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PubMed2026

Polygenic risk factors for comorbid diagnoses in individuals with substance use disorders: A phenome-wide survival analysis.

OBJECTIVE: Persons with substance use disorders (SUD) often suffer from additional comorbidities. Researchers have explored this overlap via phenome-wide association studies (PheWASs). However, PheWASs are largely cross-sectional, limiting our understanding of whether diagnoses predate the development of an SUD. We characterize whether polygenic scores (PGSs) are associated with time to comorbid diagnoses in electronic health records (EHR) after the first documented SUD diagnosis. METHODS: Using data from All of Us (N = 393,596), we explored: (1) whether social determinants of health (SDoHs) are associated with lifetime risk of SUD (N cases = 42,568) and (2) within a subset those with a diagnosed SUD and available genetic data SUD (N = 21,357), whether PGS for alcohol use disorders, cannabis use disorders, depression, externalizing, posttraumatic stress disorder, and schizophrenia were associated with subsequent diagnoses via a phenome-wide survival analysis. RESULTS: Multiple SDoHs were associated with lifetime SUD diagnosis, with annual household income having the largest overall associations (e.g. <$10 K annually vs $100 K-$150 K annually: OR = 4.18; 95% CI = 3.92, 4.45). There were 86 phenome-wide significant PGS associations with subsequent diagnoses across various bodily systems. PGSs for alcohol use disorders, posttraumatic stress disorder, and schizophrenia were each associated with time to their respective diagnoses. CONCLUSIONS: Social determinants, especially those related to income, have profound associations with lifetime SUD risk. Additionally, PGSs for psychiatric conditions are associated with multiple post-SUD diagnoses within those with a SUD, suggesting PGS may capture information beyond lifetime risk, including timing and severity of comorbidities related to SUD.

Humans↗

PubMed2026

Rare variant effect estimation and polygenic risk prediction.

Due to their low frequency, estimating the effects of rare variants is challenging. Here we propose RareEffect, a method that first estimates gene-based or region-based heritability and then each variant effect size using an empirical Bayes approach. Our method uses a variance component model, which is popular in rare variant tests, and is designed to provide two levels of effect sizes-gene/region level and variant level-that can provide better interpretation. To adjust for the case-control imbalance in phenotypes, our approach uses a fast implementation of the Firth bias correction. We demonstrate the accuracy and computational efficiency of our method through extensive simulations and analysis of UK Biobank whole-exome sequencing data for 100 traits. Additionally, we show that the effect sizes obtained from our model can be leveraged to improve polygenic score performance, thereby outperforming recently developed methods for rare variant polygenic scoring.

Humans↗

PubMed2024

Multi-ancestry polygenic mechanisms of type 2 diabetes.

Type 2 diabetes (T2D) is a multifactorial disease with substantial genetic risk, for which the underlying biological mechanisms are not fully understood. In this study, we identified multi-ancestry T2D genetic clusters by analyzing genetic data from diverse populations in 37 published T2D genome-wide association studies representing more than 1.4 million individuals. We implemented soft clustering with 650 T2D-associated genetic variants and 110 T2D-related traits, capturing known and novel T2D clusters with distinct cardiometabolic trait associations across two independent biobanks representing diverse genetic ancestral populations (African, n = 21,906; Admixed American, n = 14,410; East Asian, n =2,422; European, n = 90,093; and South Asian, n = 1,262). The 12 genetic clusters were enriched for specific single-cell regulatory regions. Several of the polygenic scores derived from the clusters differed in distribution among ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a body mass index (BMI) of 30 kg m-2 in the European subpopulation and 24.2 (22.9-25.5) kg m-2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg m-2 in the East Asian group. Thus, these multi-ancestry T2D genetic clusters encompass a broader range of biological mechanisms and provide preliminary insights to explain ancestry-associated differences in T2D risk profiles.

Humans↗

PubMed2024

The role of co-occurring conditions and genetics in the associations of eating disorders with attention-deficit/hyperactivity disorder and autism spectrum disorder.

Eating disorders (EDs) commonly co-occur with other psychiatric and neurodevelopmental disorders including attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD); however, the pattern of family history and genetic overlap among them requires clarification. This study investigated the diagnostic, familial, and genetic associations of EDs with ADHD and ASD. The nationwide population-based cohort study included all individuals born in Denmark, 1981-2008, linked to their siblings and cousins. Cox regression was used to estimate associations between EDs and ADHD or ASD, and mediation analysis was used to assess the effects of intermediate mood or anxiety disorders. Polygenic scores (PGSs) were used to investigate the genetic association between anorexia nervosa (AN) and ADHD or ASD. Significantly increased risk for any ED was observed following an ADHD or ASD diagnosis. Mediation analysis suggested that intermediate mood or anxiety disorders could account for 44%-100% of the association between ADHD or ASD and ED. Individuals with a full sibling or maternal half sibling with ASD had increased risk of AN compared to those with siblings without ASD. A positive association was found between ASD-PGS and AN risk whereas a negative association was found between AN-PGS and ADHD. In this study, positive phenotypic associations between EDs and ADHD or ASD, mediation by mood or anxiety disorder, and genetic associations between ASD-PGS and AN and between AN-PGS and ADHD were observed. These findings could guide future research in the development of new treatments that can mitigate the development of EDs among individuals with ADHD or ASD.

Humans↗

PubMed2025

GWAS highlights the neuronal contribution to multiple sclerosis susceptibility.

Multiple Sclerosis (MS) is a chronic inflammatory and neurodegenerative disease affecting the brain and spinal cord. Genetic studies have identified many risk loci, that were thought to primarily impact immune cells and microglia. Here, we performed a multi-ancestry genome-wide association study with 20,831 MS and 729,220 control participants, identifying 236 susceptibility variants outside the Major Histocompatibility Complex, including four novel loci. We derived a polygenic score for MS and, optimized for European ancestry, it is informative for African-American and Latino participants. Integrating single-cell data from blood and brain tissue, we identified 76 genes affected by MS risk variants. Notably, while T cells showed the strongest enrichment, inhibitory neurons emerged as a key cell type. The expression of IL7 and STAT3 are affected only in inhibitory neurons, highlighting the importance of neuronal and glial dysfunction in MS susceptibility.

Journal Article↗

PubMed1999

Segregation analysis of obsessive-compulsive disorder using symptom-based factor scores.

Obsessive-compulsive disorder (OCD) is a complex psychiatric disorder characterized by recurring obsessions or compulsions that cause significant distress to the patient or significantly interfere with the patient's normal home, work, or social activities [Diagnostic and Statistical Manual of Mental Disorders, 4th Edition. Washington, DC: American Psychiatric Association, 1994]. Twin and family studies have suggested that OCD has a significant genetic component. We performed complex segregation analyses using POINTER with families ascertained through an OCD-affected proband. In an attempt to resolve the phenotypic heterogeneity observed among individuals with OCD these segregation analyses used four factor-analytic symptom dimensions to subset the family sample based upon probands' symptom factor scores. Analysis of the entire sample allowed rejection of only the no transmission model; that model was also rejected in all subsequent analyses. Limiting the analyses to families with at least one OCD-affected member in addition to the proband (the demonstrably familial form of OCD) allowed rejection of all models except the mixed model. Analyses limited to families of high-factor-3 (symmetry and ordering symptoms) probands led to rejection of the polygenic model, indicating the involvement of a major locus. Additionally, the relative risk of OCD or subclinical OCD was 1.7 for relatives of probands with a factor 3 score greater than zero compared with relatives of probands with a low factor score. The symptoms attributed to high factor 3 scores (symmetry and ordering) may constitute a genetically significant symptomatic subtype of OCD. Am. J. Med. Genet. (Neuropsychiatr. Genet.) 88:669-675, 1999.

Aging↗

PubMed2026

Eating Disorders and Parkinson's Disease-2: Genetic Epidemiology and Shared Genomics.

OBJECTIVE: Individuals with anorexia nervosa (AN) share premorbid traits with Parkinson's Disease (PD) (e.g., anxiety) and exhibit a two-fold relative risk of a reported family history of PD. Published estimates of intra- and inter-disorder genetic architecture were extracted and compared prior to conducting novel analyses to provide evidence for cross-disorder genetic risk. METHODS: National register or meta-analytic familial, twin, and common variant genome-wide studies were searched; estimates and findings were extracted and compared. Novel cross-disorder conditional and conjunctional false discovery rate analyses were performed. RESULTS: Sibling relative risks and additive genetic estimates of the two disorders were similar. AN had greater common variant heritability than PD whether measured via infinitesimal model (linkage disequilibrium score regression, LDSC) or causal mixture model (MiXeR). AN had greater polygenicity than PD (mean (SD) 2.50E-03 (1.64E-04) versus 2.72E-4 (1.47E-05), p < 0.001), but lower discoverability than PD (4.20E-05 (2.69E-06) versus 1.40E-04 (6.95E-06), p < 0.001). Global genetic correlation was significant (e.g., bivariate LDSC rg = 0.10, p = 0.0033). Novel analyses identified cross-disorder enrichment, and cross-disorder risk at chr3p21.31. CONCLUSIONS: Cross-disorder AN and PD research identified shared risk variants at chr3p21.31, genes and mechanisms (e.g., conditioning, fear, and reward) linked to a shared endophenotype.

Parkinson's disease↗

PubMed2025

Polygenic Risk Factors for Comorbid Diagnoses in Individuals with Substance Use Disorders: A Phenome-Wide Survival Analysis.

OBJECTIVE: Persons with substance use disorders (SUD) often suffer from additional comorbidities. Researchers have explored this overlap via phenome wide association studies (PheWAS). However, PheWAS are largely cross-sectional, limiting our understanding of whether diagnoses predate development of an SUD. We characterize whether polygenic scores (PGS) are associated with time to comorbid diagnoses in electronic health records (EHR) after the first documented SUD diagnosis. METHODS: Using data from All of Us (N = 393,596), we explored: 1) whether social determinants of health (SDoH) are associated with lifetime risk of SUD (N cases = 42,568) and 2) within a subset those with a diagnosed SUD and available genetic data SUD (N = 21,357), whether PGS for alcohol use disorders, cannabis use disorders, depression, externalizing, post-traumatic stress disorder, and schizophrenia were associated with subsequent diagnoses via a phenome-wide survival analysis. RESULTS: Multiple SDoH were associated with lifetime SUD diagnosis, with annual household income having the largest overall associations (e.g., <$10K annually vs $100K-$150K annually: OR = 3.89, 95% CI = 3.66, 4.13). There were 101 phenome-wide significant PGS associations with subsequent diagnoses across various bodily systems. PGSs for alcohol use disorders, post-traumatic stress disorder, and schizophrenia were each associated with time to their respective diagnoses. CONCLUSIONS: Social determinants, especially those related to income, have profound associations with lifetime SUD risk. Additionally, PGS for psychiatric conditions are associated with multiple post-SUD diagnoses within those with a SUD, suggesting PGS may capture information beyond lifetime risk, including timing and severity of comorbidities related to SUD.

Journal Article↗

PubMed2026

Diabetes related genetics and outcomes after total pancreatectomy with islet autotransplantation.

CONTEXT: Approximately 20% of patients with chronic pancreatitis achieve insulin independence one year after total pancreatectomy with islet autotransplantation (TPIAT), an outcome associated with younger age and lower pre-transplant HbA1c. The contribution of diabetes-related genetics to TPIAT outcomes is unknown. Genetic risk scores (GRS) are associated with C-peptide levels in type 1 (T1D) and type 2 (T2D) diabetes. OBJECTIVE: To evaluate whether T1D and T2D GRS and partitioned metabolic polygenic scores (PGS) are associated with diabetes before TPIAT and metabolic outcomes one year after transplant. METHODS: We genotyped 318 patients with chronic pancreatitis undergoing TPIAT in the multicenter "Advancing Treatment for Pancreatitis: A Prospective Observational Study of TPIAT" using a global screening array. Regression models assessed associations of T1D- and T2D-GRS and metabolic PGS with diabetes outcomes one year after TPIAT. RESULTS: Participants were 29±17 years old, 61% female, 84% non-Hispanic White, and 13% had diabetes prior to transplant. T2D-GRS was higher among those with pre-TPIAT diabetes (-0.6±0.3 versus -0.8±0.2, p=0.003) and was associated with higher pre-transplant HbA1c (β=0.19 % per 1-SD [95% CI:0.04, 0.33], p = 0.011). Adjusted for genotype-derived ancestry and islet equivalents/kg, the beta-cell PGS was associated with higher insulin dose (β=0.05 units/kg/day per 1-SD, [95%CI:0.01, 0.09], p=0.016) and higher insulin dose adjusted A1c (IDAA1c) (β=0.34[95%CI:0.03, 0.67], p=0.029) 1-year post-transplant. The lipodystrophy PGS was associated with higher IDAA1c (β=0.38 [95%CI:0.06, 0.69], p=0.019) and lower fasting C-peptide (β=-0.09 [95%CI:-0.18, 0], p=0.041) at 1 year. CONCLUSIONS: T2D genetic risk is associated with diabetes prior to TPIAT, and beta cell and lipodystrophy PGS are associated with post-TPIAT diabetes outcomes. Diabetes-related genetics may influence transplantation outcomes and inform metabolic heterogeneity.

C-peptide↗

PubMed2025

Identification of plasma proteomic markers underlying polygenic risk of type 2 diabetes and related comorbidities.

Genomics can provide insight into the etiology of type 2 diabetes and its comorbidities, but assigning functionality to non-coding variants remains challenging. Polygenic scores, which aggregate variant effects, can uncover mechanisms when paired with molecular data. Here, we test polygenic scores for type 2 diabetes and cardiometabolic comorbidities for associations with 2,922 circulating proteins in the UK Biobank. The genome-wide type 2 diabetes polygenic score associates with 617 proteins, of which 75% also associate with another cardiometabolic score. Partitioned type 2 diabetes scores, which capture distinct disease biology, associate with 342 proteins (20% unique). In this work, we identify key pathways (e.g., complement cascade), potential therapeutic targets (e.g., FAM3D in type 2 diabetes), and biomarkers of diabetic comorbidities (e.g., EFEMP1 and IGFBP2) through causal inference, pathway enrichment, and Cox regression of clinical trial outcomes. Our results are available via an interactive portal ( https://public.cgr.astrazeneca.com/t2d-pgs/v1/ ).

Humans↗

PubMed2026

Variance Polygenic Scores (vPGS) as a Tool for Studying Gene-Environment Interactions Associated With Refractive Error.

PURPOSE: Conventional polygenic scores predict an individual's phenotype based on their genetics. By contrast, variance polygenic scores (vPGS) quantify genetic predisposition to phenotypic variance. We tested the hypothesis that a vPGS for refractive error can identify individuals with increased susceptibility to environmental risk factors for myopia. METHODS: Six vPGS construction strategies were evaluated in UK Biobank participants: three variance heterogeneity genome-wide association study (vGWAS) methods and two reweighting schemes. vPGS performance was assessed using two metrics: (i) "Diff"-difference in phenotypic variance in vPGS decile ten versus one; (ii) Spearman correlation of phenotypic variance versus vPGS decile. The optimal vPGS was used to test for vPGS × time spent reading or vPGS × time spent outdoors interactions in children aged 15 years (ALSPAC cohort; n = 3471). RESULTS: Of the vGWAS methods, conditional quantile regression outperformed SCAMPI and Levene's Test. Of the re-weighting schemes, LDpred2 outperformed pruning and thresholding. In an independent sample of UK Biobank participants (n = 19,470), the top-performing vPGS successfully stratified individuals into groups with increasing variance in refractive error, even after adjusting for a conventional PGS (Diff: 2.55, 95% confidence interval [CI], 1.64-3.47; Spearman correlation = 0.87; 95% CI, 0.43-0.93). However, in ALSPAC participants, there was minimal support for vPGS interactions with time reading (P = 0.80) or time outdoors (P = 0.89). CONCLUSIONS: A novel vPGS successfully stratified individuals into groups with relatively high or low genetic susceptibility to refractive error variance. However, the vPGS could not identify individuals at enhanced risk from lifestyle risk factors for myopia.

Humans↗

PubMed2026

PGS-GS: a framework integrating polygenic scores and genomic selection in animal breeding.

Genomic prediction has become a central paradigm in biology, enabling quantitative inference of genetic contributions to complex traits across humans, animals, and plants. Although genomic research in human genetics and animal breeding shares a highly homologous methodological foundation, significant barriers persist in their analytical paradigms and application scenarios. This study aims to promote cross-disciplinary integration by introducing human-derived polygenic scores (PGS) algorithms into animal genomic selection (GS) and proposing a PGS-GS framework with a preliminary weighting-based implementation. We systematically benchmarked the predictive performance and computational efficiency of 20 algorithms, including classical linear models, machine learning, PGS, and PGS-GS using both array and whole-genome sequencing (WGS) data across four major agricultural species: beef cattle, sheep, pigs, and chickens. Our results demonstrate that PGS and PGS-GS algorithms achieve predictive accuracy competitive with genomic best linear unbiased prediction (GBLUP) while offering markedly higher computational efficiency. Moreover, incorporating PGS-derived prior information into weighted linear and non-linear models outperformed conventional weighted GBLUP. The results provide empirical evidence to inform algorithm selection and highlight the potential of integrating human-derived PGS methodologies into animal genomic prediction frameworks.

Animals↗

PubMedDate not supplied

Mapping genes for polygenic disorders: considerations for study design in the complex trait of inflammatory bowel disease.

While the methodology for the mapping of Mendelian disorders is well established, the practical and theoretical steps required for successful gene identification in a complex trait are still difficult to predict. A number of analytical models and simulations based on repetitive drawings from predefined statistical distributions are available. To supplement these analytical models, we developed an integrated simulation approach by directly simulating entire populations under a disease model based on epidemiological data. Random mating, nonoverlapping populations and the absence of differential fitness were assumed. Samples were drawn from these homogeneous and heterogeneous populations and analyzed with established analysis tools. We investigated the properties of linkage and association studies in inflammatory bowel disease - modeled as a six-locus polygenic disorder - as an example of this approach. In nonparametric linkage studies, lod scores varied widely, with the median required sample size depending on the locus-specific relative sibling risk. A fine mapping resolution <4 cM was found to require nonparametric lod scores >10. Family-based association studies (TDT test) and case-control studies showed a similar sensitivity and can identify risk loci in populations with moderate levels of linkage disequilibrium in sample sizes of 500-800 triplets. Case-control association studies were prone to false-positive results if applied in heterogeneous populations, with the false-positive rate increasing with sample size because population heterogeneity is detected with increasing power.

Alleles↗

PubMed2024

Biobank-wide association scan identifies risk factors for late-onset Alzheimer's disease and endophenotypes.

Rich data from large biobanks, coupled with increasingly accessible association statistics from genome-wide association studies (GWAS), provide great opportunities to dissect the complex relationships among human traits and diseases. We introduce BADGERS, a powerful method to perform polygenic score-based biobank-wide association scans. Compared to traditional approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured in the same cohort. We applied BADGERS to two independent datasets for late-onset Alzheimer's disease (AD; n=61,212). Among 1738 traits in the UK biobank, we identified 48 significant associations for AD. Family history, high cholesterol, and numerous traits related to intelligence and education showed strong and independent associations with AD. Furthermore, we identified 41 significant associations for a variety of AD endophenotypes. While family history and high cholesterol were strongly associated with AD subgroups and pathologies, only intelligence and education-related traits predicted pre-clinical cognitive phenotypes. These results provide novel insights into the distinct biological processes underlying various risk factors for AD.

Alzheimer Disease↗

PubMed1997

Association of the apolipoprotein A-IV codon 360 mutation in patients with Alzheimer's disease.

Specific apolipoprotein E (apoE) alleles determines, in large part, the risk and mean age of onset familial and sporadic Alzheimer disease. The unresolved issues in this relationship support the contribution of other environmental and genetic parts. Among the candidates the apolipoprotein A-IV (apoA-IV) a component of plasma lipid particles similar to apoE has been suggested to play a role in brain metabolism. Since apoA-IV has a common DNA based protein polymorphism with a different function we determined apoA-IV (360:Gln:His) DNA polymorphism in 63 late-onset sporadic Alzheimer's patients. We found that the APOA-IV (360:His) heterozygosity occurs significantly more frequent (20.6% vs. 7.0%, P = 0.021, odds ratio 3.4 (confidence interval 1.1-10.2)) comparing age-matched controls with normal mental score. The significant difference in apoA-IV allelic distribution has been detected dominantly in patients with non-apoE4 genotype. Our data indicate that the apoA-IV-2 allele may confer one of the susceptibility markers for Alzheimer's disease (AD) and strengthen the polygenic risk determination of the variability in expression of AD.

Aged↗

PubMed2026

Alcohol use disorder and childhood adversity in the association between polygenic risk and suicidality.

OBJECTIVE: Suicidal ideation (SI) and suicide attempt (SA) are both influenced by genetic, behavioral, and environmental factors. Alcohol use disorder (AUD) and adverse childhood experiences (ACEs) may mediate or moderate the effects of genetic liability for suicidality. METHODS: Using data from 10,275 participants (43.8% female; 47.2% African-like genetic ancestry [AFR], 52.8% European-like genetic ancestry [EUR]), we tested whether polygenic scores (PGS) for SI and SA predicted lifetime suicidality outcomes. We evaluated whether AUD partially accounted for these associations and ACEs moderated the direct and indirect associations. RESULTS: The SA PGS was significantly associated with SA (AFR: b = 0.36, SE = 0.01; EUR: b = 0.17, SE = 0.01; both ps < 2e-16), but the SI PGS was not associated with SI (p > 0.55). AUD statistically mediated the association between the SA PGS and SA, accounting for approximately 2% of the total association in AFR individuals and 10% in EUR individuals (both ps < 2e-16). Notably, the proportion of the association that was accounted for by AUD decreased as ACEs exposure increased, from 4.30% to 0.54% in AFR individuals and from 13.31% to 3.44% in EUR individuals. In contrast, there was only very modest mediation and no moderated mediation for SI. CONCLUSIONS: Particularly among individuals with lower ACEs exposure, AUD accounted for a meaningful proportion of the association between genetic liability to SA and lifetime SA. These findings highlight different correlates across suicidality phenotypes and suggest potential clinical relevance for AUD in the association between genetic liability and SA.

Adult↗

PubMed2025

Multi-omics reveals cross-tissue regulatory mechanisms of autism risk loci via gut microbiota-immunity-brain axis.

Autism Spectrum Disorder (ASD) involves a multi-system interaction mechanism among genetics, immunity, and gut microbiota, yet its regulatory network remains undefined. This study conducted a meta-analysis on Genome-Wide Association Study data from four independent ASD cohorts to identify potential genetic loci. By integrating Polygenic Priority Score, brain region, and brain cell eQTL enrichment analyses, and combining summary-data-based Mendelian Randomisation (SMR) analyses of brain cis-eQTL and mQTL, bidirectional Mendelian Randomisation analyses of 473 gut microbiota, and SMR analysis of blood eQTL, SNPs such as rs2735307 and rs989134 with significant multi-dimensional associations were identified. These loci exert cross-tissue regulatory effects by participating in gut microbiota regulation, involving immune pathways such as T cell receptor signal activation and neutrophil extracellular trap formation, as well as cis-regulating neurodevelopmental genes (HMGN1 and H3C9P), or synergistically influencing epigenetic methylation modifications to regulate the expression of BRWD1 and ABT1. The cross-scale evidence chain constructed in this study provides a theoretical foundation for precision medicine research in ASD, holding promise to advance the development of innovative therapeutic strategies.

Autism spectrum disorder↗

PubMed2025

Genome-wide association study of adolescent-onset depression.

Adolescent depression is a heritable psychiatric condition with rising global prevalence and severe long-term outcomes, yet its biological underpinnings remain poorly understood. We conducted the first genome-wide association study of adolescent-onset depression, comprising 102,428 cases (diagnosis or clinical symptom thresholds) and 286,911 controls, including diverse ancestries. Cross-ancestry meta-analysis identified 52 independent variants across 17 loci; European-only analysis found 61 variants at 29 loci, with a SNP-based heritability of 9.8%. Comparative analyses revealed two genes unique to adolescent-onset versus lifetime depression, enriched in neuronal subtypes, and two genes as potential drug repurposing targets. Polygenic scores were associated with adolescent-onset depression across ancestries, persistent depression trajectories, more severe outcomes, as well as reduced cortical volume, surface area and white matter integrity. Genetic correlation and Mendelian randomisation analyses support shared genetic liability and causal links with early puberty and modifiable health and behavioural risk factors. These findings uncover novel genetic loci and refine biological pathways underlying adolescent-onset depression, revealing age-specific mechanisms and early intervention opportunities.

Journal Article↗