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PubMed2005

Gene expression variation and expression quantitative trait mapping of human chromosome 21 genes.

Inter-individual differences in gene expression are likely to account for an important fraction of phenotypic differences, including susceptibility to common disorders. Recent studies have shown extensive variation in gene expression levels in humans and other organisms, and that a fraction of this variation is under genetic control. We investigated the patterns of gene expression variation in a 25 Mb region of human chromosome 21, which has been associated with many Down syndrome (DS) phenotypes. Taqman real-time PCR was used to measure expression variation of 41 genes in lymphoblastoid cells of 40 unrelated individuals. For 25 genes found to be differentially expressed, additional analysis was performed in 10 CEPH families to determine heritabilities and map loci harboring regulatory variation. Seventy-six percent of the differentially expressed genes had significant heritabilities, and genomewide linkage analysis led to the identification of significant eQTLs for nine genes. Most eQTLs were in trans, with the best result (P=7.46 x 10(-8)) obtained for TMEM1 on chromosome 12q24.33. A cis-eQTL identified for CCT8 was validated by performing an association study in 60 individuals from the HapMap project. SNP rs965951 located within CCT8 was found to be significantly associated with its expression levels (P=2.5 x 10(-5)) confirming cis-regulatory variation. The results of our study provide a representative view of expression variation of chromosome 21 genes, identify loci involved in their regulation and suggest that genes, for which expression differences are significantly larger than 1.5-fold in control samples, are unlikely to be involved in DS-phenotypes present in all affected individuals.

Chromosome Mapping↗

PubMed2025

Multi-omics reveals cross-tissue regulatory mechanisms of autism risk loci via gut microbiota-immunity-brain axis.

Autism Spectrum Disorder (ASD) involves a multi-system interaction mechanism among genetics, immunity, and gut microbiota, yet its regulatory network remains undefined. This study conducted a meta-analysis on Genome-Wide Association Study data from four independent ASD cohorts to identify potential genetic loci. By integrating Polygenic Priority Score, brain region, and brain cell eQTL enrichment analyses, and combining summary-data-based Mendelian Randomisation (SMR) analyses of brain cis-eQTL and mQTL, bidirectional Mendelian Randomisation analyses of 473 gut microbiota, and SMR analysis of blood eQTL, SNPs such as rs2735307 and rs989134 with significant multi-dimensional associations were identified. These loci exert cross-tissue regulatory effects by participating in gut microbiota regulation, involving immune pathways such as T cell receptor signal activation and neutrophil extracellular trap formation, as well as cis-regulating neurodevelopmental genes (HMGN1 and H3C9P), or synergistically influencing epigenetic methylation modifications to regulate the expression of BRWD1 and ABT1. The cross-scale evidence chain constructed in this study provides a theoretical foundation for precision medicine research in ASD, holding promise to advance the development of innovative therapeutic strategies.

Autism spectrum disorder↗

PubMed2025

Endogenous fine-mapping and prioritization of functional regulatory elements in complex genetic loci.

Most genetic loci linked to polygenic traits are in non-coding regions, with complex regulation and linkage disequilibrium (LD), complicating causal variant and gene prioritization. We used multiplexed single-cell CRISPR interference and activation perturbations to investigate cis-regulatory element (CRE) and gene expression relationships within tight LD in the endogenous chromatin context. We demonstrated the prevalence of multiple causality in perfect LD (pLD) for independent expression quantitative trait loci (eQTLs) and uncovered fine-grained genetic effects on gene expression within pLD, which are difficult to decipher using traditional eQTL fine-mapping or existing computational methods. We found that over one-third of the causal CREs lack classical epigenetic markers prior to perturbation, and we functionally validated one of these hidden regulatory mechanisms. Leveraging Multiome single-cell epigenetic and sequence perturbations, we highlighted the regulatory plasticity of the human genome. Our study will guide the exploration of missing causal mechanisms underlying molecular trait regulation and disease development.

Humans↗

PubMed2026

Cell Type-Resolved Causal Inference and Spatial Transcriptomic Integration Reveal Immune-Specific Genetic Drivers of Autoimmune and Malignant Thyroid Disease.

BACKGROUND: Thyroid diseases, including autoimmune thyroid disease (AITD) and thyroid cancer, are characterized by immune dysregulation, yet the cell type-specific genetic mechanisms underlying these conditions remain poorly understood. Most genome-wide association studies (GWAS) have relied on bulk tissue expression quantitative trait loci (eQTL), which cannot resolve the heterogeneity of immune cell populations. METHODS: We performed two-sample Mendelian randomization (MR) analyses using single-cell cis-eQTLs from 14 immune cell subtypes (OneK1K cohort) as instrumental variables against GWAS summary statistics for four thyroid outcomes: autoimmune hyperthyroidism, autoimmune hypothyroidism, thyroid cancer and autoimmune thyroiditis. Causal associations were validated through Bayesian colocalization, phenome-wide association analysis (PheWAS) and multi-layered transcriptomic validation encompassing spatial transcriptomics of AITD tissue (GSE248205), bulk RNA-seq of thyroid cancer (GSE3678) and single-cell RNA-seq of thyroid tumours (GSE250521). gsMap spatial LD score regression was applied to map disease heritability onto spatial tissue architecture. RESULTS: We identified six Bonferroni-significant causal gene-cell type pairs for autoimmune hyperthyroidism, including protective effects of ABHD16A in naïve/immature B cells (OR = 0.440), HIST1H3H in CD8 NC T cells (OR = 0.324), HMGN4 in NK recruiting cells (OR = 0.556) and ZKSCAN4 in CD8 S100B T cells (OR = 0.427), with five pairs showing strong colocalization (PP.H4 ≥ 86%). Three pairs reached significance for autoimmune hypothyroidism, including a risk association of HLA-F in CD4 NC T cells (OR = 1.139). For autoimmune thyroiditis, FAM134B/RETREG1 showed consistent suggestive protective associations across both CD4 and CD8 NC T cells (PP.H4 ≥ 90% for both), suggesting a possible involvement of ER phagy regulation in thyroiditis susceptibility. Thyroid cancer showed a suggestive association with HLA-G in classical monocytes (OR = 1.899, PP.H4 = 53%). Spatial transcriptomic validation demonstrated progressive immune infiltration from control tissue to Graves' disease to Hashimoto's thyroiditis (7.7%-15.7%, 46.1%-54.1%, respectively) and strong spatial correlation between target gene expression and corresponding cell type enrichment (e.g., plasma cell-HLA-DQB1: r = 0.491, p < 10-300). HLA-G was independently validated in thyroid cancer bulk (log2fc = 0.542, p = 9.51 × 10-3, AUC = 0.857) and single-cell datasets. PheWAS revealed no significant associations detected for the core candidates. gsMap identified significant enrichment of autoimmune hypothyroidism heritability in gastrointestinal tract, adrenal gland and adipose tissue (all Bonferroni p < 0.002). CONCLUSIONS: This study establishes a multi-scale analytical framework integrating cell type-resolved genetic inference with spatial tissue validation, revealing distinct immunogenetic architectures underlying autoimmune versus malignant thyroid disease. Protective genetic programs in autoimmune hyperthyroidism converge on chromatin remodelling (HIST1H3H, HMGN4, ZKSCAN4) and lipid metabolism (ABHD16A) across lymphocyte subsets, whereas thyroid cancer risk involves immune escape mediated by HLA-G in myeloid cells. The ER-phagy receptor RETREG1 represents a candidate pathway warranting further investigation in autoimmune thyroiditis. These findings provide genetically supported, cell type-specific therapeutic targets and demonstrate a generalizable strategy for dissecting the immune-mediated mechanisms of complex thyroid diseases.

Mendelian randomization↗

PubMed2026

Single-cell expression quantitative trait locus Mendelian randomization reveals immune cell-specific causal regulatory networks and actionable targets in polycystic ovary syndrome.

ObjectiveTo systematically investigate whether the pathogenesis of polycystic ovary syndrome (PCOS) is causally related to dysregulated gene expression in specific immune cell subsets, and to evaluate the potential of these causal genes as actionable drug targets.MethodsThis study employed a two-sample Mendelian randomization (MR) framework using publicly available genome-wide association study (GWAS) summary statistics. The participant data included 797 PCOS cases and 140,558 controls (no direct patient recruitment was involved). Instrumental variables were derived from high-resolution immune cell-specific single-cell expression quantitative trait locus (sc-eQTL) data (OneK1K project) across 14 immune cell types. Primary analyses utilized the inverse-variance weighted (IVW) method. Shared causal variants were validated using Bayesian colocalization. Phenome-wide association analysis (PheWAS), external transcriptomic dataset validation (GSE8157), and DrugBank database screening were conducted for pleiotropy assessment and drug repositioning.ResultsMR analysis revealed genome-wide significant causal associations for GLIPR1 in non-classical monocytes (Mono NC) and XBP1 in CD4+ effector memory T cells (CD4 ET) with PCOS risk. Higher GLIPR1 expression was associated with a decreased PCOS risk (OR = 0.669, P = 4.34×10-6), whereas higher XBP1 expression was associated with an increased risk (OR = 1.406, P = 9.53×10-8). Colocalization analysis confirmed that GLIPR1 shares a causal variant with PCOS (PP.H4 = 96.73%). PheWAS and external validation confirmed the safety profile and significant upregulation (P = 0.03) of GLIPR1. Drug repositioning identified SOT-107, a Phase III protein therapy drug, as a potential interacting agent for GLIPR1.ConclusionsThis sc-eQTL MR study reveals immune cell-specific causal regulatory networks in PCOS. GLIPR1 in non-classical monocytes represents a high-confidence protective target, while XBP1 provides suggestive evidence for immune-mediated pathogenesis. The candidate drug SOT-107 highlights theoretical repositioning opportunities, though rigorous preclinical validation remains required.

Female↗

PubMed2025

Association of genetically proxied cancer-targeted drugs with cardiovascular diseases through Mendelian randomization analysis.

BACKGROUND: Cancer-targeted therapies are progressively pivotal in oncological care. Observational studies underscore the emergence of cancer therapy-related cardiovascular toxicity (CTR-CVT), impacting patient outcomes. We aimed to investigate the causal relationship between different types of cancer-targeted therapies and cardiovascular disease (CVD) outcomes through a two-sample Mendelian randomization (MR) study. METHODS: This genome-wide association study was conducted using a two-sample Mendelian randomization framework. Genetic instruments for drug target gene expression were extracted from the eQTLGen consortium (31684 individuals, 37 cohorts). Genome-wide association study (GWAS) summary statistics for 19 cardiovascular diseases were derived from the FinnGen database. Primary analysis was carried out using the summary-data-based MR (SMR) method, with sensitivity analysis for validation. Colocalization analysis identifies shared causal variants between exposure eQTLs and CVD-associated single-nucleotide polymorphisms (SNPs). RESULTS: Among the 39 drug target genes, 8 were identified with detectable cis-eQTLs and were subsequently validated through positive control analysis for further investigation. In the SMR and sensitivity analyses, genetically proxied VEGFA inhibition showed significantly strong association with stroke (odds ratio [OR] = 1.17, 95% confidence interval [CI] = 1.09-1.26, p = 1.33 × 10- 5). Additionally, the inhibition of FGFR1, FLT1, and MAP2K2 exhibited suggestive association with corresponding cardiovascular disease outcomes. Nevertheless, only VEGFA expression and stroke shared a causal variant (93.6%), whereas FGFR1, MAP2K2, and FLT1 did not share causal variants with corresponding cardiovascular diseases in the colocalization analysis. CONCLUSIONS: This genetic association study revealed evidence supporting the genetic association between the use of VEGFA inhibitors and increased stroke risk, highlighting the need for enhanced pharmacovigilance. These findings underscore the delicate balance between cardiovascular toxicity risk and the benefits of cancer-targeted therapy.

Humans↗

PubMed2026

Genetically predicted CXCL16 expression is associated with Parkinson's disease risk and peripheral immune cell dysregulation: a two-sample mendelian randomization study.

BACKGROUND: Parkinson's disease (PD) is a progressive neurodegenerative disorder with limited disease-modifying therapies. PANoptosis, an integrated form of programmed cell death involving apoptosis, pyroptosis, and necroptosis, has been implicated in neuroinflammation-related neurodegeneration. However, the roles of PANoptosis-related genes in PD remain unclear. METHODS: We performed two-sample Mendelian randomization (MR) using cis-eQTL instruments from the eQTLGen Consortium for 30 PANoptosis-related genes, with PD GWAS data from Nalls et al. 2019 as the outcome. Instrumental variables were selected using a hierarchical strategy, with genome-wide significant cis-eQTLs as primary instruments and a relaxed threshold applied only for genes with fewer than three independent SNPs. Sensitivity analyses included MR-Egger, weighted median, MR-PRESSO, MR-RAPS, and leave-one-out analyses. SMR/HEIDI testing and two-step MR mediation using 731 peripheral immune traits were also performed. RESULTS: Genetically predicted higher CXCL16 expression was associated with increased PD risk (OR = 1.115, 95% CI 1.060-1.173, p = 2.4 × 10-5), while higher FADD expression was associated with reduced PD risk (OR = 0.861, 95% CI 0.790-0.939, p = 7.1 × 10-4). CASP1 and IFI27 were nominally significant and considered exploratory. Sensitivity analyses were directionally consistent, although MR-Egger estimates were imprecise. SMR/HEIDI supported CXCL16. Exploratory mediation analysis identified 63/66 candidate immune mediators after FDR correction. CONCLUSION: These findings provide MR-based genetic evidence linking CXCL16 expression to PD risk, with exploratory mediation through peripheral immune phenotypes. The CXCL16-immune cell-PD axis warrants further experimental validation.

Humans↗

PubMed2005

Gene expression differences in mice divergently selected for methamphetamine sensitivity.

In an effort to identify genes that may be important for drug-abuse liability, we mapped behavioral quantitative trait loci (bQTL) for sensitivity to the locomotor stimulant effect of methamphetamine (MA) using two mouse lines that were selectively bred for high MA-induced activity (HMACT) or low MA-induced activity (LMACT). We then examined gene expression differences between these lines in the nucleus accumbens, using 20 U74Av2 Affymetrix microarrays and quantitative polymerase chain reaction (qPCR). Expression differences were detected for several genes, including Casein Kinase 1 Epsilon (Csnkle), glutamate receptor, ionotropic, AMPA1 (GluR1), GABA B1 receptor (Gabbr1), and dopamine- and cAMP-regulated phosphoprotein of 32 kDa (Darpp-32). We used the www.WebQTL.org database to identify QTL that regulate the expression of the genes identified by the microarrays (expression QTL; eQTL). This approach identified an eQTL for Csnkle on Chromosome 15 (LOD = 3.8) that comapped with a bQTL for the MA stimulation phenotype (LOD = 4.5), suggesting that a single allele may cause both traits. The chromosomal region containing this QTL has previously been associated with sensitivity to the stimulant effects of cocaine. These results suggest that selection was associated with (and likely caused) altered gene expression that is partially attributable to different frequencies of gene expression polymorphisms. Combining classical genetics with analysis of whole-genome gene expression and bioinformatic resources provides a powerful method for provisionally identifying genes that influence complex traits. The identified genes provide excellent candidates for future hypothesis-driven studies, translational genetic studies, and pharmacological interventions.

Animals↗

PubMed2006

Integrating genetic and gene expression data: application to cardiovascular and metabolic traits in mice.

The millions of common DNA variations that occur in the human population, or among inbred strains of mice and rats, perturb the expression (transcript levels) of a large fraction of the genes expressed in a particular tissue. The hundreds or thousands of common cis-acting variations that occur in the population may in turn affect the expression of thousands of other genes by affecting transcription factors, signaling molecules, RNA processing, and other processes that act in trans. The levels of transcripts are conveniently quantitated using expression arrays, and the cis- and trans-acting loci can be mapped using quantitative trait locus (QTL) analysis, in the same manner as loci for physiologic or clinical traits. Thousands of such expression QTL (eQTL) have been mapped in various crosses in mice, as well as other experimental organisms, and less detailed maps have been produced in studies of cells from human pedigrees. Such an integrative genetics approach (sometimes referred to as "genetical genomics") is proving useful for identifying genes and pathways that contribute to complex clinical traits. The coincidence of clinical trait QTL and eQTL can help in the prioritization of positional candidate genes. More importantly, mathematical modeling of correlations between levels of transcripts and clinical traits in genetic crosses can allow prediction of causal interactions and the identification of "key driver" genes. An important objective of such studies will be to model biological networks in physiologic processes. When combined with high-density single nucleotide polymorphism (SNP) mapping, it should be feasible to identify genes that contribute to transcript levels using association analysis in outbred populations. In this review we discuss the basic concepts and applications of this integrative genomic approach to cardiovascular and metabolic diseases.

Animals↗

PubMed2006

A review of statistical methods for expression quantitative trait loci mapping.

With high-throughput technologies now widely available, investigators can easily measure thousands of phenotypes for quantitative trait loci (QTL) mapping. Microarray measurements are particularly amenable to QTL mapping, as evidenced by a number of recent studies demonstrating utility across a broad range of biological endeavors. The early success stories have impelled a rapid increase in both the number and complexity of expression QTL (eQTL) experiments. Consequently, there is a need to consider the statistical principles involved in the design and analysis of these experiments and the methods currently being used. In this article we review these principles and methods and discuss the open questions most likely to yield significant progress toward increasing the amount of meaningful information obtained from eQTL mapping experiments.

Animals↗

PubMed2026

Prioritizing Parkinson's disease risk-associated mitochondrial candidate genes via multi-omics integrative analysis.

BACKGROUND: Mitochondrial dysfunction has been implicated in Parkinson's disease (PD), but the genetically regulated mitochondrial genes associated with PD risk remain incompletely defined. METHODS: We conducted a summary-data-based genetic epidemiology study integrating summary-based Mendelian randomization (SMR), Heterogeneity in dependent instruments (HEIDI) filtering, and Bayesian colocalization to prioritize mitochondrial-related molecular features associated with PD risk. Mitochondrial-related genes were defined using MitoCarta3.0. Genetically predicted gene expression and plasma protein abundance were evaluated using expression quantitative trait loci (eQTL) data from eQTLGen and GTEx v8, and protein quantitative trait loci (pQTL) data was assessed using International Parkinson's Disease Genomics Consortium (IPDGC) as the discovery genome-wide association study (GWAS) and FinnGen as the replication dataset. Prespecified QTL analyses were interpreted using FDR correction, HEIDI filtering, and colocalization support. DNA methylation QTL analysis, mitochondrial phenotype MR, and single-nucleus RNA-seq analysis were performed as complementary analyses. RESULTS: In the primary eQTL analysis, higher genetically predicted TTC19 expression was associated with lower PD risk (OR = 0.80, 95% CI: 0.74-0.87, PPH4 = 0.80), whereas higher MALSU1 expression was associated with increased PD risk (OR = 2.21, 95% CI: 1.59-3.06, PPH4 = 0.96). Both associations survived FDR correction, passed HEIDI filtering, and showed colocalization support. GTEx whole-blood data supported the direction of the TTC19 association. No mitochondrial protein reached significance after FDR correction and colocalization filtering in the primary pQTL analysis. Complementary methylation analysis highlighted cg06270993 as an exploratory regulatory signal for MALSU1. CONCLUSIONS: This MR-colocalization study prioritizes TTC19 and MALSU1 as genetically supported mitochondrial-related candidate genes associated with PD risk. Further validation is required to define their functional roles in PD pathogenesis.

Humans↗

PubMed2026

A cross-sectional study of oxidative stress pathway genotypes and their interactions with environmental pollutant levels identifies associations with gene expression and lung function.

BACKGROUND: Asthma is a heterogeneous disease influenced by genetic and environmental factors. Fine particulate matter (PM2.5) exacerbates asthma, likely through oxidative stress pathways, but whether genetic variation modifies this effect remains unclear. METHODS: We analysed data on 948 adults with asthma from the Severe Asthma Research Program (SARP), linking ZIP-code-level PM2.5 exposure with whole-genome sequencing data. We tested 4337 single nucleotide polymorphisms (SNPs) in 120 oxidative stress pathway genes for gene-environment (GxE) interactions with PM2.5 on lung function (forced expiratory volume in 1 s [FEV1] % predicted) using weighted linear regression. Gene expression data from bronchial epithelial cells (n = 170) were used to assess cis-expression quantitative trait loci (eQTLs). FINDINGS: Higher PM2.5 exposure was associated with lower FEV1% predicted (β per μg/m3 = -0.7, p = 0.01). We identified 20 SNPs across seven genes (OXSR1, PXDN, TPO, LRRK2, APP, MSRA, MSRB2) with significant GxE interactions after multiple-testing correction. Five SNPs were also eQTLs, linking PM2.5-modified gene expression to lung function. Minor alleles in OXSR1 and PXDN were associated with reduced gene expression and worsened FEV1% under high PM2.5 exposure. Conversely, TPO variants were associated with higher baseline expression and lower lung function, but under increasing PM2.5 exposure, minor allele carriers showed suppressed TPO expression and improved FEV1%. INTERPRETATION: This study identified 20 SNPs in oxidative stress pathway genes that modify the effect of PM2.5 on lung function in asthma. These findings highlight the importance of integrating environmental context in genetic studies and suggest potential therapeutic targets for pollution-sensitive asthma phenotypes. FUNDING: Supported by NIH grants.

Cross-Sectional Studies↗

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↗

PubMed2005

The quantitative genetics of transcription.

Quantitative geneticists have become interested in the heritability of transcription and detection of expression quantitative trait loci (eQTLs). Linkage mapping methods have identified major-effect eQTLs for some transcripts and have shown that regulatory polymorphisms in cis and in trans affect expression. It is also clear that these mapping strategies have little power to detect polygenic factors, and some new statistical approaches are emerging that paint a more complex picture of transcriptional heritability. Several studies imply pervasive non-additivity of transcription, transgressive segregation and epistasis, and future studies will soon document the extent of genotype-environment interaction and population structure at the transcriptional level. The implications of these findings for genotype-phenotype mapping and modeling the evolution of transcription are discussed.

Chi-Square Distribution↗

PubMed2005

Identification of candidate genes in the type 2 diabetes modifier locus using expression QTL.

To identify new genetic determinants relevant to type 2 diabetes (T2D), diabetic F2 progeny were generated by intercrossing F1 mice obtained from a cross of BKS.Cg-Lepr(db)+/+m and DBA/2, and T2D-related phenotypes were measured. In the F2 population, increased susceptibility to diabetes and obesity was observed. We also detected the major quantitative trait loci (QTL) modifying the severity of diabetes on chromosome 9, where peaks of logarithm of odds (LOD) overlapped for three traits. To identify candidate genes in the QTL intervals, we combined "expression QTL" (eQTL), taking mRNA levels as quantitative traits, and "interstrain sequence variations, including cSNPs." As a result, four genes were identified from cosegregation of clinical QTL with eQTL and 13 genes were found from interstrain cSNPs as candidates in the T2D modifier QTL. Our combined approach shows the acceleration of the discovery of candidate genes in the QTL of interest, spanning several megabases.

Animals↗

PubMed2025

Integrative omics of the genetic basis for wheat WUE and drought resilience reveal the function of TaMYB7-A1.

Improving wheat drought resilience and water use efficiency (WUE) is critical for sustaining productivity under increasing water scarcity. Here, we integrate genome-wide association study (GWAS), expression quantitative trait locus (eQTL) mapping, population-transcriptome analysis, and summary-data-based mendelian randomization (SMR), followed by functional validation using indexed EMS mutants and transgenic lines, to systematically identify key WUE regulators. GWAS across water conditions in 228 accessions identifies 73 quantitative trait loci (QTLs) for WUE-traits. Transcriptome profiling of 110 diverse accessions reveals 28 drought-responsive modules. eQTL mapping uncovers 146,966 regulatory variants, including condition-specific hotspots associated with key drought-related pathways. Integrative analysis underscores 85 high-confidence candidate genes, notably TaMYB7-A1. Overexpression of TaMYB7-A1 enhances photosynthesis, WUE, root development, and grain yield under drought condition by activating TaPIP2;2-B1 (water transport), TaRD20-D1 (stomatal regulation), and TaABCB4-B1 (root growth), reflecting reduced water loss and improved physiological resilience. Our study presents a comprehensive regulatory map and robust targets for wheat drought adaptation and resilient cultivar breeding.

Triticum↗

PubMed2026

Genetic evidence supports the prioritization of CD40 among prespecified immune-related candidate drug targets in myasthenia gravis.

AIM: To prioritize prespecified immune-related candidate drug targets in myasthenia gravis for further validation based on integrated genetic evidence. METHODS: We integrated drug-target Mendelian randomization (MR) using cis-expression quantitative trait loci (cis-eQTLs), protein-level MR of plasma CD40 abundance using plasma protein quantitative trait loci (pQTLs), and colocalization analyses to evaluate genetically proxied associations with overall MG, early-onset myasthenia gravis (EOMG), and late-onset myasthenia gravis (LOMG). RESULTS: In this study, CD40 showed the most consistent genetic evidence among the six prespecified targets. Effect estimates are reported as odds ratios (ORs) with 95% confidence intervals (CIs). Higher CD40 expression proxied by cis-eQTLs was associated with increased risk of overall MG (OR = 1.14, 95% CI: 1.05-1.24, Bonferroni-adjusted p = 0.022) and EOMG (OR = 1.32, 95% CI: 1.12-1.56, Bonferroni-adjusted p = 0.015). Genetically predicted higher plasma CD40 protein abundance was associated with increased overall MG risk (OR = 1.31, 95% CI: 1.08-1.57, Bonferroni-adjusted p = 0.010), whereas the protein-level MR result for EOMG was directionally consistent but not statistically significant. Colocalization analysis provided suggestive but not definitive evidence of colocalization between CD40 expression and EOMG risk. FCGRT, IL2RA, and SYK showed additional exploratory MR signals requiring further validation. CONCLUSION: CD40 showed the most consistent genetic support among the prespecified targets, supporting its prioritization for functional validation and further therapeutic investigation in MG.

CD40↗

PubMed2026

Diagnosing scientific replicability through probabilistic distinguishability.

MOTIVATION: Despite the widely recognized importance of replicability in biological research, computational methods to quantify irreplicability and identify irreplicable instances remain underdeveloped. This article presents an efficient and robust computational framework to address this gap. RESULTS: To tackle the challenge of defining an acceptable level of intrinsic heterogeneity among replicable studies, we introduce a distinguishability criterion, ensuring that replicable effects, while potentially heterogeneous, can be distinguished from zero effects and maintain consistent directions with high probability. We implement a Bayesian model criticism approach, reporting a Bayesian P-value to identify potential irreplicable instances. Through numerical experiments, we demonstrate the efficacy of the proposed methods in detecting batch effects in high-throughput experiments and identifying instances of the publication bias. Finally, we apply the framework to multi-tissue eQTL data from the GTEx consortium, uncovering tissue-specific eQTLs that represent biological heterogeneity across tissues. AVAILABILITY AND IMPLEMENTATION: An R package DiscRep implementing our method is available on GitHub (https://github.com/PengWang96/DiscRep).

Bayes Theorem↗