{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T22:42:39Z","timestamp":1782254559477,"version":"3.54.5"},"reference-count":83,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000265","name":"RCUK | Medical Research Council","doi-asserted-by":"publisher","award":["U. MC_UU_00007\/10"],"award-info":[{"award-number":["U. MC_UU_00007\/10"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Commun Biol"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Understanding the genomic basis of human proteomic variability provides powerful tools to probe potential causal relationships of proteins and disease risk, and thus to prioritise candidate drug targets. Here, we investigated 6432 plasma proteins (1533 previously unstudied in large-scale proteomic GWAS) using the SomaLogic (v4.1) aptamer-based technology in a Scottish population from the Viking Genes study. A total of 505 significant independent protein quantitative trait loci (pQTL) were found for 455 proteins in blood plasma: 382 <jats:italic>cis<\/jats:italic>- (P\u2009&lt;\u20095\u00d710<jats:sup>-8<\/jats:sup>) and 123 <jats:italic>trans<\/jats:italic>- (P\u2009&lt;\u20096.6\u00d710<jats:sup>-12<\/jats:sup>). Of these, 31 <jats:italic>cis<\/jats:italic>-pQTL were for proteins with no previous GWAS. We leveraged these pQTL to perform causal inference using bidirectional Mendelian randomisation and colocalisation against complex traits of biomedical importance. We discovered 43 colocalising associations (with a posterior probability\u2009&gt;\u200980% that pQTL and complex traits share a causal variant), pointing to plausible causal roles for the proteins. These findings include hitherto undiscovered causal links of leukocyte receptor tyrosine kinase (LTK) to type-2 diabetes and beta-1,3-glucuronyltransferase (B3GAT1) to prostate cancer. These new connections will help guide the search for new or repurposed therapies. Our findings provide strong support for continuing to increase the number of proteins studied using GWAS.<\/jats:p>","DOI":"10.1038\/s42003-025-08738-w","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T05:24:20Z","timestamp":1756445060000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Efficient candidate drug target discovery through proteogenomics in a Scottish cohort"],"prefix":"10.1038","volume":"8","author":[{"given":"Jurgis","family":"Kuliesius","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5197-1267","authenticated-orcid":false,"given":"Paul R. H. J.","family":"Timmers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5576-8584","authenticated-orcid":false,"given":"Pau","family":"Navarro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3105-8929","authenticated-orcid":false,"given":"Lucija","family":"Klaric","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5751-9178","authenticated-orcid":false,"given":"James F.","family":"Wilson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"key":"8738_CR1","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1038\/nrg.2016.86","volume":"17","author":"EA Ashley","year":"2016","unstructured":"Ashley, E. A. Towards precision medicine. Nat. Rev. Genet. 17, 507\u2013522 (2016).","journal-title":"Nat. Rev. Genet."},{"key":"8738_CR2","doi-asserted-by":"publisher","first-page":"3019","DOI":"10.1002\/pmic.200402053","volume":"5","author":"L Thadikkaran","year":"2005","unstructured":"Thadikkaran, L. et al. Recent advances in blood-related proteomics. Proteomics 5, 3019\u20133034 (2005).","journal-title":"Proteomics"},{"key":"8738_CR3","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1038\/s41576-021-00387-z","volume":"22","author":"WR Reay","year":"2021","unstructured":"Reay, W. R. & Cairns, M. J. Advancing the use of genome-wide association studies for drug repurposing. Nat. Rev. Genet. 22, 658\u2013671 (2021).","journal-title":"Nat. Rev. Genet."},{"key":"8738_CR4","doi-asserted-by":"publisher","first-page":"29","DOI":"10.3390\/proteomes4040029","volume":"4","author":"TT Duarte","year":"2016","unstructured":"Duarte, T. T. & Spencer, C. T. Personalized proteomics: the future of precision medicine. Proteomes 4, 29 (2016).","journal-title":"Proteomes"},{"key":"8738_CR5","unstructured":"SomaLogic. SomaScan\u00ae 11K Assay v5.0 Technical Note SL00000919. Rev 1, 2023\u201312 (2023)."},{"key":"8738_CR6","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1038\/s41586-023-06592-6","volume":"622","author":"BB Sun","year":"2023","unstructured":"Sun, B. B. et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature 622, 329\u2013338 (2023).","journal-title":"Nature"},{"key":"8738_CR7","unstructured":"Suhre, K. A table of all published GWAS with proteomics. Metabolomix. http:\/\/www.metabolomix.com\/a-table-of-all-published-gwas-with-proteomics\/ (Accessed: 27 April 2023)."},{"key":"8738_CR8","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1016\/j.kint.2022.07.005","volume":"102","author":"A Surapaneni","year":"2022","unstructured":"Surapaneni, A. et al. Identification of 969 protein quantitative trait loci in an African American population with kidney disease attributed to hypertension. Kidney Int. 102, 1167\u20131177 (2022).","journal-title":"Kidney Int."},{"key":"8738_CR9","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1038\/s41586-018-0175-2","volume":"558","author":"BB Sun","year":"2018","unstructured":"Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73\u201379 (2018).","journal-title":"Nature"},{"key":"8738_CR10","doi-asserted-by":"publisher","DOI":"10.1126\/science.abj1541","volume":"374","author":"M Pietzner","year":"2021","unstructured":"Pietzner, M. et al. Mapping the proteo-genomic convergence of human diseases. Science 374, eabj1541 (2021).","journal-title":"Science"},{"key":"8738_CR11","doi-asserted-by":"publisher","first-page":"W276","DOI":"10.1093\/nar\/gkac240","volume":"50","author":"F Madeira","year":"2022","unstructured":"Madeira, F. et al. Search and sequence analysis tools services from EMBL-EBI in 2022. Nucleic Acids Res. 50, W276\u2013w279 (2022).","journal-title":"Nucleic Acids Res."},{"key":"8738_CR12","doi-asserted-by":"publisher","first-page":"3555","DOI":"10.1093\/bioinformatics\/btv402","volume":"31","author":"MJ Machiela","year":"2015","unstructured":"Machiela, M. J. & Chanock, S. J. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics 31, 3555\u20133557 (2015).","journal-title":"Bioinformatics"},{"key":"8738_CR13","doi-asserted-by":"publisher","unstructured":"Macdonald-Dunlop, E. et al. Mapping genetic determinants of 184 circulating proteins in 26,494 individuals to connect proteins and diseases. medRxiv https:\/\/doi.org\/10.1101\/2021.08.03.21261494 (2021).","DOI":"10.1101\/2021.08.03.21261494"},{"key":"8738_CR14","doi-asserted-by":"publisher","unstructured":"Sun, B. B. et al. Genetic regulation of the human plasma proteome in 54,306 UK Biobank participants. bioRxiv https:\/\/doi.org\/10.1101\/2022.06.17.496443 (2022).","DOI":"10.1101\/2022.06.17.496443"},{"key":"8738_CR15","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1126\/science.aaq1327","volume":"361","author":"V Emilsson","year":"2018","unstructured":"Emilsson, V. et al. Co-regulatory networks of human serum proteins link genetics to disease. Science 361, 769\u2013773 (2018).","journal-title":"Science"},{"key":"8738_CR16","doi-asserted-by":"publisher","first-page":"1712","DOI":"10.1038\/s41588-021-00978-w","volume":"53","author":"E Ferkingstad","year":"2021","unstructured":"Ferkingstad, E. et al. Large-scale integration of the plasma proteome with genetics and disease. Nat. Genet. 53, 1712\u20131721 (2021).","journal-title":"Nat. Genet."},{"key":"8738_CR17","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-27850-z","volume":"13","author":"A Gudjonsson","year":"2022","unstructured":"Gudjonsson, A. et al. A genome-wide association study of serum proteins reveals shared loci with common diseases. Nat. Commun. 13, 480 (2022).","journal-title":"Nat. Commun."},{"key":"8738_CR18","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-19996-z","volume":"11","author":"M Pietzner","year":"2020","unstructured":"Pietzner, M. et al. Genetic architecture of host proteins involved in SARS-CoV-2 infection. Nat. Commun. 11, 6397 (2020).","journal-title":"Nat. Commun."},{"key":"8738_CR19","doi-asserted-by":"publisher","unstructured":"Elsworth, B. et al. The MRC IEU OpenGWAS data infrastructure. bioRxiv https:\/\/doi.org\/10.1101\/2020.08.10.244293 (2020).","DOI":"10.1101\/2020.08.10.244293"},{"key":"8738_CR20","doi-asserted-by":"publisher","first-page":"e1004383","DOI":"10.1371\/journal.pgen.1004383","volume":"10","author":"C Giambartolomei","year":"2014","unstructured":"Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).","journal-title":"PLoS Genet."},{"key":"8738_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.xgen.2022.100168","volume":"2","author":"KJ Karczewski","year":"2022","unstructured":"Karczewski, K. J. et al. Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes. Cell Genomics 2, 100168 (2022).","journal-title":"Cell Genomics"},{"key":"8738_CR22","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1038\/s41588-018-0142-8","volume":"50","author":"FR Schumacher","year":"2018","unstructured":"Schumacher, F. R. et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat. Genet. 50, 928\u2013936 (2018).","journal-title":"Nat. Genet."},{"key":"8738_CR23","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1038\/s41588-020-00748-0","volume":"53","author":"DV Conti","year":"2021","unstructured":"Conti, D. V. et al. Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction. Nat. Genet. 53, 65\u201375 (2021).","journal-title":"Nat. Genet."},{"key":"8738_CR24","doi-asserted-by":"publisher","unstructured":"Burrows, K. et al. Genome-wide association study of cancer risk in UK Biobank. https:\/\/doi.org\/10.5523\/bris.aed0u12w0ede20olb0m77p4b9 (MRC Integrative Epidemiology Unit, University of Bristol 2021).","DOI":"10.5523\/bris.aed0u12w0ede20olb0m77p4b9"},{"key":"8738_CR25","doi-asserted-by":"publisher","first-page":"1300","DOI":"10.1038\/s41588-021-00913-z","volume":"53","author":"U V\u00f5sa","year":"2021","unstructured":"V\u00f5sa, U. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 53, 1300\u20131310 (2021).","journal-title":"Nat. Genet."},{"key":"8738_CR26","doi-asserted-by":"publisher","unstructured":"Repetto, L. et al. Genetic mechanisms of 184 neuro-related proteins in human plasma. medRxiv https:\/\/doi.org\/10.1101\/2023.02.10.23285650 (2023).","DOI":"10.1101\/2023.02.10.23285650"},{"key":"8738_CR27","doi-asserted-by":"publisher","first-page":"1302","DOI":"10.1038\/s41593-021-00886-6","volume":"24","author":"C Yang","year":"2021","unstructured":"Yang, C. et al. Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders. Nat. Neurosci. 24, 1302\u20131312 (2021).","journal-title":"Nat. Neurosci."},{"key":"8738_CR28","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1038\/s41586-022-05275-y","volume":"610","author":"L Yengo","year":"2022","unstructured":"Yengo, L. et al. A saturated map of common genetic variants associated with human height. Nature 610, 704\u2013712 (2022).","journal-title":"Nature"},{"key":"8738_CR29","doi-asserted-by":"publisher","first-page":"6046","DOI":"10.1093\/nar\/gkw500","volume":"44","author":"A Brodie","year":"2016","unstructured":"Brodie, A., Azaria, J. R. & Ofran, Y. How far from the SNP may the causative genes be?. Nucleic Acids Res. 44, 6046\u20136054 (2016).","journal-title":"Nucleic Acids Res."},{"key":"8738_CR30","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1093\/nar\/gkw813","volume":"45","author":"J Ibn-Salem","year":"2017","unstructured":"Ibn-Salem, J., Muro, E. M. & Andrade-Navarro, M. A. Co-regulation of paralog genes in the three-dimensional chromatin architecture. Nucleic Acids Res. 45, 81\u201391 (2017).","journal-title":"Nucleic Acids Res."},{"key":"8738_CR31","doi-asserted-by":"publisher","first-page":"6570","DOI":"10.1016\/j.csbj.2022.11.041","volume":"20","author":"Y Hu","year":"2022","unstructured":"Hu, Y. et al. Paralog explorer: a resource for mining information about paralogs in common research organisms. Comput. Struct. Biotechnol. J. 20, 6570\u20136577 (2022).","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"8738_CR32","doi-asserted-by":"publisher","first-page":"1288","DOI":"10.1158\/1541-7786.MCR-16-0103","volume":"14","author":"TM Clausen","year":"2016","unstructured":"Clausen, T. M. et al. Oncofetal chondroitin sulfate glycosaminoglycans are key players in integrin signaling and tumor cell motility. Mol. Cancer Res. 14, 1288\u20131299 (2016).","journal-title":"Mol. Cancer Res."},{"key":"8738_CR33","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/sj.mp.4001382","volume":"8","author":"AR Jeffries","year":"2003","unstructured":"Jeffries, A. R. et al. \u03b2-1,3-Glucuronyltransferase-1 gene implicated as a candidate for a schizophrenia-like psychosis through molecular analysis of a balanced translocation. Mol. Psychiatry 8, 654\u2013663 (2003).","journal-title":"Mol. Psychiatry"},{"key":"8738_CR34","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1002\/(SICI)1097-0045(19990801)40:3<192::AID-PROS7>3.0.CO;2-F","volume":"40","author":"AY Liu","year":"1999","unstructured":"Liu, A. Y. et al. Analysis and sorting of prostate cancer cell types by flow cytometry. Prostate 40, 192\u2013199 (1999).","journal-title":"Prostate"},{"key":"8738_CR35","doi-asserted-by":"publisher","first-page":"1543","DOI":"10.1016\/S0002-9440(10)63412-8","volume":"165","author":"AY Liu","year":"2004","unstructured":"Liu, A. Y., Roudier, M. P. & True, L. D. Heterogeneity in primary and metastatic prostate cancer as defined by cell surface CD Profile. Am. J. Pathol. 165, 1543\u20131556 (2004).","journal-title":"Am. J. Pathol."},{"key":"8738_CR36","doi-asserted-by":"publisher","first-page":"15741","DOI":"10.1073\/pnas.1412009111","volume":"111","author":"H Zhang","year":"2014","unstructured":"Zhang, H. et al. Deorphanization of the human leukocyte tyrosine kinase (LTK) receptor by a signaling screen of the extracellular proteome. Proc. Natl. Acad. Sci. USA 111, 15741\u201315745 (2014).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"8738_CR37","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1093\/hmg\/ddh020","volume":"13","author":"N Li","year":"2004","unstructured":"Li, N. et al. Gain-of-function polymorphism in mouse and human Ltk: implications for the pathogenesis of systemic lupus erythematosus. Hum. Mol. Genet. 13, 171\u2013179 (2004).","journal-title":"Hum. Mol. Genet."},{"key":"8738_CR38","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.ymeth.2014.11.020","volume":"74","author":"S Pletscher-Frankild","year":"2015","unstructured":"Pletscher-Frankild, S., Pallej\u00e0, A., Tsafou, K., Binder, J. X. & Jensen, L. J. DISEASES: text mining and data integration of disease\u2013gene associations. Methods 74, 83\u201389 (2015).","journal-title":"Methods"},{"key":"8738_CR39","doi-asserted-by":"publisher","first-page":"1.30.31","DOI":"10.1002\/cpbi.5","volume":"54","author":"G Stelzer","year":"2016","unstructured":"Stelzer, G. et al. The GeneCards Suite: from gene data mining to disease genome sequence analyses. Curr. Protoc. Bioinforma. 54, 1.30.31\u201331.30.33 (2016).","journal-title":"Curr. Protoc. Bioinforma."},{"key":"8738_CR40","doi-asserted-by":"publisher","first-page":"D605","DOI":"10.1093\/nar\/gkaa1074","volume":"49","author":"D Szklarczyk","year":"2021","unstructured":"Szklarczyk, D. et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene\/measurement sets. Nucleic Acids Res. 49, D605\u2013d612 (2021).","journal-title":"Nucleic Acids Res."},{"key":"8738_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.molmet.2020.101020","volume":"40","author":"A Kwok","year":"2020","unstructured":"Kwok, A. et al. Truncation of Pik3r1 causes severe insulin resistance uncoupled from obesity and dyslipidaemia by increased energy expenditure. Mol. Metab. 40, 101020 (2020).","journal-title":"Mol. Metab."},{"key":"8738_CR42","doi-asserted-by":"publisher","first-page":"3067","DOI":"10.1038\/sj.onc.1201153","volume":"14","author":"H Ueno","year":"1997","unstructured":"Ueno, H. et al. The phosphatidylinositol 3\u2019 kinase pathway is required for the survival signal of leukocyte tyrosine kinase. Oncogene 14, 3067\u20133072 (1997).","journal-title":"Oncogene"},{"key":"8738_CR43","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1038\/ng.443","volume":"41","author":"J Rung","year":"2009","unstructured":"Rung, J. et al. Genetic variant near IRS1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia. Nat. Genet. 41, 1110\u20131115 (2009).","journal-title":"Nat. Genet."},{"key":"8738_CR44","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1038\/s41586-022-05473-8","volume":"613","author":"MI Kurki","year":"2023","unstructured":"Kurki, M. I. et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613, 508\u2013518 (2023).","journal-title":"Nature"},{"key":"8738_CR45","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-25968-8","volume":"12","author":"P Gautam","year":"2021","unstructured":"Gautam, P. et al. Multi-species single-cell transcriptomic analysis of ocular compartment regulons. Nat. Commun. 12, 5675 (2021).","journal-title":"Nat. Commun."},{"key":"8738_CR46","doi-asserted-by":"crossref","unstructured":"Wang, S. K. et al. Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases. Cell Genom. 2, 100164 (2022).","DOI":"10.1016\/j.xgen.2022.100164"},{"key":"8738_CR47","doi-asserted-by":"publisher","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","volume":"46","author":"DS Wishart","year":"2018","unstructured":"Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074\u2013d1082 (2018).","journal-title":"Nucleic Acids Res."},{"key":"8738_CR48","doi-asserted-by":"publisher","first-page":"2054","DOI":"10.1038\/s41588-024-01896-3","volume":"56","author":"QS Wang","year":"2024","unstructured":"Wang, Q. S. et al. Statistically and functionally fine-mapped blood eQTLs and pQTLs from 1,405 humans reveal distinct regulation patterns and disease relevance. Nat. Genet. 56, 2054\u20132067 (2024).","journal-title":"Nat. Genet."},{"key":"8738_CR49","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1038\/s41586-023-06563-x","volume":"622","author":"GH Eldjarn","year":"2023","unstructured":"Eldjarn, G. H. et al. Large-scale plasma proteomics comparisons through genetics and disease associations. Nature 622, 348\u2013358 (2023).","journal-title":"Nature"},{"key":"8738_CR50","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.abm5164","volume":"8","author":"DH Katz","year":"2022","unstructured":"Katz, D. H. et al. Proteomic profiling platforms head to head: leveraging genetics and clinical traits to compare aptamer- and antibody-based methods. Sci. Adv. 8, eabm5164 (2022).","journal-title":"Sci. Adv."},{"key":"8738_CR51","doi-asserted-by":"publisher","first-page":"e1008785","DOI":"10.1371\/journal.pgen.1008785","volume":"16","author":"AD Bretherick","year":"2020","unstructured":"Bretherick, A. D. et al. Linking protein to phenotype with Mendelian Randomization detects 38 proteins with causal roles in human diseases and traits. PLoS Genet. 16, e1008785 (2020).","journal-title":"PLoS Genet."},{"key":"8738_CR52","doi-asserted-by":"publisher","first-page":"eaax0301","DOI":"10.1126\/sciadv.aax0301","volume":"6","author":"L Klari\u0107","year":"2020","unstructured":"Klari\u0107, L. et al. Glycosylation of immunoglobulin G is regulated by a large network of genes pleiotropic with inflammatory diseases. Sci. Adv. 6, eaax0301 (2020).","journal-title":"Sci. Adv."},{"key":"8738_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.xgen.2022.100212","volume":"2","author":"K Tsuo","year":"2022","unstructured":"Tsuo, K. et al. Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity. Cell Genomics 2, 100212 (2022).","journal-title":"Cell Genomics"},{"key":"8738_CR54","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-47436-6","volume":"9","author":"SM Kerr","year":"2019","unstructured":"Kerr, S. M. et al. An actionable KCNH2 Long QT Syndrome variant detected by sequence and haplotype analysis in a population research cohort. Sci. Rep. 9, 10964 (2019).","journal-title":"Sci. Rep."},{"key":"8738_CR55","doi-asserted-by":"publisher","first-page":"19064","DOI":"10.1073\/pnas.1904761116","volume":"116","author":"E Gilbert","year":"2019","unstructured":"Gilbert, E. et al. The genetic landscape of Scotland and the Isles. Proc. Natl. Acad. Sci. USA 116, 19064\u201319070 (2019).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"8738_CR56","doi-asserted-by":"publisher","first-page":"e1008480","DOI":"10.1371\/journal.pgen.1008480","volume":"15","author":"M Halachev","year":"2019","unstructured":"Halachev, M. et al. Increased ultra-rare variant load in an isolated Scottish population impacts exonic and regulatory regions. PLoS Genet.15, e1008480 (2019).","journal-title":"PLoS Genet."},{"key":"8738_CR57","unstructured":"SomaLogic. SomaLogic SomaScan assay 4.1 Technical note SL00000572 Rev 4: 2022-01 (2022)."},{"key":"8738_CR58","unstructured":"SomaLogic. Data Standardization and File Specification Technical Note v2 SL00000048 Rev 3: 2022-01 (2022)."},{"key":"8738_CR59","doi-asserted-by":"crossref","unstructured":"Luo, J. et al. Genetic regulation of human brain proteome reveals proteins implicated in psychiatric disorders. Research Square (2022).","DOI":"10.21203\/rs.3.rs-1633422\/v1"},{"key":"8738_CR60","doi-asserted-by":"publisher","first-page":"e3379","DOI":"10.1097\/MD.0000000000003379","volume":"95","author":"Y Wang","year":"2016","unstructured":"Wang, Y. et al. The association between glycosylation of immunoglobulin G and hypertension: a multiple ethnic cross-sectional study. Medicine 95, e3379 (2016).","journal-title":"Medicine"},{"key":"8738_CR61","doi-asserted-by":"publisher","first-page":"904","DOI":"10.1038\/ng1847","volume":"38","author":"AL Price","year":"2006","unstructured":"Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904\u2013909 (2006).","journal-title":"Nat. Genet."},{"key":"8738_CR62","doi-asserted-by":"crossref","unstructured":"Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015).","DOI":"10.1186\/s13742-015-0047-8"},{"key":"8738_CR63","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1038\/ng.3643","volume":"48","author":"S McCarthy","year":"2016","unstructured":"McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279\u20131283 (2016).","journal-title":"Nat. Genet."},{"key":"8738_CR64","doi-asserted-by":"publisher","first-page":"1294","DOI":"10.1093\/bioinformatics\/btm108","volume":"23","author":"YS Aulchenko","year":"2007","unstructured":"Aulchenko, Y. S., Ripke, S., Isaacs, A. & van Duijn, C. M. GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 1294\u20131296 (2007).","journal-title":"Bioinformatics"},{"key":"8738_CR65","doi-asserted-by":"publisher","first-page":"1166","DOI":"10.1038\/ng.2410","volume":"44","author":"GR Svishcheva","year":"2012","unstructured":"Svishcheva, G. R., Axenovich, T. I., Belonogova, N. M., van Duijn, C. M. & Aulchenko, Y. S. Rapid variance components\u2013based method for whole-genome association analysis. Nat. Genet. 44, 1166\u20131170 (2012).","journal-title":"Nat. Genet."},{"key":"8738_CR66","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1093\/bib\/bbt066","volume":"16","author":"T Haller","year":"2013","unstructured":"Haller, T., Kals, M., Esko, T., M\u00e4gi, R. & Fischer, K. RegScan: a GWAS tool for quick estimation of allele effects on continuous traits and their combinations. Brief. Bioinforma. 16, 39\u201344 (2013).","journal-title":"Brief. Bioinforma."},{"key":"8738_CR67","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1002\/pro.4218","volume":"31","author":"PD Thomas","year":"2022","unstructured":"Thomas, P. D. et al. PANTHER: making genome-scale phylogenetics accessible to all. Protein Sci. 31, 8\u201322 (2022).","journal-title":"Protein Sci."},{"key":"8738_CR68","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1038\/s41596-019-0128-8","volume":"14","author":"H Mi","year":"2019","unstructured":"Mi, H. et al. Protocol Update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0). Nat. Protoc. 14, 703\u2013721 (2019).","journal-title":"Nat. Protoc."},{"key":"8738_CR69","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/75556","volume":"25","author":"M Ashburner","year":"2000","unstructured":"Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25\u201329 (2000).","journal-title":"Nat. Genet."},{"key":"8738_CR70","unstructured":"Consortium, T. G. O. et al. The Gene Ontology knowledgebase in 2023. Genetics 224, iyad031 (2023)."},{"key":"8738_CR71","doi-asserted-by":"publisher","first-page":"D988","DOI":"10.1093\/nar\/gkab1049","volume":"50","author":"F Cunningham","year":"2022","unstructured":"Cunningham, F. et al. Ensembl 2022. Nucleic Acids Res. 50, D988\u2013d995 (2022).","journal-title":"Nucleic Acids Res."},{"key":"8738_CR72","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41586-018-0579-z","volume":"562","author":"C Bycroft","year":"2018","unstructured":"Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203\u2013209 (2018).","journal-title":"Nature"},{"key":"8738_CR73","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-29189-5","volume":"13","author":"A Landini","year":"2022","unstructured":"Landini, A. et al. Genetic regulation of post-translational modification of two distinct proteins. Nat. Commun. 13, 1586 (2022).","journal-title":"Nat. Commun."},{"key":"8738_CR74","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.34408","volume":"7","author":"G Hemani","year":"2018","unstructured":"Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).","journal-title":"eLife"},{"key":"8738_CR75","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1161\/CIRCULATIONAHA.118.034532","volume":"139","author":"M Sabater-Lleal","year":"2019","unstructured":"Sabater-Lleal, M. et al. Genome-wide association transethnic meta-analyses identifies novel associations regulating coagulation factor VIII and von Willebrand factor plasma levels. Circulation 139, 620\u2013635 (2019).","journal-title":"Circulation"},{"key":"8738_CR76","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1093\/ije\/dyr036","volume":"40","author":"S Burgess","year":"2011","unstructured":"Burgess, S. & Thompson, S. G. Collaboration CCG. Avoiding bias from weak instruments in Mendelian randomization studies. Int. J. Epidemiol. 40, 755\u2013764 (2011).","journal-title":"Int. J. Epidemiol."},{"key":"8738_CR77","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1038\/s41588-020-0682-6","volume":"52","author":"J Zheng","year":"2020","unstructured":"Zheng, J. et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat. Genet. 52, 1122\u20131131 (2020).","journal-title":"Nat. Genet."},{"key":"8738_CR78","doi-asserted-by":"publisher","first-page":"D977","DOI":"10.1093\/nar\/gkac1010","volume":"51","author":"E Sollis","year":"2023","unstructured":"Sollis, E. et al. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. Nucleic Acids Res. 51, D977\u2013d985 (2023).","journal-title":"Nucleic Acids Res."},{"key":"8738_CR79","doi-asserted-by":"publisher","first-page":"e1008720","DOI":"10.1371\/journal.pgen.1008720","volume":"16","author":"C Wallace","year":"2020","unstructured":"Wallace, C. Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses. PLoS Genet. 16, e1008720 (2020).","journal-title":"PLoS Genet."},{"key":"8738_CR80","doi-asserted-by":"publisher","DOI":"10.1126\/science.aan2507","volume":"357","author":"M Uhlen","year":"2017","unstructured":"Uhlen, M. et al. A pathology atlas of the human cancer transcriptome. Science 357, eaan2507 (2017).","journal-title":"Science"},{"key":"8738_CR81","doi-asserted-by":"publisher","first-page":"D1311","DOI":"10.1093\/nar\/gkaa840","volume":"49","author":"M Ghoussaini","year":"2020","unstructured":"Ghoussaini, M. et al. Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics. Nucleic Acids Res. 49, D1311\u2013D1320 (2020).","journal-title":"Nucleic Acids Res."},{"key":"8738_CR82","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1038\/s41588-021-00945-5","volume":"53","author":"E Mountjoy","year":"2021","unstructured":"Mountjoy, E. et al. An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. Nat. Genet. 53, 1527\u20131533 (2021).","journal-title":"Nat. Genet."},{"key":"8738_CR83","doi-asserted-by":"publisher","first-page":"D1180","DOI":"10.1093\/nar\/gkad1004","volume":"52","author":"B Zdrazil","year":"2023","unstructured":"Zdrazil, B. et al. The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Res. 52, D1180\u2013D1192 (2023).","journal-title":"Nucleic Acids Res."}],"container-title":["Communications Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s42003-025-08738-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42003-025-08738-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42003-025-08738-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T21:36:34Z","timestamp":1757453794000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s42003-025-08738-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,29]]},"references-count":83,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["8738"],"URL":"https:\/\/doi.org\/10.1038\/s42003-025-08738-w","relation":{},"ISSN":["2399-3642"],"issn-type":[{"value":"2399-3642","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,29]]},"assertion":[{"value":"5 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"P.T. and L.K. are currently employed by and have share options in BioAge Labs. The remaining authors declare no conflicts of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1300"}}