychotic disorders were more heritable than cannabis phenotypes and more polygenic than cannabis use disorder. We observed positive genome-wide genetic correlations between psychotic disorders and cannabis phenotypes (range 0·22-0·35) with a mixture of positive and negative local genetic correlations. Three to 27 shared loci were identified for the psychotic disorder and cannabis phenotype pairs. Enrichment of mapped genes implicated neuronal and olfactory cells as well as drug-gene targets for nicotine, alcohol, and duloxetine. Psychotic disorders showed a causal effect on cannabis phenotypes, and lifetime cannabis use had a causal effect on bipolar disorder. Of 2181 European participants from the Norwegian Thematically Organized Psychosis cohort applied in polygenic risk score analyses, 1060 (48·6%) were females and 1121 (51·4%) were males (mean age 33·1 years [SD 11·8]). 400 participants had bipolar disorder, 697 had schizophrenia, and 1044 were healthy controls. Within this sample, polygenic scores for cannabis phenotypes predicted psychotic disorders independently and improved prediction beyond the polygenic score for the psychotic disorders.

INTERPRETATION

A subgroup of individuals might have a high genetic risk of developing a psychotic disorder and using cannabis. This finding supports public health efforts to reduce cannabis use, particularly in individuals at high risk or patients with psychotic disorders. Identified shared loci and their functional implications could facilitate development of novel treatments.

FUNDING

US National Institutes of Health, the Research Council Norway, the South-East Regional Health Authority, Stiftelsen Kristian Gerhard Jebsen, EEA-RO-NO-2018-0535, European Union's Horizon 2020 Research and Innovation Programme, the Marie Skłodowska-Curie Actions, and University of Oslo Life Science.

Dimensional clinical phenotyping using post-mortem brain donor medical records: Association with neuropathology

INTRODUCTION

Transdiagnostic dimensional phenotypes are essential to investigate the relationship between continuous symptom dimensions and pathological changes. This is a fundamental challenge to postmortem work, as assessment of newly developed phenotypic concepts needs to rely on existing records.

METHODS

We adapted well-validated methodologies to compute NIMH research domain criteria (RDoC) scores using natural language processing (NLP) from electronic health records (EHRs) obtained from post-mortem brain donors and tested whether RDoC cognitive domain scores were associated with hallmark Alzheimer's disease (AD) neuropathological measures.

RESULTS

Our results confirm an association of EHR-derived cognitive scores with hallmark neuropathological findings. Notably, higher neuropathological load, particularly neuritic plaques, was associated with higher cognitive burden scores in the frontal (ß=0.38, p=0.0004), parietal (ß=0.35, p=0.0008), temporal (ß=0.37, p=0. 0004) and occipital (ß=0.37, p=0.0003) lobes.

DISCUSSION

This proof of concept study supports the validity of NLP-based methodologies to obtain quantitative measures of RDoC clinical domains from postmortem EHR.

Recommendations for Reducing the Risk of Cannabis Use-Related Adverse Psychosis Outcomes: A Public Mental Health-Oriented Evidence Review

Objective: Cannabis use is increasingly normalized; psychosis is a major adverse health outcome. We reviewed evidence on cannabis use-related risk factors for psychosis outcomes at different stages toward recommendations for risk reduction by individuals involved in cannabis use. Methods: We searched primary databases for pertinent literature/data 2016 onward, principally relying on reviews and high-quality studies which were narratively summarized and quality-graded; recommendations were developed by international expert consensus. Results: Genetic risks, and mental health/substance use problem histories elevate the risks for cannabis-related psychosis. Early age-of-use-onset, frequency-of-use, product composition (i.e., THC potency), use mode and other substance co-use all influence psychosis risks; the protective effects of CBD are uncertain. Continuous cannabis use may adversely affect psychosis-related treatment and medication effects. Risk factor combinations further amplify the odds of adverse psychosis outcomes. Conclusions: Reductions in the identified cannabis-related risks factors-short of abstinence-may decrease risks of related adverse psychosis outcomes, and thereby protect cannabis users' health.

Genetic susceptibility for schizophrenia after adjustment by genetic susceptibility for smoking: implications in identification of risk genes and genetic correlation with related traits

BACKGROUND

Prevalence of smoking in schizophrenia (SCZ) is larger than in general population. Genetic studies provided some evidence of a causal effect of smoking on SCZ. We aim to characterize the genetic susceptibility to SCZ affected by genetic susceptibility to smoking.

METHODS

Multi-trait-based conditional and joint analysis was applied to the largest European SCZ genome-wide association studies (GWAS) to remove genetic effects on SCZ driven by smoking, estimated by generalized summary data-based Mendelian randomization. Enrichment analysis was performed to compare original v. conditional GWAS. Change in genetic correlation between SCZ and relevant traits after conditioning was assessed. Colocalization analysis was performed to identify specific loci confirming general findings.

RESULTS

Conditional analysis identified 19 new risk loci for SCZ and 42 lost loci whose association with SCZ may be partially driven by smoking. These results were strengthened by colocalization analysis. Enrichment analysis indicated a higher association of differentially expressed genes at prenatal brain stages after conditioning. Genetic correlation of SCZ with substance use and dependence, attention deficit-hyperactivity disorder, and several externalizing traits significantly changed after conditioning. Colocalization of association signal between SCZ and these traits was identified for some of the lost loci, such as CHRNA2, CUL3, and PCDH7.

CONCLUSIONS

Our approach led to identification of potential new SCZ loci, loci partially associated to SCZ through smoking, and a shared genetic susceptibility between SCZ and smoking behavior related to externalizing phenotypes. Application of this approach to other psychiatric disorders and substances may lead to a better understanding of the role of substances on mental health.

A Systematic Review of the Human Accelerated Regions in Schizophrenia and Related Disorders: Where the Evolutionary and Neurodevelopmental Hypotheses Converge

Schizophrenia is a psychiatric disorder that results from genetic and environmental factors interacting and disrupting neurodevelopmental trajectories. Human Accelerated Regions (HARs) are evolutionarily conserved genomic regions that have accumulated human-specific sequence changes. Thus, studies on the impact of HARs in the context of neurodevelopment, as well as with respect to adult brain phenotypes, have increased considerably in the last few years. Through a systematic approach, we aim to offer a comprehensive review of HARs' role in terms of human brain development, configuration, and cognitive abilities, as well as whether HARs modulate the susceptibility to neurodevelopmental psychiatric disorders such as schizophrenia. First, the evidence in this review highlights HARs' molecular functions in the context of the neurodevelopmental regulatory genetic machinery. Second, brain phenotypic analyses indicate that HAR genes' expression spatially correlates with the regions that suffered human-specific cortical expansion, as well as with the regional interactions for synergistic information processing. Lastly, studies based on candidate HAR genes and the global "HARome" variability describe the involvement of these regions in the genetic background of schizophrenia, but also in other neurodevelopmental psychiatric disorders. Overall, the data considered in this review emphasise the crucial role of HARs in human-specific neurodevelopment processes and encourage future research on this evolutionary marker for a better understanding of the genetic basis of schizophrenia and other neurodevelopmental-related psychiatric disorders. Accordingly, HARs emerge as interesting genomic regions that require further study in order to bridge the neurodevelopmental and evolutionary hypotheses in schizophrenia and other related disorders and phenotypes.

New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications

Psychiatric genetics has made substantial progress in the last decade, providing new insights into the genetic etiology of psychiatric disorders, and paving the way for precision psychiatry, in which individual genetic profiles may be used to personalize risk assessment and inform clinical decision-making. Long recognized to be heritable, recent evidence shows that psychiatric disorders are influenced by thousands of genetic variants acting together. Most of these variants are commonly occurring, meaning that every individual has a genetic risk to each psychiatric disorder, from low to high. A series of large-scale genetic studies have discovered an increasing number of common and rare genetic variants robustly associated with major psychiatric disorders. The most convincing biological interpretation of the genetic findings implicates altered synaptic function in autism spectrum disorder and schizophrenia. However, the mechanistic understanding is still incomplete. In line with their extensive clinical and epidemiological overlap, psychiatric disorders appear to exist on genetic continua and share a large degree of genetic risk with one another. This provides further support to the notion that current psychiatric diagnoses do not represent distinct pathogenic entities, which may inform ongoing attempts to reconceptualize psychiatric nosology. Psychiatric disorders also share genetic influences with a range of behavioral and somatic traits and diseases, including brain structures, cognitive function, immunological phenotypes and cardiovascular disease, suggesting shared genetic etiology of potential clinical importance. Current polygenic risk score tools, which predict individual genetic susceptibility to illness, do not yet provide clinically actionable information. However, their precision is likely to improve in the coming years, and they may eventually become part of clinical practice, stressing the need to educate clinicians and patients about their potential use and misuse. This review discusses key recent insights from psychiatric genetics and their possible clinical applications, and suggests future directions.

Bidirectional genetic overlap between bipolar disorder and intelligence

BACKGROUND

Bipolar disorder (BD) is a highly heritable psychiatric illness exhibiting substantial correlation with intelligence.

METHODS

To investigate the shared genetic signatures between BD and intelligence, we utilized the summary statistics from genome-wide association studies (GWAS) to conduct the bivariate causal mixture model (MiXeR) and conjunctional false discovery rate (conjFDR) analyses. Subsequent expression quantitative trait loci (eQTL) mapping in human brain and enrichment analyses were also performed.

RESULTS

Analysis with MiXeR suggested that approximately 10.3K variants could influence intelligence, among which 7.6K variants were correlated with the risk of BD (Dice: 0.80), and 47% of these variants predicted BD risk and intelligence in consistent allelic directions. The conjFDR analysis identified 37 distinct genomic loci that were jointly associated with BD and intelligence with a conjFDR < 0.01, and 16 loci (43%) had the same directions of allelic effects in both phenotypes. Brain eQTL analyses found that genes affected by the "concordant loci" were distinct from those modulated by the "discordant loci". Enrichment analyses suggested that genes related to the "concordant loci" were significantly enriched in pathways/phenotypes related with synapses and sleep quality, whereas genes associated with the "discordant loci" were enriched in pathways related to cell adhesion, calcium ion binding, and abnormal emotional phenotypes.

CONCLUSIONS

We confirmed the polygenic overlap with mixed directions of allelic effects between BD and intelligence and identified multiple genomic loci and risk genes. This study provides hints for the mesoscopic phenotypes of BD and relevant biological mechanisms, promoting the knowledge of the genetic and phenotypic heterogeneity of BD. The essential value of leveraging intelligence in BD investigations is also highlighted.