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PubMed2026

Cross-tissue multi-omics integration highlights BPHL and mitochondrial targets in Alzheimer's disease.

BACKGROUND: Mitochondrial dysfunction is a hallmark of Alzheimer's disease (AD), yet specific molecular targets remain to be fully characterized. METHODS: A summary-data-based Mendelian randomization (SMR) framework integrated AD genome-wide association study (GWAS) statistics (39,918 cases) with blood DNA methylation quantitative trait loci (mQTL), gene expression (eQTL), and protein (pQTL) data for 1136 mitochondria-related genes. Associations were assessed using Bayesian colocalization and HEIDI testing. Tissue relevance was evaluated in four brain regions (hippocampus, amygdala, cortex, frontal cortex) using GTEx and external transcriptomic datasets. RESULTS: Screening identified eight candidates supported across blood mQTL and eQTL layers. Stepwise central nervous system (CNS) evaluation singled out biphenyl hydrolase-like (BPHL) as the consistent candidate. Higher genetically predicted BPHL expression was associated with reduced AD risk across the hippocampus (OR=0.920, 95% CI 0.873-0.970), amygdala (OR=0.925, 95%CI 0.880-0.973), cortex (OR=0.943, 95% CI 0.908-0.978), and frontal cortex (OR=0.938, 95%CI 0.901-0.976). These findings aligned with protein-protein interactions connecting BPHL to respiratory complexes and lower BPHL expression in independent AD brains. Functional enrichment converged on oxidative phosphorylation pathways. CONCLUSIONS: By integrating multi-omics data with tissue-specific validation, this study nominates BPHL as a consistent protective candidate in the brain. These findings provide genetic support for mitochondrial molecular perturbations in AD, offering insights for future validation.

Alzheimer Disease↗

PubMed2026

Endocrine-disrupting chemical-induced gene networks confer coronary heart disease risk revealed by causal inference and single-cell analyses.

BACKGROUND: Endocrine-disrupting chemicals (EDCs) are linked to coronary heart disease (CHD), but underlying mechanisms remain unclear. We aimed to identify EDC-related genes and evaluate their causal roles in CHD. METHODS: We curated EDC-related genes from a compound-gene interaction database and integrated them with CHD genome-wide association study (GWAS) summary statistics and tissue-specific expression quantitative trait loci (eQTL) data. Two-sample Mendelian randomization (MR) and Bayesian colocalization were applied to infer causality. Functional enrichment, single-cell RNA sequencing of human coronary arteries, and EDC-gene networks were further analyzed. RESULTS: After FDR correction, 39 genes were significantly associated with CHD risk via MR. Four genes-ZNF827, FCHO1, IPO9 (protective), and RPL13 (risk-increasing)-showed strong colocalization (PPH4 > 0.9). Pathway and single-cell analyses of coronary artery tissue indicated that vascular and immune pathways mediate these effects. An interaction network highlighted associations between specific EDCs and candidate genes implicated in CHD susceptibility. CONCLUSION: This integrative genomic study provides evidence that EDCs influence CHD susceptibility through distinct gene networks, revealing potential mechanisms and molecular targets for prevention and therapy.

Humans↗

PubMed2005

Methodological aspects of the genetic dissection of gene expression.

MOTIVATION: Dissection of the genetics underlying gene expression utilizes techniques from microarray analyses as well as quantitative trait loci (QTL) mapping. Available QLT mapping methods are not tailored for the highly automated analyses required to deal with the thousand of gene transcripts encountered in the mapping of QTL affecting gene expression (sometimes referred to as eQTL). This report focuses on the adaptation of QTL mapping methodology to perform automated mapping of QTL affecting gene expression. RESULTS: The analyses of expression data on > 12,000 gene transcripts in BXD recombinant inbred mice found, on average, 629 QTL exceeding the genome-wide 5% threshold. Using additional information on trait repeatabilities and QTL location, 168 of these were classified as 'high confidence' QTL. Current sample sizes of genetical genomics studies make it possible to detect a reasonable number of QTL using simple genetic models, but considerably larger studies are needed to evaluate more complex genetic models. After extensive analyses of real data and additional simulated data (altogether > 300,000 genome scans) we make the following recommendations for detection of QTL for gene expression: (1) For populations with an unbalanced number of replicates on each genotype, weighted least squares should be preferred above ordinary least squares. Weights can be based on repeatability of the trait and the number of replicates. (2) A genome scan based on multiple marker information but analysing only at marker locations is a good approximation to a full interval mapping procedure. (3) Significance testing should be based on empirical genome-wide significance thresholds that are derived for each trait separately. (4) The significant QTL can be separated into high and low confidence QTL using a false discovery rate that incorporates prior information such as transcript repeatabilities and co-localization of gene-transcripts and QTL. (5) Including observations on the founder lines in the QTL analysis should be avoided as it inflates the test statistic and increases the Type I error. (6) To increase the computational efficiency of the study, use of parallel computing is advised. These recommendations are summarized in a possible strategy for mapping of QTL in a least squares framework. AVAILABILITY: The software used for this study is available on request from the authors.

Algorithms↗

PubMed2026

Genetic and epigenetic underpinnings of biological aging: a multi-omics study integrating Mendelian randomization, spatial transcriptomics, and drug target discovery.

Inflammaging represents a hallmark of biological aging, yet the causal inflammatory mediators driving multi-dimensional epigenetic aging and their effector genes remain poorly characterized at the genetic level. We developed a four-tier analytical framework integrating causal screening, multi-omics effector gene mapping, spatial transcriptomics, and drug target evaluation. Two-sample Mendelian randomization (MR) of 91 circulating inflammatory proteins against six aging phenotypes identified IL-12B, IFNG, and IL-2 as the most robust pro-aging mediators with consistent effects across independent outcomes. Using multi-omics summary-based MR (SMR) as the core analytical engine, we integrated four-layer whole-blood molecular QTL resources eQTL (eQTLGen, n = 31,684), sQTL (GTEx, n = 755), pQTL (INTERVAL + SCALLOP, n = 34,232), and mQTL (McRae et al., n = 1,980) - with GWAS summary statistics for four epigenetic age acceleration measures. At a stringent threshold (P_SMR < 1×10⁻¹²), seven high-confidence effector genes were identified: NHLRC1, TPMT, SELP, and RIPPLY3 for IEAA; ZNF373A and PLDN for HannumAA; and EDARADD for PhenoAA. The chromosome 6p21 NHLRC1-TPMT locus, overwhelmingly driven by methylation QTL signals (-log₁₀P = 26.06), emerged as the dominant genetic node of epigenetic aging. Spatial projection via gsMap onto a mouse E16.5 embryo atlas (121,767 cells) revealed preferential enrichment in smooth muscle and lung, with EDARADD showing marked specificity in mucosal epithelium. Cross-database drug target mining classified TPMT and SELP as repurposable known targets and NHLRC1 as a high-priority novel druggable candidate. This study provides multi-omics convergent causal evidence for inflammation-driven epigenetic aging and delivers genetically anchored targets for precision anti-aging intervention.

Aging↗

PubMed2006

Statistical methods for expression quantitative trait loci (eQTL) mapping.

Traditional genetic mapping has largely focused on the identification of loci affecting one, or at most a few, complex traits. Microarrays allow for measurement of thousands of gene expression abundances, themselves complex traits, and a number of recent investigations have considered these measurements as phenotypes in mapping studies. Combining traditional quantitative trait loci (QTL) mapping methods with microarray data is a powerful approach with demonstrated utility in a number of recent biological investigations. These expression quantitative trait loci (eQTL) studies are similar to traditional QTL studies, as a main goal is to identify the genomic locations to which the expression traits are linked. However, eQTL studies probe thousands of expression transcripts; and as a result, standard multi-trait QTL mapping methods, designed to handle at most tens of traits, do not directly apply. One possible approach is to use single-trait QTL mapping methods to analyze each transcript separately. This leads to an increased number of false discoveries, as corrections for multiple tests across transcripts are not made. Similarly, the repeated application, at each marker, of methods for identifying differentially expressed transcripts suffers from multiple tests across markers. Here, we demonstrate the deficiencies of these approaches and propose a mixture over markers (MOM) model that shares information across both markers and transcripts. The utility of all methods is evaluated using simulated data as well as data from an F(2) mouse cross in a study of diabetes. Results from simulation studies indicate that the MOM model is best at controlling false discoveries, without sacrificing power. The MOM model is also the only one capable of finding two genome regions previously shown to be involved in diabetes.

Animals↗

PubMed2026

Genetic evidence for a causal relationship between melatonin metabolism and depression.

To investigate the causal relevance of melatonin metabolism, which provides the biological basis for circulating melatonin levels, to specific depression symptom subtypes, we performed a targeted systematic review of melatonin metabolism pathways in the human brain and liver. Using two-sample Mendelian randomization (MR), we assessed the causal effects of metabolism pathways and/or individual genes on major depressive disorder (MDD) and nine symptom subtypes derived from Patient Health Questionnaire-9 (PHQ-9). Instrumental variables (IVs) were expression quantitative trait loci (eQTL) for eight individual genes, one synthesis route, and three degradation routes. Results were assessed using Bayesian colocalization and phenome-wide association analyses. At the pathway-level, the genetically proxied synthesis-route signal was associated with PHQ-9 Assessment 5 (PHQ9A5, OR: 0.89, 95% CI: 0.85-0.93), but sensitivity analyses suggested this association was primarily driven by TPH1 and may reflect serotonin-related biology. In contrast, higher brain melatonin degradation raised the risk of both PHQ9A1 (OR: 1.03, 95% CI: 1.02-1.04) and PHQ9A7 (OR: 1.03, 95% CI: 1.02-1.03). Within degradation, up-regulation of the kynurenine sub-pathway increased the odds of PHQ9A3 (OR: 1.05, 95% CI: 1.02-1.07), PHQ9A4 (OR = 1.04, 95% CI: 1.02-1.06) and PHQ9A7 (OR: 1.05, 95% CI: 1.02-1.07). Gene-level analyses were largely concordant, except for SULT1A1, whose higher expression was genetically protective for PHQ9A3 but risk-increased for PHQ9A1 and PHQ9A4. Overall, these results demonstrate that melatonin metabolism exerts symptom-specific and pathway-specific causal effects on depression. A stratified view of melatonin's role may help optimize the application of exogenous melatonin supplementation.

Melatonin↗

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↗

PubMed2026

MIF Promoter Variant rs755622 (-173G/C) in Younger and Older Turkish Adults: An Exploratory Cross-Sectional Genetic and in Silico Analysis.

Age-associated immune-inflammatory remodeling may be influenced by regulatory variation in the macrophage migration inhibitory factor gene (MIF). We conducted an exploratory cross-sectional comparison to assess whether MIF rs755622 (-173G/C) genotype distributions differ between predefined younger and older age groups in a Turkish population and to characterize the observed pattern using genetic-model and in silico analyses. We evaluated 368 individuals: 245 older adults aged 65-102 years and 123 younger controls aged 20-46 years. None of the 26 main association tests remained statistically significant after global multiplicity correction (minimum FDR q = 0.062; minimum Bonferroni-adjusted p = 0.108). Before correction, GC frequency was higher in the older group and increased across the ordered age categories, and sex-adjusted analyses yielded concordant nominal estimates. However, these nominal patterns were sensitive to younger-control genotype reclassification and were not supported by an allele-level or additive association. Younger controls showed Hardy-Weinberg disequilibrium (p < 0.001), without sequencing confirmation, and deterministic and scenario-based Monte Carlo genotype-reclassification analyses indicated sensitivity of the nominal signal to uncertainty in control genotype classification. GTEx data provide C-allele-oriented expression context but do not validate function in this cohort. These preliminary findings warrant independent genotype verification, ancestry-matched replication, and direct functional investigation in future studies.

Humans↗

PubMed2026

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

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

Humans↗

PubMed2026

Mapping the immune-genetic architecture of Epstein-Barr virus-related phenotypes and multiple sclerosis through a single-cell genetic framework for target prioritization and pharmacologic hypothesis generation.

BACKGROUND: Multiple sclerosis (MS) is a severe neuroinflammatory disease causing substantial long-term disability. Strong epidemiologic evidence links Epstein-Barr virus (EBV) exposure with MS risk, but genetic evidence for immune target prioritization in EBV-related phenotypes remains limited. METHODS: We integrated single-cell cis-eQTL data from 14 immune cell types with GWASs of an EBV-related clinical phenotype and MS using a single-cell Mendelian randomization framework with colocalization analyses. Candidate eGenes were evaluated in independent cohorts. For multi-SNP instruments, we performed heterogeneity, pleiotropy, MR-Egger, weighted median, mode-based, and MR-PRESSO sensitivity analyses. We also conducted phenome-wide association analyses and queried DrugBank to annotate candidate compounds targeting prioritized genes. RESULTS: We prioritized 43 immune-cell-specific candidate eGenes with convergent genetic support, including 6 for the EBV-related phenotype and 37 for MS. SERPINB1 in NK cells was associated with increased risk of the EBV-related phenotype, whereas HLA-G was associated with decreased risk. For MS, APOM and MSH5 showed protective associations, while AHI1 showed cell-type-dependent, bidirectional associations across immune lineages. Colocalization and independent cohort evaluation supported these findings. Among FDR-significant multi-SNP associations, MR-Egger intercept tests did not indicate directional pleiotropy, although a small subset showed heterogeneity or MR-PRESSO signals. Phenome-wide analyses identified no significant adverse phenotypic associations among evaluable genes at the prespecified threshold. DrugBank annotation nominated sodium nitroprusside, fasudil, artenimol, and choline as hypothesis-generating compounds for experimental follow-up. CONCLUSIONS: This study provides a single-cell genetic framework for prioritizing immune-cell-specific candidate targets for EBV-related phenotypes and MS, and nominates genetically supported targets and pharmacologic hypotheses for experimental investigation.

Humans↗