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PubMed2026

Exploring the shared genetic architecture of sarcopenia using genomic structural equation modeling.

Sarcopenia is a common age-associated condition characterized by the progressive loss of skeletal muscle mass, strength, and physical functionality. While large-scale genome-wide association studies (GWAS) have previously addressed isolated traits of sarcopenia, the multifactorial genetic architecture underlying this condition remains largely undefined. To characterize the common genetic basis of sarcopenia-related traits, genomic structural equation modeling (Genomic-SEM) was implemented. Multiple post-GWAS analytic approaches were integrated to pinpoint susceptibility loci. These analyses encompassed identifying enriched genetic pathways and relevant genomic elements, as well as cell-type-specific enrichment in skeletal muscle satellite stem cells, mesenchymal stem cells, and skeletal muscle satellite cells in limb muscle. Furthermore, based on the integrated GWAS data of sarcopenia-related traits, polygenic risk score (PRS) analysis was conducted to evaluate risk associations at the chromosomal level. A well-fitted Genomic-SEM successfully integrated the GWAS data, revealing the shared genetic architecture of sarcopenia-related traits. We identified 110 single nucleotide polymorphisms (SNPs) reaching genome-wide significance (p < 5 × 10-8), of which 9 represent novel discoveries. Subsequent fine-mapping procedures and gene-set analyses identified 15 causal variants alongside 77 candidate susceptibility genes. This study provides a comprehensive genetic characterization of sarcopenia via Genomic-SEM, offering new insights into the etiological pathways underlying sarcopenia.

Sarcopenia↗

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↗

PubMed2024

The Genetic Determinants and Genomic Consequences of Non-Leukemogenic Somatic Point Mutations.

Clonal hematopoiesis (CH) is defined by the expansion of a lineage of genetically identical cells in blood. Genetic lesions that confer a fitness advantage, such as point mutations or mosaic chromosomal alterations (mCAs) in genes associated with hematologic malignancy, are frequent mediators of CH. However, recent analyses of both single cell-derived colonies of hematopoietic cells and population sequencing cohorts have revealed CH frequently occurs in the absence of known driver genetic lesions. To characterize CH without known driver genetic lesions, we used 51,399 deeply sequenced whole genomes from the NHLBI TOPMed sequencing initiative to perform simultaneous germline and somatic mutation analyses among individuals without leukemogenic point mutations (LPM), which we term CH-LPMneg. We quantified CH by estimating the total mutation burden. Because estimating somatic mutation burden without a paired-tissue sample is challenging, we developed a novel statistical method, the Genomic and Epigenomic informed Mutation (GEM) rate, that uses external genomic and epigenomic data sources to distinguish artifactual signals from true somatic mutations. We performed a genome-wide association study of GEM to discover the germline determinants of CH-LPMneg. After fine-mapping and variant-to-gene analyses, we identified seven genes associated with CH-LPMneg (TCL1A, TERT, SMC4, NRIP1, PRDM16, MSRA, SCARB1), and one locus associated with a sex-associated mutation pathway (SRGAP2C). We performed a secondary analysis excluding individuals with mCAs, finding that the genetic architecture was largely unaffected by their inclusion. Functional analyses of SMC4 and NRIP1 implicated altered HSC self-renewal and proliferation as the primary mediator of mutation burden in blood. We then performed comprehensive multi-tissue transcriptomic analyses, finding that the expression levels of 404 genes are associated with GEM. Finally, we performed phenotypic association meta-analyses across four cohorts, finding that GEM is associated with increased white blood cell count and increased risk for incident peripheral artery disease, but is not significantly associated with incident stroke or coronary disease events. Overall, we develop GEM for quantifying mutation burden from WGS without a paired-tissue sample and use GEM to discover the genetic, genomic, and phenotypic correlates of CH-LPMneg.

Journal Article↗

PubMed2026

Genome-wide association study of asthma with high treatment burden and/or worse outcomes defined using electronic healthcare data in UK Biobank.

BACKGROUND: In ∼10% of asthma patients, symptoms remain uncontrolled despite maximal treatment, representing an unmet clinical need. The causal variants, genes and pathways underlying genetic risk factors have not been fully elucidated, and it is unclear whether there are unique genetic risk factors for this asthma subtype. METHODS: We used electronic healthcare records linked to UK Biobank to identify asthma patients with high treatment burden and/or worse outcomes. We performed a genome-wide association study (GWAS) with this case population and healthy controls. We sought replication for associated (p≤5×10-6) signals in four independent studies (12 152 cases and 32 316 controls). Replicated signals were fine-mapped and linked to genes and pathways. RESULTS: In total, 7681 participants met our case definition and showed enrichment for adult-onset asthma, female gender and higher body mass index compared to asthma individuals not meeting case criteria. GWAS with 7681 cases and 38 405 controls revealed 21 reproducible association signals that had previously been associated with asthma, but had a larger effect size in our study. Variant-to-gene mapping highlighted 85 candidate genes, five of which were considered high confidence (BACH2, D2HGDH, IL1RL1, RPS26, SMAD3). CONCLUSION: We present the first use of electronic healthcare records in UK Biobank to identify a subtype of asthma enriched for patients with high treatment burden and/or worse outcomes. Our findings support the role of known asthma genes, highlighting genetic risk variants with stronger effect in these groups of patients. The prioritised genes provide potential therapeutic opportunities for this difficult-to-treat patient population.

Journal Article↗

PubMed2026

CRISPR/Cas9-Mediated Mutagenesis of OsERF94 Enhances Pre-Harvest Sprouting in Rice.

Pre-harvest sprouting (PHS), where seeds germinate on panicles before harvest under humid conditions, is a serious global issue in cereal crop production, including rice. Fine-mapping of the previously reported chromosome 4 locus identified OsERF94 as a strong candidate gene for functional validation. In this study, we investigated the role of OsERF94 in PHS using CRISPR/Cas9 gene editing. The CRISPR/Cas9-mediated mutagenesis of OsERF94 induced frameshift mutations, resulting in a loss-of-function of OsERF94 in the 1-I-ET and 2-D-ET lines. The 1-I-ET and 2-D-ET lines exhibited significantly higher germination rates under PHS conditions compared to the wild type, indicating increased susceptibility to PHS. Whole-genome re-sequencing confirmed that few or no mutations could be detected at off-target candidate sites in both edited lines, ensuring the precision of the CRISPR/Cas9 gene editing. A transcriptome analysis revealed altered expression patterns of several GA-related genes, including OsLOL1, OsKO3, OsGA3ox2, and OsGA2ox5 in the OsERF94 mutant lines. The up-regulation of GA biosynthetic genes and the down-regulation of GA deactivation genes observed in both the OsERF94 mutant lines suggest possible alterations in GA metabolism during the early stages of PHS. Transient luciferase reporter assays using a single-luciferase system suggested that OsERF94 may be associated with changes in the promoter activities of several GA- and ethylene-related genes. These findings suggest that OsERF94 may contribute to the regulation of PHS, potentially through moderation of GA- and ethylene-related pathways. Overall, this study improves our understanding of the molecular role of OsERF94 in PHS and highlights its potential as a target for the genetic improvement of PHS resistance in rice-breeding programs.

OsERF94↗

PubMed2026

Quantitative trait loci mapping of gene expression and chromatin accessibility in primary fibroblasts reveals shared allelic effects between Latin American and European ancestries.

BACKGROUND: Quantitative Trait Locus (QTL) analysis of molecular data has identified genetic variants associated with traits such as gene expression, and colocalization of these functional QTL with GWAS risk loci has offered insights into the genetic basis of human disease. We employed gene expression (RNA-seq) and chromatin accessibility (ATAC-seq) obtained from human primary fibroblasts to investigate quantitative trait loci (QTLs) in cohorts ascertained for bipolar disorder of European (n = 150) and Latin American (n = 96) ancestries. RESULTS: Leveraging data from three countries of origin (The Netherlands, Colombia, Costa Rica) within our cohort, we characterized differences among individuals at the SNP, gene, and accessible-chromatin levels to compute ancestry-specific expression (e)QTLs and chromatin-accessibility (ca)QTLs. Across ancestries, we observed R2 ≥ 0.93 for eQTL effect sizes and R2 ≥ 0.95 for caQTLs, indicating a high degree of concordance. Integrating chromatin data with expression and genotype information enabled precise fine-mapping of eQTLs, yielding 203 genes with high-confidence (posterior probability > 90%) candidate regulatory pathways. In downstream analyses, transcriptome-wide (TWAS) and chromatin-wide (CWAS) association studies with brain- and skin-related GWAS identified 36 TWAS-significant genes and 77 CWAS-significant open chromatin regions. CONCLUSIONS: These findings underscore the shared genetic regulatory mechanisms across European and Latin American ancestries, while demonstrating that ancestry-specific reference panels enhance the accuracy of TWAS and CWAS in diverse populations. More broadly, this study highlights the value of paired multi-omic datasets from diverse cohorts for interpreting disease-associated genetic variation.

Humans↗

PubMed2026

Hypothesis-free evaluation of circulating metabolome provides cell-specific insights regarding the role of energy substrate availability in amyotrophic lateral sclerosis.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with limited therapeutic options. The circulating metabolome comprises small molecules present in plasma/serum which are the intermediates and end-products of cellular metabolism, and is linked to ALS pathogenesis. METHODS: We conducted hypothesis-free two-sample Mendelian randomisation (MR) analysis of the concentration of 575 plasma/serum metabolites, to determine which are causally linked to risk of ALS. Significant metabolites were validated in an independent GWAS of plasma/serum metabolite concentrations and evaluated for sex-specific effects. Correlations between directly measured patient biofluid metabolite concentrations and ALS risk/severity were examined in 94 ALS patients and 40 controls. We experimentally assessed metabolic function in a murine neurons and human astrocytes carrying an ALS-associated G4C2-repeat expansion within C9orf72. RESULTS: MR causally associated five metabolites with ALS risk after multiple-testing correction. Higher serum concentration of glycoprotein acetyls (P = 9.7e - 9, β = 0.21) and the peptide DSGEGDFXAEGGGVR (P = 8.0e - 6, β = 0.22) was associated with increased ALS risk, whereas higher plasma concentration of phenylalanylserine, isobutyrylcarnitine, and acetylcarnitine was protective (P < 5e - 5, β = - 0.29 to - 0.72). DSGEGDFXAEGGGVR has been linked to glucose metabolism but we have used genetic fine-mapping to link DSGEGDFXAEGGGVR, neuronal glucose uptake through GLUT3, and ALS risk. Direct measurement of metabolite concentrations in patient biofluids revealed elevated acetylcarnitine levels in patients with ALS, which were associated with delayed symptom onset (Cox regression, P = 0.02, HR = 0.4). Similarly, lactate is elevated in ALS patient CSF (ANOVA, P = 1.3e - 3) and in patients with longer survival time (Cox regression, P = 0.03, HR = 0.3). Plasma fructose is elevated in ALS patients with shorter survival time (Cox regression, P = 0.02, HR = 1.1). In vitro, neurons and astrocytes carrying an ALS-associated G4C2-repeat expansion within C9orf72 demonstrated reduced metabolic flexibility. CONCLUSIONS: We provide evidence that impaired energy substrate availability contributes to ALS risk and severity. CNS cell types differ in their use of energy substrates and therefore we postulate the relative importance of different cell types for different stages of disease. Our findings support further investigation of metabolic interventions to treat or prevent ALS.

Amyotrophic Lateral Sclerosis↗

PubMed2025

MDA5 variants trade antiviral activity for protection from autoimmune disease.

Loss-of-function variants in MDA5, a key sensor of double-stranded RNA from viruses and retroelements, have been associated with protection from type 1 diabetes (T1D) in genome-wide association studies (GWAS). MDA5 loss-of-function variants have also been reported to increase the risk of inflammatory bowel disease (IBD). Whether these associations are linked or extend to other diseases remains unclear. Here, fine-mapping analysis of four large GWAS datasets shows that T1D-protective loss-of-function MDA5 variants also protect against psoriasis and hypothyroidism, while increasing the risk of IBD. The degree of autoimmune protection and IBD risk were linearly proportional. The magnitudes of the odds ratios for autoimmune protection and IBD risk were larger for rare MDA5 variants than for common variants, which were differentially expressed in different geographic populations. Our analysis suggests MDA5 genetic variants offer a direct fitness trade-off between viral clearance and autoimmune tissue damage.

Interferon-Induced Helicase, IFIH1↗

PubMed2026

Unraveling 'F' factor: towards a genetic-clinical framework for the musculoskeletal-heart crosstalk in metabolic aging.

BACKGROUND: The rising co-occurrence of cardiometabolic diseases and musculoskeletal degeneration poses a critical challenge to healthy aging, yet the shared biological mechanisms underlying this multimorbidity remain poorly defined. This study aimed to establish an integrative clinical-genetic framework to elucidate the common frailty factor, the 'F' factor, that captures the systemic vulnerability linking cardiometabolic multimorbidity (CMM) and musculoskeletal aging. METHODS: Utilizing the prospective China Health and Retirement Longitudinal Study (CHARLS) cohort, we developed and validated novel Frailty-Integrated Indices for CMM risk prediction, evaluated with machine learning models interpreted via SHapley Additive exPlanations (SHAP). Independently, we applied genomic structural equation modeling (Genomic-SEM) to integrate genome-wide association data from six traits-coronary artery disease, type 2 diabetes, hypertension, bone mineral density, frailty, and telomere length-to model a shared latent genetic factor ('F' factor). This was followed by multivariate GWAS, fine-mapping, transcriptome-wide association study (TWAS), gene-based analysis, and functional annotation to prioritize causal genes, pathways, and cell types. RESULTS: Clinically, several Frailty-Integrated Indices significantly improved CMM risk prediction, with the optimal model achieving an AUC of 0.727. Genetically, we modeled a significant shared latent genetic factor ('F' factor), pinpointing novel risk loci and implicating key genes such as APOE and SLC22A3. These genes were enriched in pathways including cellular senescence and cholesterol metabolism and showed specific expression patterns in developmental brain stages and across multi-organ endothelial cells. CONCLUSION: Our findings provide converging evidence for Musculoskeletal‑Heart crosstalk of metabolic aging and inferred the 'F' factor as a genetic correlate of a transdiagnostic state, which links genetic predisposition to metabolic dysregulation, and systemic functional decline. This work provides a multi-level biological characterization of multimorbidity liability, informing early-risk detection and preventive strategies for complex aging-related comorbidities.

Humans↗

PubMed2026

Genome-wide association study of sarcopenia index reveals sex-stratified genetic architecture.

BACKGROUND: The sarcopenia index (SI), defined as the ratio of serum creatinine to cystatin C, is a proposed biomarker of muscle mass and sarcopenia, yet its genomic basis and genetic architecture remain largely unexplored. METHODS: We performed combined-sex and sex-stratified genome-wide association studies of SI in the UK Biobank. We examined the overlap between SI-associated loci and loci previously reported for sarcopenia-related traits. We assessed sexually dimorphic effects and gene-sex interactions, performed fine-mapping, and conducted credible gene prioritization, motif and transcription factor binding enrichment, gene-set enrichment, linkage disequilibrium score regression, and cross-phenotype colocalization. RESULTS: We identified 774 unique independent SI-associated loci across all analyses, with 747 detected in the combined-sex GWAS, 283 in the male-stratified GWAS, and 311 in the female-stratified GWAS; 367 of these loci had not been previously reported for conventional sarcopenia-related traits. Sex-stratified analyses highlighted the rs1145093-chr15q21.1-GATM region, where CARMA identified sex-differentiated causal variants. We prioritized 17 male-biased and 11 female-biased credible genes. Enrichment analyses implicated androgen receptor and GATA4 in males, and ESR1 and MYOD1 in females. Enrichment revealed shared pathways involving inflammation, cellular stress, and aging-related processes. LDSC showed inverse genetic correlations between SI and heart failure (rg = -0.19, p = 2.30 × 10- 9) and metabolic syndrome (rg = -0.12, p = 8.49 × 10- 8), and a positive correlation with chronic kidney disease. Compared with female SI, male SI exhibited two additional loci showing colocalization with four metabolic traits. CONCLUSIONS: These findings clarify the genetic architecture of SI and reveal sex-dependent mechanisms underlying sarcopenia, supporting precision risk assessment and targeted interventions.

Humans↗

PubMed2025

Genetic Determinants of Leisure-Time Physical Activity in the Taiwanese Population: A Genome-Wide Association Study.

BACKGROUND: Physical inactivity contributes to systemic disease burden and premature mortality worldwide. Leisure-time physical activity (LTPA) improves health outcomes; however, its genetic determinants, particularly in Asian populations, remain unclear. This study aimed to identify genetic loci associated with LTPA in the Taiwanese population. METHODS: We conducted genome-wide association studies in 122,258 Taiwan Biobank participants. LTPA was assessed both as a binary trait (regular exerciser vs non-exerciser) and an ordinal trait (categorized by MET-hours per week into low, moderate, and high physical activity levels). Logistic and ordinal logistic regression models were used under an additive genetic model, adjusting for age, age 2 , sex, body mass index, smoking, and the first 10 genetic principal components. Candidate nonsynonymous mutations were further examined in 1494 whole-genome sequenced participants. RESULTS: Binary trait genome-wide association studies identified genome-wide significant (GWS) loci at ATXN2 (12q24.12), FTO (16q12.2), and NOTCH4 (6p21.32), with associations for FTO and NOTCH4 only observed in body mass index (BMI)-adjusted models. Ordinal trait analysis (<10, 10-<20, ≥20 MET·h·wk -1 ) identified a single GWS locus at BRAP (12q24.12). Fine-mapping of 12q24.12 revealed multiple GWS single-nucleotide polymorphisms (SNPs) in strong linkage disequilibrium with lead variants; these signals largely disappeared after conditional analysis, consistent with a single underlying association. Whole-genome sequencing and linkage disequilibrium analysis identified three GWS nonsynonymous mutations, with ALDH2 rs671 emerging as the most likely causal variant. CONCLUSIONS: ATXN2-ALDH2 region on chromosome 12q24.12 was identified as a key locus for LTPA in Taiwanese individuals. These findings enhance our understanding of the genetic basis of physical activity and may inform future precision medicine and public health strategies.

Adult↗

PubMed2025

Genetic mapping in the red mason bee implicates ANTSR as an ancient sex-determining locus in bees and ants.

Haplodiploid inheritance, in which females are diploid and males are haploid, is found in all species of Hymenoptera. Sex in haplodiploids is commonly determined by the alleles present at a complementary sex determination (CSD) locus, with heterozygosity triggering the female developmental pathway. The identity of this locus differs among taxa and is only known in a few species. Here, we map a single CSD locus to a 2 kbp region in the genome of the red mason bee Osmia bicornis. It overlaps the long noncoding RNA ANTSR, which has been identified as the sex-determining gene in the invasive ant Linepithema humile. This locus is homozygous in diploid males and exhibits extremely high levels of haplotype diversity, consistent with the action of frequency-dependent selection. The elevated levels of heterozygosity in the CSD locus enable us to fine-map potentially functional genetic variation within it. We also identify elevated levels of genetic diversity in the ortholog of the CSD locus in five other bee and ant genera, suggesting that it may govern sex determination widely in Hymenoptera. Our data are consistent with the hypothesis that ANTSR evolved a role in sex determination over 150 million years ago and is the ancestral sex-determination locus of bees and ants.

Animals↗

PubMed2025

A novel and robust feature selection method with FDR control for omics-wide association analysis.

Omics-wide association analysis is a very important tool for medicine and human health study. However, the modern omics data sets collected often exhibit the high-dimensionality, unknown distribution response, unknown distribution features and unknown complex association relationships between the response and its explanatory features. Reliable association analysis results depend on an accurate modeling for such data sets. Most of the existing association analysis methods rely on the specific model assumptions and lack effective false discovery rate (FDR) control. To address these limitations, the paper firstly applies a single index model for omics data. The model shows robust performance in allowing the relationships between the response variable and linear combination of covariates to be connected by any unknown monotonic link function, and both the random error and the covariates can follow any unknown distribution. Then based on this model, the paper combines rank-based approach and symmetrized data aggregation approach to develop a novel and robust feature selection method for achieving fine-mapping of risk features while controlling the false positive rate of selection. The theoretical results support the proposed method and the analysis results of simulated data show the new method possesses effective and robust performance for all the scenarios. The new method is also used to analyze the two real datasets and identifies some risk features unreported by the existing finds.

Humans↗

PubMed2026

Integrative cross-tissue transcriptome-wide association and metabolomic analysis reveals novel genetic risk loci for aortic aneurysm.

BACKGROUND: Aortic aneurysm (AA) is a life-threatening cardiovascular condition with a strong genetic component, however, its molecular mechanisms remain poorly understood. Although genome-wide association studies (GWAS) have identified numerous risk loci, most prior studies have investigated genetic and metabolic factors separately, leaving the causal pathways from genetic variants to disease largely unexplored. METHODS: We established an integrative framework combining cross-tissue transcriptome-wide association studies (TWAS) with metabolomic mediation analysis. First, we integrated GWAS data from FinnGen R12 with multi-tissue expression quantitative trait loci (eQTL) data from Genotype-Tissue Expression Project (GTEx) V8, then performed cross-tissue TWAS using the Unified Test for MOlecular SignaTures (UTMOST) and single-tissue validation with the Functional Summary-based Imputation (FUSION) to prioritize susceptibility genes. Second, we applied Mendelian randomization (MR), colocalization, and Fine-mapping Of CaUsal gene Sets (FOCUS) to assess causality and identify high-confidence genes. Third, we performed metabolite mediation analysis to uncover metabolic pathways linking genetic variants to disease risk. Finally, we validated key findings in mouse models of thoracic aortic aneurysm (TAA) and abdominal aortic aneurysm (AAA) using Quantitative Real-Time Reverse Transcription Polymerase Chain Reaction (RT-qPCR) and Western blotting. RESULTS: We identified multiple novel susceptibility genes for AA and its subtypes. Key genes included ADH family members (ADH1A, ADH1B, ADH4, ADH6) and ZNF827, which showed cross-subtype associations with strong colocalization evidence in vascular tissues. Metabolite mediation analysis revealed significant pathways involving N-acetylphenylalanine and methionine sulfoxide. Functional enrichment revealed distinct biological mechanisms: AA and AAA were primarily associated with metabolic pathways, whereas TAA-related genes were enriched in developmental and contractile processes. PheWAS indicated no significant off-target associations. Critically, experimental validation in mouse models confirmed significant upregulation of ZNF827 in TAA and ADH6 in AAA at both mRNA and protein levels, corroborating the genetic predictions. CONCLUSION: This integrated cross-omics analysis identifies novel genetic loci and, crucially, uncovers specific nutrient-related metabolic pathways that mediate genetic risk. These findings provide a mechanistic basis for future nutritional and metabolic intervention studies in AA and its subtypes.

MAGMA↗

PubMed2026

FM-GPT: Bayesian fine mapping for phenome-wide transcriptome-wide association studies.

Transcriptome-wide association studies (TWAS) integrate genome wide association studies with expression quantitative trait locus reference panels to identify genes associated with traits of interest. However, linkage disequilibrium and correlated gene expression can induce spurious TWAS signals, motivating fine mapping methods to prioritize putatively causal genes within associated loci. The rapid growth of large-scale phenomic resources (e.g. electronic health records (EHRs)) has shifted genetic studies from single-trait analyses to phenome-wide investigations that jointly evaluate many closely related phenotypes. We introduce FM-GPT (Fine-mapping of causal Genes for Phenome-wide Transcriptome-wide association studies), a novel Bayesian fine mapping method for prioritizing causal genes across multiple correlated phenotypes with potentially mixed outcome types (e.g., binary, count or continuous) in phenome-wide TWAS. FM-GPT performs gene-guided dimension reduction of the phenotypes and reveals pleiotropic or phenotype-specific effects of the identified genes. In simulations, FM-GPT identified true causal genes more accurately than other fine mapping methods while controlling false positives. We applied FM-GPT to two applications using data from UK Biobank: a brain-wide genetic analysis of MRI data derived regional cortical thickness measures and a phenome-wide genetic analysis of clinical phenotypes derived from EHR data. FM-GPT greatly narrowed down the set size of putatively causal genes and identified: 1. genes with pleiotropic effects on regional cortical thickness across the cerebral cortex, including five genes BCAS3, LRRC37A, NOS2P3, ARL17B and UBB on chromosome 17 regulating neuronal morphology and cortical organization; and 2. genes that influence multiple medical conditions across the circulatory, metabolic, digestive, respiratory and genitourinary systems, revealing two major axes of variation among these conditions that point to a potential trade-off in gene regulation between immune and metabolic functions. These results highlight FM-GPT's power to disentangle complex gene-phenotype relationships in large-scale phenome-wide studies, uncovering shared biological mechanisms across diverse human traits and advancing translational and comorbidity research.

Bayesian fine mapping↗

PubMed2026

Developmental patterning of adipose tissue by abd-A and Abd-B homeotic genes in Drosophila melanogaster.

The Bithorax Complex (BX-C) homeobox proteins specify segmental identities along the anterior-posterior axis during Drosophila embryogenesis. Differential expression of the BX-C genes abd-A and Abd-B distinguishes abdominal from thoracic adipocytes, yet the mechanism regulating this heterogeneity remains poorly understood. Here, we identify cis-regulatory elements (CREs) and transcription factors that direct abdominal-specific expression of abd-A and Abd-B in the larval fat body. Fine-mapping analyses identified a 627-bp CRE within the Abd-B locus and a ~6-kb CRE within the abd-A locus sufficient to drive heterogeneous expression. Yeast one-hybrid screening combined with functional analyses identified Lola, Lolal, and Combgap as key repressors of Abd-B, whereas Piragua (Prg) and Seven up (Svp) function as transcriptional activators, indicating that adipocyte heterogeneity in postembryonic adipose tissue is actively regulated. In turn, lola and prg are repressed by Abd-B, whereas lolal and svp are activated, forming a feedback circuit further modulated by Wnt signaling, which promotes lola and lolal expression while repressing svp. CUT&RUN analyses suggest that these interactions are direct, with dTCF/Pan and Abd-B occupancy detected at target loci. Together, our findings define a transcriptional circuit that regulates Abd-B gene transcription to pattern adipose tissue and may establish the developmental basis of fat depot specialization.

Abd-B↗

PubMed2026

Multimodal atlas of human atherosclerosis links granular vascular cell states to coronary artery disease risk.

Advances in single-cell and spatial assays have revolutionized the scale and resolution of molecular tissue profiling. Here we present MetaPlaq, a multimodal atlas of human atherosclerotic arterial beds comprising over a million cells across single-cell transcriptomics, epigenomics and high-resolution spatial expression assays. We map granular cell states and disease-relevant transcriptional programs within the native tissue context of coronary arteries. Furthermore, we map cardiovascular GWAS signals to smooth muscle cells (SMCs) and endothelial cells (ECs) and uncover the cis-regulatory architecture governing their phenotypic transitions. Our comprehensive epigenomic reference allowed us to build cell-specific enhancer-gene link maps and multimodal gene regulatory networks (GRNs) underlying disease-relevant states such as osteogenic SMCs and ECs undergoing mesenchymal transition. We also integrate SMC and EC disease-associated gene sets with GRNs to nominate key transcription factors such as PRRX1, BNC2 and ELK3 regulating atherosclerosis-relevant transcriptional programs. Finally, we layer single-cell and spatial modalities to fine-map GWAS variants with improved cell and anatomical context. We highlight candidate cell-specific regulatory mechanisms at less characterized CAD loci, including FGD5 and MCF2L in ECs. Together, this atlas represents an important step towards fully interpreting genetic risk loci and informing new therapeutic strategies for cardiovascular disease.

Journal Article↗

PubMed2026

Integrating multi-ancestry common and rare variant mapping accelerates therapeutic target discovery.

Integrating human genetics into therapeutic discovery accelerates drug development. However, ancestral biases in historical cohorts have left critical functional variation largely uncharted. Here, we leverage the diverse NIH All of Us Research Program to conduct comprehensive common- and rare-variant association analyses for 624 quantitative traits across 369,655 ancestrally diverse individuals. We identified 6,181 genome-wide significant locus-trait associations (526 novel) and 416 gene-trait associations (105 novel) via rare-variant burden testing. By integrating fine-mapping with computational variant-effect predictors, we systematically prioritized rare, likely causal variants driving these signals. Jointly modeling common and rare variation with protein-class annotations significantly improved the identification of known drug targets compared to common-variant analysis alone. Notably, we identified NRG4 as a high-confidence candidate therapeutic target for preserving kidney function. Our findings demonstrate that characterization of rare and common variation across diverse populations enhances causal gene discovery and identifies novel, actionable therapeutic targets.

Journal Article↗