While significant progress has been made in understanding the genetic architecture of ageing in model organisms, our understanding of human ageing remains limited. We performed a multi-tissue Transcriptome-wide association study (TWAS) on human lifespan, integrating GWAS data from >1 million parental lifespans with gene expression prediction models derived from reference transcriptomic datasets; followed by replication using healthspan and longevity phenotypes as additional readouts of ageing. The TWAS uncovered 563 significant gene associations, of which 139 replicated. TOMM40 , encoding a component of the mitochondrial outer membrane translocase that is fundamental for mitochondrial function, had the strongest association with parental lifespan and longevity and was fine-mapped as a putatively causal lifespan gene at the APOE-TOMM40 region. Uniquely in our study, we identified fly orthologues of replicating genes and examined if modulating their expression impacts Drosophila longevity. The nine novel associations with all three ageing outcomes included COASY, encoding Coenzyme A synthase. Knocking down its fly orthologue, Ppat-dpck , resulted in significant lifespan extension in flies. Hence, in addition to discovering new genes associated with human ageing, by combining human TWAS with experimental Drosophila work, we provide evidence for the role of COASY ( Ppat-dpck ) in ageing across species. Significance statement Extensive research on ageing has been conducted in Drosophila and C.elegans due to their short lifespan and experimental tractability. However, in human genetic research, only a few loci have been consistently replicated. To bridge this gap, we conducted a transcriptome-wide association study (TWAS) followed by experimental validation in Drosophila . TWAS revealed 139 significant and replicating gene associations, including COASY. Knockdown of its fly orthologue ( Ppat-dpck ) significantly extended fly lifespan, validating its roles in ageing across species. Thus, integrating multiple ageing outcomes through TWAS in human genetic research can uncover robust associations and highlight genes involved in fundamental ageing mechanisms. ### Competing Interest Statement The authors have declared no competing interest. Biotechnology and Biological Sciences Research Council (BBSRC), BB/M009513/1, BB/R01356X/1, BB/S014357/1, BB/W013525/1 European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program, Grant agreement No. 948561 Malaysian Government Agency Majlis Amanah Rakyat (MARA) sponsorship

The expansion of ancestrally diverse genetic cohorts has altered the landscape of complex disease genetics. Differences in linkage disequilibrium across populations have improved fine-mapping and the identification of target genes — key steps for translating findings from genome-wide association studies into biological understanding. Whilst there is widespread sharing of genetic architecture across ancestries, loci that display heterogeneity in causal genetic effects across populations can offer unique biological insights. Here, we review how ancestral and global diversity shape genetic discoveries. As we advance towards global precision medicine, integrating genomic data with diverse environmental and social factors will be crucial to account for population-specific contexts that can influence disease risk or treatment response. Ancestral and geographic diversity in genomic resources can improve our understanding of complex disease genetics. The authors review how ancestrally diverse biobanks worldwide are reshaping genetic discovery, empowering the identification of novel variants and disease associations, with important biological implications.

Summary Background Major depressive disorder (MDD), a leading cause of disability worldwide, exhibits substantial heterogeneity in treatment outcomes. Patients who do not respond to standard antidepressant therapy account for the majority of MDD’s disease burden. Risk factors have been implicated in treatment response, including genes impacting on how antidepressants are metabolised. Yet, despite its clinical importance, risk factors for treatment-resistant depression (TRD) remain unexplored in low- and middle-income countries (LMIC). We used data from the DIVERGE study on MDD to investigate the risk factors of TRD in Pakistan. Methods DIVERGE is a genetic epidemiological study that recruited adult MDD patients (≥18 years) between Sep 27,2021 to Jun 30, 2025, from psychiatric care facilities across Pakistan. Detailed phenotypic information was collected by trained interviewers and blood samples taken. Infinium Global Diversity Array with Enhanced PGx-8 from Illumina was used for genotyping followed by DRAGEN calling to infer metaboliser phenotypes for Cytochrome P450 (CYP) enzyme genes. We defined TRD as minimal to no improvement after ≥12 weeks of adherent antidepressant therapy. We conducted multi-level logistic regression to test the association of demographic, clinical and pharmacogenetic variables with TRD. Findings Among 3,677 eligible patients, polypharmacy was rampant; 86% were prescribed another psychotropic drug along with an antidepressant. Psychological therapies were uncommon (6%) while 49% of patients had previously visited to a religious leader/faith healer in relation to their mental health problems. TRD was experienced by 34% (95%CI: 32-36%) patients. The TRD group was characterised by more psychotic symptoms and suicidal behaviour (OR=1.39, 95%CI=1.04-1.84, p=0.02; OR=1.03, 95%CI=1.01-1.05, p=0.005). Social support (OR=0.55, 95%CI=0.44-0.69, p=1.4x10 -7 ) and parents being first cousins (OR=0.81, 95%CI=0.69-0.96, p=0.01) were associated with lower odds of TRD. In 1,085 patients with CYP enzyme data, poor (OR=1.85, 95%CI=1.11-3.07, p=0.01) and ultra-rapid (OR=3.11, 95%CI=1.59-6.12, p=0.0009) metabolizers for CYP2C19 had increased risk of TRD compared with normal metabolisers. Interpretation There was an excessive use of polypharmacy in the treatment of depression while psychological therapies were uncommon highlighting the need for more evidence-based practice. This first large study of MDD from Pakistan uncovered the importance of culture-specific forms of social support in preventing TRD, highlighting opportunities for interventions in low-income settings. Pharmacogenetic markers can be leveraged to predict TRD.

Major depression (MD) treatments have limited efficacy and target few mechanisms, highlighting the need for innovative drug discovery. Drugs targeting genetically supported proteins are 2.6 times more likely to succeed in drug development. Here, we use genetic methods to identify and prioritise MD drug targets, leveraging genome-wide association study (GWAS) summary statistics from >525,000 MD cases. We derived exposure data from 10 datasets measuring protein quantitative trait loci (pQTLs) and gene expression levels (eQTLs) in blood, cerebrospinal fluid, and brain tissues. We performed cis-Mendelian randomisation (MR) on 3469 druggable targets (genes encoding proteins targeted by existing compounds or experimentally predicted to be druggable). To strengthen causal inference, we implemented robust MR estimators, colocalisation, external replication, and assessed directional consistency across tissues. We integrated cis-MR effect directions with drug mechanisms and clinical annotations to infer potential therapeutic effects. Validation analyses showed that 82% of drugs approved for depression/anxiety had ≥1 significant MR target, compared to 51% for compounds in clinical trials. For repurposing, we prioritised 54 targets of compounds developed for other conditions with estimated beneficial effects on MD (e.g., an inhibitor for a risk-increasing target). Ten high-priority targets of brain-penetrating compounds included ACE and NISCH (cardiovascular drugs), NDUFA2, NDUFB6, and NDUFS1 (metformin), CDK4, NTRK3, and MET (oncology inhibitors), and GLS and NOS2 (enzyme inhibitors). We found genetic evidence for established and novel MD targets across the drug development pipeline. Novel targets point to mechanisms beyond monoaminergic systems, most with approved drugs for other conditions, offering immediate repurposing opportunities.

Postpartum Psychosis (PP) is a severe and understudied perinatal mental illness which disproportionately affects women with bipolar disorder (BD). A relationship between sleep disturbance and PP is often assumed, but is poorly understood. From a cohort of 2099 individuals with BD, 343 parous women were identified and screened for perinatal psychiatric complications. We compared 117 women who developed PP with 226 who did not. Polygenic Risk Scores (PRS) for BD, schizophrenia, insomnia, short sleep, long sleep, sleep efficiency and sleep duration were computed using PRS-CS. Logistic regression was used to model the effect of each PRS on PP. Higher PRS for insomnia and short sleep were associated with reduced risk of PP. Individuals in the lowest decile for insomnia PRS (RR 1.96, 95% CI 1.25-3.07, p = 3.50 × 10⁻³) and short sleep PRS (RR 2.23, 95% CI 1.40-3.54, p = 7.94 × 10⁻⁴) had approximately double the risk of PP than individuals in the highest decile. The other PRS were not associated with PP. Mendelian Randomisation analyses did not support a causal relationship between sleep traits and PP. However, we demonstrate that the integration of PRS with bipolar subtype can improve prediction accuracy. Individuals with genetic vulnerability to insomnia or short sleep may develop a heightened tolerance to sleep disruption earlier in life, mitigating the impact of childbirth on mood. These findings suggest that genetic susceptibility to sleep disturbance may be important in the aetiology of PP, offering a new potential avenue for risk stratification and targeted prevention.

BACKGROUND:Genome sequencing has improved rare disease diagnosis and is now part of routine clinical care in the National Health Service in England. Automated prioritisation pipelines narrow millions of variants per patient to a small subset for clinical review, a process that relies on allele frequency resources that do not fully represent human genetic diversity. We assessed ancestry-related differences in variant prioritisation and diagnostic outcomes in patients from the UK 100,000 Genomes Project. METHODS:We analysed 29,405 rare disease probands with genome sequencing and linked clinical outcomes data. We used multivariable regression to assess ancestry-related differences in the number of variants prioritised for clinical review, the proportion of prioritised variants that were recorded as diagnostic, and diagnostic yield. We also evaluated the use of ancestry-stratified allele frequency filters derived from an independent, diverse UK cohort (n = 33,724). FINDINGS:Compared with the European ancestry group, the East African group had nearly three times more variants prioritised for clinical review (IRR 2.77, 95% CI 2.33-3.29). Other non-European groups also had significantly higher counts. Diagnostic yield was similar across ancestry groups after adjustment (LRT p = 0.1650). Prioritised variants were less likely to be recorded as diagnostic in East African (OR 0.32, 95% CI 0.22-0.46), West African (0.47, 0.39-0.57), South Asian (0.65, 0.58-0.73), and Middle Eastern (0.68, 0.54-0.86) groups. Applying ancestry-stratified allele-frequency filters removed 3.1% of prioritised variants overall-24.3% in the East African group-without loss of diagnostic sensitivity, including 29.5% of recorded VUS in this group. INTERPRETATION:Differences in the likelihood of prioritised variants being recorded as diagnostic partly reflect limitations of current allele frequency resources, which use broad population groupings that mask within-group diversity. Increased representation of diverse ancestries in reference databases and better estimation of ancestry-appropriate allele frequencies will help reduce inefficiencies and improve equity in variant prioritisation for rare disease diagnosis. FUNDING:The UK Department of Health and Social Care and the EU's Horizon 2020 Research and Innovation Programme.

Major depressive disorder (MDD) significantly contributes to the mental health burden in Africa, requiring insights into prevalence and risk factors to inform culturally responsive care. This study assessed current and lifetime MDD prevalence and associated factors among 7,073 adults from hospital settings in Uganda, Kenya, Ethiopia, and South Africa within the NeuroGAP-Psychosis study. Prevalence was 0.9% for current MDD and 5.1% for lifetime MDD, substantially lower than prior estimates from the region. Lower rates may be due to the population studied or cultural variations in expression of symptoms. Multilevel logistic regression identified significant associations between current MDD and negative life events, alcohol use, and Kessler psychological distress scores, while lifetime MDD was linked to age, female sex, chronic pain, frequent headaches, and positive psychosis screening. These findings underscore the need for targeted mental health interventions tailored to the identified factors to reduce the burden of MDD in diverse African contexts. Across Uganda, Kenya, Ethiopia and South Africa, a harmonized study identified factors associated with major depression and found country differences explained 24% of variation, indicating regional strategies should be adapted to national contexts.

BACKGROUND:Most research on genetic screening and precision oncology is based on individuals of European ancestry. We applied the National Health Service (NHS) England's cancer variant prioritisation workflow to evaluate the performance of these approaches in ethinically and ancestrally diverse populations. The second aim of the study was to assess the representativeness of the 100 000 Genomes Project cancer cohort of the population of England. METHODS:In this cross-sectional analysis, whole-genome sequencing data from patients with cancer recruited into the 100 000 Genomes Project between February 2015 to December 2018 were analysed. Clinical information, including tumour stage and grade, was gathered from the NHS England National Cancer Registration and Analysis Service. Patients with cancer types with fewer than five individuals, haematological cancers, childhood cancers, unknown primary carcinomas, patients with indeterminate sex, and patients missing somatic mutations in genes were excluded. To assess ethnicity representation in the 100 000 Genomes Project, we calculated the recruitment ratios for self-reported ethnicities for patients with cancer recruited to the 100 000 Genomes Project and patients with cancer in England. We also analysed differences in classification rates for potentially pathogenic variants to assess ancestry-related differences in germline and somatic mutations of different ancestry groups. FINDINGS:14 775 patients with cancer were recruited between February, 2015, and December, 2018, into the 100 000 Genomes Project. There was no evidence of under-representation of diverse ethnic groups in the 100 000 Genomes Project when compared with the national statistics. The recruitment rate ratio for breast cancer was 2·2 (95% CI 1·6-3·0) for Black versus White women in the 100 000 Genomes Project compared with 0·81 (0·79-0·83) for Black versus White women in the national data (fold-change in rate ratios 2·7; 95% CI 2·0-3·7, p<0·0001), suggesting higher representation of Black women in the 100 000 Genomes Project than expected given the ethnicity-specific incidence rates in England. Compared with national rates, the 100 000 Genomes Project also had higher recruitment rates of Black versus White men with prostate cancer (fold-change in rate ratios 3·7; 1·8-7·5, p=0·0004), Black versus White men with bladder cancer (fold change in rate ratios 6·1; 2·0-18·8, p=0·0016), and Asian versus White women with breast cancer (fold change in rate ratios 1·4; 1·2-1·7, p=0·0008). Ancestry had a significant association with the likelihood of carrying a variant classified as a potentially pathogenic (likelihood ratio test p=0·0011). Potentially pathogenic variants were identified in 23 (4·6%) of 500 South Asian (adjusted model odds ratio [OR] 1·88, 95% CI 1·21-2·93, p=0·0052) and 24 (5·3%) of 453 African ancestry patients (OR 2·24, 1·44-3·48, p=0·0003) compared with 263 (2·2%) of 11 955 in European-ancestry patients. However, we found that fewer tumour mutations in actionable genes were identified for patients of non-European ancestry compared with patients of European ancestry when adjusting for sex and cancer type (likelihood ratio test p<0·0001). INTERPRETATION:The was an excess of germline variants classified as potentially pathogenic variants in patients with non-European ancestry, which might impede the diagnostic process. Improved variant prioritisation workflows and more research in diverse groups are needed to ensure equitable implementation of genomics in cancer care. FUNDING:The UK Department of Health and Social Care and the EU's Horizon 2020 Research and Innovation Programme.

BACKGROUND:Sudden Unexpected Death in Epilepsy (SUDEP) is a rare and tragic outcome in epilepsy, identified by those with the condition as their most serious concern. Although several clinical factors are associated with elevated SUDEP risk, mechanisms underlying SUDEP are poorly understood, making individual risk prediction challenging, especially early in the disease course. We hypothesised that common genetic variation contributes to SUDEP risk. METHODS:Genetic data from people who had succumbed to SUDEP was compared to data from people with epilepsy who had not succumbed to SUDEP and from healthy controls. Polygenic risk scores (PRSs) for longevity, intelligence and epilepsy were compared across cohorts. Reactome pathways and gene ontology terms implicated by the contributing single nucleotide polymorphisms (SNPs) were explored. In the subset of SUDEP cases with the necessary data available, a risk score was calculated using an existing risk prediction tool (SUDEP-3); the added value to this prediction of SNP-based genomic information was evaluated. FINDINGS:Only European-ancestry participants were included. 161 SUDEP cases were compared to 768 cases with epilepsy and 1153 healthy controls. PRS for longevity was significantly reduced in SUDEP cases compared to disease (P = 0·0096) and healthy controls (P = 0·0016), as was PRS for intelligence (SUDEP cases compared to disease (P = 0·0073) and healthy controls (P = 0·00024)). The PRS for epilepsy did not differ between SUDEP cases and disease controls (P = 0·76). SNP-determined pathway and gene ontology analysis highlighted those related to inter-neuronal communication as amongst the most enriched in SUDEP. Addition of PRS for longevity and intelligence to SUDEP-3 scores improved risk prediction in a subset of cases (38) and controls (703), raising the area-under-the-curve in a receiver-operator characteristic from 0·699 using SUDEP-3 alone to 0·913 when PRSs were added. INTERPRETATION:Common genetic variation contributes to SUDEP risk, offering new approaches to improve risk prediction and to understand underlying mechanisms. FUNDING:The Amelia Roberts Fund; CURE Epilepsy; Epilepsy Society, UK; Finding A Cure for Epilepsy and Seizures (FACES).

BACKGROUND: China faces significant mental health challenges, with unique associations between mental disorders and other traits observed in its population. METHODS: Based on summary statistics of existing genome-wide association studies in East Asian ancestry (EAS) and European ancestry (EUR) populations, we tested the associations of polygenic scores (PGSs) for schizophrenia (SCZ) and major depression (MD) with 254 phenotypes in 100,640 Chinese adults. We also conducted genetic correlation and Mendelian randomization analyses to assess the consistency of these associations across ancestries and infer causality. RESULTS: The PGSs predicted SCZ (R 2 = 2.63%-3.07%) and MD (R 2 = 0.21%-0.71%) and were associated with various sociodemographic, lifestyle, and physical factors. Interestingly, based on summary statistics in the EAS population, the schizophrenia PGS was inversely associated with smoking initiation, and the MD PGS was inversely associated with body mass index. Across populations, opposing genetic correlations were observed between smoking initiation and SCZ (inverse in the EAS population, positive in the EUR population) and between body mass index and MD (inverse in the EAS population, positive in the EUR population). Univariable Mendelian randomization supported the causality of these relationships in the EUR population, but multivariable analyses suggested that pleiotropic effects on other related traits (e.g., cannabis use, unhealthy lifestyle) might have influenced the associations. CONCLUSIONS: Our study suggests the context specificity of relationships between mental disorders and other traits, highlighting a potential role of sociocultural factors.

Major depressive disorder (MDD) treatments have limited efficacy and target few mechanisms, highlighting the need for innovative drug discovery. Drugs targeting genetically supported proteins are 2.6 times more likely to succeed in drug development. Here, we use genetic methods to identify and prioritise MDD drug targets, leveraging genome-wide association study (GWAS) summary statistics from >525,000 MDD cases. We derived exposure data from 10 GWAS measuring protein quantitative trait loci (pQTLs) and gene expression levels (eQTLs) in blood, cerebrospinal fluid, and brain tissues. We performed cis-Mendelian randomisation (MR) on 3,469 druggable targets (genes encoding proteins targeted by existing compounds or experimentally predicted to be druggable). To strengthen causal inference, we implemented robust MR estimators, colocalisation, external replication, and assessed directional consistency across tissues. We integrated MR effect directions with drug mechanisms and clinical annotations to infer potential therapeutic effects. Validation analyses showed that 82% of drugs approved for depression/anxiety had ≥1 significant MR target, compared to 51% for compounds in clinical trials. For repurposing, we prioritised 54 targets of compounds developed for other conditions with estimated beneficial effects on MDD (e.g., an inhibitor for a risk-increasing target). Ten high-priority targets of brain-penetrating compounds included ACE and NISCH (cardiovascular drugs), NDUFA2, NDUFB6, and NDUFS1 (metformin), CDK4, NTRK3, and MET (oncology inhibitors), and GLS and NOS2 (enzyme inhibitors). We found genetic evidence for established and novel MDD targets across the drug development pipeline. Novel targets point to mechanisms beyond monoaminergic systems, most with approved drugs for other conditions, offering immediate repurposing opportunities. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This project has received funding from the European Research Council (ERC) under the European Union Horizon 2020 research and innovation programme (I-IRISK grant agreement No. 863981). AFS is supported by BHF grants PG/18/5033837, PG/22/10989, the UCL BHF Research Accelerator AA/18/6/34223. AFS received additional support from the National Institute for Health Research University College London Hospitals Biomedical Research Centre. SB is supported by the Great Ormond Street Hospital Children Charity. This work was funded by UK Research and Innovation (UKRI) under the UK governments Horizon Europe funding guarantee EP/Z000211/1, by the UKRI/NIHR Multimorbidity fund Mechanism and Therapeutics Research Collaborative MR/V033867/1, and by the Rosetrees Trust. The authors acknowledge the use of the UCL Myriad High Performance Computing Facility (Myriad UCL), and associated support services, in the completion of this work. This research has been conducted using the UK Biobank Resource under application number 12113. We are grateful to the UK Biobank participants. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government, and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government and the British Heart Foundation. We thank the research participants and employees of 23andMe, Inc. for their contribution to the GWAS summary data used in this study. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This research has been conducted using the UK Biobank Resource under application number 12113. The UK Biobank has ethical approval from the North West Multi-centre Research Ethics Committee as a Research Tissue Bank approval (REC reference: 21/NW/0157). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes GWAS summary statistics for MDD are available from the Psychiatric Genomics Consortium at https://pgc.unc.edu/for-researchers/download-results/. GWAS summary statistics including 23andMe data require an approved application through 23andMe available to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants (visit https://research.23andme.com/dataset-access/). UK Biobank data are available through application at https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access/. Open Targets tractability data can be downloaded from http://ftp.ebi.ac.uk/pub/databases/opentargets/platform/latest/input/target/tractability/. QTL datasets are available from: Blood plasma pQTL data: deCODE (https://www.decode.com/summarydata/), UKB-PPP (https://www.synapse.org/#!Synapse:syn51364943/), INTERVAL (http://www.phpc.cam.ac.uk/ceu/proteins/), and Gudjonsson (https://www.ebi.ac.uk/gwas/publications/35078996). Brain pQTL data: ROSMAP and Banner (https://www.synapse.org/#!Synapse:syn24172458). CSF pQTL data: Yang (https://dss.niagads.org/datasets/ng00102/). Blood eQTL data: eQTLGen (https://www.eqtlgen.org/). Brain eQTL data: MetaBrain (https://www.metabrain.nl/).

Background Major depressive disorder (MDD) treatments have limited efficacy and target few mechanisms, necessitating innovative drug discovery. Drugs targeting genetically supported proteins are 2.6 times more likely to succeed in drug development. Here, we use genetically-informed methods to identify and prioritise MDD targets among genes encoding proteins targeted by existing drugs or predicted to be compound targets (the druggable genome). Methods We used genome-wide association study (GWAS) summary statistics of over 525,000 MDD cases and 3.36 million controls of European genetic ancestry for our outcome. We derived exposure data from 10 quantitative trait loci GWAS measuring protein (pQTLs) and gene expression levels (eQTLs) in blood, cerebrospinal fluid, and brain tissues. Using Mendelian randomisation (MR), we analysed 3,650 targets with evidence of druggability by approved drugs or clinical and experimental drug candidates. To strengthen causal inference and prioritise targets, we evaluated our MR findings through robust MR estimators, colocalisation, external replication, and directional consistency across eQTLs and pQTLs. We integrated MR effect directions with drug mechanisms and clinical annotations to infer potential therapeutic effects of drugs on MDD. Results We identified 564 drug targets with significant MR effects on MDD to further evaluate. Validation analyses showed 81% of drugs approved for depression/anxiety had ≥1 significant MR target and 51% for depression/anxiety clinical trial compounds. We found 94 targets of compounds for other conditions with mechanisms that counteract target risk-increasing genetic effects on MDD. Among these, prioritised brain-penetrating target-compound pairs include cardiovascular drugs (ACE inhibitors and NISCH agonists), oncology protein kinase inhibitors (CDK4 and NTRK3), and early-stage drug development enzyme inhibitors (GLS and NOS2). Discussion We found genetic evidence supporting both novel and established MDD targets, spanning the drug development pipeline from pre-clinical candidates to approved drugs. Novel targets point to multiple pathophysiological mechanisms beyond traditional monoaminergic systems, with most targeted by approved drugs for other conditions with established safety profiles, offering promising opportunities for clinical evaluation.

Major depressive disorder (MDD) is a significant contributor to the burden of mental disorders in Africa, necessitating an understanding of its prevalence and risk factors across diverse socio-cultural contexts to address health disparities and improve care. This cross-sectional study analyzed 7,073 adult participants from hospital settings in Uganda, Kenya, Ethiopia, and South Africa within the NeuroGAP-Psychosis study. Prevalence estimates, calculated with 95% confidence intervals, revealed overall rates of 0.9% for current MDD and 5.1% for lifetime MDD, with South Africa reporting the highest prevalence (2.1% current, 8.4% lifetime). Multilevel logistic regression identified significant associations between current MDD and negative life events, alcohol use, and Kessler psychological distress scores, while lifetime MDD was linked to age, female sex, chronic pain, frequent headaches, and positive psychosis screening. These findings underscore the need for targeted mental health interventions tailored to the identified risk factors to reduce the burden of MDD in the studied populations.

Heart failure (HF) is a major contributor to global morbidity and mortality. While distinct clinical subtypes, defined by etiology and left ventricular ejection fraction, are well recognized, their genetic determinants remain inadequately understood. In this study, we report a genome-wide association study of HF and its subtypes in a sample of 1.9 million individuals. A total of 153,174 individuals had HF, of whom 44,012 had a nonischemic etiology (ni-HF). A subset of patients with ni-HF were stratified based on left ventricular systolic function, where data were available, identifying 5,406 individuals with reduced ejection fraction and 3,841 with preserved ejection fraction. We identify 66 genetic loci associated with HF and its subtypes, 37 of which have not previously been reported. Using functionally informed gene prioritization methods, we predict effector genes for each identified locus, and map these to etiologic disease clusters through phenome-wide association analysis, network analysis and colocalization. Through heritability enrichment analysis, we highlight the role of extracardiac tissues in disease etiology. We then examine the differential associations of upstream risk factors with HF subtypes using Mendelian randomization. These findings extend our understanding of the mechanisms underlying HF etiology and may inform future approaches to prevention and treatment.

Background Major Depressive Disorder (MDD) significantly contributes to the global disease burden, with genetic factors playing a crucial role in its etiology. However, the existing body of genetic research on MDD includes African Americans and Africans in the United States and the United Kingdom but not continental Africans. Gene-environment interactions can equally not be fully appreciated given substantial differences between the lived experience of continental Africa and the diaspora. This omission can perpetuate health disparities, as indigenous Africans may not benefit from new treatments and diagnostic tools derived from these findings. To address this critical research gap, our study launches an unprecedented effort to establish a genetics database for depression in Africa leveraging three African cohorts from Eastern (Uganda, Kenya and Ethiopia), Western (Nigeria) and Southern (South Africa and Malawi) regions, collectively encompassing ∼31.5K participants (∼11K cases). Methods The study will be undertaken among participants from three cohorts; i) the general population cohort (GPC) of MRC/UVRI and LSHTM Uganda Research Unit (n=10.5K), ii) the NeuroGAP study (n=7K) and iii) the DepGenAfrica study (n=14K). The GPC is on-going with 2,000 participants already recruited. Assessment for MDD is done using the Mini International Neuropsychiatric Interview (MINI, version 7.0.2) and participants’ DNA will be sequenced using the blended genome-exome (BGE) sequencing technology. Preliminary analysis 1,066 participants from this cohort has revealed a prevalence rate of 23.3 % (20.7, 25.99) for lifetime MDD. For the NeuroGAP cohort, a total of 7,073 participants (360 cases of MDD) have been recruited from Uganda, Kenya, Ethiopia and South Africa. MDD was assessed using the MINI version 7.0.2 while participants’ DNA was sequenced using the BGE technology. Both genetic and phenotypic data is available. Preliminary analysis has revealed male gender and living outside South Africa to be protective while substance use, negative life events, chronic pain and psychological distress are risk factors for MDD. For the DepGenAfrica cohort, participants recruitment is yet to commence. This study plans to recruit 3,000 cases and 2,000 controls in Nigeria, 3,000 controls and 2,000 cases in Malawi and Malawi and 4,000 cases in Ethiopia. Participants will be assessed for MDD using the Patient Health Questionnaire (PHQ-9) and their DNA will be sequenced using 4x whole-genome sequencing.Participants collectively come from six African countries of Uganda, Kenya, Ethiopia, Malawi, South Africa and Nigeria. This study will perform a discovery genome-wide association study of MDD in these participants, develop a machine learning model for prediction and test causal inferences to MDD using Mendelian randomisation. Additionally, the portability of genetic markers and polygenic risk scores across diverse ethnic backgrounds will be assessed. The study will leverage comparative analyses with data from the Psychiatric Genomics Consortium to illuminate both shared and unique genetic susceptibilities to MDD. Results Not Applicable Discussion This groundbreaking study is poised to illuminate the genetic underpinnings of MDD within African populations, potentially uncovering novel loci and elucidating the transferability of genetic risk factors.

The development of novel-acting antidepressant medications with fewer side effects and sustained efficacy requires an in-depth understanding of the aetiology of major depressive disorder (MDD) across diverse populations. Here we used a Mendelian randomization (MR) framework to identify protein levels that influence MDD risk, and that respond to MDD liability in the general population. We use summary-level data from four major ancestral groups to evaluate the consistency of genetic associations and MR estimates across populations. We identified 17 proteins that are putatively causal for MDD, with evidence of differential effects across ancestries for five proteins, which we replicate in independent individual level data. We also identified widespread protein level changes in response to disease liability in the general population. We showed that such associations can appear ancestry-specific until differential power is accounted for, after which the vast majority of associations appear consistent across ancestral groups. The protein response to disease liability can be used to generate a proteomic risk score that is strongly predictive of prospective MDD incidence. Our results indicate that multi-ancestry Mendelian randomization improves power for ancestral groups with smaller sample sizes and will inform our understanding of disease aetiology if differential marginal effects across populations arising due to gene-environment interactions can be studied.

BACKGROUND:The N100, an early auditory event-related potential, has been found to be altered in patients with psychosis. However, it is unclear if the N100 is a psychosis endophenotype that is also altered in the relatives of patients.METHODS:We conducted a family study using the auditory oddball paradigm to compare the N100 amplitude and latency across 243 patients with psychosis, 86 unaffected relatives, and 194 controls. We then conducted a systematic review and a random-effects meta-analysis pooling our results and 14 previously published family studies. We compared data from a total of 999 patients, 1192 relatives, and 1253 controls in order to investigate the evidence and degree of N100 differences.RESULTS:In our family study, patients showed reduced N100 amplitudes and prolonged N100 latencies compared to controls, but no significant differences were found between unaffected relatives and controls. The meta-analysis revealed a significant reduction of the N100 amplitude and delay of the N100 latency in both patients with psychosis (standardized mean difference [s.m.d.] = -0.48 for N100 amplitude and s.m.d. = 0.43 for N100 latency) and their relatives (s.m.d. = - 0.19 for N100 amplitude and s.m.d. = 0.33 for N100 latency). However, only the N100 latency changes in relatives remained significant when excluding studies with affected relatives.CONCLUSIONS:N100 changes, especially prolonged N100 latencies, are present in both patients with psychosis and their relatives, making the N100 a promising endophenotype for psychosis. Such changes in the N100 may reflect changes in early auditory processing underlying the etiology of psychosis.