Lumbar spinal stenosis (LSS) affects over 100 million people globally, with an increasing incidence due to an ageing population. While LSS is known to be heritable, its genetic basis remains poorly understood. We conduct a genome-wide meta-analysis of LSS in 40,303 cases and 741,469 controls. We identify 73 previously unreported loci in addition to 15 known loci, and highlight spinal degeneration as a key pathogenic mechanism. In 12,784 surgically treated cases, we discover five loci specifically associated with severe disease. Age-of-onset analyses show that most variants influence risk after midlife, but some confer susceptibility as early as age 34. Mendelian randomization further demonstrates causal effects of higher body mass and fat-free mass on LSS risk. Overall, our findings expand knowledge of the genetic background of LSS and inform future translational research.

Understanding the genetic regulation of circulating protein levels can provide new insights into disease mechanisms. Here, we present the largest proteogenomic study to date (n = 78,664 participants across 38 studies), identifying >24,000 protein quantitative trait loci (QTLs) associated with 1,116 proteins, acting near to (n = 5,040) or distant (n = 19,698) from the cognate gene. Using machine learning-guided effector gene assignment, we provide genetic evidence for pathways, cell types, and tissues that modulate circulating protein levels, highlighting N-linked glycosylation as an important regulatory pathway. We demonstrate that genetic instruments of protein production/function (“cis”) versus modulation (“trans”) reveal distinct phenotypic insights. We identify proteins as candidates for drug targets and engagement (e.g., plasma furin and cardiovascular diseases) by comparing cis-based genetic evidence with protein-disease associations. Systematic triangulation of trans-protein QTLs (pQTLs) with genetic and protein associations across many diseases highlights potential drug repurposing opportunities, e.g., tyrosine kinase 2 (TYK2) inhibitors for rheumatoid arthritis. Our multi-cohort meta-analyses generate proteogenomic insights into disease mechanisms and new treatment opportunities.

BACKGROUND:Food allergy (FA) arises from a complex interplay between an individual's genetic predisposition and environmental factors, and its prevalence is increasing. Genome-wide association studies to date have been hindered by small sample sizes and varying FA definitions. OBJECTIVE:We sought to identify novel FA risk loci by conducting a genome-wide association study meta-analysis in children and adults by using a multiphenotype approach to ensure a good trade-off between sufficient sample size and valid FA definitions. METHODS:Analyses were conducted separately in children and adults on the basis of the following FA phenotypes: self-report, doctor diagnosis, food-specific sensitization, and doctor diagnosis plus food-specific sensitization. A meta-analysis was performed of genome-wide association studies from up to 16 cohorts of people of European ancestry including 229,426 adults and 14,234 children. Models were adjusted for sex, age, principal components, and, if applicable, further study-specific confounders. Sensitivity models were additionally adjusted for hay fever. Replication was conducted in additional external cohorts and a validation in oral food challenge-defined FA cases. RESULTS:Thirty-seven single nucleotide polymorphisms met suggestive significance (P < 1 × 10-6), with two reaching genome-wide significance: rs116936231 (FGL1) in adult doctor-diagnosed FA plus food-specific sensitization phenotype (stable after additional hay fever adjustment) and rs8022829 (AKAP6-NPAS3), which was significant only in the hay fever-adjusted model in adults. However, neither variant was validated. Further, we identified 3 single nucleotide polymorphisms previously reported for FA and atopic disease. CONCLUSION:This study identified 37 single nucleotide polymorphisms suggestively associated with FA and demonstrated genetic differences across phenotypes. It highlights the need for a unified FA definition and sheds light on FA's shared genetic architecture with allergies.

Most genetic variants associated with complex heritability phenotypes lie in non-coding regions and are thought to influence disease risk by regulating gene expression. However, most transcriptome-wide association approaches primarily model local (cis) genetic effects, leaving much of gene regulation unexplained. Here, we show that incorporating distal (trans) regulatory effects improves the prediction of gene expression and the identification of disease-associated genes. Using RNA sequencing data from six human post-mortem brain regions, we developed INGENE and MODULE, two models capturing the combined influence of candidate trans-acting variants within gene coexpression networks. Integrating these models with conventional cis-based predictors improved gene expression imputation (maximum likelihood estimation, α = 0.05) for 18,744 genes across regions. Applying this framework to Psychiatric Genomics Consortium wave 3 genotypes identified 766 genes associated with schizophrenia (PFDR < 0.01), including 641 not previously reported by transcriptome-wide analyses. These findings highlight the contribution of distal regulatory mechanisms and gene network interactions to schizophrenia risk.

Myopia is a rapidly escalating global public health challenge, yet the biological mechanisms linking modern lifestyles to abnormal eye growth remain unclear. Circadian rhythms have been implicated in refractive development, but causal evidence is limited. Here, we integrate population-scale human data with an experimental animal model to determine whether circadian misalignment contributes to myopia. In >265,000 individuals from the Estonian and UK Biobanks, late chronotype was consistently associated with myopia. To assess causality, we experimentally disrupted the alignment between behavioural and environmental rhythms in mice by housing them in non-24-hour light-dark schedules. Exposure to a lengthened cycle (T26) induced a myopic shift that was, notably, reversible in early adulthood. Retinal transcriptomics revealed enrichment of mitochondrial and hypoxia-related plasticity pathways, with transcriptional changes distributed across multiple retinal cell classes. Together, these findings identify circadian misalignment as a conserved and modifiable driver of myopia, highlighting opportunities for novel preventive and therapeutic approaches.

Abstract Smartphone applications (“apps”) could be safe, cost-effective, and accessible tools for weight loss. Numerous weight-loss apps are available, but their longer-term efficacy is unclear. We conducted a meta-analysis of randomised controlled trials of weight loss apps that target eating behaviour in adults. We (a) compared the amount of weight lost in app-based interventions to other interventions and waitlists, and (b) estimated mean weight loss at six months and longer in app-based interventions. A database search identified 23 studies matching our criteria. We used three-level meta-analytic models and applied various bias-correction methods. More weight was lost in app-based interventions than on waitlists (mean difference 2.07 kg) and in non-app-based interventions (mean difference 1.64 kg) at six months. In app-based interventions, weight loss estimates ranged from 0.63 to 3.87 kg at six months; mean weight loss was 2.64 kg at longer follow-ups. We found no moderating effects of factors such as calorie counting, social support, involvement of medical workers, or automatic feedback reports. Smartphone apps could be cost-effective tools to facilitate weight loss and help maintain weight loss after surgery or drug treatments, but more studies on apps’ long-term effectiveness are needed.

Non-coding genetic variants statistically associated with complex heritability phenotypes are thought to act primarily through transcriptome regulatory mechanisms. Predictions of gene expression in tissue like the human brain traditionally rely primarily on cis -eQTLs. Here, we introduce INGENE and MODULE, trans -eQTLs models designed to enhance the prediction of gene expression by capturing the collective impact of candidate trans -eQTLs acting within co-expression networks. Exploiting RNA-seq data in six post-mortem brain regions (amygdala, caudate nucleus, dorsal/subgenual anterior cingulate cortex, dorsolateral prefrontal cortex, and hippocampus), we validate our models on two testing datasets, demonstrating increased gene predictability compared to both an original cis -based model and to EpiXcan, the leading benchmark in cis -model performance. Integration of cis - and trans -predictions significantly improves gene-level expression imputation (MLE α= 0.05) for 18,744 genes across the six brain regions considered. Applying cis and trans models to PGC wave 3 genotypes identifies 766 SCZ-associated genes across brain regions (pFDR < .01), emphasizing the complementary nature of cis and trans predictions in trait association discovery. Of these genes, 641 represent novel transcriptome-wide associations with schizophrenia, highlighting the role of trans -heritability and genetic interactions underlying risk for this disorder, in addition to further supporting 125 previous candidates. ### Competing Interest Statement A. Bertolino received consulting fees from Biogen and lecture fees from Otsuka, Janssen, and Lundbeck. D. Weinberger serves on the scientific advisory boards of Sage Therapeutics and Pasithea Therapeutics. G. Pergola and G. C. Kikidis received lecture fees from Lundbeck. A. K. Malhotra is a consultant to Genomind, InformedDNA and Concert Pharmaceuticals. M. C. O Donovan, M. J. Owen, and J. T. R. Walters are supported by collaborative research grants from Takeda Pharmaceuticals. O. A. Andreassen is a consultant for HealthLytix and received speaker s honoraria from Lundbeck. C. Arango has been a consultant to or has received honoraria or grants from Acadia, Angelini, Gedeon Richter, Janssen Cilag, Lundbeck, Minerva, Otsuka, Roche, Sage, Servier, Shire, Schering Plough, Sumitomo Dainippon Pharma, Sunovion and Takeda. Research Projects of National Relevance 2020 (PRIN 2020; 2020WSCSLZ) Research Projects of National Relevance 2022 (PRIN 2022; 2022KXJYJA) Research Projects Of National Relevance PNRR 2022 (P2022HNBJX) The LIBD funded the collection and analysis of postmortem brain tissue

Pre-existing psychiatric disorders have been associated with the severity of acute respiratory infections, including COVID-19, particularly in hospitalized populations. However, the underlying mechanisms, especially in community-based populations, remain unknown, limiting preparedness for future pandemics. We investigated the role of genetic liability for psychiatric disorders and related traits in COVID-19 and other respiratory infection severity among individuals reporting SARS-CoV-2 testing and available respiratory symptom data. We included population-based cohort data from Denmark, Estonia, Iceland, Norway, and the United Kingdom (N = 78,507; 62

Many non-coding variants influence complex traits and diseases through gene regulation, yet the mechanisms linking these variants to downstream biology remain poorly understood. Here, we present eQTLGen Phase 2, a comprehensive genome-wide analysis of gene expression quantitative trait loci (eQTLs) in 43,301 blood samples from 52 datasets. Beyond local ciseffects, this sample size enabled the first systematic mapping of trans-eQTLs at scale. We identify cis-eQTLs for nearly all expressed genes (94.7%) and trans-eQTLs for over half (56.2%). Second, by colocalizing cis-eQTLs with trans-eQTLs, we infer a directed gene regulatory network comprising 47,554 directed gene regulatory relationships. These networks reveal how genetic perturbations in upstream regulators produce dose-dependent downstream effects, supported by Perturb-seq and ChIP-seq data. Third, integrating this network with 87 genome-wide association studies allows us to systematically prioritize trait-relevant pathways and candidate genes. Variants exerting both cis- and trans-effects are markedly more likely to colocalize with trait associations than cis-only variants, delineating a subset of functionally active cis-eQTLs from a large group with limited downstream impact. This distinction provides a conceptual framework for identifying regulatory variants that truly mediate complex trait biology. Together, these results provide a publicly available resource of cis- and trans-eQTLs and an in vivo scaffold for human gene-regulatory networks, elucidating how propagation of cis-effects modulates complex disease.

Personality traits describe stable differences in how people think, feel and behave, and how they interact with and experience their social and physical environments1,2. Many questions remain unanswered about associations between DNA and personality traits, such as their robustness, their generalizability and the biological and social pathways through which they act. Here we meta-analyse data across 46 cohorts comprising 611,037 to 1.14 million participants with European-like and African-like genomes for genome-wide association studies (GWAS) of the Big Five personality traits (extraversion, agreeableness, conscientiousness, neuroticism and openness to experience), and data from up to 50,725 participants for within-family GWAS. We identify 1,260 lead genetic variants associated with personality, including 824 novel variants3. Common genetic variants explain a moderate 4.8-9.3% of the variance in measures of each trait, and 9.3-13.3% among instruments with typical measurement reliability. Genetic associations with personality are highly consistent but not identical across geography, reporter (self versus close other), age group and measurement instrument, and we find minimal spousal assortment for personality in recent history. In contrast to many other social and behavioural traits4,5, within-family GWAS and polygenic index analyses indicate that genetic associations with personality are minimally confounded by the shared family environment. Polygenic prediction, genetic correlation and Mendelian randomization analyses indicate that personality traits have widespread, potentially causal associations with consequential behaviours and life outcomes. Overall, we find that the genetic architecture of personality is robustly generalizable, minimally confounded and widely relevant to human experience.

BACKGROUND:Pneumonia risk is influenced by demographics, chronic disease burden, lifestyle, and environmental factors. Despite previous genetic studies, the impact of host genetics on pneumonia, particularly within specific patient groups, remains unclear. METHODS:We conducted genome-wide meta-analyses of pneumonia using data from FinnGen and Estonian biobank, analysing both the general population and patient subgroups based on age at first pneumonia diagnosis, recurrent pneumonia, and asthma status. Additionally, we investigated genetic correlations and causal relationships between pneumonia and other traits. FINDINGS:Our study included a total of 110,881 pneumonia cases and 509,253 controls, with subgroup analyses focussing on children (9534 cases, 509,253 controls), working-age adults (53,203 cases, 509,253 controls), elderly individuals (48,144 cases, 509,253 controls), patients with recurrent pneumonia (10,151 cases, 509,253 controls), and patients with asthma (23,943 cases, 54,456 controls). We identified 12 loci including 4 replicated (PTGER4, HLA, MUC5AC, CHRNA5) and 8 novel associations (PTPN22, CRP, CHRNA2, EML6, RP11-541P9.3, TNFSF15, CTD-2028E8.2, HNF1A). Subgroup analysis of children (HLA region), working age adults (CRP, HLA region, MUC5AC), the elderly (CRP, MUC5B, RP11-532E4.3, CHRNA5), recurrent pneumonia (CRP, EML6, RP11-541P9.3, CHRNA2, MUC5AC, CHRNA5) and patients with asthma (CRP) demonstrated significant differences in genetic associations. Loci associated with pneumonia harbour genes mainly related to acute inflammation, T cell development, antigen presentation and lung health. Further, downstream analyses suggest that well-known pneumonia risk factors, such as obesity and smoking, may be causal. INTERPRETATION:Genetics of immunology seem crucial to the development of pneumonia in early life, adulthood, and among patients with asthma, while genetics of nicotine dependency and lung health are more pronounced among the elderly and those suffering from recurrent pneumonia. FUNDING:A complete list of sources of funding is provided in the Acknowledgements section.

Interpreting the association of genetic variants with complex traits can be improved by gaining a greater understanding of the molecular consequences of these variants. Although genome-wide association studies (GWAS) for complex diseases routinely profile over one million individuals1-5, studies of molecular traits have lagged behind. Here we performed a GWAS meta-analysis for 249 circulating metabolic traits in the Estonian Biobank and the UK Biobank in up to 619,372 individuals. We identified 88,127 common and low-frequency locus-trait associations from 8,398 loci that converged on shared genes and pathways. Using statistical fine mapping, systematic phenome-wide colocalization and cis-Mendelian randomization, we explored putative causal links between metabolic traits and disease outcomes. We predict that although plasma branched-chain amino acids (BCAAs) have been associated with type 2 diabetes in observational studies6,7, lowering BCAA levels by targeting the BCAA catabolism pathway is unlikely to reduce type 2 diabetes risk. Leveraging our large sample size and high-quality genotype imputation, we found that 19.4% of the confidently fine-mapped variants had minor allele frequencies between 0.1 and 1%, and these variants were twofold enriched for predicted missense and splice-altering variants. Our results highlight the value of integrating low-frequency variants into genetic association studies.

Population-specific genome-wide association studies can reveal high-impact genomic variants that influence traits like body-mass index (BMI). Using the Estonian Biobank BMI dataset (n = 204,747 participants) we identified 214 genome-wide significant loci. Among those hits, we identified a common non-coding variant within the newly associated ADGRL3 gene (-0.18 kg/m²; P = 3.21 × 10⁻⁹). Moreover, the missense rare variant PTPRT:p.Arg1384His associated with lower BMI (-0.44 kg/m²; P = 2.51 × 10⁻¹⁰), while the protein-truncating variant POMC:p.Glu206* was associated with considerably higher BMI (+ 0.81 kg/m²; P = 1.48 × 10-12), both likely affecting the functioning of the leptin-melanocortin pathway. POMC:p.Glu206* was observed in different North-European populations, suggesting a broader, yet elusive, distribution of this damaging variant. These observations indicate the previously unrecognized roles of the ADGRL3 and PTPRT genes in body weight regulation and suggest an increased prevalence of the POMC:p.Glu206* variant in European populations, offering avenues for developing interventions in obesity management.

Interpreting genetic associations with complex traits can be greatly improved by detailed understanding of the molecular consequences of these variants. However, although genome-wide association studies (GWAS) for common complex diseases routinely profile 1M+ individuals, studies of molecular phenotypes have lagged behind. We performed a GWAS meta-analysis for 249 circulating metabolic traits in the Estonian Biobank and the UK Biobank in up to 619,372 individuals, identifying 88,604 significant locus-metabolite associations and 8,774 independent lead variants, including 987 lead variants with a minor allele frequency less than 1%. We demonstrate how common and low-frequency associations converge on shared genes and pathways, bridging the gap between rare-variant burden testing and common-variant GWAS. We used Mendelian randomisation (MR) to explore putative causal links between metabolic traits, coronary artery disease and type 2 diabetes (T2D). Surprisingly, up to 85% of the tested metabolite-disease pairs had statistically significant genome-wide MR estimates, likely reflecting complex indirect effects driven by horisontal pleiotropy. To avoid these pleiotropic effects, we used cis-MR to test the phenotypic impact of inhibiting specific drug targets. We found that although plasma levels of branched-chain amino acids (BCAAs) have been associated with T2D in both observational and genome-wide MR studies, inhibiting the BCAA catabolism pathway to lower BCAA levels is unlikely to reduce T2D risk. Our publicly available results provide a valuable novel resource for GWAS interpretation and drug target prioritisation.

Genome-wide association studies (GWAS) have significantly advanced the understanding of genetic mechanisms underlying complex human diseases and traits by systematically identifying genetic variants linked to diverse phenotype traits across diverse populations. Large-scale analyses that combine multiple phenotypes are especially valuable, as they can reveal shared genetic architectures and patterns of comorbidity, refining disease classification and risk prediction. Here, we conducted comprehensive GWAS analyses based on Estonian Biobank EHR data, focusing on 4,884 ICD-10-based disease phenotypes, in a cohort of 206,159 Estonian Biobank participants. By analysing their genotype data, altogether 18,977,777 SNV and indel variants (including common, low-frequency and rare variants), our analyses revealed 2,127 unique genome-wide significant loci, including 778 putatively novel locus-phenotype associations. Further investigation into coding variants revealed 835 significant associations, including well-established ACMG pathogenic variants and multiple putatively novel associations. Notably, a missense variant in SCN11A, which encodes the Nav1.9 sodium channel involved in pain signalling, was associated with decreased migraine risk, while an Estonian-enriched GOT1 variant considerably affected aspartate transaminase enzymatic function. Our study highlights the potential of population-based biobanks in discovering both common and rare genetic associations, contributing to the identification of novel disease associated loci and expanding the catalog of human disease related genetic variants. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was funded by the European Union through Horizon 2020 and Horizon Europe research and innovation program under grants no. 874627 (EXPANSE) (JK); 894987 (GENOMEPEP) (EA); 101153901 (CHRONOPIA) (TP); 101096888 (DISCERN) (JK); 101137201 (CLARITY) (EA, KB, LT); 101057721 (PROPHET) (AR); 101128023 (JAPreventNCD) (AR); 101080009 (CAN.HEAL) (AR); 101137154 (WISDOM) (EA); 101137278 (CVDLINK) (UV); and Estonian Research Council Grants PRG555 (Evaluation of genetic variants potentially linked to actionable health risks) (AR); and PRG1291 (Systematic phenome-wide search for genetic modulators in health and disease) (AA, EA, KB, JK, UV, PP). ### 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: The activities of the Estonian Biobank are regulated by the Human Genes Research Act, which was adopted in 2000 specifically for the operations of Estonian Biobank. Individual level data analysis in Estonian Biobank was carried out under ethical approval 1.1-12/624 from the Estonian Committee on Bioethics and Human Research (Estonian Ministry of Social Affairs), using data according to release application 3-10/GI/31689 from the Estonian Biobank. 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 will be made publicly available upon publication in a peer-reviewed journal. Pseudonymised data and/or biological samples can be accessed for research and development purposes in accordance with the Estonian Human Genome Research Act (https://www.riigiteataja.ee/en/eli/ee/531102013003/consolide/current). To access the raw data, the research proposal must be approved by the Scientific Advisory Committee of the Estonian Biobank as well as by the Estonian Committee on Bioethics and Human Research. For more details on data access and relevant documents, please see https://genomics.ut.ee/en/content/estonian-biobank#dataaccess.

Background/Objectives: Metabolomics, in combination with genetic data, is a powerful approach to study the biochemical consequences of genetic variation. We assessed the impact of human gene knockouts (KOs) on the metabolite levels of Estonia Biobank (EstBB) participants and integrated the results with electronic health record data. Methods: In 150,000 EstBB genotyped participants, we identified 723 KOs with 152 different predicted loss of function (pLoF) variants in 115 genes. For those KOs and 258 controls, 1387 metabolites were profiled using ultra-high-performance liquid chromatography-tandem mass spectrometry. Results: We identified 48 associations linking rare pLoF variants in 22 genes to 43 metabolites. Out of 48 associations, 27 (56%) were found in genes that cause inborn errors of metabolism. The top associations identified in our analysis included genes and metabolites involved in the degradation pathway of the pyrimidine bases uracil and thymine (DPYD and UPB1). We found DPYD gene KOs to be associated with elevated levels of Uracil, confirming that DPD-deficiency is a leading cause of severe 5-Fluorouracil toxicity. Overall, 54% of reported associations are gene targets of approved drugs or bioactive drug-like compounds. Conclusions: Our findings contribute to assessing the impact of human KOs on metabolite levels and offer insights into gene functions, disease mechanism, and drug target validation.

Introduction: Varenicline is an alpha(4)beta(2) nicotinic acetylcholine receptor partial agonist with the highest therapeutic efficacy of any pharmacological smoking cessation aid and a 12-month cessation rate of 26%. Genetic variation may be associated with varenicline response, but to date, no genome-wide association studies of varenicline response have been published. Methods: In this study, we investigated the genetic contribution to varenicline effectiveness using two electronic health record-derived phenotypes. We defined short-term varenicline effectiveness (SVE) and long-term varenicline effectiveness (LVE) by assessing smoking status at 3 and 12 months, respectively, after initiating varenicline treatment. In Stage 1, comprising five European cohort studies, we tested genome-wide associations with SVE (1405 cases, 2074 controls) and LVE (1576 cases, 2555 controls), defining sentinel variants (the most strongly associated variant within 1 Mb) with p-value < 5 x 10(-6) to follow up in Stage 2. In Stage 2, we tested association between sentinel variants and comparable smoking cessation endpoints in varenicline randomized controlled trials. We subsequently meta-analyzed Stages 1 and 2. Results: No variants reached genome-wide significance in the meta-analysis. In Stage 1, 10 sentinel variants were associated with SVE and five with LVE at a suggestive significance threshold (p-value < 5 x 10(-6)); none of these sentinels were previously implicated in varenicline-aided smoking cessation or in genetic studies of smoking behavior. Conclusions: We provide initial insights into the biological underpinnings of varenicline-aided smoking cessation, through implicating genes involved in various processes, including gene expression, cilium assembly, and early-stage development.

Rare copy number variants (CNVs) are a key component of the genetic basis of psychiatric conditions, but have not been well characterized for most. We conducted a genome-wide CNV analysis across six diagnostic categories (N = 574,965): autism (ASD), ADHD, bipolar disorder (BD), major depressive disorder (MDD), PTSD, and schizophrenia (SCZ). We identified 35 genome-wide significant associations at 18 loci, including novel associations in SCZ ( SMYD3, USP7 - HAPSTR1 ) and in the combined cross-disorder analysis ( ASTN2 ). Rare CNVs accounted for 1-3% of heritability across diagnoses. In ASD, associations were uniformly positive, consistent with autism having diverse etiologies and clinical presentations. By contrast, CNVs showed a dose-dependent relationship for other diagnoses, including SCZ and PTSD, with reciprocal deletions and duplications having inversely correlated effects and distinct genotype-phenotype relationships. Our findings suggest that genes have effects that are both dose-dependent and pleiotropic, such that a positive influence on one dimension of psychopathology may be accompanied by positive or negative effects on others.

Polygenic indexes (PGIs) - DNA-based predictors of individual phenotypes - have become essential tools across biomedical and social sciences. We introduce Version 2 of the Polygenic Index Repository, which expands phenotype coverage from 47 to 61, increases the number of participating datasets from 11 to 20, and adopts a more consistent and improved methodology for PGI construction. For 16 phenotypes, we leverage summary statistics from an updated GWAS meta-analysis with greater statistical power compared to the original release, thereby improving the PGI's predictive power. To improve power for family-based analyses, we provide imputed parental PGIs in all datasets with first-degree relatives and offer a framework for interpreting results from analyses that control for parental PGIs. We illustrate the utility of parental PGIs using two applications: (1) comparing PGI associations with and without parental PGI controls for all phenotypes in two Repository datasets with family data, and (2) for BMI and diastolic blood pressure, exploring the contribution of causal versus non-causal components of PGI associations to the imperfect portability of PGIs across subgroups within a genetic ancestry. Collectively, the updates enhance predictive performance, broaden the Repository's scope, and introduce novel resources that reduce confounding bias and improve interpretability.