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PubMed2026

A genome-wide cross-trait analysis characterizes the shared genetic architecture between rheumatoid arthritis and psychiatric disorders.

OBJECTIVES: Patients with RA have a 2- to 3-fold elevated risk of psychiatric disorders, suggesting an underlying genetic link between these phenotypes. However, the shared genetic architectures and pathological mechanisms driving RA-psychiatric disorder comorbidity remain to be fully elucidated. Herein, we performed cross-trait analysis to investigate the shared genetic architecture between RA and psychiatric disorders. METHODS: Leveraging European-ancestry genome-wide association studies (GWASs) datasets of RA (n = 1 026 690) and 10 major psychiatric disorders (n = 14 307-1 222 882), we performed cross-trait pleiotropic analysis to identify the shared pleiotropic loci and genes between RA and psychiatric disorders, followed by functional annotation and Mendelian randomization analysis to explore the pathological mechanisms underlying RA-psychiatric disorder comorbidity. RESULTS: Our analysis revealed significant positive genetic correlations between RA and seven psychiatric disorders, such as major depressive disorder. From these correlations, we identified 61 pleiotropic loci jointly influencing RA and psychiatric disorder risk, along with 208 pleiotropic genes predominantly involved in immune and inflammatory response biological processes. Druggable target exploration identified 21 drug-gene interactions involving pleiotropic genes, with two genes (RHOA and TRAF3) classified in the clinically actionable category, representing potential therapeutic targets for both RA and psychiatric disorders. Mendelian randomization further demonstrated a bidirectional causal relationship between RA and schizophrenia, while supporting the causal roles of attention-deficit/hyperactivity disorder, major depressive disorder and post-traumatic stress disorder in increasing RA risk. CONCLUSION: Our findings elucidate the shared genetic architecture between RA and psychiatric disorders, providing novel insights into the pathological mechanisms underlying their comorbidity and laying the groundwork for improved comorbidity management.

Arthritis, Rheumatoid↗

PubMed2026

Shared genetic architecture and cellular convergence between female reproductive disorders and pulmonary function: a genome-wide cross-trait analysis.

Female reproductive disorders (FRDs), including polycystic ovary syndrome, endometriosis, uterine leiomyomata, and infertility, have been epidemiologically associated with impaired pulmonary function. However, it remains unclear whether this cross-organ link reflects shared genetic etiology and, if so, which cellular mechanisms mediate it. We performed a systematic genome-wide cross-trait analysis of three FRDs and lung function traits (FEV₁, FVC, FEV₁/FVC) using GWAS summary statistics from individuals of European ancestry, integrating genetic correlation, bidirectional causal inference, pleiotropy mapping, and single-cell enrichment analyses. We identified significant negative genetic correlations between FRDs and lung volume traits, most prominently for FVC (rg range: - 0.077 to - 0.178). Bidirectional causal analyses indicated that FRDs have a detrimental effect on lung volume, with higher FRD genetic liability associated with reduced lung volume. Cross-trait meta-analysis identified 17 pleiotropic variants across 11 loci, with the 19q13.2 (LTBP4) and 12q13.13 (HOXC6/HOXC9) loci showing strong evidence of shared causal variants. Critically, single-cell analyses revealed that shared genetic risk converged on mesenchymal lineages across organs, specifically alveolar adventitial fibroblasts in the lung and stromal/smooth muscle cells in the endometrium. Transcriptome-wide analyses further nominated the estrogen-responsive gene RERG as a convergent gene linking these conditions with lung function. Our study revealed a shared genetic architecture between female reproductive disorders and lung function traits, providing a basis for further mechanistic investigations and potential clinical evaluation. Furthermore, our findings suggest that shared fibroproliferative and hormone-responsive pathways may offer insights into the biological mechanisms underlying these conditions.

Female↗

PubMed2026

Genetic overlap between depression and C-reactive protein levels: Evidence from a cross-trait analysis.

Inflammation and depression have been consistently associated, with elevated C-reactive protein (CRP) levels observed in a significant subset of affected individuals. However, the genetic mechanisms underlying this association remain poorly understood. We integrated results from large-scale genome-wide association studies (GWAS) of depression and CRP levels in a cross-trait analysis specifically focusing on identifying horizontally pleiotropic loci. Identified variants were stratified as concordant versus discordant based on their direction of effects on the two traits and followed up using functional annotation, gene set enrichment, and colocalization analyses. We also explored causal relationships using Mendelian Randomization (MR) analysis with extensive sensitivity analyses, including adjustment for body mass index (BMI). We identified 9 novel loci. Functional analyses revealed that concordant loci were enriched in genes linked to immune and inflammatory processes, while discordant loci mostly mapped to metabolic pathways, including lipid regulation. MR provided strong evidence for body mass index driving a causal relationship between the genetic liability of depression on CRP levels. Our findings suggest that the association between depression and CRP levels is partly driven by shared genetic influences, pointing to different biological pathways depending on whether genetic effects are concordant or discordant. These results underscore the importance of considering effect direction when assessing the genetic overlap between depression and inflammatory processes. In addition, they highlight BMI as a key factor in the causal relationship between depression and systemic inflammation.

C-Reactive Protein↗

PubMed2026

Shared genetic architecture of smoking dependence and Crohn's disease: A cross-trait analysis of GWAS summary statistics.

INTRODUCTION: Smoking dependence (SD) and Crohn's disease (CD) are epidemiologically associated, but whether this relationship reflects shared genetic susceptibility remains unclear. METHODS: We conducted a cross-trait genetic analysis of SD and CD using publicly available genome-wide association study (GWAS) summary statistics from European-ancestry populations. Genome-wide genetic correlation was estimated using linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL). Pleiotropic variants were identified using PLACO and mapped to genomic loci using FUMA. Regional signal sharing was assessed by Bayesian colocalization. Functional analyses included stratified LDSC, Multi-marker Analysis of GenoMic Annotation (MAGMA), GTEx tissue analysis, and Metascape. Expression-linked candidate genes were prioritized using expression quantitative trait locus (eQTL)-based summary-data-based Mendelian randomization (SMR) with heterogeneity in dependent instruments (HEIDI) testing. Genetically informed spatial mapping of cells for complex traits (gsMap) was used for spatial mapping. RESULTS: SD and CD showed positive genetic correlation by LDSC (rg=0.2090, p=0.0008) and HDL (rg=0.3817, p=0.00106). PLACO identified 81 genome-wide significant pleiotropic SNPs, which were mapped by FUMA to three loci at 1p31.3, 5p13.1, and 12q12, represented by rs11209031, rs1395152, and rs17467116, respectively. MAGMA identified 22 FDR-significant genes, four of which remained Bonferroni significant: LRRK2, TNFRSF6B, ZGPAT, and RP4-583P15.15. Cross-trait tissue analysis showed significant enrichment of the shared genetic signal in whole blood and small intestine, while gene-set analysis highlighted inflammatory response (pbon=1.86×10-5) and T-helper 17 cell differentiation (pbon=7.37×10-4). SMR/HEIDI analysis further prioritized RPS6KB1 as a shared expression-linked candidate. Spatial mapping revealed a prominent signal in the embryonic gastrointestinal tract and gene-specific regional patterns involving LRRK2 and SLC2A13 in the adult mouse brain. CONCLUSIONS: SD and CD showed measurable shared genetic susceptibility, with convergent evidence from pleiotropic loci, immune-inflammatory pathway enrichment, tissue-level associations, and spatial transcriptomic mapping.

Crohn's disease↗

PubMed2026

Shared genetic architecture between ADHD and intelligence varies across ADHD subtypes.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental condition frequently accompanied by cognitive difficulties. Although previous genetic studies have demonstrated substantial overlap between ADHD and intelligence, most have treated ADHD as a single phenotype. However, whether this shared genetic architecture differs across ADHD subtypes remains unclear. METHODS: We conducted a genome-wide cross-trait analysis integrating large-scale genome-wide association study (GWAS) datasets of overall ADHD, its subtypes-childhood ADHD, persistent ADHD, and late-diagnosed ADHD-and intelligence (total N > 300,000). Genome-wide genetic correlations, polygenic overlap, local genetic correlations, and variant-level associations between ADHD phenotypes and intelligence were evaluated to characterize their shared genetic architecture. Shared variants were identified through cross-trait enrichment analyses and subsequently mapped to genes for functional annotation and gene-set enrichment. Bidirectional associations were evaluated using two-sample Mendelian randomization with sensitivity analyses. Additional GWAS datasets were used to validate the robustness of shared loci by assessing the consistency of effect directions. RESULTS: All ADHD phenotypes showed significant negative genetic correlations with intelligence (rg ranging from -0.3442 to -0.4205). Despite these modest genome-wide correlations, cross-trait analyses revealed substantial genetic overlap, including polygenic overlap, local genetic correlations, and variant-level associations. We identified 184 loci jointly associated with ADHD traits and intelligence, including 64 novel loci, whereas no shared loci were detected for persistent ADHD under the current analysis. Functional annotation revealed biologically distinct enrichment patterns across subtypes: childhood ADHD loci were linked to early neurodevelopmental processes, while late-diagnosed ADHD loci were enriched in synapse-related and neuronal signaling pathways. Mendelian randomization analyses suggested bidirectional associations, with stronger evidence supporting a directional association from intelligence to ADHD risk. Furthermore, these shared loci showed largely consistent effect directions across additional GWAS datasets, providing support for the robustness of the findings. CONCLUSIONS: The shared genetic architecture between ADHD and intelligence varies across ADHD subtypes, highlighting distinct biological pathways underlying cognitive heterogeneity in ADHD. These findings suggest that the relationship between ADHD liability and general cognitive ability is not uniform across ADHD subtypes and may inform future research on risk stratification and early identification in child and adolescent psychiatry.

Humans↗

PubMed2026

Multi-omics uncovers the pleiotropic genetic mechanisms linking MASLD and cardiometabolic syndromes.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) and cardiovascular-kidney-metabolic (CKM) syndrome are interrelated conditions with shared pathophysiological features; however, the genetic architecture underlying their relationship has not been fully elucidated. Deciphering this shared genetic basis holds promise for advancing mechanistic insights and therapeutic discovery. METHODS: We performed an integrated genome-wide cross-trait analysis using GWAS summary statistics for MASLD and 38 CKM traits. Our analysis estimated genetic correlations, inferred causal relationships, and identified pleiotropic variants. Candidate causal genes and druggable targets were subsequently prioritized through integrating multi-omics data. RESULTS: MASLD exhibited significant genetic correlations with 16 CKM traits, especially metabolic and cardiovascular conditions. Bidirectional causal relationships were observed between MASLD and T2D, adiposity, and lipid traits. We discovered 116 pleiotropic loci, including 65 shared causal variants such as rs429358 near APOE, which exerted influence across multiple traits. Gene-based analyses prioritized 152 unique candidate pleiotropic genes, enriched in lipid and cholesterol metabolism, and highly expressed in the liver, adipose, and immune-related cell types, such as macrophages and endothelial cells. Multi-omics integration validated 131 genes using eQTL and pQTL data from multiple tissues and cohorts. Notably, FTO and APOE emerged as central pleiotropic hubs, and druggability evaluation highlighted APOE, LPL, PPARG, and GPBAR1 as established therapeutic targets for metabolic diseases. CONCLUSION: This study provides a comprehensive map of the shared genetic architecture between MASLD and CKM syndrome, reveals novel causal genes and repurposable drug targets, and offers insights into precision medicine approaches for cardiometabolic and liver diseases.

Humans↗

PubMed2026

Dissecting the shared genetic architecture between migraine subtypes and cardiovascular diseases: a multi-layered genomic analysis.

BACKGROUND: Epidemiological studies have linked migraine to an increased risk of cardiovascular disease (CVD); however, the shared genetic basis and putative causal relationships between migraine subtypes and cardiovascular traits remain poorly understood. METHODS: Leveraging large-scale GWAS summary statistics for migraine phenotypes (overall migraine, migraine with aura [MA], and migraine without aura [MO]) from FinnGen R12, along with seven cardiovascular diseases from publicly available consortia, we conducted a multi-layered genetic analysis. This integrative framework encompassed genetic correlation [linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL)], cross-trait meta-analysis (CPASSOC and PLACO), Bayesian colocalization, summary-data-based Mendelian randomization (SMR) using GTEx v8 eQTL data, and bidirectional two-sample Mendelian randomization (MR). RESULTS: Significant genetic correlations were identified between migraine and multiple cardiovascular traits, with hypertension and coronary artery disease (CAD) showing the most robust associations. MA exhibited broader genetic overlap with cardiovascular diseases than MO, including a notably stronger correlation with ischemic stroke, whereas MO demonstrated a stronger correlation with hypertension. Cross-trait meta-analysis identified 160 pleiotropic loci across 17 of 21 trait pairs. Colocalization analysis confirmed 32 loci harboring shared causal variants, mapped to 13 candidate genes, of which 7 (PHACTR1, LRP1, SOX7, ABO, FHOD3, MEI1, XKR6) were further validated by SMR as exhibiting tissue-specific regulatory effects. Among these, PHACTR1 displayed the broadest pleiotropic profile across migraine phenotypes and vascular diseases. After MR-PRESSO outlier removal, bidirectional MR identified 10 MR-supported associations, two of which (genetic liability to hypertension on overall migraine, and CAD on MA) survived Bonferroni correction, all free of detectable horizontal pleiotropy. Genetic liability to hypertension was associated with increased migraine risk (OR = 1.90, 95% CI 1.25-2.90, P = 2.64 × 10⁻³), atherosclerotic diseases showed subtype-specific effects (inverse for MO, positive for MA), and, in the reverse direction, migraine was associated with increased ischemic stroke risk. CONCLUSIONS: This study provides a comprehensive and systematic characterization of the shared genetic architecture between migraine subtypes and cardiovascular diseases. By identifying pleiotropic genes and bidirectional putative causal relationships with subtype-specific patterns, our findings carry implications for the development of targeted therapeutics and subtype-specific cardiovascular risk stratification.

Humans↗

PubMed2026

Genetic interconnections between personality-related phenotypes and psychiatric disorders.

BACKGROUND: Personality-related phenotypes are genetically correlated with psychiatric disorders, but whether these relationships reflect shared genetic loci and differ across individual phenotypes remains unclear. We investigated their shared genetic architecture at the level of specific phenotype-disorder pairs. METHODS: We analyzed genome-wide association study summary statistics for 13 personality-related phenotypes and eight psychiatric disorders in populations of European ancestry. Genetic correlations were evaluated separately for 104 phenotype-disorder pairs using linkage disequilibrium score regression and high-definition likelihood. For pairs supported by both methods, MTAG and CPASSOC were applied separately to identify pleiotropic signals, followed by linkage disequilibrium clumping, Bayesian colocalization, gene prioritization, functional enrichment and bidirectional two-sample Mendelian randomization analyses. No composite personality or psychiatric-disorder phenotype was constructed. RESULTS: Among the 104 evaluated pairs, 77 showed significant positive genetic correlations in both analyses. Joint screening of MTAG and CPASSOC results identified pleiotropic signals in 61 pairs, comprising 1088 independent lead SNV-pair associations and 776 unique SNVs. Bayesian colocalization supported 351 signals across 42 pairs and 284 unique lead SNVs. MAGMA identified 1293 unique genes, of which 379 were prioritized by PoPS and 151 were further supported by SMR. These genes were enriched in brain tissues and biological processes involving nervous system development, synaptic organization and intercellular connectivity. Inverse-variance weighted Mendelian randomization identified 41 forward and 32 reverse associations after false-discovery-rate correction, including 21 pairs with bidirectional evidence. CONCLUSION: These item-resolved analyses identify widespread but heterogeneous genetic sharing between personality-related phenotypes and psychiatric disorders. The findings provide a pair-specific map of shared loci and prioritized genes, while the Mendelian randomization results should be interpreted cautiously because of residual heterogeneity and potential horizontal pleiotropy. Further validation in diverse populations and functional studies is required.

Colocalization↗

PubMed2026

Multi-ancestry multi-trait analysis reveals shared genetics across major psychiatric disorders and Alzheimer's disease.

The clinical overlap between major psychiatric disorders (MPDs) and Alzheimer's disease (AD) implicates complex shared etiology. Previous studies demonstrated that both diseases are genetically complex and highly heritable, suggesting that more endeavors are necessary to be made from the very bottom to understand their genetic basis. With the advance of post-genomic analysis, multi-ancestry meta-analysis allows the generalizability of the genetic architecture across different populations to uncover ancestry-specific variants, while multi-trait analysis enables the discovery of the co-colocalized risk genomic regions across diseases. Therefore, in this study, we leveraged published GWAS summary statistics from European, East Asian, Hispanic and African American populations to report schizophrenia, major depressive disorders, and Alzheimer's disease risk loci and further fine-mapping to credible sets with >95% PP inclusion of the causal variant. We distilled 2871 potential traits from publicly available and found 134 traits significantly genetically correlated with both MPDs and AD using batch LD score regression. We then prioritized the identified loci from multi-ancestry results for cross-trait colocalization analysis to assess shared genetic etiology and further nominated 2 colocalized loci across both conditions, including rs2532240 and rs6504163. In the end, we finalized our analysis by validation and functional inference of the underlying susceptibility genes as well as putative mechanisms using evidence from multiple resources, including FIVEx, Open Targets, and scQTLbase.

Humans↗

PubMed2026

Integrative multi-omics reveals a fibroblast-centered, ZFHX3-prioritized regulatory framework linking sick sinus syndrome and atrial fibrillation.

OBJECTIVE: To define shared genetic and multi-scale mechanisms underlying comorbidity between sick sinus syndrome (SSS) and atrial fibrillation (AF). METHODS: We integrated genome-wide association study (GWAS) summary statistics for SSS and AF with Genotype-Tissue Expression (GTEx) expression and splicing quantitative trait loci (eQTL/sQTL), atrial single-cell and spatial transcriptomics, and epigenomics. We identified trait-relevant tissues and pathways, prioritized shared cell types, quantified genome-wide and local genetic sharing, detected joint loci by cross-trait meta-analysis, and linked loci to regulatory programs via colocalization and cell-prioritized co-expression networks. RESULTS: Both traits showed strongest enrichment in cardiac tissue, especially Heart Atrial Appendage. Fibroblasts from the left atrial appendage were consistently prioritized as the key shared cell population. SSS and AF displayed significant positive genome-wide genetic correlation, with multiple locally shared regions, including six major loci. Cross-trait meta-analysis identified eight joint-phenotype SNPs implicating four susceptibility genes. ZFHX3 was the leading tissue-cell-gene candidate, acting as a hub in fibroblast co-expression modules and colocalizing with cardiac regulatory signals. CONCLUSION: Shared liability for SSS and AF is highly tissue- and cell-specific, converging on regulatory networks in atrial appendage fibroblasts, with ZFHX3 serving as a central mechanistic and biomarker node.

Humans↗

PubMed2026

From gut to pancreas: Shared genetic susceptibility and biological convergence in acute pancreatitis and Crohn's disease.

BACKGROUND: Acute pancreatitis (AP) and Crohn's disease (CD) exhibit overlapping clinical presentations and an unexpectedly high rate of comorbidity. Whether this reflects shared genetic susceptibilities remains unclear. METHODS: We performed a cross-trait genome-wide association analysis leveraging European-ancestry summary statistics for AP (Ncases = 8446; Ncontrols = 437,418) and CD (Ncases = 12,194; Ncontrols = 28,072). Firstly, cross-trait genetic correlation was estimated using linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL). Secondly, to pinpoint specific pleiotropic loci and prioritize candidate genes, we employed PLACO under a rigorous composite null hypothesis, integrated with Bayesian colocalization and SMR/HEIDI analyses. Finally, we dissected the underlying biological context by mapping tissue-specific regulatory enrichment and pathway convergence using FUMA, MAGMA, and Stratified LD Score Regression (S-LDSC). RESULTS: AP and CD showed significant positive genetic correlation (LDSC: rg = 0.178, SE = 0.079, P = 0.025; HDL: rg = 0.294, SE = 0.096, P = 0.0021). Pleiotropy analyses revealed 86 SNPs and 6 independent genome-wide significant pleiotropic loci (lead variants at 5q33.1, 6q22.33, 7q34, 10q24.2, 15q22.33 and 19q13.11, PPLACO < 5 × 10-8). Colocalization showed suggestive evidence of a shared causal signal at 6q22.33 (PP4 = 0.666). Gene-based tests of the AP-CD cross-trait statistics prioritized eight pleiotropic genes-RSPO3, ATG16L1, SMAD3, FADS1, ZPBP2, FADS2, PRKAA1 and IRGM. Gene-set analyses highlighted IL-23/Th17-related and broader inflammatory response pathways. CONCLUSIONS: AP and CD share polygenic susceptibility and converge on immune and inflammatory processes, with prioritized genes pointing to autophagy, lipid metabolism and TGF-β/SMAD-related biology.

Humans↗

PubMed2026

Cell-type-specific genetic associations in Lewy body dementia identified using single-cell eQTL-based Mendelian randomization.

BACKGROUND: Lewy body dementia (LBD) is a complex neurodegenerative disorder marked by α-synuclein aggregation and dual impairment of cognitive and motor function.While genome-wide association studies have identified risk loci, the cellular mechanisms linking genetic variation to disease susceptibility remain largely unexplored. METHODS: We performed single-cell transcriptome-wide Mendelian randomization using brain cell-type-specific eQTLs across eight major cell types. Genetic associations were evaluated using inverse-variance weighted models, followed by Bayesian colocalization analysis. Replication was performed in independent stratified LBD cohorts based on APOE ε4 carrier status. Phenome-wide association analysis was included as a supplementary, descriptive assessment of cross-trait associations. RESULTS: Expression of ANKRD65 in excitatory neurons was significantly associated with reduced LBD risk (odds ratio = 0.65, 95 % CI: 0.52-0.81, p = 0.00013). This association passed a false discovery rate of 0.1 and showed strong evidence of colocalization (posterior probability = 0.93). Effect direction was consistent across APOE ε4+ and ε4- LBD subgroups in independent cohorts. No genome-wide significant associations were observed with non-neurological traits in the phenome-wide analysis. CONCLUSIONS: Our findings identify a genetically supported, cell-type-resolved association between ANKRD65 expression in excitatory neurons and LBD risk. This study demonstrates the value of integrating cell-resolved transcriptomic regulation with genetic inference to pinpoint functionally relevant targets in neurodegenerative diseases.

Humans↗

PubMed2026

Integrative post-GWAS analysis prioritizes immune regulatory pathways and candidate effector signals in systemic lupus erythematosus.

BACKGROUND: Systemic lupus erythematosus (SLE) has a complex polygenic architecture, but translating genome-wide association signals into biologically interpretable candidates remains challenging. We applied an integrative post-GWAS framework to refine SLE-associated loci and prioritize candidate regulatory mechanisms. METHODS: European-ancestry SLE GWAS summary statistics from FinnGen and Bentham et al. were meta-analysed, comprising 8417 cases and 354,277 controls. After quality filtering, 6,782,131 SNPs were retained. Downstream analyses included LAVA regional prioritization, Bayesian colocalization with GTEx v8 whole-blood and spleen eQTLs, independent replication in the Julià et al. Spanish cohort, pathway enrichment, bivariate LAVA cross-trait local genetic correlation, and therapeutic annotation. RESULTS: The discovery meta-analysis identified 46 genome-wide significant SLE-associated loci, including putative novel signals requiring database/literature qualification. LAVA identified 14 candidate index variants across 12 high-confidence regions, of which nine index variants were retained as the primary prioritized set based on LAVA support and/or convergent regulatory evidence. The strongest association mapped to the chr6p21.3/MHC region (rs389884), where four genes showed colocalization support, including CLIC1 in whole blood and C4A in spleen. Because the chr6p21.3/MHC rs389884 region lead variant was unavailable for replication and no suitable proxy was identified, this signal was interpreted as an emerging candidate for functional validation rather than a replicated causal signal. Seven available variants replicated with concordant effects. An exploratory Roadmap immune chromatin-state overlap analysis placed 15 of 45 non-MHC lead variants (33.3%) directly, and 34 of 45 (75.6%) within ±10 kb, in active immune enhancer/promoter states. Pathway analyses highlighted type I interferon, JAK-STAT signaling, cytokine regulation, and antigen presentation, while bivariate LAVA analyses supported shared local genetic architecture with rheumatoid arthritis, systemic sclerosis, and Sjögren syndrome. CONCLUSIONS: This integrative post-GWAS analysis refines SLE association signals into biologically interpretable candidate regions and supports interferon and JAK-STAT signaling as central genetically supported pathways in SLE.

CLIC1↗

PubMed2026

Conventional and Shared Genetic Association Analysis Between Diabetes Mellitus and Sensorineural Hearing Loss.

PURPOSE: This study aims to investigate the epidemiological and genetic associations between diabetes mellitus (DM) and sensorineural hearing loss (SNHL) across different subtypes. METHODS: We analyzed 502,490 participants from the UK Biobank using multivariate logistic regression to examine the association between DM and SNHL, considering gender, age, and HbA1c levels. Genetic correlations and causality were examined by linkage disequilibrium score regression and bidirectional Mendelian randomization. Cross-trait meta-analyses identified shared loci between DM and SNHL, followed by gene annotation, functional analysis, and drug candidate exploration for the shared traits. RESULTS: Observational analysis revealed significant associations between DM and SNHL, consistent in subgroups based on age, sex, and certain HbA1c levels. A positive genetic correlation was found between type 2 diabetes mellitus (T2D) and SNHL (Rg = 0.0982, p = 0.0095) between T2D and SNHL, and four loci were identified, with ARHGEF28 and TCF7L2 prioritized as credible pleiotropic genes. Enrichment was indicated in glucose metabolism and organogenesis, with shared heritability in metabolic tissues and outer hair cells. Metformin was identified as potential drug candidates for the T2D-SNHL comorbidity. CONCLUSION: These findings progress our understanding of the epidemiological association, shared genetic basis, and potential therapeutic targets between T2D and SNHL, which might contribute to the management of their comorbidity.

Humans↗

PubMed2026

Multi-Ancestry Survival GWAS of Substance Use Initiation in the ABCD Study.

BACKGROUND: Substance use initiation in adolescence is influenced by both genetic and environmental factors; however, large-scale genetic studies often treat initiation as a binary outcome and underuse longitudinal timing information. METHODS: We conducted time-to-event (survival) genome-wide association analyses (GWAS) of initiation for four outcomes-alcohol, nicotine, cannabis, and any substance use-using longitudinal follow-up data from the Adolescent Brain Cognitive Development (ABCD) Study. We performed ancestry-stratified GWAS within European (EUR), African (AFR), and Hispanic (HISP) groups, applying consistent quality control and covariate adjustment. Summary statistics were harmonized across ancestries and meta-analyzed using inverse-variance weighted fixed-effects and DerSimonian-Laird random-effects models. We evaluated genomic inflation and heterogeneity (Cochran's Q and I 2), identified independent lead variants at genome-wide and suggestive significance thresholds, and assessed cross-trait overlap of associated loci. RESULTS: In the multi-ancestry meta-analysis, we observed suggestive association signals across traits (minimum p-values: alcohol ~ 1 × 10-7, any ~ 1 × 10-7, cannabis ~ 5 × 10-8, nicotine ~ 1 × 10-8). Nicotine initiation showed one genome-wide significant variant in both fixed- and random-effects meta-analyses (p < 5 × 10-8). Across traits, suggestive loci demonstrated limited overlap, with the strongest concordance between alcohol and any substance use, consistent with shared liability. Heterogeneity statistics indicated that some loci exhibited cross-ancestry variation in effect estimates. CONCLUSIONS: Survival GWAS leveraging initiation timing can identify genetic signals that may be missed by binary designs and enables principled multi-ancestry synthesis. Our results highlight both shared and trait-specific genetic contributions to early substance initiation and provide a foundation for downstream functional annotation and integrative modeling with environmental risk factors. These findings demonstrate the value of incorporating developmental timing into genetic discovery and provide a framework for integrating longitudinal risk modeling with genomic analyses.

ABCD↗

PubMed2026

Genetic Correlation Between Brain Imaging Phenotypes and Externalizing Behavior: A Large-Scale LDSC Analysis of UK Biobank IDPs.

Externalizing has been associated with differences in brain structure and function; however, it remains unclear whether these associations reflect shared common-variant genetic influences. Cross-trait linkage disequilibrium score regression was used to estimate genome-wide genetic correlations between externalizing genome-wide association study (GWAS) results and 3,935 brain imaging-derived phenotypes from the UK Biobank BIG40 resource. The imaging phenotypes covered structural magnetic resonance imaging (MRI), diffusion MRI, susceptibility-weighted imaging, resting-state functional MRI, and task-based functional MRI. Results were included in the primary analysis when the imaging phenotype had positive single-nucleotide polymorphism (SNP) heritability, a heritability Z statistic of at least 1.96, a mean GWAS chi-square statistic of at least 1.02, at least 200,000 regression SNPs, and a complete LDSC result without a fatal error. Technical imaging quality-control phenotypes were excluded from biological inference. Individual results were corrected using the Benjamini-Hochberg false discovery rate procedure. Aggregated Cauchy association tests (ACATs) were used to evaluate evidence across all imaging phenotypes and within predefined imaging categories. Statistical power, simultaneous confidence bounds, and alternative quality-control definitions were examined in sensitivity analyses. Of the 3,935 imaging phenotypes, 3,716 produced estimable genetic correlations, 2,980 met the primary LDSC quality-control criteria, and 2,967 were classified as biological imaging phenotypes. No individual phenotype survived false discovery rate correction. The smallest unadjusted P value was 0.0005, and the minimum adjusted q value was 0.486. The distribution of genetic correlations was centered near zero, with a median genetic correlation of 0.0014 and a median absolute genetic correlation of 0.0338. ACAT provided no evidence of an aggregate association across all biological imaging phenotypes (P = 0.302), and no predefined imaging category survived multiple-testing correction. The median minimum detectable genetic correlation at 80% power was 0.216. Bonferroni-adjusted simultaneous confidence intervals were fully contained within the interval [-0.30, 0.30] for 80.0% of phenotypes in the primary analysis and 88.0% under the stringent heritability quality-control definition. Broad and stringent sensitivity analyses produced the same overall conclusions. In this study, no statistically robust evidence of genome-wide genetic correlations between externalizing and individual UK Biobank brain imaging phenotypes was found. Nevertheless, small, localized, mixed-direction, or developmentally specific genetic effects remain possible.

Journal Article↗

PubMed2026

Shared genetic architecture between major depression and intrinsic brain functional connectome organization.

BACKGROUND: Major depression (MD) is increasingly understood as a disorder characterized by widespread abnormalities in intrinsic brain functional network organization. Although both MD and brain functional connectome architecture are highly heritable, the genetic architecture underlying their relationship remains poorly characterized. METHODS: We integrated genome-wide association studies of MD with 191 ICA-based resting-state functional connectome traits to investigate their shared genetic architecture. These traits captured intrinsic connectome organization across amplitude, functional connectivity, and global connectivity domains. Cross-trait genetic analyses were used to assess pleiotropic overlap between traits. Locus-level and gene-based analyses integrating multi-omics evidence were performed to characterize the biological relevance of shared genetic signals. RESULTS: We identified significant genetic overlap between MD and 148 of 191 brain functional connectome traits. Cross-trait analyses revealed widespread shared genetic signals organized into 627 genomic loci across amplitude, functional connectivity, and global connectivity measures. Among these, 193 loci showed evidence consistent with shared causal variants based on colocalization analyses. Gene-level integration mapped these loci to 1459 protein-coding genes (390 unique genes). Multi-layer prioritization identified 17 high-confidence genes supported by convergent genomic, transcriptomic, and proteomic evidence, with enrichment in neurodevelopmental and lipid-related metabolism pathways. CONCLUSIONS: This study provides a multi-scale characterization of the shared genetic architecture between MD and intrinsic brain functional connectome organization, revealing that shared genetic signals between MD and brain functional systems are distributed across multiple functional levels and converge at the molecular level.

Connectome↗

PubMed2024

Multivariate genetic architecture reveals testosterone-driven sexual antagonism in contemporary humans.

Sex difference (SD) is ubiquitous in humans despite shared genetic architecture (SGA) between the sexes. A univariate approach, i.e., studying SD in single traits by estimating genetic correlation, does not provide a complete biological overview, because traits are not independent and are genetically correlated. The multivariate genetic architecture between the sexes can be summarized by estimating the additive genetic (co)variance across shared traits, which, apart from the cross-trait and cross-sex covariances, also includes the cross-sex-cross-trait covariances, e.g., between height in males and weight in females. Using such a multivariate approach, we investigated SD in the genetic architecture of 12 anthropometric, fat depositional, and sex-hormonal phenotypes. We uncovered sexual antagonism (SA) in the cross-sex-cross-trait covariances in humans, most prominently between testosterone and the anthropometric traits - a trend similar to phenotypic correlations. 27% of such cross-sex-cross-trait covariances were of opposite sign, contributing to asymmetry in the SGA. Intriguingly, using multivariate evolutionary simulations, we observed that the SGA acts as a genetic constraint to the evolution of SD in humans only when selection is sexually antagonistic and not concordant. Remarkably, we found that the lifetime reproductive success in both the sexes shows a positive genetic correlation with anthropometric traits, but not with testosterone. Moreover, we demonstrated that genetic variance is depleted along multivariate trait combinations in both the sexes but in different directions, suggesting absolute genetic constraint to evolution. Our results indicate that testosterone drives SA in contemporary humans and emphasize the necessity and significance of using a multivariate framework in studying SD.

Humans↗