Expression Quantitative Trait Loci Research Articles

Impact of putatively beneficial genomic loci on gene expression in little brown bats (Myotis lucifugus, Le Conte, 1831) affected by white-nose syndrome.

Genome-wide scans for selection have become a popular tool for investigating evolutionary responses in wildlife to emerging diseases. However, genome scans are susceptible to false positives and do little to demonstrate specific mechanisms by which loci impact survival. Linking putatively resistant genotypes to observable phenotypes increases confidence in genome scan results and provides evidence of survival mechanisms that can guide conservation and management efforts. Here we used an expression quantitative trait loci (eQTL) analysis to uncover relationships between gene expression and alleles associated with the survival of little brown bats (Myotis lucifugus) despite infection with the causative agent of white-nose syndrome. We found that 25 of the 63 single-nucleotide polymorphisms (SNPs) associated with survival were related to gene expression in wing tissue. The differentially expressed genes have functional annotations associated with the innate immune system, metabolism, circadian rhythms, and the cellular response to stress. In addition, we observed differential expression of multiple genes with survival implications related to loci in linkage disequilibrium with focal SNPs. Together, these findings support the selective function of these loci and suggest that part of the mechanism driving survival may be the alteration of immune and other responses in epithelial tissue.

Systems genetic analysis of lignin biosynthesis in Populus tremula.

The genetic control underlying natural variation in lignin content and composition in trees is not fully understood. We performed a systems genetic analysis to uncover the genetic regulation of lignin biosynthesis in a natural 'SwAsp' population of aspen (Populus tremula) trees. We analyzed gene expression by RNA sequencing (RNA-seq) in differentiating xylem tissues, and lignin content and composition using Pyrolysis-GC-MS in mature wood of 268 trees from 99 genotypes. Abundant variation was observed for lignin content and composition, and genome-wide association study identified proteins in the pentose phosphate pathway and arabinogalactan protein glycosylation among the top-ranked genes that are associated with these traits. Variation in gene expression and the associated genetic polymorphism was revealed through the identification of 312 705 local and 292 003 distant expression quantitative trait loci (eQTL). A co-expression network analysis suggested modularization of lignin biosynthesis and novel functions for the lignin-biosynthetic CINNAMYL ALCOHOL DEHYDROGENASE 2 and CAFFEOYL-CoA O-METHYLTRANSFERASE 3. PHENYLALANINE AMMONIA LYASE 3 was co-expressed with HOMEOBOX PROTEIN 5 (HB5), and the role of HB5 in stimulating lignification was demonstrated in transgenic trees. The systems genetic approach allowed linking natural variation in lignin biosynthesis to trees´ responses to external cues such as mechanical stimulus and nutrient availability.

Regulatory networks of coresident subgenomes during rapid fiber cell elongation in upland cotton

Cotton, an intriguing plant species shaped by polyploidization, evolution, and domestication, holds particular interest due to the complex mechanisms governing fiber traits across its two subgenomes. However, the regulatory elements or transcriptional networks between subgenomes during fiber elongation remain to be fully clarified. Here, we analyzed 1462 cotton fiber samples to reconstruct the gene-expression regulatory networks that influence fiber cell elongation. Inter-subgenome expression quantitative trait loci (eQTLs) largely dictate gene transcription, with a notable tendency for the D subgenome to regulate A-subgenome eGenes. This regulation reveals synchronized homoeologous gene expression driven by co-localized eQTLs and divergent patterns that diminish genetic correlations, thus leading to preferential expression in the A and D subgenomes. Hotspot456 emerged as a key regulator of fiber initiation and elongation, and artificial selection of trans-eQTLs in hotspot456 that positively regulate KCS1 has facilitated cell elongation. Experiments designed to clarify the roles of trans-eQTLs in improved fiber breeding confirmed the inhibition of GhTOL9 by a specific trans-eQTL via GhWRKY28, which negatively affects fiber elongation. We propose a model in which the GhWRKY28–GhTOL9 module regulates this process through the ESCRT (endosomal sorting complex required for transport) pathway. This research significantly advances our understanding of cotton’s evolutionary and domestication processes and the intricate regulatory mechanisms that underlie significant plant traits.

Identifying genetically-supported drug repurposing targets for non-small cell lung cancer through mendelian randomization of the druggable genome.

Lung cancer is responsible for most cancer-related deaths, and non-small cell lung cancer (NSCLC) accounts for the majority of cases. Targeted therapy has made promising advancements in systemic treatment for NSCLC over the last two decades, but inadequate drug targets with clinically proven survival benefits limit its universal application in clinical practice compared to chemotherapy and immunotherapy. There is an urgent need to explore new drug targets to expand the beneficiary group. This study aims to identify druggable genes and to predict the efficacy and prognostic value of the corresponding targeted drugs in NSCLC. Two-sample mendelian randomization (MR) of druggable genes was performed to predict the efficacy of their corresponding targeted therapy for NSCLC. Subsequent sensitivity analyses were performed to assess potential confounders. Accessible RNA sequencing data were incorporated for subsequent verifications, and Kaplan-Meier survival curves of different gene expressions were used to explore the prognostic value of candidate druggable genes. MR screening encompassing 4,863 expression quantitative trait loci (eQTL) and 1,072 protein quantitative trait loci (pQTL, with 453 proteins overlapping) were performed. Seven candidate druggable genes were identified, including CD33, ENG, ICOSLG and IL18R1 for lung adenocarcinoma, and VSIR, FSTL1 and TIMP2 for lung squamous cell carcinoma. The results were validated by further transcriptomic investigations. Drugs targeting genetically supported genomes are considerably more likely to yield promising efficacy and succeed in clinical trials. We provide compelling genetic evidence to prioritize drug development for NSCLC.

Mendelian randomization reveals potential causal relationships between cellular senescence-related genes and multiple cancer risks

Cellular senescence is widely acknowledged as having strong associations with cancer. However, the intricate relationships between cellular senescence-related (CSR) genes and cancer risk remain poorly explored, with insights on causality remaining elusive. In this study, Mendelian Randomization (MR) analyses were used to draw causal inferences from 866 CSR genes as exposures and summary statistics for 18 common cancers as outcomes. We focused on genetic variants affecting gene expression, DNA methylation, and protein expression quantitative trait loci (cis-eQTL, cis-mQTL, and cis-pQTL, respectively), which were strongly linked to CSR genes alterations. Variants were selected as instrumental variables (IVs) and analyzed for causality with cancer using both summary-data-based MR (SMR) and two-sample MR (TSMR) approaches. Bayesian colocalization was used to unravel potential regulatory mechanisms underpinning risk variants in cancer, and further validate the robustness of MR results. We identified five CSR genes (CNOT6, DNMT3B, MAP2K1, TBPL1, and SREBF1), 18 DNA methylation genes, and LAYN protein expression which were all causally associated with different cancer types. Beyond causality, a comprehensive analysis of gene function, pathways, and druggability values was also conducted. These findings provide a robust foundation for unravelling CSR genes molecular mechanisms and promoting clinical drug development for cancer.

Genetic analysis from multiple cohorts implies causality between 2200 druggable genes, telomere length, and leukemia

BackgroundClinical therapeutic targets for leukemia remain to be identified and the causality between leukemia and telomere length is unclear. MethodsThis work employed cis expression quantitative trait locus (eQTL) for 2,200 druggable genes from the eQTLGen Consortium and genome-wide association studies (GWAS) summary data for telomere length in seven blood cell types from the UK Biobank, Netherlands Cohort as exposures. GWAS data for lymphoid leukemia (LL) and myeloid leukemia (ML) from FinnGen and Lee Lab were used as outcomes for discovery and replication cohorts, respectively. Robust Mendelian randomization (MR) findings were generated from seven MR models and a series of sensitivity analyses. Summary-data-based MR (SMR) analysis and transcriptome-wide association studies (TWAS) were further implemented to verify the association between identified druggable genes and leukemia. Single-cell type expression analysis was employed to identify the specific expression of leukemia casual genes on human bone marrow and peripheral blood immune cells. Multivariable MR analysis, linkage disequilibrium score regression (LDSC), and Bayesian colocalization analysis were performed to further validate the relationship between telomere length and leukemia. Mediation analysis was used to assess the effects of identified druggable genes affecting leukemia via telomere length. Phenome-wide MR (Phe-MR) analysis for assessing the effect of leukemia causal genes and telomere length on 1,403 disease phenotypes. ResultsCombining the results of the meta-analysis for MR estimates from two cohorts, SMR and TWAS analysis, we identified five LL causal genes (TYMP, DSTYK, PPIF, GDF15, FAM20A) and three ML causal genes (LY75, ADA, ABCA2) as promising drug targets for leukemia. Univariable MR analysis showed genetically predicted higher leukocyte telomere length increased the risk of LL (odds ratio [OR] = 2.33, 95 % confidence interval [95 % CI] 1.70–3.18; P = 1.33E-07), and there was no heterogeneity and horizontal pleiotropy. Evidence from the meta-analysis of two cohorts strengthened this finding (OR = 1.88, 95 % CI 1.06–3.05; P = 0.01). Multivariable MR analysis showed the causality between leukocyte telomere length and LL without interference from the other six blood cell telomere length (OR = 2.72, 95 % CI 1.88–3.93; P = 1.23E-07). Evidence from LDSC supported the positive genetic correlation between leukocyte telomere length and LL (rg = 0.309, P = 0.0001). Colocalization analysis revealed that the causality from leukocyte telomere length on LL was driven by the genetic variant rs770526 in the TERT region. The mediation analysis via two-step MR showed that the causal effect from TYMP on LL was partly mediated by leukocyte telomere length, with a mediated proportion of 12 %. ConclusionOur study identified several druggable genes associated with leukemia risk and provided new insights into the etiology and drug development of leukemia. We also found that genetically predicted higher leukocyte telomere length increased LL risk and its potential mechanism of action.

Identification of genetic variants associated with clinical features of sickle cell disease

Sickle cell disease (SCD) is an inherited blood disorder marked by homozygosity of hemoglobin S, which is a defective hemoglobin caused by a missense mutation in the β-globin gene. However, clinical phenotypes of SCD vary among patients. To investigate genetic variants associated with various clinical phenotypes of SCD, we genotyped DNA samples from 520 SCD subjects and used a genome-wide association study (GWAS) approach to identify genetic variants associated with phenotypic features of SCD. For HbF levels, the previously reported 2p16.1 locus (BCL11A) reached genome significance (rs1427407, P = 8.58 × 10–10) in our GWAS as expected. In addition, we found a new genome-wide significance locus at 15q14 (rs8182015, P = 2.07 × 10–8) near gene EMC7. GWAS of acute chest syndrome (ACS) detected a locus (rs79915189, P = 3.70 × 10–8) near gene IDH2 at 15q26.1. The SNP, rs79915189, is also an expression quantitative trait locus (eQTL) of IDH2 in multiple tissues. For vasoocclusive episode (VOE), GWAS detected multiple significant signals at 2p25.1 (rs62118798, P = 4.27 × 10–8), 15q26.1 (rs62020555, P = 2.04 × 10–9) and 15q26.3 (rs117797325, P = 4.63 × 10–8). Our findings provide novel insights into the genetic mechanisms of SCD suggesting that common genetic variants play an important role in the presentation of the clinical phenotypes of patients with SCD.

Extensive co-regulation of neighboring genes complicates the use of eQTLs in target gene prioritization

Identifying causal genes underlying genome-wide association studies (GWAS) is a fundamental problem in human genetics. Although colocalisation with gene expression quantitative trait loci (eQTLs) is often used to prioritise GWAS target genes, systematic benchmarking has been limited due to unavailability of large ground truth datasets. Here, we re-analysed plasma protein QTL data from 3,301 individuals of the INTERVAL cohort together with 131 eQTL Catalogue datasets. Focusing on variants located within or close to the affected protein identified 793 proteins with at least one cis-pQTL where we could assume that the most likely causal gene was the gene coding for the protein. We then benchmarked the ability of cis-eQTLs to recover these causal genes by comparing three Bayesian colocalisation methods (coloc.susie, coloc.abf and CLPP) and five Mendelian randomisation (MR) approaches (three varieties of inverse-variance weighted MR, MR-RAPS, and MRLocus). We found that assigning fine-mapped pQTLs to their closest protein coding genes outperformed all colocalisation methods regarding both precision (71.9%) and recall (76.9%). Furthermore, the colocalisation method with the highest recall (coloc.susie - 46.3%) also had the lowest precision (45.1%). Combining evidence from multiple conditionally distinct colocalising QTLs with MR increased precision to 81%, but this was accompanied by a large reduction in recall to 7.1%. Furthermore, the choice of the MR method greatly affected performance, with the standard inverse-variance weighted MR often producing many false positives. Our results highlight that linking GWAS variants to target genes remains challenging with eQTL evidence alone, and prioritising novel targets requires triangulation of evidence from multiple sources.

Novel genetic structures associated with adverse response to chemotherapy in breast cancer.

Introduction: The role of genetic variants in response to chemotherapy has been investigated in several studies. This study aimed to investigate genetic variants associated with response to chemotherapy in breast cancer (BC) patients. Methods: Significant variants (p < 5 × 10-8) associated with response to chemotherapy were obtained from GWA studies. Candidate variants were identified by haplotype analysis (r2 ≥ 0.9, D'≥0.9) using 1000Genome LD data. To determine the effects of the variants on gene expression, expression quantitative trait loci (eQTL) were evaluated. To compare the expression of the identified genes in tumor samples, expression levels were compared between TCGA tumor types and adjacent normal tissues. Results: Six rs3820706, rs147451859, rs4784750, rs17587029, rs16830728, and rs16972207 variants were significantly associated with response to chemotherapy in BC patients (p < 5 × 10-8). Seven novel haplotypic structures were identified to be associated with adverse response to chemotherapy in BC patients. These haplotypes formed two genetic structures associated with neutropenia, leukopenia, chemotherapy-induced cytotoxicity (GAG-TTAT), and chemotherapy-induced alopecia (CC-CAACTCCCGTTGCGG). These variants are located on PPCDC, NLRC5, STAM2, and TNFSF13B genes, and the expression of these genes significantly changed in BC tissues than normal tissues (P ≤ 0.05), also showing gene-gene correlation (P ≤ 0.05). Conclusions: These genetic variants and their associated novel haplotypic structures can predict adverse response to chemotherapy in BC patients and could potentially form BC-associated genetic panel for adverse response to chemotherapy.

Integrative genomic analyses identify neuroblastoma risk genes involved in neuronal differentiation

Genome-Wide Association Studies (GWAS) have been decisive in elucidating the genetic predisposition of neuroblastoma (NB). The majority of genetic variants identified in GWAS are found in non-coding regions, suggesting that they can be causative of pathogenic dysregulations of gene expression. Nonetheless, pinpointing the potential causal genes within implicated genetic loci remains a major challenge. In this study, we integrated NB GWAS and expression Quantitative Trait Loci (eQTL) data from adrenal gland to identify candidate genes impacting NB susceptibility. We found that ZMYM1, CBL, GSKIP and WDR81 expression was dysregulated by NB predisposing variants. We further investigated the functional role of the identified genes through computational analysis of RNA sequencing (RNA-seq) data from single-cell and whole-tissue samples of NB, neural crest, and adrenal gland tissues, as well as through in vitro differentiation assays in NB cell cultures. Our results indicate that dysregulation of ZMYM1, CBL, GSKIP, WDR81 may lead to malignant transformation by affecting early and late stages of normal program of neuronal differentiation. Our findings enhance the understanding of how specific genes contribute to NB pathogenesis by highlighting their influence on neuronal differentiation and emphasizing the impact of genetic risk variants on the regulation of genes involved in critical biological processes.

Genetic Associations With an Amyotrophic Lateral Sclerosis Reversal Phenotype.

The term "ALS Reversal" describes patients who initially meet diagnostic criteria for amyotrophic lateral sclerosis (ALS) or had clinical features most consistent with progressive muscular atrophy (PMA) but subsequently demonstrated substantial and sustained clinical improvement. The objective of this genome-wide association study (GWAS) was to identify correlates of this unusual clinical phenotype. Participants were recruited from a previously created database of individuals with the ALS Reversal phenotype. Whole-genome sequencing (WGS) data were compared with ethnicity-matched patients with typically progressive ALS enrolled through the CReATe Consortium's Phenotype-Genotype-Biomarker (PGB) study. These results were replicated using an independent ethnically matched WGS data set from Target ALS. Significant results were further explored with available databases of genetic regulatory markers and expression quantitative trait loci (eQTL) analysis. WGS from 22 participants with documented ALS Reversals was compared with the PGB primary cohort (n = 103) and the Target ALS validation cohort (n = 140). Two genetic loci met predefined criteria for statistical significance (two-sided permutation p ≤ 0.01) and remained plausible after fine-mapping. The lead single nucleotide variant (SNV) from the first locus was rs4242007 (primary cohort GWAS OR = 12.0, 95% CI 4.1 to 34.6), which is in an IGFBP7 intron and is in near-perfect linkage disequilibrium with a SNV in the IGFBP7 promoter region. Both SNVs are associated with decreased frontal cortex IGFBP7 expression in eQTL data sets. Notably, 3 Reversals, but none of the typically progressive individuals (n = 243), were homozygous for rs4242007. The importance of the second locus, located near GRIP1, is uncertain given the absence of an associated effect on nearby gene transcription. We found a significant association between the Reversal phenotype and an IGFBP7 noncoding SNV that is associated with IGFBP7 expression. This is biologically relevant as IGFBP7 is a reported inhibitor of the insulin growth factor-1 (IGF-1) receptor that activates the possibly neuroprotective IGF-1 signaling pathway. This finding is limited by small sample size but suggests that there may be merit in further exploration of IGF-1 pathway signaling as a therapeutic mechanism for ALS. This study was registered with ClinicalTrials.gov (NCT03464903) on March 14, 2018. The first participant was enrolled on June 22, 2018.

Multi-Omics Analysis Revealed the rSNPs Potentially Involved in T2DM Pathogenic Mechanism and Metformin Response.

The goal of our study was to identify and assess the functionally significant SNPs with potentially important roles in the development of type 2 diabetes mellitus (T2DM) and/or their effect on individual response to antihyperglycemic medication with metformin. We applied a bioinformatics approach to identify the regulatory SNPs (rSNPs) associated with allele-asymmetric binding and expression events in our paired ChIP-seq and RNA-seq data for peripheral blood mononuclear cells (PBMCs) of nine healthy individuals. The rSNP outcomes were analyzed using public data from the GWAS (Genome-Wide Association Studies) and Genotype-Tissue Expression (GTEx). The differentially expressed genes (DEGs) between healthy and T2DM individuals (GSE221521), including metformin responders and non-responders (GSE153315), were searched for in GEO RNA-seq data. The DEGs harboring rSNPs were analyzed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We identified 14,796 rSNPs in the promoters of 5132 genes of human PBMCs. We found 4280 rSNPs to associate with both phenotypic traits (GWAS) and expression quantitative trait loci (eQTLs) from GTEx. Between T2DM patients and controls, 3810 rSNPs were detected in the promoters of 1284 DEGs. Based on the protein-protein interaction (PPI) network, we identified 31 upregulated hub genes, including the genes involved in inflammation, obesity, and insulin resistance. The top-ranked 10 enriched KEGG pathways for these hubs included insulin, AMPK, and FoxO signaling pathways. Between metformin responders and non-responders, 367 rSNPs were found in the promoters of 131 DEGs. Genes encoding transcription factors and transcription regulators were the most widely represented group and many were shown to be involved in the T2DM pathogenesis. We have formed a list of human rSNPs that add functional interpretation to the T2DM-association signals identified in GWAS. The results suggest candidate causal regulatory variants for T2DM, with strong enrichment in the pathways related to glucose metabolism, inflammation, and the effects of metformin.

Genetic regulation of B cell receptor signaling pathway: Insights from expression quantitative trait locus analysis using a mixed model

The B cell receptor (BCR) signaling pathway regulates non-immune cellular response through various pathways like MAPK, NF-kB, and PI3K-Akt. This study aimed to identify expression quantitative trait loci (eQTL) and their regulatory functions on BCR signaling pathway genes. A mixed model was employed to analyze eQTL using RNA expression levels in lymphoblastoid from 376 Europeans in the GEUVADIS dataset. In total, 266 SNPs, including 115 cis-acting SNPs, were found for association with transcription of 13 genes (P < 5 × 10−8), revealing 19 independent signals for five genes through linkage disequilibrium analysis. Functional analysis, aligning them with DNase sensitive sites, transcription factor binding sites, histone modification, promoters/enhancers, CpG islands, and ChIA-PET, identified regulatory variants targeting SYK, VAV2, and PLCG2. Notably, rs2562397 was validated as a SYK promoter variant, and rs694505, rs636667, and rs4889409 were confirmed as enhancer variants for VAV2 and PLCG2. Their allelic differences in gene expression were also confirmed using ENCODE ChIP-seq and Sei neural network prediction. Persistent differential expression of these genes by alleles might impact the adaptive immune system, vascular development, and/or relevant diseases that have been previously associated with other variants of the genes. Comprehensive genetic architecture studies of the BCR signaling pathway, along with experiments demonstrating related mechanisms, will greatly contribute to understanding the underlying mechanisms of relevant disease development and implementing precision medicine.

A comprehensive assessment of the association between common drugs and psychiatric disorders using Mendelian randomization and real-world pharmacovigilance database

Medications prescribed for chronic diseases can lead to short-term neuropsychiatric symptoms, but their long-term effects on brain structures and psychiatric conditions remain unclear. We comprehensively analyzed the FDA Adverse Event Reporting System database and conducted drug target Mendelian Randomization (MR) studies on six categories of common drugs, 477 brain imaging-derived phenotypes (IDPs) and eight psychiatric disorders. Genetic instruments were extracted from expression quantitative trait loci (eQTLs) in blood, brain, and other target tissues, protein quantitative trait loci (pQTLs) in blood, and genome-wide association studies (GWAS) of hemoglobin and cholesterol. Summary statistics for brain IDPs, psychiatric disorders, and gut microbiome were obtained from the BIG40, Psychiatric Genomics Consortium, and MiBioGen. A two-step MR and mediation analysis were employed to screen possible mediators of drug-IDP effects from 119 gut microbiota genera and identify their mediation proportions. Among 19 drug classes, six drugs were found to be associated with higher risks of psychiatric adverse events, while 11 drugs were associated with higher risks of gastrointestinal adverse events in the FAERS analysis. We identified ten drug-psychiatric disorder associations, 202 drug-IDP associations, 16 drug-microbiota associations, and four drug-microbiota-IDP causal links. For example, PPARG activation mediated HbA1c reduction caused a higher risk of bipolar disorder (BD) II. Genetically proxied GLP-1R agonists were significantly associated with an increase in the volume of the CA3-head of the right hippocampus and the area of the left precuneus cortex, both of which have been shown to correlate with cognition in previous studies. Common drugs may affect brain structure and risk of psychiatric disorder. Oral medications in particular may exert some of these effects by influencing gut microbiota. This study calls for greater attention to be paid to the neuropsychiatric adverse effects of drugs and encourages drug repurposing. National Natural Science Foundation of China (grant No. 82330035, 82130043, 82172685, and 82001223), National Natural Science Foundation of Hunan Province (grant No. 2021SK1010), and the Science Foundation for Distinguished Young Scholars of Changsha (grant No. kq2209006).

Multi-omic characterization of air pollution effects: Applications of AirSigOmniTWP Hub

Air pollution, particularly fine particulate matter and gaseous pollutants including NO2 and NOx, presents significant public health challenges. While the harmful effects of these pollutants are well-documented, the molecular mechanisms underlying their impact on health remain incompletely understood. In this study, we utilized genome-wide association study (GWAS) data from the UK Biobank, expression quantitative trait loci (eQTL) data from the Genotype-Tissue Expression (GTEx) project, and protein quantitative trait loci (pQTL) data from the Atherosclerosis Risk in Communities (ARIC) study to conduct comprehensive analyses using the Unified Test for Molecular Signatures (UTMOST), Transcriptome-wide Association Studies (TWAS), and Proteome-wide Association Studies (PWAS). To integrate and synthesize these analyses, we developed the AirSigOmniTWP Hub, a specialized platform designed to consolidate and interpret the results from UTMOST, TWAS, and PWAS. TWAS analysis identified a significant association between PM10 exposure and the gene INO80E in females (P = 4.37×10⁻⁵, FDR = 0.0383), suggesting a potential role in chromatin remodeling. PWAS analysis revealed a significant association between NOx exposure and the gene PIP in females (P = 2.28×10⁻⁵, FDR = 0.0299), implicating its involvement in inflammatory pathways. Additionally, UTMOST analyses uncovered significant associations between various pollutants and genes including NCOA4P3 and SPATS2L with PM2.5 exposure, indicating potential mechanisms related to transcriptional regulation and gene-environment interactions.

The impact of immunity on the risk of coronary artery disease: insights from a multiomics study.

Immune inflammation is intricately associated with coronary artery disease (CAD) progression, necessitating the pursuit of more efficacious therapeutic strategies. This study aimed to uncover potential therapeutic targets for CAD and myocardial infarction (MI) by elucidating the causal connection between regulatory immune-related genes (RIRGs) and these disorders. We performed summary data-based Mendelian randomization analysis to assess the therapeutic targets linked to expression quantitative trait loci and methylation quantitative trait loci of RIRGs in relation to CAD and MI. Independent validation cohorts and datasets from coronary artery and left ventricular heart tissue were analyzed. To strengthen causal inference, colocalization analysis and PhenoScanner phenotype scans were employed. Utilizing multiomics integration, we pinpointed EIF2B2, FCHO1, and DDT as CAD risk genes. Notably, EIF2B2 and FCHO1 displayed significant associations with MI. High EIF2B2 expression, regulated by cg16144293, heightened CAD and MI risk at rs175438. In contrast, enhanced FCHO1 expression, modulated by cg18329931, reduced CAD and MI risk at rs13382133. DDT upregulation influenced by cg11060661 and cg09664220 was associated with decreased CAD risk at rs5760120. Colocalization analysis firmly established these relationships. EIF2B2, FCHO1, and DDT represent risk loci for CAD progression within RIRGs. Our identification of these genes enhances understanding of CAD pathogenesis and directs future drug development efforts.

Deciphering the role of oxidative stress genes in idiopathic pulmonary fibrosis: a multi-omics mendelian randomization approach.

Oxidative stress (OS) is crucial in idiopathic pulmonary fibrosis (IPF) pathogenesis, with its genes potentially acting as both causes and consequences of the disease. We identified OS-related genes from GeneCards and performed a meta-analysis on pulmonary transcriptome datasets to discover differentially expressed genes (DEGs) related to OS in IPF. We integrated this data with the largest available IPF GWAS summaries, expression quantitative trait loci (eQTLs), and DNA methylation QTLs (mQTLs) from blood. This approach aimed to identify blood OS genes and regulatory elements linked to IPF risk, incorporating the latest pulmonary eQTLs and bronchoalveolar lavage fluid microbial QTLs (bmQTLs) for a comprehensive view of gene-lung microbiota interactions through SMR and colocalization analyses. Sensitivity analyses were conducted using two additional mendelian randomization (MR) methods. Meta-analysis revealed 1090 differentially expressed OS genes between IPF patients and controls. Integration with IPF GWAS, eQTL, and mQTL data identified key genes and regulatory elements involved in IPF pathogenesis, highlighting the role of specific genes such as KCNMA1 and SLC22A5 in modulating IPF risk through epigenetic mechanisms. Colocalization analysis further identified potential interactions between gene expression and lung microbiota. Our findings elucidate the complex interplay between OS genes and IPF, suggesting potential therapeutic targets and highlighting the importance of considering epigenetic and microbial interactions in the disease's etiology and progression.

SexAnnoDB, a knowledgebase of sex-specific regulations from multi-omics data of human cancers

BackgroundSexual differences across molecular levels profoundly impact cancer biology and outcomes. Patient gender significantly influences drug responses, with divergent reactions between men and women to the same drugs. Despite databases on sex differences in human tissues, understanding regulations of sex disparities in cancer is limited. These resources lack detailed mechanistic studies on sex-biased molecules.MethodsIn this study, we conducted a comprehensive examination of molecular distinctions and regulatory networks across 27 cancer types, delving into sex-biased effects. Our analyses encompassed sex-biased competitive endogenous RNA networks, regulatory networks involving sex-biased RNA binding protein-exon skipping events, sex-biased transcription factor-gene regulatory networks, as well as sex-biased expression quantitative trait loci, sex-biased expression quantitative trait methylation, sex-biased splicing quantitative trait loci, and the identification of sex-biased cancer therapeutic drug target genes. All findings from these analyses are accessible on SexAnnoDB (https://ccsm.uth.edu/SexAnnoDB/).ResultsFrom these analyses, we defined 126 cancer therapeutic target sex-associated genes. Among them, 9 genes showed sex-biased at both the mRNA and protein levels. Specifically, S100A9 was the target of five drugs, of which calcium has been approved by the FDA for the treatment of colon and rectal cancers. Transcription factor (TF)-gene regulatory network analysis suggested that four TFs in the SARC male group targeted S100A9 and upregulated the expression of S100A9 in these patients. Promoter region methylation status was only associated with S100A9 expression in KIRP female patients. Hypermethylation inhibited S100A9 expression and was responsible for the downregulation of S100A9 in these female patients.ConclusionsComprehensive network and association analyses indicated that the sex differences at the transcriptome level were partially the result of corresponding sex-biased epigenetic and genetic molecules. Overall, SexAnnoDB offers a discipline-specific search platform that could potentially assist basic experimental researchers or physicians in developing personalized treatment plans.

Providing biological context for GWAS results using eQTL regulatory and co-expression networks in Populus.

Our study utilized genome-wide association studies (GWAS) to link nucleotide variants to traits in Populus trichocarpa, a species with rapid linkage disequilibrium decay. The aim was to overcome the challenge of interpreting statistical associations at individual loci without sufficient biological context, which often leads to reliance solely on gene annotations from unrelated model organisms. We employed an integrative approach that included GWAS targeting multiple traits using three individual techniques for lignocellulose phenotyping, expression quantitative trait loci (eQTL) analysis to construct transcriptional regulatory networks around each candidate locus and co-expression analysis to provide biological context for these networks, using lignocellulose biosynthesis in Populus trichocarpa as a case study. The research identified three candidate genes potentially involved in lignocellulose formation, including one previously recognized gene (Potri.005G116800/VND1, a critical regulator of secondary cell wall formation) and two genes (Potri.012G130000/AtSAP9 and Potri.004G202900/BIC1) with newly identified putative roles in lignocellulose biosynthesis. Our integrative approach offers a framework for providing biological context to loci associated with trait variation, facilitating the discovery of new genes and regulatory networks.

Genetics of cell-type-specific post-transcriptional gene regulation during human neurogenesis

The function of some genetic variants associated with brain-relevant traits has been explained through colocalization with expression quantitative trait loci (eQTL) conducted in bulk postmortem adult brain tissue. However, many brain-trait associated loci have unknown cellular or molecular function. These genetic variants may exert context-specific function on different molecular phenotypes including post-transcriptional changes. Here, we identified genetic regulation of RNA editing and alternative polyadenylation (APA) within a cell-type-specific population of human neural progenitors and neurons. More RNA editing and isoforms utilizing longer polyadenylation sequences were observed in neurons, likely due to higher expression of genes encoding the proteins mediating these post-transcriptional events. We also detected hundreds of cell-type-specific editing quantitative trait loci (edQTLs) and alternative polyadenylation QTLs (apaQTLs). We found colocalizations of a neuron edQTL in CCDC88A with educational attainment and a progenitor apaQTL in EP300 with schizophrenia, suggesting that genetically mediated post-transcriptional regulation during brain development leads to differences in brain function.