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.

Population-Based Risk of Psychiatric Disorders Associated With Recurrent Copy Number Variants.

Importance: Recurrent copy number variants (rCNVs) have been associated with increased risk of psychiatric disorders in case-control studies, but their population-level impact is unknown. Objective: To provide unbiased population-based estimates of prevalence and risk associated with psychiatric disorders for rCNVs and to compare risks across outcomes, rCNV dosage type (deletions or duplications), and locus features. Design, Setting, and Participants: This genetic association study is an analysis of data from the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) case-cohort sample of individuals born in Denmark in 1981-2008 and followed up until 2015, including (1) all individuals (n = 92 531) with a hospital discharge diagnosis of attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder, major depressive disorder (MDD), or schizophrenia spectrum disorder (SSD) and (2) a subcohort (n = 50 625) randomly drawn from the source population. Data were analyzed from January 2021 to August 2023. Exposures: Carrier status of deletions and duplications at 27 autosomal rCNV loci was determined from neonatal blood samples genotyped on single-nucleotide variant microarrays. Main Outcomes and Measures: Population-based rCNV prevalence was estimated with a survey model using finite population correction to account for oversampling of cases. Hazard ratio (HR) estimates and 95% CIs for psychiatric disorders were derived using weighted Cox proportional hazard models. Risks were compared across outcomes, dosage type, and locus features using generalized estimating equation models. Results: A total of 3547 rCNVs were identified in 64 735 individuals assigned male at birth (53.8%) and 55 512 individuals assigned female at birth (46.2%) whose age at the end of follow-up ranged from 7.0 to 34.7 years (mean, 21.8 years). Most observed increases in rCNV-associated risk for ADHD, ASD, or SSD were moderate, and risk estimates were highly correlated across these disorders. Notable exceptions included high ASD-associated risk observed for Prader-Willi/Angelman syndrome duplications (HR, 20.8; 95% CI, 7.9-55). No rCNV was associated with increased MDD risk. Also, rCNV-associated risk was positively correlated with locus size and gene constraint but not with dosage type. Comparison with published case-control and community-based studies revealed a higher prevalence of deletions and lower associated increase in risk for several rCNVs in iPSYCH2015. Conclusions and Relevance: This study found that several rCNVs were more prevalent and conferred less risk of psychiatric disorders than estimated previously. Most case-control studies overestimate rCNV-associated risk of psychiatric disorders, likely because of selection bias. In an era where genetics is increasingly being clinically applied, these results highlight the importance of population-based risk estimates for genetics-based predictions.

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

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

COSGAP: COntainerized Statistical Genetics Analysis Pipelines.

Summary: The collection and analysis of sensitive data in large-scale consortia for statistical genetics is hampered by multiple challenges, due to their non-shareable nature. Time-consuming issues in installing software frequently arise due to different operating systems, software dependencies, and limited internet access. For federated analysis across sites, it can be challenging to resolve different problems, including format requirements, data wrangling, setting up analysis on high-performance computing (HPC) facilities, etc. Easier, more standardized, automated protocols and pipelines can be solutions to overcome these issues. We have developed one such solution for statistical genetic data analysis using software container technologies. This solution, named COSGAP: "COntainerized Statistical Genetics Analysis Pipelines," consists of already established software tools placed into Singularity containers, alongside corresponding code and instructions on how to perform statistical genetic analyses, such as genome-wide association studies, polygenic scoring, LD score regression, Gaussian Mixture Models, and gene-set analysis. Using provided helper scripts written in Python, users can obtain auto-generated scripts to conduct the desired analysis either on HPC facilities or on a personal computer. COSGAP is actively being applied by users from different countries and projects to conduct genetic data analyses without spending much effort on software installation, converting data formats, and other technical requirements. Availability and implementation: COSGAP is freely available on GitHub (https://github.com/comorment/containers) under the GPLv3 license.

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.

Distinct genomic signatures and modifiable risk factors underly 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. Using genomic data, this study elucidates biological mechanisms, key risk factors, and causal pathways underlying their comorbidity. We show that CVDs share a large proportion of their genetic risk factors with MDD. Multivariate genome-wide association analysis of the shared genetic liability between MDD and atherosclerotic CVD (ASCVD) revealed seven novel loci and distinct patterns of tissue and brain cell-type enrichments, suggesting a role for the thalamus. Part of the genetic overlap was explained by shared inflammatory, metabolic, and psychosocial/lifestyle risk factors. Finally, we found support for causal effects of genetic liability to MDD on CVD risk, but not from most CVDs to MDD, and demonstrated that the causal effects were partly explained by metabolic and psychosocial/lifestyle factors. The distinct signature of MDD-ASCVD comorbidity aligns with the idea of an immunometabolic sub-type of MDD more strongly associated with CVD than overall MDD. In summary, we identify plausible biological mechanisms underlying MDD-CVD comorbidity, as well as key modifiable risk factors for prevention of CVD in individuals with MDD.

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.

Epidemiological overview of major depressive disorder in Scandinavia using nationwide registers.

Background: Major depressive disorder (MDD) is a common psychiatric disorder associated with a high disease burden. This study gives a comprehensive overview of the prevalence, outcomes, treatment, and genetic epidemiology of MDD within and across the Scandinavian countries. Methods: This study has aimed to assess and compare across Norway, Denmark, and Sweden 1) the prevalence and trajectories of MDD and comorbidity, 2) outcomes and treatment, and 3) heritability (Denmark and Sweden only). The analyses leveraged data on 272,944 MDD cases (and 6.2 million non-cases) from Norway, Sweden, and Denmark in specialist care in national longitudinal health registers covering 1975-2013. Relying on harmonized public data global comparisons of socioeconomic and health metrics were performed to assess to what extent findings are generalizable. Findings: MDD ranked among the most prevalent psychiatric disorders. For many cases, the disorder trajectory was severe, with varying proportions experiencing recurrence, developing comorbid disorders, requiring inpatient treatment, or dying of suicide. Important country differences in specialist care prevalence and treatment were observed. Heritability estimates were moderate (35-48%). In terms of socioeconomic and health indices, the Scandinavian nations were comparable to one another and grouped with other Western nations. Interpretation: The Scandinavian countries were similar with regards to MDD epidemiological measures, but we show that differences in health care organization need to be taken into consideration when comparing countries. This study demonstrates the utility of using comprehensive population-wide registry data, outlining possibilities for other applications. The findings will be of use to policy makers for developing better prevention and intervention strategies. Funding: Swedish Research Council (Vetenskapsrådet, award D0886501 to PFS), US National Institutes of Mental HealthR01 MH123724 (to PFS), European Union's Horizon 2020 Research and Innovation Program (847776 and 964874, to OA) and European Research Council grant (grant agreement ID 101042183, to YL).

Shared genetic influences on depression and menopause symptoms.

BACKGROUND: Women experience major depression and post-traumatic stress disorder (PTSD) approximately twice as often as men. Estrogen is thought to contribute to sex differences in these disorders, and reduced estrogen is also known to be a key driver of menopause symptoms such as hot flashes. Moreover, estrogen is used to treat menopause symptoms. In order to test for potential shared genetic influences between menopause symptoms and psychiatric disorders, we conducted a genome-wide association study (GWAS) of estrogen medication use (as a proxy for menopause symptoms) in the UK Biobank. METHODS: The analysis included 232 993 women aged 39-71 in the UK Biobank. The outcome variable for genetic analyses was estrogen medication use, excluding women using hormonal contraceptives. Trans-ancestry GWAS meta-analyses were conducted along with genetic correlation analyses on the European ancestry GWAS results. Hormone usage was also tested for association with depression and PTSD. RESULTS: GWAS of estrogen medication use (compared to non-use) identified a locus in the TACR3 gene, which was previously linked to hot flashes in menopause [top rs77322567, odds ratio (OR) = 0.78, p = 7.7 × 10-15]. Genetic correlation analyses revealed shared genetic influences on menopause symptoms and depression (rg = 0.231, s.e.= 0.055, p = 2.8 × 10-5). Non-genetic analyses revealed higher psychiatric symptoms scores among women using estrogen medications. CONCLUSIONS: These results suggest that menopause symptoms have a complex genetic etiology which is partially shared with genetic influences on depression. Moreover, the TACR3 gene identified here has direct clinical relevance; antagonists for the neurokinin 3 receptor (coded for by TACR3) are effective treatments for hot flashes.

Infection Polygenic Factors Account for a Small Proportion of the Relationship Between Infections and Mental Disorders.

BACKGROUND: Several recent studies have suggested a role for infections in the development of mental disorders; however, the genetic contribution to this association is understudied. METHODS: We use the iPSYCH case-cohort genotyped sample (n = 65,534) and Danish health care registry data to study the genetic association between infections and mental disorders. To test the hypothesis that these associations are due to genetic pleiotropy, we estimated the genetic correlation between infection and mental disorders. Polygenic risk scores (PRSs) were used to assess whether genetic pleiotropy of infections and mental disorders was mediated by actual infection diagnoses. RESULTS: We observed that schizophrenia, attention-deficit/hyperactivity disorder, major depressive disorder, bipolar disorder, and posttraumatic stress disorder (rg ranging between 0.18 and 0.83), but not autism spectrum disorder and anorexia nervosa, were significantly genetically correlated with infection diagnoses. PRSs for infections were associated with modest increase in risk of attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia in the iPSYCH case-cohort (hazard ratios = 1.04 to 1.10) but was not associated with risk of anorexia, autism, or bipolar disorder. Using mediation analysis, we show that infection diagnoses account for only a small proportion (6%-14%) of the risk for mental disorders conferred by infection PRSs. CONCLUSIONS: Infections and mental disorders share a modest genetic architecture. Infection PRSs can predict risk of certain mental disorders; however, this effect is moderate. Finally, recorded infections partially explain the relationship between infection PRSs and mental disorders.

A comprehensive map of genetic relationships among diagnostic categories based on 48.6 million relative pairs from the Danish genealogy.

For more than half a century, Denmark has maintained population-wide demographic, health care, and socioeconomic registers that provide detailed information on the interaction between all residents and the extensive national social services system. We leverage this resource to reconstruct the genealogy of the entire nation based on all individuals legally residing in Denmark since 1968. We cross-reference 6,691,426 individuals with nationwide health care registers to estimate heritability and genetic correlations of 10 broad diagnostic categories involving all major organs and systems. Heritability estimates for mental disorders were consistently the highest across demographic cohorts (average h2 = 0.406, 95% CI = [0.403, 0.408]), whereas estimates for cancers were the lowest (average h2 = 0.130, 95% CI = [0.125, 0.134]). The average genetic correlation of each of the 10 diagnostic categories with the other nine was highest for gastrointestinal conditions (average rg = 0.567, 95% CI = [0.566, 0.567]) and lowest for urogenital conditions (average rg = 0.386, 95% CI = [0.385, 0.388]). Mental, pulmonary, gastrointestinal, and neurological conditions had similar genetic correlation profiles.

Using tree-based methods for detection of gene-gene interactions in the presence of a polygenic signal: simulation study with application to educational attainment in the Generation Scotland Cohort Study.

Motivation: The genomic architecture of human complex diseases is thought to be attributable to single markers, polygenic components and epistatic components. No study has examined the ability of tree-based methods to detect epistasis in the presence of a polygenic signal. We sought to apply decision tree-based methods, C5.0 and logic regression, to detect epistasis under several simulated conditions, varying strength of interaction and linkage disequilibrium (LD) structure. We then applied the same methods to the phenotype of educational attainment in a large population cohort. Results: LD pruning improved the power and reduced the type I error. C5.0 had a conservative type I error rate whereas logic regression had a type I error rate that exceeded 5%. Despite the more conservative type I error, C5.0 was observed to have higher power than logic regression across several conditions. In the presence of a polygenic signal, power was generally reduced. Applying both methods on educational attainment in a large population cohort yielded numerous interacting SNPs; notably a SNP in RCAN3 which is associated with reading and spelling and a SNP in NPAS3, a neurodevelopmental gene. Availability and implementation: All methods used are implemented and freely available in R. Supplementary information: Supplementary data are available at Bioinformatics online.

Phenotypic and genetic analysis of cognitive performance in Major Depressive Disorder in the Generation Scotland: Scottish Family Health Study.

Lower performances in cognitive ability in individuals with Major Depressive Disorder (MDD) have been observed on multiple occasions. Understanding cognitive performance in MDD could provide a wider insight in the aetiology of MDD as a whole. Using a large, well characterised cohort (N = 7012), we tested for: differences in cognitive performance by MDD status and a gene (single SNP or polygenic score) by MDD interaction effect on cognitive performance. Linear regression was used to assess the association between cognitive performance and MDD status in a case-control, single-episode-recurrent MDD and control-recurrent MDD study design. Test scores on verbal declarative memory, executive functioning, vocabulary, and processing speed were examined. Cognitive performance measures showing a significant difference between groups were subsequently analysed for genetic associations. Those with recurrent MDD have lower processing speed versus controls and single-episode MDD (ß = -2.44, p = 3.6 × 10-04; ß = -2.86, p = 1.8 × 10-03, respectively). There were significantly higher vocabulary scores in MDD cases versus controls (ß = 0.79, p = 2.0 × 10-06), and for recurrent MDD versus controls (ß = 0.95, p = 5.8 × 10-05). Observed differences could not be linked to significant single-locus associations. Polygenic scores created from a processing speed meta-analysis GWAS explained 1% of variation in processing speed performance in the single-episode versus recurrent MDD study (p = 1.7 × 10-03) and 0.5% of variation in the control versus recurrent MDD study (p = 1.6 × 10-10). Individuals with recurrent MDD showed lower processing speed and executive function while showing higher vocabulary performance. Within MDD, persons with recurrent episodes show lower processing speed and executive function scores relative to individuals experiencing a single episode.