Expression Quantitative Trait Loci Research Articles

Association of genetically predicted human metabolomic gene expression with incident heart failure

Abstract Background Heart failure (HF) represents a significant challenge both for patients and healthcare systems worldwide. Despite ongoing efforts, therapeutic options are limited. The underlying pathophysiology involves intricate changes in cardiac metabolism that strongly influence the clinical phenotype, thus potentially offering novel options for therapeutic targeting. Systematic prioritisation of metabolic genes regarding their phenotypic impact on HF is outstanding. Purpose We investigate the association of genetically predicted metabolic gene expression with the onset of HF within UK Biobank (UKB). We followed up on identified candidate genes using human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) models of cardiomyocyte hypertrophy. Methods Utilising genomic data of 484,372 UKB participants and left ventricle-specific expression quantitative trait loci (eQTL) information from Genotype-Tissue Expression (GTEx) project, we predicted genetically regulated gene expression using established methods (PrediXcan) for all metabolic genes. Subsequently, we employed per-gene Cox proportional hazards (COX-PH) models to assess the association of genetically predicted metabolic gene expression with incident HF. We adjusted models for clinical risk using established risk models (Pooled Cohort Equations to Prevent HF (PCP-HF)). To validate our findings in vitro, we subjected hiPSC-CMs to a sympathomimetic stimulus via treatment with endothelin-1 (ET-1; 10 nM). We evaluate alterations in expression via real-time quantitative polymerase chain reaction (RT-qPCR) and establish methods for assessing hypertrophic remodelling. Results COX-PH models revealed the predicted expression of 104/1515 metabolic genes to be independently associated with the onset of HF. Subsequent refinement with a meta-analysis of commonly dysregulated genes in end-stage HF narrowed our findings to 46 candidate genes, of which several were known for their role in HF pathophysiology. We further traced nine genes for validation in hiPSC-CM models and showed significant dysregulation of several genes following the treatment with ET-1. Flow cytometry measured hiPSC-CM hypertrophy following sympathomimetic stimulation (p=0.036). Extracellular flux analysis revealed canonical changes following treatment with ET-1. Conclusions The study revealed several metabolic genes associated with incident HF, thus confirming previous findings and highlighting novel, putative therapeutic target genes. We confirmed transcriptional changes in an in vitro model. Ongoing work includes further validation via silencing candidate genes in hiPSC-CMs and subjection to the established phenotypic in vitro models.

Hypnotic use and the risk of cardiovascular diseases in insomnia patients

Abstract Background Insomnia, commonly treated with hypnotic agents, is an independent risk factor of cardiovascular diseases. Previous research demonstrated the detrimental effect of hypnotics on the prognosis of cardiovascular patients. Purpose We aimed to examine the association between hypnotic agents and cardiovascular outcomes in general individuals with insomnia. Methods In a propensity score matched cohort of UK Biobank (UKB) participants with insomnia, Cox proportional hazard model was used to estimate the association between regular use of hypnotic agents and predetermined cardiovascular outcomes including incident coronary heart diseases (CHD), heart failure (HF), stroke, and cardiovascular death. Inverse probability of treatment weighting and competing risk models were further performed during sensitivity analysis. Drug-target Mendelian randomization (MR) analyses were employed for further evaluation of hypnotics-related genes and cardiovascular disease association. The genome-wide association data of CHD, HF, and stroke were there large-scale genome-wide association analyses. Tissue-specified expression quantitative trait loci (eQTL) data was derived from BrainSeq phase II. Results During a median follow-up of 14.3 years, the matched cohort documented a total of 1,019 CHD cases, 406 HF cases, 285 stroke cases, and 197 cardiovascular deaths. No significant association was detected between Z-meds and CHD, stroke, and cardiovascular mortality. Benzodiazepine use was significantly associated with the increased risk of CHD, HF, and cardiovascular mortality (Figure 1). The inverse probability of treatment weighting and competing risk models didn’t alter the above associations. Moreover, drug-target MR analyses corroborated the safety of Z-meds in the general population regarding cardiovascular health. However, GABRG1—a drug target for benzodiazepines—was associated with heightened risks for heart failure, and ischemic stroke (Figure 2). Conclusions Our findings suggested the heterogeneous associations between different categories of hypnotics and incident cardiovascular events in individuals with insomnia. Z-meds are safe regarding the risk of cardiovascular outcomes in the UKB population with insomnia.Figure 1Figure 2

Systematic identification of therapeutic targets for coronary artery calcification: an integrated transcriptomic and proteomic Mendelian randomization

BackgroundCoronary artery calcification (CAC) is associated with an increased risk of mortality and cardiovascular events. However, none therapeutic drugs have been proven effective for CAC treatment. The objective of this study was to identify potential therapeutic targets for CAC through the utilization of Mendelian randomization (MR) and colocalization analysis.MethodsThe expression quantitative trait loci (eQTLs) of 16,943 genes from the eQTLGen consortium and protein quantitative trait loci (pQTLs) of 4,412 proteins from a plasma proteome were utilized as genetic instruments. Genetic associations with CAC were derived from a GWAS meta-analysis of 26,909 individuals. The MR and colocalization analysis were utilized to identify potential target genes.ResultsA total of 671 genes were found to be significantly associated with the risk of CAC based on transcriptomic MR analysis at a false discovery rate <0.05, while proteomic MR analysis identified 15 genes with significant associations with CAC at the same threshold. With robust evidence from colocalization analysis, we observed positive associations between CWF19L2, JARID2, and MANBA and the risk of CAC, while KLB exhibited an inverse association. In summary, our study identified 23 potential therapeutic targets for CAC. Further downstream analysis revealed IGFBP3, ABCC6, ULK3, DOT1L, KLB and AMH as promising candidates for repurposing in the treatment of CAC.ConclusionThe integrated MR analysis of transcriptomic and proteomic data identified multiple potential drug targets for the treatment of CAC. ULK3, DOT1L, and AMH were recognized as novel targets for drug repurposing for CAC and deserve further investigation.

A novel functional IKBKE variant activating NFAT in a patient with polyarthritis and a remittent fever

BackgroundIKBKE is a negative regulator of T cell activation and one of the key activators of type I interferon (IFN) and NFκB signaling via non-classical pathways. The upstream single nucleotide polymorphism of IKBKE (rs2297550-G) is a genome-wide association study risk variant of systemic lupus erythematosus, and is associated with decreased IKBKE expression in T cells by expression quantitative trait locus analysis.Case presentationA 48-year-old female had a remittent fever, arthritis, and oral ulcers for 20 years. She had a poor response to corticosteroids or disease-modifying antirheumatic drugs, including the tumor necrosis factor-α antagonist, etanercept, and the anti-interleukin-6 receptor antibody, tocilizumab.MethodShe participated in the Initiative on Rare and Undiagnosed Disease (IRUD), and whole-exome sequencing (WES) was performed. Functional analyses were conducted by transfecting the identified variants into reporter cells to assess the activation of NFAT and NFκB signaling. Additionally, peripheral blood RNA- sequencing (RNA-seq) data were compared with those from healthy individuals to evaluate the gene expression profiles of immune cells.ResultWES identified a novel heterozygous c.1877G>A, p(Cys626Tyr) variant in IKBKE. Functional analysis indicated that this variant led to increased activity of NFAT (p = 0.015) and decreased activity of NFκB and type I IFN (p = 0.00068 and 0.00044, respectively). The patient had a remarkably low proportion of Naïve CD4 T cells. RNA-seq of peripheral blood immune cell subsets revealed significant differences in gene expression, especially in T cells.ConclusionA novel functional heterozygous variant in IKBKE is described in a patient with a remittent fever and arthritis. The data suggest that IKBKE is an important negative regulator of inflammation, particularly in T cells, and this IKBKE variant might be the underlying cause of a novel autoinflammatory pathology.

Identification of JAZF1, KNOP1, and PLEKHA1 as causally associated genes and drug targets for Alzheimer's disease: a summary data-based Mendelian randomization study.

There is a growing body of evidence indicating the significant role of the immune system and immune cells in the progression of Alzheimer's disease (AD). However, the exact role of genes from various immune cell types in AD remains unclear. We aimed to utilize summary data-based Mendelian randomization (SMR) to explore the potential causal relationships between genes in specific immune cells and the risk of AD. By utilizing data sets of expression quantitative trait loci (eQTL) for 14 different immune cell types and large-scale AD genome-wide association study (GWAS), we employed SMR to identify key genes associated with AD within specific immune cells. Sensitivity analyses, including F-statistic, colocalization, and assessment of horizontal pleiotropy, were further conducted to validate the discovered genes. In addition, replication analyses were performed in AD GWAS from the FinnGen consortium. Finally, we further identified existing drugs that target or interact with the druggable genes and reviewed the studies about the associations between these drugs and AD. SMR analysis revealed 342 genes associated with AD across 14 immune cell types. Further sensitivity analyses identified nine genes, CTSH, FCER1G, FNBP4, HLA-E, JAZF1, KNOP1, PLEKHA1, RP11-960L18.1, and ZNF638 that had significant associations with AD across nine specific immune cell types. JAZF1, KNOP1 and PLEKHA1 were replicated in an independent analysis usingthe GWAS data. The review on gene-related drugs also supported these findings. Our research suggests that the expression of the genes JAZF1, KNOP1, and PLEKHA1 in specific immune cell types is related to the risk of AD.

An Expression Quantitative Trait Locus of Fc Gamma Receptor Genes is Associated with Anti-Malarial IgG Responses and Infection Levels in Burkinabe Families.

The interaction between antibodies and Fc gamma receptors (FcγRs) plays a critical role in regulating immune responses to Plasmodium falciparum. Polymorphisms in genes encoding FcγRs influence the host's capacity to control parasite infection. This study investigates whether non-coding variants influencing FcγR expression are associated with anti-malarial immunization and infection traits. We utilized eQTL databases and functional annotations to identify non-coding variants, specifically rs1771575, rs2099684, and rs6700241, within the FCGR gene cluster. In addition, we examined the coding variants rs1801274 (p.His167Arg) and rs1050501 (p.Ile231Thr), which affect the affinity of FcγRIIa and FcγRIIb for IgG. These variants were genotyped in 163 individuals from Burkinabe families. Family-based linear mixed regression and Quantitative Transmission Disequilibrium Tests (QTDT) analyses were performed to assess associations with IgG levels and malaria infection, accounting for relevant covariates. Linear mixed models identified rs1771575 as associated with total IgG levels, while both rs1771575 and rs1801274 were linked to IgG2, and rs1050501 to IgG1 levels. A haplotype combining rs2099684 and rs6700241 was positively associated with IgG1. The rs1771575-CC and rs1050501-TT genotypes correlated with higher infection levels in children. QTDT models confirmed the association of rs1771575 with IgG2 and infection in children. Our findings suggest that the intergenic variant rs1771575 serves as an independent marker for IgG levels and blood infection in children. This highlights the interplay between regulatory variants and coding mutations in FCGR, which may influence immune function and antibody production. These results underscore the potential for personalized strategies to monitor humoral responses in malaria-endemic regions.

Transcriptional Responses to Priority Effects in Nectar Yeast.

Priority effects, where the order and timing of species arrival influence the assembly of ecological communities, have been observed in a variety of taxa and habitats. However, the genetic and molecular basis of priority effects remains unclear, hindering a better understanding of when priority effects will be strong. We sought to gain such an understanding for the nectar yeast Metschnikowia reukaufii commonly found in the nectar of our study plant, the hummingbird-pollinated Diplacus (Mimulus) aurantiacus. In this plant, M. reukaufii can experience strong priority effects when it reaches flowers after other nectar yeasts, such as M. rancensis. After inoculation into two contrasting types of synthetic nectar simulating early arrival of M. rancensis, we conducted whole-transcriptome sequencing of 108 strains of M. reukaufii. We found that several genes were differentially expressed in M. reukaufii strains when the nectar had been conditioned by growth of M. rancensis. Many of these genes were associated with amino acid metabolism, suggesting that M. reukaufii strains responded molecularly to the reduction in amino acid availability caused by M. rancensis. Furthermore, investigation of expression quantitative trait loci (eQTLs) revealed that genes involved in amino acid transport and resistance to antifungal compounds were enriched in some genetic variants of M. reukaufii. We also found that gene expression was associated with population growth rate, particularly when amino acids were limited. These results suggest that intraspecific genetic variation in the ability of nectar yeasts to respond to nutrient limitation and direct fungal competition underpins priority effects in this microbial system.

Genetic Variants Associated with Sensitive Skin: A Genome-Wide Association Study in Korean Women

Sensitive skin (SS) is associated with discomfort, including burning, stinging, and itching. These symptoms are often exacerbated by environmental factors and personal care products. In this genome-wide association study (GWAS), we aimed to identify the genetic variants associated with SS in 1690 Korean female participants; 389 and 1301 participants exhibited sensitive and non-sensitive skin, respectively. Using a combination of self-reported questionnaires, patch tests, and sting tests, we selected 115 sensitive and 181 non-sensitive participants for genetic analysis. A GWAS was performed to identify the loci associated with SS. Although none of the single-nucleotide polymorphisms (SNPs) met the genome-wide significance threshold, we identified several SNPs with suggestive associations. SNP rs11689992 in the 2q11.3 region increased SS risk by approximately 3.67 times. SNP rs7614738 in the USP4 locus elevated SS risk by 2.34 times and was found to be an expression quantitative trait locus for GPX1, a gene involved in oxidative stress and inflammation. Additionally, SNPs rs12306124 in the RASSF8 locus and rs10483893 in the NRXN3 region were identified. These results suggest that the genetic variations affecting oxidative stress, cell growth regulation, and neurobiology potentially influence skin sensitivity, providing a basis for further investigation and the development of personalized approaches to manage sensitive skin.

Discovering Novel Loci of Chronic Kidney Disease via Principal Component Analysis based Multiple-trait Genome‑wide Association Study.

Introduction Chronic kidney diseases (CKD) encompass a spectrum of complex pathophysiological processes. While numerous genome-wide association studies (GWASs) have focused on individual traits such as albuminuria, estimated glomerular filtration rate (eGFR), and eGFR change, there remains a paucity of genetic studies integrating these traits collectively for comprehensive evaluation. Methods In this study, we performed individual GWASs for albuminuria, baseline eGFR, and eGFR slope utilizing data from non-diabetic individuals enrolled from the Taiwan Biobank (TWB). Subsequently, we employed principal component analysis to transform these three quantitative traits into principal components (PCs) and performed GWAS based on these principal components (PC-based GWAS). Results The individual GWAS analyses of albuminuria, baseline eGFR, and eGFR slope identified 10, 13, and 210 candidate loci respectively, with 2, 3, and 99 of them representing previously reported loci. PC-based GWAS identified additional 20 novel candidate loci linked to CKD (P-values ranging from 5.8 × 10-7 to 9.1 × 10-6). Notably, 4 of these 20 single nucleotide polymorphisms (rs9332641, rs10737429, rs117231653, and rs73360624) exhibited significant associations with kidney expression quantitative trait loci. Conclusion To our knowledge, this study represents the first PC-based GWAS integrating albuminuria, baseline eGFR, and eGFR slope. Our approach found 20 novel candidate loci suggestively associated with CKD, underscoring the value of integrating multiple kidney traits in unraveling the pathophysiology of this complex disorder.

Phosphodiesterase 5 and its inhibitors with ischemic heart disease: a Mendelian randomization analysis and a real-world study.

Accumulating studies reported that several phosphodiesterases (PDEs) inhibitors might have cardiovascular benefits. This study aimed to explore the relationship between genetically-predicted PDEs and ischemia heart disease via drug target Mendelian randomization (MR) approach, and then examine the effect of inhibitors of identified target on the outcomes by using real-world data. In the two-sample MR study, the expression of genes encoding PDEs was used to proxy the level of PDEs and available expression quantitative trait loci (eQTLs) for each target gene were identified as the genetic instruments. The outcomes included coronary heart disease (CHD) and myocardial infarction (MI). In the real-world study, a retrospective cohort was conducted to compare the incidence of outcomes between PDE5 inhibitors and alprostadil use by linking Swedish nationwide registers. MR analyses identified two types of PDEs, PDE5 and PDE8, genetically-predicted expression in blood of the encoded genes was significantly associated with the risk of CHD (ORPDE5A=1.22,95% CI=1.06-1.40; ORPDE8A=1.26,95% CI=1.07-1.49) and MI (ORPDE5A=1.27,95% CI=1.09-1.48; ORPDE8A=1.24,95% CI=1.04-1.48). Notably, the highest expression of PDE5A was observed in artery aorta, which was also positively related to CHD (OR=1.17,95% CI=1.05-1.32) and MI (OR=1.15,95% CI=1.02-1.30). Real-world study provided supportive evidence that as compared to alprostadil use, PDE5 inhibitors use significantly reduced the incidence of CHD (adjusted HR=0.70,95% CI=0.66-0.73) and MI (adjusted HR=0.79,95% CI=0.73-0.84). This study provided observational and genetic evidence about the protective role of PDE5 inhibition against ischemic heart disease, indicating the potential of these drugs to be repurposed for ischemia heart disease prevention and treatment.

ScTWAS Atlas: an integrative knowledgebase of single-cell transcriptome-wide association studies.

Single-cell transcriptome-wide association studies (scTWAS) is a new method for conducting TWAS analysis at the cellular level to identify gene-trait associations with higher precision. This approach helps overcome the challenge of interpreting cell-type heterogeneity in traditional TWAS results. As the field of scTWAS rapidly advances, there is a growing need for additional database platforms to integrate this wealth of data and knowledge effectively. To address this gap, we present scTWAS Atlas (https://ngdc.cncb.ac.cn/sctwas/), a comprehensive database of scTWAS information integrating literature curation and data analysis. The current version of scTWAS Atlas amasses 2,765,211 associations encompassing 34 traits, 30 cell types, 9 cell conditions and 16,470 genes. The database features visualization tools, including an interactive knowledge graph that integrates single-cell expression quantitative trait loci (sc-eQTL) and scTWAS associations to build a multi-omics level regulatory network at the cellular level. Additionally, scTWAS Atlas facilitates cross-cell-type analysis, highlighting cell-type-specific and shared TWAS genes. The database is designed with user-friendly interfaces and allows for easy browsing, searching, and downloading of relevant information. Overall, scTWAS Atlas is instrumental in exploring the genetic regulatory mechanisms at the cellular level and shedding light on the role of various cell types in biological processes, offering novel insights for human health research.

A regulatory variant rs9379874 in T1D risk region 6p22.2 affects BTN3A1 expression regulating T cell function.

Genome-wide association studies (GWAS) have identified that 6p22.2 region is associated with type 1 diabetes (T1D) risk in the Chinese Han population. This study aims to reveal associations between this risk region and T1D subgroups and related clinical features, and further identify causal variant(s) and target gene(s) in this region. 2608 T1D and 4814 healthy controls were recruited from East, Central, and South China. Baseline data and genotyping for rs4320356 were collected. The most likely causal variant and gene were identified by bioinformatics analysis, dual-luciferase reporter assays, expression quantitative trait loci (eQTL), and functional annotation of the non-coding region within the 6p22.2 region. The leading variant rs4320356 in the 6p22.2 region was associated with T1D risk in the Chinese and Europeans. However, this variant was not significantly associated with islet function or autoimmunity. In silico analysis suggested rs9379874 was the most potential causal variant for T1D risk among thymus, spleen, and T cells, overlapping with the enhancer-related histone mark in multiple T cell subsets. Dual luciferase reporter assay and eQTL showed that the T allele of rs9379874 increased BTN3A1 expression by binding to FOXA1. Public single-cell RNA sequencing analysis indicated that BTN3A1 was related to T-cell activation, ATP metabolism, and cytokine metabolism pathways, which might contribute to T1D development. This study indicates that a functional variant rs9379874 regulates BTN3A1 expression, expanding the genomic landscape of T1D risk and offering a potential target for developing novel therapies.

Evaluating the causal effect of circulating proteome on the glycemic traits: Evidence from Mendelian randomization.

Exploring the mechanisms underlying abnormal glycemic traits is important for deciphering type 2 diabetes and characterizing novel drug targets. This study aimed to decipher the causal associations of circulating proteins with fasting glucose (FG), 2-h glucose after an oral glucose challenge (2hGlu), fasting insulin (FI), and glycated hemoglobin (HbA1c) using large-scale proteome-wide Mendelian randomization (MR) analyses. Genetic data on plasma proteomes were obtained from ten proteomic genome-wide association studies (GWAS). Both cis- and cis+trans-protein quantitative trait loci (pQTLs) MR analyses were conducted. Bayesian colocalization, Steiger filtering analysis, assessment of protein-altering variants, and mapping expression quantitative trait loci to protein quantitative trait loci were performed to investigate the reliability of the MR findings. Protein-protein interaction, pathway enrichment analysis, and evaluation of drug targets were performed. Thirty-three proteins were identified with causal effects on FG, FI, or HbA1c but not 2hGlu in the cis-pQTLs analysis, and 93 proteins had causal effects on glycemic traits in the cis+trans-pQTLs analysis. Most proteins were either considered druggable or drug targets. In conclusion, many novel circulating protein biomarkers were identified to be causally associated with glycemic traits. These biomarkers enhance the understanding of molecular etiology and provide insights into the screening, monitoring, and treatment of diabetes.

EQTL in diseased colon tissue identifies novel target genes associated with IBD.

Genome-wide association studies (GWAS) have identified over 300 loci associated with the inflammatory bowel diseases (IBD), but putative causal genes for most are unknown. We conducted the largest disease-focused expression quantitative trait loci (eQTL) analysis using colon tissue from 252 IBD patients to determine genetic effects on gene expression and potential contribution to IBD. Combined with two non-IBD colon eQTL studies, we identified 194 potential target genes for 108 GWAS loci. eQTL in IBD tissue were enriched for IBD GWAS loci colocalizations, provided novel evidence for IBD-associated genes such as ABO and TNFRSF14 , and identified additional target genes compared to non-IBD tissue eQTL. IBD-associated eQTL unique to diseased tissue had distinct regulatory and functional characteristics with increased effect sizes. Together, these highlight the importance of eQTL studies in diseased tissue for understanding functional consequences of genetic variants, and elucidating molecular mechanisms and regulation of key genes involved in IBD.

Population-wide DNA methylation polymorphisms at single-nucleotide resolution in 207 cotton accessions reveal epigenomic contributions to complex traits.

DNA methylation plays multiple regulatory roles in crop development. However, the relationships of methylation polymorphisms with genetic polymorphisms, gene expression, and phenotypic variation in natural crop populations remain largely unknown. Here, we surveyed high-quality methylomes, transcriptomes, and genomes obtained from the 20-days-post-anthesis (DPA) cotton fibers of 207 accessions and extended the classical framework of population genetics to epigenetics. Over 287 million single methylation polymorphisms (SMPs) were identified, 100 times more than the number of single nucleotide polymorphisms (SNPs). These SMPs were significantly enriched in intragenic regions while depleted in transposable elements. Association analysis further identified a total of 5,426,782 cis-methylation quantitative trait loci (cis-meQTLs), 5078 cis-expression quantitative trait methylation (cis-eQTMs), and 9157 expression quantitative trait loci (eQTLs). Notably, 36.39% of cis-eQTM genes were not associated with genetic variation, indicating that a large number of SMPs associated with gene expression variation are independent of SNPs. In addition, out of the 1715 epigenetic loci associated with yield and fiber quality traits, only 36 (2.10%) were shared with genome-wide association study (GWAS) loci. The construction of multi-omics regulatory networks revealed 43 cis-eQTM genes potentially involved in fiber development, which cannot be identified by GWAS alone. Among these genes, the role of one encoding CBL-interacting protein kinase 10 in fiber length regulation was successfully validated through gene editing. Taken together, our findings prove that DNA methylation data can serve as an additional resource for breeding purposes and can offer opportunities to enhance and expedite the crop improvement process.

Genetic variations in NER pathway gene polymorphisms and Wilms tumor risk: A six-center case-control study in East China.

The nucleotide excision repair (NER) system is one of the main ways to protect organisms from DNA damage caused by endogenous and exogenous carcinogens. NER deficiency increases genome mutations, chromosomal aberrations, and cancer viability. However, the genetic association between Wilms tumor and NER pathway gene polymorphisms needs to be further validated. We assessed the associations between 19 NER gene polymorphisms and Wilms tumor susceptibility in 416 cases and 936 controls from East China via the TaqMan method. We found that xeroderma pigmentosum group D (XPD) rs238406 and rs13181 significantly decreased the risk of Wilms tumor [adjusted odds ratio (OR) = 0.59, 95% confidence interval (CI) = 0.46-0.75, p <.0001; adjusted OR = 0.63, 95% CI = 0.44-0.89, p = .009, respectively]. Furthermore, the rs751402 and rs2296147 polymorphisms in the xeroderma pigmentosum group G (XPG) gene were significantly correlated with an increased risk for Wilms tumor (adjusted OR = 1.47, 95% CI = 1.03-2.09, p = .034; adjusted OR = 2.14, 95% CI = 1.29-3.56, p = .003, respectively). Expression quantitative trait loci (eQTL) analysis revealed that these four polymorphisms may affect the expression of genes that are adjacent to XPD and XPG. Our study provides evidence that XPD and XPG gene polymorphisms are associated with Wilms tumor risk. Nonetheless, these findings should be confirmed in a larger sample size.

Multiomics integration unravels genotype‐microbiome interactions shaping the conjunctival transcriptome

AbstractGene expression is a molecular phenotype shaped by the interplay between genotype and environment. The microbiome represents a critical environmental exposure for the host. However, the genotype‐microbiome interactions (GMIs) shaping the host transcriptome remain largely unexplored. Here, we integrated conjunctival multiomics data from 120 pairs of twins to investigate GMIs. We identified 272,972 expression quantitative trait loci associated with 5946 genes and 241,073 genotype‐controlled correlations between gene expression and microbial abundance. We developed a modeling approach, MicroGenix, that screens for GMIs from host genome, transcriptome, and microbiome data and identifies GMIs associated with the disease through gene‐based association tests. We applied MicroGenix to the conjunctival data set and found that incorporating GMIs into gene expression prediction models significantly increased prediction accuracy. The genes with increased accuracy were overrepresented by those encoding cell adhesion molecules. We further used MicroGenix to predict the transcriptome from conjunctival metagenomes and identified GMIs associated with ocular surface disease. Our work provides a resource for studying host‐microbiome interactions at the conjunctiva and a computational approach to investigate GMIs using multiomics data.

HMGCR as a promising molecular target for therapeutic intervention in aortic aneurisms: a mendelian randomization study

BackgroundDespite the exploration of the connections between serum low-density lipoprotein cholesterol (LDL-C) levels and aneurisms in epidemiological studies, causality remains unclear. Therefore, this study aimed to assess the causal impact of LDL-C-lowering targets (HMGCR, PCSK9, NPC1L1, CETP, APOB, and LDLR) on various forms of aneurisms using Mendelian Randomization (MR) analysis.MethodsTwo genetic instruments acted as proxies for exposure to LDL-C-lowering drugs: expression quantitative trait loci of drug target genes and genetic variants linked to LDL-C near drug target genes. Summary-data-based MR (SMR), inverse-variance-weighted MR (IVW-MR), and multivariable MR (MVMR) methods were employed to compute the effect estimates.ResultsThe SMR analysis revealed substantial associations between increased HMGCR expression and a heightened risk of aortic aneurism (odds ratio [OR] = 1.603, 95% confidence interval [CI] = 1.209–2.124), thoracic aortic aneurism (OR = 1.666, 95% CI = 1.122–2.475), and abdominal aortic aneurism (OR = 1.910, 95% CI = 1.278–2.856). Likewise, IVW-MR analysis demonstrated positive correlations between HMGCR-mediated LDL-C and aortic aneurism (OR = 2.228, 95% CI = 1.702–2.918), thoracic aortic aneurism (OR = 1.751, 95% CI = 1.191–2.575), abdominal aortic aneurism (OR = 4.784, 95% CI = 3.257–7.028), and cerebral aneurism (OR = 1.993, 95% CI = 1.277–3.110). Furthermore, in the MVMR analysis, accounting for body mass index, smoking, and hypertension, a significant positive relationship was established between HMGCR-mediated LDL-C levels and the development of aortic aneurisms, encompassing both thoracic and abdominal subtypes. Similarly, consistent positive associations were observed for PCSK9 and CETP genes, as well as PCSK9-mediated and CETP-mediated LDL-C levels, with the occurrence of aortic aneurism and abdominal aortic aneurism. Nonetheless, the evidence for potential associations between APOB, NPC1L1 and LDLR with specific subtypes of aortic aneurisms lacked consistent support from both SMR and IVW-MR analyses.ConclusionsOur MR analysis offered compelling evidence of a plausible causal link between HMGCR and an increased risk of aortic aneurism, encompassing both thoracic and abdominal types. These groundbreaking findings further bolster the case for the deployment of HMGCR inhibitors in the treatment of aortic aneurisms, including both thoracic and abdominal variants.

SNP-Based and Kmer-Based eQTL Analysis Using Transcriptome Data.

Traditional expression quantitative trait locus (eQTL) mapping associates single nucleotide polymorphisms (SNPs) with gene expression, where the SNPs are derived from large-scale whole-genome sequencing (WGS) data or transcriptome data. While WGS provides a high SNP density, it also incurs substantial sequencing costs. In contrast, RNA-seq data, which are more accessible and less expensive, can simultaneously yield gene expressions and SNPs. Thus, eQTL analysis based on RNA-seq offers significant potential applications. Two primary strategies were employed for eQTL in this study. The first involved analyzing expression levels in relation to variant sites detected between populations from RNA-seq data. The second approach utilized kmers, which are sequences of length k derived from RNA-seq reads, to represent variant sites and associated these kmer genotypes with gene expression. We discovered 87 significant association signals involving eGene on the basis of the SNP-based eQTL analysis. These genes include DYNLT1, NMNAT1, and MRLC2, which are closely related to neurological functions such as motor coordination and homeostasis, play a role in cellular energy metabolism, and function in regulating calcium-dependent signaling in muscle contraction, respectively. This study compared the results obtained from eQTL mapping using RNA-seq identified SNPs and gene expression with those derived from kmers. We found that the vast majority (23/30) of the association signals overlapping the two methods could be verified by haplotype block analysis. This comparison elucidates the strengths and limitations of each method, providing insights into their relative efficacy for eQTL identification.

Multiomics identification of ALDH9A1 as a crucial immunoregulatory molecule involved in calcific aortic valve disease

Mitochondrial dysfunction and immune cell infiltration play crucial yet incompletely understood roles in the pathogenesis of calcific aortic valve disease (CAVD). This study aimed to identify immune-related mitochondrial genes critical to the pathological process of CAVD using multiomics approaches. The CIBERSORT algorithm was employed to evaluate immune cell infiltration characteristics in CAVD patients. An integrative analysis combining weighted gene coexpression network analysis (WGCNA), machine learning, and summary data-based Mendelian randomization (SMR) was performed to identify key mitochondrial genes implicated in CAVD. Spearman’s rank correlation analysis was also performed to assess the relationships between key mitochondrial genes and infiltrating immune cells. Compared with those in normal aortic valve tissue, an increased proportion of M0 macrophages and resting memory CD4 T cells, along with a decreased proportion of plasma cells and activated dendritic cells, were observed in CAVD patients. Additionally, eight key mitochondrial genes associated with CAVD, including PDK4, LDHB, SLC25A36, ALDH9A1, ECHDC2, AUH, ALDH2, and BNIP3, were identified through the integration of WGCNA and machine learning methods. Subsequent SMR analysis, incorporating multiomics data, such as expression quantitative trait loci (eQTLs) and methylation quantitative trait loci (mQTLs), revealed a significant causal relationship between ALDH9A1 expression and a reduced risk of CAVD. Moreover, ALDH9A1 expression was inversely correlated with M0 macrophages and positively correlated with M2 macrophages. These findings suggest that increased ALDH9A1 expression is significantly associated with a reduced risk of CAVD and that it may exert its protective effects by modulating mitochondrial function and immune cell infiltration. Specifically, ALDH9A1 may contribute to the shift from M0 macrophages to anti-inflammatory M2 macrophages, potentially mitigating the pathological progression of CAVD. In conclusion, ALDH9A1 represents a promising molecular target for the diagnosis and treatment of CAVD. However, further validation through in vivo and n vitro studies is necessary to confirm its role in CAVD pathogenesis and therapeutic potential.