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PubMed2026

Assessment of Genetic Correlations Between Tobacco or Alcohol Use and Neurodegenerative Diseases Using East Asian Genetic Ancestry Genome-Wide Association Study Results.

Alzheimer's disease (AD) and Parkinson's disease (PD) are the most prevalent late-onset neurodegenerative diseases worldwide. Both are influenced in part by genetic factors and are currently incurable. Tobacco and alcohol, the two most common substances used among the general adult population, are potential AD/PD risk factors and are also heritable. Although important progress has been made, most existing research on the genetics of AD and PD has been carried out in individuals of European genetic ancestry. Investigations in a broad range of groups are crucial to understand disease mechanisms. Given the current availability of ancestry-specific tobacco and alcohol use as well as AD and PD genome-wide association study summary statistics, we performed global and local genetic correlation analyses using East Asian datasets. Genes within the correlated genetic regions were subsequently used to identify potentially enriched biological pathways between substance use and neurodegenerative diseases. We identified a global genetic correlation between smoking cessation and PD, which we confirmed in complementary European genetic ancestry data. Gene set enrichment analyses highlighted potentially shared genetic mechanisms between breast cancer and AD, which warrants further exploration. This work aims to promote further analyses across genetic ancestry groups.

Female↗

PubMed2026

Consistent and idiosyncratic pleiotropy in shaping genetic correlations.

Pleiotropy, the phenomenon where a single mutation influences multiple phenotypic traits, creates genetic correlations that can constrain evolutionary trajectories. Yet genetic correlations differ in their persistence: some remain stable over long evolutionary timescales, whereas others change rapidly across generations or environments. One explanation is that similar values of genetic correlation, rG, can arise from different pleiotropic architectures: broadly aligned effects across many loci, or disproportionate covariance contributions from a few large effect loci. Motivated by the distinction between vertical and horizontal pleiotropy, here, we develop a bivariate marker effect framework for recombinant mapping populations that separates candidate large covariance contributors from the polygenic background correlation, rD. We define rD as the correlation among marker effects after trimming markers with unusually large covariance contributions. rD is a trait-pair summary of how consistently small and moderate effect markers align across the genome; high rD is expected when many perturbations propagate through shared developmental, physiological, causal, or geometric structure. Applying this framework to high-dimensional yeast single-cell morphology, we show that trait pairs with similar rG can differ substantially in rD, and that a small number of candidate outlier regions can strongly influence some marker effect correlations. We then test whether rD predicts the environmental stability of genetic correlations under geldanamycin-mediated Hsp90 perturbation. Trait pairs with stronger rD show smaller absolute changes in rG. These results suggest that genetic correlations supported by a strong polygenic marker effect background are more environmentally stable than correlations shaped primarily by a few large covariance contributors.

Genetic Pleiotropy↗

PubMed2026

Unraveling causal links between chronic rhinosinusitis and peripheral artery diseases: insights from genetic correlations through genome-wide association studies.

OBJECTIVES: Chronic Rhinosinusitis (CRS) shares epidemiological links with Cardiovascular Diseases (CVDs), however, their shared genetic basis remains unclear. We hypothesized that pleiotropic genetic variants underlie CRS-CVDs links via distinct biological pathways. METHODS: Using large-scale GWAS data from European-ancestry individuals, we assessed global and local genetic correlations. We applied Genomic Structural Equation Modeling (Genomic SEM) to dissect shared genetic architecture, performed bidirectional Mendelian Randomization (MR) to infer causality, and conducted cis-eQTL colocalization to identify shared genetic signals. Finally, in vitro endothelial models (HUVECs) validated the functional dynamics of candidate genes under CRS-mimicking inflammatory stress. RESULTS: CRS showed significant genetic correlations with multiple CVDs. Genomic SEM revealed a latent factor structuring shared genetic risk through three pathways: artery diseases, myocardial diseases, and heart failure. Local genetic correlations identified significant local genetic correlations specifically between CRS and Peripheral Atherosclerosis (PAS)/Peripheral Artery Disease (PAD) specifically within the chr6: 31.57‒33.24 Mb locus. MR demonstrated causal effects of CRS on PAD (OR = 1.23, p = 0.022) and PAS (OR = 1.21, p = 0.011), but not vice versa. Genetically predicted HLA-DRB1, APOM, and COL11A2 expression conferred protection, while HLA-DQA2 increased risk. Crucially, in vitro validation corroborated these pathogenic trajectories, inflammatory stress significantly downregulated the protective APOM and upregulated the risk-associated HLA-DQA2 alongside pro-atherogenic VCAM-1, while HLA-DRB1 exhibited a compensatory upregulation (p < 0.05). CONCLUSION: CRS shares global genetic liability with CVDs, structured through three primary etiological pathways. Causal effects of CRS on peripheral artery diseases are mediated by immune and lipid-related genes within the chr6 locus, revealing divergent pleiotropic mechanisms. Our integrated genetic and in vitro evidence provides a mechanistic framework wherein chronic mucosal inflammation contributes to systemic endothelial vulnerability, thereby highlighting candidate targets for mechanism-directed therapy.

Humans↗

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

Investigating the causal role of smoking in gout: A triangulation approach combining NHANES data, genetic correlation, and Mendelian randomization.

The relationship between smoking and the development of gout is not well understood. To address this, we adopted a triangulation framework that integrates observational analysis, genetic correlation estimation, and two-sample Mendelian randomization (MR) to examine whether smoking confers a causal risk for gout. We first performed a cross-sectional analysis using information for 13,626 participants from the National Health and Nutrition Examination Survey between 2013 and 2018. The association of smoking with gout was subsequently assessed through logistic regression models. We next investigated the extent of shared genetic factors between smoking phenotypes and gout. We were able to demonstrate this using the linkage disequilibrium score regression applied to genome-wide association study data of European ancestry. Finally, to verify the causality of our relationship, we carried out a two-sample MR analysis. We selected the inverse-variance weighted (IVW) method and confirmed the consistency of using the IVW method with other statistical methods, including weighted median, weighted mode, and simple mode, as well as MR-Egger regression. We performed sensitivity analyses to investigate the heterogeneity of the hypothesis and stability of the data. Our findings based on National Health and Nutrition Examination Survey data reveal that there is a strong positive association between smoking and the risk of gout (odds ratio [OR] = 1.94, 95% confidence interval [CI] = 1.48-2.55, P < .001). This association persisted after confounding adjustments (OR = 1.41, 95% CI = 1.04-1.91, P = .027). In the subgroup analyses, former smokers and current smokers of 10 to 20 cigarettes per day had a substantially increased risk. Post-linkage disequilibrium score regression analysis revealed that the significantly positive genetic correlations of smoking initiation and lifetime smoking index with gout risk were both significantly positive. Additional evidence for causality is presented by MR. Genetic prediction of smoking initiation statistically increases gout risk (IVW OR = 1.55, 95% CI = 1.26-1.90, P = 3.17 × 10-5). A much stronger association is evident for lifetime smoking index (IVW OR = 1.99, 95% CI = 1.44-2.76, P = 3.24 × 10-5). These findings are the same with or without heterogeneity by sensitivity analysis. In light of our integrated analysis, smoking is a causative factor for gout. This suggests that public health interventions like anti-smoking campaigns might reduce gout incidence.

Humans↗

PubMed2026

Multi-ancestral genome-wide association study of chronic pain reveals widespread genetic correlations with mental and physical health traits.

Chronic pain (CP) is common and debilitating, affecting 12-40% of people worldwide. In this study, we conducted a genome-wide association study (GWAS) of CP in the All of Us Research Program across six genetic ancestries (Ntotal = 313 931, Ncase = 64 894, Ncontrol = 249 037). In the cross-ancestral meta-analysis, one locus on chromosome 3 reached genome-wide (GW) significance (α = 5E-08; lead SNP: rs3849410, p = 2.64E-08,). This same lead SNP, rs3849410, also reached GW significance in the European subsample (p = 7.45E-10) and in European females (p = 4.25E-08). Two additional loci, with lead SNPs rs7652179 and rs4760489, reached GW significance (p = 8.57E-09, and 3.07E-08, respectively) in European ancestry. Sex-stratified analyses revealed one locus on chromosome 11 (lead SNP: rs77607049) in males (p = 1.13E-08); in females, two other loci on chromosomes 11 (lead SNP: rs368001205) and 12 were also identified (lead SNP: rs80043169; p = 1.58E-08, 9.14E-09, respectively; p < 2.5E-08). CP was genetically correlated with psychiatric, physical, and immune traits, including anxiety (rg = 0.72, p = 2.00E-46), generalized addiction risk (rg = 0.38, p = 2.08E-17), higher C-reactive protein levels (rg = 0.36, p = 6.38E-22) and greater body mass index (rg = 0.43, p = 8.03E-47). This study represents one of the largest cross-ancestral investigations of the genetics of CP to date and demonstrates shared genetic effects between CP and multiple health conditions. PERSPECTIVE: This article presents multi-ancestral cross-sex and sex-stratified GWAS of chronic pain (CP). One significant cross-ancestral locus and 3 sex-specific loci were identified; a previously published locus for multisite CP met traditional genome-wide significance in the current European ancestry GWAS. This study identifies 4 novel genetic loci associated with CP.

Chronic pain↗

PubMed2025

aPhyloGeo: a Python application for correlating genetic and climatic conditions.

MOTIVATION: Environmental variation and its influence on genetic diversity is a central topic in evolutionary biology and phylogeography. Accurate correlations between genetic and climatic datasets to understand the genetic adaptations of different species to specific environments. It requires integrated and reproducible workflows. RESULTS: We developed aPhyloGeo, an open-source and multiplatform application implemented in Python, for investigating correlations between genetic variation and environmental data within a phylogenetic framework. The workflow integrates multiple analytical steps, including sequence alignment, sliding window phylogenetic inference, and statistical approaches such as the Mantel test and the Procrustean randomization test. These analyses enable the identification of mutation hotspots that exhibit strong associations with environmental variables. In addition, aPhyloGeo supports multicore data processing and provides a fully reproducible pipeline for evaluating localized relationships between genomic variation and climatic distributions. AVAILABILITY AND IMPLEMENTATION: aPhyloGeo is freely available on GitHub at: https://github.com/tahiri-lab/aPhyloGeo, as both a PyPI package and as Python scripts for Linux, macOS, and Windows.

Software↗

PubMed2026

Refining the link between REM sleep behavior disorder and neurodegeneration: Genetic correlation, Mendelian randomization, and colocalization evidence.

Observational studies have proposed a link between isolated rapid eye movement sleep behavior disorder (iRBD) and several neurodegenerative diseases. We employed genome-wide linkage disequilibrium score regression (LDSC), standard two-sample Mendelian randomization (MR), and colocalization analysis to assess the causal links between iRBD and these neurodegenerative conditions. iRBD demonstrated a positive causal association with Alzheimer disease (odds ratio [OR] = 1.02, 95% confidence interval [CI]: 1.00-1.03, P = 1.10E-02), Parkinson disease (OR = 1.10, 95% CI: 1.03-1.16, P = 2.96E-03), and multiple sclerosis (OR = 1.09, 95% CI: 1.02-1.17, P = 1.61E-02). A strong positive genetic correlation with dementia with Lewy bodies was observed (rg = 1.6313, P = .0002), along with a causal association (OR = 1.45, 95% CI: 1.03-2.06, P = 3.53E-02), further supported by colocalization analysis. No significant causal relationship was identified between iRBD and amyotrophic lateral sclerosis (all P > .05). Additionally, reverse Mendelian randomization analyses did not reveal any causal relationships between the neurodegenerative diseases studied and iRBD. Our findings provide robust genetic evidence supporting a causal relationship between iRBD and the risk of multiple neurodegenerative diseases, highlighting the potential for shared pathophysiological mechanisms.

Humans↗

PubMed2025

Is there a genetic correlation between tinnitus and temporomandibular joint disorder?: A two-sample Mendelian randomization study.

Tinnitus and temporomandibular disorder (TMD) are frequent coexistence of clinical symptoms caused by some systemic diseases in all humans. The observational research results on the correlation between tinnitus and TMD are inconsistent with the reported findings. The purpose of the study was to explore the bidirectional causal relationship between tinnitus and TMD. This study adopts a two-sample Mendelian randomization (MR) methodology. Single-nucleotide polymorphisms from the genome-wide association study were used as instrumental variables to elucidate the bidirectional causal relationship between tinnitus and TMD. The quality of our study was evaluated in accordance with the STROBE-MR guidelines. The MR analysis results showed that tinnitus (yes, most or all of the time now) could be significantly reduced TMD muscular pain linked with fibromyalgia (OR = 0.239, 95% CI: 0.087-0.66, P = .0057), but there was no correlation between other frequencies of tinnitus and the increased risk of TMD in genetic predisposition (all P > .05). The reverse MR analysis revealed no causal relationship and correlation between TMD exposure and increased risk of tinnitus. Sensitivity analysis indicated no horizontal pleiotropy and insignificant heterogeneity. This MR study supports evidence of a correlation between high-frequency tinnitus and reduced risk of TMD muscle pain associated with fibromyalgia. Further research on disease mechanisms are needed to explore the correlation between tinnitus and TMD.

Humans↗

PubMed2025

Interstrain Recombinants of Human Cytomegalovirus Reveal Complex Genetic Correlates and Epistasis Influencing Glycoprotein Display, Virion Infectivity and Spread Characteristics.

Most of the nucleotide diversity in the human cytomegalovirus (HCMV) genome is due to approximately 17 genes with 2-14 alleles each. These allelic genes are interspersed among longer stretches of highly conserved sequences with signatures of extensive recombination that would shuffle the allelic genes into a vast number of allelic haplotypes. Bacterial artificial chromosome clones derived from 3 independent clinical isolates (TB40/e (TB), TR and Merlin (ME)) display dramatic differences in the abundance of entry-mediating glycoproteins gH/gL/gO and gH/gL/UL128-131, virion infectivity and efficiency of cell-free and cell-to-cell modes of spread. Of these, TB and ME are the most phenotypically different and share only 2 of the 17 allelic genes. A set of recombinant HCMV was generated by coinfecting cells with TB and ME and restriction fragment length polymorphism (RFLP) analyses demonstrated complex crossover patterns. Most recombinants were either "TB-like" with much more gH/gL/gO than gH/gL/UL128-131, or "ME-like" with much more gH/gL/UL128-131. This correlated with a TB or ME UL128 sequence, consistent with a G/T polymorphism affecting UL128 pre-mRNA splicing. One recombinant had a gH/gL/gO:gH/gL/UL128-131 ratio of 0.8, suggesting genetic determinants beyond UL128. Virion infectivity correlated with TB versus ME-like glycoprotein display, but intragroup variability indicated additional factors and variability in spread efficiency and the contribution of cell-free and cell-to-cell spread modes indicated an influence of characteristics beyond virion infectivity. Results suggest that the relationships among these three phenotypes are not strictly causal and that all three phenotypes are genetically complex and influenced by epistasis among polymorphic loci across the genome.

Journal Article↗

PubMed2026

Robust inference and correlates from genetic associations with personality.

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.

Journal Article↗

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

Multilevel Exploration of Shared Genetic Architecture Between Primary Biliary Cholangitis and Four Autoimmune Diseases.

INTRODUCTION: Primary Biliary Cholangitis (PBC) frequently coexists with various autoimmune diseases, such as Multiple Sclerosis (MS), Psoriasis (PS), Rheumatoid Arthritis (RA), and Sjögren's Syndrome (SS). Understanding the genetic associations between these diseases is crucial for providing deeper insights into their shared pathogenic mechanisms and comorbidity patterns. METHODS: This study utilized genome-wide association study summary data of PBC and four autoimmune diseases (MS, PS, RA, and SS). A multi-stage analytical pipeline was employed to systematically investigate the genetic associations between the diseases. The analytical approach consisted of three stages: first, linkage disequilibrium score regression and high-definition likelihood methods were applied to estimate overall genetic correlations between the diseases; second, local genetic correlation analysis was conducted to pinpoint genetic signals in specific chromosomal regions; third, conditional/conjunctional false discovery rate (cond/conjFDR) algorithms were used to quantitatively assess genetic overlap and identify shared susceptibility loci. RESULTS: Genome-wide analysis revealed significant genetic associations between PBC and the four autoimmune diseases (MS, PS, RA, and SS). Regional analysis showed local genetic correlations across various chromosomal segments. cond/conjFDR analysis confirmed genetic intersections among the diseases and identified several critical genetic polymorphic loci that influence disease susceptibility. DISCUSSION: This study comprehensively delineates the shared genetic architecture underlying PBC and four autoimmune diseases through integrative analyses of multiple genome-wide approaches. The results highlight strong genetic correlations, particularly between PBC and MS, PS, RA, and SS, and identify key shared susceptibility genes, including CLEC16A, CD58, CD86, STAT4, IRF5, TYK2, and TNFAIP3, which collectively mediate immune dysregulation through autophagy, cytokine signaling, and NF-κB pathways. These findings not only extend current understanding of the molecular mechanisms driving autoimmune comorbidity but also provide potential genetic targets for future functional validation and therapeutic exploration. CONCLUSION: This study provides comprehensive genomic evidence for the genetic connections between PBC and the four autoimmune diseases (MS, PS, RA, and SS), offering valuable insights into the shared pathological mechanisms underlying their comorbidities.

Humans↗

PubMed2026

Unravelling sex differences in the genetic architecture of anxiety.

BACKGROUND: Anxiety disorders show striking sex differences in prevalence, symptoms, and clinical characteristics, shaping how they manifest and are experienced. METHODS: Here, we report the first sex-specific meta-analysis of genome-wide association studies (GWAS) of anxiety, leveraging two of the largest biobank datasets, UK Biobank and All of Us, comprising 85,042 female cases with 196,789 controls and 36,732 male cases with 136,924 controls. Functional annotation, sex-specific polygenic scores (PGS), and genetic correlations were performed to assess genetic differences and functional implications. RESULTS: In females, 21 lead SNPs were significantly associated with anxiety, compared to five in males. Although the genetic correlation between sexes was high, it was significantly different from one, indicating partially distinct genetic architectures. In addition, both the SNP-based observed and liability-scale heritabilities (assuming a 2:1 female-to-male prevalence ratio) were significantly higher in females. Gene-based tests and functional prioritization identified different genes associated with anxiety in females and males. Moreover, genetic correlation analyses revealed stronger associations of female anxiety with attention-deficit/hyperactivity disorder (ADHD) and body mass index (BMI), whereas male anxiety showed stronger correlations with waist-hip-ratio-adjusted BMI. CONCLUSIONS: While the overall genetic architecture of anxiety is largely shared, our findings reveal distinct sex-specific genetic associations and correlations, highlighting the value of analyzing the sexes separately to uncover genetic signals that may be masked in sex-combined samples.

Female↗

PubMed2025

Shared genetic risk and causal associations between Post-traumatic stress disorder and migraine with antithrombotic agents and other medications.

Post-traumatic stress disorder (PTSD) is a psychiatric disorder that frequently co-occurs with pain disorders including migraine. There are proposed biological, genetic and environmental factors associated with both PTSD and migraine suggesting shared etiology. Genome-Wide Association Studies (GWAS) have been used to identify genomic risk loci associated with various disorders and to investigate genetic overlap between traits. There is a significant genetic correlation between PTSD and migraine with no evidence of a causal relationship that could be attributed to pleiotropy. Cross-disorder genetic analyses were applied to investigate the genetic overlap and causal associations using GWAS summary statistics of PTSD (n = 214408), migraine (n = 873341) and 23 medication use traits (n = 78808-305913) including anti-depressants, anti-migraine preparations and beta-blocking agents. Across the entire genome, anti-thrombotic agents had a significant and negative genetic correlation with PTSD (rG = -0.2, P FDR = 0.032) and a positive genetic correlation with migraine (rG = 0.26, P FDR = 2.23 x 10-8). PTSD showed significant genetic correlation with 11 other medication use traits including beta blocking agents (rG = -0.11, P FDR = 0.034). Of the 2495 genomic regions tested, PTSD showed significant local genetic correlation with 12 medication use traits at 43 loci; while migraine showed significant genetic correlation with only anti-inflammatory agents and anti-rheumatic products at locus 12:57522282-57607142 (DAB1) (P < 2 x 10-5). The genetic liability to PTSD had a causal effect on increased risk of using pain medication such as opioids (β ivw = 0.59, P = 5.21 x 10-5) while the genetic liability to migraine had a causal effect on the increased risk of using anti-thrombotic agents (β ivw = 0.59, P = 1.69 x 10-7). The genes in the genomic regions shared between PTSD and medication use traits were enriched in neural-related pathways such as neuron development, neurogenesis and protein kinase activity. These results provide further insight into the genetically controlled biological and environmental factors underlying the shared etiology between PTSD and migraine. The identified biomarkers can be used as a basis for investigation as potential drug targets for both disorders. These findings are significant for drug re-purposing and treatment of PTSD and migraine using monotherapy.

GWAS↗

PubMed2025

The role of the brain-bone axis in skeletal degenerative diseases and psychiatric disorders, A genome-wide pleiotropic analysis.

INTRODUCTION: Skeletal degenerative diseases and psychiatric disorders often coexist clinically. However, the genetic correlations and underlying biological mechanisms between these two types of diseases remain unclear. OBJECTIVES: To investigate the genetic correlations between skeletal degenerative diseases and psychiatric disorders and to identify shared genomic loci, genes, and pathways. METHODS: This comprehensive genome-wide pleiotropic association study utilized summary statistics from publicly available genome-wide association data. Various statistical genetic correlation methods were employed, including LDSC, HDL, PLACO, Coloc, Hyprcoloc, and Mendelian randomization (MR) analysis, along with immune cell colocalization analysis. The study aimed to identify potential shared genetic factors among three skeletal degenerative diseases (osteoarthritis, intervertebral disc degeneration, and osteoporosis) and three psychiatric disorders (schizophrenia, anxiety disorder, and major depressive disorder). RESULTS: Analyses using LDSC, HDL, and Bonferroni corrections revealed significant genetic correlations between intervertebral disc degeneration (IVDD) and anxiety disorder (ANX); fractures, IVDD, and arthritis with major depressive disorder (MDD); and arthritis with schizophrenia (SCZ). Significant genetic correlations were also observed between VDD and ANX, fractures, IVDD, hip osteoarthritis (HipOA), knee osteoarthritis (KneeOA) and MDD, and KneeOA and SCZ. Pleiotropy analysis using PLACO, MAGMA, and multitrait colocalization Hyprcoloc identified 65 pleiotropic loci, 27 shared causal loci, and 9 shared risk loci involving immune cells related to both psychiatric and bone-related diseases. Additionally, tissue-specific enrichment analysis showed that genes mapped to these loci were enriched in brain, cardiovascular, pancreatic, and other tissues. The IVW method demonstrated that MDD increased the risk of IVDD and KneeOA, while IVDD increased the risk of ANX and MDD. Conversely, SCZ was associated with a reduced risk of KneeOA. Multiple sensitivity analyses further supported a positive causal effect of IVDD on MDD. CONCLUSION: These findings suggest significant genetic correlations between skeletal degenerative diseases and psychiatric disorders, highlighting multiple shared comorbid genes and key immune cell types. Importantly, the study supports the role of the brain-bone axis in the regulation of skeletal degenerative diseases and psychiatric disorders, which could provide valuable insights for potential therapeutic targets and interventions for these conditions.

Humans↗

PubMed2021

Genetic Relationship Between Endometriosis and Melanoma.

Epidemiological studies have observed that risk of endometriosis is associated with history of cutaneous melanoma and vice versa. Evidence for shared biological mechanisms between the two traits is limited. The aim of this study was to investigate the genetic correlation and causal relationship between endometriosis and melanoma. Summary statistics from genome-wide association meta-analyses (GWAS) for endometriosis and melanoma were used to estimate the genetic correlation between the traits and Mendelian randomization was used to test for a causal association. When using summary statistics from separate female and male melanoma cohorts we identified a significant positive genetic correlation between melanoma in females and endometriosis (r g = 0.144, se = 0.065, p = 0.025). However, we find no evidence of a correlation between endometriosis and melanoma in males or a combined melanoma dataset. Endometriosis was not genetically correlated with skin color, red hair, childhood sunburn occasions, ease of skin tanning, or nevus count suggesting that the correlation between endometriosis and melanoma in females is unlikely to be influenced by pigmentary traits. Mendelian Randomization analyses also provided evidence for a relationship between the genetic risk of melanoma in females and endometriosis. Colocalization analysis identified 27 genomic loci jointly associated with the two diseases regions that contain different causal variants influencing each trait independently. This study provides evidence of a small genetic correlation and relationship between the genetic risk of melanoma in females and endometriosis. Genetic risk does not equate to disease occurrence and differences in the pathogenesis and age of onset of both diseases means it is unlikely that occurrence of melanoma causes endometriosis. This study instead provides evidence that having an increased genetic risk for melanoma in females is related to increased risk of endometriosis. Larger GWAS studies with increased power will be required to further investigate these associations.

endometriosis↗

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↗