eQTL Data Research Articles

Causal effects of gut microbiota on sepsis and sepsis-related death: insights from genome-wide Mendelian randomization, single-cell RNA, bulk RNA sequencing, and network pharmacology

BackgroundGut microbiota alterations have been implicated in sepsis and related infectious diseases, but the causal relationship and underlying mechanisms remain unclear.MethodsWe evaluated the association between gut microbiota composition and sepsis using two-sample Mendelian randomization (MR) analysis based on published genome-wide association study (GWAS) summary statistics. Sensitivity analyses were conducted to validate the robustness of the results. Reverse MR analysis and integration of GWAS and expression quantitative trait loci (eQTL) data were performed to identify potential genes and therapeutic targets.ResultsOur analysis identified 11 causal bacterial taxa associated with sepsis, with increased abundance of six taxa showing positive causal relationships. Ten taxa had causal effects on the 28-day survival outcome of septic patients, with increased abundance of six taxa showing positive associations. Sensitivity analyses confirmed the robustness of these associations. Reverse MR analysis did not provide evidence of reverse causality. Integration of GWAS and eQTL data revealed 76 genes passing the summary data-based Mendelian randomization (SMR) test. Differential expression of these genes was observed between sepsis patients and healthy individuals. These genes represent potential therapeutic targets for sepsis. Molecular docking analysis predicted potential drug-target interactions, further supporting their therapeutic potential.ConclusionOur study provides insights for the development of personalized treatment strategies for sepsis and offers preliminary candidate targets and drugs for future drug development.

Identification of functional enhancer variants associated with type I diabetes in CD4+ T cells.

Type I diabetes is an autoimmune disease mediated by T-cell destruction of β cells in pancreatic islets. Currently, there is no known cure, and treatment consists of daily insulin injections. Genome-wide association studies and twin studies have indicated a strong genetic heritability for type I diabetes and implicated several genes. As most strongly associated variants are noncoding, there is still a lack of identification of functional and, therefore, likely causal variants. Given that many of these genetic variants reside in enhancer elements, we have tested 121 CD4+ T-cell enhancer variants associated with T1D. We found four to be functional through massively parallel reporter assays. Three of the enhancer variants weaken activity, while the fourth strengthens activity. We link these to their cognate genes using 3D genome architecture or eQTL data and validate them using CRISPR editing. Validated target genes include CLEC16A and SOCS1. While these genes have been previously implicated in type 1 diabetes and other autoimmune diseases, we show that enhancers controlling their expression harbor functional variants. These variants, therefore, may act as causal type 1 diabetic variants.

Mendelian Randomization Integrating GWAS, eQTL and mQTL Data to Identify Pleiotropic Genes and DNA Methylation Loci Associated with Insomnia

Mendelian Randomization Integrating GWAS, eQTL and mQTL Data to Identify Pleiotropic Genes and DNA Methylation Loci Associated with Insomnia

Cathepsin-mediated regulation of alpha-synuclein in Parkinson's disease: a Mendelian randomization study.

The observational association between cathepsin and Parkinson's disease (PD) has been partially explored in previous research. However, the causal relationship remains unclear. In this study, our objective is to investigate the causal link between cathepsin and PD using Mendelian randomization (MR) analysis and elucidate the underlying mechanisms governing their interaction. Utilizing bidirectional two-sample MR and multivariable MR, we systematically investigates the causal relationship between nine cathepsins and PD. The data pertaining to cathepsins were obtained from the Integrative Epidemiology Unit (IEU) Open GWAS Project, while data related to PD were sourced from versions R9 and R10 of the FinnGen database. The primary analytical method utilized was the inverse variance weighted (IVW), with MR analysis initially conducted using PD data from R9, complemented by a series of sensitivity analyses. Subsequently, replication analysis was performed on the R10 dataset, and meta-analysis were employed to merge the findings from both datasets. To explore potential mechanisms by which Cathepsins may impact PD, MR analyses were performed on significant Cathepsins with alpha-synuclein. MR analysis and colocalization analysis were conducted on expression quantitative trait loci (eQTL) data of gene related to alpha-synuclein with PD data. Forward MR analyses revealed more cathepsin B (CTSB) associated with less PD risk (OR = 0.898, 95%CI: 0.834-0.966, p = 0.004), while more cathepsin H (CTSH) (OR = 1.076, 95%CI: 1.007-1.149, p = 0.029) and more cathepsin S (CTSS) (OR = 1.076, 95%CI: 1.007-1.150, p = 0.030) associated with increasing PD risk. Meta-analyses validated these associations. Multivariate MR Results were consistent with those before adjustment. No significant results were observed in bidirectional MR analysis. In the investigation of the underlying mechanism, our findings demonstrate that CTSB significantly reduces the levels of alpha-synuclein (OR = 0.909, 95%CI: 0.841-0.983, p = 0.017). Concurrently, a genetically determined positive correlation between alpha-synuclein and PD is illuminated by both eQTL MR and colocalization analysis. In conclusion, this MR study yields robust evidence suggesting an association between elevated levels of CTSB and reduced PD risk, mediated by the downregulation of alpha-synuclein levels. Conversely, higher levels of CTSH and CTSS are associated with an increased risk of PD. These findings offer novel insights into the pathophysiological mechanisms of PD and identify potential drug targets for disease prevention and treatment warranting further clinical investigations.

Multi-omics Mendelian randomization integrating GWAS, eQTL, and mQTL data identified genes associated with breast cancer.

Breast cancer (BC) remains a major disease posing a threat to women's health, but the underlying biological interpretation remains largely unknown. Here, we aimed to identify genes associated with breast cancer and analyze their pathophysiological mechanisms based on multi-omics Mendelian randomization (MR). Summary-data-based MR (SMR) was performed to estimate the causal effects of blood and breast mammary tissue expression quantitative trait loci (eQTLs) on BC. External validation analysis was used to validate the identified genes. Integration analyses BC GWAS summaries with eQTLs and DNA methylation QTLs (mQTLs) from the blood were conducted using SMR to prioritize putative blood genes and their regulatory elements associated with BC risk. Finally, two prior genes (ATG10 and RCCD1) from blood tissue reached significant levels in both BCAC (ATG10: ORBRCR = 0.91, PBRCR = 1.29 × 10-11; RCCD1: ORBRCR = 0.90, PBRCR = 3.72 × 10-15) and FinnGen cohorts (ATG10: ORFinnGen = 0.89, PFinnGen = 8.55 × 10-5; RCCD1: ORFinnGen = 0.89, PFinnGen = 2.38 × 10-8). Additionally, those two genes from breast tissues also replicated in both BCAC (ATG10: ORBRCR = 0.95, PBRCR = 1.02 × 10-9; RCCD1: ORBRCR = 0.87, PBRCR = 4.70 × 10-10) and FinnGen cohorts (ATG10: ORFinnGen = 0.93, PFinnGen = 2.38 × 10-4; RCCD1: ORFinnGen = 0.85, PFinnGen = 3.81 × 10-6). Sensitive analysis and external validation analysis validated those two identified genes. Multi-omics MR analysis showed that the SNP signals associated with ATG10 and RCCD1 were significant across the data from BC Genome-wide association study (GWAS), eQTL, and mQTL studies. In conclusion, we identified two priority genes that are potentially associated with BC. These findings improve our limited understanding of the mechanism of BC and shed light on the development of therapeutic agents for treating BC.

Role of telomere dysfunction and immune infiltration in idiopathic pulmonary fibrosis: new insights from bioinformatics analysis.

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease characterized by unexplained irreversible pulmonary fibrosis. Although the etiology of IPF is unclear, studies have shown that it is related to telomere length shortening. However, the prognostic value of telomere-related genes in IPF has not been investigated. We utilized the GSE10667 and GSE110147 datasets as the training set, employing differential expression analysis and weighted gene co-expression network analysis (WGCNA) to screen for disease candidate genes. Then, we used consensus clustering analysis to identify different telomere patterns. Next, we used summary data-based mendelian randomization (SMR) analysis to screen core genes. We further evaluated the relationship between core genes and overall survival and lung function in IPF patients. Finally, we performed immune infiltration analysis to reveal the changes in the immune microenvironment of IPF. Through differential expression analysis and WGCNA, we identified 35 significant telomere regulatory factors. Consensus clustering analysis revealed two distinct telomere patterns, consisting of cluster A (n = 26) and cluster B (n = 19). Immune infiltration analysis revealed that cluster B had a more active immune microenvironment, suggesting its potential association with IPF. Using GTEx eQTL data, our SMR analysis identified two genes with potential causal associations with IPF, including GPA33 (PSMR = 0.0013; PHEIDI = 0.0741) and MICA (PSMR = 0.0112; PHEIDI = 0.9712). We further revealed that the expression of core genes is associated with survival time and lung function in IPF patients. Finally, immune infiltration analysis revealed that NK cells were downregulated and plasma cells and memory B cells were upregulated in IPF. Further correlation analysis showed that GPA33 expression was positively correlated with NK cells and negatively correlated with plasma cells and memory B cells. Our study provides a new perspective for the role of telomere dysfunction and immune infiltration in IPF and identifies potential therapeutic targets. Further research may reveal how core genes affect cell function and disease progression, providing new insights into the complex mechanisms of IPF.

Multi-ancestry genome-wide association meta-analysis of Parkinson’s disease

Although over 90 independent risk variants have been identified for Parkinson’s disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson’s disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations.

A genome-wide association study meta-analysis in a European sample of stage III/IV grade C periodontitis patients ≤35 years of age identifies new risk loci.

Few genome-wide association studies (GWAS) have been conducted for severe forms of periodontitis (stage III/IV grade C), and the number of known risk genes is scarce. To identify further genetic risk variants to improve the understanding of the disease aetiology, a GWAS meta-analysis in cases with a diagnosis at ≤35 years of age was performed. Genotypes from German, Dutch and Spanish GWAS studies of III/IV-C periodontitis diagnosed at age ≤35 years were imputed using TopMed. After quality control, a meta-analysis was conducted on 8,666,460 variants in 1306 cases and 7817 controls with METAL. Variants were prioritized using FUMA for gene-based tests, functional annotation and a transcriptome-wide association study integrating eQTL data. The study identified a novel genome-wide significant association in the FCER1G gene (p = 1.0 × 10-9 ), which was previously suggestively associated with III/IV-C periodontitis. Six additional genes showed suggestive association with p < 10-5 , including the known risk gene SIGLEC5. HMCN2 showed the second strongest association in this study (p = 6.1 × 10-8 ). This study expands the set of known genetic loci for severe periodontitis with an age of onset ≤35 years. The putative functions ascribed to the associated genes highlight the significance of oral barrier tissue stability, wound healing and tissue regeneration in the aetiology of these periodontitis forms and suggest the importance of tissue regeneration in maintaining oral health.

Alzheimer’s disease genetics and real‐world patient data fuel target and drug discovery in European and African American ancestries

AbstractBackgroundTranslation of massive genetic and genomic data into Alzheimer’s target and drug discovery has not materialized. One challenge in this process is ethnicity‐based differences in the field of Alzheimer’s disease (AD). For example, African Americans (AAs) and European Americans (EAs) differ in AD prevalence, risk factors, and symptomatic presentation, and AAs have historically been less frequently enrolled in AD clinical trials.MethodWe conducted ancestry genetic‐specific target and drug discovery by gathering large genetic data from a diverse population with real‐world patient data to boost the likelihood of identifying disease‐modifying drug targets and treatments for AD. We analyzed genetic data from 275,540 AD cases and 1.55 million controls across 7 AD genome‐wide association studies (GWAS) datasets for both EA and AA ancestries, using Mendelian randomization. We utilized the genetic instruments from 9 expression quantitative trait loci (eQTL) and 3 protein quantitative trait loci (pQTL) datasets from three large human brain biobanks: a) Mount Sinai Brain Bank (MSBB), b) Mayo clinic (Mayo), and c) Religious Orders Study (ROS) or the Rush Memory and Aging Project (MAP) (ROS/MAP).ResultWe identified 25 drug targets in EAs and 6 new drug targets in AAs among 1,176 investigated druggable targets. Notably, the inflammatory target of epoxide hydrolase 2 (EPHX2) emerged as a potent AD target in EAs, and treatment of transgenic TgF344‐AD rats with an EPHX2 inhibitor was therapeutic. Among 6 AA‐specific targets, we identified that TRPV3 is a potent drug target for AA individuals with AD, and further replicated our finding in AA‐specific eQTL data. We additionally identified 23 candidate drugs associated with reduced risk of AD in mild cognitive impairment (MCI) patients after analysis of ∼80 million electronic health records. Using a propensity score‐matched design, we identified that usage of either apixaban (hazard ratio [HR] = 0.74, 95% confidence interval [CI] 0.69 – 0.80) or amlodipine (HR = 0.91, 95% CI 0.88 – 0.94) were both significantly associated with reduced progression to AD in people with MCI.ConclusionCombining genetics and real‐world patient data has identified ancestry‐specific therapeutic targets and medicines for AD.

Altered HCAR3 expression may underlying the blunted niacin responses of the psychiatric disorders and the risk of schizophrenia.

Blunted niacin response (BNR) was an endophenotype of schizophrenia, but the underlying mechanism remains unclarified. The objective of this study was to verify whether genes associated with BNR pathway constitute the genetic basis and the pathological mechanism of BNR phenotypic psychiatric patients. Two independent sample sets consisting of 971 subjects were enrolled in this study. A total of 62 variants were genotyped in the discovery set, then the related variants were verified in the verification set. The published PGC GWAS data were used to validate the associations between the variants and psychiatry disorders. RT-PCR analysis, eQTL data, and Dual-Luciferase Reporter experiment were used to investigate the potential molecular mechanisms of the variants underlying BNR. The results showed that two SNPs, rs56959712 in HCAR2 and rs2454721 in HCAR3 were significantly associated with niacin response. The risk allele T of rs2454721 could affect the niacin responses of psychiatric patients through elevated HCAR3 gene expression. These two genes, especially HCAR3, were significantly associated with the risk of schizophrenia, as identified in this study and verified using the published GWAS data. HCAR3 is a novel schizophrenia susceptibility gene which is significantly associated with blunted niacin response in schizophrenia. In-depth investigation of HCAR3 is of great significance for uncovering the pathogenesis and propose new therapeutic targets for psychiatric disorders, especially for the BNR subgroup patients.

Abstract 18642: GWAS to Function Highlights the Role of REST in Endothelial Biology and Atherosclerosis

Introduction: Despite the emergence of new lipid-lowering therapies, coronary artery disease (CAD) remains the leading cause of death worldwide. Genome-wide association studies (GWAS) for CAD have discovered multiple novel loci that contribute to CAD pathogenesis. However, limitations in the interpretability of those GWAS associations have hindered the clinical translation of those findings. Hypothesis: In this study, we sought to evaluate the mechanisms of the association between chromosomal locus 4q12 and CAD, replicated in multiple independent GWAS for CAD. Methods: We utilized a combination of computational methods and in vitro assays to achieve our objectives. Results: Meta-analysis of public expression quantitative trait locus (eQTL) data and multi-trait colocalization across atherosclerosis-relevant cell types revealed that the CAD 4q12 locus regulates the expression of REST, a transcriptional repressor, in human aortic endothelial cells (HAEC) and prioritized 2 enhancer variants as candidate causal variants for the GWAS association. CRISPR interference confirmed that the rs6853156 variant regulates REST expression in inducible human endothelial cells. Pathway enrichment analysis in HAEC after REST silencing showed that epithelial to mesenchymal transition was the most upregulated pathway suggesting a potential mechanism via which silencing REST contributes to atherosclerosis. Endothelial to mesenchymal transition (endMT) was confirmed in HAEC after REST silencing at the RNA and protein level by rt-qPCR, western blot, and immunofluorescence. Concordantly, REST silencing increased endothelial permeability and matrix metalloproteinase activity in endothelial cell supernatants in vitro. L1CAM, a known endMT activator was only expressed in HAEC upon REST silencing. Chromatin immunoprecipitation sequencing supports direct binding of REST to the L1CAM promoter. Simultaneous silencing of L1CAM and REST in HAEC inhibited the observed upregulation of mesenchymal genes induced by REST silencing. Conclusions: In summary, our data reveal a novel role of REST as a repressor that functions as a constitutive inhibitor of endMT in human arterial endothelial cells.

Identification of asthma-related genes using asthmatic blood eQTLs of Korean patients

BackgroundMore than 200 asthma-associated genetic variants have been identified in genome-wide association studies (GWASs). Expression quantitative trait loci (eQTL) data resources can help identify causal genes of the GWAS signals, but it can be difficult to find an eQTL that reflects the disease state because most eQTL data are obtained from normal healthy subjects.MethodsWe performed a blood eQTL analysis using transcriptomic and genotypic data from 433 Korean asthma patients. To identify asthma-related genes, we carried out colocalization, Summary-based Mendelian Randomization (SMR) analysis, and Transcriptome-Wide Association Study (TWAS) using the results of asthma GWASs and eQTL data. In addition, we compared the results of disease eQTL data and asthma-related genes with two normal blood eQTL data from Genotype-Tissue Expression (GTEx) project and a Japanese study.ResultsWe identified 340,274 cis-eQTL and 2,875 eGenes from asthmatic eQTL analysis. We compared the disease eQTL results with GTEx and a Japanese study and found that 64.1% of the 2,875 eGenes overlapped with the GTEx eGenes and 39.0% with the Japanese eGenes. Following the integrated analysis of the asthmatic eQTL data with asthma GWASs, using colocalization and SMR methods, we identified 15 asthma-related genes specific to the Korean asthmatic eQTL data.ConclusionsWe provided Korean asthmatic cis-eQTL data and identified asthma-related genes by integrating them with GWAS data. In addition, we suggested these asthma-related genes as therapeutic targets for asthma. We envisage that our findings will contribute to understanding the etiological mechanisms of asthma and provide novel therapeutic targets.

Endoplasmic reticulum stress related genome-wide Mendelian randomization identifies therapeutic genes for ulcerative colitis and Crohn's disease.

Background: Endoplasmic reticulum stress (ERS) is an important pathophysiological mechanism in ulcerative colitis (UC) and Crohn's disease (CD). ERS-related genes may be influenced by genetic factors and intestinal inflammation. However, the role of ERS as a trigger or potential etiological factor for UC and CD is unclear, as the expression of ERS-related genes in UC and CD may be the cause or subsequent changes in intestinal inflammation. Here, we used a three-step summary data-based Mendelian randomization (SMR) approach integrating multi-omics data to identify putative causal effects of ERS-related genes in UC and CD. Methods: Genome-wide association study (GWAS) summary data for UC (6,968 cases and 20,464 controls) and CD (5,956 cases and 14,927 controls) were extracted as outcome, and DNA methylation quantitative trait loci (mQTL, 1,980 participants) data and expression QTL data (eQTL, 31,684 participants) from the blood were obtained as exposure. The ERS-related genes were extracted from the GeneCards database, and then the GWAS summary data were integrated with the mQTL and eQTL data associated with ERS genes by SMR. Sensitivity analysis included two-sample MR analysis, power calculations, Bayesian co-localization analysis, and phenotype scanning were performed to evaluate the robustness of the results. Results: A total of 1,193 ERS-related genes were obtained. The three-step SMR analysis showed that cg24011261 CpG site regulating GPX1 expression was associated with a low risk of UC, whereas GPX1 expression regulated by a combination of cg05055782, cg24011261, and cg05551922 CpG sites was associated with a low risk of CD. Sensitivity analysis further supports these findings. Conclusion: This multi-omics integration study identifies a causal relationship between the role of ERS in UC and CD and suggests potential new therapeutic targets for clinical practice.

An organism-wide ATAC-seq peak catalog for the bovine and its use to identify regulatory variants.

We report the generation of an organism-wide catalog of 976,813 cis-acting regulatory elements for the bovine detected by the assay for transposase accessible chromatin using sequencing (ATAC-seq). We regroup these regulatory elements in 16 components by nonnegative matrix factorization. Correlation between the genome-wide density of peaks and transcription start sites, correlation between peak accessibility and expression of neighboring genes, and enrichment in transcription factor binding motifs support their regulatory potential. Using a previously established catalog of 12,736,643 variants, we show that the proportion of single-nucleotide polymorphisms mapping to ATAC-seq peaks is higher than expected and that this is owing to an approximately 1.3-fold higher mutation rate within peaks. Their site frequency spectrum indicates that variants in ATAC-seq peaks are subject to purifying selection. We generate eQTL data sets for liver and blood and show that variants that drive eQTL fall into liver- and blood-specific ATAC-seq peaks more often than expected by chance. We combine ATAC-seq and eQTL data to estimate that the proportion of regulatory variants mapping to ATAC-seq peaks is approximately one in three and that the proportion of variants mapping to ATAC-seq peaks that are regulatory is approximately one in 25. We discuss the implication of these findings on the utility of ATAC-seq information to improve the accuracy of genomic selection.

DNA methylation regulator-mediated modification patterns and risk of intracranial aneurysm: a multi-omics and epigenome-wide association study integrating machine learning, Mendelian randomization, eQTL and mQTL data

BackgroundIntracranial aneurysms (IAs) pose a significant and intricate challenge. Elucidating the interplay between DNA methylation and IA pathogenesis is paramount to identify potential biomarkers and therapeutic interventions.MethodsWe employed a comprehensive bioinformatics investigation of DNA methylation in IA, utilizing a transcriptomics-based methodology that encompassed 100 machine learning algorithms, genome-wide association studies (GWAS), Mendelian randomization (MR), and summary-data-based Mendelian randomization (SMR). Our sophisticated analytical strategy allowed for a systematic assessment of differentially methylated genes and their implications on the onset, progression, and rupture of IA.ResultsWe identified DNA methylation-related genes (MRGs) and associated molecular pathways, and the MR and SMR analyses provided evidence for potential causal links between the observed DNA methylation events and IA predisposition.ConclusionThese insights not only augment our understanding of the molecular underpinnings of IA but also underscore potential novel biomarkers and therapeutic avenues. Although our study faces inherent limitations and hurdles, it represents a groundbreaking initiative in deciphering the intricate relationship between genetic, epigenetic, and environmental factors implicated in IA pathogenesis.Graphical

Enrichment of Spatial eGenes Colocalized with Type 2 Diabetes Mellitus Genome-Wide Association Study Signals in the Lysosomal Pathway

Genome-wide association studies (GWAS) have identified genetic markers associated with type 2 diabetes mellitus (T2DM). Additionally, tissue-specific expression quantitative trait loci (eQTL) studies have revealed regulatory elements influencing gene expression in specific tissues. We performed enrichment analyses using spatial eGenes corresponding to known T2DM GWAS signals to uncover T2DM pathological pathways. T2DM GWAS signals were obtained from the GWAS Catalog, and spatial eQTL data from T2DM-associated tissues, including visceral adipose tissue, liver, skeletal muscle, and pancreas, were sourced from the Genotype-Tissue Expression Consortium. The eGenes were enriched in Kyoto Encyclopedia of Genes and Genomes biological pathways using the Benjamini–Hochberg method. Colocalization analysis of 2857 independent T2DM GWAS signals identified 556 eGenes in visceral adipose tissue, 176 in liver, 715 in skeletal muscle, and 384 in pancreas (PFDR &lt; 0.05 where PFDR is the false discovery rate). These eGenes showed enrichment in various pathways (PBH &lt; 0.05 where PBH is the corrected P for the Benjamini–Hochberg multiple testing), especially the lysosomal pathway in pancreatic tissue. Unlike the mTOR pathway in T2DM autophagy dysregulation, the role of lysosomes remains poorly understood. The enrichment analysis of spatial eGenes associated with T2DM GWAS signals highlights the importance of the lysosomal pathway in autophagic termination. Thus, investigating the processes involving autophagic termination associated with lysosomes is a priority for understanding T2DM pathogenesis.

Whole-genome sequencing analysis of suicide deaths integrating brain-regulatory eQTLs data to identify risk loci and genes

Recent large-scale genome-wide association studies (GWAS) have started to identify potential genetic risk loci associated with risk of suicide; however, a large portion of suicide-associated genetic factors affecting gene expression remain elusive. Dysregulated gene expression, not assessed by GWAS, may play a significant role in increasing the risk of suicide death. We performed the first comprehensive genomic association analysis prioritizing brain expression quantitative trait loci (eQTLs) within regulatory regions in suicide deaths from the Utah Suicide Genetic Risk Study (USGRS). 440,324 brain-regulatory eQTLs were obtained by integrating brain eQTLs, histone modification ChIP-seq, ATAC-seq, DNase-seq, and Hi-C results from publicly available data. Subsequent genomic analyses were conducted in whole-genome sequencing (WGS) data from 986 suicide deaths of non-Finnish European (NFE) ancestry and 415 ancestrally matched controls. Additional independent USGRS suicide deaths with genotyping array data (n = 4657) and controls from the Genome Aggregation Database were explored for WGS result replication. One significant eQTL locus, rs926308 (p = 3.24e−06), was identified. The rs926308-T is associated with lower expression of RFPL3S, a gene important for neocortex development and implicated in arousal. Gene-based analyses performed using Sherlock Bayesian statistical integrative analysis also detected 20 genes with expression changes that may contribute to suicide risk. From analyzing publicly available transcriptomic data, ten of these genes have previous evidence of differential expression in suicide death or in psychiatric disorders that may be associated with suicide, including schizophrenia and autism (ZNF501, ZNF502, CNN3, IGF1R, KLHL36, NBL1, PDCD6IP, SNX19, BCAP29, and ARSA). Electronic health records (EHR) data was further merged to evaluate if there were clinically relevant subsets of suicide deaths associated with genetic variants. In summary, our study identified one risk locus and ten genes associated with suicide risk via gene expression, providing new insight into possible genetic and molecular mechanisms leading to suicide.

De novo identification of complex traits associated with asthma.

Asthma is a heterogeneous inflammatory disease often associated with other complex phenotypes. Identifying asthma-associated diseases and uncovering the molecular mechanisms mediating their interaction can help detangle the heterogeneity of asthma. Network analysis is a powerful approach for untangling such inter-disease relationships. Here, we integrated information on physical contacts between common single nucleotide polymorphisms (SNPs) and gene expression with expression quantitative trait loci (eQTL) data from the lung and whole blood to construct two tissue-specific spatial gene regulatory networks (GRN). We then located the asthma GRN (level 0) within each tissue-specific GRN by identifying the genes that are functionally affected by asthma-associated spatial eQTLs. Curated protein interaction partners were subsequently identified up to four edges or levels away from the asthma GRN. The eQTLs spatially regulating genes on levels 0-4 were queried against the GWAS Catalog to identify the traits enriched (hypergeometric test; FDR ≤ 0.05) in each level. We identified 80 and 82 traits significantly enriched in the lung and blood GRNs, respectively. All identified traits were previously reported to be comorbid or associated (positively or negatively) with asthma (e.g., depressive symptoms and lung cancer), except 8 traits whose association with asthma is yet to be confirmed (e.g., reticulocyte count). Our analysis additionally pinpoints the variants and genes that link asthma to the identified asthma-associated traits, a subset of which was replicated in a comorbidity analysis using health records of 26,781 asthma patients in New Zealand. Our discovery approach identifies enriched traits in the regulatory space proximal to asthma, in the tissue of interest, without a priori selection of the interacting traits. The predictions it makes expand our understanding of possible shared molecular interactions and therapeutic targets for asthma, where no cure is currently available.

Multiomics Data Analysis Identified CpG Sites That Mediate the Impact of Smoking on Cardiometabolic Traits.

Understanding the epigenome paths through which smoking contributes to cardiometabolic traits is important for downstream applications. In this study, an SNP-based analytical pipeline was used to integrate several publicly available datasets in order to identify CpG sites that mediate the impact of smoking on cardiometabolic traits and to investigate the underlying molecular mechanisms. After applying stringent statistical criteria, 11 CpG sites were detected that showed significant association (p < 5 × 10-8) with cardiometabolic traits at both the discovery and replication stages. By integrating eQTL data, I found genes behind a number of these associations. cg05228408 was hypomethylated in smokers and contributed to higher blood pressure by lowering the expression of the CLCN6 gene. cg08639339 was hypermethylated in smokers and lowered the metabolic rate by increasing the expression of RAB29; furthermore, I noted TMEM120A mediated the impact of smoking-cg17325771 on LDL, and LTBP3 mediated the smoking-cg07029024 effect on heart rate. The pathway analysis identified processes through which the identified genes impact their traits. This study provides a list of CpG sites that mediates the impact of smoking on cardiometabolic traits and a framework to investigate the underlying molecular paths using publicly available data.

Lipids, lipid-modifying drug target genes and migraine: a Mendelian randomization study

IntroductionMigraine, a prevalent headache disorder with unclear mechanisms and limited treatments, may be influenced by dyslipidemia and genetic factors. Statins and emerging lipid-modifying agents show potential but lack evidence for migraine management. Mendelian Randomization analysis offers insights into causal relationships and therapeutic targets. This study aims to explore genetically predicted lipid traits, drug targets, and their association with migraine risk.MethodWe conducted Mendelian randomization (MR) analyses utilizing genetic variants associated with lipid traits and variants in genes encoding the protein targets of various classes of lipid-lowering drugs. The specific drug classes investigated included HMGCR, PCSK9, NPC1L1, ABCG5/ABCG8, LDLR, LPL, ANGPTL3, APOB, CETP, and APOC3. To determine the effects on migraine risk, we meta-analyzed MR estimates for regional variants using data from two large sample sets. The genetic variants were weighted based on their associations with specific lipid traits, such as low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), Apolipoprotein A1, and Apolipoprotein B. To obtain association weights, we utilized data from lipid genetics consortia. For lipid-modifying drug targets that exhibited suggestive significance, we further employed expression quantitative trait locus (eQTL) data. Additionally, we performed colocalization analysis to assess genetic confounding.ResultThe use of genetic proxies for HMGCR inhibition demonstrated a significant association with a decreased risk of migraine in the FinnGen dataset (OR = 0.64, 95% CI: 0.46–0.88, p = 0.0006) and a nearly significant association in the Choquet dataset (OR = 0.78, 95% CI: 0.60–1.01, p = 0.06). When pooling the estimates, the overall effect size showed a reduced risk of migraine (OR = 0.73, 95% CI: 0.60–0.89, p = 0.0016). Similarly, genetic mimicry of LPL enhancement was associated with a lower risk of migraine in the FinnGen dataset (OR = 0.82, 95% CI: 0.69–0.96, p = 0.01) and the Choquet dataset (OR = 0.91, 95% CI: 0.83–0.99, p = 0.03). Pooling the estimates showed a consistent effect size (OR = 0.89, 95% CI: 0.83–0.96, p = 0.002). Sensitivity analyses yielded no statistically significant evidence of bias arising from pleiotropy or genetic confounding.ConclusionIn the study, it was observed that among the 10 lipid-lowering drug targets investigated, LPL and HMGCR showed significant associations with migraine risk. These findings indicate that LPL and HMGCR have the potential to serve as candidate drug targets for the treatment or prevention of migraines.