{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:19:32Z","timestamp":1759331972059,"version":"3.37.3"},"reference-count":6,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2018,2,8]],"date-time":"2018-02-08T00:00:00Z","timestamp":1518048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 GM122078 and R21 CA209848"],"award-info":[{"award-number":["R01 GM122078 and R21 CA209848"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,6,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Integration of genetic studies for multiple phenotypes is a powerful approach to improving the identification of genetic variants associated with complex traits. Although it has been shown that leveraging shared genetic basis among phenotypes, namely pleiotropy, can increase statistical power to identify risk variants, it remains challenging to effectively integrate genome-wide association study (GWAS) datasets for a large number of phenotypes. We previously developed graph-GPA, a Bayesian hierarchical model that integrates multiple GWAS datasets to boost statistical power for the identification of risk variants and to estimate pleiotropic architecture within a unified framework. Here we propose a novel improvement of graph-GPA which incorporates external knowledge about phenotype\u2013phenotype relationship to guide the estimation of genetic correlation and the association mapping. The application of graph-GPA to GWAS datasets for 12 complex diseases with a prior disease graph obtained from a text mining of biomedical literature illustrates its power to improve the identification of risk genetic variants and to facilitate understanding of genetic relationship among complex diseases.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>graph-GPA is implemented as an R package \u2018GGPA\u2019, which is publicly available at http:\/\/dongjunchung.github.io\/GGPA\/. DDNet, a web interface to query diseases of interest and download a prior disease graph obtained from a text mining of biomedical literature, is publicly available at http:\/\/www.chunglab.io\/ddnet\/.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty061","type":"journal-article","created":{"date-parts":[[2018,2,6]],"date-time":"2018-02-06T20:11:24Z","timestamp":1517947884000},"page":"2139-2141","source":"Crossref","is-referenced-by-count":5,"title":["Improving SNP prioritization and pleiotropic architecture estimation by incorporating prior knowledge using graph-GPA"],"prefix":"10.1093","volume":"34","author":[{"given":"Hang J","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH, USA"}]},{"given":"Zhenning","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA"}]},{"given":"Andrew","family":"Lawson","sequence":"additional","affiliation":[{"name":"Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA"}]},{"given":"Hongyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA"},{"name":"Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA"},{"name":"Department of Genetics, Yale School of Medicine, New Haven, CT, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8072-5671","authenticated-orcid":false,"given":"Dongjun","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA"}]}],"member":"286","published-online":{"date-parts":[[2018,2,8]]},"reference":[{"key":"2023012713391000900_bty061-B1","doi-asserted-by":"crossref","first-page":"e1004787.","DOI":"10.1371\/journal.pgen.1004787","article-title":"GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation","volume":"10","author":"Chung","year":"2014","journal-title":"PLoS Genet"},{"key":"2023012713391000900_bty061-B2","doi-asserted-by":"crossref","first-page":"e1005388.","DOI":"10.1371\/journal.pcbi.1005388","article-title":"graph-GPA: a graphical model for prioritizing GWAS results and investigating pleiotropic architecture","volume":"13","author":"Chung","year":"2017","journal-title":"PLoS Comput. Biol"},{"key":"2023012713391000900_bty061-B3","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1038\/ng.2711","article-title":"Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs","volume":"45","author":"Cross-Disorder Group of the Psychiatric Genomics Consortium","year":"2013","journal-title":"Nat. Genet"},{"key":"2023012713391000900_bty061-B4","doi-asserted-by":"crossref","first-page":"10576.","DOI":"10.1038\/srep10576","article-title":"A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data","volume":"5","author":"Lu","year":"2015","journal-title":"Sci. Rep"},{"key":"2023012713391000900_bty061-B5","doi-asserted-by":"crossref","first-page":"e1005947.","DOI":"10.1371\/journal.pgen.1005947","article-title":"Integrative tissue-specific functional annotations in the human genome provide novel insights on many complex traits and improve signal prioritization in genome wide association studies","volume":"12","author":"Lu","year":"2016","journal-title":"PLoS Genet"},{"key":"2023012713391000900_bty061-B6","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1172\/JCI34772","article-title":"A HapMap harvest of insights into the genetics of common disease","volume":"118","author":"Manolio","year":"2008","journal-title":"J. Clin. Investig"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/12\/2139\/48935795\/bioinformatics_34_12_2139.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/12\/2139\/48935795\/bioinformatics_34_12_2139.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T14:18:53Z","timestamp":1674829133000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/34\/12\/2139\/4844127"}},"subtitle":[],"editor":[{"given":"Oliver","family":"Stegle","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2018,2,8]]},"references-count":6,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2018,6,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bty061","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"type":"print","value":"1367-4803"},{"type":"electronic","value":"1367-4811"}],"subject":[],"published-other":{"date-parts":[[2018,6,15]]},"published":{"date-parts":[[2018,2,8]]}}}