Oleksandr Frei

• Kopal, Jakub; Bakken, N. R.; Parekh, Pravesh; Shadrin, Alexey; Jaholkowski, Piotr Pawel & Ystaas, L. A. R. [Show all 14 contributors for this article] (2026). Early-childhood temperament trajectories map onto transdiagnostic psychiatric risk. Molecular Psychiatry. ISSN 1359-4184. doi: 10.1038/s41380-026-03881-0. Full text in Research Archive
• Ystaas, Lars A.R.; Parekh, Pravesh; Parker, Nadine; Akkouh, Ibrahim Ahmed; Birkenæs, Viktoria & Sønderby, Ida Elken [Show all 12 contributors for this article] (2026). Genetic liability to addiction underlies comorbid bipolar and substance use disorders. Biological Psychiatry. ISSN 0006-3223. doi: 10.1016/j.biopsych.2026.08.008. Full text in Research Archive
• Fuhrer, Julian; Shadrin, Alexey; Hughes, Timothy; Parker, Nadine; Hindley, Guy Frederick Lanyon & Frei, Evgeniia [Show all 12 contributors for this article] (2026). Beyond exons: Linking noncoding heritability and polygenicity across complex human traits and disorders. American Journal of Human Genetics. ISSN 0002-9297. p. 1–13. doi: 10.1016/j.ajhg.2026.08.012. Full text in Research Archive
• Koch, Elise; Parker, Nadine; Mohammad, Helin; Lenk, Hasan Cagin; Ystaas, Lars Andreas Rømo & Jaholkowski, Piotr Pawel [Show all 13 contributors for this article] (2026). Genetic overlap between treatment-resistant schizophrenia and smoking initiation. International Journal of Neuropsychopharmacology. ISSN 1461-1457. 29(7). doi: 10.1093/ijnp/pyag035. Full text in Research Archive Show summary

  Background Early detection of treatment-resistant schizophrenia (TRS) is of substantial clinical importance. While TRS is heritable, the associated genetic variants have been difficult to identify. Cigarette smoking is associated with non-response to antipsychotics, and smoking behavior and schizophrenia have a shared genetic basis. Thus, TRS may also have a shared genetic basis with smoking behavior. Objective Here, we aim to identify genetic variants associated with TRS by leveraging overlapping genetic variants with smoking initiation to increase statistical power. Methods We analyzed genome-wide data for TRS and smoking initiation with the conditional/conjunctional false discovery rate (cond/conjFDR) to identify shared loci, and LD score regression to determine genetic correlations. To investigate potential causal effects of shared loci, we performed Mendelian randomization analyses. Shared loci were mapped to genes, which were further investigated for enrichment of drug target genes. Results We observed a significant positive genetic correlation between TRS and smoking initiation (rg = 0.47 P = .0002). Leveraging the genetic overlap between TRS and smoking initiation, we identified four novel loci jointly associated with TRS and smoking initiation. The condFDR results improved polygenic prediction of TRS. Mendelian randomization indicates putative evidence for a causal effect of genetic liability to TRS on smoking initiation. The functional genetic analyses suggest that alpha-1-adrenergic receptors may be involved in the pathophysiology of TRS and possibly related to the efficacy of clozapine versus other antipsychotic drugs. Conclusion In conclusion, our results show that shared genetic mechanisms influence both TRS and smoking behavior, which provide new insights into the biological underpinnings of TRS.

• Corfield, Elizabeth Claire; Shadrin, Alexey; Frei, Oleksandr; Rahman, Mohammad Zillur; Akdeniz, Bayram Cevdet & Filiz, Tahir Tekin [Show all 29 contributors for this article] (2026). Family genetic designs in MoBa provide insights into health and functioning. Nature. ISSN 0028-0836. doi: 10.1038/s41586-026-10926-5. Full text in Research Archive Show summary

  Genome-wide association studies using large, population-based samples of unrelated individuals have discovered thousands of genetic associations with health and disease1. These studies can help explain genetic and environmental risks. However, increasing evidence suggests that population-based estimates, while precise, can also reflect confounding that affects their use and interpretation. This confounding can be overcome using data from genotyped family members, such as nuclear mother–father–child trios2,3. However, samples of genotyped families are rare4,5,6,7,8,9,10,11. Here we illustrate some of the advantages of familial data using the Norwegian Mother, Father and Child Cohort Study (MoBa), a population-based cohort of parents and offspring with extensive genotype data (n ≈ 230,000) (ref. 3), along with broad and longitudinal phenotyping of health and functioning. We provide an overview of MoBa and describe the quality control of genotype data tailored to this extensively related sample. We then use trio data to illustrate how family-based genomic designs can identify distinct direct and indirect sources of genetic influence and structural confounding. As examples, we analyse children’s height, educational achievement, depressive symptoms and sleep duration. These demonstrations highlight MoBa as a broadly valuable resource for advancing understanding of health and functioning across the lifecourse and generations.

• Parekh, Pravesh; Parker, Nadine; Pecheva, Diliana; Frei, Evgeniia; Vaudel, Marc & Smith, Diana M. [Show all 26 contributors for this article] (2026). FEMA-Long: Modeling unstructured covariances for discovery of time-dependent effects in large-scale longitudinal datasets. PLoS Genetics. ISSN 1553-7390. 22(6). doi: 10.1371/journal.pgen.1012184. Full text in Research Archive Show summary

  While linear mixed-effects (LME) models are common for analyzing longitudinal data, most users rely on random intercepts or simple stationary covariance, due to unavailability of computationally tractable solutions. Here, we extend the Fast and Efficient Mixed-Effects Algorithm (FEMA) and present FEMA-Long, a computationally tractable approach to flexibly modeling longitudinal covariance suitable for high-dimensional data. FEMA-Long can: i) model unstructured covariance, ii) model covariates as smooth functions using splines, iii) discover time-dependent effects of covariates with spline interactions, and iv) use these flexible longitudinal modeling strategies to perform longitudinal genome-wide association studies and discover time-dependent genetic effects, in a computationally scalable manner, suitable for high-dimensional data. Through extensive simulations, we show that estimates from FEMA-Long are accurate, while being up to several thousand times faster and with minimal carbon footprint. To show the utility of FEMA-Long for discovering novel biological signal, using data from the Norwegian Mother, Father and Child Cohort Study (MoBa), we performed a longitudinal genome-wide association study with non-linear SNP-by-time interaction on length, weight, and BMI of 68,273 infants with up to six measurements in the first year of life. We found dynamic patterns of random effects including time-varying heritability and genetic correlations, as well as several genetic variants showing time-dependent effects, highlighting the applicability of FEMA-Long to enable novel discoveries.

• Tesfaye, Markos; Shadrin, Alexey; Parker, Nadine; Jaholkowski, Piotr Pawel; Parekh, Pravesh & Kutrolli, Gleda [Show all 16 contributors for this article] (2026). Comorbidity Alters the Genetic Relationship Between Anxiety Disorders and Major Depression. Biological Psychiatry. ISSN 0006-3223. 100(6), p. 689–698. doi: 10.1016/j.biopsych.2026.04.026. Full text in Research Archive
• Rødevand, Linn Nilsen; Rahman, Mohammad Zillur; Jaholkowski, Piotr Pawel; Parker, Nadine; Valdimarsdóttir, Unnur Anna & Smeland, Olav Bjerkehagen [Show all 14 contributors for this article] (2026). Evidence for overlapping genetic architecture between lifestyle factors and severe mental disorders with different patterns across diagnoses. EBioMedicine. 128. doi: 10.1016/j.ebiom.2026.106304. Full text in Research Archive
• Wang, Xin; Sofer, Tamar; Frei, Oleksandr; Kaplan, Robert; Perreira, Krista M. & Franceschini, Nora [Show all 12 contributors for this article] (2026). Explicitly modeling genetic ancestry to improve polygenic prediction accuracy for height in a large, admixed cohort of US Latinos: Findings from HCHS/SOL. Human Genetics and Genomics Advances (HGG Advances). 7(3). doi: 10.1016/j.xhgg.2026.100597. Full text in Research Archive
• Muntané, Gerard; Shadrin, Alexey; Guardiola-Ripoll, M.; O'Connell, Kevin Sean; Frei, Oleksandr & Nærland, Terje [Show all 8 contributors for this article] (2026). Genetic overlap and shared risk loci between autism spectrum disorder and cardiometabolic traits. Molecular Psychiatry. ISSN 1359-4184. doi: 10.1038/s41380-026-03563-x. Full text in Research Archive Show summary

  Autism spectrum disorder (ASD) is a neurodevelopmental condition affecting 2% of the global population. Beyond core symptoms such as social communication deficits and repetitive behaviors, individuals with ASD are at increased risk of cardiometabolic comorbidities, including obesity, diabetes, and cardiovascular disease. Here, we investigate the shared genetic architecture between ASD and cardiometabolic traits using large genome-wide association studies datasets and advanced statistical approaches: the bivariate causal mixture (MiXeR) model and pleiotropy-informed conditional false discovery rate (pleioFDR). Our results show significant polygenic overlap between ASD and several cardiometabolic phenotypes, despite almost negligible genetic correlation between the traits. Specifically, we observed positive genetic correlations within the shared component for ASD and metabolic traits, such as body mass index (rg=0.03), type 2 diabetes (rg=0.23), and total cholesterol (rg=0.78). In contrast, negative correlations emerged between ASD and cardiovascular traits, including diastolic and systolic blood pressure (rg = -0,22, for both), pulse pressure (rg = -0.25), and coronary artery disease (rg = -0.90). Finally, we identified 100 shared loci between ASD and cardiometabolic traits, mapping to 124 genes and suggesting shared biological mechanisms underlying these phenotypes and pointing to potential therapeutic targets. Shared loci between ASD and metabolic traits predominantly showed concordant effects, whereas those overlapping with cardiovascular traits—particularly blood pressure–related traits—tended to exhibit discordant effects. Together, these findings deepen our understanding of the biological connections between ASD and cardiometabolic comorbidities and may help inform more personalized strategies for managing ASD and its associated long-term health risks.

• Holen, Børge; Rahman, Mohammad Zillur; Shadrin, Alexey; Icick, Romain; O'Connell, Kevin Sean & Rødevand, Linn [Show all 14 contributors for this article] (2026). Shared genetic variants across substance use disorders implicate common neurobiological pathways, a genome‐wide mixed methods study. BMJ General Psychiatry (GPSYCH). ISSN 2096-5923. 39(2). doi: 10.1002/gps3.70017. Full text in Research Archive Show summary

  ABSTRACT Background Substance use disorders (SUDs) are highly heritable, but the extent of shared and distinct genetic architecture across different SUDs is unclear. Aims To compare the genetic architectures of alcohol use disorder (AUD), cannabis use disorder (CUD) and opioid use disorder (OUD) and to identify shared and unique genetic loci. Methods We analysed large‐scale genome‐wide association study (GWAS) summary statistics from individuals of European ancestry recruited in Europe and the USA. The mixture model MiXeR was used to estimate the unique genetic architecture characteristics of each SUD, including its polygenicity, single nucleotide polymorphism (SNP)‐heritability and discoverability, a measure of the distribution of genetic signal across all causal variants. Pairwise conditional/conjunctional false discovery rate (cond/conjFDR) analyses identified shared loci, followed by biological annotation of implicated genes. Results AUD demonstrated the highest polygenicity, followed by CUD and OUD. SNP‐based heritability was 0.10 for AUD and OUD and 0.01 for CUD. Discoverability was highest for OUD, followed by AUD and CUD. Currently, genome‐wide significant SNPs explain 2.0% of AUD, 0.3% of CUD and 0.2% of OUD variance. Cond/conjFDR identified 39 novel loci for AUD, 10 for CUD and 1 for OUD. Of implicated genes, most were expressed in the brain, including several involved in gamma‐aminobutyric acid and dopaminergic neurotransmission, opioid neurophysiology, myelination, DNA recombination, apoptosis and ubiquitin‐dependent protein catabolism. Conclusions SUDs have polygenic architectures with many shared loci and are similar with regards to some characteristics. However, the level of polygenicity differs across SUDs, with AUD being considerably more polygenic than OUD, whereas CUD is intermediate in terms of its polygenicity. The novel loci implicate genes primarily expressed in the brain, involving a variety of biological functions. The findings expand our view of the aetiology of these disorders, while supporting the hypothesis of a shared set of pleiotropic SUD genes.

• Parker, Nadine; Koch, Elise; Shadrin, Alexey; Fuhrer, Julian; Hindley, Guy F. L. & Stinson, Sara Elizabeth [Show all 15 contributors for this article] (2026). Leveraging the genetics of psychiatric disorders to prioritize potential drug targets and compounds. Neuropsychopharmacology. ISSN 0893-133X. doi: 10.1038/s41386-026-02380-8. Full text in Research Archive
• Frei, Evgeniia; Frei, Oleksandr; Hagen, Espen; Shadrin, Alexey; Bakken, Nora Refsum & Birkenæs, Viktoria [Show all 9 contributors for this article] (2026). Prediction of adolescent internalizing disorder risk: Evidence from the Norwegian mother, father, and child cohort study. Journal of Affective Disorders. ISSN 0165-0327. 404, p. 1–9. doi: 10.1016/j.jad.2026.121481. Full text in Research Archive Show summary

  Background Internalizing disorders are among the most common psychiatric conditions in adolescence, often associated with long-term adverse outcomes. Early identification of at-risk youth is important for effective intervention, though it remains challenging due to the multifactorial nature of risk. Machine learning (ML) offers opportunities to integrate multiple data sources and improve risk prediction for internalizing disorders. Methods We used data from 13,743 adolescents (mean age 14.45 years; 52.7% female) participating in the Norwegian Mother, Father and Child Cohort Study (MoBa), linked to national health registries. Logistic regression with elastic net regularization was applied to predict the risk of an internalizing disorder (mood, anxiety or stress-related) occurring within one to five years after assessment. Nested models of increasing complexity incorporated sociodemographic, clinical, lifestyle, mental health, psychosocial, and genetic predictors. Model performance was evaluated in a hold-out test set. Simplified models combining three questionnaire scales were also evaluated. Results Test-set performance increased with model complexity, reaching area under the receiver operating characteristic curve (AUC) of 0.731 for the full model. Mental health self-reported symptoms and psychosocial predictors contributed most to the discrimination. Simplified models using three questionnaire scales, alongside age and sex, achieved AUCs up to 0.718 and effectively stratified adolescents into high- and low-risk groups (OR80/20 ranged 6.11–9.35). Conclusion Multimodal ML models integrating registry information, mental health symptoms, psychosocial factors, and genetic data demonstrated moderate predictive performance. Simplified models with three questionnaire items reached comparable performance, highlighting their potential utility in the early identification of adolescents at elevated internalizing disorder risk.

• Moberget, Torgeir; Meer, Dennis van der; Bahrami, Shahram; Roelfs, Daniël; Frei, Oleksandr & Kaufmann, Tobias Herbert [Show all 15 contributors for this article] (2026). The genetic architecture of human cerebellar morphology supports a key role for the cerebellum in human evolution and psychopathology. Communications Biology. 9(1). doi: 10.1038/s42003-026-09664-1. Full text in Research Archive Show summary

  The functional domain of the cerebellum has expanded beyond motor control to also include cognitive and affective functions. In line with this notion, cerebellar volume has increased over recent primate evolution, and cerebellar alterations have been linked to heritable mental disorders. To map the genetic architecture of human cerebellar morphology, we here studied a large imaging genetics sample from the UK Biobank (n discovery = 27,302; n replication: 11,264) with state-of-the art neuroimaging and biostatistics tools. Multivariate GWAS on regional cerebellar MRI features yielded 351 significant genetic loci (226 novel, 94% replicated). Lead SNPs showed positive enrichment for relatively recent genetic mutations over the last 20-40k years (i.e., overlapping the Upper Paleolithic, a period characterized by rapid cultural evolution), while gene level analyses revealed enrichment for human-specific evolution over the last ∼6-8 million years. Finally, we observed genetic overlap with major mental disorders, supporting cerebellar involvement in psychopathology.

• Stinson, Sara E.; Shadrin, Alexey; Rahman, Mohammad Zillur; Rødevand, Linn; Broce, Iris J. & Selbæk, Geir [Show all 17 contributors for this article] (2026). Distinct metabolic signatures of Alzheimer's and Parkinson's disease revealed through genetic overlap. EBioMedicine. 127. doi: 10.1016/j.ebiom.2026.106254. Full text in Research Archive Show summary

  Background: Metabolic dysfunction is a major risk factor for neurodegeneration, yet the genetic architecture linking systemic metabolism to Alzheimer's disease (AD) and Parkinson's disease (PD) remains unclear. Methods: We integrated genome-wide association data for 249 circulating metabolites and proglucagon with summary statistics for AD, PD, and cardiometabolic traits. Genetic correlations, polygenic overlap, causal relationships, and shared genetic loci were quantified using linkage disequilibrium score regression, high-definition likelihood, bivariate mixture modelling, Mendelian randomisation, and conjunctional false discovery rate analyses, followed by functional and tissue-specific enrichment analyses. Findings: AD displayed a metabolic-genetic profile aligned with body mass index, type 2 diabetes, coronary artery disease, and stroke, whereas PD exhibited largely opposing patterns (Spearman's rs = −0.26). Mendelian randomization analyses supported causal effects of lipoprotein subclasses, glutamine, and proglucagon on AD risk, with opposite or null effects in PD. Shared loci between metabolites and AD were enriched for lipid metabolism and cholesterol transport, whereas PD-associated loci were enriched for mitochondrial function, vesicle trafficking, and stress-response signalling. Interpretation: AD and PD are shaped by fundamentally distinct metabolic-genetic architectures. Metabolically targeted interventions, particularly those modulating lipid, amino acid, and proglucagon pathways, may require disease-specific and genetically informed strategies for prevention and treatment of neurodegenerative diseases.

• Akdeniz, Bayram Cevdet; Bahrami, Shahram; Hagen, Espen; Fuhrer, Julian; Fominykh, Vera & Shadrin, Alexey [Show all 108 contributors for this article] (2026). Polygenic Hazard Score for Predicting Age-associated Risk of Alzheimer’s Disease in European Populations: Development and Validation. medRxiv. doi: 10.1101/2025.07.28.25332293. Full text in Research Archive Show summary

  Abstract Objectives Polygenic hazard score (PHS) models can be used to predict the age-associated risk for complex diseases, including Alzheimer’s disease (AD). In this study, we present an improved PHS model for AD that incorporates a large number of genetic variants and demonstrates enhanced predictive accuracy for age of onset in European populations compared to alternative models. Methods We used the genotyped European Alzheimer & Dementia Biobank (EADB) sample (n=42,120) to develop and evaluate the performance of the PHS model. We developed a PHS model building on 720 genetic variants, including Apolipoprotein E ( APOE ) ε2 and ε4 alleles. We used Elastic Net-regularized Cox regression approach to develop the PHS model. Results The new PHS model (EADB720) improved prediction accuracy compared to alternative models in European populations, with the Odds Ratio OR80/20 from the highest quintile of risk (80th risk percentile and above) to the lowest quintile of risk (20th risk percentile and below) varying between 5.10 and 13.15 within the range of age of onset from 65 – 85 years. Our model also improved risk stratification across ε3/3 individuals of European ancestry (OR80/20 ranges from 1.95 to 3.52). It was also successfully validated in independent datasets (HUSK, DemGene and ADNI) by achieving OR80/20 up to 10.00 in each independent dataset. Conclusion Our EADB720 model significantly improves the accuracy of age-associated risk of AD across European populations (pval<0.03). Accurately predicting the age of onset of AD is of large clinical importance to implementing new AD medication and early intervention in clinical settings.

• Wiström, Erik David; Fuhrer, Julian; Shadrin, Alexey; Fominykh, Vera; O'Connell, Kevin Sean & Jaholkowski, Piotr Pawel [Show all 17 contributors for this article] (2026). Dissecting the genetic relationship between severe mental disorders and autoimmune diseases. Brain, Behavior, and Immunity. ISSN 0889-1591. 133. doi: 10.1016/j.bbi.2025.106249. Full text in Research Archive Show summary

  Severe mental disorders have been linked to immune system dysfunction. While a genetic association between mental disorders and autoimmune diseases has been suggested, their genetic relationship remains incompletely understood. Utilizing a complementary set of statistical analyses, we conducted a comprehensive investigation of the genetic architecture between severe mental disorders (major depression (MD), bipolar disorder (BD), and schizophrenia (SCZ)) and seven autoimmune diseases (autoimmune thyroiditis, celiac disease, inflammatory bowel disease (IBD), multiple sclerosis, psoriasis, rheumatoid arthritis, and type 1 diabetes), involving a total of 667,518 cases from 10 genome-wide association studies. While MD was positively genetically correlated with five autoimmune diseases, BD and SCZ were only positively correlated with IBD, suggesting differences in the genetic signal shared with autoimmunity across these mental disorders. A considerable fraction of genetic variants influencing autoimmune diseases (range 17.1–88.4 %) was estimated to overlap with mental disorders; however, this constitutes only a minor part of genetic variants influencing the more polygenic mental disorders. Finally, we identified 172 genetic loci jointly affecting mental disorders and autoimmune diseases, implicating both lipid metabolism and TNF signaling. In conclusion, MD, BD, and SCZ have a small but distinct genetic overlap with autoimmune diseases, which may inform new possible immune targets for treatment in mental illness.

• Grotzinger, Andrew D.; Werme, Josefin; Peyrot, Wouter J.; Frei, Oleksandr; Leeuw, Christiaan de & Bicks, Lucy K. [Show all 72 contributors for this article] (2025). Mapping the genetic landscape across 14 psychiatric disorders. Nature. ISSN 0028-0836. 649(8096), p. 406–415. doi: 10.1038/s41586-025-09820-3. Full text in Research Archive Show summary

  Psychiatric disorders display high levels of comorbidity and genetic overlap, challenging current diagnostic boundaries. For disorders for which diagnostic separation has been most debated, such as schizophrenia and bipolar disorder, genomic methods have revealed that the majority of genetic signal is shared. While over a hundred pleiotropic loci have been identified by recent cross-disorder analyses, the full scope of shared and disorder-specific genetic influences remains poorly defined. Here we addressed this gap by triangulating across a suite of cutting-edge statistical and functional genomic analyses applied to 14 childhood- and adult-onset psychiatric disorders (1,056,201 cases). Using genetic association data from common variants, we identified and characterized five underlying genomic factors that explained the majority of the genetic variance of the individual disorders (around 66% on average) and were associated with 238 pleiotropic loci. The two factors defined by (1) Schizophrenia and bipolar disorders (SB factor); and (2) major depression, PTSD and anxiety (Internalizing factor) showed high levels of polygenic overlap and local genetic correlation and very few disorder-specific loci. The genetic signal shared across all 14 disorders was enriched for broad biological processes (for example, transcriptional regulation), while more specific pathways were shared at the level of the individual factors. The shared genetic signal across the SB factor was substantially enriched in genes expressed in excitatory neurons, whereas the Internalizing factor was associated with oligodendrocyte biology. These observations may inform a more neurobiologically valid psychiatric nosology and implicate targets for therapeutic development designed to treat commonly occurring comorbid presentations.

• Frei, Evgeniia; Filiz, Tahir Tekin; Frei, Oleksandr; Loughnan, Robert; Jaholkowski, Piotr Pawel & Parker, Nadine [Show all 12 contributors for this article] (2025). Genome-wide analysis of screen behaviors among adolescents identifies novel loci and overlap with educational attainment and mental disorders. Scientific Reports. 15(1). doi: 10.1038/s41598-025-17450-y. Full text in Research Archive
• Frei, Evgeniia; Frei, Oleksandr; Jaholkowski, Piotr Pawel; Parker, Nadine; Parekh, Pravesh & Shadrin, Alexey [Show all 12 contributors for this article] (2025). The phenotypic and genetic relationship between adolescent mental health and time spent on social media, gaming, and TV. Psychiatry Research. ISSN 0165-1781. 353. doi: 10.1016/j.psychres.2025.116761. Full text in Research Archive
• Shadrin, Alexey; Hindley, Guy; Hagen, Espen; Parker, Nadine; Tesfaye, Markos & Jaholkowski, Piotr Pawel [Show all 16 contributors for this article] (2025). Distinct patterns of genetic overlap among multimorbidities revealed with trivariate MiXeR. Genome Medicine. 17. doi: 10.1186/s13073-025-01528-3. Full text in Research Archive Show summary

  Background: Multimorbidities are a global health challenge. Accumulating evidence indicates that overlapping genetic architectures underlie comorbid complex human traits and disorders. This can be quantified for a pair of phenotypes using various techniques. Still, the pattern of genetic overlap between three distinct complex phenotypes, which is important for understanding multimorbidities, has not been possible to quantify. Methods: Here, we present and validate the novel trivariate MiXeR tool, which disentangles the pattern of genetic overlap between three complex phenotypes using summary statistics from genome-wide association studies. Our simulations show that trivariate MiXeR can reliably reconstruct different patterns of genetic overlap and estimate the proportions of genetic overlap between three phenotypes. Results: We found substantial genetic overlap between gastro-intestinal and brain diseases supporting a genetic basis of the gut-brain axis—the pattern consistent with pairwise analysis. However, the pattern of genetic overlap between three diverse cardiometabolic and renal health indicators and three immune-linked disorders revealed a much larger genomic component shared between all phenotypes than expected from separate pairwise analyses. This suggests the existence of core pathways underlying distinct but related chronic conditions. Conclusions: Overall, trivariate MiXeR offers a novel and efficient tool for investigating patterns of genetic overlap among three complex phenotypes. This contributes to a better understanding of genetic relationships between complex traits and disorders, potentially providing new insights into the mechanisms underlying common multimorbidities. Trivariate MiXeR is freely available at https://github.com/precimed/mix3r .

• Hope, Sigrun; Lin, Aihua; Rødevand, Linn; Hübenette, Saira Jameela; Quintana, Daniel & Sønderby, Ida Elken [Show all 17 contributors for this article] (2025). Shared genetic architecture between autism spectrum disorder, loneliness, and social isolation reveals novel genetic loci. Psychiatric Genetics. ISSN 0955-8829. doi: 10.1097/ypg.0000000000000406. Full text in Research Archive Show summary

  Objective: Deficits in social communication and social interaction are core features of autism spectrum disorder (ASD), and studies suggest that loneliness and social isolation are common. ASD has a strong genetic basis, but the genetic architecture and overlap with social phenotypes are not clear. Methods: We analyzed summary statistics from genome-wide association studies on ASD (46 350), loneliness (452 302), and social isolation (288 950), using linkage disequilibrium score regression, local analysis of covariant annotation (LAVA), bivariate causal mixture model (MiXeR), and the conditional/conjunctional false discovery rate (cond/conjFDR). Results: For ASD and social isolation, we found nonsignificant global genetic correlation ( r g = 0.02, P  = 0.8), but LAVA identified 72 genomic regions with bidirectional correlations, and MiXeR estimated that 8.7 k of 13.1 k variants (81%) were shared, of which 53% had concordant effect directions. For ASD and loneliness, we found a positive genetic correlation ( r g = 0.26, P  = 2e-10), LAVA identified 80 genomic regions with bidirectional genetic correlations, and MiXeR suggested that at least 3.8 k variants were shared. We identified nine specific shared genetic loci between ASD and loneliness and eight between ASD and social isolation (conjFDR < 0.05). Of these, 12 loci were novel for ASD. Genes mapped to these loci are involved in γ-aminobutyric acid (GABA), glutamate, calcium, and stress hormone signaling, cerebral glucose transport, TAU-accumulation, and immune function. Conclusion: We found extensive overlap in genetic architecture between ASD, loneliness, and social isolation, with bidirectional effects. By leveraging data for ASD and social traits, we identified 12 novel ASD related genetic loci implicating several genes, thereby elucidating potential pathways underlying their shared genetic architecture.

• Smeland, Olav B.; Kutrolli, Gleda; Bahrami, Shahram; Fominykh, Vera; Parker, Nadine & Fuhrer, Julian [Show all 21 contributors for this article] (2025). A genome-wide analysis of the shared genetic risk architecture of complex neurological and psychiatric disorders. Nature Neuroscience. ISSN 1097-6256. 28(12), p. 2439–2450. doi: 10.1038/s41593-025-02090-2. Full text in Research Archive Show summary

  Although neurological and psychiatric disorders have historically been considered to reflect distinct pathogenic entities, recent findings suggest shared pathophysiological mechanisms. However, the extent to which these heritable disorders share genetic influences remains unclear. Here we performed a comprehensive analysis of genome-wide association study data, involving nearly 1 million cases across ten neurological diseases and ten psychiatric disorders, to compare their common genetic signal and biological associations. Using complementary statistical tools, we demonstrate that a large set of common genetic variants impacts the risk of multiple neurological and psychiatric disorders, even in the absence of genetic correlations. Furthermore, genome-wide association studies on psychiatric disorders consistently implicate neuronal biology, whereas neurological diseases are associated with diverse neurobiological processes. Together, this study elucidates the genetic relationship between complex neurological and psychiatric disorders, indicating a larger degree of genetic pleiotropy than previously recognized. The findings have implications for disease classification, precision medicine and clinical practice.

• Shorter, John R.; Pasman, Joëlle A.; Kurvits, Siim; Jangmo, Andreas; Naamanka, Joonas & Harder, Arvid [Show all 31 contributors for this article] (2025). Genome-wide association analyses identify distinct genetic architectures for early-onset and late-onset depression. Nature Genetics. ISSN 1061-4036. 57, p. 2972–2979. doi: 10.1038/s41588-025-02396-8. Full text in Research Archive Show summary

  Major depressive disorder (MDD) is a common and heterogeneous disorder of complex etiology. Studying more homogeneous groups stratified according to clinical characteristics, such as age of onset, can improve the identification of the underlying genetic causes and lead to more targeted treatment strategies. We leveraged Nordic biobanks with longitudinal health registries to investigate differences in the genetic architectures of early-onset (eoMDD; n  = 46,708 cases) and late-onset (loMDD; n  = 37,168 cases) MDD. We identified 12 genomic loci for eoMDD and two for loMDD. Overall, the two MDD subtypes correlated moderately (genetic correlation, r g  = 0.58) and differed in their genetic correlations with related traits. These findings suggest that eoMDD and loMDD have partially distinct genetic signatures, with a specific developmental brain signature for eoMDD. Importantly, we demonstrate that polygenic risk scores (PRS) for eoMDD predict suicide attempts within the first 10 years after the initial diagnosis: the absolute risk for suicide attempt was 26% in the top PRS decile, compared to 12% and 20% in the bottom decile and the intermediate group, respectively. Taken together, our findings can inform precision psychiatry approaches for MDD.

• Koch, Elise; Puusepp, Tuuli; Einarsson, Guðmundur; Mitchell, Brittany L.; Harder, Arvid & Lin, Yuhao [Show all 31 contributors for this article] (2025). Genome-wide meta-analyses of non-response to antidepressants provide insights into underlying molecular genetics and suggest potential pharmacotherapies. Molecular Psychiatry. ISSN 1359-4184. doi: 10.1038/s41380-025-03357-7. Full text in Research Archive Show summary

  Antidepressants exhibit a considerable variation in efficacy, and increasing evidence suggests that individual genetics contribute to antidepressant treatment response. Here, we combined data on antidepressant non-response measured using rating scales for depressive symptoms, questionnaires of treatment effect, and data from electronic health records, to increase statistical power to detect genomic loci associated with non-response to antidepressants in a total sample of 135,471 individuals prescribed antidepressants (25,255 non-responders and 110,216 responders). We performed genome-wide association meta-analyses, genetic correlation analyses, leave-one-out polygenic prediction, and bioinformatics analyses for genetically informed drug prioritization. We identified one novel locus (rs1106260) associated with non-response to selective serotonin reuptake inhibitors (SSRIs), and one novel locus (rs60847828) associated with non-response to SSRIs and serotonin-norepinephrine reuptake inhibitors (SNRIs) and showed significant polygenic prediction in independent samples. Genetic correlation analyses show positive associations between non-response to antidepressants and most psychiatric traits, and negative associations with cognitive traits and subjective well-being. In addition, we investigated drugs that target proteins likely involved in mechanisms underlying antidepressant non-response, and shortlisted drugs that warrant further replication and validation of their potential to reduce depressive symptoms in individuals who do not respond to first-line antidepressant medications. These results suggest that meta-analyses of GWAS utilizing real-world measures of treatment outcomes can increase sample sizes to improve the discovery of variants associated with non-response to antidepressants.

• Haram, Marit; Jangmo, Andreas; Jaholkowski, Piotr Pawel; Pasman, Joëlle; Meijsen, Joeri & Shorter, John R. [Show all 15 contributors for this article] (2025). Associations of polygenic risk scores for major depression and depression severity: an investigation of 105 623 individuals with 16 years follow-up. Molecular Psychiatry. ISSN 1359-4184. p. 1–8. doi: 10.1038/s41380-025-03243-2. Full text in Research Archive Show summary

  Genetic risk could be informative for identifying individuals at risk of depression with severe outcomes. With the novel approach of combined health care registries and self-report measures related to depression, the authors aimed to identify the impact of polygenic risk scores (PRS) for major depression on self report measures and a diagnosis of depression across diagnostic thresholds. The study sample comprised participants from the Norwegian Mother, Father and Child Cohort Study with linked information of depression diagnoses during 2006–2022 from health registries. Linear and logistic models were used to estimate the associations between PRS for major depression and self-reported measures related to depression (Symptom Checklist- 5, Satisfaction With Life Scale, Rosenberg Self-Esteem Scale), and a diagnosis of depression. Analyses were performed in groups stratified by level of depression severity defined by registrations in primary and specialist health care. Among the 105 623 individuals included in the study, mean (SD) age was 33.9 (5.3) and 58.5% were female. The associations between PRS for major depression and Rosenberg Self-Esteem Scale were more prominent in individuals with a history of inpatient status compared to outpatient status (β = 0.095, cluster robust 95% CI 0.029–0.162; P  = 0.005) and status of primary care (β = 0.098, cluster robust 95% CI 0.035–0.160; P  = 0.002). PRS for major depression were associated with a diagnosis of depression in all groups of depression severity, with highest effect sizes for more severe types (history of inpatient status: OR = 1.85, cluster robust 95% CI 1.75–1.94; P  < 0.01). The results provide new knowledge of how PRS for major depression vary with depression severity.

• Koch, Elise; Shadrin, Alexey; Parker, Nadine; Lock, Siobhan K.; Smith, Robert Løvsletten & Frei, Oleksandr [Show all 11 contributors for this article] (2025). Polygenic overlap with granulocyte counts identifies novel loci for clozapine metabolism and clozapine-induced agranulocytosis. Neuropsychopharmacology. ISSN 0893-133X. 50, p. 947–955. doi: 10.1038/s41386-025-02054-x. Full text in Research Archive Show summary

  While clozapine is the most effective antipsychotic drug, its use is limited due to hematological adverse effects involving the reduction of granulocyte counts with potential life-threatening agranulocytosis. It is not yet possible to predict or prevent the risk of agranulocytosis, and the mechanisms are unknown but likely related to clozapine metabolism. Genome-wide association studies (GWASs) of clozapine metabolism and clozapine-induced agranulocytosis have identified few genetic loci. We used the largest available GWAS summary statistics of clozapine metabolism (clozapine-to-norclozapine ratio) and clozapine-induced agranulocytosis, applying the conditional false discovery rate (condFDR) method to increase power for genetic discovery by conditioning on granulocyte counts variants. To investigate potential causal effects of shared loci, we performed Mendelian Randomization analyses. After conditioning on granulocyte counts, we identified two novel loci associated with clozapine-to-norclozapine ratio. These loci were significantly associated with clozapine metabolism in a validation sample of 392 clozapine-treated individuals. For clozapine-induced agranulocytosis, five loci were identified after conditioning on granulocyte counts. These five loci were significantly associated with reduced granulocyte counts in a small independent sample of clozapine-treated individuals. Genetic liability to slow clozapine metabolism (high clozapine-to-norclozapine ratio) showed evidence of a causal effect on reduced neutrophil counts, and genetic liability to low neutrophil counts exhibited weak evidence of a causal effect on clozapine-induced agranulocytosis. Our findings of shared genetic variants associated with clozapine metabolism and granulocyte counts may form the basis for developing prediction models for clozapine-induced agranulocytosis.

• O'Connell, Kevin Sean; Koromina, Maria; Veen, Tracy van der; Boltz, Toni; David, Friederike S. & Yang, Jessica Mei Kay [Show all 313 contributors for this article] (2025). Genomics yields biological and phenotypic insights into bipolar disorder. Nature. ISSN 0028-0836. 639, p. 968–975. doi: 10.1038/s41586-024-08468-9. Full text in Research Archive Show summary

  Bipolar disorder is a leading contributor to the global burden of disease1. Despite high heritability (60–80%), the majority of the underlying genetic determinants remain unknown2. We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.8 million controls), combining clinical, community and self-reported samples. We identified 298 genome-wide significant loci in the multi-ancestry meta-analysis, a fourfold increase over previous findings3, and identified an ancestry-specific association in the East Asian cohort. Integrating results from fine-mapping and other variant-to-gene mapping approaches identified 36 credible genes in the aetiology of bipolar disorder. Genes prioritized through fine-mapping were enriched for ultra-rare damaging missense and protein-truncating variations in cases with bipolar disorder4, highlighting convergence of common and rare variant signals. We report differences in the genetic architecture of bipolar disorder depending on the source of patient ascertainment and on bipolar disorder subtype (type I or type II). Several analyses implicate specific cell types in the pathophysiology of bipolar disorder, including GABAergic interneurons and medium spiny neurons. Together, these analyses provide additional insights into the genetic architecture and biological underpinnings of bipolar disorder.

• Wiström, Erik David; O'Connell, Kevin Sean; Koch, Elise; Jaholkowski, Piotr Pawel; Hindley, Guy & Steen, Nils Eiel [Show all 14 contributors for this article] (2025). The genetic overlap between major depressive disorder, white blood cell counts and interleukin 6. Journal of Affective Disorders Reports. 20, p. 1–10. doi: 10.1016/j.jadr.2025.100889. Full text in Research Archive Show summary

  Background: Immune dysregulation may contribute to the pathophysiology of major depressive disorder (MDD). Here we aimed to identify genetic architecture jointly associated with MDD, white blood cell (WBC) count and interleukin 6 (IL-6) levels. Methods: Using genome-wide association studies summary statistics on MDD (330,173 cases and 727,595 controls), WBC counts (nmax = 563,946) and IL-6 (n = 52,654), we performed linkage disequilibrium (LD) score regression, bivariate causal mixture model (MiXeR), conjunctional false discovery rate (conjFDR) and Mendelian randomization (MR) analyses. Additionally, we used an independent MDD dataset (9,582 cases and 84,670 controls) from the Norwegian Mother, Father and Child Cohort Study for polygenic risk score (PRS) analyses. Findings: We found a significant positive genetic correlation (rg = 0.22) between MDD and IL-6. MiXeR estimates indicated substantial differences in the polygenicity of MDD (13.7K variants), WBC subgroups (0.8K-1.8K variants), and IL-6 (0.2K variants), with 10.1 %-31.4 % of the variants influencing WBC subgroups overlapping with MDD. We identified MDD risk loci shared with basophils (8), eosinophils (17), lymphocytes (23), monocytes (14), neutrophils (20), and total WBC counts (20), as well as two loci shared between MDD and IL-6, at conjFDR <0.05. PRS analysis showed a weak, but significantly increased risk for MDD dependent on monocyte count. Limitations: The analyses only included European ancestry samples, and the causal genes associated with the identified genetic loci were not experimentally validated. Conclusions: MDD shares genetic underpinnings with immune system components, which implicates immune-mediated pathways in the pathophysiology of MDD. However, this connection may only be relevant for a minority of patients.

• Fominykh, Vera; Shadrin, Alexey; Jaholkowski, Piotr Pawel; Fuhrer, Julian; Parker, Nadine & Wiström, Erik David [Show all 11 contributors for this article] (2025). Mapping the genetic landscape of immune-mediated disorders: potential implications for classification and therapeutic strategies. Frontiers in Immunology. 16. doi: 10.3389/fimmu.2025.1543781. Full text in Research Archive Show summary

  Objectives: Based on clinical, biomarker, and genetic data, McGonagle and McDermott suggested that autoimmune and autoinflammatory disorders can be classified as a disease continuum from purely autoimmune to autoinflammatory with mixed diseases in between. However, the genetic architecture of this spectrum has not been systematically described. Here, we investigate the continuum of polygenic immune-mediated disorders using genome-wide association studies (GWAS) and statistical genetics methods. Methods: We mapped the genetic landscape of 15 immune-mediated disorders using GWAS summary statistics and methods including genomic structural equation modeling (genomic SEM), linkage disequilibrium score regression, Local Analysis of [co]Variant Association, and Gaussian causal mixture modeling (MiXeR). We performed enrichment analyses of tissues and biological gene sets using MAGMA. Results: Genomic SEM suggested a continuum structure with four underlying latent factors from autoimmune diseases at one end to autoinflammatory on the opposite end. Across disorders, we observed a balanced mixture of negative and positive local genetic correlations within the major histocompatibility complex, while outside this region, local genetic correlations were predominantly positive. MiXeR analysis showed large genetic overlap in accordance with the continuum landscape. MAGMA analysis implicated genes associated with known monogenic immune diseases for prominent autoimmune and autoinflammatory component. Conclusions: Our findings support a polygenic continuum across immune-mediated disorders, with four genetic clusters. The “polygenic autoimmune” and “polygenic autoinflammatory” clusters reside on margins of this continuum. These findings provide insights and lead us to hypothesize that the identified clusters could inform future therapeutical strategies, with patients in the same clusters potentially responding similarly to specific therapies.

• Meer, Dennis van der; Hindley, Guy; Shadrin, Alexey; Smeland, Olav Bjerkehagen; Parker, Nadine & Dale, Anders M. [Show all 8 contributors for this article] (2025). Mapping the Genetic Landscape of Psychiatric Disorders With the MiXeR Toolset. Biological Psychiatry. ISSN 0006-3223. 98(6), p. 455–465. doi: 10.1016/j.biopsych.2025.02.886. Full text in Research Archive Show summary

  Psychiatric disorders have complex genetic architectures with substantial genetic overlap across conditions, which may partially explain their high levels of comorbidity. This presents significant challenges to research. Genome-wide association studies (GWASs) have uncovered hundreds of loci associated with single disorders, but the genetic landscape of psychiatric disorders has remained largely obscure. Moving beyond the conventional infinitesimal model, uni-, bi-, and trivariate MiXeR tools, applied to GWAS summary statistics, has enabled us to more comprehensively describe the genetic architecture of complex disorders and traits and their overlap. Furthermore, the GSA-MiXeR tool improves biological interpretation of GWAS findings to better elucidate causal mechanisms. Here, we outline the methodology that underlies the MiXeR tools together with instructions for their optimal use. We review results from studies that have investigated the genetic architecture of psychiatric disorders and their overlap using the MiXeR toolset. These studies have revealed generally high polygenicity and low discoverability among psychiatric disorders, particularly in contrast to somatic disorders. There is also pervasive genetic overlap across psychiatric disorders and behavioral traits, while their overlap with somatic traits is smaller, consistent with differences in polygenicity. Finally, GSA-MiXeR has quantified the contribution of gene sets to the heritability of psychiatric disorders, prioritizing small, biologically coherent gene sets. Together, these findings have implications for our understanding of the complex relationships between psychiatric disorders and related traits. MiXeR tools have provided new insights into the genetic architecture of psychiatric disorders, generating a better understanding of their underlying biological mechanisms and potential for clinical utility.

• Icick, Romain; Shadrin, Alexey; Holen, Børge; Karadag, Naz; Parker, Nadine & O'Connell, Kevin Sean [Show all 20 contributors for this article] (2025). Identification of risk variants and cross-disorder pleiotropy through multi-ancestry genome-wide analysis of alcohol use disorder. Nature Mental Health. 3(2), p. 253–265. doi: 10.1038/s44220-024-00353-8. Full text in Research Archive Show summary

  Alcohol use disorder (AUD) is highly heritable and burdensome worldwide. Genome-wide association studies can provide new evidence regarding the etiology of AUD. We report a multi-ancestry genome-wide association study focusing on a narrow AUD phenotype, using novel statistical tools in a total sample of 1,041,450 individuals (102,079 cases; European, 75,583; African, 20,689 (mostly African American); Hispanic American, 3,449; East Asian, 2,254; South Asian, 104; descent). Cross-ancestry functional analyses were performed with European and African samples. Thirty-seven genome-wide significant loci (105 variants) were identified, of which seven were novel for AUD and six for other alcohol phenotypes. Loci were mapped to genes, which show altered expression in brain regions relevant for AUD (striatum, hypothalamus and prefrontal cortex) and encode potential drug targets (GABAergic, dopaminergic and serotonergic neurons). African-specific analysis yielded a unique pattern of immune-related gene sets. Polygenic overlap and positive genetic correlations showed extensive shared genetic architecture between AUD and both mental and general medical phenotypes, suggesting that they are not only complications of alcohol use but also share genetic liability with AUD. Leveraging a cross-ancestry approach allowed identification of novel genetic loci for AUD and underscores the value of multi-ancestry genetic studies. These findings advance our understanding of AUD risk and clinically relevant comorbidities.

• Meer, Dennis van der; Rahman, Mohammad Zillur; Ottas, Aigar; Parekh, Pravesh; Kutrolli, Gleda & Stinson, Sara Elizabeth [Show all 37 contributors for this article] (2025). Pleiotropic and sex-specific genetic mechanisms of circulating metabolic markers. Nature Communications. 16(1). doi: 10.1038/s41467-025-60058-z. Full text in Research Archive Show summary

  Metabolites in plasma form biosignatures of a range of common complex human diseases. Discovering variants with pleiotropic effects across metabolites can reveal underlying biological mechanisms. We therefore performed uni- and multivariate genome-wide association studies (GWAS) on 249 circulating metabolic markers across 328,006 UK Biobank and Estonian Biobank participants. We investigated rare variation through whole exome sequencing gene burden tests, analysed the role of body mass index through Mendelian randomization, and performed genome-wide interaction analyses with sex. We discovered 15,585 loci summed over the univariate GWAS, with high pleiotropy across markers, linked to a wide range of disorders. Findings from common and rare variant gene tests converged on lipid homeostasis pathways. 31 loci interacted with sex, mapped to genes involved in cholesterol processing. The findings offer insights into the genetic architecture of circulating metabolites, revealing pleiotropic loci, highlighting the role of rare variation, and uncovering sex-specific molecular mechanisms of lipid metabolism.

• Jaholkowski, Piotr Pawel; Bahrami, Shahram; Fominykh, Vera; Hindley, Guy; Tesfaye, Markos & Parekh, Pravesh [Show all 23 contributors for this article] (2024). Charting the shared genetic architecture of Alzheimer's disease, cognition, and educational attainment, and associations with brain development. Neurobiology of Disease. ISSN 0969-9961. 203. doi: 10.1016/j.nbd.2024.106750. Full text in Research Archive Show summary

  The observation that the risk of developing Alzheimer's disease is reduced in individuals with high premorbid cognitive functioning, higher educational attainment, and occupational status has led to the ‘cognitive reserve’ hypothesis. This hypothesis suggests that individuals with greater cognitive reserve can tolerate a more significant burden of neuropathological changes before the onset of cognitive decline. The underpinnings of cognitive reserve remain poorly understood, although a shared genetic basis between measures of cognitive reserve and Alzheimer's disease has been suggested. Using the largest samples to date and novel statistical tools, we aimed to investigate shared genetic variants between Alzheimer's disease, and measures of cognitive reserve; cognition and educational attainment to identify molecular and neurobiological foundations. We applied the causal mixture model (MiXeR) to estimate the number of trait-influencing variants shared between Alzheimer's disease, cognition, and educational attainment, and condFDR/conjFDR to identify shared loci. To provide biological insights loci were functionally characterized. Subsequently, we constructed a Structural Equation Model (SEM) to determine if the polygenic foundation of cognition has a direct impact on Alzheimer's disease risk, or if its effect is mediated through established risk factors for the disease, using a case-control sample from the UK Biobank. Univariate MiXeR analysis (after excluding chromosome 19) revealed that Alzheimer's disease was substantially less polygenic (450 trait-influencing variants) compared to cognition (11,100 trait-influencing variants), and educational attainment (12,700 trait-influencing variants). Bivariate MiXeR analysis estimated that Alzheimer's disease shared approximately 70 % of trait-influencing variants with cognition, and approximately 40 % with educational attainment, with mixed effect directions. Using condFDR analysis, we identified 18 loci jointly associated with Alzheimer's disease and cognition and 6 loci jointly associated with Alzheimer's disease and educational attainment. Genes mapped to shared loci were associated with neurodevelopment, expressed in early life, and implicated the dendritic tree and phosphatidylinositol phosphate binding mechanisms. Spatiotemporal gene expression analysis of the identified genes showed that mapped genes were highly expressed during the mid-fetal period, further suggesting early neurodevelopmental stages as critical periods for establishing cognitive reserve which affect the risk of Alzheimer's disease in old age. Furthermore, our SEM analysis showed that genetic variants influencing cognition had a direct effect on the risk of developing Alzheimer's disease, providing evidence in support of the neurodevelopmental hypothesis of the disease.

• Roelfs, Daniel; Meer, Dennis van der; Alnæs, Dag; Frei, Oleksandr; Shadrin, Alexey & Loughnan, Robert [Show all 11 contributors for this article] (2024). Genetic overlap between multivariate measures of human functional brain connectivity and psychiatric disorders. Nature Mental Health. 2(2), p. 189–199. doi: 10.1038/s44220-023-00190-1. Full text in Research Archive
• Hestetun, Sigrid Valen; Rudsari, Hamid Khoshfekr; Jaholkowski, Piotr Pawel; Shadrin, Alexey; Haftorn, Kristine Løkås & Andersen, Svend [Show all 13 contributors for this article] (2024). Incidence and Genetic Risk of Juvenile Idiopathic Arthritis in Norway by Latitude. Arthritis & Rheumatology. ISSN 2326-5191. 77(4), p. 458–467. doi: 10.1002/art.43040. Full text in Research Archive Show summary

  Objective: We aimed to investigate the incidence of juvenile idiopathic arthritis (JIA) in the three geographic regions of Norway and whether potential regional incidence differences are explained by environmental or genetic factors across regions. Methods: We conducted a register-based cohort study including all Norwegian children born from 2004 to 2019, with follow-up throughout 2020. The JIA diagnosis, defined by at least two International Classification of Diseases, Tenth Revision codes for JIA, was validated against medical records. The incidence rate (IR) and hazard ratio (HR) for JIA were estimated for all Norway and for the North, Mid, and South regions. In a subsample from the Norwegian Mother, Father, and Child Cohort Study (MoBa), the genetic risk for JIA was assessed in the three regions. Results: After median 9.1 (range 0.3-16.0) years of follow-up, we identified 1,184 patients with JIA and 910,058 controls. The IR for JIA/100,000 person-years was 14.4 in all of Norway, 25.9 in the North region, 17.9 in the Mid region, and 12.5 in the South region. The HR (95% confidence interval [CI]) of JIA in the North region was 2.07 (1.77-2.43) and in the Mid region HR 1.43 (95% CI 1.23-1.67) compared with the South region. Adjustments for perinatal factors, socioeconomic status, and early antibiotic exposure did not change our estimates substantially. In MoBa (238 patients with JIA, 57,392 controls), the association between JIA and region of birth was no longer significant when adjusting for genetic factors. Conclusion: We found a higher incidence of JIA with increasing latitude without evidence for available environmental factors explaining the observed gradient. In contrast, genetic factors modified the association, but further studies are warranted.

• Smith, Diana M.; Parekh, Pravesh; Kennedy, Joseph; Loughnan, Robert; Frei, Oleksandr & Nichols, Thomas E. [Show all 9 contributors for this article] (2024). Partitioning variance in cortical morphometry into genetic, environmental, and subject-specific components. Cerebral Cortex. ISSN 1047-3211. 34(6). doi: 10.1093/cercor/bhae234. Full text in Research Archive
• Bergstedt, Jacob; Pasman, Joëlle A.; Ma, Ziyan; Harder, Arvid; Yao, Shuyang & Parker, Nadine [Show all 26 contributors for this article] (2024). Distinct biological signature and modifiable risk factors underlie the comorbidity between major depressive disorder and cardiovascular disease. Nature Cardiovascular Research. 3(6), p. 754–769. doi: 10.1038/s44161-024-00488-y. Full text in Research Archive Show summary

  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.

• Tissink, E.P.; Shadrin, Alexey; Meer, Dennis van der; Parker, Nadine; Hindley, Guy & Roelfs, Daniel [Show all 18 contributors for this article] (2024). Abundant pleiotropy across neuroimaging modalities identified through a multivariate genome-wide association study. Nature Communications. 15. doi: 10.1038/s41467-024-46817-4. Full text in Research Archive Show summary

  Genetic pleiotropy is abundant across spatially distributed brain characteristics derived from one neuroimaging modality (e.g. structural, functional or diffusion magnetic resonance imaging [MRI]). A better understanding of pleiotropy across modalities could inform us on the integration of brain function, micro- and macrostructure. Here we show extensive genetic overlap across neuroimaging modalities at a locus and gene level in the UK Biobank (N = 34,029) and ABCD Study (N = 8607). When jointly analysing phenotypes derived from structural, functional and diffusion MRI in a genome-wide association study (GWAS) with the Multivariate Omnibus Statistical Test (MOSTest), we boost the discovery of loci and genes beyond previously identified effects for each modality individually. Cross-modality genes are involved in fundamental biological processes and predominantly expressed during prenatal brain development. We additionally boost prediction of psychiatric disorders by conditioning independent GWAS on our multimodal multivariate GWAS. These findings shed light on the shared genetic mechanisms underlying variation in brain morphology, functional connectivity, and tissue composition.

• Karadag, Naz; Hagen, Espen; Shadrin, Alexey; Meer, Dennis van der; O'Connell, Kevin Sean & Rahman, Mohammad Zillur [Show all 18 contributors for this article] (2024). Dissecting the Shared Genetic Architecture of Common Epilepsies With Cortical Brain Morphology. Neurology: Genetics. 10(3). doi: 10.1212/NXG.0000000000200143. Full text in Research Archive Show summary

  Background and Objectives: Epilepsies are associated with differences in cortical thickness (TH) and surface area (SA). However, the mechanisms underlying these relationships remain elusive. We investigated the extent to which these phenotypes share genetic influences. Methods: We analyzed genome-wide association study data on common epilepsies (n = 69,995) and TH and SA (n = 32,877) using Gaussian mixture modeling MiXeR and conjunctional false discovery rate (conjFDR) analysis to quantify their shared genetic architecture and identify overlapping loci. We biologically interrogated the loci using a variety of resources and validated in independent samples. Results: The epilepsies (2.4 k–2.9 k variants) were more polygenic than both SA (1.8 k variants) and TH (1.3 k variants). Despite absent genome-wide genetic correlations, there was a substantial genetic overlap between SA and genetic generalized epilepsy (GGE) (1.1 k), all epilepsies (1.1 k), and juvenile myoclonic epilepsy (JME) (0.7 k), as well as between TH and GGE (0.8 k), all epilepsies (0.7 k), and JME (0.8 k), estimated with MiXeR. Furthermore, conjFDR analysis identified 15 GGE loci jointly associated with SA and 15 with TH, 3 loci shared between SA and childhood absence epilepsy, and 6 loci overlapping between SA and JME. 23 loci were novel for epilepsies and 11 for cortical morphology. We observed a high degree of sign concordance in the independent samples. Discussion: Our findings show extensive genetic overlap between generalized epilepsies and cortical morphology, indicating a complex genetic relationship with mixed-effect directions. The results suggest that shared genetic influences may contribute to cortical abnormalities in epilepsies.

• Ivarsdottir, Erna V.; Gudmundsson, Julius; Tragante, Vinicius; Sveinbjornsson, Gardar; Kristmundsdottir, Snaedis & Stacey, Simon N. [Show all 58 contributors for this article] (2024). Gene-based burden tests of rare germline variants identify six cancer susceptibility genes. Nature Genetics. ISSN 1061-4036. 56(11), p. 2422–2433. doi: https:/rdcu.be/dYuc6. Full text in Research Archive
• Parekh, Pravesh; Fan, Chun Chieh; Frei, Oleksandr; Palmer, Clare E.; Smith, Diana M. & Makowski, Carolina [Show all 17 contributors for this article] (2024). FEMA: Fast and efficient mixed-effects algorithm for large sample whole-brain imaging data. Human Brain Mapping. ISSN 1065-9471. 45(2). doi: 10.1002/hbm.26579. Full text in Research Archive Show summary

  The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME, however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross-sectional and longitudinal effects of age on region-of-interest level and vertex-wise cortical thickness, as well as connectome-wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive DevelopmentSM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex-wise cortical thickness and connectome-wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/.

• Tesfaye, Markos; Jaholkowski, Piotr Pawel; Shadrin, Alexey; Meer, Dennis van der; Hindley, Guy & Holen, Børge [Show all 18 contributors for this article] (2024). Identification of novel genomic loci for anxiety symptoms and extensive genetic overlap with psychiatric disorders. Psychiatry and Clinical Neurosciences. ISSN 1323-1316. 78(12), p. 783–791. doi: 10.1111/pcn.13742. Full text in Research Archive Show summary

  Aims: Anxiety disorders are prevalent and anxiety symptoms (ANX) co-occur with many psychiatric disorders. We aimed to identify genomic loci associated with ANX, characterize its genetic architecture, and genetic overlap with psychiatric disorders. Methods: We included a genome-wide association study of ANX (meta-analysis of UK Biobank and Million Veterans Program, n = 301,732), schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD), and validated the findings in the Norwegian Mother, Father, and Child Cohort (n = 95,841). We employed the bivariate causal mixture model and local analysis of covariant association to characterize the genetic architecture including overlap between the phenotypes. Conditional and conjunctional false discovery rate analyses were performed to boost the identification of loci associated with anxiety and shared with psychiatric disorders. Results: Anxiety was polygenic with 12.9k genetic variants and overlapped extensively with psychiatric disorders (4.1k–11.4k variants) with predominantly positive genetic correlations between anxiety and psychiatric disorders. We identified 119 novel loci for anxiety by conditioning on the psychiatric disorders, and loci shared between anxiety and MD (n=47), BIP (n=33), SCZ (n=71), ADHD (n=20), and ASD (n=5). Genes annotated to anxiety loci exhibit enrichment for a broader range of biological pathways including cell adhesion and neurofibrillary tangle compared with genes annotated to the shared loci. Conclusions: Anxiety is highly polygenic phenotype with extensive genetic overlap with psychiatric disorders, and we identified novel loci for anxiety implicating new molecular pathways. The shared genetic architecture may underlie the extensive cross-disorder comorbidity of anxiety, and the identified molecular underpinnings may lead to potential drug targets.

• Frei, Oleksandr; Hindley, Guy; Shadrin, Alexey; Meer, Dennis van der; Akdeniz, Bayram Cevdet & Hagen, Espen [Show all 50 contributors for this article] (2024). Improved functional mapping of complex trait heritability with GSA-MiXeR implicates biologically specific gene sets. Nature Genetics. ISSN 1061-4036. 56(6), p. 1310–1318. doi: 10.1038/s41588-024-01771-1. Full text in Research Archive
• Loughnan, Robert; Ahern, Jonathan; Boyle, Mary; Jernigan, Terry L.; Jr, Donald J. Hagler & Iversen, John R. [Show all 15 contributors for this article] (2024). Hemochromatosis neural archetype reveals iron disruption in motor circuits. Science Advances. 10(47). doi: 10.1126/sciadv.adp4431. Full text in Research Archive Show summary

  Our understanding of brain iron regulation and its disruption in disease is limited. Excess iron affects motor circuitry, contributing to Parkinson’s disease (PD) risk. The molecular mechanisms regulating central iron levels, beyond a few well-known genes controlling peripheral iron, remain unclear. We generated scores based on the archetypal brain iron accumulation observed in magnetic resonance imaging scans of individuals with excessive dietary iron absorption and hemochromatosis risk. Genome-wide analysis revealed that this score is highly heritable, identifying loci associated with iron homeostasis, and driven by peripheral iron levels. Our score predicted gait abnormalities and showed a U-shaped relationship with PD risk, identifying individuals with threefold increased risk. These results establish a hormetic relationship between brain iron and PD risk, where central iron levels are strongly determined by genetics via peripheral iron. This framework combining forward and reverse genetics is a powerful study design to understand genomic drivers underlying high dimensional phenotypes.

• Karadag, Naz; Hagen, Espen; Shadrin, Alexey; Meer, Dennis van der; O'Connell, Kevin Sean & Rahman, Mohammad Zillur [Show all 19 contributors for this article] (2024). Unraveling the shared genetics of common epilepsies and general cognitive ability. Seizure. ISSN 1059-1311. 122, p. 105–112. doi: 10.1016/j.seizure.2024.09.016. Full text in Research Archive Show summary

  Purpose Cognitive impairment is prevalent among individuals with epilepsy, and increasing evidence indicates that genetic factors can underlie this relationship. However, the extent to which epilepsy subtypes differ in their genetic relationship with cognitive function, and information about the specific genetic variants involved remain largely unknown. Methods We investigated the genetic relationship between epilepsies and general cognitive ability (COG) using complementary statistical tools, including linkage disequilibrium score (LDSC) regression, MiXeR and conjunctional false discovery rate (conjFDR). We analyzed genome-wide association study data on COG (n = 269,867) and common epilepsies (n = 27,559 cases, 42,436 controls), including the broad phenotypes ‘all epilepsy’, focal epilepsies and genetic generalized epilepsies (GGE), as well as specific subtypes. We functionally annotated the identified loci using several biological resources and validated the results in independent samples. Results Using MiXeR, COG (11.2k variants) was estimated to be almost four times more polygenic than ‘all epilepsy’, GGE, juvenile myoclonic epilepsy (JME), and childhood absence epilepsy (CAE) (2.5k – 2.9k variants). The other epilepsy phenotypes were insufficiently powered for MiXeR analysis. We quantified extensive genetic overlap between COG and epilepsy types, but with varying negative genetic correlations (-0.23 to -0.04). COG was estimated to share 2.9k variants with both GGE and ‘all epilepsy’, and 2.3k variants with both JME and CAE. Using conjFDR, we identified 66 distinct loci shared between COG and epilepsies, including novel associations for GGE (27), ‘all epilepsy’ (5), JME (5) and CAE (5). The implicated genes were significantly expressed in multiple brain regions. The results were validated in independent samples (COG: p = 3.62 × 10–7; ‘all epilepsy’: p = 2.58 × 10–3). Conclusion Our study further dissects the substantial genetic basis shared between epilepsies and COG and identifies novel shared loci. An improved understanding of the genetic relationship between epilepsies and COG may lead to the development of novel comorbidity-targeted epilepsy treatments.

• Akdeniz, Bayram Cevdet; Frei, Oleksandr; Shadrin, Alexey; Vetrov, Dmitry; Kropotov, Dmitry & Hovig, Eivind [Show all 8 contributors for this article] (2024). Finemap-MiXeR: A variational Bayesian approach for genetic finemapping. PLoS Genetics. ISSN 1553-7390. 20(8). doi: 10.1371/journal.pgen.1011372. Full text in Research Archive Show summary

  Genome-wide association studies (GWAS) implicate broad genomic loci containing clusters of highly correlated genetic variants. Finemapping techniques can select and prioritize variants within each GWAS locus which are more likely to have a functional influence on the trait. Here, we present a novel method, Finemap-MiXeR, for finemapping causal variants from GWAS summary statistics, controlling for correlation among variants due to linkage disequilibrium. Our method is based on a variational Bayesian approach and direct optimization of the Evidence Lower Bound (ELBO) of the likelihood function derived from the MiXeR model. After obtaining the analytical expression for ELBO’s gradient, we apply Adaptive Moment Estimation (ADAM) algorithm for optimization, allowing us to obtain the posterior causal probability of each variant. Using these posterior causal probabilities, we validated Finemap-MiXeR across a wide range of scenarios using both synthetic data, and real data on height from the UK Biobank. Comparison of Finemap-MiXeR with two existing methods, FINEMAP and SuSiE RSS, demonstrated similar or improved accuracy. Furthermore, our method is computationally efficient in several aspects. For example, unlike many other methods in the literature, its computational complexity does not increase with the number of true causal variants in a locus and it does not require any matrix inversion operation. The mathematical framework of Finemap-MiXeR is flexible and may also be applied to other problems including cross-trait and cross-ancestry finemapping.

• Gurholt, Tiril Pedersen; Elvsåshagen, Torbjørn; Bahrami, Shahram; Rahman, Mohammad Zillur; Shadrin, Alexey & Askeland-Gjerde, Daniel E. [Show all 13 contributors for this article] (2024). Large-scale brainstem neuroimaging and genetic analyses provide new insights into the neuronal mechanisms of hypertension. Human Genetics and Genomics Advances (HGG Advances). 6(1). doi: 10.1016/j.xhgg.2024.100392. Full text in Research Archive Show summary

  While brainstem regions are central regulators of blood pressure, the neuronal mechanisms underlying their role in hypertension remain poorly understood. Here, we investigated the structural and genetic relationships between global and regional brainstem volumes and blood pressure. We used magnetic resonance imaging data from n = 32,666 UK Biobank participants, and assessed the association of volumes of the whole brainstem and its main regions with blood pressure. We applied powerful statistical genetic tools, including bivariate causal mixture modeling (MiXeR) and conjunctional false discovery rate (conjFDR), to non-overlapping genome-wide association studies of brainstem volumes (n = 27,034) and blood pressure (n = 321,843) in the UK Biobank cohort. We observed negative associations between the whole brainstem and medulla oblongata volumes and systolic blood and pulse pressure, and positive relationships between midbrain and pons volumes and blood pressure traits when adjusting for the whole brainstem volume (all partial correlation coefficients ∣r∣ effects between 0.03 and 0.05, p ≤ 0.0042). We observed the largest genetic overlap for the whole brainstem, sharing 77% of its trait-influencing variants with blood pressure. We identified 65 shared loci between brainstem volumes and blood pressure traits and mapped these to 71 genes, implicating molecular pathways linked to sympathetic nervous system development, metal ion transport, and vascular homeostasis. The present findings support a link between brainstem structures and blood pressure and provide insights into their shared genetic underpinnings. The overlapping genetic architectures and mapped genes offer mechanistic information about the roles of brainstem regions in hypertension.

• Akdeniz, Bayram Cevdet; Frei, Oleksandr; Hagen, Espen; Filiz, Tahir Tekin; Karthikeyan, Sandeep & Pasman, Joëlle [Show all 18 contributors for this article] (2024). COSGAP: COntainerized Statistical Genetics Analysis Pipelines. Bioinformatics Advances. 4(1). doi: 10.1093/bioadv/vbae067. Full text in Research Archive Show 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.

• Wootton, Olivia; Shadrin, Alexey; Bjella, Thomas; Smeland, Olav Bjerkehagen; Meer, Dennis van der & Frei, Oleksandr [Show all 11 contributors for this article] (2024). Genomic insights into the shared and distinct genetic architecture of cognitive function and schizophrenia. Scientific Reports. 14(1). doi: 10.1038/s41598-024-66085-y. Full text in Research Archive Show summary

  Cognitive impairment is a major determinant of functional outcomes in schizophrenia, however, understanding of the biological mechanisms underpinning cognitive dysfunction in the disorder remains incomplete. Here, we apply Genomic Structural Equation Modelling to identify latent cognitive factors capturing genetic liabilities to 12 cognitive traits measured in the UK Biobank. We identified three broad factors that underly the genetic correlations between the cognitive tests. We explore the overlap between latent cognitive factors, schizophrenia, and schizophrenia symptom dimensions using a complementary set of statistical approaches, applied to data from the latest schizophrenia genome-wide association study (Ncase = 53,386, Ncontrol = 77,258) and the Thematically Organised Psychosis study (Ncase = 306, Ncontrol = 1060). Global genetic correlations showed a significant moderate negative genetic correlation between each cognitive factor and schizophrenia. Local genetic correlations implicated unique genomic regions underlying the overlap between schizophrenia and each cognitive factor. We found substantial polygenic overlap between each cognitive factor and schizophrenia and biological annotation of the shared loci implicated gene-sets related to neurodevelopment and neuronal function. Lastly, we show that the common genetic determinants of the latent cognitive factors are not predictive of schizophrenia symptoms in the Norwegian Thematically Organized Psychosis cohort. Overall, these findings inform our understanding of cognitive function in schizophrenia by demonstrating important differences in the shared genetic architecture of schizophrenia and cognitive abilities.

• Steen, Nils Eiel; Rahman, Zillur; Szabo, Attila; Hindley, Guy; Parker, Nadine & Cheng, Weiqiu [Show all 20 contributors for this article] (2023). Shared Genetic Loci Between Schizophrenia and White Blood Cell Counts Suggest Genetically Determined Systemic Immune Abnormalities. Schizophrenia Bulletin. ISSN 0586-7614. 49(5), p. 1345–1354. doi: 10.1093/schbul/sbad082. Full text in Research Archive Show summary

  Background: Immune mechanisms are indicated in schizophrenia (SCZ). Recent genome-wide association studies (GWAS) have identified genetic variants associated with SCZ and immune-related phenotypes. Here, we use cutting edge statistical tools to identify shared genetic variants between SCZ and white blood cell (WBC) counts and further understand the role of the immune system in SCZ. Study Design: GWAS results from SCZ (patients, n = 53 386; controls, n = 77 258) and WBC counts (n = 56 3085) were analyzed. We applied linkage disequilibrium score regression, the conditional false discovery rate method and the bivariate causal mixture model for analyses of genetic associations and overlap, and 2 sample Mendelian randomization to estimate causal effects. Study Results: The polygenicity for SCZ was 7.5 times higher than for WBC count and constituted 32%–59% of WBC count genetic loci. While there was a significant but weak positive genetic correlation between SCZ and lymphocytes (rg = 0.05), the conditional false discovery rate method identified 383 shared genetic loci (53% concordant effect directions), with shared variants encompassing all investigated WBC subtypes: lymphocytes, n = 215 (56% concordant); neutrophils, n = 158 (49% concordant); monocytes, n = 146 (47% concordant); eosinophils, n = 135 (56% concordant); and basophils, n = 64 (53% concordant). A few causal effects were suggested, but consensus was lacking across different Mendelian randomization methods. Functional analyses indicated cellular functioning and regulation of translation as overlapping mechanisms. Conclusions: Our results suggest that genetic factors involved in WBC counts are associated with the risk of SCZ, indicating a role of immune mechanisms in subgroups of SCZ with potential for stratification of patients for immune targeted treatment.

• Rødevand, Linn Nilsen; Rahman, Zillur; Hindley, Guy Frederick Lanyon; Smeland, Olav Bjerkehagen; Frei, Oleksandr & Filiz, Tahir Tekin [Show all 15 contributors for this article] (2023). Characterizing the Shared Genetic Underpinnings of Schizophrenia and Cardiovascular Disease Risk Factors. American Journal of Psychiatry. ISSN 0002-953X. 180(11), p. 815–826. doi: 10.1176/APPI.AJP.20220660. Full text in Research Archive
• Jaholkowski, Piotr Pawel; Shadrin, Alexey; Jangmo, Anders Andreas Krister; Frei, Evgeniia; Woldeyohannes, Markos Tesfaye & Hindley, Guy Frederick Lanyon [Show all 23 contributors for this article] (2023). Associations Between Symptoms of Premenstrual Disorders and Polygenic Liability for Major Psychiatric Disorders. JAMA psychiatry. ISSN 2168-6238. doi: 10.1001/jamapsychiatry.2023.1137. Full text in Research Archive
• Hindley, Guy; Drange, Ole Kristian; Lin, Aihua; Kutrolli, Gleda; Shadrin, Alexey & Parker, Nadine [Show all 20 contributors for this article] (2023). Cross-trait genome-wide association analysis of C-reactive protein level and psychiatric disorders. Psychoneuroendocrinology. ISSN 0306-4530. 157. doi: 10.1016/j.psyneuen.2023.106368. Full text in Research Archive Show summary

  C-reactive protein (CRP) tends to be elevated in individuals with psychiatric disorders. Recent findings have suggested a protective effect of the genetic liability to elevated CRP on schizophrenia risk and a causative effect on depression despite weak genetic correlations, while causal relationships with bipolar disorder were inconclusive. We investigated the shared genetic underpinnings of psychiatric disorders and variation in CRP levels. Genome-wide association studies for CRP (n = 575,531), bipolar disorder (n = 413,466), depression (n = 480,359), and schizophrenia (n = 130,644) were used in causal mixture models to compare CRP with psychiatric disorders based on polygenicity, discoverability, and genome-wide genetic overlap. The conjunctional false discovery rate method was used to identify specific shared genetic loci. Shared variants were mapped to putative causal genes, which were tested for overrepresentation among gene ontology gene-sets. CRP was six to ten times less polygenic (n = 1400 vs 8600–14,500 variants) and had a discoverability one to two orders of magnitude higher than psychiatric disorders. Most CRP-associated variants were overlapping with psychiatric disorders. We identified 401 genetic loci jointly associated with CRP and psychiatric disorders with mixed effect directions. Gene-set enrichment analyses identified predominantly CNS-related gene sets for CRP and each of depression and schizophrenia, and basic cellular processes for CRP and bipolar disorder. In conclusion, CRP has a markedly different genetic architecture to psychiatric disorders, but the majority of CRP associated variants are also implicated in psychiatric disorders. Shared genetic loci implicated CNS-related processes to a greater extent than immune processes, which may have implications for how we conceptualise causal relationships between CRP and psychiatric disorders.

• Campbell, Megan L.; Dalvie, Shareefa; Shadrin, Alexey; Meer, Dennis van der; O'Connell, Kevin Sean & Frei, Oleksandr [Show all 9 contributors for this article] (2023). Distributed Genetic Effects of the Corpus Callosum Subregions Suggest Links to Neuropsychiatric Disorders and Related Traits. Acta Neuropsychiatrica. ISSN 0924-2708. doi: 10.1017/neu.2023.32. Full text in Research Archive Show summary

  Background: The corpus callosum (CC) is a brain structure with a high heritability and potential role in psychiatric disorders. However, the genetic architecture of the CC and the genetic link with psychiatric disorders remain largely unclear. We investigated the genetic architectures of the volume of the CC and its subregions and the genetic overlap with psychiatric disorders. Methods: We applied multivariate genome-wide association study (GWAS) to genetic and T1-weighted magnetic resonance imaging (MRI) data of 40,894 individuals from the UK Biobank, aiming to boost genetic discovery and to assess the pleiotropic effects across volumes of the five subregions of the CC (posterior, mid-posterior, central, mid-anterior and anterior) obtained by FreeSurfer 7.1. Multivariate GWAS was run combining all subregions, co-varying for relevant variables. Gene-set enrichment analyses were performed using MAGMA. Linkage disequilibrium score regression (LDSC) was used to determine Single nucleotide polymorphism (SNP)-based heritability of total CC volume and volumes of its subregions as well as their genetic correlations with relevant psychiatric traits. Results: We identified 70 independent loci with distributed effects across the five subregions of the CC (p < 5 × 10−8). Additionally, we identified 33 significant loci in the anterior subregion, 23 in the mid-anterior, 29 in the central, 7 in the mid-posterior and 56 in the posterior subregion. Gene-set analysis revealed 156 significant genes contributing to volume of the CC subregions (p < 2.6 × 10−6). LDSC estimated the heritability of CC to (h2SNP = 0.38, SE = 0.03) and subregions ranging from 0.22 (SE = 0.02) to 0.37 (SE = 0.03). We found significant genetic correlations of total CC volume with bipolar disorder (BD, rg = −0.09, SE = 0.03; p = 5.9 × 10−3) and drinks consumed per week (rg = −0.09, SE = 0.02; p = 4.8 × 10−4), and volume of the mid-anterior subregion with BD (rg = −0.12, SE = 0.02; p = 2.5 × 10−4), major depressive disorder (MDD) (rg = −0.12, SE = 0.04; p = 3.6 × 10−3), drinks consumed per week (rg = −0.13, SE = 0.04; p = 1.8 × 10−3) and cannabis use (rg = −0.09, SE = 0.03; p = 8.4 × 10−3). Conclusions: Our results demonstrate that the CC has a polygenic architecture implicating multiple genes and show that CC subregion volumes are heritable. We found that distinct genetic factors are involved in the development of anterior and posterior subregions, consistent with their divergent functional specialisation. Significant genetic correlation between volumes of the CC and BD, drinks per week, MDD and cannabis consumption subregion volumes with psychiatric traits is noteworthy and deserving of further investigation.

• Hope, Sigrun; Shadrin, Alexey; Lin, Aihua; Bahrami, Shahram; Rødevand, Linn & Frei, Oleksandr [Show all 17 contributors for this article] (2023). Bidirectional genetic overlap between autism spectrum disorder and cognitive traits. Translational Psychiatry. 13(1), p. 1–9. doi: 10.1038/s41398-023-02563-7. Full text in Research Archive Show summary

  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.

• Akdeniz, Bayram Cevdet; Mattingsdal, Morten; Dominguez-Valentin, Mev; Frei, Oleksandr; Shadrin, Alexey & Puustusmaa, Mikk [Show all 12 contributors for this article] (2023). A Breast Cancer Polygenic Risk Score Is Feasible for Risk Stratification in the Norwegian Population. Cancers. 15(16). doi: 10.3390/cancers15164124. Full text in Research Archive Show summary

  Background: Statistical associations of numerous single nucleotide polymorphisms with breast cancer (BC) have been identified in genome-wide association studies (GWAS). Recent evidence suggests that a Polygenic Risk Score (PRS) can be a useful risk stratification instrument for a BC screening strategy, and a PRS test has been developed for clinical use. The performance of the PRS is yet unknown in the Norwegian population. Aim: To evaluate the performance of PRS models for BC in a Norwegian dataset. Methods: We investigated a sample of 1053 BC cases and 7094 controls from different regions of Norway. PRS values were calculated using four PRS models, and their performance was evaluated by the area under the curve (AUC) and the odds ratio (OR). The effect of the PRS on the age of onset of BC was determined by a Cox regression model, and the lifetime absolute risk of developing BC was calculated using the iCare tool. Results: The best performing PRS model included 3820 SNPs, which yielded an AUC = 0.625 and an OR = 1.567 per one standard deviation increase. The PRS values of the samples correlate with an increased risk of BC, with a hazard ratio of 1.494 per one standard deviation increase (95% confidence interval of 1.406–1.588). The individuals in the highest decile of the PRS have at least twice the risk of developing BC compared to the individuals with a median PRS. The results in this study with Norwegian samples are coherent with the findings in the study conducted using Estonian and UK Biobank samples. Conclusion: The previously validated PRS models have a similar observed accuracy in the Norwegian data as in the UK and Estonian populations. A PRS provides a meaningful association with the age of onset of BC and lifetime risk. Therefore, as suggested in Estonia, a PRS may also be integrated into the screening strategy for BC in Norway.

• Bang, Lasse; Bahrami, Shahram; Hindley, Guy; Smeland, Olav Bjerkehagen; Rødevand, Linn & Jaholkowski, Piotr Pawel [Show all 18 contributors for this article] (2023). Genome-wide analysis of anorexia nervosa and major psychiatric disorders and related traits reveals genetic overlap and identifies novel risk loci for anorexia nervosa. Translational Psychiatry. 13. doi: 10.1038/s41398-023-02585-1. Full text in Research Archive Show summary

  Anorexia nervosa (AN) is a heritable eating disorder (50–60%) with an array of commonly comorbid psychiatric disorders and related traits. Although significant genetic correlations between AN and psychiatric disorders and related traits have been reported, their shared genetic architecture is largely understudied. We investigated the shared genetic architecture of AN and schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), mood instability (Mood), neuroticism (NEUR), and intelligence (INT). We applied the conditional false discovery rate (FDR) method to identify novel risk loci for AN, and conjunctional FDR to identify loci shared between AN and related phenotypes, to summarize statistics from relevant genome-wide association studies (GWAS). Individual GWAS samples varied from 72,517 to 420,879 participants. Using conditional FDR we identified 58 novel AN loci. Furthermore, we identified 38 unique loci shared between AN and major psychiatric disorders (SCZ, BIP, and MD) and 45 between AN and psychological traits (Mood, NEUR, and INT). In line with genetic correlations, the majority of shared loci showed concordant effect directions. Functional analyses revealed that the shared loci are involved in 65 unique pathways, several of which overlapped across analyses, including the “signal by MST1” pathway involved in Hippo signaling. In conclusion, we demonstrated genetic overlap between AN and major psychiatric disorders and related traits, and identified novel risk loci for AN by leveraging this overlap. Our results indicate that some shared characteristics between AN and related disorders and traits may have genetic underpinnings.

• Karadag, Naz; Shadrin, Alexey; O'Connell, Kevin Sean; Hindley, Guy Frederick Lanyon; Rahman, Zillur & Parker, Nadine [Show all 18 contributors for this article] (2023). Identification of novel genomic risk loci shared between common epilepsies and psychiatric disorders. Brain. ISSN 0006-8950. 146(8), p. 3392–3403. doi: 10.1093/brain/awad038. Full text in Research Archive Show summary

  Psychiatric disorders and common epilepsies are heritable disorders with a high comorbidity and overlapping symptoms. However, the causative mechanisms underlying this relationship are poorly understood. Here we aimed to identify overlapping genetic loci between epilepsy and psychiatric disorders to gain a better understanding of their comorbidity and shared clinical features. We analysed genome-wide association study data for all epilepsies (n = 44 889), genetic generalized epilepsy (n = 33 446), focal epilepsy (n = 39 348), schizophrenia (n = 77 096), bipolar disorder (n = 406 405), depression (n = 500 199), attention deficit hyperactivity disorder (n = 53 293) and autism spectrum disorder (n = 46 350). First, we applied the MiXeR tool to estimate the total number of causal variants influencing the disorders. Next, we used the conjunctional false discovery rate statistical framework to improve power to discover shared genomic loci. Additionally, we assessed the validity of the findings in independent cohorts, and functionally characterized the identified loci. The epilepsy phenotypes were considerably less polygenic (1.0 K to 3.4 K causal variants) than the psychiatric disorders (5.6 K to 13.9 K causal variants), with focal epilepsy being the least polygenic (1.0 K variants), and depression having the highest polygenicity (13.9 K variants). We observed cross-trait genetic enrichment between genetic generalized epilepsy and all psychiatric disorders and between all epilepsies and schizophrenia and depression. Using conjunctional false discovery rate analysis, we identified 40 distinct loci jointly associated with epilepsies and psychiatric disorders at conjunctional false discovery rate <0.05, four of which were associated with all epilepsies and 39 with genetic generalized epilepsy. Most epilepsy risk loci were shared with schizophrenia (n = 31). Among the identified loci, 32 were novel for genetic generalized epilepsy, and two were novel for all epilepsies. There was a mixture of concordant and discordant allelic effects in the shared loci. The sign concordance of the identified variants was highly consistent between the discovery and independent datasets for all disorders, supporting the validity of the findings. Gene-set analysis for the shared loci between schizophrenia and genetic generalized epilepsy implicated biological processes related to cell cycle regulation, protein phosphatase activity, and membrane and vesicle function; the gene-set analyses for the other loci were underpowered. The extensive genetic overlap with mixed effect directions between psychiatric disorders and common epilepsies demonstrates a complex genetic relationship between these disorders, in line with their bi-directional relationship, and indicates that overlapping genetic risk may contribute to shared pathophysiological and clinical features between epilepsy and psychiatric disorders.

• Jaholkowski, Piotr Pawel; Hindley, Guy; Shadrin, Alexey; Tesfaye, Markos; Bahrami, Shahram & Nerhus, Mari [Show all 19 contributors for this article] (2023). Genome-wide Association Analysis of Schizophrenia and Vitamin D Levels Shows Shared Genetic Architecture and Identifies Novel Risk Loci. Schizophrenia Bulletin. ISSN 0586-7614. 49(6), p. 1654–1664. doi: 10.1093/schbul/sbad063. Full text in Research Archive Show summary

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

• Bøen, Rune; Kaufmann, Tobias Herbert; Meer, Dennis van der; Frei, Oleksandr; Agartz, Ingrid & Ames, David [Show all 53 contributors for this article] (2023). Beyond the Global Brain Differences: Intraindividual Variability Differences in 1q21.1 Distal and 15q11.2 BP1-BP2 Deletion Carriers. Biological Psychiatry. ISSN 0006-3223. 95(2), p. 147–160. doi: 10.1016/j.biopsych.2023.08.018. Full text in Research Archive Show summary

  Background Carriers of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants exhibit regional and global brain differences compared with noncarriers. However, interpreting regional differences is challenging if a global difference drives the regional brain differences. Intraindividual variability measures can be used to test for regional differences beyond global differences in brain structure. Methods Magnetic resonance imaging data were used to obtain regional brain values for 1q21.1 distal deletion (n = 30) and duplication (n = 27) and 15q11.2 BP1-BP2 deletion (n = 170) and duplication (n = 243) carriers and matched noncarriers (n = 2350). Regional intra-deviation scores, i.e., the standardized difference between an individual’s regional difference and global difference, were used to test for regional differences that diverge from the global difference. Results For the 1q21.1 distal deletion carriers, cortical surface area for regions in the medial visual cortex, posterior cingulate, and temporal pole differed less and regions in the prefrontal and superior temporal cortex differed more than the global difference in cortical surface area. For the 15q11.2 BP1-BP2 deletion carriers, cortical thickness in regions in the medial visual cortex, auditory cortex, and temporal pole differed less and the prefrontal and somatosensory cortex differed more than the global difference in cortical thickness. Conclusions We find evidence for regional effects beyond differences in global brain measures in 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants. The results provide new insight into brain profiling of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants, with the potential to increase understanding of the mechanisms involved in altered neurodevelopment.

• Jaholkowski, Piotr; Shadrin, Alexey A.; Jangmo, Andreas; Frei, Evgeniia; Woldeyohannes, Markos Tesfaye & Hindley, Guy F. L. [Show all 23 contributors for this article] (2023). Associations Between Symptoms of Premenstrual Disorders and Polygenic Liability for Major Psychiatric Disorders. JAMA psychiatry. ISSN 2168-6238. 80(7), p. 738–742. doi: 10.1001/jamapsychiatry.2023.1137. Full text in Research Archive
• Oddsson, Asmundur; Sulem, Patrick; Sveinbjornsson, Gardar; Arnadottir, Gudny A.; Steinthorsdottir, Valgerdur & Halldorsson, Gisli H. [Show all 54 contributors for this article] (2023). Deficit of homozygosity among 1.52 million individuals and genetic causes of recessive lethality. Nature Communications. 14(1). doi: 10.1038/s41467-023-38951-2. Full text in Research Archive Show summary

  Genotypes causing pregnancy loss and perinatal mortality are depleted among living individuals and are therefore difficult to find. To explore genetic causes of recessive lethality, we searched for sequence variants with deficit of homozygosity among 1.52 million individuals from six European populations. In this study, we identified 25 genes harboring protein-altering sequence variants with a strong deficit of homozygosity (10% or less of predicted homozygotes). Sequence variants in 12 of the genes cause Mendelian disease under a recessive mode of inheritance, two under a dominant mode, but variants in the remaining 11 have not been reported to cause disease. Sequence variants with a strong deficit of homozygosity are over-represented among genes essential for growth of human cell lines and genes orthologous to mouse genes known to affect viability. The function of these genes gives insight into the genetics of intrauterine lethality. We also identified 1077 genes with homozygous predicted loss-of-function genotypes not previously described, bringing the total set of genes completely knocked out in humans to 4785.

• Askeland, Ragna Bugge; Hannigan, Laurie John; O'Connell, Kevin Sean; Corfield, Elizabeth Claire; Frei, Oleksandr & Thapar, Anita [Show all 11 contributors for this article] (2023). Developmental manifestations of polygenic risk for bipolar disorder from infancy to middle childhood. Translational Psychiatry. 13(1). doi: 10.1038/s41398-023-02522-2. Full text in Research Archive Show summary

  Abstract Knowledge on how genetic risk for bipolar disorder manifests in developmental, emotional or behavioral traits during childhood is lacking. This issue is important to address to inform early detection and intervention efforts. We investigated whether polygenic risk for bipolar disorder is associated with developmental outcomes during early to middle childhood in the general population, and if associations differ between boys and girls. Our sample consisted of 28 001 children from the Norwegian Mother, Father and Child Cohort study, a prospective pregnancy cohort with available genotype and developmental data. Mothers reported on a range of developmental outcomes in their children at 6 and 18 months, 3, 5 and 8 years. Polygenic risk scores reflecting common variant liability to bipolar disorder were calculated. Linear regression models were used in a multi-group framework to investigate associations between polygenic risk score and developmental outcomes, using sex as a grouping variable. We found robust evidence for an association between polygenic risk scores for bipolar disorder and conduct difficulties (β = 0.041, CI = 0.020–0.062) and oppositional defiant difficulties (β = 0.032, CI = 0.014–0.051) at 8 years. Associations with most other outcomes were estimated within the region of practical equivalence to zero (equivalence range D  = −0.1 to 0.1), with the exceptions of negative association for activity levels (β = −0.028, CI = −0.047– −0.010) at age 5 and benevolence (β = −0.025, CI = –0.043 to –0.008) at age 8, and positive association for motor difficulties (β = 0.025, CI = 0.008–0.043) at age 3, inattention (β = 0.021, CI = 0.003–0.041) and hyperactivity (β = 0.025, CI = 0.006–0.044) at age 8. Our results suggest that genetic risk for bipolar disorder manifests as disruptive behaviors like oppositional defiant and conduct difficulties in childhood in the general population.

• Holen, Børge; Shadrin, Alexey; Icick, Romain; Filiz, Tahir Tekin; Hindley, Guy Frederick Lanyon & Rødevand, Linn [Show all 19 contributors for this article] (2023). Genome-wide analyses reveal novel opioid use disorder loci and genetic overlap with schizophrenia, bipolar disorder, and major depression. Addiction Biology. ISSN 1355-6215. 28(6). doi: 10.1111/adb.13282. Full text in Research Archive Show summary

  Opioid use disorder (OUD) and mental disorders are often comorbid, with increased morbidity and mortality. The causes underlying this relationship are poorly understood. Although these conditions are highly heritable, their shared genetic vulnerabilities remain unaccounted for. We applied the conditional/conjunctional false discovery rate (cond/conjFDR) approach to analyse summary statistics from independent genome wide association studies of OUD, schizophrenia (SCZ), bipolar disorder (BD) and major depression (MD) of European ancestry. Next, we characterized the identified shared loci using biological annotation resources. OUD data were obtained from the Million Veteran Program, Yale-Penn and Study of Addiction: Genetics and Environment (SAGE) (15 756 cases, 99 039 controls). SCZ (53 386 cases, 77 258 controls), BD (41 917 cases, 371 549 controls) and MD (170 756 cases, 329 443 controls) data were provided by the Psychiatric Genomics Consortium. We discovered genetic enrichment for OUD conditional on associations with SCZ, BD, MD and vice versa, indicating polygenic overlap with identification of 14 novel OUD loci at condFDR < 0.05 and 7 unique loci shared between OUD and SCZ (n = 2), BD (n = 2) and MD (n = 7) at conjFDR < 0.05 with concordant effect directions, in line with estimated positive genetic correlations. Two loci were novel for OUD, one for BD and one for MD. Three OUD risk loci were shared with more than one psychiatric disorder, at DRD2 on chromosome 11 (BD and MD), at FURIN on chromosome 15 (SCZ, BD and MD) and at the major histocompatibility complex region (SCZ and MD). Our findings provide new insights into the shared genetic architecture between OUD and SCZ, BD and MD, indicating a complex genetic relationship, suggesting overlapping neurobiological pathways.

• Parker, Nadine; Cheng, Weiqiu; Hindley, Guy; O'Connell, Kevin Sean; Karthikeyan, Sandeep & Holen, Børge [Show all 19 contributors for this article] (2023). Genetic Overlap Between Global Cortical Brain Structure, C-Reactive Protein, and White Blood Cell Counts. Biological Psychiatry. ISSN 0006-3223. 95(1). doi: 10.1016/j.biopsych.2023.06.008. Full text in Research Archive Show summary

  Background For many brain disorders, a subset of patients jointly exhibit alterations in cortical brain structure and elevated levels of circulating immune markers. This may be driven in part by shared genetic architecture. Therefore, we investigated the phenotypic and genetic associations linking global cortical surface area and thickness with blood immune markers (i.e., white blood cell counts and plasma C-reactive protein levels). Methods Linear regression was used to assess phenotypic associations in 30,823 UK Biobank participants. Genome-wide and local genetic correlations were assessed using linkage disequilibrium score regression and local analysis of covariance annotation. The number of shared trait-influencing genetic variants was estimated using MiXeR. Shared genetic architecture was assessed using a conjunctional false discovery rate framework, and mapped genes were included in gene-set enrichment analyses. Results Cortical structure and blood immune markers exhibited predominantly inverse phenotypic associations. There were modest genome-wide genetic correlations, the strongest of which were for C-reactive protein levels (rg_surface_area = −0.13, false discovery rate–corrected p = 4.17 × 10−3; rg_thickness = −0.13, false discovery rate–corrected p = 4.00 × 10−2). Meanwhile, local genetic correlations showed a mosaic of positive and negative associations. White blood cells shared on average 46.24% and 38.64% of trait-influencing genetic variants with surface area and thickness, respectively. Additionally, surface area shared 55 unique loci with the blood immune markers while thickness shared 15. Overall, monocyte count exhibited the largest genetic overlap with cortical brain structure. A series of gene enrichment analyses implicated neuronal-, astrocytic-, and schizophrenia-associated genes. Conclusions The findings indicate shared genetic underpinnings for cortical brain structure and blood immune markers, with implications for neurodevelopment and understanding the etiology of brain-related disorders.

• Smith, Diana M.; Loughnan, Robert; Friedman, Naomi P.; Parekh, Pravesh; Frei, Oleksandr & Thompson, Wesley K. [Show all 10 contributors for this article] (2023). Heritability Estimation of Cognitive Phenotypes in the ABCD Study^® Using Mixed Models. Behavior Genetics. ISSN 0001-8244. 53(3), p. 169–188. doi: 10.1007/s10519-023-10141-2. Full text in Research Archive Show summary

  Abstract Twin and family studies have historically aimed to partition phenotypic variance into components corresponding to additive genetic effects ( A ), common environment ( C ), and unique environment ( E ). Here we present the ACE Model and several extensions in the Adolescent Brain Cognitive Development℠ Study (ABCD Study ® ), employed using the new Fast Efficient Mixed Effects Analysis (FEMA) package. In the twin sub-sample ( n  = 924; 462 twin pairs), heritability estimates were similar to those reported by prior studies for height (twin heritability = 0.86) and cognition (twin heritability between 0.00 and 0.61), respectively. Incorporating SNP-derived genetic relatedness and using the full ABCD Study ® sample ( n  = 9,742) led to narrower confidence intervals for all parameter estimates. By leveraging the sparse clustering method used by FEMA to handle genetic relatedness only for participants within families, we were able to take advantage of the diverse distribution of genetic relatedness within the ABCD Study ® sample.

• Mufford, Mary S.; Meer, Dennis van der; Kaufmann, Tobias Herbert; Frei, Oleksandr; Ramesar, Raj & Thompson, Paul M. [Show all 11 contributors for this article] (2023). The Genetic Architecture of Amygdala Nuclei. Biological Psychiatry. ISSN 0006-3223. doi: 10.1016/j.biopsych.2023.06.022. Full text in Research Archive Show summary

  Background Whereas genetic variants influencing total amygdala volume have been identified, the genetic architecture of its distinct nuclei has yet to be explored. We aimed to investigate whether increased phenotypic specificity through nuclei segmentation aids genetic discoverability and elucidates the extent of shared genetic architecture and biological pathways with related disorders. Methods T1-weighted brain magnetic resonance imaging scans (N = 36,352, 52% female) from the UK Biobank were segmented into 9 amygdala nuclei with FreeSurfer (version 6.1). Genome-wide association analyses were performed on the entire sample, a European-only subset (n = 31,690), and a generalization (transancestry) subset (n = 4662). We estimated single nucleotide polymorphism–based heritability; derived polygenicity, discoverability, and power estimates; and investigated genetic correlations and shared loci with psychiatric disorders. Results The heritability of the nuclei ranged from 0.17 to 0.33. Across the whole amygdala and the nuclei volumes, we identified 28 novel genome-wide significant (padj < 5 × 10−9) loci in the European analysis, with significant en masse replication for the whole amygdala and central nucleus volumes in the generalization analysis, and we identified 10 additional candidate loci in the combined analysis. The central nucleus had the highest statistical power for discovery. The significantly associated genes and pathways showed unique and shared effects across the nuclei, including immune-related pathways. Shared variants were identified between specific nuclei and autism spectrum disorder, Alzheimer’s disease, Parkinson’s disease, bipolar disorder, and schizophrenia. Conclusions Through investigation of amygdala nuclei volumes, we have identified novel candidate loci in the neurobiology of amygdala volume. These nuclei volumes have unique associations with biological pathways and genetic overlap with psychiatric disorders.

• Nordengen, Kaja; Cappelletti, Chiara; Bahrami, Shahram; Frei, Oleksandr; Pihlstrøm, Lasse & Henriksen, Sandra Pilar [Show all 11 contributors for this article] (2023). Pleiotropy with sex-specific traits reveals genetic aspects of sex differences in Parkinson’s disease. Brain. ISSN 0006-8950. 147(3), p. 858–870. doi: 10.1093/brain/awad297. Full text in Research Archive Show summary

  Parkinson’s disease is an age-related neurodegenerative disorder with a higher incidence in males than females. The causes for this sex difference are unknown. Genome-wide association studies (GWAS) have identified 90 Parkinson’s disease risk loci, but the genetic studies have not found sex-specific differences in allele frequency on autosomal chromosomes or sex chromosomes. Genetic variants, however, could exert sex-specific effects on gene function and regulation of gene expression. To identify genetic loci that might have sex-specific effects, we studied pleiotropy between Parkinson’s disease and sex-specific traits. Summary statistics from GWASs were acquired from large-scale consortia for Parkinson’s disease (n cases = 13 708; n controls = 95 282), age at menarche (n = 368 888 females) and age at menopause (n = 69 360 females). We applied the conditional/conjunctional false discovery rate (FDR) method to identify shared loci between Parkinson’s disease and these sex-specific traits. Next, we investigated sex-specific gene expression differences in the superior frontal cortex of both neuropathologically healthy individuals and Parkinson’s disease patients (n cases = 61; n controls = 23). To provide biological insights to the genetic pleiotropy, we performed sex-specific expression quantitative trait locus (eQTL) analysis and sex-specific age-related differential expression analysis for genes mapped to Parkinson’s disease risk loci. Through conditional/conjunctional FDR analysis we found 11 loci shared between Parkinson’s disease and the sex-specific traits age at menarche and age at menopause. Gene-set and pathway analysis of the genes mapped to these loci highlighted the importance of the immune response in determining an increased disease incidence in the male population. Moreover, we highlighted a total of nine genes whose expression or age-related expression in the human brain is influenced by genetic variants in a sex-specific manner. With these analyses we demonstrated that the lack of clear sex-specific differences in allele frequencies for Parkinson’s disease loci does not exclude a gen- etic contribution to differences in disease incidence. Moreover, further studies are needed to elucidate the role that the candidate genes identified here could have in determining a higher incidence of Parkinson’s disease in the male population.

• Hindley, Guy Frederick Lanyon; Shadrin, Alexey; Meer, Dennis van der; Parker, Nadine; Cheng, Weiqiu & O'Connell, Kevin Sean [Show all 23 contributors for this article] (2023). Multivariate genetic analysis of personality and cognitive traits reveals abundant pleiotropy. Nature Human Behaviour. 7, p. 1584–1600. doi: 10.1038/s41562-023-01630-9. Full text in Research Archive
• Leonardsen, Esten Høyland; Vidal-Piñeiro, Didac; Roe, James Michael; Frei, Oleksandr; Shadrin, Alexey & Iakunchykova, Olena [Show all 14 contributors for this article] (2023). Genetic architecture of brain age and its casual relations with brain and mental disorders. Molecular Psychiatry. ISSN 1359-4184. doi: 10.1038/s41380-023-02087-y. Full text in Research Archive Show summary

  Abstract The difference between chronological age and the apparent age of the brain estimated from brain imaging data—the brain age gap (BAG)—is widely considered a general indicator of brain health. Converging evidence supports that BAG is sensitive to an array of genetic and nongenetic traits and diseases, yet few studies have examined the genetic architecture and its corresponding causal relationships with common brain disorders. Here, we estimate BAG using state-of-the-art neural networks trained on brain scans from 53,542 individuals (age range 3–95 years). A genome-wide association analysis across 28,104 individuals (40–84 years) from the UK Biobank revealed eight independent genomic regions significantly associated with BAG ( p  &lt; 5 × 10 −8 ) implicating neurological, metabolic, and immunological pathways – among which seven are novel. No significant genetic correlations or causal relationships with BAG were found for Parkinson’s disease, major depressive disorder, or schizophrenia, but two-sample Mendelian randomization indicated a causal influence of AD ( p  = 7.9 × 10 −4 ) and bipolar disorder ( p  = 1.35 × 10 −2 ) on BAG. These results emphasize the polygenic architecture of brain age and provide insights into the causal relationship between selected neurological and neuropsychiatric disorders and BAG.

• O'Connell, Kevin Sean; Koch, Elise; Lenk, Hasan Cagin; Akkouh, Ibrahim Ahmed; Hindley, Guy Frederick Lanyon & Jaholkowski, Piotr Pawel [Show all 16 contributors for this article] (2023). Polygenic overlap with body-mass index improves prediction of treatment-resistant schizophrenia. Psychiatry Research. ISSN 0165-1781. 325. doi: 10.1016/j.psychres.2023.115217. Full text in Research Archive Show summary

  Treatment resistant schizophrenia (TRS) is characterized by repeated treatment failure with antipsychotics. A recent genome-wide association study (GWAS) of TRS showed a polygenic architecture, but no significant loci were identified. Clozapine is shown to be the superior drug in terms of clinical effect in TRS; at the same time it has a serious side effect profile, including weight gain. Here, we sought to increase power for genetic discovery and improve polygenic prediction of TRS, by leveraging genetic overlap with Body Mass Index (BMI). We analysed GWAS summary statistics for TRS and BMI applying the conditional false discovery rate (cFDR) framework. We observed cross-trait polygenic enrichment for TRS conditioned on associations with BMI. Leveraging this cross-trait enrichment, we identified 2 novel loci for TRS at cFDR <0.01, suggesting a role of MAP2K1 and ZDBF2. Further, polygenic prediction based on the cFDR analysis explained more variance in TRS when compared to the standard TRS GWAS. These findings highlight putative molecular pathways which may distinguish TRS patients from treatment responsive patients. Moreover, these findings confirm that shared genetic mechanisms influence both TRS and BMI and provide new insights into the biological underpinnings of metabolic dysfunction and antipsychotic treatment.

• Parker, Nadine; Cheng, Weiqiu; Hindley, Guy; Parekh, Pravesh; Shadrin, Alexey & Maximov, Ivan [Show all 12 contributors for this article] (2023). Psychiatric disorders and brain white matter exhibit genetic overlap implicating developmental and neural cell biology. Molecular Psychiatry. ISSN 1359-4184. doi: 10.1038/s41380-023-02264-z. Full text in Research Archive
• Makowski, Carolina; Wang, Hao; Srinivasan, Anjali; Qi, Anna; Qiu, Yuqi & Meer, Dennis van der [Show all 11 contributors for this article] (2023). Larger cerebral cortex is genetically correlated with greater frontal area and dorsal thickness. Proceedings of the National Academy of Sciences of the United States of America (PNAS). ISSN 0027-8424. 120(11). doi: 10.1073/pnas.2214834120. Full text in Research Archive Show summary

  Human cortical expansion has occurred non-uniformly across the brain. We assessed the genetic architecture of cortical global expansion and regionalization by comparing two sets of genome-wide association studies of 24 cortical regions with and without adjustment for global measures (i.e., total surface area, mean cortical thickness) using a genetically informed parcellation in 32,488 adults. We found 393 and 756 significant loci with and without adjusting for globals, respectively, where 8% and 45% loci were associated with more than one region. Results from analyses without adjustment for globals recovered loci associated with global measures. Genetic factors that contribute to total surface area of the cortex particularly expand anterior/frontal regions, whereas those contributing to thicker cortex predominantly increase dorsal/frontal-parietal thickness. Interactome-based analyses revealed significant genetic overlap of global and dorsolateral prefrontal modules, enriched for neurodevelopmental and immune system pathways. Consideration of global measures is important in understanding the genetic variants underlying cortical morphology.

• Andreassen, Ole; Hindley, Guy Frederick Lanyon; Frei, Oleksandr & Smeland, Olav Bjerkehagen (2023). New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications. World Psychiatry. ISSN 1723-8617. 22(1), p. 4–24. doi: 10.1002/wps.21034. Full text in Research Archive
• Bøen, Rune; Kaufmann, Tobias; Frei, Oleksandr; Meer, Dennis van der; Djurovic, Srdjan & Andreassen, Ole [Show all 9 contributors for this article] (2023). No signs of neurodegenerative effects in 15q11.2 BP1-BP2 copy number variant carriers in the UK Biobank. Translational Psychiatry. 13:61, p. 1–6. doi: 10.1038/s41398-023-02358-w. Full text in Research Archive Show summary

  The 15q11.2 BP1-BP2 copy number variant (CNV) is associated with altered brain morphology and risk for atypical development, including increased risk for schizophrenia and learning difficulties for the deletion. However, it is still unclear whether differences in brain morphology are associated with neurodevelopmental or neurodegenerative processes. This study derived morphological brain MRI measures in 15q11.2 BP1-BP2 deletion (n = 124) and duplication carriers (n = 142), and matched deletion-controls (n = 496) and duplication-controls (n = 568) from the UK Biobank study to investigate the association with brain morphology and estimates of brain ageing. Further, we examined the ageing trajectory of age-affected measures (i.e., cortical thickness, surface area, subcortical volume, reaction time, hand grip strength, lung function, and blood pressure) in 15q11.2 BP1-BP2 CNV carriers compared to non-carriers. In this ageing population, the results from the machine learning models showed that the estimated brain age gaps did not differ between the 15q11.2 BP1-BP2 CNV carriers and non-carriers, despite deletion carriers displaying thicker cortex and lower subcortical volume compared to the deletion-controls and duplication carriers, and lower surface area compared to the deletion-controls. Likewise, the 15q11.2 BP1-BP2 CNV carriers did not deviate from the ageing trajectory on any of the age-affected measures examined compared to non-carriers. Despite altered brain morphology in 15q11.2 BP1-BP2 CNV carriers, the results did not show any clear signs of apparent altered ageing in brain structure, nor in motor, lung or heart function. The results do not indicate neurodegenerative effects in 15q11.2 BP1-BP2 CNV carriers.

• Holen, Børge; Kutrolli, Gleda; Shadrin, Alexey; Icick, Romain; Hindley, Guy & Rødevand, Linn [Show all 16 contributors for this article] (2023). Genome-wide analyses reveal shared genetic architecture and novel risk loci between opioid use disorder and general cognitive ability. Drug and Alcohol Dependence. ISSN 0376-8716. 256. doi: 10.1016/j.drugalcdep.2023.111058. Full text in Research Archive Show summary

  Background Opioid use disorder (OUD), a serious health burden worldwide, is associated with lower cognitive function. Recent studies have demonstrated a negative genetic correlation between OUD and general cognitive ability (COG), indicating a shared genetic basis. However, the specific genetic variants involved, and the underlying molecular mechanisms remain poorly understood. Here, we aimed to quantify and identify the genetic basis underlying OUD and COG. Methods We quantified the extent of genetic overlap between OUD and COG using a bivariate causal mixture model (MiXeR) and identified specific genetic loci applying conditional/conjunctional FDR. Finally, we investigated biological function and expression of implicated genes using available resources. Results We estimated that ~94% of OUD variants (4.8k out of 5.1k variants) also influence COG. We identified three novel OUD risk loci and one locus shared between OUD and COG. Loci identified implicated biological substrates in the basal ganglia. Conclusion We provide new insights into the complex genetic risk architecture of OUD and its genetic relationship with COG.

• Roelfs, Daniel; Frei, Oleksandr; Meer, Dennis van der; Tissink, Elleke; Shadrin, Alexey & Alnæs, Dag [Show all 9 contributors for this article] (2023). Shared genetic architecture between mental health and the brain functional connectome in the UK Biobank. BMC Psychiatry. 23(1). doi: 10.1186/s12888-023-04905-7. Full text in Research Archive Show summary

  Psychiatric disorders are complex clinical conditions with large heterogeneity and overlap in symptoms, genetic liability and brain imaging abnormalities. Building on a dimensional conceptualization of mental health, previous studies have reported genetic overlap between psychiatric disorders and population-level mental health, and between psychiatric disorders and brain functional connectivity. Here, in 30,701 participants aged 45–82 from the UK Biobank we map the genetic associations between self-reported mental health and resting-state fMRI-based measures of brain network function. Multivariate Omnibus Statistical Test revealed 10 genetic loci associated with population-level mental symptoms. Next, conjunctional FDR identified 23 shared genetic variants between these symptom profiles and fMRI-based brain network measures. Functional annotation implicated genes involved in brain structure and function, in particular related to synaptic processes such as axonal growth (e.g. NGFR and RHOA). These findings provide further genetic evidence of an association between brain function and mental health traits in the population.

• Fominykh, Vera; Shadrin, Alexey; Jaholkowski, Piotr Pawel; Bahrami, Shahram; Athanasiu, Lavinia & Wightman, Douglas P. [Show all 13 contributors for this article] (2023). Shared genetic loci between Alzheimer's disease and multiple sclerosis: Crossroads between neurodegeneration and immune system. Neurobiology of Disease. ISSN 0969-9961. 183. doi: 10.1016/j.nbd.2023.106174. Full text in Research Archive Show summary

  Background Neuroinflammation is involved in the pathophysiology of Alzheimer's disease (AD), including immune-linked genetic variants and molecular pathways, microglia and astrocytes. Multiple Sclerosis (MS) is a chronic, immune-mediated disease with genetic and environmental risk factors and neuropathological features. There are clinical and pathobiological similarities between AD and MS. Here, we investigated shared genetic susceptibility between AD and MS to identify putative pathological mechanisms shared between neurodegeneration and the immune system. Methods We analysed GWAS data for late-onset AD (N cases = 64,549, N controls = 634,442) and MS (N cases = 14,802, N controls = 26,703). Gaussian causal mixture modelling (MiXeR) was applied to characterise the genetic architecture and overlap between AD and MS. Local genetic correlation was investigated with Local Analysis of [co]Variant Association (LAVA). The conjunctional false discovery rate (conjFDR) framework was used to identify the specific shared genetic loci, for which functional annotation was conducted with FUMA and Open Targets. Results MiXeR analysis showed comparable polygenicities for AD and MS (approximately 1800 trait-influencing variants) and genetic overlap with 20% of shared trait-influencing variants despite negligible genetic correlation (rg = 0.03), suggesting mixed directions of genetic effects across shared variants. conjFDR analysis identified 16 shared genetic loci, with 8 having concordant direction of effects in AD and MS. Annotated genes in shared loci were enriched in molecular signalling pathways involved in inflammation and the structural organisation of neurons. Conclusions Despite low global genetic correlation, the current results provide evidence for polygenic overlap between AD and MS. The shared loci between AD and MS were enriched in pathways involved in inflammation and neurodegeneration, highlighting new opportunities for future investigation.

• Woldeyohannes, Markos Tesfaye; Jaholkowski, Piotr Pawel; Hindley, Guy; Shadrin, Alexey; Rahman, Zillur & Bahrami, Shahram [Show all 17 contributors for this article] (2023). Shared genetic architecture between irritable bowel syndrome and psychiatric disorders reveals molecular pathways of the gut-brain axis. Genome Medicine. 15(1). doi: 10.1186/s13073-023-01212-4. Full text in Research Archive Show summary

  Background: Irritable bowel syndrome (IBS) often co-occurs with psychiatric and gastrointestinal disorders. A recent genome-wide association study (GWAS) identified several genetic risk variants for IBS. However, most of the heritability remains unidentified, and the genetic overlap with psychiatric and somatic disorders is not quantified beyond genome-wide genetic correlations. Here, we characterize the genetic architecture of IBS, further, investigate its genetic overlap with psychiatric and gastrointestinal phenotypes, and identify novel genomic risk loci. Methods: Using GWAS summary statistics of IBS (53,400 cases and 433,201 controls), and psychiatric and gastrointestinal phenotypes, we performed bivariate casual mixture model analysis to characterize the genetic architecture and genetic overlap between these phenotypes. We leveraged identified genetic overlap to boost the discovery of genomic loci associated with IBS, and to identify specific shared loci associated with both IBS and psychiatric and gastrointestinal phenotypes, using the conditional/conjunctional false discovery rate (condFDR/conjFDR) framework. We used functional mapping and gene annotation (FUMA) for functional analyses. Results: IBS was highly polygenic with 12k trait-influencing variants. We found extensive polygenic overlap between IBS and psychiatric disorders and to a lesser extent with gastrointestinal diseases. We identified 132 independent IBS-associated loci (condFDR < 0.05) by conditioning on psychiatric disorders (n = 127) and gastrointestinal diseases (n = 24). Using conjFDR, 70 unique loci were shared between IBS and psychiatric disorders. Functional analyses of shared loci revealed enrichment for biological pathways of the nervous and immune systems. Genetic correlations and shared loci between psychiatric disorders and IBS subtypes were different. Conclusions: We found extensive polygenic overlap of IBS and psychiatric and gastrointestinal phenotypes beyond what was revealed with genetic correlations. Leveraging the overlap, we discovered genetic loci associated with IBS which implicate a wide range of biological pathways beyond the gut-brain axis. Genetic differences may underlie the clinical subtype of IBS. These results increase our understanding of the pathophysiology of IBS which may form the basis for the development of individualized interventions.

• Reas, Emilie T.; Shadrin, Alexey; Frei, Oleksandr; Motazedi, Ehsan; McEvoy, Linda & Bahrami, Shahram [Show all 22 contributors for this article] (2023). Improved multimodal prediction of progression from MCI to Alzheimer's disease combining genetics with quantitative brain MRI and cognitive measures. Alzheimer's & Dementia. ISSN 1552-5260. 19(11), p. 5151–5158. doi: 10.1002/alz.13112. Full text in Research Archive Show summary

  Introduction There is a pressing need for non-invasive, cost-effective tools for early detection of Alzheimer's disease (AD). Methods Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), Cox proportional models were conducted to develop a multimodal hazard score (MHS) combining age, a polygenic hazard score (PHS), brain atrophy, and memory to predict conversion from mild cognitive impairment (MCI) to dementia. Power calculations estimated required clinical trial sample sizes after hypothetical enrichment using the MHS. Cox regression determined predicted age of onset for AD pathology from the PHS. Results The MHS predicted conversion from MCI to dementia (hazard ratio for 80th versus 20th percentile: 27.03). Models suggest that application of the MHS could reduce clinical trial sample sizes by 67%. The PHS alone predicted age of onset of amyloid and tau. Discussion The MHS may improve early detection of AD for use in memory clinics or for clinical trial enrichment.

• Cheng, Weiqiu; Parker, Nadine; Karadag, Naz; Koch, Elise; Hindley, Guy Frederick Lanyon & Icick, Romain [Show all 22 contributors for this article] (2023). The relationship between cannabis use, schizophrenia, and bipolar disorder: a genetically informed study. The Lancet Psychiatry. ISSN 2215-0374. 10(6), p. 441–451. doi: 10.1016/S2215-0366(23)00143-8. Full text in Research Archive Show summary

  Background The relationship between psychotic disorders and cannabis use is heavily debated. Shared underlying genetic risk is one potential explanation. We investigated the genetic association between psychotic disorders (schizophrenia and bipolar disorder) and cannabis phenotypes (lifetime cannabis use and cannabis use disorder). Methods We used genome-wide association summary statistics from individuals with European ancestry from the Psychiatric Genomics Consortium, UK Biobank, and International Cannabis Consortium. We estimated heritability, polygenicity, and discoverability of each phenotype. We performed genome-wide and local genetic correlations. Shared loci were identified and mapped to genes, which were tested for functional enrichment. Shared genetic liabilities to psychotic disorders and cannabis phenotypes were explored using causal analyses and polygenic scores, using the Norwegian Thematically Organized Psychosis cohort. Findings Psychotic disorders were more heritable than cannabis phenotypes and more polygenic than cannabis use disorder. We observed positive genome-wide genetic correlations between psychotic disorders and cannabis phenotypes (range 0·22–0·35) with a mixture of positive and negative local genetic correlations. Three to 27 shared loci were identified for the psychotic disorder and cannabis phenotype pairs. Enrichment of mapped genes implicated neuronal and olfactory cells as well as drug–gene targets for nicotine, alcohol, and duloxetine. Psychotic disorders showed a causal effect on cannabis phenotypes, and lifetime cannabis use had a causal effect on bipolar disorder. Of 2181 European participants from the Norwegian Thematically Organized Psychosis cohort applied in polygenic risk score analyses, 1060 (48·6%) were females and 1121 (51·4%) were males (mean age 33·1 years [SD 11·8]). 400 participants had bipolar disorder, 697 had schizophrenia, and 1044 were healthy controls. Within this sample, polygenic scores for cannabis phenotypes predicted psychotic disorders independently and improved prediction beyond the polygenic score for the psychotic disorders. Interpretation A subgroup of individuals might have a high genetic risk of developing a psychotic disorder and using cannabis. This finding supports public health efforts to reduce cannabis use, particularly in individuals at high risk or patients with psychotic disorders. Identified shared loci and their functional implications could facilitate development of novel treatments.

• Meer, Dennis van der; Gurholt, Tiril Pedersen; Sønderby, Ida Elken; Shadrin, Alexey; Hindley, Guy Frederick Lanyon & Rahman, Zillur [Show all 18 contributors for this article] (2022). The link between liver fat and cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body composition. Communications Biology. 5(1). doi: 10.1038/s42003-022-04237-4. Full text in Research Archive Show summary

  Abstract Obesity and associated morbidities, metabolic associated fatty liver disease (MAFLD) included, constitute some of the largest public health threats worldwide. Body composition and related risk factors are known to be heritable and identification of their genetic determinants may aid in the development of better prevention and treatment strategies. Recently, large-scale whole-body MRI data has become available, providing more specific measures of body composition than anthropometrics such as body mass index. Here, we aimed to elucidate the genetic architecture of body composition, by conducting genome-wide association studies (GWAS) of these MRI-derived measures. We ran both univariate and multivariate GWAS on fourteen MRI-derived measurements of adipose and muscle tissue distribution, derived from scans from 33,588 White European UK Biobank participants (mean age of 64.5 years, 51.4% female). Through multivariate analysis, we discovered 100 loci with distributed effects across the body composition measures and 241 significant genes primarily involved in immune system functioning. Liver fat stood out, with a highly discoverable and oligogenic architecture and the strongest genetic associations. Comparison with 21 common cardiometabolic traits revealed both shared and specific genetic influences, with higher mean heritability for the MRI measures (h 2  = .25 vs. .13, p  = 1.8x10 −7 ). We found substantial genetic correlations between the body composition measures and a range of cardiometabolic diseases, with the strongest correlation between liver fat and type 2 diabetes (r g  = .49, p  = 2.7x10 −22 ). These findings show that MRI-derived body composition measures complement conventional body anthropometrics and other biomarkers of cardiometabolic health, highlighting the central role of liver fat, and improving our knowledge of the genetic architecture of body composition and related diseases.

• Fan, Chun Chieh; Loughnan, Robert; Makowski, Carolina; Pecheva, Diliana; Chen, Chi-Hua & Hagler, Donald J. [Show all 12 contributors for this article] (2022). Multivariate genome-wide association study on tissue-sensitive diffusion metrics highlights pathways that shape the human brain. Nature Communications. 13(1). doi: 10.1038/s41467-022-30110-3. Full text in Research Archive Show summary

  Abstract The molecular determinants of tissue composition of the human brain remain largely unknown. Recent genome-wide association studies (GWAS) on this topic have had limited success due to methodological constraints. Here, we apply advanced whole-brain analyses on multi-shell diffusion imaging data and multivariate GWAS to two large scale imaging genetic datasets (UK Biobank and the Adolescent Brain Cognitive Development study) to identify and validate genetic association signals. We discover 503 unique genetic loci that have impact on multiple regions of human brain. Among them, more than 79% are validated in either of two large-scale independent imaging datasets. Key molecular pathways involved in axonal growth, astrocyte-mediated neuroinflammation, and synaptogenesis during development are found to significantly impact the measured variations in tissue-specific imaging features. Our results shed new light on the biological determinants of brain tissue composition and their potential overlap with the genetic basis of neuropsychiatric disorders.

• Saevarsdottir, Saedis; Stefansdottir, Lilja; Sulem, Patrick; Thorleifsson, Gudmar; Ferkingstad, Egil & Rutsdottir, Gudrun [Show all 78 contributors for this article] (2022). Multiomics analysis of rheumatoid arthritis yields sequence variants that have large effects on risk of the seropositive subset. Annals of the Rheumatic Diseases. ISSN 0003-4967. 81(8), p. 1085–1095. doi: 10.1136/annrheumdis-2021-221754. Full text in Research Archive Show summary

  Objectives To find causal genes for rheumatoid arthritis (RA) and its seropositive (RF and/or ACPA positive) and seronegative subsets. Methods We performed a genome-wide association study (GWAS) of 31 313 RA cases (68% seropositive) and ~1 million controls from Northwestern Europe. We searched for causal genes outside the HLA-locus through effect on coding, mRNA expression in several tissues and/or levels of plasma proteins (SomaScan) and did network analysis (Qiagen). Results We found 25 sequence variants for RA overall, 33 for seropositive and 2 for seronegative RA, altogether 37 sequence variants at 34 non-HLA loci, of which 15 are novel. Genomic, transcriptomic and proteomic analysis of these yielded 25 causal genes in seropositive RA and additional two overall. Most encode proteins in the network of interferon-alpha/beta and IL-12/23 that signal through the JAK/STAT-pathway. Highlighting those with largest effect on seropositive RA, a rare missense variant in STAT4 (rs140675301-A) that is independent of reported non-coding STAT4-variants, increases the risk of seropositive RA 2.27-fold (p=2.1×10−9), more than the rs2476601-A missense variant in PTPN22 (OR=1.59, p=1.3×10−160). STAT4 rs140675301-A replaces hydrophilic glutamic acid with hydrophobic valine (Glu128Val) in a conserved, surface-exposed loop. A stop-mutation (rs76428106-C) in FLT3 increases seropositive RA risk (OR=1.35, p=6.6×10−11). Independent missense variants in TYK2 (rs34536443-C, rs12720356-C, rs35018800-A, latter two novel) associate with decreased risk of seropositive RA (ORs=0.63–0.87, p=10−9–10−27) and decreased plasma levels of interferon-alpha/beta receptor 1 that signals through TYK2/JAK1/STAT4. Conclusion Sequence variants pointing to causal genes in the JAK/STAT pathway have largest effect on seropositive RA, while associations with seronegative RA remain scarce.

• Bahrami, Shahram; Nordengen, Kaja; Shadrin, Alexey; Frei, Oleksandr; Meer, Dennis van der & Dale, Anders [Show all 9 contributors for this article] (2022). Distributed genetic architecture across the hippocampal formation implies common neuropathology across brain disorders. Nature Communications. 13(1). doi: 10.1038/s41467-022-31086-w. Full text in Research Archive Show summary

  Abstract Despite its major role in complex human functions across the lifespan, most notably navigation, learning and memory, much of the genetic architecture of the hippocampal formation is currently unexplored. Here, through multivariate genome-wide association analysis in volumetric data from 35,411 white British individuals, we reveal 177 unique genetic loci with distributed associations across the hippocampal formation. We identify genetic overlap with eight brain disorders with typical onset at different stages of life, where common genes suggest partly age- and disorder-independent mechanisms underlying hippocampal pathology.

• Bahrami, Shahram; Hindley, Guy Frederick Lanyon; Winsvold, Bendik K S; O'Connell, Kevin Sean; Frei, Oleksandr & Shadrin, Alexey [Show all 39 contributors for this article] (2022). Dissecting the shared genetic basis of migraine and mental disorders using novel statistical tools. Brain. ISSN 0006-8950. 145(1), p. 142–153. doi: 10.1093/brain/awab267. Full text in Research Archive Show summary

  Migraine is three times more prevalent in people with bipolar disorder or depression. The relationship between schizophrenia and migraine is less certain although glutamatergic and serotonergic neurotransmission are implicated in both. A shared genetic basis to migraine and mental disorders has been suggested but previous studies have reported weak or non-significant genetic correlations and five shared risk loci. Using the largest samples to date and novel statistical tools, we aimed to determine the extent to which migraine’s polygenic architecture overlaps with bipolar disorder, depression and schizophrenia beyond genetic correlation, and to identify shared genetic loci. Summary statistics from genome-wide association studies were acquired from large-scale consortia for migraine (n cases = 59 674; n controls = 316 078), bipolar disorder (n cases = 20 352; n controls = 31 358), depression (n cases = 170 756; n controls = 328 443) and schizophrenia (n cases = 40 675, n controls = 64 643). We applied the bivariate causal mixture model to estimate the number of disorder-influencing variants shared between migraine and each mental disorder, and the conditional/conjunctional false discovery rate method to identify shared loci. Loci were functionally characterized to provide biological insights. Univariate MiXeR analysis revealed that migraine was substantially less polygenic (2.8 K disorder-influencing variants) compared to mental disorders (8100–12 300 disorder-influencing variants). Bivariate analysis estimated that 800 (SD = 300), 2100 (SD = 100) and 2300 (SD = 300) variants were shared between bipolar disorder, depression and schizophrenia, respectively. There was also extensive overlap with intelligence (1800, SD = 300) and educational attainment (2100, SD = 300) but not height (1000, SD = 100). We next identified 14 loci jointly associated with migraine and depression and 36 loci jointly associated with migraine and schizophrenia, with evidence of consistent genetic effects in independent samples. No loci were associated with migraine and bipolar disorder. Functional annotation mapped 37 and 298 genes to migraine and each of depression and schizophrenia, respectively, including several novel putative migraine genes such as L3MBTL2, CACNB2 and SLC9B1. Gene-set analysis identified several putative gene sets enriched with mapped genes including transmembrane transport in migraine and schizophrenia. Most migraine-influencing variants were predicted to influence depression and schizophrenia, although a minority of mental disorder-influencing variants were shared with migraine due to the difference in polygenicity. Similar overlap with other brain-related phenotypes suggests this represents a pool of ‘pleiotropic’ variants that influence vulnerability to diverse brain-related disorders and traits. We also identified specific loci shared between migraine and each of depression and schizophrenia, implicating shared molecular mechanisms and highlighting candidate migraine genes for experimental validation.

• Loughnan, Robert J.; Shadrin, Alexey; Frei, Oleksandr; Meer, Dennis van der; Zhao, Weiqi & Palmer, Clare E. [Show all 12 contributors for this article] (2022). Generalization of cortical MOSTest genome-wide associations within and across samples. NeuroImage. ISSN 1053-8119. 263. doi: 10.1016/j.neuroimage.2022.119632. Full text in Research Archive Show summary

  Genome-Wide Association studies have typically been limited to univariate analysis in which a single outcome measure is tested against millions of variants. Recent work demonstrates that a Multivariate Omnibus Statistic Test (MOSTest) is well powered to discover genomic effects distributed across multiple phenotypes. Applied to cortical brain MRI morphology measures, MOSTest has resulted in a drastic improvement in power to discover loci when compared to established approaches (min-P). One question that arises is how well these discovered loci replicate in independent data. Here we perform 10 times cross validation within 34,973 individuals from UK Biobank for imaging measures of cortical area, thickness and sulcal depth (>1,000 dimensionality for each). By deploying a replication method that aggregates discovered effects distributed across multiple phenotypes, termed PolyVertex Score (MOSTest-PVS), we demonstrate a higher replication yield and comparable replication rate of discovered loci for MOSTest (# replicated loci: 242–496, replication rate: 96–97%) in independent data when compared with the established min-P approach (# replicated loci: 26–55, replication rate: 91–93%). An out-of-sample replication of discovered loci was conducted with a sample of 4,069 individuals from the Adolescent Brain Cognitive Development® (ABCD) study, who are on average 50 years younger than UK Biobank individuals. We observe a higher replication yield and comparable replication rate of MOSTest-PVS compared to min-P. This finding underscores the importance of using well-powered multivariate techniques for both discovery and replication of high dimensional phenotypes in Genome-Wide Association studies.

• Motazedi, Ehsan; Cheng, Weiqiu; Thomassen, Jesper Qvist; Frei, Oleksandr; Rongve, Arvid & Athanasiu, Lavinia [Show all 30 contributors for this article] (2022). Using Polygenic Hazard Scores to Predict Age at Onset of Alzheimer's Disease in Nordic Populations. Journal of Alzheimer's Disease. ISSN 1387-2877. 88(4), p. 1533–1544. doi: 10.3233/JAD-220174. Full text in Research Archive Show summary

  Background: Polygenic hazard scores (PHS) estimate age-dependent genetic risk of late-onset Alzheimer’s disease (AD), but there is limited information about the performance of PHS on real-world data where the population of interest differs from the model development population and part of the model genotypes are missing or need to be imputed. Objective: The aim of this study was to estimate age-dependent risk of late-onset AD using polygenic predictors in Nordic populations. Methods: We used Desikan PHS model, based on Cox proportional hazards assumption, to obtain age-dependent hazard scores for AD from individual genotypes in the Norwegian DemGene cohort (n = 2,772). We assessed the risk discrimination and calibration of Desikan model and extended it by adding new genotype markers (the Desikan Nordic model). Finally, we evaluated both Desikan and Desikan Nordic models in two independent Danish cohorts: The Copenhagen City Heart Study (CCHS) cohort (n = 7,643) and The Copenhagen General Population Study (CGPS) cohort (n = 10,886). Results: We showed a robust prediction efficiency of Desikan model in stratifying AD risk groups in Nordic populations, even when some of the model SNPs were missing or imputed. We attempted to improve Desikan PHS model by adding new SNPs to it, but we still achieved similar risk discrimination and calibration with the extended model. Conclusion: PHS modeling has the potential to guide the timing of treatment initiation based on individual risk profiles and can help enrich clinical trials with people at high risk to AD in Nordic populations.

• Cheng, Weiqiu; Meer, Dennis van der; Parker, Nadine; Hindley, Guy Frederick Lanyon; O'Connell, Kevin Sean & Wang, Yunpeng [Show all 20 contributors for this article] (2022). Shared genetic architecture between schizophrenia and subcortical brain volumes implicates early neurodevelopmental processes and brain development in childhood. Molecular Psychiatry. ISSN 1359-4184. doi: 10.1038/s41380-022-01751-z. Full text in Research Archive Show summary

  Patients with schizophrenia have consistently shown brain volumetric abnormalities, implicating both etiological and pathological processes. However, the genetic relationship between schizophrenia and brain volumetric abnormalities remains poorly understood. Here, we applied novel statistical genetic approaches (MiXeR and conjunctional false discovery rate analysis) to investigate genetic overlap with mixed effect directions using independent genome-wide association studies of schizophrenia (n = 130,644) and brain volumetric phenotypes, including subcortical brain and intracranial volumes (n = 33,735). We found brain volumetric phenotypes share substantial genetic variants (74–96%) with schizophrenia, and observed 107 distinct shared loci with sign consistency in independent samples. Genes mapped by shared loci revealed (1) significant enrichment in neurodevelopmental biological processes, (2) three co-expression clusters with peak expression at the prenatal stage, and (3) genetically imputed thalamic expression of CRHR1 and ARL17A was associated with the thalamic volume as early as in childhood. Together, our findings provide evidence of shared genetic architecture between schizophrenia and brain volumetric phenotypes and suggest that altered early neurodevelopmental processes and brain development in childhood may be involved in schizophrenia development.

• Trubetskoy, Vassily; Pardiñas, Antonio F.; Qi, Ting; Panagiotaropoulou, Georgia; Awasthi, Swapnil & Bigdeli, Tim B. [Show all 434 contributors for this article] (2022). Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature. ISSN 0028-0836. 604(7906), p. 502–508. doi: 10.1038/s41586-022-04434-5. Full text in Research Archive Show summary

  Schizophrenia has a heritability of 60–80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.

• Hindley, Guy Frederick Lanyon; O'Connell, Kevin Sean; Rahman, Zillur; Frei, Oleksandr; Bahrami, Shahram & Shadrin, Alexey [Show all 17 contributors for this article] (2022). The shared genetic basis of mood instability and psychiatric disorders: A cross-trait genome-wide association analysis. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics. ISSN 1552-4841. 189(6), p. 207–218. doi: 10.1002/ajmg.b.32907. Full text in Research Archive Show summary

  Recent genome-wide association studies of mood instability (MOOD) have found significant positive genetic correlation with major depression (DEP) and weak correlations with other psychiatric disorders. We investigated the polygenic overlap between MOOD and psychiatric disorders beyond genetic correlation to better characterize putative shared genetic determinants. GWAS summary statistics for schizophrenia (SCZ, n = 105,318), bipolar disorder (BIP, n = 413,466), DEP (n = 450,619), attention-deficit hyperactivity disorder (ADHD, n = 53,293), and MOOD (n = 363,705) were analyzed using the bivariate causal mixture model and conjunctional false discovery rate methods. MOOD correlated positively with all psychiatric disorders, but with wide variation in strength (rg = 0.10–0.62). Of 10.4 K genomic variants influencing MOOD, 4 K–9.4 K influenced psychiatric disorders. Furthermore, MOOD was jointly associated with DEP at 163 loci, SCZ at 110, BIP at 60 and ADHD at 25. Fifty-three jointly associated loci were overlapping across two or more disorders, seven of which had discordant effect directions on psychiatric disorders. Genes mapped to loci associated with MOOD and all four disorders were enriched in a single gene-set, “synapse organization.” The extensive polygenic overlap indicates shared molecular underpinnings across MOOD and psychiatric disorders. However, distinct patterns of genetic correlation and effect directions may relate to differences in the core clinical features of each disorder.

• Hindley, Guy; Frei, Oleksandr; Shadrin, Alexey; Cheng, Weiqiu; O'Connell, Kevin Sean & Icick, Romain [Show all 17 contributors for this article] (2022). Charting the Landscape of Genetic Overlap Between Mental Disorders and Related Traits Beyond Genetic Correlation. American Journal of Psychiatry. ISSN 0002-953X. 179(11), p. 833–843. doi: 10.1176/appi.ajp.21101051. Full text in Research Archive
• Meer, Dennis van der; Shadrin, Alexey; O'Connell, Kevin Sean; Bettella, Francesco; Djurovic, Srdjan & Wolfers, Thomas [Show all 17 contributors for this article] (2022). Boosting Schizophrenia Genetics by Utilizing Genetic Overlap With Brain Morphology. Biological Psychiatry. ISSN 0006-3223. 92(4), p. 291–298. doi: 10.1016/j.biopsych.2021.12.007. Full text in Research Archive Show summary

  Background Schizophrenia is a complex polygenic disorder with subtle, distributed abnormalities in brain morphology. There are indications of shared genetic architecture between schizophrenia and brain measures despite low genetic correlations. Through the use of analytical methods that allow for mixed directions of effects, this overlap may be leveraged to improve our understanding of underlying mechanisms of schizophrenia and enrich polygenic risk prediction outcome. Methods We ran a multivariate genome-wide analysis of 175 brain morphology measures using data from 33,735 participants of the UK Biobank and analyzed the results in a conditional false discovery rate together with schizophrenia genome-wide association study summary statistics of the Psychiatric Genomics Consortium (PGC) Wave 3. We subsequently created a pleiotropy-enriched polygenic score based on the loci identified through the conditional false discovery rate approach and used this to predict schizophrenia in a nonoverlapping sample of 743 individuals with schizophrenia and 1074 healthy controls. Results We found that 20% of the loci and 50% of the genes significantly associated with schizophrenia were also associated with brain morphology. The conditional false discovery rate analysis identified 428 loci, including 267 novel loci, significantly associated with brain-linked schizophrenia risk, with functional annotation indicating high relevance for brain tissue. The pleiotropy-enriched polygenic score explained more variance in liability than conventional polygenic scores across several scenarios. Conclusions Our results indicate strong genetic overlap between schizophrenia and brain morphology with mixed directions of effect. The results also illustrate the potential of exploiting polygenetic overlap between brain morphology and mental disorders to boost discovery of brain tissue–specific genetic variants and its use in polygenic risk frameworks.

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• Hindley, Guy; Drange, Ole Kristian; Lin, Aihua; Kutrolli, Gleda; Shadrin, Alexey & Parker, Nadine [Show all 20 contributors for this article] (2024). Corrigendum to “Cross-trait genome-wide association analysis of C-reactive protein level and psychiatric disorders” [Psychoneuroendocrinology 157 (2023) 106368] (Psychoneuroendocrinology (2023) 157, (S0306453023003463), (10.1016/j.psyneuen.2023.106368)). Psychoneuroendocrinology. ISSN 0306-4530. doi: 10.1016/j.psyneuen.2024.106980. Full text in Research Archive
• Corfield, Elizabeth Claire; Frei, Oleksandr; Shadrin, Alexey; Rahman, Zillur; Lin, Aihua & Athanasiu, Lavinia [Show all 26 contributors for this article] (2022). The Norwegian Mother, Father, and Child cohort study (MoBa) genotyping data resource: the MoBaPsychGen_v1 pipeline. Full text in Research Archive
• Askeland, Ragna Bugge; Hannigan, Laurie John; O'Connell, Kevin Sean; Corfield, Elizabeth Claire; Frei, Oleksandr & Thapar, Anita [Show all 11 contributors for this article] (2022). Developmental manifestations of polygenic risk for bipolar disorder from infancy to middle childhood. Full text in Research Archive
• Corfield, Elizabeth Claire; Shadrin, Alexey; Frei, Oleksandr; Rahman, Zillur; Lin, Aihua & Athanasiu, Lavinia [Show all 15 contributors for this article] (2022). Genetic profile of the Norwegian Mother, Father, and Child Cohort Study (MoBa): Results from the MoBaPsychGen pipeline. Full text in Research Archive
• Shorter, John; Pasman, Joëlle; Jangmo, Andreas; Kurvits, Siim; Meijsen, Joeri & Frei, Oleksandr [Show all 15 contributors for this article] (2022). A LARGE META-ANALYSIS ACROSS FOUR NORDIC COUNTRIES REVEALS SEVERAL SIGNIFICANT GENETIC ASSOCIATIONS FOR EARLY ONSET MAJOR DEPRESSIVE DISORDER. European Neuropsychopharmacology. ISSN 0924-977X. Full text in Research Archive
• Corfield, Elizabeth Claire; Frei, Oleksandr; Shadrin, Alexey; Rahman, Zillur; Lin, Aihua & Athanasiu, Lavinia [Show all 26 contributors for this article] (2022). The Norwegian Mother, Father, and Child cohort study (MoBa) genotyping data resource: MoBaPsychGen pipeline v.1. Cold Spring Harbor Laboratory Press (CSHL). doi: 10.1101/2022.06.23.496289. Full text in Research Archive Show summary

  Background: The Norwegian Mother, Father, and Child Cohort Study (MoBa) is a population-based pregnancy cohort, which includes approximately 114,500 children, 95,200 mothers, and 75,200 fathers. Genotyping of MoBa has been conducted through multiple research projects, spanning several years; using varying selection criteria, genotyping arrays, and genotyping centres. MoBa contains numerous interrelated families, which necessitated the implementation of a family-based quality control (QC) pipeline that verifies and accounts for diverse types of relatedness. Methods: The MoBaPsychGen pipeline, comprising pre-imputation QC, phasing, imputation, and post-imputation QC, was developed based on current best-practice protocols and implemented to account for the complex structure of the MoBa genotype data. The pipeline includes QC on both single nucleotide polymorphism (SNP) and individual level. Phasing and imputation were performed using the publicly available Haplotype Reference Consortium release 1.1 panel as a reference. Information from the Medical Birth Registry of Norway and MoBa questionnaires were used to identify biological sex, year of birth, reported parent-offspring (PO) relationships, and multiple births (only available in the offspring generation). Results: In total, 207,569 unique individuals (90% of the unique individuals included in the study) and 6,981,748 SNPs passed the MoBaPsychGen pipeline. The relatedness checks performed throughout the pipeline allowed identification of within-generation and across-generation first-degree, second-degree, and third-degree relatives. The individuals passing post-imputation QC comprised 64,471 families ranging in size from singletons to 84 unique individuals (singletons are included as families as other family members may not have been genotyped, imputed, or passed post-imputation QC). The relationships identified include 287 monozygotic twin pairs, 22,884 full siblings, 117,004 PO pairs, 23,299 second-degree relative pairs, and 10,828 third-degree relative pairs. Discussion: MoBa contains a highly complex relatedness structure, with a variety of family structures including singletons, PO duos, full (mother, father, child) PO trios, nuclear families, blended families, and extended families. The availability of robustly quality-controlled genetic data for such a large cohort with a unique extended family structure will allow many novel research questions to be addressed. Furthermore, the MoBaPsychGen pipeline has potential utility in similar cohorts.

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