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PubMed2006

Mapping determinants of gene expression plasticity by genetical genomics in C. elegans.

Recent genetical genomics studies have provided intimate views on gene regulatory networks. Gene expression variations between genetically different individuals have been mapped to the causal regulatory regions, termed expression quantitative trait loci. Whether the environment-induced plastic response of gene expression also shows heritable difference has not yet been studied. Here we show that differential expression induced by temperatures of 16 degrees C and 24 degrees C has a strong genetic component in Caenorhabditis elegans recombinant inbred strains derived from a cross between strains CB4856 (Hawaii) and N2 (Bristol). No less than 59% of 308 trans-acting genes showed a significant eQTL-by-environment interaction, here termed plasticity quantitative trait loci. In contrast, only 8% of an estimated 188 cis-acting genes showed such interaction. This indicates that heritable differences in plastic responses of gene expression are largely regulated in trans. This regulation is spread over many different regulators. However, for one group of trans-genes we found prominent evidence for a common master regulator: a transband of 66 coregulated genes appeared at 24 degrees C. Our results suggest widespread genetic variation of differential expression responses to environmental impacts and demonstrate the potential of genetical genomics for mapping the molecular determinants of phenotypic plasticity.

Animals↗

PubMed2005

eQTL Explorer: integrated mining of combined genetic linkage and expression experiments.

The development of computational resources to visualize and explore data from combined genome-wide expression and linkage studies is essential for the development of testable hypotheses. eQTL Explorer stores expression profiles, linkage data and information from external sources in a relational database and enables simultaneous visualization and intuitive interpretation of the combined data via a Java graphical interface. eQTL Explorer provides a new and powerful tool to interrogate these very large and complex datasets.

Algorithms↗

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↗

PubMed2004

Colocation between a gene encoding the bZip factor SPA and an eQTL for a high-molecular-weight glutenin subunit in wheat (Triticum aestivum).

The quality of wheat grain is largely determined by the quantity and composition of storage proteins (prolamins) and depends on mechanisms underlying the regulation of expression of prolamin genes. The endosperm-specific wheat basic region leucine zipper (bZIP) factor storage protein activator (SPA) is a positive regulator that binds to the promoter of a prolamin gene. The aim of this study was to map SPA (the gene encoding bZIP factor SPA) and genomic regions associated with quantitative variations of storage protein fractions using F7 recombinant inbred lines (RILs) derived from a cross between Triticum aestivum "Renan" and T. aestivum "Récital". SPA was mapped through RFLP using a cDNA probe and a specific single nucleotide polymorphism (SNP) marker. Storage protein fractions in the parents and RILs were quantified using capillary electrophoresis. Quantitative trait loci (QTLs) for protein were detected and mapped on six chromosome regions. One QTL, located on the long arm of chromosome 1B, explained 70% of the variation in quantity of the x subunit of Glu-B1. Genetic mapping suggested that SPA is located on chromosome arm 1L and is also present in the confidence interval of the corresponding QTL for Glu-B1x on 1BL, suggesting that SPA might be a candidate gene for this QTL.

Base Sequence↗

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↗

PubMed2026

Genetic architecture of endometriosis: risk factors, comorbidities and clinical implications.

BACKGROUND: In 1999, Dr Susan Treloar and colleagues conducted a landmark twin study in Australia and reported their estimate of 51% for the heritability of endometriosis. This important result led several groups to begin mapping genetic factors contributing to increased endometriosis risk. Despite early challenges, advances in genome-wide association studies (GWAS) have identified multiple genetic risk factors and some target genes implicated in follow-up studies on genetic regulation of transcription. Access to large publicly available genetic datasets and analysis with endometriosis GWAS results is also providing new opportunities to answer important questions about comorbid conditions associated with endometriosis and their implications for clinical practice. OBJECTIVE AND RATIONALE: The objective of the review is to summarize the last 25 years of genetic studies in endometriosis, outline contributions to our understanding of the disease, and suggest future directions to accelerate biological insights from genetic studies to improve clinical outcomes. SEARCH METHODS: A comprehensive review of scientific literature on the genetics of endometriosis was conducted through searches in PubMed and Google Scholar up to June 2026. Search terms included "endometriosis AND (genetics OR GWAS OR genetic risk factors)", For studies addressing the functional characterization of genetic risk loci, additional searches employed the terms "endometriosis AND (genotype-phenotype associations OR colocalization OR eQTL OR mQTL OR multi omics methods)". To identify studies examining shared genetic risk between endometriosis and comorbid conditions, the search strategy included "endometriosis AND (genetic correlation OR colocalization OR Mendelian randomisation)". Publications reporting discoveries related to genetic risk factors for endometriosis and studies interpreting their biological and clinical significance were critically evaluated, and 144 publications were discussed in the review. OUTCOMES: Discovery of genetic risk factors started slowly and has accelerated in recent years with developments in technology and international collaborations to combine data and increase statistical power. GWAS have mapped 80 genetic risk factors that implicate gene regulation of hormonal targets, development of the reproductive tract, regulation of cell proliferation, and regulation of epithelial cell differentiation. In common with most other complex diseases, effects of individual common genetic risk factors are small. However, several examples demonstrate that small effect sizes are not a good predictor for the impact of drugs developed against genetically validated targets. Genetic risk factors implicate five genes regulating gonadotrophin release and oestrogen action, the major target pathway of current drugs for treatment of endometriosis demonstrating proof-of-principal for biologically meaningful results. Genetic correlation and Mendelian Randomization studies highlight important causal relationships between endometriosis and comorbid conditions including a possible role for testosterone during development and shared genetic risk factors for gynaecological, gastrointestinal, pain, psychiatric, and inflammatory conditions. Understanding causal relationships between endometriosis and related conditions will aid clinical management and more personalized treatments. WIDER IMPLICATIONS: Genetic studies provide novel insights into endometriosis pathogenesis and associations with related comorbid conditions. Genetic factors modifying gene regulation and disease risk likely act in specific cell types, and access to datasets from genetically informed cell-based models, single-cell and spatial omics data are needed to accelerate progress. Future studies should address critical questions of heterogeneity and disease subtypes, expand the search for genetic risk factors to non-European populations, evaluate the role of rare and structural variants, and better integrate data from functional, genomics, genetics, and clinical studies to reduce diagnostic delay, develop novel treatment strategies, and translate discoveries into personalized management strategies for affected individuals. REGISTRATION NUMBER: N/A.

comorbid conditions↗

PubMed2026

Mapping the Immune cell-specific gene regulatory network in bipolar disorder: A framework from scTWMR to exploratory drug-target annotation.

BACKGROUND: Although the involvement of the immune system in the genetic susceptibility of bipolar disorder (BD) is widely acknowledged, the causal relationship between gene expression in specific immune cell subtypes and BD requires systematic elucidation. METHODS: We implemented an analytical framework integrating single-cell transcriptome-wide Mendelian randomization (scTWMR) with colocalization analysis. This approach utilized cis-expression quantitative trait loci (cis-eQTLs) derived from 14 distinct immune cell types as instrumental variables to interrogate BD genome-wide association study (GWAS) summary statistics (comprising 41,917 cases and 371,549 controls). Subsequent investigations encompassed functional enrichment analysis, protein-protein interaction (PPI) network construction, phenome-wide association study (PheWAS), and performed an exploratory drug-target annotation. RESULTS: Our analysis identified 33 gene-immune cell associations. Colocalization analysis provided robust evidence (PPH4 > 90%) for shared causal variants implicating the MAD1L1, APOM, and NFKBIL1 loci. Significantly enriched biological pathways included cell cycle regulation, circadian rhythm entrainment, and neuroinflammation. The PPI network revealed a core regulatory module centered on histone-encoding and immune-related genes. Exploratory drug-target annotation nominated compounds for further investigation for compounds targeting APOM, TMEM258, and NFKBIL1. CONCLUSION: This study systematically delineates a genetically supported regulatory network of immune cell-specific gene expression in BD, predominantly implicating CD8⁺ effector T cells, plasma cells, and B cells. The findings corroborate established pathological pathways while uncovering novel cell type-specific therapeutic targets, thereby providing a genetic framework for prioritizing candidate targets for future investigation.

Bipolar disorder↗

PubMed2025

Genetic effects on chromatin accessibility reveal the molecular mechanisms of complex traits in maize.

Cis-regulatory elements (CREs) are critical for modulating gene expression and phenotypic diversity in maize. While genome-wide association study (GWAS) hits and expression quantitative trait loci (eQTLs) are often enriched in CREs, their molecular mechanisms remain poorly understood. Characterizing CREs within accessible chromatin regions (ACRs) offers a powerful approach to link noncoding variants to chromatin structure alterations and phenotypic variation. Here, we generated ATAC-seq profiles from seedling leaves of 214 maize inbred lines, identifying 82 174 consensus ACRs. Notably, 39.55% of these ACRs exhibited significant population-wide chromatin accessibility variation. By mapping chromatin accessibility quantitative trait loci (caQTLs), we discovered 27 004 loci, including 1398 predicted to disrupt transcription factor (TF)-binding sites. Integration with multi-omics data revealed 7405 caACR-target gene pairs and linked 56 caACRs to GWAS signals for 51 agronomic traits, with significant enrichment in flowering-related pathways. Functional candidates such as ZmZIM30 - putatively regulated by caACRs - emerged as key regulators of flowering time. At the fad7 locus associated with linolenic acid content, allelic variants overlapping a caQTL showed differential chromatin accessibility. Our study provides a high-resolution cis-elements of maize leaves, deciphers the genetic basis of chromatin accessibility variation, and bridges noncoding caQTLs to molecular mechanisms underlying GWAS hits.

Zea mays↗

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↗

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↗

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↗

PubMed2026

Integrative post-GWAS analysis prioritizes immune regulatory pathways and candidate effector signals in systemic lupus erythematosus.

BACKGROUND: Systemic lupus erythematosus (SLE) has a complex polygenic architecture, but translating genome-wide association signals into biologically interpretable candidates remains challenging. We applied an integrative post-GWAS framework to refine SLE-associated loci and prioritize candidate regulatory mechanisms. METHODS: European-ancestry SLE GWAS summary statistics from FinnGen and Bentham et al. were meta-analysed, comprising 8417 cases and 354,277 controls. After quality filtering, 6,782,131 SNPs were retained. Downstream analyses included LAVA regional prioritization, Bayesian colocalization with GTEx v8 whole-blood and spleen eQTLs, independent replication in the Julià et al. Spanish cohort, pathway enrichment, bivariate LAVA cross-trait local genetic correlation, and therapeutic annotation. RESULTS: The discovery meta-analysis identified 46 genome-wide significant SLE-associated loci, including putative novel signals requiring database/literature qualification. LAVA identified 14 candidate index variants across 12 high-confidence regions, of which nine index variants were retained as the primary prioritized set based on LAVA support and/or convergent regulatory evidence. The strongest association mapped to the chr6p21.3/MHC region (rs389884), where four genes showed colocalization support, including CLIC1 in whole blood and C4A in spleen. Because the chr6p21.3/MHC rs389884 region lead variant was unavailable for replication and no suitable proxy was identified, this signal was interpreted as an emerging candidate for functional validation rather than a replicated causal signal. Seven available variants replicated with concordant effects. An exploratory Roadmap immune chromatin-state overlap analysis placed 15 of 45 non-MHC lead variants (33.3%) directly, and 34 of 45 (75.6%) within ±10 kb, in active immune enhancer/promoter states. Pathway analyses highlighted type I interferon, JAK-STAT signaling, cytokine regulation, and antigen presentation, while bivariate LAVA analyses supported shared local genetic architecture with rheumatoid arthritis, systemic sclerosis, and Sjögren syndrome. CONCLUSIONS: This integrative post-GWAS analysis refines SLE association signals into biologically interpretable candidate regions and supports interferon and JAK-STAT signaling as central genetically supported pathways in SLE.

CLIC1↗

PubMed2026

Dissecting the shared genetic architecture between migraine subtypes and cardiovascular diseases: a multi-layered genomic analysis.

BACKGROUND: Epidemiological studies have linked migraine to an increased risk of cardiovascular disease (CVD); however, the shared genetic basis and putative causal relationships between migraine subtypes and cardiovascular traits remain poorly understood. METHODS: Leveraging large-scale GWAS summary statistics for migraine phenotypes (overall migraine, migraine with aura [MA], and migraine without aura [MO]) from FinnGen R12, along with seven cardiovascular diseases from publicly available consortia, we conducted a multi-layered genetic analysis. This integrative framework encompassed genetic correlation [linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL)], cross-trait meta-analysis (CPASSOC and PLACO), Bayesian colocalization, summary-data-based Mendelian randomization (SMR) using GTEx v8 eQTL data, and bidirectional two-sample Mendelian randomization (MR). RESULTS: Significant genetic correlations were identified between migraine and multiple cardiovascular traits, with hypertension and coronary artery disease (CAD) showing the most robust associations. MA exhibited broader genetic overlap with cardiovascular diseases than MO, including a notably stronger correlation with ischemic stroke, whereas MO demonstrated a stronger correlation with hypertension. Cross-trait meta-analysis identified 160 pleiotropic loci across 17 of 21 trait pairs. Colocalization analysis confirmed 32 loci harboring shared causal variants, mapped to 13 candidate genes, of which 7 (PHACTR1, LRP1, SOX7, ABO, FHOD3, MEI1, XKR6) were further validated by SMR as exhibiting tissue-specific regulatory effects. Among these, PHACTR1 displayed the broadest pleiotropic profile across migraine phenotypes and vascular diseases. After MR-PRESSO outlier removal, bidirectional MR identified 10 MR-supported associations, two of which (genetic liability to hypertension on overall migraine, and CAD on MA) survived Bonferroni correction, all free of detectable horizontal pleiotropy. Genetic liability to hypertension was associated with increased migraine risk (OR = 1.90, 95% CI 1.25-2.90, P = 2.64 × 10⁻³), atherosclerotic diseases showed subtype-specific effects (inverse for MO, positive for MA), and, in the reverse direction, migraine was associated with increased ischemic stroke risk. CONCLUSIONS: This study provides a comprehensive and systematic characterization of the shared genetic architecture between migraine subtypes and cardiovascular diseases. By identifying pleiotropic genes and bidirectional putative causal relationships with subtype-specific patterns, our findings carry implications for the development of targeted therapeutics and subtype-specific cardiovascular risk stratification.

Humans↗

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↗

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↗

PubMed2005

Cis-acting expression quantitative trait loci in mice.

We previously reported the analysis of genome-wide expression profiles and various diabetes-related traits in a segregating cross between inbred mouse strains C57BL/6J (B6) and DBA/2J (DBA). By considering transcript levels as quantitative traits, we identified several thousand expression quantitative trait loci (eQTL) with LOD score >4.3. We now experimentally address the problem of multiple comparisons by estimating the fraction of false-positive eQTL that are under cis-acting regulation. For this, we have utilized a classic cis-trans test with (B6 x DBA)F(1) mice to determine the relative levels of transcripts from the B6 and DBA alleles. The results suggest that at least 64% of cis-acting eQTL with LOD >4.3 are true positives, while the remaining 36% could not be confirmed as truly cis-acting. Moreover, we find that >96% of apparent cis-acting eQTL occur in regions that do not share SNP haplotypes. Cis-acting eQTL serve as an important new resource for the identification of positional candidates in QTL studies in mice. Also, we use the analysis of the correlation structures between genotypes, gene expression traits, and phenotypic traits to further characterize genes expressed in liver that are under cis-acting control, and highlight the advantages and disadvantages of integrating genetics and gene expression data in segregating populations.

Alleles↗

PubMed2026

Mitochondria-Related Pathogenic Genes in Paediatric Asthma: A Multi-Omics Mendelian Randomization Study.

Mitochondrial dysfunction is implicated in asthma pathogenesis, but causal roles of mitochondrial-related genes in paediatric asthma remain unclear. We performed a multi-omics Mendelian randomization study integrating GWAS data from paediatric asthma cohorts with blood-based methylation quantitative trait loci (mQTLs), expression QTLs (eQTLs) and protein QTLs (pQTLs) datasets. Causal inference was assessed using Summary-data-based Mendelian Randomization (SMR) and HEIDI testing, complemented by colocalization analysis. Findings were validated in independent cohorts and evaluated for tissue specificity using GTEx. Functional enrichment and protein-protein interaction (PPI) network analyses were conducted. SMR analysis identified 80 methylation sites spanning 54 genes, 26 gene expressions, and three proteins significantly associated with paediatric asthma. Colocalization analysis confirmed strong evidence for 10 methylation sites (7 genes), the STX17 eQTL (PP.H4 = 0.98) and the UNG pQTL (PP.H4 = 0.84). Tissue-specific eQTL validation replicated the STX17 association. Multi-omics integration associated ALAS1 (cg13241645, cg15698299) and TXNRD1 (cg09884423) with asthma at both methylation and expression levels, with colocalization supporting both ALAS1 associations. Furthermore, integrated mQTL-eQTL analysis suggests that DNA methylation potentially regulates ALAS1 and TXNRD1 expression. Functional enrichment and network analyses revealed that these candidate genes converge on mitochondrial metabolic pathways and identified seven hub genes with potential regulatory significance (SDHB, MFN2, GLDC, PHB2, TXNRD1, ATP5MC1 and PHB). This study provides multi-omics evidence supporting a causal role for mitochondrial-related genes, particularly ALAS1 and TXNRD1, in paediatric asthma, offering new insights into pathogenesis and potential therapeutic targets.

Humans↗

PubMed2006

How replicable are mRNA expression QTL?

Applying quantitative trait analysis methods to genome-wide microarray-derived mRNA expression phenotypes in segregating populations is a valuable tool in the attempt to link high-level traits to their molecular causes. The massive multiple-testing issues involved in analyzing these data make the correct level of confidence to place in mRNA abundance quantitative trait loci (QTL) a difficult problem. We use a unique resource to directly test mRNA abundance QTL replicability in mice: paired recombinant inbred (RI) and F(2) data sets derived from C57BL/6J (B6) and DBA/2J (D2) inbred strains and phenotyped using the same Affymetrix arrays. We have one forebrain and one striatum data set pair. We describe QTL replication at varying stringencies in these data. For instance, 78% of mRNA expression QTL (eQTL) with genome-wide adjusted p < or = 0.0001 in RI data replicate at a genome-wide adjusted p < 0.05 or better. Replicated QTL are disproportionately putatively cis-acting, and approximately 75% have higher apparent expression levels associated with B6 genotypes, which may be partly due to probe set generation using B6 sequence. Finally, we note that while trans-acting QTL do not replicate well between data sets in general, at least one cluster of trans-acting QTL on distal Chr 1 is notably preserved between data sets.

Animals↗