{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T06:37:56Z","timestamp":1773383876144,"version":"3.50.1"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,1,12]],"date-time":"2025-01-12T00:00:00Z","timestamp":1736640000000},"content-version":"vor","delay-in-days":17,"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":["12301383"],"award-info":[{"award-number":["12301383"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Fine-mapping aims to prioritize causal variants underlying complex traits by accounting for the linkage disequilibrium of genome-wide association study risk locus. The expanding resources of functional annotations serve as auxiliary evidence to improve the power of fine-mapping. However, existing fine-mapping methods tend to generate many false positive results when integrating a large number of annotations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this study, we propose a unified method to integrate high-dimensional functional annotations with fine-mapping (Funmap). Funmap can effectively improve the power of fine-mapping by borrowing information from hundreds of functional annotations. Meanwhile, it relates the annotation to the causal probability with a random effects model that avoids the over-fitting issue, thereby producing a well-controlled false positive rate. Paired with a fast algorithm, Funmap enables scalable integration of a large number of annotations to facilitate prioritizing multiple causal single nucleotide polymorphisms. Our comprehensive simulations across a wide range of annotation relevance settings demonstrate that Funmap is the only method that produces well-calibrated false discovery rate under the setting of high-dimensional annotations while achieving better or comparable power gains as compared to existing methods. By integrating genome-wide association studies of 4 lipid traits with 187 functional annotations, Funmap consistently identified more variants that can be replicated in an independent cohort, achieving 15.5%\u201326.2% improvement over the runner-up in terms of replication rate.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The Funmap software and all analysis code are available at https:\/\/github.com\/LeeHITsz\/Funmap.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf017","type":"journal-article","created":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T23:20:44Z","timestamp":1736378444000},"source":"Crossref","is-referenced-by-count":4,"title":["Funmap: integrating high-dimensional functional annotations to improve fine-mapping"],"prefix":"10.1093","volume":"41","author":[{"given":"Yuekai","family":"Li","sequence":"first","affiliation":[{"name":"Department of Biostatistics, City University of Hong Kong , Hong Kong,","place":["China"]}]},{"given":"Jiashun","family":"Xiao","sequence":"additional","affiliation":[{"name":"Shenzhen International Center for Industrial and Applied Mathematics, Shenzhen Research Institute of Big Data , Shenzhen 518172,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7059-4156","authenticated-orcid":false,"given":"Jingsi","family":"Ming","sequence":"additional","affiliation":[{"name":"Academy of Statistics and Interdisciplinary Sciences, KLATASDS-MOE, East China Normal University , Shanghai 200062,","place":["China"]}]},{"given":"Yicheng","family":"Zeng","sequence":"additional","affiliation":[{"name":"Shenzhen International Center for Industrial and Applied Mathematics, Shenzhen Research Institute of Big Data , Shenzhen 518172,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4011-8292","authenticated-orcid":false,"given":"Mingxuan","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, City University of Hong Kong , Hong Kong,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,1,12]]},"reference":[{"key":"2025012702463483400_btaf017-B1","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1093\/bioinformatics\/btw018","article-title":"Finemap: efficient variable selection using summary data from genome-wide association studies","volume":"32","author":"Benner","year":"2016","journal-title":"Bioinformatics"},{"key":"2025012702463483400_btaf017-B2","article-title":"Efficient bounds for the softmax function and applications to approximate inference in hybrid 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