The iPSYCH2012 case-cohort sample: new directions for unravelling genetic and environmental architectures of severe mental disorders

Polygenic Analyses Show Important Differences Between Major Depressive Disorder Symptoms Measured Using Various Instruments

BACKGROUND

Symptoms of major depressive disorder (MDD) are commonly assessed using self-rating instruments like the Patient Health Questionnaire-9 (PHQ-9) (current symptoms) and the Composite International Diagnostic Interview Short-Form (CIDI-SF) (worst-episode symptoms). We performed a systematic comparison between them for their genetic architecture and utility in investigating MDD heterogeneity.

METHODS

Using data from the UK Biobank (n = 41,948-109,417), we assessed the single nucleotide polymorphism heritability and genetic correlation (rg) of both sets of MDD symptoms. We further compared their rg with non-MDD traits and used Mendelian randomization to assess whether either set of symptoms has more genetic sharing with non-MDD traits. We also assessed how specific each set of symptoms is to MDD using the metric polygenic risk score pleiotropy. Finally, we used genomic structural equation modeling to identify factors that explain the genetic covariance between each set of symptoms.

RESULTS

Corresponding symptoms reported through the PHQ-9 and CIDI-SF have low to moderate genetic correlations (rg = 0.43-0.87), and this cannot be fully attributed to different severity thresholds or the use of a skip structure in the CIDI-SF. Both Mendelian randomization and polygenic risk score pleiotropy analyses showed that PHQ-9 symptoms are more associated with traits that reflect general dysphoria, whereas the skip structure in the CIDI-SF allows for the identification of heterogeneity among likely MDD cases. Finally, the 2 sets of symptoms showed different factor structures in genomic structural equation modeling, reflective of their genetic differences.

CONCLUSIONS

MDD symptoms assessed using the PHQ-9 and CIDI-SF are not interchangeable; the former better indexes general dysphoria, while the latter is more informative about within-MDD heterogeneity.

Shared familial risk for type 2 diabetes mellitus and psychiatric disorders: a nationwide multigenerational genetics study

BACKGROUND

Psychiatric disorders and type 2 diabetes mellitus (T2DM) are heritable, polygenic, and often comorbid conditions, yet knowledge about their potential shared familial risk is lacking. We used family designs and T2DM polygenic risk score (T2DM-PRS) to investigate the genetic associations between psychiatric disorders and T2DM.

METHODS

We linked 659 906 individuals born in Denmark 1990-2000 to their parents, grandparents, and aunts/uncles using population-based registers. We compared rates of T2DM in relatives of children with and without a diagnosis of any or one of 11 specific psychiatric disorders, including neuropsychiatric and neurodevelopmental disorders, using Cox regression. In a genotyped sample (iPSYCH2015) of individuals born 1981-2008 (n = 134 403), we used logistic regression to estimate associations between a T2DM-PRS and these psychiatric disorders.

RESULTS

Among 5 235 300 relative pairs, relatives of individuals with a psychiatric disorder had an increased risk for T2DM with stronger associations for closer relatives (parents:hazard ratio = 1.38, 95% confidence interval 1.35-1.42; grandparents: 1.14, 1.13-1.15; and aunts/uncles: 1.19, 1.16-1.22). In the genetic sample, one standard deviation increase in T2DM-PRS was associated with an increased risk for any psychiatric disorder (odds ratio = 1.11, 1.08-1.14). Both familial T2DM and T2DM-PRS were significantly associated with seven of 11 psychiatric disorders, most strongly with attention-deficit/hyperactivity disorder and conduct disorder, and inversely with anorexia nervosa.

CONCLUSIONS

Our findings of familial co-aggregation and higher T2DM polygenic liability associated with psychiatric disorders point toward shared familial risk. This suggests that part of the comorbidity is explained by shared familial risks. The underlying mechanisms still remain largely unknown and the contributions of genetics and environment need further investigation.

Diagnosed eating disorders in Danish registers - incidence, prevalence, mortality, and polygenic risk

Eating disorders are a group of severe and potentially enduring psychiatric disorders associated with increased mortality. Compared to other severe mental illnesses, they have received relatively limited research attention. Epidemiological studies often only report relative measures despite these being difficult to interpret having limited practical use. The aims of this study were to evaluate the incidence and prevalence of diagnosed anorexia nervosa (AN), bulimia nervosa, and eating disorder not otherwise specified recorded in Danish hospital registers and estimate both relative and absolute measures of subsequent mortality - both all-cause and cause-specific in a general nationwide population of 1,667,374 individuals. In a smaller, genetically informed case-cohort sample, the prediction of polygenic scores for AN, body fat percentage, and body mass index on AN prevalence and severity was estimated. Despite males being less likely to be diagnosed with an eating disorder, those that do have significantly increased rates of mortality. AN prevalence was highest for individuals with high AN and low body fat percentage/body mass index polygenic scores.

Phenomewide Association Study of Health Outcomes Associated With the Genetic Correlates of 25 Hydroxyvitamin D Concentration and Vitamin D Binding Protein Concentration

While it is known that vitamin D deficiency is associated with adverse bone outcomes, it remains unclear whether low vitamin D status may increase the risk of a wider range of health outcomes. We had the opportunity to explore the association between common genetic variants associated with both 25 hydroxyvitamin D (25OHD) and the vitamin D binding protein (DBP, encoded by the GC gene) with a comprehensive range of health disorders and laboratory tests in a large academic medical center. We used summary statistics for 25OHD and DBP to generate polygenic scores (PGS) for 66,482 participants with primarily European ancestry and 13,285 participants with primarily African ancestry from the Vanderbilt University Medical Center Biobank (BioVU). We examined the predictive properties of PGS25OHD, and two scores related to DBP concentration with respect to 1322 health-related phenotypes and 315 laboratory-measured phenotypes from electronic health records. In those with European ancestry: (a) the PGS25OHD and PGSDBP scores, and individual SNPs rs4588 and rs7041 were associated with both 25OHD concentration and 1,25 dihydroxyvitamin D concentrations; (b) higher PGS25OHD was associated with decreased concentrations of triglycerides and cholesterol, and reduced risks of vitamin D deficiency, disorders of lipid metabolism, and diabetes. In general, the findings for the African ancestry group were consistent with findings from the European ancestry analyses. Our study confirms the utility of PGS and two key variants within the GC gene (rs4588 and rs7041) to predict the risk of vitamin D deficiency in clinical settings and highlights the shared biology between vitamin D-related genetic pathways a range of health outcomes.

Genome-wide association study identifies new loci associated with OCD

To date, four genome-wide association studies (GWAS) of obsessive-compulsive disorder (OCD) have been published, reporting a high single-nucleotide polymorphism (SNP)-heritability of 28% but finding only one significant SNP. A substantial increase in sample size will likely lead to further identification of SNPs, genes, and biological pathways mediating the susceptibility to OCD. We conducted a GWAS meta-analysis with a 2-3-fold increase in case sample size (OCD cases: N = 37,015, controls: N = 948,616) compared to the last OCD GWAS, including six previously published cohorts (OCGAS, IOCDF-GC, IOCDF-GC-trio, NORDiC-nor, NORDiC-swe, and iPSYCH) and unpublished self-report data from 23andMe Inc. We explored the genetic architecture of OCD by conducting gene-based tests, tissue and celltype enrichment analyses, and estimating heritability and genetic correlations with 74 phenotypes. To examine a potential heterogeneity in our data, we conducted multivariable GWASs with MTAG. We found support for 15 independent genome-wide significant loci (14 new) and 79 protein-coding genes. Tissue enrichment analyses implicate multiple cortical regions, the amygdala, and hypothalamus, while cell type analyses yielded 12 cell types linked to OCD (all neurons). The SNP-based heritability of OCD was estimated to be 0.08. Using MTAG we found evidence for specific genetic underpinnings characteristic of different cohort-ascertainment and identified additional significant SNPs. OCD was genetically correlated with 40 disorders or traits-positively with all psychiatric disorders and negatively with BMI, age at first birth and multiple autoimmune diseases. The GWAS meta-analysis identified several biologically informative genes as important contributors to the aetiology of OCD. Overall, we have begun laying the groundwork through which the biology of OCD will be understood and described.

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

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

The genetic architecture of youth anxiety: a study protocol

BACKGROUND

Anxiety disorders are the most common psychiatric problems among Canadian youth and typically have an onset in childhood or adolescence. They are characterized by high rates of relapse and chronicity, often resulting in substantial impairment across the lifespan. Genetic factors play an important role in the vulnerability toward anxiety disorders. However, genetic contribution to anxiety in youth is not well understood and can change across developmental stages. Large-scale genetic studies of youth are needed with detailed assessments of symptoms of anxiety disorders and their major comorbidities to inform early intervention or preventative strategies and suggest novel targets for therapeutics and personalization of care.

METHODS

The Genetic Architecture of Youth Anxiety (GAYA) study is a Pan-Canadian effort of clinical and genetic experts with specific recruitment sites in Calgary, Halifax, Hamilton, Toronto, and Vancouver. Youth aged 10-19 (n = 13,000) will be recruited from both clinical and community settings and will provide saliva samples, complete online questionnaires on demographics, symptoms of mental health concerns, and behavioural inhibition, and complete neurocognitive tasks. A subset of youth will be offered access to a self-managed Internet-based cognitive behavioral therapy resource. Analyses will focus on the identification of novel genetic risk loci for anxiety disorders in youth and assess how much of the genetic risk for anxiety disorders is unique or shared across the life span.

DISCUSSION

Results will substantially inform early intervention or preventative strategies and suggest novel targets for therapeutics and personalization of care. Given that the GAYA study will be the biggest genomic study of anxiety disorders in youth in Canada, this project will further foster collaborations nationally and across the world.

Gene Discovery and Biological Insights into Anxiety Disorders from a Multi-Ancestry Genome-wide Association Study of >1.2 Million Participants

We leveraged information from more than 1.2 million participants to investigate the genetics of anxiety disorders across five continental ancestral groups. Ancestry-specific and cross-ancestry genome-wide association studies identified 51 anxiety-associated loci, 39 of which are novel. Additionally, polygenic risk scores derived from individuals of European descent were associated with anxiety in African, Admixed-American, and East Asian groups. The heritability of anxiety was enriched for genes expressed in the limbic system, the cerebral cortex, the cerebellum, the metencephalon, the entorhinal cortex, and the brain stem. Transcriptome- and proteome-wide analyses highlighted 115 genes associated with anxiety through brain-specific and cross-tissue regulation. We also observed global and local genetic correlations with depression, schizophrenia, and bipolar disorder and putative causal relationships with several physical health conditions. Overall, this study expands the knowledge regarding the genetic risk and pathogenesis of anxiety disorders, highlighting the importance of investigating diverse populations and integrating multi-omics information.

Interplay of polygenic liability with birth-related, somatic, and psychosocial factors in anorexia nervosa risk: a nationwide study

BACKGROUND

Although several types of risk factors for anorexia nervosa (AN) have been identified, including birth-related factors, somatic, and psychosocial risk factors, their interplay with genetic susceptibility remains unclear. Genetic and epidemiological interplay in AN risk were examined using data from Danish nationwide registers. AN polygenic risk score (PRS) and risk factor associations, confounding from AN PRS and/or parental psychiatric history on the association between the risk factors and AN risk, and interactions between AN PRS and each level of target risk factor on AN risk were estimated.

METHODS

Participants were individuals born in Denmark between 1981 and 2008 including nationwide-representative data from the iPSYCH2015, and Danish AN cases from the Anorexia Nervosa Genetics Initiative and Eating Disorder Genetics Initiative cohorts. A total of 7003 individuals with AN and 45 229 individuals without a registered AN diagnosis were included. We included 22 AN risk factors from Danish registers.

RESULTS

Risk factors showing association with PRS for AN included urbanicity, parental ages, genitourinary tract infection, and parental socioeconomic factors. Risk factors showed the expected association to AN risk, and this association was only slightly attenuated when adjusted for parental history of psychiatric disorders or/and for the AN PRS. The interaction analyses revealed a differential effect of AN PRS according to the level of the following risk factors: sex, maternal age, genitourinary tract infection, C-section, parental socioeconomic factors and psychiatric history.

CONCLUSIONS

Our findings provide evidence for interactions between AN PRS and certain risk-factors, illustrating potential diverse risk pathways to AN diagnosis.

Associations between ADHD and risk of six psychiatric disorders: a Mendelian randomization study

BACKGROUND

Observational studies and diagnostic criteria have indicated that Attention Deficit Hyperactivity Disorder (ADHD) frequently comorbid with various psychiatric disorders. Therefore, we conducted a Mendelian randomization (MR) study to explore this potential genetic association between ADHD and six psychiatric disorders.

METHODS

Using a two-sample Mendelian randomization (MR) design, this study systematically screened genetic instrumental variables (IVs) based on the genome-wide association studies (GWAS) of ADHD and six psychiatric disorders, with the inverse variance weighted (IVW) method as the primary approach.

RESULTS

The study revealed a positive and causal association between ADHD and the risk of ASD, with an odds ratio (OR) of 2.328 (95%CI: 1.241-4.368) in the IVW MR analysis. Additionally, ADHD showed a positive causal effect on an increased risk of schizophrenia, with an OR of 1.867 (95%CI: 1.260-2.767) in the IVW MR analysis. However, no causal effect of Tic disorder, Mental retardation, Mood disorders and Anxiety disorder with ADHD was found in the analysis mentioned above.

CONCLUSION

Our MR analysis provides robust evidence of the causal role of ADHD in increasing the risk of ASD and schizophrenia. However, ADHD is not associated with the risk of Tic Disorder, Mental Retardation, Mood Disorders and Anxiety Disorder. This suggests the need for increased attention to the co-occurrence of ADHD-ASD or ADHD-schizophrenia and the implementation of timely intervention and treatment.

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

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

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

Background

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

Aims

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

Methods

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

Results

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

Conclusions

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

100 ancient genomes show repeated population turnovers in Neolithic Denmark

Major migration events in Holocene Eurasia have been characterized genetically at broad regional scales1-4. However, insights into the population dynamics in the contact zones are hampered by a lack of ancient genomic data sampled at high spatiotemporal resolution5-7. Here, to address this, we analysed shotgun-sequenced genomes from 100 skeletons spanning 7,300 years of the Mesolithic period, Neolithic period and Early Bronze Age in Denmark and integrated these with proxies for diet (13C and 15N content), mobility (87Sr/86Sr ratio) and vegetation cover (pollen). We observe that Danish Mesolithic individuals of the Maglemose, Kongemose and Ertebølle cultures form a distinct genetic cluster related to other Western European hunter-gatherers. Despite shifts in material culture they displayed genetic homogeneity from around 10,500 to 5,900 calibrated years before present, when Neolithic farmers with Anatolian-derived ancestry arrived. Although the Neolithic transition was delayed by more than a millennium relative to Central Europe, it was very abrupt and resulted in a population turnover with limited genetic contribution from local hunter-gatherers. The succeeding Neolithic population, associated with the Funnel Beaker culture, persisted for only about 1,000 years before immigrants with eastern Steppe-derived ancestry arrived. This second and equally rapid population replacement gave rise to the Single Grave culture with an ancestry profile more similar to present-day Danes. In our multiproxy dataset, these major demographic events are manifested as parallel shifts in genotype, phenotype, diet and land use.

Curation of causal interactions mediated by genes associated with autism accelerates the understanding of gene-phenotype relationships underlying neurodevelopmental disorders

Autism spectrum disorder (ASD) comprises a large group of neurodevelopmental conditions featuring, over a wide range of severity and combinations, a core set of manifestations (restricted sociality, stereotyped behavior and language impairment) alongside various comorbidities. Common and rare variants in several hundreds of genes and regulatory regions have been implicated in the molecular pathogenesis of ASD along a range of causation evidence strength. Despite significant progress in elucidating the impact of few paradigmatic individual loci, such sheer complexity in the genetic architecture underlying ASD as a whole has hampered the identification of convergent actionable hubs hypothesized to relay between the vastness of risk alleles and the core phenotypes. In turn this has limited the development of strategies that can revert or ameliorate this condition, calling for a systems-level approach to probe the cross-talk of cooperating genes in terms of causal interaction networks in order to make convergences experimentally tractable and reveal their clinical actionability. As a first step in this direction, we have captured from the scientific literature information on the causal links between the genes whose variants have been associated with ASD and the whole human proteome. This information has been annotated in a computer readable format in the SIGNOR database and is made freely available in the resource website. To link this information to cell functions and phenotypes, we have developed graph algorithms that estimate the functional distance of any protein in the SIGNOR causal interactome to phenotypes and pathways. The main novelty of our approach resides in the possibility to explore the mechanistic links connecting the suggested gene-phenotype relations.

The correlates of neonatal complement component 3 and 4 protein concentrations with a focus on psychiatric and autoimmune disorders

Complement components have been linked to schizophrenia and autoimmune disorders. We examined the association between neonatal circulating C3 and C4 protein concentrations in 68,768 neonates and the risk of six mental disorders. We completed genome-wide association studies (GWASs) for C3 and C4 and applied the summary statistics in Mendelian randomization and phenome-wide association studies related to mental and autoimmune disorders. The GWASs for C3 and C4 protein concentrations identified 15 and 36 independent loci, respectively. We found no associations between neonatal C3 and C4 concentrations and mental disorders in the total sample (both sexes combined); however, post-hoc analyses found that a higher C3 concentration was associated with a reduced risk of schizophrenia in females. Mendelian randomization based on C4 summary statistics found an altered risk of five types of autoimmune disorders. Our study adds to our understanding of the associations between C3 and C4 concentrations and subsequent mental and autoimmune disorders.

Polygenic Risk of Mental Disorders and Subject-Specific School Grades

BACKGROUND

Education is essential for socioeconomic security and long-term mental health; however, mental disorders are often detrimental to the educational trajectory. Genetic correlations between mental disorders and educational attainment do not always align with corresponding phenotypic associations, implying heterogeneity in the genetic overlap.

METHODS

We unraveled this heterogeneity by investigating associations between polygenic risk scores for 6 mental disorders and fine-grained school outcomes: school grades in language and mathematics in ninth grade and high school, as well as educational attainment by age 25, using nationwide-representative data from established cohorts (N = 79,489).

RESULTS

High polygenic liability of attention-deficit/hyperactivity disorder was associated with lower grades in language and mathematics, whereas high polygenic risk of anorexia nervosa or bipolar disorder was associated with higher grades in language and mathematics. Associations between polygenic risk and school grades were mixed for schizophrenia and major depressive disorder and neutral for autism spectrum disorder.

CONCLUSIONS

Polygenic risk scores for mental disorders are differentially associated with language and mathematics school grades.

Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder

Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each. We use phenotype imputation to integrate information across hundreds of MDD-relevant phenotypes, which significantly increases genome-wide association study (GWAS) power and polygenic risk score (PRS) prediction accuracy of the deepest available MDD phenotype in UK Biobank, LifetimeMDD. We demonstrate that imputation preserves specificity in its genetic architecture using a novel PRS-based pleiotropy metric. We further find that integration via summary statistics also enhances GWAS power and PRS predictions, but can introduce nonspecific genetic effects depending on input. Our work provides a simple and scalable approach to improve genetic studies in large biobanks by integrating shallow and deep phenotypes.

Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals

Problematic alcohol use (PAU), a trait that combines alcohol use disorder and alcohol-related problems assessed with a questionnaire, is a leading cause of death and morbidity worldwide. Here we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals (European, N = 903,147; African, N = 122,571; Latin American, N = 38,962; East Asian, N = 13,551; and South Asian, N = 1,716 ancestries). We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses. Cross-ancestry fine mapping improved the identification of likely causal variants. Prioritizing genes through gene expression and chromatin interaction in brain tissues identified multiple genes associated with PAU. We identified existing medications for potential pharmacological studies by a computational drug repurposing analysis. Cross-ancestry polygenic risk scores showed better performance of association in independent samples than single-ancestry polygenic risk scores. Genetic correlations between PAU and other traits were observed in multiple ancestries, with other substance use traits having the highest correlations. This study advances our knowledge of the genetic etiology of PAU, and these findings may bring possible clinical applicability of genetics insights-together with neuroscience, biology and data science-closer.

Prenatal antidepressant exposure and the risk of decreased gestational age and lower birthweight: A polygenic score approach to investigate confounding by indication

INTRODUCTION

Prenatal antidepressant exposure has been associated with lower gestational age and birthweight. Yet, unmeasured residual confounding may inflate this association. We explored if maternal genetic liability for major depression explains part of the association of antidepressant use in pregnancy with lower gestational age and birthweight.

MATERIAL AND METHODS

We employed the maternal polygenic score (PGS) for major depression as a measure of genetic liability. We used generalised linear models to estimate the differences in gestational age and birthweight at each PGS quintile between children whose mothers continued antidepressant use during pregnancy (continuation group), children whose mothers discontinued antidepressant use during pregnancy (discontinuation group) and unexposed children.

RESULTS

After adjusting for confounders, we found significant differences in birthweight between PGS quintiles in the continuation and unexposed group. Yet, this relationship was not linear. Furthermore, at the lowest and highest PGS quintiles, the continuation group had significantly reduced mean gestational ages (adjusted β ranges: 1.7-4.5 days, p < 0.001-0.008) and lower mean birthweights (adjusted β ranges: 58.6-165.4 g, p = 0.001-0.008) than the discontinuation and unexposed groups.

CONCLUSION

We confirmed that antidepressant use in pregnancy was associated with small reductions in gestational age and birthweight but found that genetic liability for depression was not linearly associated with this risk. The causality of the observed associations could not be established due to the observational nature of the study. Residual confounding linked to the underlying disease was likely still present.

Postpartum and non-postpartum depression: a population-based matched case-control study comparing polygenic risk scores for severe mental disorders

It remains inconclusive whether postpartum depression (PPD) and depression with onset outside the postpartum period (MDD) are genetically distinct disorders. We aimed to investigate whether polygenic risk scores (PGSs) for major mental disorders differ between PPD cases and MDD cases in a nested case-control study of 50,057 women born from 1981 to 1997 in the iPSYCH2015 sample in Demark. We identified 333 women with first-onset postpartum depression (PPD group), who were matched with 993 women with first-onset depression diagnosed outside of postpartum (MDD group), and 999 female population controls. Data on genetics and depressive disorders were retrieved from neonatal biobanks and the Psychiatric Central Research Register. PGSs were calculated from both individual-level genetic data and meta-analysis summary statistics from the Psychiatric Genomics Consortium. Conditional logistic regression was used to calculate the odds ratio (OR), accounting for the selection-related reproductive behavior. After adjustment for covariates, higher PGSs for severe mental disorders were associated with increased ORs of both PPD and MDD. Compared with MDD cases, MDD PGS and attention-deficit/hyperactivity disorder PGS were marginally but not statistically higher for PPD cases, with the OR of PPD versus MDD being 1.12 (95% CI: 0 .97-1.29) and 1.11 (0.97-1.27) per-standard deviation increase, respectively. The ORs of PPD versus MDD did not statistically differ by PGSs of bipolar disorder, schizophrenia, or autism spectrum disorder. Our findings suggest that relying on PGS data, there was no clear evidence of distinct genetic make-up of women with depression occurring during or outside postpartum, after taking the selection-related reproductive behavior into account.

Is adjustment disorder genetically correlated with depression, anxiety, or risk-tolerant personality trait?

Adjustment disorder has three main subtypes: adjustment disorder with depressed mood, adjustment disorder with anxiety, and adjustment disorder with disturbance of conduct. The disorder is moderately heritable and has lifetime comorbidities with major depressive disorder (MDD), anxiety disorders, or risk-tolerant personality. However, it remains unclear whether the degrees of genetic correlations between adjustment disorder and other psychiatric disorders and intermediate phenotypes are similar or different to those between MDD, anxiety disorders or risk-tolerant personality and these other psychiatric disorders and intermediate phenotypes. To compare patterns of genetic correlations, we utilized large-scale genome-wide association study summary statistics for adjustment disorder-related disorders and personality trait, eleven other psychiatric disorders and fifteen intermediate phenotypes. Adjustment disorder had highly positive genetic correlations with MDD, anxiety disorders, and risk-tolerant personality. Among other psychiatric disorders, adjustment disorder, MDD, anxiety disorders and risk-tolerant personality were positively correlated with risks for schizophrenia (SCZ), bipolar disorder (BD), SCZ + BD, attention-deficit/hyperactivity disorder, and cross disorders. In contrast, adjustment disorder was not significantly correlated with risks for obsessive-compulsive disorder, Tourette syndrome, or posttraumatic stress disorder despite significant genetic correlations of MDD or anxiety disorders with these disorders. Among intermediate phenotypes, adjustment disorder, MDD, anxiety disorders, and risk-tolerant personality commonly had a younger age at first sexual intercourse, first birth, and menopause, lower cognitive ability, and higher rate of smoking initiation. Adjustment disorder was not genetically correlated with extraversion, although the related disorder and personality were correlated with extraversion. Only adjustment disorder was correlated with a higher smoking quantity. These findings suggest that adjustment disorder could share a genetic etiology with MDD, anxiety disorders and risk-tolerant personality trait, as well as have a disorder-specific genetic etiology.

Identification of women at high risk of postpartum psychiatric episodes: A population-based study quantifying relative and absolute risks following exposure to selected risk factors and genetic liability

BACKGROUND

We quantified relative and absolute risks of postpartum psychiatric episodes (PPE) following risk factors: Young age, past personal or family history of psychiatric disorders, and genetic liability.

METHODS

We conducted a register-based study using the iPSYCH2012 case-cohort sample. Exposures were personal history of psychiatric episodes prior to childbirth, being a young mother (giving birth before the age of 21.5 years), having a family history of psychiatric disorders, and a high (highest quartile) polygenic score (PGS) for major depression. PPE was defined within 12 months postpartum by prescription of psychotropic medication or in- and outpatient contact to a psychiatric facility. We included primiparous women born 1981-1999, giving birth before January 1st, 2016. We conducted Cox regression to calculate hazard ratios (HRs) of PPE, absolute risks were calculated using cumulative incidence functions.

RESULTS

We included 8174 primiparous women, and the estimated baseline PPE risk was 6.9% (95% CI 6.0%-7.8%, number of PPE cases: 2169). For young mothers with a personal and family history of psychiatric disorders, the absolute risk of PPE was 21.6% (95% CI 15.9%-27.8%). Adding information on high genetic liability to depression, the risk increased to 29.2% (95% CI 21.3%-38.4%) for PPE.

CONCLUSIONS

Information on prior personal and family psychiatric episodes as well as age may assist in estimating a personalized risk of PPE. Furthermore, additional information on genetic liability could add even further to this risk assessment.

Bidirectional genetic overlap between autism spectrum disorder and cognitive traits

Autism spectrum disorder (ASD) is a highly heritable condition with a large variation in cognitive function. Here we investigated the shared genetic architecture between cognitive traits (intelligence (INT) and educational attainment (EDU)), and risk loci jointly associated with ASD and the cognitive traits. We analyzed data from genome-wide association studies (GWAS) of INT (n = 269,867), EDU (n = 766,345) and ASD (cases n = 18,381, controls n = 27,969). We used the bivariate causal mixture model (MiXeR) to estimate the total number of shared genetic variants, local analysis of co-variant annotation (LAVA) to estimate local genetic correlations, conditional false discovery rate (cond/conjFDR) to identify specific overlapping loci. The MiXeR analyses showed that 12.7k genetic variants are associated with ASD, of which 12.0k variants are shared with EDU, and 11.1k are shared with INT with both positive and negative relationships within overlapping variants. The majority (59-68%) of estimated shared loci have concordant effect directions, with a positive, albeit modest, genetic correlation between ASD and EDU (rg = 0.21, p = 2e-13) and INT (rg = 0.22, p = 4e-12). We discovered 43 loci jointly associated with ASD and cognitive traits (conjFDR<0.05), of which 27 were novel for ASD. Functional analysis revealed significant differential expression of candidate genes in the cerebellum and frontal cortex. To conclude, we quantified the genetic architecture shared between ASD and cognitive traits, demonstrated mixed effect directions, and identified the associated genetic loci and molecular pathways. The findings suggest that common genetic risk factors for ASD can underlie both better and worse cognitive functioning across the ASD spectrum, with different underlying biology.

ADuLT: An efficient and robust time-to-event GWAS

Proportional hazards models have been proposed to analyse time-to-event phenotypes in genome-wide association studies (GWAS). However, little is known about the ability of proportional hazards models to identify genetic associations under different generative models and when ascertainment is present. Here we propose the age-dependent liability threshold (ADuLT) model as an alternative to a Cox regression based GWAS, here represented by SPACox. We compare ADuLT, SPACox, and standard case-control GWAS in simulations under two generative models and with varying degrees of ascertainment as well as in the iPSYCH cohort. We find Cox regression GWAS to be underpowered when cases are strongly ascertained (cases are oversampled by a factor 5), regardless of the generative model used. ADuLT is robust to ascertainment in all simulated scenarios. Then, we analyse four psychiatric disorders in iPSYCH, ADHD, Autism, Depression, and Schizophrenia, with a strong case-ascertainment. Across these psychiatric disorders, ADuLT identifies 20 independent genome-wide significant associations, case-control GWAS finds 17, and SPACox finds 8, which is consistent with simulation results. As more genetic data are being linked to electronic health records, robust GWAS methods that can make use of age-of-onset information will help increase power in analyses for common health outcomes.

Multi-ancestry genome-wide association study of 4069 children with glioma identifies 9p21.3 risk locus

BACKGROUND

Although recent sequencing studies have revealed that 10% of childhood gliomas are caused by rare germline mutations, the role of common variants is undetermined and no genome-wide significant risk loci for pediatric central nervous system tumors have been identified to date.

METHODS

Meta-analysis of 3 population-based genome-wide association studies comprising 4069 children with glioma and 8778 controls of multiple genetic ancestries. Replication was performed in a separate case-control cohort. Quantitative trait loci analyses and a transcriptome-wide association study were conducted to assess possible links with brain tissue expression across 18 628 genes.

RESULTS

Common variants in CDKN2B-AS1 at 9p21.3 were significantly associated with astrocytoma, the most common subtype of glioma in children (rs573687, P-value of 6.974e-10, OR 1.273, 95% CI 1.179-1.374). The association was driven by low-grade astrocytoma (P-value of 3.815e-9) and exhibited unidirectional effects across all 6 genetic ancestries. For glioma overall, the association approached genome-wide significance (rs3731239, P-value of 5.411e-8), while no significant association was observed for high-grade tumors. Predicted decreased brain tissue expression of CDKN2B was significantly associated with astrocytoma (P-value of 8.090e-8).

CONCLUSIONS

In this population-based genome-wide association study meta-analysis, we identify and replicate 9p21.3 (CDKN2B-AS1) as a risk locus for childhood astrocytoma, thereby establishing the first genome-wide significant evidence of common variant predisposition in pediatric neuro-oncology. We furthermore provide a functional basis for the association by showing a possible link to decreased brain tissue CDKN2B expression and substantiate that genetic susceptibility differs between low- and high-grade astrocytoma.

Population-based Risk of Psychiatric Disorders Associated with Recurrent CNVs

Recurrent copy number variants (rCNVs) are associated with increased risk of neuropsychiatric disorders but their pathogenic population-level impact is unknown. We provide population-based estimates of rCNV-associated risk of neuropsychiatric disorders for 34 rCNVs in the iPSYCH2015 case-cohort sample (n=120,247). Most observed significant increases in rCNV-associated risk for ADHD, autism or schizophrenia were moderate (HR:1.42-5.00), and risk estimates were highly correlated across these disorders, the most notable exception being high autism-associated risk with Prader-Willi/Angelman Syndrome duplications (HR=20.8). No rCNV was associated with significant increase in depression risk. Also, rCNV-associated risk was positively correlated with locus size and gene constraint. Comparison with published rCNV studies suggests that prevalence of some rCNVs is higher, and risk of psychiatric disorders lower, than previously estimated. In an era where genetics is increasingly being clinically applied, our results highlight the importance of population-based risk estimates for genetics-based predictions.

Risk factors for anorexia nervosa: A population-based investigation of sex differences in polygenic risk and early life exposures

OBJECTIVE

To examine sex differences in risk factors for anorexia nervosa (AN).

METHOD

This population-based study involved 44,743 individuals (6,239 AN cases including 5,818 females and 421 males, and 38,504 controls including 18,818 females and 19,686 males) born in Denmark between May 1981 and December 2009. Follow-up began on the individual's sixth birthday and ended at AN diagnosis, emigration, death, or December 31, 2016, whichever occurred first. Exposures included socioeconomic status (SES), pregnancy, birth, and early childhood factors based on data from Danish registers, and psychiatric and metabolic polygenic risk scores (PRS) based on genetic data. Hazard ratios were estimated using weighted Cox proportional hazards models stratified by sex (assigned at birth), with AN diagnosis as the outcome.

RESULTS

The effects of early life exposures and PRS on AN risk were comparable between females and males. Although we observed some differences in the magnitude and direction of effects, there were no significant interactions between sex and SES, pregnancy, birth, or early childhood exposures. The effects of most PRS on AN risk were highly similar between the sexes. We observed significant sex-specific effects of parental psychiatric history and body mass index PRS, though these effects did not survive corrections for multiple comparisons.

CONCLUSIONS

Risk factors for AN are comparable between females and males. Collaboration across countries with large registers is needed to further investigate sex-specific effects of genetic, biological, and environmental exposures on AN risk, including exposures in later childhood and adolescence as well as the additive effects of exposures.

PUBLIC SIGNIFICANCE

Sex differences in the prevalence and clinical presentation of AN warrant examination of sex-specific risk factors. This population-based study indicates that the effects of polygenic risk and early life exposures on AN risk are comparable between females and males. Collaboration between countries with large registers is needed to further investigate sex-specific AN risk factors and improve early identification of AN.

Whole Person Modeling: a transdisciplinary approach to mental health research

The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.

Multi-PGS enhances polygenic prediction by combining 937 polygenic scores

The predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones. In this study, the multi-PGS framework increases prediction accuracy over single PGS for all included psychiatric disorders and other available outcomes, with prediction R2 increases of up to 9-fold for attention-deficit/hyperactivity disorder compared to a single PGS. We also generate multi-PGS for phenotypes without an existing GWAS and for case-case predictions. We benchmark the multi-PGS framework against other methods and highlight its potential application to new emerging biobanks.

Exploring opportunities for drug repurposing and precision medicine in cannabis use disorder using genetics

Cannabis use disorder (CUD) remains a significant public health issue globally, affecting up to one in five adults who use cannabis. Despite extensive research into the molecular underpinnings of the condition, there are no effective pharmacological treatment options available. Therefore, we sought to further explore genetic analyses to prioritise opportunities to repurpose existing drugs for CUD. Specifically, we aimed to identify druggable genes associated with the disorder, integrate transcriptomic/proteomic data and estimate genetic relationships with clinically actionable biochemical traits. Aggregating variants to genes based on genomic position, prioritised the phosphodiesterase gene PDE4B as an interesting target for drug repurposing in CUD. Credible causal PDE4B variants revealed by probabilistic finemapping in and around this locus demonstrated an association with inflammatory and other substance use phenotypes. Gene and protein expression data integrated with the GWAS data revealed a novel CUD associated gene, NPTX1, in whole blood and supported a role for hyaluronidase, a key enzyme in the extracellular matrix in the brain and other tissues. Finally, genetic correlation with biochemical traits revealed a genetic overlap between CUD and immune-related markers such as lymphocyte count, as well as serum triglycerides.

Genetic liability to posttraumatic stress disorder and its association with postpartum depression

BACKGROUND

Childbirth may be a traumatic experience and vulnerability to posttraumatic stress disorder (PTSD) may increase the risk of postpartum depression (PPD). We investigated whether genetic vulnerability to PTSD as measured by polygenic score (PGS) increases the risk of PPD and whether a predisposition to PTSD in PPD cases exceeds that of major depressive disorder (MDD) outside the postpartum period.

METHODS

This case-control study included participants from the iPSYCH2015, a case-cohort of all singletons born in Denmark between 1981 and 2008. Restricting to women born between 1981 and 1997 and excluding women with a first diagnosis other than depression (N = 22 613), 333 were identified with PPD. For each PPD case, 999 representing the background population and 993 with MDD outside the postpartum were matched by calendar year at birth, cohort selection, and age. PTSD PGS was calculated from summary statistics from the Psychiatric Genomics Consortium with LDpred2-auto. Odds ratios (ORs) were estimated using conditional logistic regression adjusted for parental psychiatric history and country of origin, PGS for MDD and age at first birth, and the first 10 principal components.

RESULTS

The PTSD PGS was significantly associated with PPD (OR 1.42, 95% CI 1.20-1.68 per standard deviation increase in PTSD PGS) compared to healthy female controls. Genetic PTSD vulnerability in PPD cases did not exceed that of matched female depression cases outside the postpartum period (OR 1.10, 95% CI 0.94-1.30 per standard deviation increase).

CONCLUSIONS

Genetic vulnerability to PTSD increased the risk of PPD but did not differ between PPD cases and women with depression at other times.

Deep integrative models for large-scale human genomics

Polygenic risk scores (PRSs) are expected to play a critical role in precision medicine. Currently, PRS predictors are generally based on linear models using summary statistics, and more recently individual-level data. However, these predictors mainly capture additive relationships and are limited in data modalities they can use. We developed a deep learning framework (EIR) for PRS prediction which includes a model, genome-local-net (GLN), specifically designed for large-scale genomics data. The framework supports multi-task learning, automatic integration of other clinical and biochemical data, and model explainability. When applied to individual-level data from the UK Biobank, the GLN model demonstrated a competitive performance compared to established neural network architectures, particularly for certain traits, showcasing its potential in modeling complex genetic relationships. Furthermore, the GLN model outperformed linear PRS methods for Type 1 Diabetes, likely due to modeling non-additive genetic effects and epistasis. This was supported by our identification of widespread non-additive genetic effects and epistasis in the context of T1D. Finally, we constructed PRS models that integrated genotype, blood, urine, and anthropometric data and found that this improved performance for 93% of the 290 diseases and disorders considered. EIR is available at https://github.com/arnor-sigurdsson/EIR.

Depression pathophysiology, risk prediction of recurrence and comorbid psychiatric disorders using genome-wide analyses

Depression is a common psychiatric disorder and a leading cause of disability worldwide. Here we conducted a genome-wide association study meta-analysis of six datasets, including >1.3 million individuals (371,184 with depression) and identified 243 risk loci. Overall, 64 loci were new, including genes encoding glutamate and GABA receptors, which are targets for antidepressant drugs. Intersection with functional genomics data prioritized likely causal genes and revealed new enrichment of prenatal GABAergic neurons, astrocytes and oligodendrocyte lineages. We found depression to be highly polygenic, with ~11,700 variants explaining 90% of the single-nucleotide polymorphism heritability, estimating that >95% of risk variants for other psychiatric disorders (anxiety, schizophrenia, bipolar disorder and attention deficit hyperactivity disorder) were influencing depression risk when both concordant and discordant variants were considered, and nearly all depression risk variants influenced educational attainment. Additionally, depression genetic risk was associated with impaired complex cognition domains. We dissected the genetic and clinical heterogeneity, revealing distinct polygenic architectures across subgroups of depression and demonstrating significantly increased absolute risks for recurrence and psychiatric comorbidity among cases of depression with the highest polygenic burden, with considerable sex differences. The risks were up to 5- and 32-fold higher than cases with the lowest polygenic burden and the background population, respectively. These results deepen the understanding of the biology underlying depression, its disease progression and inform precision medicine approaches to treatment.

DanMAC5: a browser of aggregated sequence variants from 8,671 whole genome sequenced Danish individuals

OBJECTIVES

Allele counts of sequence variants obtained by whole genome sequencing (WGS) often play a central role in interpreting the results of genetic and genomic research. However, such variant counts are not readily available for individuals in the Danish population. Here, we present a dataset with allele counts for sequence variants (single nucleotide variants (SNVs) and indels) identified from WGS of 8,671 (5,418 females) individuals from the Danish population. The data resource is based on WGS data from three independent research projects aimed at assessing genetic risk factors for cardiovascular, psychiatric, and headache disorders. To enable the sharing of information on sequence variation in Danish individuals, we created summarized statistics on allele counts from anonymized data and made them available through the European Genome-phenome Archive (EGA, https://identifiers.org/ega.

DATASET

EGAD00001009756 ) and in a dedicated browser, DanMAC5 (available at www.danmac5.dk ). The summary level data and the DanMAC5 browser provide insight into the allelic spectrum of sequence variants segregating in the Danish population, which is important in variant interpretation.

DATA DESCRIPTION

Three WGS datasets with an average coverage of 30x were processed independently using the same quality control pipeline. Subsequently, we summarized, filtered, and merged allele counts to create a high-quality summary level dataset of sequence variants.

Circulating S100B levels at birth and risk of six major neuropsychiatric or neurological disorders: a two-sample Mendelian Randomization Study

Circulating levels of the astrocytic marker S100B have been associated with risk of neuropsychiatric or neurological disorders. However, reported effects have been inconsistent, and no causal relations have yet been established. We applied two-sample Mendelian Randomization (MR) on the association statistics from genome-wide association studies (GWAS) for circulating S100B levels measured 5-7 days after birth (the iPSYCH sample) and in an older adult sample (mean age, 72.5 years; the Lothian sample), upon those derived from major depression disorder (MDD), schizophrenia (SCZ), bipolar disorder (BIP), autism spectral disorder (ASD), Alzheimer's disease (AD), and Parkinson's disease (PD). We studied the causal relations in the two S100B datasets for risk of these six neuropsychiatric disorders. MR suggested increased S100B levels 5-7 days after birth to causally increase the risk of MDD (OR = 1.014; 95%CI = 1.007-1.022; FDR-corrected p = 6.43×10-4). In older adults, MR suggested increased S100B levels to have a causal relation to the risk of BIP (OR = 1.075; 95%CI = 1.026-1.127; FDR-corrected p = 1.35×10-2). No significant causal relations were found for the other five disorders. We did not observe any evidence for reverse causality of these neuropsychiatric or neurological disorders on altered S100B levels. Sensitivity analyses using more stringent SNP-selection criteria and three alternative MR models suggested the results are robust. Altogether, our findings imply a small cause-effect relation for the previously reported associations of S100B and mood disorders. Such findings may provide a novel avenue for the diagnosis and management of disorders.

An immunogenetic investigation of 30 autoimmune and autoinflammatory diseases and their links to psychiatric disorders in a nationwide sample

Autoimmune and autoinflammatory diseases (AIIDs) involve a deficit in an individual's immune system function, whereby the immune reaction is directed against self-antigens. Many AIIDs have a strong genetic component, but they can also be triggered by environmental factors. AIIDs often have a highly negative impact on the individual's physical and mental wellbeing. Understanding the genetic underpinning of AIIDs is thus crucial both for diagnosis and for identifying individuals at high risk of an AIID and mental illness as a result thereof. The aim of the present study was to perform systematic statistical and genetic analyses to assess the role of human leukocyte antigen (HLA) alleles in 30 AIIDs and to study the links between AIIDs and psychiatric disorders. We leveraged the Danish iPSYCH Consortium sample comprising 65 534 individuals diagnosed with psychiatric disorders or selected as part of a random population sample, for whom we also had genetic data and diagnoses of AIIDs. We employed regression analysis to examine comorbidities between AIIDs and psychiatric disorders and associations between AIIDs and HLA alleles across seven HLA genes. Our comorbidity analyses showed that overall AIID and five specific AIIDs were associated with having a psychiatric diagnosis. Our genetic analyses found 81 significant associations between HLA alleles and AIIDs. Lastly, we show connections across AIIDs, psychiatric disorders and infection susceptibility through network analysis of significant HLA associations in these disease classes. Combined, our results include both novel associations as well as replications of previously reported associations in a large sample, and highlight the genetic and epidemiological links between AIIDs and psychiatric disorders.

Developmental language disorder - a comprehensive study of more than 46,000 individuals

Developmental language disorder (DLD) is characterized by enduring low language abilities with a significant functional impact, in the absence of biomedical conditions in which language impairment is part of a complex of impairments. There is a lack of awareness of DLD even among healthcare professionals. Here we estimated the prevalence of DLD and its links to reading and learning difficulties and physical and mental health in the Danish Blood Donor Study (N = 46,547), where DLD-related information is based on questionnaires (self-report). We compared the questionnaire-derived DLD status with the relevant language-related diagnoses from hospital registers. We also investigated the genetic architecture of DLD in a subset of the cohort (N = 18,380). DLD was significantly associated with reading and learning difficulties and poorer mental and physical health. DLD prevalence was 3.36%-3.70% based on questionnaires, compared with 0.04% in hospital registers. Our genetic analyses identified one genome-wide significant locus, but not a significant heritability estimate. Our study shows that DLD has health-related implications that may last into adulthood, and that DLD may be undiagnosed in general healthcare. Furthermore, DLD is likely more genetically heterogeneous than narrower developmental language phenotypes. Our results emphasize the need to raise awareness of DLD and consider criteria for molecular studies of DLD to reduce case heterogeneity.

A Review of Major Danish Biobanks: Advantages and Possibilities of Health Research in Denmark

Biobank research may lead to an improved understanding of disease etiology and advance personalized medicine. Denmark (population ~5.9 million) provides a unique setting for population-based health research. The country is a rich source of biobanks and the universal, tax-funded healthcare system delivers routinely collected data to numerous registries and databases. By virtue of the civil registration number (assigned uniquely to all Danish citizens), biological specimens stored in biobanks can be combined with clinical and demographic data from these population-based health registries and databases. In this review, we aim to provide an understanding of advantages and possibilities of biobank research in Denmark. As knowledge about the Danish setting is needed to grasp the full potential, we first introduce the Danish healthcare system, the Civil Registration System, the population-based registries, and the interface with biobanks. We then describe the biobank infrastructures, comprising the Danish National Biobank Initiative, the Bio- and Genome Bank Denmark, and the Danish National Genome Center. Further, we briefly provide an overview of fourteen selected biobanks, including: The Danish Newborn Screening Biobank; The Danish National Birth Cohort; The Danish Twin Registry Biobank; Diet, Cancer and Health; Diet, Cancer and Health - Next generations; Danish Centre for Strategic Research in Type 2 Diabetes; Vejle Diabetes Biobank; The Copenhagen Hospital Biobank; The Copenhagen City Heart Study; The Copenhagen General Population Study; The Danish Cancer Biobank; The Danish Rheumatological Biobank; The Danish Blood Donor Study; and The Danish Pathology Databank. Last, we inform on practical aspects, such as data access, and discuss future implications.

Polygenic analyses show important differences between MDD symptoms collected using PHQ9 and CIDI-SF

Symptoms of Major Depressive Disorder (MDD) are commonly assessed using self-rating instruments like the Patient Health Questionnaire 9 (PHQ9, for current symptoms), and the Composite International Diagnostic Interview Short-Form (CIDI-SF, for lifetime worst-episode symptoms). Using data from the UKBiobank, we show that corresponding symptoms endorsed through PHQ9 and CIDI-SF have low to moderate genetic correlations (rG=0.43-0.87), and this cannot be fully attributed to different severity thresholds or the use of a skip-structure in CIDI-SF. Through a combination of Mendelian Randomization (MR) and polygenic prediction analyses, we find that PHQ9 symptoms are more associated with traits which reflect general dysphoria, while the skip-structure in CIDI-SF allows for the identification of heterogeneity among likely MDD cases. This has important implications on factor analyses performed on their respective genetic covariance matrices for the purpose of identification of genetic factors behind MDD symptom dimensions and heterogeneity.

Polygenic Risk and Episode Polarity Among Individuals With Bipolar Disorder

OBJECTIVE

The authors investigated associations between polygenic liabilities for bipolar disorder, major depression, and schizophrenia and episode polarity among individuals with bipolar disorder.

METHODS

The sample consisted of 2,705 individuals diagnosed with bipolar disorder at Danish psychiatric hospitals between January 1995 and March 2017. DNA was obtained from dried blood spots collected at birth as part of routine screening. Polygenic risk scores (PRSs) for bipolar disorder, major depression, and schizophrenia were generated using a meta-PRS method combining internally and externally trained components. Associations between PRS and polarity at first episode, polarity at any episode, and number of episodes with a given polarity were evaluated for each disorder-specific PRS using logistic and negative binominal regressions adjusted for the other two PRSs, age, sex, genotype platform, and five ancestral principal components.

RESULTS

PRS for bipolar disorder was positively associated with any manic episodes (odds ratio=1.23, 95% CI=1.09-1.38). PRS for depression was positively associated with any depressive (odds ratio=1.11, 95% CI=1.01-1.23) and mixed (odds ratio=1.15, 95% CI=1.03-1.28) episodes and negatively associated with any manic episodes (odds ratio=0.76, 95% CI=0.69-0.84). PRS for schizophrenia was positively associated with any manic episodes (odds ratio=1.13, 95% CI=1.01-1.27), but only when psychotic symptoms were present (odds ratio for psychotic mania: 1.27, 95% CI=1.05-1.54; odds ratio for nonpsychotic mania: 1.06, 95% CI=0.93-1.20). These patterns were similar for first-episode polarity and for the number of episodes within each pole.

CONCLUSIONS

PRSs for bipolar disorder, major depression, and schizophrenia are associated with episode polarity and psychotic symptoms in a congruent manner among individuals with bipolar disorder.

Genetic correlates of vitamin D-binding protein and 25-hydroxyvitamin D in neonatal dried blood spots

The vitamin D binding protein (DBP), encoded by the group-specific component (GC) gene, is a component of the vitamin D system. In a genome-wide association study of DBP concentration in 65,589 neonates we identify 26 independent loci, 17 of which are in or close to the GC gene, with fine-mapping identifying 2 missense variants on chromosomes 12 and 17 (within SH2B3 and GSDMA, respectively). When adjusted for GC haplotypes, we find 15 independent loci distributed over 10 chromosomes. Mendelian randomization analyses identify a unidirectional effect of higher DBP concentration and (a) higher 25-hydroxyvitamin D concentration, and (b) a reduced risk of multiple sclerosis and rheumatoid arthritis. A phenome-wide association study confirms that higher DBP concentration is associated with a reduced risk of vitamin D deficiency. Our findings provide valuable insights into the influence of DBP on vitamin D status and a range of health outcomes.

School performance and genetic propensities for educational attainment and depression in the etiology of self-harm: a Danish population-based study

BACKGROUND

Poor school performance is linked to higher risks of self-harm. The association might be explained through genetic liabilities for depression or educational attainment. We investigated the association between school performance and self-harm in a population-based sample while assessing the potential influence of polygenic risk scores (PRSs) for depression (PRSMDD) and for educational attainment (PRSEDU).

METHOD

We conducted a follow-up study of individuals born 1987-98 and followed from age 18 until 2016. The total sample consisted of a case group (23,779 diagnosed with mental disorders; schizophrenia, bipolar disorder, depression, autism, and attention deficit hyperactivity disorder (ADHD) and a randomly sampled comparison group (n = 10,925). Genome-wide data were obtained from the Neonatal Screening Biobank and information on school performance, family psychiatric history, and socioeconomic status from national administrative registers.

RESULTS

Individuals in the top PRSMDD decile were at higher self-harm risk in the case group (IRR: 1.30; 95% CI 1.15-1.46), whereas individuals in the top PRSEDU decile were at lower self-harm risk (IRR: 0.63; 95% CI: 0.55-0.74). Poorer school performance was associated with higher self-harm risk in persons diagnosed with any mental disorder (IRR: 1.69; 95% CI: 1.44-1.99) and among the comparison group (IRR: 7.93; 95% CI: 4.47-15.18). Observed effects of PRSMDD and PRSEDU on self-harm risk were strongest for individuals with poor school performance.

CONCLUSION

Associations between PRSMDD and self-harm risk and between PRSEDU and self-harm risk were found. Nevertheless, these polygenic scores seem currently of limited clinical utility for identifying individuals at high self-harm risk.

Associations of psychiatric disorders with sex chromosome aneuploidies in the Danish iPSYCH2015 dataset: a case-cohort study

BACKGROUND

Increased prevalence of mental illness has been reported in clinical studies of sex chromosome aneuploidies, but accurate population-based estimates of the prevalence and clinical detection rate of sex chromosome aneuploidies and the associated risks of psychiatric disorders are needed. In this study, we provide such estimates, valid for children and young adults of the contemporary Danish population.

METHODS

We used the iPSYCH2015 case-cohort dataset, which is based on a source population of single-born individuals born in Denmark between May 1, 1981, and Dec 31, 2008. The case sample comprises all individuals from the source population with a diagnosis of any index psychiatric disorder (schizophrenia spectrum disorder, bipolar disorder, major depressive disorder, autism spectrum disorder, or ADHD) by the end of follow-up (Dec 31, 2015), registered in the hospital-based Danish Psychiatric Central Research Register. The cohort consists of individuals randomly selected from the source population, and overlaps with the case sample. Biobanked blood samples for individuals in the case and cohort samples underwent genotyping and quality-control filtering, after which we analysed microarray data to detect sex chromosome aneuploidy karyotypes (45,X, 47,XXX, 47,XXY, and 47,XYY). We estimated the population-valid prevalence of these karyotypes from the cohort sample. Weighted Cox proportional hazards models were used to estimate the risks of each index psychiatric disorder associated with each sex chromosome aneuploidy karyotype, by use of date of first hospitalisation with the index disorder in the respective case group and the cohort as outcome. The clinical detection rate was determined by comparing records of clinical diagnoses of genetic conditions from the Danish National Patient Register with sex chromosome aneuploidy karyotype determined by our study.

FINDINGS

The assessed sample comprised 119 481 individuals (78 726 in the case sample and 43 326 in the cohort) who had genotyped and quality-control-filtered blood samples, including 64 533 (54%) people of gonadal male sex and 54 948 (46%) of gonadal female sex. Age during follow-up ranged from 0 to 34·7 years (mean 10·9 years [SD 3·5 years]). Information on ethnicity was not available. We identified 387 (0·3%) individuals as carriers of sex chromosome aneuploidies. The overall prevalence of sex chromosome aneuploidies was 1·5 per 1000 individuals. Each sex chromosome aneuploidy karyotype was associated with an increased risk of at least one index psychiatric disorder, with hazard ratios (HRs) of 2·20 (95% CI 1·42-3·39) for 47,XXY; 2·73 (1·25-6·00) for 47,XXX; 3·56 (1·01-12·53) for 45,X; and 4·30 (2·48-7·55) for 47,XYY. All karyotypes were associated with an increased risk of ADHD (HRs ranging from 1·99 [1·24-3·19] to 6·15 [1·63-23·19]), autism spectrum disorder (2·72 [1·72-4·32] to 8·45 [2·49-28·61]), and schizophrenia spectrum disorder (1·80 [1·15-2·80] to 4·60 [1·57-13·51]). Increased risk of major depressive disorder was found for individuals with 47,XXY (1·88 [1·07-3·33]) and 47,XYY (2·65 [1·12-5·90]), and of bipolar disorder for those with 47,XXX (4·32 [1·12-16·62]). The proportion of sex chromosome aneuploidy carriers who had been clinically diagnosed was 93% for 45,X, but lower for 47,XXY (22%), 47,XXX (15%), and 47,XYY (15%). Among carriers, the risk of diagnosis of at least one index psychiatric disorder did not significantly differ between those who had and had not been clinically diagnosed with sex chromosome aneuploidies (p=0·65).

INTERPRETATION

Increased risks of psychiatric disorders associated with sex chromosome aneuploidies, combined with low rates of clinical diagnosis of sex chromosome aneuploidies, compromise the adequate provision of necessary health care and counselling to affected individuals and their families, which might be helped by increased application of genetic testing in clinical settings.

FUNDING

Lundbeck Foundation and National Institutes of Health.

Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains

Attention-deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder with a major genetic component. Here, we present a genome-wide association study meta-analysis of ADHD comprising 38,691 individuals with ADHD and 186,843 controls. We identified 27 genome-wide significant loci, highlighting 76 potential risk genes enriched among genes expressed particularly in early brain development. Overall, ADHD genetic risk was associated with several brain-specific neuronal subtypes and midbrain dopaminergic neurons. In exome-sequencing data from 17,896 individuals, we identified an increased load of rare protein-truncating variants in ADHD for a set of risk genes enriched with probable causal common variants, potentially implicating SORCS3 in ADHD by both common and rare variants. Bivariate Gaussian mixture modeling estimated that 84-98% of ADHD-influencing variants are shared with other psychiatric disorders. In addition, common-variant ADHD risk was associated with impaired complex cognition such as verbal reasoning and a range of executive functions, including attention.

Deep Learning for Cross-Diagnostic Prediction of Mental Disorder Diagnosis and Prognosis Using Danish Nationwide Register and Genetic Data

Importance

Diagnoses and treatment of mental disorders are hampered by the current lack of objective markers needed to provide a more precise diagnosis and treatment strategy.

Objective

To develop deep learning models to predict mental disorder diagnosis and severity spanning multiple diagnoses using nationwide register data, family and patient-specific diagnostic history, birth-related measurement, and genetics.

Design, Setting, and Participants

This study was conducted from May 1, 1981, to December 31, 2016. For the analysis, which used a Danish population-based case-cohort sample of individuals born between 1981 and 2005, genotype data and matched longitudinal health register data were taken from the longitudinal Danish population-based Integrative Psychiatric Research Consortium 2012 case-cohort study. Included were individuals with mental disorders (attention-deficit/hyperactivity disorder [ADHD]), autism spectrum disorder (ASD), major depressive disorder (MDD), bipolar disorder (BD), schizophrenia spectrum disorders (SCZ), and population controls. Data were analyzed from February 1, 2021, to January 24, 2022.

Exposure

At least 1 hospital contact with diagnosis of ADHD, ASD, MDD, BD, or SCZ.

Main Outcomes and Measures

The predictability of (1) mental disorder diagnosis and (2) severity trajectories (measured by future outpatient hospital contacts, admissions, and suicide attempts) were investigated using both a cross-diagnostic and single-disorder setup. Predictive power was measured by AUC, accuracy, and Matthews correlation coefficient (MCC), including an estimate of feature importance.

Results

A total of 63 535 individuals (mean [SD] age, 23 [7] years; 34 944 male [55%]; 28 591 female [45%]) were included in the model. Based on data prior to diagnosis, the specific diagnosis was predicted in a multidiagnostic prediction model including the background population with an overall area under the curve (AUC) of 0.81 and MCC of 0.28, whereas the single-disorder models gave AUCs/MCCs of 0.84/0.54 for SCZ, 0.79/0.41 for BD, 0.77/0.39 for ASD, 0.74/0.38, for ADHD, and 0.74/0.38 for MDD. The most important data sets for multidiagnostic prediction were previous mental disorders and age (11%-23% reduction in prediction accuracy when removed) followed by family diagnoses, birth-related measurements, and genetic data (3%-5% reduction in prediction accuracy when removed). Furthermore, when predicting subsequent disease trajectories of the disorder, the most severe cases were the most easily predictable, with an AUC of 0.72.

Conclusions and Relevance

Results of this diagnostic study suggest the possibility of combining genetics and registry data to predict both mental disorder diagnosis and disorder progression in a clinically relevant, cross-diagnostic setting prior to clinical assessment.

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.

Multi-ancestry study of the genetics of problematic alcohol use in >1 million individuals

Problematic alcohol use (PAU) is a leading cause of death and disability worldwide. To improve our understanding of the genetics of PAU, we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals. We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses. Cross-ancestry fine-mapping improved the identification of likely causal variants. Prioritizing genes through gene expression and/or chromatin interaction in brain tissues identified multiple genes associated with PAU. We identified existing medications for potential pharmacological studies by drug repurposing analysis. Cross-ancestry polygenic risk scores (PRS) showed better performance in independent sample than single-ancestry PRS. Genetic correlations between PAU and other traits were observed in multiple ancestries, with other substance use traits having the highest correlations. The analysis of diverse ancestries contributed significantly to the findings, and fills an important gap in the literature.

Accuracy of haplotype estimation and whole genome imputation affects complex trait analyses in complex biobanks

Sample recruitment for research consortia, biobanks, and personal genomics companies span years, necessitating genotyping in batches, using different technologies. As marker content on genotyping arrays varies, integrating such datasets is non-trivial and its impact on haplotype estimation (phasing) and whole genome imputation, necessary steps for complex trait analysis, remains under-evaluated. Using the iPSYCH dataset, comprising 130,438 individuals, genotyped in two stages, on different arrays, we evaluated phasing and imputation performance across multiple phasing methods and data integration protocols. While phasing accuracy varied by choice of method and data integration protocol, imputation accuracy varied mostly between data integration protocols. We demonstrate an attenuation in imputation accuracy within samples of non-European origin, highlighting challenges to studying complex traits in diverse populations. Finally, imputation errors can bias association tests, reduce predictive utility of polygenic scores. Carefully optimized data integration strategies enhance accuracy and replicability of complex trait analyses in complex biobanks.

The genetic background of hydrocephalus in a population-based cohort: implication of ciliary involvement

Hydrocephalus is one of the most common congenital disorders of the central nervous system and often displays psychiatric co-morbidities, in particular autism spectrum disorder. The disease mechanisms behind hydrocephalus are complex and not well understood, but some association with dysfunctional cilia in the brain ventricles and subarachnoid space has been indicated. A better understanding of the genetic aetiology of hydrocephalus, including the role of ciliopathies, may bring insights into a potentially shared genetic aetiology. In this population-based case-cohort study, we, for the first time, investigated variants of postulated hydrocephalus candidate genes. Using these data, we aimed to investigate potential involvement of the ciliome in hydrocephalus and describe genotype-phenotype associations with an autism spectrum disorder. One-hundred and twenty-one hydrocephalus candidate genes were screened in a whole-exome-sequenced sub-cohort of the Lundbeck Foundation Initiative for Integrative Psychiatric Research study, comprising 72 hydrocephalus patients and 4181 background population controls. Candidate genes containing high-impact variants of interest were systematically evaluated for their involvement in ciliary function and an autism spectrum disorder. The median age at diagnosis for the hydrocephalus patients was 0 years (range 0-27 years), the median age at analysis was 22 years (11-35 years), and 70.5% were males. The median age for controls was 18 years (range 11-26 years) and 53.3% were males. Fifty-two putative hydrocephalus-associated variants in 34 genes were identified in 42 patients (58.3%). In hydrocephalus cases, we found increased, but not significant, enrichment of high-impact protein altering variants (odds ratio 1.51, 95% confidence interval 0.92-2.51, P = 0.096), which was driven by a significant enrichment of rare protein truncating variants (odds ratio 2.71, 95% confidence interval 1.17-5.58, P = 0.011). Fourteen of the genes with high-impact variants are part of the ciliome, whereas another six genes affect cilia-dependent processes during neurogenesis. Furthermore, 15 of the 34 genes with high-impact variants and three of eight genes with protein truncating variants were associated with an autism spectrum disorder. Because symptoms of other diseases may be neglected or masked by the hydrocephalus-associated symptoms, we suggest that patients with congenital hydrocephalus undergo clinical genetic assessment with respect to ciliopathies and an autism spectrum disorder. Our results point to the significance of hydrocephalus as a ciliary disease in some cases. Future studies in brain ciliopathies may not only reveal new insights into hydrocephalus but also, brain disease in the broadest sense, given the essential role of cilia in neurodevelopment.

Genome-wide association study of school grades identifies genetic overlap between language ability, psychopathology and creativity

Cognitive functions of individuals with psychiatric disorders differ from that of the general population. Such cognitive differences often manifest early in life as differential school performance and have a strong genetic basis. Here we measured genetic predictors of school performance in 30,982 individuals in English, Danish and mathematics via a genome-wide association study (GWAS) and studied their relationship with risk for six major psychiatric disorders. When decomposing the school performance into math and language-specific performances, we observed phenotypically and genetically a strong negative correlation between math performance and risk for most psychiatric disorders. But language performance correlated positively with risk for certain disorders, especially schizophrenia, which we replicate in an independent sample (n = 4547). We also found that the genetic variants relating to increased risk for schizophrenia and better language performance are overrepresented in individuals involved in creative professions (n = 2953) compared to the general population (n = 164,622). The findings together suggest that language ability, creativity and psychopathology might stem from overlapping genetic roots.