SEARCH · PubMed Health

Results for “Quantitative Trait Loci”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources

PubMed2024

Complementation testing identifies genes mediating effects at quantitative trait loci underlying fear-related behavior.

Knowing the genes involved in quantitative traits provides an entry point to understanding the biological bases of behavior, but there are very few examples where the pathway from genetic locus to behavioral change is known. To explore the role of specific genes in fear behavior, we mapped three fear-related traits, tested fourteen genes at six quantitative trait loci (QTLs) by quantitative complementation, and identified six genes. Four genes, Lamp, Ptprd, Nptx2, and Sh3gl, have known roles in synapse function; the fifth, Psip1, was not previously implicated in behavior; and the sixth is a long non-coding RNA, 4933413L06Rik, of unknown function. Variation in transcriptome and epigenetic modalities occurred preferentially in excitatory neurons, suggesting that genetic variation is more permissible in excitatory than inhibitory neuronal circuits. Our results relieve a bottleneck in using genetic mapping of QTLs to uncover biology underlying behavior and prompt a reconsideration of expected relationships between genetic and functional variation.

Animals↗

PubMed2026

Identifying Single-Cell Expression Quantitative Trait Loci Using a Bootstrap Penalized Hurdle Model.

BACKGROUND: Expression quantitative trait loci (eQTL) analysis links genetic variants to gene expression levels, helping to uncover how genetic variation contributes to gene regulation. While traditional eQTL analyses rely on bulk RNA-seq data, recent advances in single-cell RNA sequencing (scRNA-seq) have made it possible to detect cell-type-specific eQTLs. However, the inherent sparsity and heterogeneity of scRNA-seq data present major challenges for standard modeling approaches. METHODS: In this paper, we propose a novel statistical framework, Bootstrap Penalized Hurdle regression model (BPHurdle), designed specifically for scRNA-seq data. BPHurdle employs a hurdle modeling framework, where a logistic component accounts for the excess zeros in single-cell expression data, and a Poisson component jointly evaluates the effects of multiple SNPs on positive gene expression levels. RESULTS: Through simulation studies, we show that BPHurdle achieves high accuracy and robustness in identifying regulatory variants. We further demonstrate its utility on a real dataset through a case study focusing on a subset of differentially expressed genes, where it successfully identifies reliable cell-type-specific eQTLs. CONCLUSIONS: Overall, BPHurdle offers an advanced and flexible approach for single-cell eQTL mapping, providing deeper insight into the genetic regulation of gene expression at cellular resolution.

Quantitative Trait Loci↗

PubMed2026

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

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

Humans↗

PubMed2025

Quantitative trait loci for Globodera pallida resistance derived from wild potato species Solanum gourlayi.

Globodera pallida is a major pest that is responsible for huge losses in potato yields worldwide. Expanding the gene pool of cultivated potatoes with clones resistant to this pest is made possible by searching for resistance genes in wild Solanum species. The aim of this study was to identify quantitative trait loci (QTLs) for potato resistance to Globodera pallida derived from Solanum gourlayi. A resistant diploid potato clone, Sg 2/7 (Solanum gourlayi, accession CGN17592), was crossed with a susceptible potato hybrid clone, DW 94-4235, to generate an F1 mapping population. All clones were tested for nematode resistance using G. pallida, pathotypes Pa2 and Pa3, in 2 or 3 years (2017-2019), respectively. Diversity Array Technology (DArTseq) was used for genotyping and genetic map construction. QTLs for nematode resistance were identified on potato chromosomes II, IV, V, VI, VII, X, XI, and XII, explaining from 10.1 to 21.5% of phenotypic variance. The most significant QTL for resistance to G. pallida pathotype Pa2 was identified on chromosome XII, explaining 20.9% of the phenotypic variance in the dataset from 2017. The most significant QTL for resistance to the G. pallida Pa3 pathotype was identified on chromosome VI, with a CAPS marker Exp928 in its peak, explaining 21.5% of the phenotypic variance in the dataset from 2017. The novel QTLs for resistance to S. gourlayi may be useful for breeding resistant potato cultivars, further studies of candidate genes, and host responses of potato to G. pallida infection.

Quantitative Trait Loci↗

PubMed2026

X chromosome-wide association studies for quantitative trait loci based on the mixture of general pedigrees and additional unrelated individuals.

Genome-wide association studies have successfully identified many genetic variants associated with complex traits. However, most existing methods target autosomes rather than X chromosome, and several existing X chromosome-wide association studies (XWAS) at quantitative trait loci (QTL) largely focus on unrelated individuals, with limited attention to general pedigrees or mixture of general pedigrees and additional unrelated individuals (called the mixed data for brevity). In this study, we propose nine novel methods for XWAS at QTL in the mixed data (${\mathrm{MQX}}_{\mathrm{cat}}$, ${\mathrm{MQZ}}_{\mathrm{max}}$, ${\mathrm{MT}}_{\mathrm{plinkw}}$, ${\mathrm{MT}}_{\mathrm{chenw}}$, $\mathrm{MwM}3\mathrm{VNA}$, ${\mathrm{MQMVX}}_{\mathrm{cat}}$, ${\mathrm{MQMVZ}}_{\mathrm{max}}$, $\mathrm{MpMV}$, and $\mathrm{McMV}$), also applicable to general pedigrees alone. The first four methods test for mean differences across genotypes; the latter four test for differences in both means and variances; $\mathrm{MwM}3\mathrm{VNA}$ tests for variance differences only. All mean-based and mean-variance-based methods incorporate X chromosome inactivation information, and all nine methods consider genetic relatedness in pedigrees. Simulation studies confirm well-controlled type I error rates, and inclusion of pedigrees significantly improves statistical power. Note that there has been no study focusing on X chromosome for the mixed data or general pedigrees from UK Biobank database, so we apply our proposed methods to this dataset, which identify five total cholesterol (TC)-associated and 13 low-density lipoprotein cholesterol (LDL-C)-associated single nucleotide polymorphisms (SNPs). Linkage disequilibrium (LD) analysis reveals that these SNPs fall into three distinct LD blocks. Functional annotation and gene ontology enrichment analysis reveal 16 and 28 enriched pathways for TC-associated and LDL-C-associated genes, respectively. These methods provide robust and powerful tools for XWAS at QTL in both mixed data and general pedigrees.

Quantitative Trait Loci↗

PubMed2026

Tonsillar expression quantitative trait loci verify and expand genetic contributors to childhood atopic diseases.

BACKGROUND: The spectrum of causal variants, mechanisms, and immunologic gene networks that influence pediatric atopic traits is not completely understood. Human genetic variation associated with transcript abundance (expression quantitative trait loci [eQTLs]) can help to advance our understanding, yet prior work has focused on profiling immune cell populations collected from peripheral blood primarily in adult populations, leaving tissue-resident lymphocytes collected from children uncharacterized. OBJECTIVE: We sought to characterize gene expression of 4 populations of tonsil-derived immune cell types collected from pediatric patients. METHODS: We collected naive B, germinal center B, naive T, and T follicular helper cells from the discarded tonsils of 103 children across development (age range 1-19). Following genotyping and RNA sequencing of samples, we performed differential expression and eQTL analysis, then statistically linked eQTL signals to relevant atopic traits via colocalization. RESULTS: We found differentially expressed genes across cell types and identified 13,393 expression genes (eGenes) (1,793 eGenes not previously reported in similar datasets) influenced by 27,603 eQTLs (5,199 eQTLs not previously reported). We linked eQTLs to associations identified in pediatric and adult asthma and atopy traits, nominating 78 eGenes including TRAF3, ZBTB10, and JAZF1 in disease-relevant cell types. CONCLUSIONS: Our freely available resource exemplifies the importance of discovery in native tissues and across human development.

Expression quantitative trait locus↗

PubMed2026

Genome-wide cis-expression Quantitative Trait Loci (eQTL) and transcriptomic signals reveal distinct molecular regulation across correlated feed efficiency traits.

INTRODUCTION: Feed efficiency (FE) is a complex trait which determines livestock production profitability, yet the molecular mechanisms behind it remain unclear. This study investigated the blood transcriptomic profile of lambs, alongside genotype data with the aim to uncover the genetic basis of FE traits such as absolute dry matter intake (DMIabsolute), DMI adjusted for body size (DMIadjusted), average daily live weight gain (ADG), and residual feed intake (RFI). MATERIALS AND METHODS: Bulk RNA-Seq and genotype data were analysed using three complementary approaches: differential gene expression (DGE) analysis, weighted gene co-expression network analysis (WGCNA), and cis-expression Quantitative Trait Loci (cis-eQTL) mapping. These methods were used independently to identify genes and regulatory networks associated with FE traits and to investigate evidence supporting multi-trait candidate gene selection. RESULTS: DGE analysis revealed 2, 24, 85 and 4 differentially expressed genes for DMIabsolute, DMIadjusted, ADG, and RFI (Padjusted < 0.05), functionally enriched in sensory perception, ATP-dependent chromatin remodeling, Notch signaling and immune response pathways. 9 gene modules significantly associated with the FE traits (P ≤ 0.05) with correlations ranging from r = -0.56 to 0.49, were identified using WGCNA. Single nucleotide polymorphism (SNP)-level cis-eQTL analysis identified 93 eSNPs associated with 74 genes (false discovery rate (FDR) < 0.05), while permutation-derived gene level analysis identified 280 eGenes (FDR < 0.2, empirical P < 0.03). Across the three analyses, applying thresholds of DGE (Padjusted < 0.05), WGCNA (correlation, P ≤ 0.05), and cis-eQTL gene-level significance (empirical P < 0.05), multiple overlapping genes were identified including DNMT3A, KANSL1, NCOR1 for DMIadjusted, ACOX2, FANCF, CIMIP2B, LOC101115106, ARMH2, LOC132657496 for ADG, and LOC114114576 for RFI representing regulators of variations in FE. DISCUSSION: The integration of DGE, WGCNA, and cis-eQTL analyses identified key genes and regulatory mechanisms associated with variation in FE traits. These results highlight that integrated multi-trait candidate gene identification approaches can reveal key genes that lower feed intake while maintaining animal growth, supporting breeding strategies aimed at improving efficiency and long-term economic sustainability in sheep.

average daily gain (ADG)↗

PubMed2026

Integrative multi-omics quantitative trait loci prioritize CASP7 as a candidate protective gene for cataract.

Cataracts are the leading cause of vision loss worldwide. Despite surgery being the only effective treatment, its economic burden highlights the necessity of exploring the pathogenesis of cataracts. In this study, we analyzed 4 large-scale GWAS (genome-wide association study) datasets for cataracts and performed SMR analysis along with heterogeneity in dependent instruments (HEIDI) testing to explore the effects of methylation, expression, and protein QTLs on cataracts. We further validated shared genetic variants through COLOC analysis. Additionally, we searched datasets related to cataracts from the Gene Expression Omnibus (GEO) database for differentially expressed genes (DEGs) and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses. By integrating summary-based Mendelian randomization (SMR) results with bioinformatics findings, CASP7 showed a consistent protective-direction association with cataract risk (mQTL: OR [95% CI] = 0.959 [0.941-0.977], FDR-adjusted P = .039; eQTL: OR [95% CI] = 0.897 [0.860-0.937], FDR-adjusted P = .0046; pQTL: OR [95% CI] = 0.597 [0.483-0.738], FDR-adjusted P = .00083). GEO-based analyses provided transcriptomic support for CASP7 involvement in cataract-related lens biology. These findings prioritize CASP7 as a genetically supported candidate protective gene associated with cataract risk. Because this study is based on public summary-level and transcriptomic datasets, the results should be interpreted cautiously and require functional validation in human lens-relevant systems.

Quantitative Trait Loci↗

PubMed2026

Quantitative trait loci associated with improved fruit yield under heat-stress conditions in fresh-market tomato.

Rising temperatures and more frequent heat stress events pose a major challenge to global tomato production, particularly in tropical and subtropical regions such as the southern United States. High temperatures during flowering and fruit set lead to poor fruit set and reduced yield. Although several commercial cultivars and breeding lines are described as heat-tolerant, the genetic basis of yield performance under heat stress conditions in fresh-market tomato remains poorly understood. This study aimed to identify genomic regions associated with fruit yield under natural heat stress. A biparental recombinant inbred line (RIL) population developed by the UF/IFAS tomato breeding program was evaluated under natural field heat stress in the fall seasons of 2016, 2017, and 2018, with fruit yield recorded as the primary trait. Genotyping of RILs was performed with the AgriPlex commercial tomato panel. Multi-environment QTL analysis was conducted to identify loci associated with fruit yield under heat stress. A major locus on chromosome 12 was selected for validation. Backcross populations segregating for this region were evaluated in a randomized block design during the fall of 2020 at the Gulf Coast Research and Education Center (GCREC), Balm, Florida. Multi-environment QTL analysis identified several loci on chromosome 4, 5, 6, and 12 associated with fruit yield under natural heat stress conditions. Among these, a locus on chromosome 12 showed consistent effects across multiple harvests and environments and explained a relatively larger proportion of phenotypic variance. Validation using backcross populations confirmed that genotype carrying the chromosome 12 QTL produced significantly higher yield under natural heat stress than susceptible genotypes. Overall, this study identified an agronomically important region on chromosome 12 that can be targeted to improve tomato yield under heat stress. The results also highlight multiple genomic regions contributing to higher yield under heat stress. These findings provide a foundation for developing breeding strategies for developing heat-tolerant fresh-market tomato cultivars.

QTL analysis↗

PubMed2025

Cross-Phenotype Genome-Wide Association Study on the Shared Genetic Susceptibility to Systemic Sclerosis and Primary Biliary Cholangitis.

OBJECTIVE: An increased risk of primary biliary cholangitis (PBC) has been reported in patients with systemic sclerosis (SSc). Our study aims to investigate the shared genetic susceptibility between the two disorders and to define candidate causal genes using cross-phenotype genome-wide association study (GWAS) meta-analysis. METHODS: We performed cross-phenotype GWAS meta-analysis and Bayesian colocalization analysis for patients with SSc and patients with PBC. We performed both genome-wide and locus-based analysis, including tissue and pathway enrichment analyses, fine-mapping, Bayesian colocalization analyses with expression quantitative trait loci and protein quantitative trait loci (pQTL) datasets, and phenome-wide association studies. Finally, we used an integrative approach to prioritize candidate causal genes from the novel loci. RESULTS: We detected a strong genetic correlation between SSc and PBC (global genetic correlation = 0.84, P = 1.7 × 10-6). In the cross-phenotype GWAS meta-analysis, we identified 44 nonhuman leukocyte antigens loci that reached genome-wide significance (P < 5 × 10-8). Evidence of shared causal variants between patients with SSc and patients with PBC was found for nine loci, five of which were novel. Integrating multiple sources of evidence, we prioritized CD40, ERAP1, PLD4, SPPL3, and CCDC113 as novel candidate causal genes. The CD40 risk locus colocalized with trans-pQTLs of multiple plasma proteins involved in B cell function. CONCLUSION: Our study supports a strong shared genetic susceptibility between SSc and PBC. Using cross-phenotype analyses, we have prioritized several novel candidate causal genes and pathways for these disorders.

Humans↗

PubMed2025

EP300-mediated lactylation leads to ulcerative colitis via CD86-positive plasmacytoid dendritic cells: A Mendelian randomization and mediation analysis.

This study explores the potential mechanism between lactylation and ulcerative colitis (UC) using two-sample Mendelian randomization and multi-omics analysis. This study employed expression quantitative trait loci and protein quantitative trait loci as exposures, with UC from the Finnish database as the outcome, to conduct Mendelian randomization analysis on lactylation-related target genes, aiming to investigate the causal relationships between these exposures and the outcome. Sensitivity and pleiotropy tests, combined with colocalization analysis, are performed to identify the best target genes and ensure the robustness of the results. Finally, immune cells are included for mediation analysis between lactylation and UC to explore potential mechanisms of action. Through Mendelian randomization analysis combined with sensitivity and pleiotropy tests, 2 lactylation target genes were found to have a significant causal relationship with UC. Subsequent colocalization analysis confirmed EP300 as a potential gene target. After including immune cells in the mediation analysis, it was discovered that there is a potential mechanism involving EP300, CD86+ plasmacytoid dendritic cells (pDCs), and UC. There is a significant causal relationship between lactylation and UC. Furthermore, the lactylation-modified gene EP300 may lead to UC occurrence by regulating CD86+ pDCs.

Humans↗

PubMed2026

Identification of Rare Noncoding Variants in Familial Nonmedullary Thyroid Carcinoma.

BACKGROUND: Familial nonmedullary thyroid carcinoma (FNMTC) occurs when three or more family members are affected by usually papillary thyroid carcinoma (PTC), the most common form of NMTC. While the heritability to NMTC is among the highest of all cancers, the genetic determinants among NMTC families are not well understood. Here, we aim to understand the contribution of rare noncoding germline variants in the etiology of FNMTC. METHODS: We previously reported whole-genome sequencing (WGS) and linkage analysis in 17 PTC families and reported on 41 protein-coding variants in 40 genes that cosegregated with PTC in 11 of the families. Herein, we further leveraged our WGS data to include noncoding variants in our analysis for all 17 families. We hypothesized that most of the pathogenic noncoding variants would be located in theoretical or empirically determined regulatory regions that demonstrate at a minimum, basal thyroid expression, a positive family linkage score, and co-segregation among PTC-affected individuals. To test this hypothesis, we adopted a unique filtering strategy to identify variants that occurred in known DNA elements and transcription factor binding sites, near regions known to impact on gene expression or splicing in thyroid tissue, and/or in characterized thyroid enhancers. We annotated variants using two analyses (ENCODE and transcription factor binding site) within the BasePlayer software. We separately analyzed (1) expression quantitative trait loci, (2) splicing quantitative trait loci, and (3) thyroid enhancers. We then ranked variants according to predicted pathogenicity and performed Sanger sequencing in all individuals of each family. RESULTS: In total, 121 variants were selected based on in-silico prediction and our custom ranking analysis in each pedigree. Of these, 56 variants showed cosegregation among all PTC-affected individuals and were absent from unaffected individuals. This included candidate variants from five of the six PTC families for whom no protein-coding variants were previously found. CONCLUSION: Our data suggest that noncoding variants are important in the etiology of FNMTC and provide a framework for identifying noncoding germline variants using a novel approach. Further studies are needed to functionally characterize these variants to better understand the molecular mechanism of their pathogenicity.

Humans↗

PubMed2026

Colocalization and functional analyses identify GBE1 as a gene linking muscle strength and cardiometabolic fitness.

Handgrip strength is a proxy for muscular fitness, an indicator for general health status, and is associated with cardiometabolic health. The mechanisms connecting handgrip strength to skeletal muscle function are incompletely understood. We applied integrated linkage-disequilibrium-adjusted colocalization analysis of genome-wide association study summary statistics for handgrip strength, combined with expression and splicing quantitative trait loci from skeletal muscle, and identified glycogen branching enzyme 1 (GBE1) as a candidate gene for handgrip strength. CRISPR-interference knockdown of GBE1 in immortalized human skeletal muscle cells (HMCL-7304) demonstrated decreased glycogen content and accumulation of polyglucosan bodies. Knockdown of GBE1 led to increased oxygen consumption rate, oxidative stress, and changes in mitochondrial morphology. Transcriptomic profiling of GBE1 knockdown cells identified upregulation of the human superoxide dismutase 2 and enrichment of pathways related to muscle contraction and oxidative stress responses. These functional genomic analyses prioritize GBE1 as a muscle-relevant candidate gene for handgrip strength and provide mechanistic insights to muscle fitness.NEW & NOTEWORTHY Colocalization of genome-wide association study (GWAS) loci with quantitative trait loci (QTL) in skeletal muscle tissue identified GBE1 as a candidate for handgrip strength. Cellular phenotypes with GBE1 knockdown in immortalized human skeletal muscle cells include decreased glycogen content, accumulation of polyglucosan bodies, changes in mitochondrial function and morphology, and increased expression of reactive oxygen species (ROS) scavengers. Transcriptomic changes suggest a role for GBE1 in muscle contraction and oxidative stress-mediated responses.

Humans↗

PubMed2025

Multi-omics Mendelian randomization integrating RNA-seq, eQTL and pQTL data revealed CPXM1 as a potential drug target for osteoporosis.

Osteoporosis, a prevalent skeletal disorder characterized by decreased bone mineral density and increased fracture risk, continues to be a major global health concern. Traditional treatments for osteoporosis have limited efficacy and safety profiles, highlighting the need for novel therapeutic targets. This study integrates multi-omics data, including RNA-seq, expression quantitative trait loci (eQTL), and protein quantitative trait loci (pQTL) data, through Mendelian randomization (MR) to identify potential drug targets for osteoporosis. By leveraging bidirectional two-sample MR analysis, we identified CPXM1 (Carboxypeptidase X, M14 family member 1) as a novel gene that is causally linked to osteoporosis risk. Through transcriptomic and proteomic validation, we demonstrate that CPXM1 was upregulated in aged bone tissues and osteoporotic conditions in both human and murine models. Gene set enrichment analysis (GSEA) revealed significant dysregulation of bone homeostasis pathways, including increased extracellular matrix degradation and suppression of osteoblast differentiation in aged mice. Furthermore, phenome-wide association studies (PheWAS) confirmed minimal off-target effects of CPXM1, reinforcing its potential as a therapeutic target. Finally, computational drug repurposing predicted several promising drug candidates, including Doxorubicin, 5-Fluorouracil, and 2-Methylcholine, which may target CPXM1 pathways for osteoporosis treatment. These findings highlight CPXM1 as a potential biomarker and therapeutic target, offering new avenues for osteoporosis therapy.

Osteoporosis↗

PubMed2025

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

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

Journal Article↗

PubMed2025

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

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

Quantitative Trait Loci↗

PubMed2026

Plasma proteins are integral to cross-tissue gene regulatory networks implicated in cardiometabolic disorders and coronary artery disease.

The plasma proteome has demonstrated promise for identifying diagnostic markers for cardiometabolic disorders (CMDs) and coronary artery disease (CAD). However, identifying the organ of origin for these biomarkers is critical for establishing biological relevance. We performed a multi-omic integrative analysis across multiple tissues from the STARNET study by profiling 974 plasma proteins in 532 CAD patients, integrating RNA sequencing (RNA-seq) data from the arterial wall, major metabolic organs, and blood. We identified 144 cis-protein quantitative trait loci in plasma, colocalizing with tissue cis-expression quantitative trait loci. Additionally, by mapping tissue mRNA "seed genes," we traced 262 plasma proteins to their source organs, primarily the liver. Crucially, we found that 851 plasma proteins are associated with the activity of cross-tissue gene regulatory networks (GRNs), including GRNs implicated in CMD and CAD development. Our findings demonstrate that plasma proteins are integral components of GRNs, with potential for developing reliable diagnostics and precise therapeutic targets. A record of this paper's transparent peer review process is included in the supplemental information.

cardiometabolic disorders↗

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↗