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PubMed2026

Genetically-predicted placental gene expression links to uterine fibroids and endometriosis.

INTRODUCTION: Mother-to-child disease transmission begins in utero, with the placenta playing a critical role in pregnancy and offspring health. Uterine leiomyomata (fibroids, UFs) and endometriosis (ENDO) are common gynecologic diseases that have substantial overlaps in symptomology and risk factors, however drivers of disease risk remain unclear. The objective of this study was to investigate shared placental genetic associations across ENDO and UFs. METHODS: Genome-wide association study (GWAS) summary statistics were utilized from a published study of UFs (PMID: 40050615) and meta-analyzed for ENDO (24,092 cases and 548,255 controls). To improve our statistical power, we applied Multi-Trait Analysis of GWAS to the ENDO and UF GWAS. We estimated genetically predicted gene expression using S-PrediXcan across 49 tissues using GTEx v7 and a placental tissue expression model. RESULTS: We identified 54 and 14 genes where predicted expression in the placenta was significantly associated with UFs and ENDO, respectively. Twenty-one of these genes were shared between UFs and ENDO. Significant gene associations in placenta tissue were compared to the other 48 GTEx v7 tissue types to identify placenta specific associations. There were 40 and 13 significant gene-tissue associations specific to the placenta across UFs and ENDO, respectively. Eight of the placenta-specific genes were shared across UFs and ENDO. The strongest shared placenta-specific associations included PRKCI and HRH1. CONCLUSIONS: Our findings demonstrate a shared genetic relationship between UFs and ENDO in the placenta. The placenta specific associations suggest that dysregulation of early developmental pathways may contribute to a shared genetic origin of these diseases.

Female↗

PubMed2025

Post-genome-wide association study dissects genetic vulnerability and risk gene expression of Sjögren's disease for cardiovascular disease.

OBJECTIVES: This study aims to clarify the genetic associations between Sjögren's Disease (SD) and cardiovascular disease (CVD) outcomes, and to conduct an in-depth exploration of specific pleiotropic susceptibility genes. METHODS: We performed two-sample and multivariable Mendelian randomization (MR) analysis to investigate the association between SD and the risk of ischemic heart disease (IHD) and stroke. Linkage disequilibrium score regression (LDSC) and Bayesian co-localization analyses were employed to assess the genetic associations between traits. Cross-phenotype analyses were employed to identify shared variants and genes, followed by a Transcriptome-Wide Association Study (TWAS) and Multi-marker Analysis of Genomic Annotation (MAGMA) based on Multi-Trait Analysis of GWAS (MTAG) results. To validate the pleiotropic genes, we further analyzed tissue-specific differentially expressed genes (DEGs) related to SD using RNA sequencing data. RESULTS: The two-sample and multivariable MR analyses revealed that SD confers a genetic vulnerability to IHD and stroke. LDSC and co-localization analyses indicated a strong genetic linkage between SD and CVDs. Cross-phenotype analyses identified 38 and 37 pleiotropic single nucleotide polymorphisms (SNPs) for SD-Stroke and SD-IHD, respectively, primarily located within the MHC class region on 6p21.32:33 loci. Additionally, TWAS and MAGMA analyses identified pleiotropic genes located outside the MHC regions-seven associated with stroke (UHRF1BP1, SNRPC, BLK, FAM167A, ARHGAP27, C8orf12, and PLEKHM1) and two associated with IHD (UHRF1BP1 and SNRPC). Proxy variants within these genes in SD suggested an increased causal risk for stroke or IHD. Co-localization analysis further reinforced that SD and stroke share significant SNPs within the loci of FAM167A, BLK, C8orf12, SNRPC, and UHRF1BP1. DEG analysis revealed a significant up-regulation of the identified genes in SD-specific tissues. CONCLUSIONS: SD appears genetically predisposed to an increased risk of CVDs. Moreover, this research not only identified pleiotropic genes shared between SD and CVDs, but also, for the first time, detected key gene expressions that elevate CVD risk in SD patients-findings that may offer promising therapeutic targets for patient management.

Humans↗

PubMed2026

Genetic evidence that advanced COVID-19 accelerates longitudinal brain atrophy: A Mendelian randomization study.

Coronavirus disease 2019 (COVID-19) was reported to persist long-term in the brain and leave several long-term neurologic sequelae. However, the causal relationship between COVID-19 and brain aging is still unknown. The genome-wide association study (GWAS) data on COVID-19 phenotypes (susceptibility, hospitalization, and severity), involving a total of 5,779,391 participants, were collected from the COVID-19 Host Genetics Initiative. In addition, GWAS data on longitudinal changes in 15 brain structures, assessed via magnetic resonance imaging across the lifespan, were sourced from the ENIGMA Consortium and involved 15,640 participants. Two-sample Mendelian randomization was conducted to infer the causal relationship between COVID-19 and longitudinal brain changes. Multi-trait GWAS meta-analysis, colocalization, and fine-mapping analyses were performed to identify shared genetic etiologies. H3K27me3 ChIP-seq was used to evaluate the regulatory effect of colocalized loci. Two-step Mendelian randomization was applied to explore potential mediating mechanisms across multi-omics layers, including proteomics, metabolomics, and immunomics. Our results showed that COVID-19 hospitalization (β = -262.405, P = .041) and severity (β = -177.676, P = .049) were genetically associated with atrophied volume of total brain during longitudinal change. This suggests that individuals with advanced COVID-19 may be more susceptible to accelerated global brain aging. Caudate was genetically affected by all COVID-19 phenotypes. Seven variants were shared between advanced COVID-19 and global brain aging. rs117169628 was colocalized between advanced COVID-19 and global brain aging, and exerted an inhibitory effect on CDH15 expression, further strengthening the causality. Six metabolites, 1 protein, and 1 immune trait were identified as potential mediators. Our study indicates that advanced COVID-19 might be genetically associated with accelerated brain aging. Brain health should be paid more attention in long COVID-19.

Humans↗

PubMed2026

Multidimensional GWAS analyses on longitudinal phenotypes reveal candidate genes regulating multi-stage egg production traits in Wannan yellow chicken.

Egg production performance directly determines the economic viability of indigenous chicken breeding. However, the genetic regulation of multi-stage egg production traits remains difficult to characterize due to their complex and dynamic nature. Here, we integrated a multidimensional GWAS framework, including single-trait GWAS, multi-trait GWAS (MTAG), and longitudinal trajectory-based GWAS (TrajGWAS), to identify stage-specific and shared genetic effects underlying egg production traits in Wannan yellow chickens (WNY). Whole-genome sequencing of 354 WNY hens (10× depth) and quality control yielded 14,253,816 SNPs for analysis. Selective sweep analyses comparing red jungle fowl, commercial layers, and WNY identified a genomic region containing IGF1 under significant selection pressure. Single-trait GWAS identified SNPs 4_57990480 (BMPR1B) and 17_370912 (LOC112531479) associated with egg production across three laying stages (21-30, 31-40, and 21-40 weeks). MTAG further identified loci 8_4336468 (FASLG) and 21_654726 (CHD5) with shared effects across the laying period, whereas TrajGWAS revealed longitudinal associations involving PRKG1 and identified dynamic loci associated with clutch traits, including GRID1. For clutch traits, stage-specific loci were detected for average clutch size (ACS) and maximum clutch size (MCS), including SNP 8_8542036 at 21-30 weeks, PROK1 at 31-40 weeks, and CUL5, ALKBH8 across the entire laying period. These results demonstrate that integrating complementary GWAS strategies improves the resolution of genetic architecture underlying egg production traits by capturing trait-specific, shared, and stage-dependent genetic effects. The identified GWAS loci and selective-sweep candidate regions provide insights into the genetic architecture of egg production traits and breed differentiation.

Egg production↗

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

Multi-trait GWAS identifies pleiotropic loci shared between early pregnancy bleeding and psychiatric traits.

INTRODUCTION: Early pregnancy bleeding is a common pregnancy complication, yet its genetic basis and potential links with psychiatric traits remain poorly understood. This study aimed to characterize the shared genetic architecture between early pregnancy bleeding and reproductive, psychiatric, and cardiometabolic traits. METHODS: We integrated linkage disequilibrium score regression (LDSC), local genetic correlation analysis (LAVA), and multi-trait genome-wide association analysis (MTAG). LDSC was used to estimate genome-wide genetic correlations, including sex-stratified analyses. LAVA was applied to identify genomic regions contributing to local genetic sharing. Guided by these correlation patterns, MTAG was performed to improve locus discovery, followed by cis-eQTL analysis using GTEx v8 to explore potential regulatory mechanisms. RESULTS: LDSC revealed significant positive genetic correlations between early pregnancy bleeding and reproductive traits, including endometriosis, miscarriage, and uterine fibroids. Strong positive correlations were also observed with several psychiatric disorders, including major depressive disorder, post-traumatic stress disorder, and attention deficit hyperactivity disorder. Sex-stratified analyses suggested stronger genetic correlations with emotional reactivity-related traits in females, whereas social and behavioral traits were more prominent in males. LAVA localized these shared signals to specific genomic regions and identified pleiotropic hotspots at 8q21 near RUNX1T1 and 9p21 near CDKN2A/B. MTAG identified two novel loci, 15q15.1 marked by rs45457497 and 11q13.1 marked by rs2452681. Cis-eQTL analysis showed that the lead variant at 15q15.1 regulates RMDN3 expression across multiple brain regions, while the 11q13.1 locus regulates PACS1, GAL3ST3, and SF3B2 expression in brain tissues and the pituitary. DISCUSSION: These findings position early pregnancy bleeding as a multifactorial trait shaped by shared reproductive, psychiatric, neuroendocrine, and stress-related biology. The implication of RMDN3, which encodes a mitochondrial outer membrane protein involved in ER-mitochondria tethering and calcium homeostasis, suggests a potential molecular link between neuroendocrine stress pathways, psychiatric susceptibility, and reproductive vulnerability.

RMDN3↗

PubMed2026

Hypertrophic cardiomyopathy: a genome-wide association meta-analysis and polygenic risk score.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is a heritable trait with marked variability in expression and outcomes. Our aims were to discover new genetic loci associated with HCM and to test the effect of a new polygenic risk score (PRS) on incidence, phenotype and outcomes stratified by genotype status. METHODS: A discovery genome-wide association study (GWAS) was performed on 2284 HCM cases and 4525 controls. Two fixed-effects meta-analyses combined our discovery GWAS with single-trait and multi-trait results from a published study. Discovered loci underwent comprehensive bioinformatic analysis including functional and druggability annotations. A PRS using loci from the two meta-analyses was evaluated for association with HCM diagnosis in 411 213 individuals from UK Biobank (UKBB); imaging phenotypes in individuals without HCM; a composite endpoint (including all-cause mortality and transplantation); and sudden cardiac death (SCD) in 1756 HCM cases. PRS analyses were stratified by genotype status. RESULTS: Three loci were found in the discovery GWAS (BAG3, FHOD3 and novel locus PPP1R3A). In the meta-analyses, 70 unique loci were identified, four novel (MYPN, YWHAE, NOS1AP and OBSCN). Bioinformatic analyses identified NOS1AP as a candidate HCM gene. A new PRS was significantly associated with HCM diagnosis (HR=3.19, 95% CI 2.46 to 4.14 for top 5% vs lower 95%; HR=1.88, 95% CI 1.72 to 2.06 per SD increase). Significant associations were found between PRS and greater left ventricular (LV) wall thickness and higher LV ejection fraction in UKBB participants without HCM. Genotype-negative HCM cases in the top 20% of the PRS distribution had an increased risk of SCD (HR=2.72, 95% CI 1.03 to 7.17). CONCLUSIONS: We report novel HCM loci. A new PRS predicted the risk of HCM development and associated imaging characteristics in the UKBB and outcomes in an HCM cohort.

Cardiomyopathies↗

PubMed2026

Research on multi-trait genome association study method based on Shannon information entropy.

BACKGROUND: Genetic analysis of complex traits is crucial for elucidating disease mechanisms and biological inheritance processes. However, traditional Genome-wide Association Study (GWAS) for single trait often fail to capture the synergistic effects of genetic loci on multiple traits. METHODS: This study proposes a method for analyzing the association between multiple traits and gene regions based on Shannon information entropy. Innovatively, Shannon information entropy is introduced to integrate gene region information as genetic entropy, thereby constructing an Inverse Shannon Entropy-Multi-Trait Association Analysis of Gene Region genetic model (InvSE-MTAGR). Furthermore, a partial regression test is applied to the model to establish the Inverse Partial Shannon Entropy-Multi-Trait Association Analysis of Gene Region method (InvPSE-MTAGR). When performing multi-trait analysis with InvSE-MTAGR, the method achieved statistical significance by accumulating minor effects, thereby enhancing the ability to identify pleiotropic gene regions. RESULTS: The simulation results showed that the proposed multi-trait gene region association analysis method performed well in terms of both Type I error rate control and statistical power. Leveraging tomato and sorghum datasets for validation, the proposed multi-trait gene region association analysis method based on Shannon information entropy accurately pinpointed most of the gene regions harboring candidate genes. CONCLUSION: The study reveals the advantage of multi-trait method in integrating weak-effect pleiotropic signals and capturing the correlation among traits, which provides an efficient theoretical tool for dynamic analysis of complex multi-trait genetic networks and multi-target collaborative breeding of crops.

Genome-Wide Association Study↗

PubMed2026

Decoding Primary Open-Angle Glaucoma: A Multi-Omics Approach to Identify Druggable Effector Genes.

PURPOSE: Genomewide association studies (GWAS) have identified numerous primary open angle glaucoma (POAG) risk loci, yet most reside in non-coding regions with unclear function. Mapping these loci to effector genes can elucidate disease mechanisms, identify functionally conserved variants, improve cross-ancestry risk prediction by reducing population-specific noise, and uncover shared therapeutic targets. METHODS: Here, we integrate European POAG GWAS with six types of multi-omics molecular Quantitative Trait Locis (xQTLs) using multi-trait colocalization to identify candidate effector variants and evaluate their cross-population relevance using genetic risk score (GRS) analysis, and their therapeutic potential through drug target prioritization. RESULTS: We identified 25 POAG effector variants colocalized with at least one xQTLs. In non-European populations, effector variants showed stronger effect size correlations with Europeans than non-colocalized variants (Pearson r2 = African 0.85 vs. 0.71; East Asian 0.81 vs. 0.69; and Latin American 0.91 vs. 0.75). Effector variants also had smaller allele frequency variations across populations (average interquartile range [IQR] = 0.15 vs. 0.20). The genetic risk score based on effector variants performed comparably to the genome-wide significant single-nucleotide polymorphism (SNP)-based GRS in non-European populations. Drug prioritization identified zinc, copper, sunitinib, probucol, and astemizole as potential common therapeutic agents for POAG and its subtypes. CONCLUSIONS: Our findings offer deeper insight into the molecular mechanisms underlying glaucoma and effector variants for developing more robust GRS models and broadly effective therapeutic strategies for POAG.

Humans↗

PubMed2026

Multi-trait polygenic scores for COPD and COPD exacerbations implicate druggable proteins.

BACKGROUNDWe constructed multi-trait polygenic risk scores (PRSs) predicting chronic obstructive pulmonary disease (COPD) and exacerbations, validated their performance in diverse cohorts, and identified PRS-related proteins for potential therapeutic targeting.METHODSPRSmix+, a multi-trait PRS framework, is used to train a composite PRS (PRSmulti) in COPDGene non-Hispanic White participants (n = 6,647). Associations of PRSmulti with COPD status (GOLD 2-4 vs. GOLD 0 or ICD) and exacerbation frequency were tested in COPDGene African American (n = 2,466), ECLIPSE (n = 1,858), Mass General Brigham Biobank (n = 15,152), and All of Us (n = 118,566). Protein prediction models were applied to GWAS summary statistics from traits contributing to PRSmulti and were validated with proteomic data in COPDGene (n = 5,173) and UK Biobank (n = 5,012).RESULTSPRSmix+ selected 7 traits for PRSmulti. In multivariable models, PRSmulti was associated with COPD status (meta-analysis random effects [RE] OR 1.58 [95% CI: 1.28-1.94]) and exacerbation frequency (meta-analysis RE β 0.21 [95% CI: 0.11-0.31]), with higher effect sizes observed in smoking-enriched cohorts. PRSmulti outperformed traditional single-trait PRS in all tested cohorts. Using protein prediction models, we identified 73 proteins associated with the PRSs that were also validated with measured protein levels in COPDGene and UK Biobank. Of these proteins, 25 were linked to approved or investigational drugs. Notable targets include RAGE/sRAGE, IL1RL1, and SCARF2, all implicated in COPD pathogenesis and exacerbations.CONCLUSIONSMulti-trait PRS improves prediction of COPD and exacerbation risk. Integration with proteomic data identifies druggable protein targets, offering a promising avenue for precision medicine in COPD management.TRIAL REGISTRATIONCOPDGene: ClinicalTrials.gov NCT00608764; ECLIPSE: ClinicalTrials.gov NCT00292552.

Humans↗

PubMed2026

Dissecting the genetics of forage quality traits in soft red winter wheat in the U.S. southeast region.

Winter wheat plays a viable role in agriculture, not only as a primary grain crop but also as a valuable forage source that bridges fall-spring forage gaps in many regions, including the southeastern (SE) U.S. Despite its nutritive potential, genetic basis of forage-quality traits remains insufficiently understood, limiting breeding efforts for dual-purpose cultivars. This study aimed to dissect the genetic architecture of forage quality in 182 soft red winter wheat (SRWW) genotypes adapted to the SE U.S. using genome-wide association study (GWAS). Field experiments were carried out in randomized complete block design across three Georgia locations over two growing seasons (2023-2025), with forage sampled at the end of tillering and evaluated using near-infrared reflectance spectroscopy. Significant phenotypic variation was observed for dry matter (DM), crude protein (CP), acid and neutral detergent fiber (ADF, NDF), acid detergent lignin (ADL), total digestible nutrients (TDN), sugars (SUG), and relative forage quality (RFQ). Heritability estimates ranged from low-to-moderate in combined environments and from low-to-high within individual locations. Correlation analysis revealed strong positive associations among fiber-related traits and negative associations with TDN, RFQ, and SUG, while CP declined with increasing fiber. Genome-wide association analysis identified 282 significant marker-trait associations (P < 1×10-4) across 19 chromosomes, which were consolidated into 121 QTLs, including 27 major-effect QTLs. Three QTLs QRfq.uga-3B.1, QRfq.uga-3B.2 (RFQ) and QDm/Sug.uga-7A (DM, SUG) were stable across locations while QAdf/Adl.uga-2A (ADF, ADL) and QDm/Sug.uga-7A (DM, SUG) indicated multi-trait control. Notably, 25 of the 27 major QTLs were putatively novel, highlighting substantial untapped allelic diversity for forage-quality improvement in SE SRWW. Favorable allele accumulation resulted in an overall improvement in forage quality, increasing desirable nutritive traits (DM, RFQ, SUG, CP) while reducing undesirable traits (ADF, ADL). Candidate gene analysis linked six major QTLs with genes implicated in abiotic stress response, plant development, and metabolic regulation, supporting their functional relevance in forage-quality determination. Incorporating these loci into breeding programs provides a robust genetic framework for marker-assisted selection, enabling the development of dual-purpose wheat cultivars with enhanced forage quality, thereby strengthening wheat's utility as a reliable forage resource during periods of seasonal feed scarcity in SE production systems.

GWAS↗