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Results for “expression quantitative trait loci”

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PubMed2004

A major suppressor of cell death, slm1, modifies the expression of the maize (Zea mays L.) lesion mimic mutation les23.

Disease lesion mimics provide an excellent biological system to study the genetic basis of cell death in plants. Many lesion mimics show variation in phenotype expression in different genetic backgrounds. Our goal was to identify quantitative trait loci (QTL) modifying lesion mimic expression thereby identifying genetic modifiers of cell death. A recessive lesion mimic, les23, in a severe-expressing line was crossed to the maize inbred line Mo20W, a lesion-suppressing line, and an F(2) population was developed for QTL analysis. In addition to locating les23 to the short arm of chromosome 2, this analysis detected significant loci for modification of lesion expression. One highly significant locus was found on the long arm of chromosome 2. The Mo20W allele at this QTL significantly delayed initiation of the lesion phenotype and decreased the final lesion severity. Other QTL with lesser effect affected severity of lesion expression without affecting lesion initiation date. Our results demonstrate that dramatic change in lesion phenotype can be controlled by a single major QTL. The presumed function of this QTL in normal plants is to regulate some aspect of the cell death pathway underlying the les23 phenotype.

Alleles↗
PubMed2026

Integrative TWAS and multi-omics analyses prioritize HSPE1 as a candidate risk gene for bipolar disorder with immune cell-specific regulatory evidence.

BACKGROUND: Bipolar disorder (BD) is a severe psychiatric disorder associated with substantial disability. Although genome-wide association studies have identified multiple BD-associated loci, the underlying genes and mechanisms remain incompletely understood. METHODS: We integrated a European-ancestry BD genome-wide association dataset with cross-tissue and tissue-specific transcriptome-wide association studies (TWAS) and complementary gene-based analysis. Candidate genes were further evaluated using differential expression analysis, consensus clustering, immune infiltration analysis, machine learning, summary-data-based Mendelian randomization, Mendelian randomization using single-cell expression quantitative trait locus data, single-nucleus transcriptomics, phenome-wide association analysis, and virtual screening. RESULTS: The integrative analyses prioritized 37 candidate genes. Peripheral-blood differential-expression analysis identified 14 genes that remained significant after FDR correction, and their expression profiles separated BD samples into two expression-defined clusters. Machine-learning analysis selected UNC50, LMAN2L, LYG2, HSPE1, and KANSL3 for an exploratory classification nomogram. SMR associated genetically predicted higher HSPE1 expression with increased BD risk in two blood eQTL datasets. Cell-type-specific analyses indicated HSPE1-related associations in T-cell and natural killer cell subsets, while single-nucleus analysis descriptively showed higher HSPE1 expression in medial thalamic T cells from BD samples. PheWAS identified no genome-wide significant associations for HSPE1, whereas virtual screening identified candidate compounds with favorable predicted docking scores against the HSPE1 structure. CONCLUSION: This integrative multi-omics study identified HSPE1 as a candidate BD risk gene with immune-cell-related regulatory evidence, providing insight into BD pathogenesis and supporting functional validation.

Humans↗
PubMed2000

Pharmacogenomics and addiction to opiates.

The risk of initiating and maintaining the use of opiates up to the point of abuse and dependence is to a large degree genetically transmitted and is separate from genetic risk factors for addiction to other drugs of abuse. Pharmacogenetic studies have so far focused on obvious candidate genes that are expected to be involved either in the pharmacokinetics or in the pharmacodynamics of opioids in the mesolimbic reward system of the brain. The few findings of a positive allelic association rarely withstand replication in independent case-control or less stratification-prone family-based association samples. A pharmacogenomic approach in the best sense of the word, however, involves an unbiased, genome-wide, parallel search for risk genes and gene expression patterns. So far, only quantitative trait loci mapping studies of inbred rodent strains and differential expression studies using high-density DNA microarrays fulfill these requirements. The present state of pharmacogenomic and pharmacogenetic studies in animals and humans with respect to opiate addiction is reviewed in this paper.

Animals↗
PubMed2026

Integrative Multi-Omics Mendelian Randomization Analysis Identifies NIT2 as a Potential Metabolic Risk Gene in Hepatocellular Carcinoma.

BACKGROUND: Metabolic pathways are crucial in hepatocellular carcinoma (HCC) pathogenesis, but causal metabolic genes remain unclear. This study used Summary data-based Mendelian Randomization (SMR) and colocalization to identify metabolism-related genetic loci influencing HCC risk. METHODS: Differentially expressed genes in hepatic malignancy phenotype versus normal tissues from TCGA and GTEx were analyzed. Metabolism-related candidates were examined via SMR and colocalization using multi-omics data: methylation (mQTL), expression (eQTL), and protein (pQTL) quantitative trait loci. RESULTS: Multi-omics integration identified NIT2 as a key metabolic regulator for HCC. The cg13016775 locus of NIT2 was associated with elevated HCC risk at gene (OR = 1.618, 95% CI: 1.199-2.182) and protein (OR = 4.432, 95% CI: 1.783-11.018) levels. Colocalization supported a shared causal variant (PPH4 > 0.6), linking NIT2 to hepatocarcinogenesis via metabolic regulation. CONCLUSIONS: This study provides multi-omics evidence for NIT2 as a potential causal gene in HCC, enhancing understanding of metabolic contributions to HCC pathogenesis and highlighting integrative genomics for uncovering causal relationships.

Carcinoma, Hepatocellular↗
PubMed2000

Bayesian mapping of quantitative trait loci for complex binary traits.

A complex binary trait is a character that has a dichotomous expression but with a polygenic genetic background. Mapping quantitative trait loci (QTL) for such traits is difficult because of the discrete nature and the reduced variation in the phenotypic distribution. Bayesian statistics are proved to be a powerful tool for solving complicated genetic problems, such as multiple QTL with nonadditive effects, and have been successfully applied to QTL mapping for continuous traits. In this study, we show that Bayesian statistics are particularly useful for mapping QTL for complex binary traits. We model the binary trait under the classical threshold model of quantitative genetics. The Bayesian mapping statistics are developed on the basis of the idea of data augmentation. This treatment allows an easy way to generate the value of a hypothetical underlying variable (called the liability) and a threshold, which in turn allow the use of existing Bayesian statistics. The reversible jump Markov chain Monte Carlo algorithm is used to simulate the posterior samples of all unknowns, including the number of QTL, the locations and effects of identified QTL, genotypes of each individual at both the QTL and markers, and eventually the liability of each individual. The Bayesian mapping ends with an estimation of the joint posterior distribution of the number of QTL and the locations and effects of the identified QTL. Utilities of the method are demonstrated using a simulated outbred full-sib family. A computer program written in FORTRAN language is freely available on request.

Algorithms↗
PubMed2005

Klk1 as one of the genes contributing to hypertension in Dahl salt-sensitive rat.

A genome-wide quantitative trait loci analysis for blood pressure was performed using 107 male F2 rats derived from Dahl salt-sensitive and Lewis rats. Blood pressure was assessed by telemetry, and >400 microsatellite markers were used for genotyping. Two major quantitative trait loci for blood pressure were identified at chromosome 1 and chromosome 10. The expression levels of 366 transcripts around the chromosome 1 quantitative trait loci were assessed by RT-PCR, and we found that the Klk1 (kallikrein 1) and Ngfg (nerve growth factor gamma) mRNA levels were significantly reduced in the kidneys of Dahl salt-sensitive rats compared with those in Lewis rats. The expression levels of kallikrein 1 protein were also suppressed in Dahl salt-sensitive rats compared with those in Lewis rats. Because the kallikrein-kinin system has been shown to be involved in renal function, including salt homeostasis, it is likely that the reduced expression of Klk1 contributes to salt-sensitive hypertension in Dahl salt-sensitive rats.

Animals↗
PubMed2026

Genome-wide association study of estimated glomerular filtration rate using repeated measurements in the Taiwan Biobank.

BACKGROUND: Chronic kidney disease (CKD) is a major global public health issue, with genetic factors playing a significant role in kidney function. Although genome-wide association studies (GWAS) have identified numerous loci associated with estimated glomerular filtration rate (eGFR), most studies relied on a single time-point measurement, which limits the capacity to account for within-individual measurement variability. METHODS: We performed a repeated-measurement GWAS in the prospective Taiwan Biobank (Taiwanese ancestry; n = 25,004) using two repeated creatinine-based eGFR measurements. Repeated eGFR values were analyzed using a linear mixed-effects model with a subject-specific random intercept and time-varying covariates, providing a more precise estimate of eGFR level. Identified loci underwent functional annotation (expression quantitative trait locus, deleteriousness prediction, and epigenetic markers) and were compared with results from a single-measurement GWAS. RESULTS: Six loci associated with eGFR were identified, including four previously reported regions (1q22, 4q21.1, 11p14.1, and 17q21.2) and two additional loci (6p21.32 and 15q24.2). Functional annotation implicated several candidate genes-such as MUC1/EFNA1, SHROOM3, HLA-DQB1, MPPED2, NRG4, and PGAP3/FBXL20-in the regulation of kidney function. CONCLUSION: Incorporating repeated eGFR measurements into GWAS may improve phenotypic precision for identifying genetic associations with kidney function. This study identified eGFR-associated loci and biologically plausible candidate genes in a Taiwanese population, which require further replication and functional validation.

Chronic kidney disease↗
PubMed2005

Comparison of quantitative trait loci for adaptive traits between oak and chestnut based on an expressed sequence tag consensus map.

A comparative genetic and QTL mapping was performed between Quercus robur L. and Castanea sativa Mill., two major forest tree species belonging to the Fagaceae family. Oak EST-derived markers (STSs) were used to align the 12 linkage groups of the two species. Fifty-one and 45 STSs were mapped in oak and chestnut, respectively. These STSs, added to SSR markers previously mapped in both species, provided a total number of 55 orthologous molecular markers for comparative mapping within the Fagaceae family. Homeologous genomic regions identified between oak and chestnut allowed us to compare QTL positions for three important adaptive traits. Colocation of the QTL controlling the timing of bud burst was significant between the two species. However, conservation of QTL for height growth was not supported by statistical tests. No QTL for carbon isotope discrimination was conserved between the two species. Putative candidate genes for bud burst can be identified on the basis of colocations between EST-derived markers and QTL.

Adaptation, Physiological↗
PubMed2026

Deep learning and statistical methods identify novel asthma risk variants in Europeans.

BACKGROUND: Asthma is a common heritable respiratory disorder with a complex genetic basis. Although large-scale genome-wide association studies have identified many risk loci, the full spectrum of its polygenic architecture remains to be defined. OBJECTIVE: We refined the genetic landscape of asthma in individuals of European ancestry and improve polygenic risk prediction through statistical and deep learning-based methods. METHODS: We conducted the largest genome-wide association study meta-analysis of asthma in individuals of European ancestry, combining data from the Global Biobank Meta-analysis Initiative (121,940 cases, 1,254,131 controls) and the Million Veteran Program (36,823 cases, 398,278 controls). To enhance discovery, we applied pleiotropy-informed multitrait analysis and conditional false discovery rate approaches, each incorporating eosinophil counts as a secondary trait. In parallel, we used a Transformer-based deep learning framework to further prioritize variants and improve polygenic risk prediction. RESULTS: The meta-analysis identified 69 independent genome-wide significant loci (P&#x2009;<&#x2009;5 &#xd7; 10-8) not previously reported in asthma. Multitrait analysis of genome-wide association studies, conditional false discovery rate, and deep learning approaches uncovered additional candidate loci. Functional annotation and expression quantitative trait locus mapping implicated novel genes in immune regulation, airway remodeling, and metabolic processes. Polygenic risk score models derived from deep learning-prioritized variants outperformed those based on conventional genome-wide association study and standard statistical approaches. CONCLUSIONS: Our study yields a comprehensive map of asthma-associated loci in European ancestry populations, improves genetic risk prediction, and informs future mechanistic studies.

Humans↗
PubMed1994

Quantitative trait loci underlying gene product variation: a novel perspective for analyzing regulation of genome expression.

A methodology to dissect the genetic architecture of quantitative variation of numerous gene products simultaneously is proposed. For each individual of a segregating progeny, proteins extracted from a given organ are separated using two-dimensional electrophoresis, and their amounts are estimated with a computer-assisted system for spot quantification. Provided a complete genetic map is available, statistical procedures allow determination of the number, effects and chromosomal locations of factors controlling the amounts of individual proteins. This approach was applied to anonymous proteins of etiolated coleoptiles of maize, in an F2 progeny between two distant lines. The genetic map included both restriction fragment length polymorphism and protein markers. Minimum estimates of one to five unlinked regulatory factors were found for 42 of the 72 proteins analyzed, with a large diversity of effects. Dominance and epistasis interactions were involved in the control of 38% and 14% of the 72 proteins, respectively. Such a methodology might help understanding the architecture of regulatory networks and the possible adaptive or phenotypic significance of the polymorphism of the genes involved.

Chromosome Mapping↗
PubMed2005

Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics'.

We combined large-scale mRNA expression analysis and gene mapping to identify genes and loci that control hematopoietic stem cell (HSC) function. We measured mRNA expression levels in purified HSCs isolated from a panel of densely genotyped recombinant inbred mouse strains. We mapped quantitative trait loci (QTLs) associated with variation in expression of thousands of transcripts. By comparing the physical transcript position with the location of the controlling QTL, we identified polymorphic cis-acting stem cell genes. We also identified multiple trans-acting control loci that modify expression of large numbers of genes. These groups of coregulated transcripts identify pathways that specify variation in stem cells. We illustrate this concept with the identification of candidate genes involved with HSC turnover. We compared expression QTLs in HSCs and brain from the same mice and identified both shared and tissue-specific QTLs. Our data are accessible through WebQTL, a web-based interface that allows custom genetic linkage analysis and identification of coregulated transcripts.

Carrier Proteins↗
PubMed2004

In silico study of transcriptome genetic variation in outbred populations.

Dissecting the genetic architecture of regulatory elements on a genome-wide basis is now technically feasible. The potential medical and genetical implications of this kind of experiment being very large, it is paramount to assess the reliability and repeatability of the results. This is especially relevant in outbred populations, such as humans, where the genetic architecture is necessarily more complex than in crosses between inbred lines. Here we simulated a chromosome-wide SNP association study using real human microarray data. Our model predicted, as observed, a highly significant clustering of quantitative trait loci (QTL) for gene expression. Importantly, the estimates of QTL positions were often unstable, and a decrease in the number of individuals of 16% resulted in a loss of power of approximately 30% and a large shift in the position estimate in approximately 30-40% of the remaining significant QTL. We also found that the analysis of two repeated measures of the same mRNA can also result in two QTL that are located far apart. The intrinsic difficulties of analyzing outbred populations should not be underestimated. We anticipate that (many) conflicting results may be collected in the future if whole-genome association studies for mRNA levels are carried out in outbred populations.

Alleles↗
PubMed2025

Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.

INTRODUCTION: Stem rust (SR), caused by Puccinia graminis f. sp. tritici (Pgt), remains a major threat to global barley production, particularly in regions with conducive environments and evolving pathogen populations. Despite progress in understanding seedling resistance, adult plant resistance (APR) to SR remains underexplored in diverse barley germplasm. This study aimed to dissect the genetic architecture of APR to SR in a panel of diverse origins of two-row spring barley using a genome-wide association study (GWAS). METHODS: A total of 273 barley accessions were evaluated for APR to SR in two distinct environments in Kazakhstan. Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. Transcriptomic data from 16 barley tissues were used to identify candidate genes within major QTL regions. Substantial phenotypic variation in SR severity was observed across environments. RESULTS: A total of 204 MTAs were identified, among which 96 were stable across models, resulting in 19 model-stable QTLs spanning all seven barley chromosomes. Six QTLs co-localized with known SR-resistance QTLs and genes, including Rpg1 and Rpg6. Q_rpg_7H.1 (coinciding with Rpg1) was one of the strongest and most consistent QTL, harboring 42 highly expressed candidate genes. A novel major-effect QTL on chromosome 5H, Q_rpg_5H.1 (3.5 - 9.9 Mb), not previously associated with known resistance loci, contained 10 highly expressed genes grouped into three co-expression clusters, including WRKY transcription factors and PR-5 proteins. CONCLUSION: This study provides new insights into the complex, multilayered genetic control of SR resistance in barley. The discovery of both known and novel QTLs offers valuable targets for marker-assisted selection and lays the foundation for breeding durable SR-resistant barley adapted to diverse agroecological conditions.

Hordeum vulgare L.↗
PubMed2026

Quantitative trait locus qLDC5 regulates primary root branching in an auxin-dependent manner.

L-type lateral root (LLR) density determines root system architecture, affecting nutrient acquisition in rice (Oryza sativa L.), particularly under low-phosphorus conditions. Previous studies identified genotypic differences in LLR density and a quantitative trait locus (QTL) enhancing LLR density on crown roots (qLDC5). We showed that LLR densities on crown and primary roots were closely correlated and confirmed higher LLR density on primary roots in qLDC5 donor DJ123 compared with the African variety NERICA4 using X-ray micro-computed tomography. We confirmed the qLDC5 effect in a field experiment for LLR density on primary roots. LLR densities on primary and crown roots, therefore, appear under similar genetic control. Developmental analyses revealed that DJ123 and NDJ188-a derivative line harboring qLDC5-initiate more lateral root primordia than NERICA4, with a higher proportion progressing to elongation, but that exogenous auxin application reversed this ranking. Within qLDC5, auxin biosynthesis gene OsYUCCA2 and auxin response factor OsARF15 were up-regulated in DJ123. Transcriptome analysis revealed an indirect auxin-mediated regulatory network underlying LLR variation. Differentially expressed genes in DJ123 and NDJ188 were enriched for ent-kaurene and gibberellin metabolism, including the robust induction of OsGA2ox5. These findings suggest qLDC5 increases lateral root density by coordinating gibberellin, auxin, and terpene pathways.

Oryza↗
PubMed2026

A novel reusable transcriptome-wide association study workflow used to map key genes linked to important cattle traits.

Transcriptome-wide association studies (TWAS) are a powerful approach for studying the genes underlying complex traits by directly integrating GWAS and gene expression datasets. In cattle, they have been previously applied to identify genes driving fertility, milk production, and health. However, these studies have also highlighted several challenges, from difficulties in reproducing these complex analyses to limitations from poor genotype calls, especially when called directly from RNA sequencing data. To address these and other challenges, for the H2020 BovReg Project, we have developed a streamlined, species-agnostic, and reusable Nextflow TWAS workflow to integrate transcriptomic and GWAS summary statistic datasets. Our workflow first generates accurate genotype calls and gene expression prediction models from transcriptomic datasets and then applies these tools to impute gene expression levels into GWAS cohorts, enabling the association of genes with traits of interest. We explore optimal strategies for calling genetic variants directly from transcriptomic data and illustrate that using imputation approaches specifically designed for low-pass sequencing data can improve variant calling over previously adopted methods. We demonstrate the utility of our TWAS workflow by applying it to both novel and publicly available GWAS cohorts for cattle, detecting novel gene-trait associations for complex traits. Using a new transcriptome annotation of the cattle genome generated for the BovReg project we also illustrate how previously un-assayable associations can be detected. The results and the workflow we present, provide a new resource for the community and contribute to a better understanding of the molecular drivers of complex traits in cattle with the goal of eventually leveraging this information in future breeding decisions.

Animals↗
PubMed2001

Murine inter-strain polymorphisms alter gene targeting frequencies at the mu opioid receptor locus in embryonic stem cells.

Chromosomal regions near the mu opioid receptor gene are implicated in morphine preference by quantitative trait loci studies. Differences in expression of the mu opioid receptor are expected to contribute to differences in inter-individual (humans) or strain-specific (mice) responses to painful stimuli, opiate drugs, and addictive behaviors. The search for relevant genetic elements is hindered by a lack of inter-strain (or inter-individual) genomic sequence information. This work describes 9.3 kb of DNA sequence surrounding exons 2 and 3 of the murine mu opioid receptor gene from both 129/Sv and C57BL/6 strains. While the exons are perfectly conserved, intronic sequences demonstrate approximately a 2.5% divergence between the strains. Polymorphism within these intronic regions may effect either primary transcript stability or C-terminal splicing. Homologous recombination frequencies of targeting vectors harboring mu opioid receptor gene sequences have also been compared in embryonic stem cells derived from these strains. Non-isogenic targeting reduces homologous recombination in both 129/Sv and C57BL/6 embryonic stem cells by greater than 15-fold. These findings are the first to examine C57BL/6 embryonic stem cells for non-isogenic targeting frequencies and to define polymorphisms that exist between these mouse strains which might contribute to opioid behaviors.

Alleles↗
PubMed2026

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

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

Software↗