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PubMed2026

Network-based stratification of allele-specific expression reveals patient subgroups in Huntington's disease.

MOTIVATION: Huntington's disease (HD) exhibits substantial variability in age of onset and disease progression that is not fully explained by CAG repeat length alone. Part of this residual variation is heritable, implicating additional genetic mechanisms. cis-regulatory variation, genetic variants that alter transcription and splicing of nearby genes, represents one such mechanism that can be quantified through allele-specific expression (ASE) analysis. However, methods for integrating ASE profiles into patient stratification frameworks remain underdeveloped, particularly for rare diseases with small cohorts and sparse data. RESULTS: We adapt a network-based stratification algorithm, originally developed for somatic tumour mutations, to ASE data. By propagating gene-level ASE imbalance profiles through a protein-protein interaction network, we stratified 20 HD patients into three distinct biological patient subgroups. Differential gene expression analysis highlights neuroinflammatory pathways, including microglial activation, immune cell activation, and cytokine regulation, as key sources of inter-patient heterogeneity, while differential ASE analysis implicates proteasomal and ubiquitin-dependent protein catabolic processes, immune activation, and central nervous system development. Intersection of differentially imbalanced and expressed genes identified FAM181B as a candidate gene with potential eQTL-mediated regulation, supported by independent cis-eQTL evidence for rs3780 in the caudate and putamen, the primary HD-affected striatal regions. FAM181B encodes a nuclear protein expressed in neural tissues acting as an interactor of the Hippo pathway TEAD transcription factors, implicating transcriptional regulatory variation as a potential contributor to molecular heterogeneity between patient subgroups. Differences in cortical and striatal neuropathological scores between clusters, even when adjusted for CAG repeat length, provide clinical support for the biological relevance of the identified subgroups. AVAILABILITY: All analysis code, Docker containers, and conda environments are available at https://github.com/macsbio/HD-ASE-NBS.

Huntington Disease↗

PubMed2026

SNPannotator: automated functional annotation of genetic variants and linked proxies.

SUMMARY: Genome-wide association studies (GWASs) have identified thousands of genetic variants associated with complex traits and diseases. However, explaining the mechanisms underlying phenotypic variation remains challenging. Here, we introduce SNPannotator, an automated post-GWAS analysis software package designed to streamline the interpretation of GWAS findings. Our pipeline implements a multi-step process that identifies proxy variants in high linkage disequilibrium (LD) with associated lead variants, then queries comprehensive resources (including Ensembl, the GTEx Portal, the eQTL Catalog, and STRING DB) for genomic position, deleteriousness, regulatory annotations, clinical significance, trait associations, expression (eQTLs) and splicing quantitative trait loci (sQTLs), and functional enrichment analyses and compiles the results into user-friendly reports. This package is implemented in the R programming language and includes auxiliary functions for variant lookup and LD exploration. SNPannotator provides a practical framework for efficiently deriving biologically meaningful insights from GWAS data and for assisting researchers in prioritizing candidate variants for functional validation. AVAILABILITY AND IMPLEMENTATION: The SNPannotator package is available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/SNPannotator. The development version and tutorial is available on GitHub (https://github.com/omicslaboratory/SNPannotator). The online version of the package is available at https://omicslab.org/snpannotator.

Software↗

PubMed2026

Integrative genomic and transcriptomic analysis of hypertension in a Taiwanese population.

OBJECTIVES: Hypertension is highly prevalent in Asian populations and represents a major cardiovascular risk factor. However, most genome-wide association studies (GWASs) and transcriptome-wide association studies (TWASs) have focused primarily on Caucasian cohorts. This study aimed to identify genetic loci and gene expression signatures associated with hypertension in an Asian population. METHODS: We analyzed 10 739 hypertensive patients and 49 668 controls from the Taiwan Biobank, testing 4 512 191 genome-wide single nucleotide polymorphisms (SNPs). Integrated GWAS, TWAS, and expression quantitative trait locus (eQTL) analyses were conducted to characterize genetic risk. Additionally, a polygenic risk score (PRS) was constructed using a split-sample design to evaluate genetic risk stratification. RESULTS: We identified 14 loci significantly associated with hypertension, including a novel locus at 5p13.1. eQTL analysis linked this locus to DAB2 expression in whole blood. TWAS detected 55 hypertension-associated genes, with 20 (36%) overlapping GWAS loci. Several novel genes outside GWAS loci, including FBXL15, KCNIP2, and CRIP3, were highly significant and implicated in vascular biology and hypertension mechanisms. PRS analysis effectively differentiated hypertension risk, with individuals in the top 10% showing a > 3.5-fold increased risk compared to the bottom 10%. CONCLUSIONS: Our findings provide new insights into the genetic and transcriptomic landscape of hypertension in Asians. The identification of novel loci and genes advances understanding of disease biology and may guide precision medicine approaches for risk prediction and therapeutic development.

Female↗

PubMed2025

Expanded Chromatin Accessibility Mapping Explains Genetic Variation Associated with Complex Traits in Liver.

Genome-wide association studies (GWAS) have identified thousands of loci associated with a variety of common, complex human traits. Recent efforts have focused on characterizing chromatin accessibility to discover regulatory elements that modify the expression of nearby genes, suggesting that trait associations are mediated through changes in gene regulation. Genetic variants associated with differences in chromatin accessibility, known as chromatin accessibility quantitative trait loci (caQTLs), are established contributors to gene expression differences, providing mechanistic hypotheses for signals identified by GWAS. Using the assay for transposase-accessible chromatin with sequencing (ATAC-seq), we assessed chromatin accessibility in 189 diverse human liver samples, identifying over two million accessible chromatin regions enriched for gene regulatory features and, in 175 of these samples, over 14,000 caQTLs. Focusing subsequently on liver-relevant complex traits, we obtained publicly available blood lipids GWAS data and identified 157 loci where caQTLs, expression quantitative trait loci (eQTLs), and GWAS signals colocalized. This generated specific molecular hypotheses about regulatory elements, affected genes, and, in some cases, implicated transcription factors. Finally, we enumerated the set of blood lipid trait signals that lack an obvious proposed mechanism beyond catalogs of liver caQTLs and eQTLs. After integrating 10 multi-omic QTL regulatory mechanism datasets whilst considering limitations in statistical power, we found that approximately 20% of blood lipid GWAS signals lacked a statistical link to a proposed mechanism. Our results demonstrate the value of integrating multiple genomic datasets to improve understanding of GWAS signals, while emphasizing the need for additional experimental approaches to fully characterize complex trait associations.

Journal Article↗

PubMed2025

Complex Genetics and Regulatory Drivers of Hypermobile Ehlers-Danlos Syndrome: Insights from Genome-Wide Association Study Meta-analysis.

BACKGROUND: Hypermobile Ehlers-Danlos syndrome (hEDS) is the most common subtype of EDS, a group of heritable connective tissue disorders. Clinically, hEDS is defined by generalized joint hypermobility and chronic musculoskeletal pain, but its impact extends beyond the musculoskeletal system. Affected individuals frequently experience autonomic, gastrointestinal, immune, and neuropsychiatric involvement, highlighting both the multisystemic nature of the condition and challenges of diagnosis. In contrast to other EDS subtypes with defined genetic causes, the molecular basis of hEDS has remained elusive. METHODS: We conducted a genome-wide association study (GWAS) of hEDS across three case controls studies, including 1,815 cases and 5,008 ancestry-matched controls. Fixed-effects meta-analysis of 6.2 million variants was complemented with LDAK gene-based association testing, transcriptome-wide association studies, and integrative annotation across multiple tissues and cell types including eQTLs, enhancer marks and open chromatin accessibility profiles, supported by luciferase assays on one candidate variant. LD-score genetic correlations were assessed between hEDS and 19 frequently reported comorbid conditions. RESULTS: Two loci reached genome-wide significance, including a regulatory region near the atypical chemokine receptor 3 gene (ACKR3) on chromosome 2. Functional annotation supports ACKR3 risk alleles colocalize with eQTLs in tibial nerve, alter enhancer activity, and generate a de novo AHR transcription factor regulatory site, implicating neuroimmune and pain signaling pathways. Gene-based and transcriptome-wide analyses identified common variants in a locus containing multiple candidates, including SLC39A13, a zinc transporter critical for connective tissue development previously implicated in a rare form of EDS, and PSMC3, a gene involved in central nervous system development. LD-score regression revealed significant genetic correlations between hEDS and joint hypermobility, myalgic encephalomyelitis/chronic fatigue syndrome, fibromyalgia, depression, anxiety, autism spectrum disorder, migraine, and gastrointestinal diseases. CONCLUSIONS: These results establish the first evidence of common variant contributions to hEDS, supporting a complex, multisystem model involving neuroimmune-stromal dysregulation. Our findings add novel indications to hEDS pathogenesis and provide solid foundations for future molecular definition and therapeutic discovery.

Genome-wide association study↗

PubMed2026

Integrative Multi-Omics Analysis Identifies Thrombosis-Associated Molecular Features Linked to Germline Susceptibility and Immune Cell Communication in Gastric Cancer.

Emerging evidence indicates that coagulation-related molecular programs are associated with thrombosis, tumor progression, and molecular dysregulation in gastric cancer (GC). However, thrombosis-associated molecular features in GC and their potential links to inherited susceptibility remain insufficiently understood. Integrated analyses of transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were performed to identify thrombosis-associated genes and establish a machine learning-based prognostic signature. Genome-wide association study (GWAS), expression quantitative trait loci (eQTL), transcriptome-wide association study (TWAS), and Mendelian randomization (MR) analyses were conducted to investigate susceptibility-associated transcriptional programs in GC. Functional assays were used to evaluate candidate genes associated with malignant phenotypes. Single-cell RNA sequencing (scRNA-seq) and cell-cell communication analyses were further performed to characterize cell-type-specific expression patterns and potential intercellular interactions. A total of 22 differentially expressed thrombosis-associated genes were identified, and a prognostic signature comprising 14 genes was established. The signature stratified patients into high- and low-risk groups and showed prognostic performance in both the training and validation cohorts. Integrative GWAS, eQTL, and TWAS analyses identified susceptibility-associated transcriptional programs that were positively correlated with the thrombosis-associated risk score. Silencing ACTN2 and CRYAB significantly reduced GC cell migration and invasion. scRNA-seq analysis revealed relatively high CRYAB expression in neutrophils, and CellChat analysis suggested potential neutrophil-B cell interactions involving COLLAGEN-related signaling. This integrative multi-omics study identified a thrombosis-associated molecular signature linked to prognosis and germline susceptibility-associated transcriptional programs in GC. ACTN2 and CRYAB may represent candidate genes associated with GC cell migration and invasion, while single-cell analysis suggested potential immune-related communication features.

Humans↗

PubMed2006

Genetical genomics in livestock: potentials and pitfalls.

Genetical genomics combines gene mapping and gene expression approaches to identify loci controlling gene expression (eQTLs) that may underlie functional trait variation. The combination of genomic tools has great potential to facilitate dissection of complex traits, but studies need careful design and interpretation. Here we explore both the potential and the pitfalls of this approach with illustrations from actual studies. There are now an appreciable number of studies in model species and even humans demonstrating the feasibility of genetical genomics. However, most studies are too limited in size and design to unlock the full potential of the approach. Limited statistical power of studies exacerbates the problem of detection of false-positive eQTL and some reported results should be interpreted with caution. As one approach to more successful implementation of genetical genomics, we propose to combine expression studies with fine mapping of functional trait loci. This synergistic approach facilitates the implementation of genetical genomics for species without inbred resources but is equally applicable to model species. These properties make it particularly suitable for livestock populations where many QTL are already in the public domain and potentially very large pedigreed populations can be accessed.

Animals↗

PubMed2006

Elucidating the murine brain transcriptional network in a segregating mouse population to identify core functional modules for obesity and diabetes.

Complex biological systems are best modeled as highly modular, fluid systems exhibiting a plasticity that allows them to adapt to a vast array of changing conditions. Here we highlight several novel network-based approaches to elucidate genetic networks underlying complex traits. These integrative genomic approaches combine large-scale genotypic and gene expression results in segregating mouse populations to reconstruct reliable genetic networks underlying complex traits such as disease or drug response. We apply these novel approaches to one of the most extensive surveys of gene expression studies ever undertaken in whole brain in a segregating mouse population. More than 23,000 genes were monitored in whole brain samples from more than 300 mice derived from an F2 intercross population and genotyped at over 1200 SNP markers uniformly spread over the entire genome. We explore the topological properties of the brain transcriptional network and highlight different approaches to inferring causal associations among genes by integrating genotypic and expression data. We demonstrate the utility of these approaches by identifying and experimentally validating brain gene expression traits predicted to respond to a strong expression quantitative trait locus (eQTL) for the pituitary tumor-transforming 1 gene (Pttg1) that coincides with the physical location of this gene (a cis eQTL). We identify core functional modules making up the brain transcriptional network in mice that are coherent for core biological processes associated with metabolic disease traits including obesity and diabetes.

Animals↗

PubMed2006

Review of microarray experimental design strategies for genetical genomics studies.

Genetical genomics approaches provide a powerful tool for studying the genetic mechanisms governing variation in complex traits. By combining information on phenotypic traits, pedigree structure, molecular markers, and gene expression, such studies can be used for estimating heritability of mRNA transcript abundances, for mapping expression quantitative trait loci (eQTL), and for inferring regulatory gene networks. Microarray experiments, however, can be extremely costly and time consuming, which may limit sample sizes and statistical power. Thus it is crucial to optimize experimental designs by carefully choosing the subjects to be assayed, within a selective profiling approach, and by cautiously controlling systematic factors affecting the system. Also, a rigorous strategy should be used for allocating mRNA samples across assay batches, slides, and dye labeling, so that effects of interest are not confounded with nuisance factors. In this presentation, we review some selective profiling strategies for genetical genomics studies, including the selection of individuals for increased genetic dissimilarity and for a higher number of recombination events. Efficient designs for studying epistasis are also discussed, as well as experiments for inferring heritability of transcriptional levels. It is shown that solving an optimal design problem generally requires a numerical implementation and that the optimality criteria should be intimately related to the goals of the experiment, such as the estimation of additive, dominance, and interacting effects, localizing putative eQTL, or inferring genetic and environmental variance components associated with transcriptional abundances.

Animals↗

PubMed2006

Population and systems genetics analyses of cortisol in pigs divergently selected for stress.

This study presents a systems genetic analysis on the physiology of cortisol in mice and pigs with an aim to show the potential of a comprehensive computational approach to quickly identify candidate genes and avoid a costly whole-genome quantitative trait locus (QTL) mapping. Population genetics analyses were performed on measurements of cortisol from a pig selection experiment. Expression QTL were mapped and gene networks were built using gene expressions for Crhr1 (corticotrophin-releasing hormone receptor) gene and single nucleotide polymorphisms from public mouse data. Results from mouse data were used to infer potential candidate regulatory genes involved in pig cortisol regulation, using a comparative or translational systems genetics approach. The pig data used were from a 10-yr divergent genetic selection experiment, providing data on 417 individuals. Population genetics analysis showed that cortisol is highly genetically determined with heritabilities of 0.40-0.70. Furthermore, a major gene with an additive effect of 86 ng/ml is segregating. Genetical-genomics investigations revealed two trans-acting eQTL for Crhr1 gene expression on chromosomes 2 and 13. Candidate gene search under trans-eQTL peaks yielded 63 genes for Crhr1 expression phenotypes. Functional links for Crhr1 genes with other genes/proteins in the gene network using mouse data were shown for the first 10 statistically significant genes involved. Results show translational or comparative systems genetics approaches reduce costs and time in large-scale genetics and "-omics" investigations. This is the first study to report a strong genetic basis for cortisol physiology using a systems approach.

Animals↗

PubMed2006

Cis-regulatory variations: a study of SNPs around genes showing cis-linkage in segregating mouse populations.

BACKGROUND: Changes in gene expression are known to be responsible for phenotypic variation and susceptibility to diseases. Identification and annotation of the genomic sequence variants that cause gene expression changes is therefore likely to lead to a better understanding of the cause of disease at the molecular level. In this study we investigate the pattern of single nucleotide polymorphisms (SNPs) in genes for which the mRNA levels show cis-genetic linkage (gene expression quantitative trait loci mapping in cis, or cis-eQTLs) in segregating mouse populations. Such genes are expected to have polymorphisms near their physical location (cis-variations) that affect their mRNA levels by altering one or more of the cis-regulatory elements. This led us to characterize the SNPs in promoter (5 Kb upstream) and non-coding gene regions (introns and 5 Kb downstream) (cis-SNPs) and the effects they may have on putative transcription factor binding sites. RESULTS: We demonstrate that the cis-eQTL genes (CEGs) have a significantly higher frequency of cis-SNPs compared to non-CEGs (when both sets are taken from the non-IBD regions, i.e. regions not identical by descent). Most CEGs having cis-SNPs do not contain these SNPs in the phylogenetically conserved regions. In those CEGs that contain cis-SNPs in the phylogenetically conserved regions, enrichment of cis-SNPs occurs both within and outside of the conserved sequences. A higher fraction of CEGs are also seen to harbor cis-SNP that affect predicted transcription factor binding sites, a likely consequence of the higher cis-SNPs density in these genes. CONCLUSION: This present study provides the first genome-wide investigation of the putative cis-regulatory variations in a large set of genes whose levels of expression give rise to cis-linkage in segregating mammalian populations. Our results provide insights into the challenges that exist in identifying polymorphisms regulating gene expression using bioinformatic sequence analysis approaches. The data provided herein should benefit future investigations in this area.

Adipose Tissue↗

PubMed2026

Genetic variants in IGF2BP family genes are associated with glioma risk in Chinese children.

BACKGROUND: Glioma is a highly prevalent malignant tumor of the central nervous system in children and is driven by complex genetic and environmental factors. IGF2BP family genes (IGF2BP1, IGF2BP2, and IGF2BP3) encode critical RNA epigenetic "readers" that participate in posttranscriptional gene regulation and modulate various cellular processes. However, the contributions of these gene variants to glioma risk remain unclear. METHODS: A multicenter case-control study was conducted, enrolling 360 patients with glioma and 547 cancer-free controls. Genotyping of 11 potentially functional polymorphisms within IGF2BP family genes was performed using the TaqMan assay. Unconditional logistic regression models were employed to estimate odds ratios and 95% confidence intervals. Furthermore, expression quantitative trait loci (eQTL) and The Cancer Genome Atlas (TCGA) clinical database analyses were conducted to investigate the potential regulatory mechanisms and clinical significance of the identified variants. RESULTS: We found that the IGF2BP1 rs2270575 polymorphism was significantly associated with a decreased risk of glioma. Conversely, the IGF2BP2 rs17289925 and rs7646419 polymorphisms were linked to increased glioma risk. Stratification and cumulative effect analyses revealed that harboring multiple risk genotypes of IGF2BP1 or IGF2BP2 substantially increased glioma susceptibility. This cumulative risk was especially notable among males, younger children (< 60 months), and patients diagnosed with early-stage (I + II) tumors. Additionally, eQTL and TCGA analyses revealed that the rs2270575 and rs7646419 alleles correlated significantly with altered mRNA expression levels of CALCOCO2 and AC099661.1 (ENSG00000286086), respectively, which further correlated with favorable molecular subtypes (IDH mutation status) and WHO tumor grades. CONCLUSION: Genetic polymorphisms within IGF2BP family genes significantly modulate pediatric glioma susceptibility and have cumulative, subtype-specific, and age-dependent effects; thus, these genes may serve as promising noninvasive biomarkers for early risk stratification of childhood glioma.

Adolescent↗

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

The tissue-specific effects of glucose-lowering drug targets on aging mediated through DNA methylation: a multi-omics genetic study.

BACKGROUND: DNA methylation plays a key role in mediating the anti-aging effects of glucose-lowering drugs. This study aims to systematically explore the potential anti-aging effects of target genes of FDA-approved glucose-lowering drugs and the underlying epigenetic mediators. METHODS: We conducted a two-sample Mendelian randomization (MR) study to investigate the putative causal relationships between the gene expression levels of glucose-lowering drug targets and 10 aging-related phenotypes, followed by a two-step MR to estimate the mediation effect of DNA methylation. Drug candidates were selected according to the latest review of clinical drug use for type 2 diabetes, and their target genes were obtained from the DGIdb. Tissue-specific cis-expression quantitative trait loci (eQTLs) from GTEx Consortium were selected as genetic instruments to proxy the expression level of drug-target genes. Glycemic phenotypes were used as positive controls to validate the instruments. The cis- and trans-methylation QTLs of Cytosine-phosphate-Guanine sites near the drug target genes were obtained from GoDMC Consortium. Additionally, we performed enrichment analyses focused on tissue specificity and aging pathways to further corroborate our findings. RESULTS: We obtained 194 target genes interacting with 36 FDA-approved anti-diabetic drugs, of which the tissue-specific eQTLs were used to proxy the drug target effects. MR showed strong evidence that nine interacting genes of six glucose-lowering drugs showed anti-aging potential on one or more aging-related phenotypes mediated by DNA methylation: EHMT2, HSPA4, IGF2BP2, IRS1, LPL, NDUFAF1, NDUFS3, SLC22A3, and TCF7L2. These genes were distributed in 17 tissues, especially in the central nervous system, suggesting a potential neural component in their anti-aging effects. For instance, expression of EHMT2 in several brain basal ganglia regions, where the gene interacted with Tolazamide, showed a protective effect on frailty (odds ratio (OR) in caudate = 1.02, 95%CI = 1.01-1.04, FDR adjusted P = 1.69 × 10-2; OR in putamen = 1.02, 95% CI = 1.01-1.03, PFDR = 3.37 × 10-2, OR in nucleus accumbens = 1.02, 95% CI = 1.01-1.04, PFDR = 3.37 × 10-2). These associations were externally validated by searching literature evidence in existing EWAS and TWAS studies, as well as evidence from enrichment analyses. CONCLUSIONS: This study prioritizes nine glucose-lowering genes as anti-aging drug targets in specific tissues and prioritizes their epigenetic regulation through DNA methylation for future drug development.

DNA Methylation↗

PubMed2025

Possible linking and treatment between Parkinson's disease and inflammatory bowel disease: a study of Mendelian randomization based on gut-brain axis.

BACKGROUND: Mounting evidence suggests that Parkinson's disease (PD) and inflammatory bowel disease (IBD) are closely associated and becoming global health burdens. However, the causal relationships and common pathogeneses between them are uncertain. Furthermore, they are uncurable. Thus, we aimed to identify the causal relationships and novel therapeutic targets shared between them based on their common pathophysiological mechanisms in gut-brain-axis (GBA). METHODS: A meta-analysis on bidirectional Mendelian randomization (MR) utilizing various datasets was performed to estimate their causal relationship. Then, pleiotropic analysis under the composite null hypothesis (PLACO) with functional mapping combined with annotation of genetic associations (FUMA) analysis were conducted to identify pleiotropic genes. Next, blood, brain and intestine expression quantitative trait locus (eQTL) were taken to perform drug-target MR finding common causal genes in two diseases. Colocalization analysis ensured the eQTLs of corresponding gene colocalized with disease. Enrichment analysis and protein‒protein interaction (PPI) network were done to explore common pathogenesis pathways. Genes passed all analysis were regarded as drug targets. RESULTS: Our MR meta-analysis revealed the bidirectional causal relationship between diseases, with combined ORs for PD on IBD, CD, UC (1.050 [95% CI 1.014-1.086], 1.044 [95% CI 0.995-1.095], 1.063 [95% CI 1.016-1.120]); for IBD, CD, UC on PD (1.003 [95% CI 0.973-1.034], 1.035 [95% CI 1.004-1.067], 1.008 [95% CI 0.977-1.040]). Overall, 277, 216 and 201 genes were identified as pleiotropic genes between PD and IBD, CD, UC. Total of 733 genes were classified as tier 3 (found in only one tissue) druggable targets, 57 as tier 2 (found in two tissues, 51 protein-coding genes) and 9 as tier 3 (found in three tissues). Among 60 protein-coding druggable targets over tier 2, 18 overlapped with pleiotropic genes and enriched in mitochondria, antigen presentation, processing and immune cell regulation pathways. Three druggable genes (LRRK2, RAB29 and HLA-DQA2) passed colocalization analysis. LRRK2 and RAB29 were reported to be pleiotropic genes, and RAB29 and HLA-DQA2 were reported for the first time as potential drug targets. CONCLUSIONS: This study established a reliable causal relationship, possible shared drug targets and common pathogenesis pathways of two diseases, which had important implications for intervention and treatment of two diseases simultaneously.

Humans↗

PubMed2026

Programmed cell death and risk of diabetic retinopathy: a Mendelian randomization study.

BACKGROUND: Programmed cell death (PCD) plays an important role in diabetic retinopathy (DR); however, the underlying genetic mechanisms remain unclear. We used Mendelian randomization (MR) to investigate the causal relationships between PCD-related genes and DR. This study aimed to investigate the effects of PCD on the risk of DR by conducting MR analysis. METHODS: Summary statistics from gene expression quantitative trait loci (eQTL) studies (31,684 Europeans) were analyzed. Genetic instrumental variables were selected using cis-eQTL single-nucleotide polymorphisms (SNPs; P < 5 × 10- 8). Summary data-based MR (SMR) was employed to assess causal associations between PCD-related genes and DR, with three additional MR methods used for sensitivity testing. Bayesian colocalization was used to examine the shared regulatory mechanisms between PCD QTLs and DR risk loci. RESULTS: Sensitivity and colocalization analyses revealed six genes that affected DR: cathepsin H (CTSH), NAD(P)H: quinone oxidoreductase 1 (NQO1), tribbles pseudokinase 3 (TRIB3), and phosphoglycerate mutase 5 (PGAM5), which increased DR risk, and iron-responsive element binding protein 2 (IREB2) and tumor necrosis factor (TNF), which exhibited protective effects. Multivariate MR confirmed significant causal effects for CTSH, IREB2, and PGAM5 (p < 0.050). Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis (including 10 STRING-derived genes) revealed that 13 genes were enriched in necroptosis, apoptosis, mitophagy, and TNF signaling pathways in DR. CONCLUSIONS: This MR study supports the causal involvement of PCD in DR and identifies candidate genes (CTSH, IREB2, PGAM5, NQO1, TRIB3, and TNF) for therapeutic targeting or biomarker development in DR prevention or diagnosis.

Humans↗

PubMed2025

Prioritization of causal genes from genome-wide association studies by Bayesian data integration across loci.

MOTIVATION: Genome-wide association studies (GWAS) have identified genetic variants, usually single-nucleotide polymorphisms (SNPs), associated with human traits, including disease and disease risk. These variants (or causal variants in linkage disequilibrium with them) usually affect the regulation or function of a nearby gene. A GWAS locus can span many genes, however, and prioritizing which gene or genes in a locus are most likely to be causal remains a challenge. Better prioritization and prediction of causal genes could reveal disease mechanisms and suggest interventions. RESULTS: We describe a new Bayesian method, termed SigNet for significance networks, that combines information both within and across loci to identify the most likely causal gene at each locus. The SigNet method builds on existing methods that focus on individual loci with evidence from gene distance and expression quantitative trait loci (eQTL) by sharing information across loci using protein-protein and gene regulatory interaction network data. In an application to cardiac electrophysiology with 226 GWAS loci, only 46 (20%) have within-locus evidence from Mendelian genes, protein-coding changes, or colocalization with eQTL signals. At the remaining 180 loci lacking functional information, SigNet selects 56 genes other than the minimum distance gene, equal to 31% of the information-poor loci and 25% of the GWAS loci overall. Assessment by pathway enrichment demonstrates improved performance by SigNet. Review of individual loci shows literature evidence for genes selected by SigNet, including PMP22 as a novel causal gene candidate.

Genome-Wide Association Study↗

PubMed2025

A spectral framework to map QTLs affecting joint differential networks of gene co-expression.

Studying the mechanisms underlying the genotype-phenotype association is crucial in genetics. Gene expression studies have deepened our understanding of the genotype  →  expression  →  phenotype mechanisms. However, traditional expression quantitative trait loci (eQTL) methods often overlook the critical role of gene co-expression networks in translating genotype into phenotype. This gap highlights the need for more powerful statistical methods to analyze genotype  →  network  →  phenotype mechanism. Here, we develop a network-based method, called spectral network quantitative trait loci analysis (snQTL), to map quantitative trait loci affecting gene co-expression networks. Our approach tests the association between genotypes and joint differential networks of gene co-expression via a tensor-based spectral statistics, thereby overcoming the ubiquitous multiple testing challenges in existing methods. We demonstrate the effectiveness of snQTL in the analysis of three-spined stickleback (Gasterosteus aculeatus) data. Compared to conventional methods, our method snQTL uncovers chromosomal regions affecting gene co-expression networks, including one strong candidate gene that would have been missed by traditional eQTL analyses. Our framework suggests the limitation of current approaches and offers a powerful network-based tool for functional loci discoveries.

Quantitative Trait Loci↗