{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T14:15:20Z","timestamp":1780582520315,"version":"3.54.1"},"update-to":[{"DOI":"10.1371\/journal.pgen.1012157","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000}}],"reference-count":44,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T00:00:00Z","timestamp":1779840000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AR080662"],"award-info":[{"award-number":["R01AR080662"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["UC2 AR081033"],"award-info":[{"award-number":["UC2 AR081033"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01HG009124"],"award-info":[{"award-number":["R01HG009124"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01GM144960"],"award-info":[{"award-number":["R01GM144960"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosgenetics.org"],"crossmark-restriction":false},"short-container-title":["PLoS Genet"],"abstract":"<jats:p>\n                    Recent advances in transcriptome-wide association study (TWAS) fine-mapping have enabled the joint modeling of multiple genes to improve causal gene prioritization. However, existing methods have been developed primarily for quantitative traits and most of them rely on gene expression data from a single tissue. Here, we present mFABIO, a multi-tissue TWAS fine-mapping method specifically designed for binary traits. mFABIO employs a probit model to directly link genetically regulated expression (GReX) of genes within a locus across multiple tissues to a binary outcome, while accounting for correlations in GReX across genes and tissues. As a result, mFABIO offers substantial power gains for binary traits, while maintaining robust control of false discovery rates (FDR). We evaluated mFABIO through extensive simulations and applied it to an in-depth analysis of six binary disease traits (asthma, breast cancer, gout, hypertension, prostate cancer, and rheumatoid arthritis) in the UK Biobank, using expression data spanning 38 Genotype-Tissue Expression (GTEx) tissues. mFABIO identified an average of 42 likely causal genes and 65 tissue-gene pairs per disease (FDR\u2009&lt;\u20090.05). Notably, 60.9% of the genes and 77.2% of the gene-tissue pairs were supported by existing TWAS or GWAS evidence. This represented at least a 14.9% increase in evidence-supported genes and a 14.8% increase in evidence-supported gene-tissue pairs, compared to existing approaches. Additionally, mFABIO was also able to narrow down the list of potentially causal candidates by at least 51.3% for genes, and 50.8% for gene-tissue pairs, compared to single-tissue approaches. Leveraging its improved power, mFABIO successfully prioritized multiple potentially causal gene-tissue pairs associated with these diseases, with biological support. Notable examples include\n                    <jats:italic>D2HGDH<\/jats:italic>\n                    in lung tissue for asthma,\n                    <jats:italic>CYBRD1<\/jats:italic>\n                    in breast mammary tissue for breast cancer, and\n                    <jats:italic>CCR6<\/jats:italic>\n                    in spleen tissue for rheumatoid arthritis. Overall, mFABIO serves as an effective tool for multi-tissue TWAS fine-mapping of binary traits.\n                  <\/jats:p>","DOI":"10.1371\/journal.pgen.1012157","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T17:42:36Z","timestamp":1779903756000},"page":"e1012157","update-policy":"https:\/\/doi.org\/10.1371\/journal.pgen.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["mFABIO: An integrative multi-tissue TWAS fine-mapping approach to prioritize potentially causal genes and tissues underlying binary traits"],"prefix":"10.1371","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5902-7289","authenticated-orcid":true,"given":"Haihan","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lam C.","family":"Tsoi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4331-7599","authenticated-orcid":true,"given":"Xiang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,5,27]]},"reference":[{"issue":"3","key":"pgen.1012157.ref001","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1038\/ng.3506","article-title":"Integrative approaches for large-scale transcriptome-wide association studies","volume":"48","author":"A Gusev","year":"2016","journal-title":"Nat Genet"},{"issue":"9","key":"pgen.1012157.ref002","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1038\/ng.3367","article-title":"A gene-based association method for mapping traits using reference transcriptome data","volume":"47","author":"ER Gamazon","year":"2015","journal-title":"Nat Genet"},{"issue":"5","key":"pgen.1012157.ref003","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1038\/ng.3538","article-title":"Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets","volume":"48","author":"Z Zhu","year":"2016","journal-title":"Nat Genet"},{"issue":"4","key":"pgen.1012157.ref004","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1038\/s41588-019-0385-z","article-title":"Opportunities and challenges for transcriptome-wide association studies","volume":"51","author":"M Wainberg","year":"2019","journal-title":"Nat Genet"},{"issue":"4","key":"pgen.1012157.ref005","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1038\/s41588-019-0367-1","article-title":"Probabilistic fine-mapping of transcriptome-wide association studies","volume":"51","author":"N Mancuso","year":"2019","journal-title":"Nat Genet"},{"issue":"2","key":"pgen.1012157.ref006","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1038\/s41588-023-01645-y","article-title":"Conditional transcriptome-wide association study for fine-mapping candidate causal genes","volume":"56","author":"L Liu","year":"2024","journal-title":"Nat Genet"},{"issue":"2","key":"pgen.1012157.ref007","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1038\/s41588-023-01648-9","article-title":"Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits","volume":"56","author":"S Zhao","year":"2024","journal-title":"Nat Genet"},{"issue":"2","key":"pgen.1012157.ref008","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and Variable Selection Via the Elastic Net","volume":"67","author":"H Zou","year":"2005","journal-title":"Journal of the Royal Statistical Society Series B: Statistical Methodology"},{"issue":"6509","key":"pgen.1012157.ref009","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1126\/science.aaz1776","article-title":"The GTEx Consortium atlas of genetic regulatory effects across human tissues","volume":"369","author":"Consortium GTEx","year":"2020","journal-title":"Science"},{"issue":"1","key":"pgen.1012157.ref010","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/nrg2918","article-title":"Network medicine: a network-based approach to human disease","volume":"12","author":"A-L Barab\u00e1si","year":"2011","journal-title":"Nat Rev Genet"},{"issue":"1","key":"pgen.1012157.ref011","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1038\/s41588-024-01994-2","article-title":"Fine-mapping causal tissues and genes at disease-associated loci","volume":"57","author":"BJ Strober","year":"2025","journal-title":"Nat Genet"},{"issue":"2","key":"pgen.1012157.ref012","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1038\/s41588-021-00990-0","article-title":"Genome-wide analysis of 102,084 migraine cases identifies 123 risk loci and subtype-specific risk alleles","volume":"54","author":"H Hautakangas","year":"2022","journal-title":"Nat Genet"},{"issue":"6","key":"pgen.1012157.ref013","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1038\/s41588-021-00857-4","article-title":"Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology","volume":"53","author":"N Mullins","year":"2021","journal-title":"Nat Genet"},{"issue":"2","key":"pgen.1012157.ref014","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pgen.1003264","article-title":"Polygenic modeling with bayesian sparse linear mixed models","volume":"9","author":"X Zhou","year":"2013","journal-title":"PLoS Genet"},{"issue":"12","key":"pgen.1012157.ref015","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pgen.1011503","article-title":"FABIO: TWAS fine-mapping to prioritize causal genes for binary traits","volume":"20","author":"H Zhang","year":"2024","journal-title":"PLoS Genet"},{"issue":"9","key":"pgen.1012157.ref016","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pgen.1009733","article-title":"Identifying causal variants by fine mapping across multiple studies","volume":"17","author":"N LaPierre","year":"2021","journal-title":"PLoS Genet"},{"issue":"422","key":"pgen.1012157.ref017","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1080\/01621459.1993.10476321","article-title":"Bayesian Analysis of Binary and Polychotomous Response Data","volume":"88","author":"JH Albert","year":"1993","journal-title":"Journal of the American Statistical Association"},{"issue":"7","key":"pgen.1012157.ref018","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1038\/ng.608","article-title":"Common SNPs explain a large proportion of the heritability for human height","volume":"42","author":"J Yang","year":"2010","journal-title":"Nat Genet"},{"issue":"5","key":"pgen.1012157.ref019","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1111\/rssb.12388","article-title":"A simple new approach to variable selection in regression, with application to genetic fine mapping","volume":"82","author":"G Wang","year":"2020","journal-title":"J R Stat Soc Series B Stat Methodol"},{"issue":"506","key":"pgen.1012157.ref020","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1080\/01621459.2013.869223","article-title":"EMVS: The EM Approach to Bayesian Variable Selection","volume":"109","author":"V Ro\u010dkov\u00e1","year":"2014","journal-title":"Journal of the American Statistical Association"},{"issue":"2","key":"pgen.1012157.ref021","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1093\/bioinformatics\/btv546","article-title":"Approximately independent linkage disequilibrium blocks in human populations","volume":"32","author":"T Berisa","year":"2016","journal-title":"Bioinformatics"},{"issue":"1","key":"pgen.1012157.ref022","doi-asserted-by":"crossref","first-page":"3861","DOI":"10.1038\/s41467-020-17668-6","article-title":"Testing and controlling for horizontal pleiotropy with probabilistic Mendelian randomization in transcriptome-wide association studies","volume":"11","author":"Z Yuan","year":"2020","journal-title":"Nat Commun"},{"issue":"3","key":"pgen.1012157.ref023","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1038\/s41588-019-0345-7","article-title":"A statistical framework for cross-tissue transcriptome-wide association analysis","volume":"51","author":"Y Hu","year":"2019","journal-title":"Nat Genet"},{"issue":"6","key":"pgen.1012157.ref024","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1038\/s41588-020-0625-2","article-title":"Quantifying genetic effects on disease mediated by assayed gene expression levels","volume":"52","author":"DW Yao","year":"2020","journal-title":"Nat Genet"},{"key":"pgen.1012157.ref025","first-page":"66","article-title":"Breakthroughs in statistics: Methodology and distribution","author":"RA Fisher","year":"1970"},{"issue":"1","key":"pgen.1012157.ref026","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing","volume":"57","author":"Y Benjamini","year":"1995","journal-title":"Journal of the Royal Statistical Society Series B: Statistical Methodology"},{"key":"pgen.1012157.ref027","doi-asserted-by":"crossref","unstructured":"Efron, B. Size, power and false discovery rates. 2007.","DOI":"10.1214\/009053606000001460"},{"issue":"7726","key":"pgen.1012157.ref028","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41586-018-0579-z","article-title":"The UK Biobank resource with deep phenotyping and genomic data","volume":"562","author":"C Bycroft","year":"2018","journal-title":"Nature"},{"issue":"2","key":"pgen.1012157.ref029","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac039","article-title":"PGS-server: accuracy, robustness and transferability of polygenic score methods for biobank scale studies","volume":"23","author":"S Yang","year":"2022","journal-title":"Brief Bioinform"},{"key":"pgen.1012157.ref030","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s13742-015-0047-8","article-title":"Second-generation PLINK: rising to the challenge of larger and richer datasets","volume":"4","author":"CC Chang","year":"2015","journal-title":"Gigascience"},{"issue":"6","key":"pgen.1012157.ref031","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1038\/s41588-018-0121-0","article-title":"A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases","volume":"50","author":"Z Zhu","year":"2018","journal-title":"Nat Genet"},{"issue":"11","key":"pgen.1012157.ref032","doi-asserted-by":"crossref","first-page":"3014","DOI":"10.1111\/all.15809","article-title":"D-2-hydroxyglutarate suppresses allergic sensitization in a murine model of experimental asthma","volume":"78","author":"A Tharakan","year":"2023","journal-title":"Allergy"},{"issue":"5509","key":"pgen.1012157.ref033","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.1126\/science.1057206","article-title":"An iron-regulated ferric reductase associated with the absorption of dietary iron","volume":"291","author":"AT McKie","year":"2001","journal-title":"Science"},{"issue":"2","key":"pgen.1012157.ref034","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1158\/1078-0432.CCR-07-1054","article-title":"Overexpression of cellular iron import proteins is associated with malignant progression of esophageal adenocarcinoma","volume":"14","author":"J Boult","year":"2008","journal-title":"Clin Cancer Res"},{"issue":"1","key":"pgen.1012157.ref035","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1186\/s13058-017-0814-9","article-title":"DCYTB is a predictor of outcome in breast cancer that functions via iron-independent mechanisms","volume":"19","author":"DJ Lemler","year":"2017","journal-title":"Breast Cancer Res"},{"issue":"1","key":"pgen.1012157.ref036","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1186\/s13059-021-02460-6","article-title":"Functional genomics atlas of synovial fibroblasts defining rheumatoid arthritis heritability","volume":"22","author":"X Ge","year":"2021","journal-title":"Genome Biol"},{"key":"pgen.1012157.ref037","doi-asserted-by":"crossref","first-page":"129","DOI":"10.2174\/1874312901408010129","article-title":"Felty\u2019s Syndrome, Insights and Updates","volume":"8","author":"MB Owlia","year":"2014","journal-title":"Open Rheumatol J"},{"issue":"9","key":"pgen.1012157.ref038","first-page":"15043","article-title":"The abnormal expression of CCR4 and CCR6 on Tregs in rheumatoid arthritis","volume":"8","author":"N Li","year":"2015","journal-title":"Int J Clin Exp Med"},{"issue":"18","key":"pgen.1012157.ref039","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1093\/bioinformatics\/btab186","article-title":"LocusZoom.js: interactive and embeddable visualization of genetic association study results","volume":"37","author":"AP Boughton","year":"2021","journal-title":"Bioinformatics"},{"issue":"2","key":"pgen.1012157.ref040","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/S0378-3758(99)00195-0","article-title":"Noncanonical links in generalized linear models \u2013 when is the effort justified?","volume":"87","author":"C Czado","year":"2000","journal-title":"Journal of Statistical Planning and Inference"},{"issue":"4","key":"pgen.1012157.ref041","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.ajhg.2017.08.012","article-title":"Prospects of Fine-Mapping Trait-Associated Genomic Regions by Using Summary Statistics from Genome-wide Association Studies","volume":"101","author":"C Benner","year":"2017","journal-title":"Am J Hum Genet"},{"issue":"4","key":"pgen.1012157.ref042","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1038\/ng.2213","article-title":"Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits","volume":"44","author":"Genetic Investigation of ANthropometric Traits (GIANT) Consortium, DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium","year":"2012","journal-title":"Nat Genet"},{"issue":"2","key":"pgen.1012157.ref043","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.ajhg.2020.12.006","article-title":"Multi-trait transcriptome-wide association studies with probabilistic Mendelian randomization","volume":"108","author":"L Liu","year":"2021","journal-title":"Am J Hum Genet"},{"issue":"1","key":"pgen.1012157.ref044","doi-asserted-by":"crossref","first-page":"6646","DOI":"10.1038\/s41467-024-50983-w","article-title":"SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning","volume":"15","author":"RL Parrish","year":"2024","journal-title":"Nat Commun"}],"updated-by":[{"DOI":"10.1371\/journal.pgen.1012157","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000}}],"container-title":["PLOS Genetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pgen.1012157","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T18:09:12Z","timestamp":1780337352000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pgen.1012157"}},"subtitle":[],"editor":[{"given":"Lin S.","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,5,27]]},"references-count":44,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5,27]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pgen.1012157","relation":{},"ISSN":["1553-7404"],"issn-type":[{"value":"1553-7404","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,27]]}}}