{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T23:28:53Z","timestamp":1781047733562,"version":"3.54.1"},"reference-count":44,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T00:00:00Z","timestamp":1747353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32300500"],"award-info":[{"award-number":["32300500"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022M723666"],"award-info":[{"award-number":["2022M723666"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["323E0454"],"award-info":[{"award-number":["323E0454"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["32300500"],"award-info":[{"award-number":["32300500"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022M723666"],"award-info":[{"award-number":["2022M723666"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["323E0454"],"award-info":[{"award-number":["323E0454"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Talent Start-up Foundation of JiaYing University","award":["32300500"],"award-info":[{"award-number":["32300500"]}]},{"name":"Talent Start-up Foundation of JiaYing University","award":["2022M723666"],"award-info":[{"award-number":["2022M723666"]}]},{"name":"Talent Start-up Foundation of JiaYing University","award":["323E0454"],"award-info":[{"award-number":["323E0454"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biology"],"abstract":"<jats:p>Background: The tissues of origin and molecular mechanisms underlying human complex diseases remain incompletely understood. Previous studies have leveraged transcriptomic data to interpret genome-wide association studies (GWASs) for identifying disease-relevant tissues and fine-mapping causal genes. However, according to the central dogma, proteins more directly reflect cellular molecular activities than RNA. Therefore, in this study, we integrated proteomic data with GWAS to identify disease-associated tissues and genes. Methods: We compiled proteomic and paired transcriptomic data for 12,229 genes across 32 human tissues from the GTEx project. Using three tissue inference approaches\u2014S-LDSC, MAGMA, and DESE\u2014we analyzed GWAS data for six representative complex diseases (bipolar disorder, schizophrenia, coronary artery disease, Crohn\u2019s disease, rheumatoid arthritis, and type 2 diabetes), with an average sample size of 260 K. We systematically compared disease-associated tissues and genes identified using proteomic versus transcriptomic data. Results: Tissue-specific protein abundance showed a moderate correlation with RNA expression (mean correlation coefficient = 0.46, 95% CI: 0.42\u20130.49). Proteomic data accurately identified disease-relevant tissues, such as the association between brain regions and schizophrenia and between coronary arteries and coronary artery disease. Compared to GWAS-based gene association estimates alone, incorporating proteomic data significantly improved gene association detection (AUC difference test, p = 0.0028). Furthermore, proteomic data revealed unique disease-associated genes that were not identified using transcriptomic data, such as the association between bipolar disorder and CREB1. Conclusions: Integrating proteomic data enables accurate identification of disease-associated tissues and provides irreplaceable advantages in fine-mapping genes for complex diseases.<\/jats:p>","DOI":"10.3390\/biology14050554","type":"journal-article","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T05:11:57Z","timestamp":1747372317000},"page":"554","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Integrating Proteomics and GWAS to Identify Key Tissues and Genes Underlying Human Complex Diseases"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3481-4224","authenticated-orcid":false,"given":"Chao","family":"Xue","sequence":"first","affiliation":[{"name":"Medical College, Jiaying University, Meizhou 514031, China"},{"name":"Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Medical College, Jiaying University, Meizhou 514031, China"},{"name":"Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1016\/j.ajhg.2018.04.002","article-title":"The Post-GWAS Era: From Association to Function","volume":"102","author":"Gallagher","year":"2018","journal-title":"Am. 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