Expression Quantitative Trait Loci Data Research Articles

Statins as a risk factor for diabetic retinopathy: a Mendelian randomization and cross-sectional observational study

BackgroundDiabetic retinopathy (DR) is the foremost cause of vision loss among the global working-age population, and statins are among the most frequently prescribed drugs for lipid management in patients with DR. The exact relationship between statins and DR has not been determined. This study sought to validate the causal association between statins usage and diabetic retinopathy.MethodsThe summary-data-based Mendelian randomization (SMR) method and inverse-variance-weighted Mendelian randomization (IVW-MR) were used to identify the causal relationship between statins and DR via the use of expression quantitative trait loci (eQTL) data for 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) (31,684 blood samples), low density lipoprotein cholesterol-related GWAS data (sample size: 440,546), and DR-related GWAS data (14,584 cases and 176,010 controls). Additionally, a cross-sectional observational study based on the data from the National Health and Nutrition Examination Survey (NHANES) was conducted to supplement the association between DR and statins (sample size: 106,911). The odds ratios (ORs) with corresponding 95% confidence intervals (CIs) was employed to evaluate the results.ResultsBased on the results of the MR analysis, HMGCR inhibitors were causally connected with a noticeably greater incidence of DR (IVW: OR = 0.54, 95% CI [0.42, 0.69], p = 0.000002; SMR: OR = 0.66, 95% CI [0.52, 0.84], p = 0.00073). Subgroup analysis revealed that the results were not affected by the severity of DR. The sensitivity analysis revealed the stability and reliability of the MR analysis results. The results from the cross-sectional study based on NHANES also support the association between not taking statins and a decreased risk of DR (OR = 0.54, 95% CI [0.37, 0.79], p = 0.001).ConclusionsThis study revealed that a significant increase in DR risk was causally related to statins use, providing novel insights into the role of statins in DR. However, further investigations are needed to verify these findings.

Proteome-Wide Mendelian Randomization and Colocalization Analysis Identify Therapeutic Targets for Knee and Hip Osteoarthritis.

Osteoarthritis (OA) is a common degenerative disease. Although some biomarkers and drug targets of OA have been discovered and employed, limitations and challenges still exist in the targeted therapy of OA. Mendelian randomization (MR) analysis has been regarded as a reliable analytic method to identify effective therapeutic targets. Thus, we aimed to identify novel therapeutic targets for OA and investigate their potential side effects based on MR analysis. In this study, two-sample MR, colocalization analysis, summary-data-based Mendelian randomization (SMR) and Mendelian randomization phenome-wide association study (MR-PheWAS) were conducted. We firstly analyzed data from 4907 plasma proteins to identify potential therapeutic targets associated with OA. In addition, blood expression quantitative trait loci (eQTLs) data sources were used to perform additional validation. A protein-protein interaction (PPI) network was also constructed to delve into the interactions among identified proteins. Then, MR-PheWASs were utilized to assess the potential side effects of core therapeutic targets. After MR analysis and FDR correction, we identified twelve proteins as potential therapeutic targets for knee OA or hip OA. Colocalization analysis and additional validation supported our findings, and PPI networks revealed the interactions among identified proteins. Finally, we identified MAPK3 (OR = 0.855, 95% CI: 0.791-0.923, p = 6.88 × 10-5) and GZMK (OR = 1.278, 95% CI: 1.131-1.444, p = 8.58 × 10-5) as the core therapeutic targets for knee OA, and ITIH1 (OR = 0.847, 95% CI: 0.784-0.915, p = 2.44 × 10-5) for hip OA. A further MR phenome-wide association study revealed the potential side effects of treatments targeting MAPK3, GZMK, and ITIH1. This comprehensive study indicates twelve plasma proteins with potential roles in knee and hip OA as therapeutic targets. This advancement holds promise for the progression of OA drug development, and paves the way for more efficacious treatments of OA.

Potential drug targets for gastroesophageal reflux disease and Barrett's esophagus identified through Mendelian randomization analysis.

Gastroesophageal reflux disease (GERD) is a prevalent chronic ailment, and present therapeutic approaches are not always effective. This study aimed to find new drug targets for GERD and Barrett's esophagus (BE). We obtained genetic instruments for GERD, BE, and 2004 plasma proteins from recently published genome-wide association studies (GWAS), and Mendelian randomization (MR) was employed to explore potential drug targets. We further winnowed down MR-prioritized proteins through replication, reverse causality testing, colocalization analysis, phenotype scanning, and Phenome-wide MR. Furthermore, we constructed a protein-protein interaction network, unveiling potential associations among candidate proteins. Simultaneously, we acquired mRNA expression quantitative trait loci (eQTL) data from another GWAS encompassing four different tissues to identify additional drug targets. Meanwhile, we searched drug databases to evaluate these targets. Under Bonferroni correction (P < 4.8 × 10-5), we identified 11 plasma proteins significantly associated with GERD. Among these, 7 are protective proteins (MSP, GPX1, ERBB3, BT3A3, ANTR2, CCM2, and DECR2), while 4 are detrimental proteins (TMEM106B, DUSP13, C1-INH, and LINGO1). Ultimately, C1-INH and DECR2 successfully passed the screening process and exhibited similar directional causal effects on BE. Further analysis of eQTLs highlighted 4 potential drug targets, including EDEM3, PBX3, MEIS1-AS3, and NME7. The search of drug databases further supported our conclusions. Our study indicated that the plasma proteins C1-INH and DECR2, along with 4 genes (EDEM3, PBX3, MEIS1-AS3, and NME7), may represent potential drug targets for GERD and BE, warranting further investigation.

Potential association of genetically predicted lipid and lipid-modifying drugs with rheumatoid arthritis: A Mendelian randomization study.

Past studies have demonstrated that patients diagnosed with rheumatoid arthritis (RA) often exhibit abnormal levels of lipids. Furthermore, certain lipid-modifying medications have shown effectiveness in alleviating clinical symptoms associated with RA. However, the current understanding of the causal relationship between lipids, lipid-modifying medications, and the risk of developing RA remains inconclusive. This study employed Mendelian randomization (MR) to investigate the causal connection between lipids, lipid-modifying drugs, and the occurrence of RA. We obtained genetic variation for lipid traits and drug targets related to lipid modification from three sources: the Global Lipids Genetics Consortium (GLGC), UK Biobank, and Nightingale Health 2020. The genetic data for RA were acquired from two comprehensive meta-analyses and the R8 of FINNGEN, respectively. These variants were employed in drug-target MR analyses to establish a causal relationship between genetically predicted lipid-modifying drug targets and the risk of RA. For suggestive lipid-modified drug targets, we conducted Summary-data-based Mendelian Randomization (SMR) analyses and using expression quantitative trait loci (eQTL) data in relevant tissues. In addition, we performed co-localization analyses to assess genetic confounders. Our analysis revealed no significant causal relationship between lipid and RA. We observed that the genetically predicted 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) -mediated low density lipoprotein cholesterol (LDL-C) (OR 0.704; 95% CI 0.56, 0.89; P = 3.43×10-3), Apolipoprotein C-III (APOC3) -mediated triglyceride (TG) (OR 0.844; 95% CI 0.77, 0.92; P = 1.50×10-4) and low density lipoprotein receptor (LDLR) -mediated LDL-C (OR 0.835; 95% CI 0.73, 0.95; P = 8.81×10-3) were significantly associated with a lowered risk of RA. while Apolipoprotein B-100 (APOB) -mediated LDL-C (OR 1.212; 95%CI 1.05,1.40; P = 9.66×10-3) was significantly associated with an increased risk of RA. Our study did not find any supporting evidence to suggest that lipids are a risk factor for RA. However, we observed significant associations between HMGCR, APOC3, LDLR, and APOB with the risk of RA.

Five genes identified as prognostic markers for colorectal cancer through the integration of genome-wide association study and expression quantitative trait locidata.

Background: Colorectal cancer (CRC) is a prominent form of cancer globally, ranking second in terms of prevalence and serving as a leading cause of cancer-related deaths, but the underlying biological interpretation remains largely unknown. Methods: We used the summary data-based Mendelian randomizationmethod to integrate CRC genome-wide association studies(ncase=7062;ncontrol=195,745) and expression quantitative trait loci summary data in peripheral whole blood (Consortium for Architecture of Gene Expression: n = 2765; Genotype-Tissue Expression [v8]: n = 755) and colon tissue (colon-transverse: n = 406;colon-sigmoid: n = 373) and identified related genes. Results: Genes ABTB1, CYP21A2, NLRP1, PHKG1 and PIP5K1C have emerged as significant prognostic markers for CRC patient survival. Functional analysis revealed their involvement in cancer cell migration and invasion mechanisms, providing valuable insights for the development of future anti-CRC drugs. Conclusion: We successfully identified five CRC risk genes, providing new insights and research directions for the effective mechanisms of CRC.

Genetic susceptibility and causal pathway analysis of eye disorders coexisting in multiple sclerosis.

The comorbidity of optic neuritis with multiple sclerosis has been well recognized. However, the causal association between multiple sclerosis and optic neuritis, as well as other eye disorders, remains incompletely understood. To address these gaps, we investigated the genetically relationship between multiple sclerosis and eye disorders, and explored potential drugs. In order to elucidate the genetic susceptibility and causal links between multiple sclerosis and eye disorders, we performed two-sample Mendelian randomization analyses to examine the causality between multiple sclerosis and eye disorders. Additionally, causal single-nucleotide polymorphisms were annotated and searched for expression quantitative trait loci data. Pathway enrichment analysis was performed to identify the possible mechanisms responsible for the eye disorders coexisting with multiple sclerosis. Potential therapeutic chemicals were also explored using the Cytoscape. Mendelian randomization analysis revealed that multiple sclerosis increased the incidence of optic neuritis while reducing the likelihood of concurrent of cataract and macular degeneration. Gene Ontology enrichment analysis implicated that lymphocyte proliferation, activation and antigen processing as potential contributors to the pathogenesis of eye disorders coexisting with multiple sclerosis. Furthermore, pharmaceutical agents traditionally employed for allograft rejection exhibited promising therapeutic potential for the eye disorders coexisting with multiple sclerosis. Multiple sclerosis genetically contributes to the development of optic neuritis while mitigating the concurrent occurrence of cataract and macular degeneration. Further research is needed to validate these findings and explore additional mechanisms underlying the comorbidity of multiple sclerosis and eye disorders.

Causal role of immune cell traits in stroke: A Mendelian randomization study

ObjectivesImmune mechanisms play a crucial role in the development of stroke. However, immune-related phenotypes are diverse and their associations with stroke are largely unknown. Here, we aimed to systematically explore the causal role of immune cell traits in stroke and its subtypes by leveraging data from genome-wide association studies (GWASs). Materials and MethodsExposure data were obtained from a recent GWAS on 731 immune cell traits profiled by flow cytometry involving 3757 individuals. By conducting two-sample univariable Mendelian randomization (MR) analyses, each immune cell trait was assessed for causal relationships with stroke outcomes from the MEGASTROKE Consortium (40,585 cases and 406,111 controls). The robustness of the MR results was verified by a series of sensitivity analyses. ResultsWe identified three significant associations after Bonferroni correction (P < 1.37E-05). Increased CD27 expression on memory B cell (OR = 1.23, 95% CI = 1.14-1.33, P = 2.78E-08), IgD−CD38dim B cell (OR = 1.16, 95% CI = 1.09-1.23, P = 5.98E-06) and unswitched memory B cell (OR = 1.18, 95% CI = 1.10-1.27, P = 1.09E-05) were associated with a higher risk of large-artery atherosclerotic stroke (LAS). Furthermore, expression quantitative trait loci data also indicated elevated blood CD27 mRNA level was a risk factor for LAS (OR = 1.37, 95% CI = 1.02-1.84, P = 0.037). ConclusionsThis study provided genetic evidence of the causal relationship between immune cell traits and stroke, highlighting the role of CD27 on memory B cell as a novel factor for LAS risk.

Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits

Many methods have been developed to leverage expression quantitative trait loci (eQTL) data to nominate candidate genes from genome-wide association studies. These methods, including colocalization, transcriptome-wide association studies (TWAS) and Mendelian randomization-based methods; however, all suffer from a key problem—when assessing the role of a gene in a trait using its eQTLs, nearby variants and genetic components of other genes’ expression may be correlated with these eQTLs and have direct effects on the trait, acting as potential confounders. Our extensive simulations showed that existing methods fail to account for these ‘genetic confounders’, resulting in severe inflation of false positives. Our new method, causal-TWAS (cTWAS), borrows ideas from statistical fine-mapping and allows us to adjust all genetic confounders. cTWAS showed calibrated false discovery rates in simulations, and its application on several common traits discovered new candidate genes. In conclusion, cTWAS provides a robust statistical framework for gene discovery.

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.

Transcriptional risk score in Alzheimer’s disease: from pathology to cognition

AbstractBackgroundTranscriptional risk scores (TRS), a summation of polarized expression levels of functional genes, have been proposed for disease risk assessment using transcriptome profiles. Our previously developed blood‐based TRS showed associations with diagnosis and neuroimaging biomarkers for Alzheimer’s disease (AD). Here, we developed brain‐based TRS models and calculated TRS using brain tissue‐based transcriptome profiles.MethodWe integrated AD genome‐wide association study summary and expression quantitative trait locus data to prioritize target genes using Mendelian randomization and then calculated individual TRS by summing the polarized z‐scores over the target genes. We used brain transcriptome data from the Religious Orders Study and Rush Memory and Aging Project cohort (ROSMAP; N = 634) as a discovery set and the Mount Sinai Brain Bank cohort (MSBB; N = 244) as a replication set. We performed association analysis of TRS with AD diagnosis, amyloidopathy, tauopathy, and cross‐sectional and longitudinal cognitive function. We compared AD classification performance of TRS with polygenic risk scores (PRS) using a machine learning approach (Fig. 1).ResultHigher TRS values were significantly associated with AD compared to cognitively normal controls (Table 1). Higher TRS values were also related with amyloidopathy, tauopathy, poor cognitive performance, and faster cognitive decline. The AD classification performance of PRS was increased with the inclusion of TRS up to 12% with the area under the curve value of 0.842 (Fig. 2). These findings were replicated in the replication dataset.ConclusionOur results showed that brain‐based TRS is associated with AD pathology and clinical course. Furthermore, TRS improves the AD classification performance of PRS. Along with static PRS, dynamic TRS based on transcriptional expression levels at a given time could be a potential disease‐stage specific AD biomarker.

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.

GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors.

Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and cross-validated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS meta-analysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures. This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses. Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values <5×10-8. These loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The multi-ancestry SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p=5.7×10-80). Significant brain tissue gene expression and drug set enrichment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major depressive disorder and posttraumatic stress disorder. Genetic causal proportion analyses implicated shared genetic risk for specific health factors. This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death.

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.

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.

Transcriptional risk scores in Alzheimer's disease: From pathology to cognition.

Our previously developed blood-based transcriptional risk scores (TRS) showed associations with diagnosis and neuroimaging biomarkers for Alzheimer's disease (AD). Here, we developed brain-based TRS. We integrated AD genome-wide association study summary and expression quantitative trait locus data to prioritize target genes using Mendelian randomization. We calculated TRS using brain transcriptome data of two independent cohorts (N=878) and performed association analysis of TRS with diagnosis, amyloidopathy, tauopathy, and cognition. We compared AD classification performance of TRS with polygenic risk scores (PRS). Higher TRS values were significantly associated with AD, amyloidopathy, tauopathy, worse cognition, and faster cognitive decline, which were replicated in an independent cohort. The AD classification performance of PRS was increased with the inclusion of TRS up to 16% with the area under the curve value of 0.850. Our results suggest brain-based TRS improves the AD classification of PRS and may be a potential AD biomarker. Transcriptional risk score (TRS) is developed using brain RNA-Seq data. Higher TRS values are shown in Alzheimer's disease (AD). TRS improves the AD classification power of PRS up to 16%. TRS is associated with AD pathology presence. TRS is associated with worse cognitive performance and faster cognitive decline.

Shared Genetics and Comorbid Genes of Amyotrophic Lateral Sclerosis and Parkinson's Disease.

Comorbidity exists between amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD), but the role of genetic factors is unclear. We aim to investigate genetic correlation, causal relationship, and comorbid genes between ALS and PD. Leveraging the largest genome-wide association study data (ALS: 27,205 cases, 110,881 controls; PDG: 33,674 cases, 449,056 controls), we used linkage disequilibrium score regression and Mendelian randomization analysis for genetic correlation and causal inference. We performed genome-wide cross-trait analysis via Multi-Trait Analysis of Genome-Wide Association Studies and Cross-Phenotype Association to identify specific single-nucleotide polymorphisms, followed by functional mapping and annotation. Integrating expression quantitative trait loci data from 13 brain regions, we conducted a transcriptome-wide association study via functional summary-based imputation and joint-tissue imputation to explore comorbid genes, followed by pathway enrichment analysis. We found that PD positively correlates with ALS (rg = 0.144, P = 0.026) and confers a causal effect (odds ratio = 1.09, 95% confidence interval: 1.03-1.15, P = 3.00 × 10-3 ). We identified nine single-nucleotide polymorphisms (eight new), associating with three risk loci (chromosomes 4, 10, and 17) and seven genes (TMEM175, MAPT, NSF, LRRC37A2, ARHGAP27, GAK, and FGFRL1). In transcriptome-wide association study analysis, we showed six previously unreported pleiotropic genes (KANSL1, ARL17B, EFNA1, WNT3, ERCC8, and ADAM15), and we found these candidate genes are mainly enriched in negative regulation of neuron projection development (GO:0010977). Our work demonstrates shared genetic architecture between ALS and PD, reports new pleiotropic genes, and sheds light on the comorbid mechanism. © 2023 International Parkinson and Movement Disorder Society.

Bidirectional Mendelian Randomization and Multiphenotype GWAS Show Causality and Shared Pathophysiology Between Depression and Type 2 Diabetes.

Depression is a common comorbidity of type 2 diabetes. We assessed the causal relationships and shared genetics between them. We applied two-sample, bidirectional Mendelian randomization (MR) to assess causality between type 2 diabetes and depression. We investigated potential mediation using two-step MR. To identify shared genetics, we performed 1) genome-wide association studies (GWAS) separately and 2) multiphenotype GWAS (MP-GWAS) of type 2 diabetes (19,344 case subjects, 463,641 control subjects) and depression using major depressive disorder (MDD) (5,262 case subjects, 86,275 control subjects) and self-reported depressive symptoms (n = 153,079) in the UK Biobank. We analyzed expression quantitative trait locus (eQTL) data from public databases to identify target genes in relevant tissues. MR demonstrated a significant causal effect of depression on type 2 diabetes (odds ratio 1.26 [95% CI 1.11-1.44], P = 5.46 × 10-4) but not in the reverse direction. Mediation analysis indicated that 36.5% (12.4-57.6%, P = 0.0499) of the effect from depression on type 2 diabetes was mediated by BMI. GWAS of type 2 diabetes and depressive symptoms did not identify shared loci. MP-GWAS identified seven shared loci mapped to TCF7L2, CDKAL1, IGF2BP2, SPRY2, CCND2-AS1, IRS1, CDKN2B-AS1. MDD has not brought any significant association in either GWAS or MP-GWAS. Most MP-GWAS loci had an eQTL, including single nucleotide polymorphisms implicating the cell cycle gene CCND2 in pancreatic islets and brain and the insulin signaling gene IRS1 in adipose tissue, suggesting a multitissue and pleiotropic underlying mechanism. Our results highlight the importance to prevent type 2 diabetes at the onset of depressive symptoms and the need to maintain a healthy weight in the context of its effect on depression and type 2 diabetes comorbidity.

Mendelian Randomization and Transcriptome-Wide Association Analysis Identified Genes That Were Pleiotropically Associated with Intraocular Pressure.

Intraocular pressure (IOP) is a major modifiable risk factor for glaucoma. However, the mechanisms underlying the controlling of IOP remain to be elucidated. To prioritize genes that are pleiotropically associated with IOP. We adopted a two-sample Mendelian randomization method, named summary-based Mendelian randomization (SMR), to examine the pleiotropic effect of gene expression on IOP. The SMR analyses were based on summarized data from a genome-wide association study (GWAS) on IOP. We conducted separate SMR analyses using Genotype-Tissue Expression (GTEx) and Consortium for the Architecture of Gene Expression (CAGE) expression quantitative trait loci (eQTL) data. Additionally, we performed a transcriptome-wide association study (TWAS) to identify genes whose cis-regulated expression levels were associated with IOP. We identified 19 and 25 genes showing pleiotropic association with IOP using the GTEx and CAGE eQTL data, respectively. RP11-259G18.3 (PSMR = 2.66 × 10-6), KANSL1-AS1 (PSMR = 2.78 × 10-6), and RP11-259G18.2 (PSMR = 2.91 × 10-6) were the top three genes using the GTEx eQTL data. LRRC37A4 (PSMR = 1.19 × 10-5), MGC57346 (PSMR = 1.19 × 10-5), and RNF167 (PSMR = 1.53 × 10-5) were the top three genes using the CAGE eQTL data. Most of the identified genes were found in or near the 17q21.31 genomic region. Additionally, our TWAS analysis identified 18 significant genes whose expression was associated with IOP. Of these, 12 and 4 were also identified by the SMR analysis using the GTEx and CAGE eQTL data, respectively. Our findings suggest that the 17q21.31 genomic region may play a critical role in the regulation of IOP.

Twas_sim, a Python-based tool for simulation and power analysis of transcriptome-wide association analysis.

Genome-wide association studies (GWASs) have identified numerous genetic variants associated with complex disease risk; however, most of these associations are non-coding, complicating identifying their proximal target gene. Transcriptome-wide association studies (TWASs) have been proposed to mitigate this gap by integrating expression quantitative trait loci (eQTL) data with GWAS data. Numerous methodological advancements have been made for TWAS, yet each approach requires ad hoc simulations to demonstrate feasibility. Here, we present twas_sim, a computationally scalable and easily extendable tool for simplified performance evaluation and power analysis for TWAS methods. Software and documentation are available at https://github.com/mancusolab/twas_sim.