Lauren J Donoghue, Christian Benner, Diana Chang, Flaviyan Jerome Irudayanathan, Rion K Pendergrass, Brian L Yaspan, Anubha Mahajan, Mark I McCarthy
{"title":"生物库规模遗传学和血浆蛋白质组学的整合揭示了哮喘风险和异质性因果过程的证据。","authors":"Lauren J Donoghue, Christian Benner, Diana Chang, Flaviyan Jerome Irudayanathan, Rion K Pendergrass, Brian L Yaspan, Anubha Mahajan, Mark I McCarthy","doi":"10.1016/j.xgen.2025.100840","DOIUrl":null,"url":null,"abstract":"<p><p>Hundreds of genetic associations for asthma have been identified, yet translating these findings into mechanistic insights remains challenging. We leveraged plasma proteomics from the UK Biobank Pharma Proteomics Project (UKB-PPP) to identify biomarkers and effectors of asthma risk or heterogeneity using genetic causal inference approaches. We identified 609 proteins associated with asthma status (269 proteins after controlling for body mass index [BMI] and smoking). Analysis of genetically predicted protein levels identified 70 proteins with putative causal roles in asthma risk, including known drug targets and proteins without prior genetic evidence in asthma (e.g., GCHFR, TDRKH, and CLEC7A). The genetic architecture of causally associated proteins provided evidence for a Toll-like receptor (TLR)1-interleukin (IL)-27 asthma axis. Lastly, we identified evidence of causal relationships between proteins and heterogeneous aspects of asthma biology, including between TSPAN8 and neutrophil counts. These findings illustrate that integrating biobank-scale genetics and plasma proteomics can provide a framework to identify therapeutic targets and mechanisms underlying disease risk and heterogeneity.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":" ","pages":"100840"},"PeriodicalIF":11.1000,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integration of biobank-scale genetics and plasma proteomics reveals evidence for causal processes in asthma risk and heterogeneity.\",\"authors\":\"Lauren J Donoghue, Christian Benner, Diana Chang, Flaviyan Jerome Irudayanathan, Rion K Pendergrass, Brian L Yaspan, Anubha Mahajan, Mark I McCarthy\",\"doi\":\"10.1016/j.xgen.2025.100840\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Hundreds of genetic associations for asthma have been identified, yet translating these findings into mechanistic insights remains challenging. We leveraged plasma proteomics from the UK Biobank Pharma Proteomics Project (UKB-PPP) to identify biomarkers and effectors of asthma risk or heterogeneity using genetic causal inference approaches. We identified 609 proteins associated with asthma status (269 proteins after controlling for body mass index [BMI] and smoking). Analysis of genetically predicted protein levels identified 70 proteins with putative causal roles in asthma risk, including known drug targets and proteins without prior genetic evidence in asthma (e.g., GCHFR, TDRKH, and CLEC7A). The genetic architecture of causally associated proteins provided evidence for a Toll-like receptor (TLR)1-interleukin (IL)-27 asthma axis. Lastly, we identified evidence of causal relationships between proteins and heterogeneous aspects of asthma biology, including between TSPAN8 and neutrophil counts. These findings illustrate that integrating biobank-scale genetics and plasma proteomics can provide a framework to identify therapeutic targets and mechanisms underlying disease risk and heterogeneity.</p>\",\"PeriodicalId\":72539,\"journal\":{\"name\":\"Cell genomics\",\"volume\":\" \",\"pages\":\"100840\"},\"PeriodicalIF\":11.1000,\"publicationDate\":\"2025-03-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cell genomics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.xgen.2025.100840\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell genomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.xgen.2025.100840","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
Integration of biobank-scale genetics and plasma proteomics reveals evidence for causal processes in asthma risk and heterogeneity.
Hundreds of genetic associations for asthma have been identified, yet translating these findings into mechanistic insights remains challenging. We leveraged plasma proteomics from the UK Biobank Pharma Proteomics Project (UKB-PPP) to identify biomarkers and effectors of asthma risk or heterogeneity using genetic causal inference approaches. We identified 609 proteins associated with asthma status (269 proteins after controlling for body mass index [BMI] and smoking). Analysis of genetically predicted protein levels identified 70 proteins with putative causal roles in asthma risk, including known drug targets and proteins without prior genetic evidence in asthma (e.g., GCHFR, TDRKH, and CLEC7A). The genetic architecture of causally associated proteins provided evidence for a Toll-like receptor (TLR)1-interleukin (IL)-27 asthma axis. Lastly, we identified evidence of causal relationships between proteins and heterogeneous aspects of asthma biology, including between TSPAN8 and neutrophil counts. These findings illustrate that integrating biobank-scale genetics and plasma proteomics can provide a framework to identify therapeutic targets and mechanisms underlying disease risk and heterogeneity.