Genomic atlas of the human plasma proteome.

BackgroundAlzheimer’s disease (AD) is the main cause of dementia with few effective therapies. We aimed to identify potential plasma biomarkers or drug targets for AD by investigating the causal association between plasma proteins and AD by integrating comprehensive Mendelian randomization (MR) and multi-omics data.MethodsUsing two-sample MR, cis protein quantitative trait loci (cis-pQTLs) for 1,916 plasma proteins were used as an exposure to infer their causal effect on AD liability in individuals of European ancestry, with two large-scale AD genome-wide association study (GWAS) datasets as the outcome for discovery and replication. Significant causal relationships were validated by sensitivity analyses, reverse MR analysis, and Bayesian colocalization analysis. Additionally, we investigated the causal associations at the transcriptional level with cis gene expression quantitative trait loci (cis-eQTLs) data across brain tissues and blood in European ancestry populations, as well as causal plasma proteins in African ancestry populations.ResultsIn those of European ancestry, the genetically predicted levels of five plasma proteins (BLNK, CD2AP, GRN, PILRA, and PILRB) were causally associated with AD. Among these five proteins, GRN was protective against AD, while the rest were risk factors. Consistent causal effects were found in the brain for cis-eQTLs of GRN, BLNK, and CD2AP, while the same was true for PILRA in the blood. None of the plasma proteins were significantly associated with AD in persons of African ancestry.ConclusionsComprehensive MR analyses with multi-omics data identified five plasma proteins that had causal effects on AD, highlighting potential biomarkers or drug targets for better diagnosis and treatment for AD.

IntroductionAcute myeloid leukemia (AML) remains a therapeutic challenge due to its high relapse rate and limited treatment options. This study aimed to identify and validate novel circulating protein biomarkers with causal roles in AML pathogenesis using an integrative multi-omics approach.MethodsWe performed proteome-wide Mendelian randomization (MR) analyses using protein quantitative trait locus (pQTL) data from two large-scale proteomic studies (deCODE and UK Biobank Pharma Proteomics Project) and genome-wide association study (GWAS) data from two cohorts (FinnGen and UK Biobank). Single-cell RNA sequencing was used to analyze the expression patterns of candidate proteins in hematopoietic progenitor and immune cells. Plasma protein levels were experimentally validated via ELISA in AML patients and healthy controls, and their dynamic changes relative to disease status were assessed. Drug repurposing analysis and phenome-wide association studies (PheWAS) were conducted to evaluate potential therapeutic agents and their safety profiles.ResultsThree independent MR analyses identified TNFAIP8, TCL1A, and WFDC1 as risk factors for AML, while TNFSF8 was identified as a protective factor. Single-cell RNA sequencing revealed distinct expression patterns of these proteins within hematopoietic progenitor and immune cells, suggesting roles in microenvironmental dysregulation. ELISA validation confirmed elevated plasma levels of TNFAIP8, TCL1A, and WFDC1 and reduced levels of TNFSF8 in AML patients compared to healthy controls. Dynamic changes were observed for TNFAIP8 and TNFSF8, supporting their potential for disease monitoring. Drug repurposing analysis prioritized 13 candidates targeting these proteins, including FDA-approved agents, and PheWAS supported their safety.ConclusionThis study provides the first genetic evidence supporting the causal roles of TNFAIP8, TCL1A, WFDC1, and TNFSF8 in AML, offering new insights for targeted therapy development and biomarker-based disease monitoring.

SARS-CoV-2 is responsible for the coronavirus disease 2019 (COVID-19) and the current health crisis. Despite intensive research efforts, the genes and pathways that contribute to COVID-19 remain poorly understood. We, therefore, used an integrative genomics (IG) approach to identify candidate genes responsible for COVID-19 and its severity. We used Bayesian colocalization (COLOC) and summary-based Mendelian randomization to combine gene expression quantitative trait loci (eQTLs) from the Lung eQTL (n = 1,038) and eQTLGen (n = 31,784) studies with published COVID-19 genome-wide association study (GWAS) data from the COVID-19 Host Genetics Initiative. Additionally, we used COLOC to integrate plasma protein quantitative trait loci (pQTL) from the INTERVAL study (n = 3,301) with COVID-19 loci. Finally, we determined any causal associations between plasma proteins and COVID-19 using multi-variable two-sample Mendelian randomization (MR). The expression of 18 genes in lung and/or blood co-localized with COVID-19 loci. Of these, 12 genes were in suggestive loci (PGWAS < 5 × 10–05). LZTFL1, SLC6A20, ABO, IL10RB and IFNAR2 and OAS1 had been previously associated with a heightened risk of COVID-19 (PGWAS < 5 × 10–08). We identified a causal association between OAS1 and COVID-19 GWAS. Plasma ABO protein, which is associated with blood type in humans, demonstrated a significant causal relationship with COVID-19 in the MR analysis; increased plasma levels were associated with an increased risk of COVID-19 and, in particular, severe COVID-19. In summary, our study identified genes associated with COVID-19 that may be prioritized for future investigations. Importantly, this is the first study to demonstrate a causal association between plasma ABO protein and COVID-19.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00439-021-02264-5.

BackgroundCharacterization of regulatory variants (e.g., gene expression quantitative trait loci, eQTL; gene splicing QTL, sQTL) is crucial for biologically interpreting molecular mechanisms underlying loci associated with complex traits. However, regulatory variants in dairy cattle, particularly in specific biological contexts (e.g., distinct lactation stages), remain largely unknown. In this study, we explored regulatory variants in whole blood samples collected during early to mid-lactation (22–150 days after calving) of 101 Holstein cows and analyzed them to decipher the regulatory mechanisms underlying complex traits in dairy cattle.ResultsWe identified 14,303 genes and 227,705 intron clusters expressed in the white blood cells of 101 cattle. The average heritability of gene expression and intron excision ratio explained by cis-SNPs is 0.28 ± 0.13 and 0.25 ± 0.13, respectively. We identified 23,485 SNP-gene expression pairs and 18,166 SNP-intron cluster pairs in dairy cattle during early to mid-lactation. Compared with the 2,380,457 cis-eQTLs reported to be present in blood in the Cattle Genotype-Tissue Expression atlas (CattleGTEx), only 6,114 cis-eQTLs (P < 0.05) were detected in the present study. By conducting colocalization analysis between cis-e/sQTL and the results of genome-wide association studies (GWAS) from four traits, we identified a cis-e/sQTL (rs109421300) of the DGAT1 gene that might be a key marker in early to mid-lactation for milk yield, fat yield, protein yield, and somatic cell score (PP4 > 0.6). Finally, transcriptome-wide association studies (TWAS) revealed certain genes (e.g., FAM83H and TBC1D17) whose expression in white blood cells was significantly (P < 0.05) associated with complex traits.ConclusionsThis study investigated the genetic regulation of gene expression and alternative splicing in dairy cows during early to mid-lactation and provided new insights into the regulatory mechanisms underlying complex traits of economic importance.

ABSTRACTIntroductionGenetic generalized epilepsy (GGE) involves polygenic inheritance, with emerging evidence implicating immune mechanisms in seizure pathogenesis. Unlike previous studies focusing on inflammation following seizures, we employed an integrative multi‐omics approach to identify precipitating immune factors in GGE development.MethodsSummary data on plasma protein levels were extracted from two large protein quantitative trait loci (pQTLs) studies, measuring 4907 and 2923 plasma proteins in 35,559 and 54,219 individuals, respectively. Immune cell trait data were derived from a genome‐wide association study (GWAS) involving 3757 individuals. GGE data, comprising 7407 cases and 52,538 controls, were sourced from a GWAS meta‐analysis by the International League Against Epilepsy (ILAE). Mendelian randomization (MR) analysis identified associations between plasma proteins, immune cell phenotypes, and GGE. Colocalization analysis assessed whether plasma proteins and GGE share a common causal variant. Transcriptome‐wide association studies (TWAS) from GTEx v8 brain tissue and whole blood were conducted for validation. Drug target prediction and molecular docking identified potential therapeutic interventions.ResultsWe identified 62 potential susceptibility proteins by integrating GWAS data for GGE and its subsyndromes with plasma proteomics data. Of these, eight proteins showed strong evidence of colocalization, primarily within immune‐related pathways. The absolute count of TD CD4+ cells was significantly associated with GGE (OR [95% CI]: 0.69 [0.59, 0.81]). Seven genes (CD46, ITGAM, PRPSAP2, PYDC1, STX4, TMEM106A, and VAT1) were significantly associated with GGE in at least one brain tissue in TWAS analysis. Drug target prediction and molecular docking identified several natural compounds (quercetin, cholecalciferol, resveratrol, curcumin, epigallocatechin gallate, and vitamin E) that may provide ideas for the intervention of GGE.ConclusionThese findings revealed causal associations between plasma proteins and GGE, prioritized immune‐related biological pathways, and proposed potential therapeutic hypotheses targeting immunomodulatory mechanisms.

BackgroundThe causal relationship between Transient receptor potential (TRP) and hepatocellular carcinoma (HCC) remains unclear. Our study aimed to identify potential drug targets for HCC within the TRP family using Mendelian randomization (MR).MethodsThe gene expression quantitative trait loci (eQTL) data for TRP was sourced from eQTLGen Consortium. Summary statistics for HCC came from European (nCase = 379, nControl = 475,259) and East Asian population (nCase = 2122, nControl = 159,201). We undertook main MR analysis in the European population using the R package ‘TwosampleMR’, with significance determined through Bonferroni correction. The East Asian population serves as the validation cohort. Sensitivity analyses include Steiger filtering, bidirectional MR analysis, multivariable MR (MVMR) analysis, and phenotype scanning for further validation of causal relationships.ResultsMain MR analysis had identified two causal TRPs, MCOLN3 (OR = 1.59, 95% CI: 1.24–2.06) and TRPV4 (OR = 0.597, 95% CI: 0.407–0.875). No heterogeneity or pleiotropy was detected. The basal metabolic rate may partially mediate the causal effect of TRPV4 on HCC. Drugs such as cisplatin and Cannabidiol were identified for their potential action on causal TRPs. High expression of MCOLN3 may lead to increased sensitivity to sorafenib, while patients with low expression of MCOLN3 and TRPV4 were more likely to benefit from immunotherapy. Furthermore, we revealed the expression landscape of causal TRPs in HCC by performing integrated multi-omics analyses.ConclusionsThis MR analysis revealed a causal relationship between TRP and HCC, and MCOLN3 and TRPV4 were potential drug targets. They also served as potential molecular biomarkers for the efficacy of immunotherapy and/or targeted therapy, providing a strong theoretical basis for the clinical application of TRPs.Graphical Supplementary InformationThe online version contains supplementary material available at 10.1186/s40246-025-00807-9.

Genome-wide Mendelian randomization mapping the influence of plasma proteome on major depressive disorder.

Ankylosing spondylitis (AS) is a chronic inflammatory disease affecting the spine and sacroiliac joints. Recent genetic studies suggest certain plasma proteins may play a causal role in AS development. This study aims to identify and characterize these proteins using Mendelian randomization (MR) and colocalization analyses. Plasma protein data were obtained from recent publications in Nature Genetics, integrating data from five previous GWAS datasets, including 738 cis-pQTLs for 734 plasma proteins. GWAS summary data for AS were sourced from IGAS and other European cohorts. MR analyses were conducted using "TwoSampleMR" to assess causal links between plasma protein levels and AS. Colocalization analysis was performed with the coloc R package to identify shared genetic variants. Sensitivity analyses and protein-protein interaction (PPI) network analyses were conducted to validate findings and explore therapeutic targets. We performed Phenome-wide association study (PheWAS) to examine the potential side effects of drug protein on AS treatment. After FDR correction, eight significant proteins were identified: IL7R, TYMP, IL12B, CCL8, TNFAIP6, IL18R1, IL23R, and ERAP1. Elevated levels of IL7R, IL12B, CCL8, IL18R1, IL23R, and ERAP1 increased AS risk, whereas elevated TYMP and TNFAIP6 levels decreased AS risk. Colocalization analysis indicated that IL23R, IL7R, and TYMP likely share causal variants with AS. PPI network analysis identified IL23R and IL7R as potential new therapeutic targets. This study identified eight plasma proteins with significant associations with AS risk, suggesting IL23R, IL7R, and TYMP as promising therapeutic targets. Further research is needed to explore underlying mechanisms and potential for drug repurposing.

Many gene expression quantitative trait locus (eQTL) studies have published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization. However, technical differences between these datasets are a barrier to their widespread use. Consequently, target genes for most genome-wide association study (GWAS) signals have still not been identified. In the present study, we present the eQTL Catalogue (https://www.ebi.ac.uk/eqtl), a resource of quality-controlled, uniformly re-computed gene expression and splicing QTLs from 21 studies. We find that, for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies. Although most QTLs were shared between most bulk tissues, we identified a greater diversity of cell-type-specific QTLs from purified cell types, a subset of which also manifested as new disease co-localizations. Our summary statistics are freely available to enable the systematic interpretation of human GWAS associations across many cell types and tissues.

The aim of this study was to explore the application of Mendelian randomization (MR) Egger and inverse variance weighted (IVW) in a causal effect on depression and ankylosing spondylitis (AS). Instrumental variables (IVs) were determined using genome-wide association studies. The 2-sample MR analysis was conducted by MR Egger to test the causal effect between depression and AS. The pleiotropy of potential instrumental variables was evaluated. The results of MR Egger and IVW were further compared. A total of 3 single nucleotide polymorphisms as the construct IVs were included. IVW results showed a significant causal effect between depression and AS (P < .001). Depression could promote the risk of AS (odds ratio = 1.060, 95% confidence interval: 1.026-1.094). However, the MR Egger showed no causal effect (P = .311). Heterogeneity statistics suggested that no heterogeneity was existed (P > .05). It was also suggested that there was no horizontal pleiotropy in IVs (MR Egger intercept: -0.0004, P = .471). Reverse MR analysis suggested that there was no causal effect between AS and depression (P > .05). Gene expression quantitative trait locus (QTLs) suggested that rs2517601 and RNF39 were positively correlated (beta = 1.066, P < .001). Depression may be one of the causes of AS by MR analysis in a European population. We can estimate the causal effect based on IVW when horizontal pleiotropy is very tiny.

Background Currently, the treatment and prevention of rheumatoid arthritis (RA) face significant challenges. In the pursuit of new therapeutic avenues, Mendelian randomization (MR) analysis has emerged as a crucial research method. Building on this, we conducted a comprehensive genome-wide analysis of MR of drug targets to identify potential therapeutic intervention points for RA.MethodS In this study, we constructed a comprehensive analytical framework aimed at identifying and validating potential biomarkers for RA. The framework begins with a two-sample MR study utilizing two large plasma protein datasets. Building upon this foundation, we conducted an in-depth exploration of the identified positive proteins using the summary data-based Mendelian randomization (SMR) method, combined with Bayesian co-localization analysis of coding genes. This approach allowed us to reveal RA multi-omics biomarkers, and we employed the LDSC analysis method to investigate the genetic correlation between the identified genes and complex diseases. Additionally, a phenome-wide association study (PheWAS) was performed on the positive genes mapped by the identified proteins, alongside an exploration of their expression in various tissues. Subsequently, we expanded our analysis to include protein-protein interaction (PPI) network analysis, gene ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Finally, we conducted drug prediction and molecular docking studies. The purpose of these comprehensive analytical methods is to thoroughly investigate the biological functions and mechanisms of action of these biomarkers, thereby providing a scientific basis for the development of more effective and targeted therapeutic drugs. Our findings encompass RA and its multiple subtypes, including seropositive RA, seronegative RA, and juvenile RA.Results This study presents a multidimensional analysis of plasma proteins in relation to RA and its subtypes. In the MR analysis of Icelandic plasma protein - Quantitative Trait Loci(pQTLs) associated with RA, the findings revealed 137, 150, 95, and 69 positive associations for RA, seropositive RA, seronegative RA, and juvenile RA, respectively. Additionally, the MR analysis of plasma pQTLs from the UK Biobank database identified 156, 167, 106, and 81 positive plasma proteins for the same conditions. After applying false discovery rate (FDR) correction, the MR analysis of plasma pQTLs and RA in Iceland identified PPA2, JUND, AGER, F2, and PMEL as significantly positive proteins. In the MR analysis of plasma pQTLs and RA within the UK Biobank database, the significantly positive proteins included AIF1, ARG2, ATP5IF1, CCL19, CDSN, CEP43, MXRA8, PADI2, RPA2, SLC16A1, TNF, and TNFRSF14. For the MR analysis of plasma pQTLs and seropositive RA in Iceland, TGFBR3, FCGR3B, TIMP4, and PMEL were identified as significantly positive proteins. In the UK Biobank MR analysis of plasma pQTLs and seropositive RA, the following proteins were significantly positive: AIF1, APOBR, ATP6V1G2, BCL2L15, C1QTNF6, CCL19, CD40, CDSN, CEP43, CX3CL1, FCGR2B, FCRL1, IL6R, MXRA8, TGFBR3, TNF, and TNFRSF14. The MR analysis of plasma pQTLs and seronegative RA in the UK Biobank identified AIF1, CEP43, and TNF as significantly positive proteins. Following Bonferroni correction, the MR analysis of UK Biobank plasma pQTLs and RA highlighted AIF1, CCL19, CDSN, CEP43, and TNF as significantly positive proteins. For seropositive RA in the UK Biobank, AIF1, ATP6V1G2, BCL2L15, CCL19, CDSN, CEP43, IL6R, and TNF were identified as significantly positive proteins. Lastly, the MR analysis of plasma pQTLs and seronegative RA in the UK Biobank confirmed AIF1 and TNF as significantly positive proteins.In the context of single-gene SMR analysis, the examination of Icelandic plasma pQTLs in relation to RA—specifically seropositive RA, seronegative RA, and juvenile RA—identified 28, 34, 21, and 15 positive plasma associations, respectively. For proteins, MR analysis of UK Biobank plasma pQTLs revealed 38, 37, 21, and 12 positive plasma proteins, respectively. Building on the findings from the previous two-sample MR analysis, Bayesian co-localization was subsequently performed. Among the Icelandic plasma pQTLs, F2 emerged as a significantly positive gene associated with RA. In the UK Biobank plasma pQTLs, the genes ATP5IF1, CCL19, CX3CL1, HDGF, MXRA8, and TNFRSF14 were identified as significantly positive. LDSC analysis demonstrated a significant positive genetic correlation between CCL19 and both RA and seropositive RA, as well as a significant positive genetic correlation between TNFRSF14 and both RA and seropositive RA. These results suggest that FCGR3A, ADAM15, CCL19, CX3CL1, NFKBIE, TNFRSF14, F2, ATP5IF1, HDGF, and MXRA8 may serve as key therapeutic targets for RA. Notably, TNFRSF14 and CCL19 warrant further investigation as important genes for understanding the pathogenesis and potential therapeutic strategies for RA and its subtypes.Conclusion Through a comprehensive analysis of plasma proteomic and transcriptomic data, we successfully identified key therapeutic targets for RA and its three clinical subtypes. Specifically, we identified FCGR3A, ADAM15, CCL19, CX3CL1, NFKBIE, TNFRSF14, F2, ATP5IF1, HDGF, and MXRA8 as potential therapeutic targets for RA. By integrating genetic relatedness scores, we further elucidated the significance of these findings. Notably, TNFRSF14 and CCL19 emerged as critical genes warranting in-depth exploration regarding the pathogenesis of RA and its subtypes, as well as their potential as therapeutic targets. These results provide a scientific basis for the development of new immunotherapy approaches, combination treatment regimens, or targeted intervention strategies, and are anticipated to advance research progress in the treatment of RA.

Autophagy dysregulation has been implicated in the toxic protein aggregates of amyotrophic lateral sclerosis (ALS). However, the causal relationship between impaired autophagy and ALS remains ambiguous, necessitating further elucidation. This Mendelian randomization (MR) study employs a two-sample design, utilizing genetic instruments to proxy autophagy dysregulation as the exposure and ALS as the outcome. It incorporates summary statistics of ALS (27,205 cases, 110,881 controls), along with data on DNA methylation, RNA splicing, gene expression, and protein abundance quantitative trait loci (QTLs) in both blood and brain tissues (mQTL, sQTL, eQTL, and pQTL, respectively) sourced from European cohorts. Cis-variants situated proximal to or within the 604 autophagy-related genes, exhibiting robust associations with molecular alterations in autophagy, are employed as instrumental variables. Their causal links with ALS are assessed via summary-data-based MR (SMR) analyses, followed by Bayesian colocalization, sensitivity analyses, brain cell-specific MR analyses, protein-protein interaction (PPI), and druggable analyses. Consistent evidence supported the causal effects of two lysosome genes (FNBP1 and IDUA), one autophagy core gene (C9orf72), and one mitophagy gene (USP35) on ALS risk. Specifically, brain FNBP1 splicing level (OR = 1.18, p = 3.38E-5) and blood USP35 expression level (OR = 1.17, p = 5.94E-5) were positively associated with higher ALS risk. In contrast, we found strong causal evidence of brain IDUA methylation level (OR = 0.96, p = 8.36E-6) and blood C9orf72 methylation level (OR = 0.55, p = 7.59E-12) with lower ALS risk. Cell-type-specific MR analyses, PPI, and druggable analyses further nominated the key brain cell type (astrocytes), potential interaction with known causative genes (SQSTM1 and PFN1), and promising druggability for FNBP1 in ALS. This multi-omics MR study identified causal associations between the regulation of four autophagy-related genes and ALS risk, shedding light on autophagy-mediated mechanisms and offering early evidence of novel therapeutic targets for ALS.

Proteomics plays a pivotal role in clinical diagnostics and monitoring. We conducted proteome-wide Mendelian randomization (MR) study to estimate the causal association between plasma proteins and Herpes simplex virus (HSV) infection. Data for 2,923 plasma protein levels were obtained from a large-scale protein quantitative trait loci study involving 54,219 individuals, conducted by the UK Biobank Pharma Proteomics Project. HSV-associated SNPs were derived from the FinnGen study, which included a total of 400,098 subjects infected with HSV. MR analysis was performed to assess the links between protein levels and the risk of HSV infection. Furthermore, a Phenome-wide MR analysis was utilized to explore potential alternative indications or predict adverse drug events. Finally, we evaluated the impact of 1,949 plasma proteins on HSV infection, identifying 48 proteins that were negatively associated with HSV infection and 54 proteins that were positively associated. Genetically higher HLA-E levels were significantly associated with increased HSV infection risk (OR = 1.39, 95% CI: 1.17–1.65, P = 2.13 × 10−4, while ULBP2 showed a significant negative association with HSV infection risk (OR = 0.81, 95% CI: 0.73–0.90, P = 6.25 × 10−5) in the primary analysis. No significant heterogeneity or pleiotropy was observed in any of the results. Additionally, we found a suggestive association of Lymphotoxin-beta, SMOC1, MICB_MICA, ASGR1, and ANXA10 with HSV infection risk (P < 0.003). In Phenome-wide MR analysis, HLA-E was associated with 214 phenotypes (PFDR < 0.10) while ULBP2 did not show significant associations with any diseases after FDR adjustment. The comprehensive MR analysis established a causal link between multiple plasma proteins and HSV infection, emphasizing the roles of HLA-E and ULBP2. These results provide new insights into the biological mechanisms of HSV and support the potential for early intervention and treatment strategies, although further research is needed to validate these plasma protein biomarkers.

目的通过孟德尔随机化（Mendelian randomization, MR）及共定位分析，系统探索2型气道炎症性疾病潜在的病理分子和治疗靶点。方法本研究以4302个可用药血浆蛋白作为暴露因素，利用其顺式表达数量性状位点（cis-expression quantitative trait loci, cis-eQTL）作为工具变量，进行转录水平MR分析。疾病结局数据集来源于英国生物银行和芬兰队列，分别用于发现分析和重复验证。对于成功验证的蛋白，进一步使用顺式蛋白质数量性状位点（cis-protein quantitative trait loci, cis-pQTL）进行蛋白水平MR分析。结合共定位分析、反向MR分析及中介分析，探讨这些血浆蛋白与过敏性鼻炎（allergic rhinitis, AR）、哮喘（asthma, AS）和鼻息肉（nasal polyps, NP）的关联。结果对2528个蛋白进行了cis-eQTL MR分析，发现10个与AR相关的蛋白（TLR10、ERBB3、PNMT等），7个与AS相关的蛋白（ERBB3、SLC40A1、PRKCQ等），3个与NP相关的蛋白（IL18RAP、AXL、ERBB3）。cis-pQTL MR分析显示，IL18RAP与较低的NP疾病风险相关，ERBB3与较低的AR、AS和NP疾病风险相关。共定位分析结果支持ERBB3与AR的关联（pp.H4=0.910）。中介分析显示，ERBB3与AR、AS的关联由嗜酸性粒细胞介导，中介效应比例分别为12.51%和17.64%。结论本研究揭示了2型气道炎症性疾病独有和共有的分子靶点，其中ERBB3可能是AR、AS和NP共有的保护性因素及生物标志物。

Chronic kidney disease (CKD) causes detrimental systemic effects, including inflammation or apoptosis, which lead to substantial morbidity and mortality. However, the causal effect of reduced kidney function on systemic proteomic signatures is incompletely understood. We performed an integrated Mendelian randomization (MR) and observational analyses to identify the causal association between kidney function and plasma protein levels, based on 1815 plasma protein profiles in 50,407 UK Biobank participants and the CKDGen Phase 4 genome-wide association study (GWAS) meta-analysis for the genetic instruments of eGFR. The MR analysis revealed 383 plasma proteins causally associated with eGFR. Reduced kidney function was found to be causally associated with an increase in the plasma levels of 381 proteins, among which TNF and IGFBP4 were increased, while the level of two proteins, NPHS1 and SPOCK1, decreased. Apoptosis-related pathway was significantly enriched in the gene-set enrichment analysis. In network analysis, TNF was identified as a hub protein with multiple linkages to molecules included in the TNF-signaling pathways, involved in inflammation, fibrosis, and apoptosis. In this proteo-genomic analysis, we identified 383 plasma proteins causally associated with eGFR, highlighting TNF-associated pathways as pathologically relevant processes in kidney disease progression, systemic inflammation, and organ fibrosis, warranting further investigation.