El Hierro Genome Study: A Genomic and Health Study in an Isolated Canary Island Population.

El Hierro is the smallest and westernmost island of the Canary Islands, whose population derives from an admixture of different ancestral components and that has been subjected to genetic isolation. We established the "El Hierro Genome Study" to characterize the health status and the genetic composition of ~10% of the current population of the island, accounting for a total of 1054 participants. Detailed demographic and clinical data and a blood sample for DNA extraction were obtained from each participant. Genomic genotyping was performed with the Global Screening Array (Illumina). The genetic composition of El Hierro was analyzed in a subset of 416 unrelated individuals by characterizing the mitochondrial DNA (mtDNA) and Y-chromosome haplogroups and performing principal component analyses (PCAs). In order to explore signatures of isolation, runs of homozygosity (ROHs) were also estimated. Among the participants, high blood pressure, hypercholesterolemia, and diabetes were the most prevalent conditions. The most common mtDNA haplogroups observed were of North African indigenous origin, while the Y-chromosome ones were mainly European. The PCA showed that the El Hierro population clusters near 1000 Genomes' European population but with a shift toward African populations. Moreover, the ROH analysis revealed some individuals with an important portion of their genomes with ROHs exceeding 400 Mb. Overall, these results confirmed that the "El Hierro Genome" cohort offers an opportunity to study the genetic basis of several diseases in an unexplored isolated population.

Pixel walking along the boreal forest-Arctic tundra ecotone: Large scale ground-truthing of satellite-derived greenness (NDVI).

In this Technical Advance, we describe a novel method to improve ecological interpretation of remotely sensed vegetation greenness measurements that involved sampling 24,395 Landsat pixels (30 m) across 639 km of Alaska's central Brooks Range. The method goes well beyond the spatial scale of traditional plot-based sampling and thereby more thoroughly relates ground-based observations to satellite measurements. Our example dataset illustrates that, along the boreal-Arctic boundary, vegetation with the greatest Landsat Normalized Difference Vegetation Index (NDVI) is taller than 1 m, woody, and deciduous; whereas vegetation with lower NDVI tends to be shorter, evergreen, or non-woody. The field methods and associated analyses advance efforts to inform satellite data with ground-based vegetation observations using field samples collected at spatial scales that closely match the resolution of remotely sensed imagery.

Causes and consequences of major depressive disorder: An encompassing Mendelian randomization study.

Background: Major depressive disorder (MDD) is a prevalent and debilitating disorder that has been associated with a range of risk factors and outcomes. Causal pathways between MDD and other traits can be studied using genetic variants as instrumental variables. Methods: A literature review was conducted to identify 201 MDD-associated traits. For 115 traits, there were well-powered genome-wide association study (GWAS) results available that could be used to assess the genetic correlation with MDD. Of these, there were 89 meeting criteria for investigating causal associations in both directions using two-sample Mendelian randomization (TSMR). Of the traits that were not captured by GWAS, 43 could be included as outcomes of MDD using one-sample MR (OSMR). A range of methods and sensitivity tests was applied to gauge robustness of results, together with statistical power analyses to aid interpretation. Outcomes: Moderate to strong genetic overlap was found between MDD and most traits. Support for causal effects of MDD liability were found for circadian, cognitive, diet, medical disease, endocrine, functional, inflammatory, metabolic, mortality, physical activity, reproduction, risk behavior, social, socioeconomic, and suicide outcomes. Most associations were bidirectional, although there was less evidence for diet, disease, and endocrine traits causing MDD risk. Results were robust across sensitivity analyses. Interpretation: This study provides a systematic overview of traits putatively causally related to MDD, confirming previous findings as well as identifying new associations. Our results highlight the importance of MDD as a risk factor cross-cutting across medical, functional, and psychosocial domains and emphasize the need for concerted efforts at reducing this highly prevalent disorder.

Multi-organ imaging-derived polygenic indexes for brain and body health.

The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.

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

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

Association of Occupational Dysfunction and Hospital Admissions With Different Polygenic Profiles in Bipolar Disorder.

OBJECTIVE: Many but not all persons with bipolar disorder require hospital care because of severe mood episodes. Likewise, some but not all patients experience long-term occupational dysfunction that extends beyond acute mood episodes. It is not known whether these dissimilar outcomes of bipolar disorder are driven by different polygenic profiles. Here, polygenic scores (PGSs) for major psychiatric disorders and educational attainment were assessed for associations with occupational functioning and psychiatric hospital admissions in bipolar disorder. METHODS: A total of 4,782 patients with bipolar disorder and 2,963 control subjects were genotyped and linked to Swedish national registers. Longitudinal measures from at least 10 years of registry data were used to derive percentage of years without employment, percentage of years with long-term sick leave, and mean number of psychiatric hospital admissions per year. Ordinal regression was used to test associations between outcomes and PGSs for bipolar disorder, schizophrenia, major depressive disorder, attention deficit hyperactivity disorder (ADHD), and educational attainment. Replication analyses of hospital admissions were conducted with data from the Bipolar Disorder Research Network cohort (N=4,219). RESULTS: Long-term sick leave and unemployment in bipolar disorder were significantly associated with PGSs for schizophrenia, ADHD, major depressive disorder, and educational attainment, but not with the PGS for bipolar disorder. By contrast, the number of hospital admissions per year was associated with higher PGSs for bipolar disorder and schizophrenia, but not with the other PGSs. CONCLUSIONS: Bipolar disorder severity (indexed by hospital admissions) was associated with a different polygenic profile than long-term occupational dysfunction. These findings have clinical implications, suggesting that mitigating occupational dysfunction requires interventions other than those deployed to prevent mood episodes.

A cross ancestry genetic study of psychiatric disorders from India.

Genome-wide association studies across diverse populations may help validate and confirm genetic contributions to risk of disease. We estimated the extent of population stratification as well as the predictive accuracy of polygenic scores (PGS) derived from European samples to a data set from India. We analysed 2685 samples from two data sets, a population neurodevelopmental study (cVEDA) and a hospital-based sample of bipolar affective disorder (BD) and obsessive-compulsive disorder (OCD). Genotyping was conducted using Illumina's Global Screening Array. Population structure was examined with principal component analysis (PCA), uniform manifold approximation and projection (UMAP), support vector machine (SVM) ancestry predictions, and admixture analysis. PGS were calculated from the largest available European discovery GWAS summary statistics for BD, OCD, and externalizing traits using two Bayesian methods that incorporate local linkage disequilibrium structures (PGS-CS-auto) and functional genomic annotations (SBayesRC). Our analyses reveal global and continental PCA overlap with other South Asian populations. Admixture analysis revealed a north-south genetic axis within India (FST 1.6%). The UMAP partially reconstructed the contours of the Indian subcontinent. The Bayesian PGS analyses indicates moderate-to-high predictive power for BD. This was despite the cross-ancestry bias of the discovery GWAS dataset, with the currently available data. However, accuracy for OCD and externalizing traits was much lower. The predictive accuracy was perhaps influenced by the sample size of the discovery GWAS and phenotypic heterogeneity across the syndromes and traits studied. Our study results highlight the accuracy and generalizability of newer PGS models across ancestries. Further research, across diverse populations, would help understand causal mechanisms that contribute to psychiatric syndromes and traits.

Meta-analysis of epigenetic aging in schizophrenia reveals multifaceted relationships with age, sex, illness duration, and polygenic risk.

BACKGROUND: The study of biological age acceleration may help identify at-risk individuals and reduce the rising global burden of age-related diseases. Using DNA methylation (DNAm) clocks, we investigated biological aging in schizophrenia (SCZ), a mental illness that is associated with an increased prevalence of age-related disabilities and morbidities. In a whole blood DNAm sample of 1090 SCZ cases and 1206 controls across four European cohorts, we performed a meta-analysis of differential aging using three DNAm clocks (i.e., Hannum, Horvath, and Levine). To dissect how DNAm aging contributes to SCZ, we integrated information on duration of illness and SCZ polygenic risk, as well as stratified our analyses by chronological age and biological sex. RESULTS: We found that blood-based DNAm aging is significantly altered in SCZ independent from duration of the illness since onset. We observed sex-specific and nonlinear age effects that differed between clocks and point to possible distinct age windows of altered aging in SCZ. Most notably, intrinsic cellular age (Horvath clock) is decelerated in SCZ cases in young adulthood, while phenotypic age (Levine clock) is accelerated in later adulthood compared to controls. Accelerated phenotypic aging was most pronounced in women with SCZ carrying a high polygenic burden with an age acceleration of + 3.82 years (CI 2.02-5.61, P = 1.1E-03). Phenotypic aging and SCZ polygenic risk contributed additively to the illness and together explained up to 14.38% of the variance in disease status. CONCLUSIONS: Our study contributes to the growing body of evidence of altered DNAm aging in SCZ and points to intrinsic age deceleration in younger adulthood and phenotypic age acceleration in later adulthood in SCZ. Since increased phenotypic age is associated with increased risk of all-cause mortality, our findings indicate that specific and identifiable patient groups are at increased mortality risk as measured by the Levine clock. Our study did not find that DNAm aging could be explained by the duration of illness of patients, but we did observe age- and sex-specific effects that warrant further investigation. Finally, our results show that combining genetic and epigenetic predictors can improve predictions of disease outcomes and may help with disease management in schizophrenia.

Machine learning methods for predicting guide RNA effects in CRISPR epigenome editing experiments.

CRISPR epigenomic editing technologies enable functional interrogation of non-coding elements. However, current computational methods for guide RNA (gRNA) design do not effectively predict the power potential, molecular and cellular impact to optimize for efficient gRNAs, which are crucial for successful applications of these technologies. We present "launch-dCas9" (machine LeArning based UNified CompreHensive framework for CRISPR-dCas9) to predict gRNA impact from multiple perspectives, including cell fitness, wildtype abundance (gauging power potential), and gene expression in single cells. Our launchdCas9, built and evaluated using experiments involving >1 million gRNAs targeted across the human genome, demonstrates relatively high prediction accuracy (AUC up to 0.81) and generalizes across cell lines. Method-prioritized top gRNA(s) are 4.6-fold more likely to exert effects, compared to other gRNAs in the same cis-regulatory region. Furthermore, launchdCas9 identifies the most critical sequence-related features and functional annotations from >40 features considered. Our results establish launch-dCas9 as a promising approach to design gRNAs for CRISPR epigenomic experiments.

Connecting genomic results for psychiatric disorders to human brain cell types and regions reveals convergence with functional connectivity.

Understanding the temporal and spatial brain locations etiological for psychiatric disorders is essential for targeted neurobiological research. Integration of genomic insights from genome-wide association studies with single-cell transcriptomics is a powerful approach although past efforts have necessarily relied on mouse atlases. Leveraging a comprehensive atlas of the adult human brain, we prioritized cell types via the enrichment of SNP-heritabilities for brain diseases, disorders, and traits, progressing from individual cell types to brain regions. Our findings highlight specific neuronal clusters significantly enriched for the SNP-heritabilities for schizophrenia, bipolar disorder, and major depressive disorder along with intelligence, education, and neuroticism. Extrapolation of cell-type results to brain regions reveals important patterns for schizophrenia with distinct subregions in the hippocampus and amygdala exhibiting the highest significance. Cerebral cortical regions display similar enrichments despite the known prefrontal dysfunction in those with schizophrenia highlighting the importance of subcortical connectivity. Using functional MRI connectivity from cases with schizophrenia and neurotypical controls, we identified brain networks that distinguished cases from controls that also confirmed involvement of the central and lateral amygdala, hippocampal body, and prefrontal cortex. Our findings underscore the value of single-cell transcriptomics in decoding the polygenicity of psychiatric disorders and offer a promising convergence of genomic, transcriptomic, and brain imaging modalities toward common biological targets.

Arctic sea ice retreat fuels boreal forest advance.

Climate-induced northward advance of boreal forest is expected to lessen albedo, alter carbon stocks, and replace tundra, but where and when this advance will occur remains largely unknown. Using data from 19 sites across 22 degrees of longitude along the tree line of northern Alaska, we show a stronger temporal correlation of tree ring growth with open water uncovered by retreating Arctic sea ice than with air temperature. Spatially, our results suggest that tree growth, recruitment, and range expansion are causally linked to open water through associated warmer temperatures, deeper snowpacks, and improved nutrient availability. We apply a meta-analysis to 82 circumarctic sites, finding that proportionally more tree lines have advanced where proximal to ongoing sea ice loss. Taken together, these findings underpin how and where changing sea ice conditions facilitate high-latitude forest advance.

Key subphenotypes of bipolar disorder are differentially associated with polygenic liabilities for bipolar disorder, schizophrenia, and major depressive disorder.

Bipolar disorder (BD) features heterogenous clinical presentation and course of illness. It remains unclear how subphenotypes associate with genetic loadings of BD and related psychiatric disorders. We investigated associations between the subphenotypes and polygenic risk scores (PRS) for BD, schizophrenia, and major depressive disorder (MDD) in two BD cohorts from Sweden (N = 5180) and the UK (N = 2577). Participants were assessed through interviews and medical records for inter-episode remission, psychotic features during mood episodes, global assessment of functioning (GAF, function and symptom burden dimensions), and comorbid anxiety disorders. Meta-analyses based on both cohorts showed that inter-episode remission and GAF-function were positively correlated with BD-PRS but negatively correlated with schizophrenia-PRS (SCZ-PRS) and MDD-PRS. Moreover, BD-PRS was negatively, and MDD-PRS positively, associated with the risk of comorbid anxiety disorders. Finally, SCZ-PRS was positively associated with psychotic symptoms during mood episodes. Assuming a higher PRS of certain psychiatric disorders in cases with a positive family history, we further tested the associations between subphenotypes in index BD people and occurrence of BD, schizophrenia, or MDD in their relatives using Swedish national registries. BD patients with a relative diagnosed with BD had: (1) higher GAF and lower risk of comorbid anxiety than those with a relative diagnosed with schizophrenia or MDD, (2) lower risk of psychotic symptoms than those with a relative diagnosed with schizophrenia. Our findings shed light on the genetic underpinnings of the heterogeneity in clinical manifestations and course of illness in BD, which ultimately provide insights for developing personalized approaches to the diagnosis and treatment.

How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry.

Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements-well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms-to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders.

SETD1A variant-associated psychosis: A systematic review of the clinical literature and description of two new cases.

OBJECTIVE: SETD1A encodes a histone methyltransferase involved in various cell cycle regulatory processes. Loss-of-function SETD1A variants have been associated with numerous neurodevelopmental phenotypes, including intellectual disability and schizophrenia. While the association between rare coding variants in SETD1A and schizophrenia has achieved genome-wide significance by rare variant burden testing, only a few studies have described the psychiatric phenomenology of such individuals in detail. This systematic review and case report aims to characterize the neurodevelopmental and psychiatric phenotypes of SETD1A variant-associated schizophrenia. METHODS: A PubMed search was completed in July 2022 and updated in May 2023. Only studies that reported individuals with a SETD1A variant as well as a primary psychotic disorder were ultimately included. Additionally, another two previously unpublished cases of SETD1A variant-associated psychosis from our own sequencing cohort are described. RESULTS: The search yielded 32 articles. While 15 articles met inclusion criteria, only five provided case descriptions. In total, phenotypic information was available for 11 individuals, in addition to our own two unpublished cases. Our findings suggest that although individuals with SETD1A variant-associated schizophrenia may share a number of common features, phenotypic variability nonetheless exists. Moreover, although such individuals may exhibit numerous other neurodevelopmental features suggestive of the syndrome, their psychiatric presentations appear to be similar to those of general schizophrenia populations. CONCLUSIONS: Loss-of-function SETD1A variants may underlie the development of psychosis in a small percentage of individuals with schizophrenia. Identifying such individuals may become increasingly important, given the potential for advances in precision medicine treatment approaches.

Distinct genomic signatures and modifiable risk factors underly the comorbidity between major depressive disorder and cardiovascular disease.

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

COVID-19 illness severity and 2-year prevalence of physical symptoms: an observational study in Iceland, Sweden, Norway and Denmark.

Background: Although the persistence of physical symptoms after SARS-CoV-2 infection is a major public health concern, evidence from large observational studies beyond one year post diagnosis remain scarce. We aimed to assess the prevalence of physical symptoms in relation to acute illness severity up to more than 2-years after diagnosis of COVID-19. Methods: This multinational study included 64,880 adult participants from Iceland, Sweden, Denmark, and Norway with self-reported data on COVID-19 and physical symptoms from April 2020 to August 2022. We compared the prevalence of 15 physical symptoms, measured by the Patient Health Questionnaire (PHQ-15), among individuals with or without a confirmed COVID-19 diagnosis, by acute illness severity, and by time since diagnosis. We additionally assessed the change in symptoms in a subset of Swedish adults with repeated measures, before and after COVID-19 diagnosis. Findings: During up to 27 months of follow-up, 34.5% participants (22,382/64,880) were diagnosed with COVID-19. Individuals who were diagnosed with COVID-19, compared to those not diagnosed, had an overall 37% higher prevalence of severe physical symptom burden (PHQ-15 score ≥15, adjusted prevalence ratio [PR] 1.37 [95% confidence interval [CI] 1.23-1.52]). The prevalence was associated with acute COVID-19 severity: individuals bedridden for seven days or longer presented with the highest prevalence (PR 2.25 [1.85-2.74]), while individuals never bedridden presented with similar prevalence as individuals not diagnosed with COVID-19 (PR 0.92 [0.68-1.24]). The prevalence was statistically significantly elevated among individuals diagnosed with COVID-19 for eight of the fifteen measured symptoms: shortness of breath, chest pain, dizziness, heart racing, headaches, low energy/fatigue, trouble sleeping, and back pain. The analysis of repeated measurements rendered similar results as the main analysis. Interpretation: These data suggest an elevated prevalence of some, but not all, physical symptoms during up to more than 2 years after diagnosis of COVID-19, particularly among individuals suffering a severe acute illness, highlighting the importance of continued monitoring and alleviation of these targeted core symptoms. Funding: This work was mainly supported by grants from NordForsk (COVIDMENT, grant number 105668 and 138929) and Horizon 2020 (CoMorMent, 847776). See Acknowledgements for further details on funding.

Elevated symptoms of depression and anxiety among family members and friends of critically ill COVID-19 patients - an observational study of five cohorts across four countries.

Background: Little is known regarding the mental health impact of having a significant person (family member and/or close friend) with COVID-19 of different severity. Methods: The study included five prospective cohorts from four countries (Iceland, Norway, Sweden, and the UK) with self-reported data on COVID-19 and symptoms of depression and anxiety during March 2020-March 2022. We calculated prevalence ratios (PR) of depression and anxiety in relation to having a significant person with COVID-19 and performed a longitudinal analysis in the Swedish cohort to describe temporal patterns. Findings: 162,237 and 168,783 individuals were included in the analysis of depression and anxiety, respectively, of whom 24,718 and 27,003 reported a significant person with COVID-19. Overall, the PR was 1.07 (95% CI: 1.05-1.10) for depression and 1.08 (95% CI: 1.03-1.13) for anxiety in relation to having a significant person with COVID-19. The respective PRs for depression and anxiety were 1.15 (95% CI: 1.08-1.23) and 1.24 (95% CI: 1.14-1.34) if the patient was hospitalized, 1.42 (95% CI: 1.27-1.57) and 1.45 (95% CI: 1.31-1.60) if the patient was ICU-admitted, and 1.34 (95% CI: 1.22-1.46) and 1.36 (95% CI: 1.22-1.51) if the patient died. Individuals with a significant person with hospitalized, ICU-admitted, or fatal COVID-19 showed elevated prevalence of depression and anxiety during the entire year after the COVID-19 diagnosis. Interpretation: Family members and close friends of critically ill COVID-19 patients show persistently elevated prevalence of depressive and anxiety symptoms. Funding: This study was primarily supported by NordForsk (COVIDMENT, 105668) and Horizon 2020 (CoMorMent, 847776).

Swedish multimodal cohort of patients with anxiety or depression treated with internet-delivered psychotherapy (MULTI-PSYCH).

PURPOSE: Depression and anxiety afflict millions worldwide causing considerable disability. MULTI-PSYCH is a longitudinal cohort of genotyped and phenotyped individuals with depression or anxiety disorders who have undergone highly structured internet-based cognitive-behaviour therapy (ICBT). The overarching purpose of MULTI-PSYCH is to improve risk stratification, outcome prediction and secondary preventive interventions. MULTI-PSYCH is a precision medicine initiative that combines clinical, genetic and nationwide register data. PARTICIPANTS: MULTI-PSYCH includes 2668 clinically well-characterised adults with major depressive disorder (MDD) (n=1300), social anxiety disorder (n=640) or panic disorder (n=728) assessed before, during and after 12 weeks of ICBT at the internet psychiatry clinic in Stockholm, Sweden. All patients have been blood sampled and genotyped. Clinical and genetic data have been linked to several Swedish registers containing a wide range of variables from patient birth up to 10 years after the end of ICBT. These variable types include perinatal complications, school grades, psychiatric and somatic comorbidity, dispensed medications, medical interventions and diagnoses, healthcare and social benefits, demographics, income and more. Long-term follow-up data will be collected through 2029. FINDINGS TO DATE: Initial uses of MULTI-PSYCH include the discovery of an association between PRS for autism spectrum disorder and response to ICBT, the development of a machine learning model for baseline prediction of remission status after ICBT in MDD and data contributions to genome wide association studies for ICBT outcome. Other projects have been launched or are in the planning phase. FUTURE PLANS: The MULTI-PSYCH cohort provides a unique infrastructure to study not only predictors or short-term treatment outcomes, but also longer term medical and socioeconomic outcomes in patients treated with ICBT for depression or anxiety. MULTI-PSYCH is well positioned for research collaboration.

The pivotal role of the X-chromosome in the genetic architecture of the human brain.

Genes on the X-chromosome are extensively expressed in the human brain, resulting in substantial influences on brain development, intellectual disability, and other brain-related disorders. To comprehensively investigate the X-chromosome's impact on the cerebral cortex, white matter tract microstructures, and intrinsic and extrinsic brain functions, we examined 2,822 complex brain imaging traits obtained from n=34,000 subjects in the UK Biobank. We unveiled potential autosome-X-chromosome interaction, while proposing an atlas of dosage compensation (DC) for each set of traits. We observed a pronounced X-chromosome impact on the corticospinal tract and the functional amplitude and connectivity of visual networks. In association studies, we identified 50 genome-wide significant trait-locus pairs enriched in Xq28, 22 of which replicated in independent datasets (n=4,900). Notably, 13 newly identified pairs were in the X-chromosome's non-pseudo-autosomal regions (NPR). The volume of the right ventral diencephalon shared genetic architecture with schizophrenia and educational attainment in a locus indexed by rs2361468 (located ~3kb upstream of PJA1, a conserved and ubiquitously expressed gene implicated in multiple psychiatric disorders). No significant associations were identified in the pseudo-autosomal regions (PAR) or the Y-chromosome. Finally, we explored sex-specific associations on the X-chromosome and compared differing genetic effects between sexes. We found much more associations can be identified in males (33 versus 9) given a similar sample size. In conclusion, our research provides invaluable insights into the X-chromosome's role in the human brain, contributing to the observed sex differences in brain structure and function.

Polygenic risk scores of lithium response and treatment resistance in major depressive disorder.

Treatment response and resistance in major depressive disorder (MDD) are suggested to be heritable. Due to significant challenges in defining treatment-related phenotypes, our understanding of their genetic bases is limited. This study aimed to derive a stringent definition of treatment resistance and to investigate the genetic overlap between treatment response and resistance in MDD. Using electronic medical records on the use of antidepressants and electroconvulsive therapy (ECT) from Swedish registers, we derived the phenotype of treatment-resistant depression (TRD) and non-TRD within ~4500 individuals with MDD in three Swedish cohorts. Considering antidepressants and lithium are first-line treatment and augmentation used for MDD, respectively, we generated polygenic risk scores (PRS) of antidepressants and lithium response for individuals with MDD and evaluated their associations with treatment resistance by comparing TRD with non-TRD. Among 1778 ECT-treated MDD cases, nearly all (94%) used antidepressants before their first ECT and the vast majority had at least one (84%) or two (61%) antidepressants of adequate duration, suggesting these MDD cases receiving ECT were resistant to antidepressants. We did not observe a significant difference in the mean PRS of antidepressant response between TRD and non-TRD; however, we found that TRD cases had a significantly higher PRS of lithium response compared to non-TRD cases (OR = 1.10-1.12 under various definitions). The results support the evidence of heritable components in treatment-related phenotypes and highlight the overall genetic profile of lithium-sensitivity in TRD. This finding further provides a genetic explanation for lithium efficacy in treating TRD.