{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:33:34Z","timestamp":1772138014025,"version":"3.50.1"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T00:00:00Z","timestamp":1639008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01HL129132"],"award-info":[{"award-number":["R01HL129132"]}],"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":["R01GM105785"],"award-info":[{"award-number":["R01GM105785"]}],"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":["U544 HD079124"],"award-info":[{"award-number":["U544 HD079124"]}],"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":["U01HG011720"],"award-info":[{"award-number":["U01HG011720"]}],"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":["KL2TR002490"],"award-info":[{"award-number":["KL2TR002490"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Multiple statistical methods for aggregate association testing have been developed for whole-genome sequencing (WGS) data. Many aggregate variants in a given genomic window and ignore existing knowledge to define test regions, resulting in many identified regions not clearly linked to genes, and thus, limiting biological understanding. Functional information from new technologies (such as Hi-C and its derivatives), which can help link enhancers to their effector genes, can be leveraged to predefine variant sets for aggregate testing in WGS data. Here, we propose the eSCAN (scan the enhancers) method for genome-wide assessment of enhancer regions in sequencing studies, combining the advantages of dynamic window selection in SCANG (SCAN the Genome), a previously developed method, with the advantages of incorporating putative regulatory regions from annotation. eSCAN, by searching in putative enhancers, increases statistical power and aids mechanistic interpretation, as demonstrated by extensive simulation studies. We also apply eSCAN for blood cell traits using NHLBI Trans-Omics for Precision Medicine WGS data. Results from real data analysis show that eSCAN is able to capture more significant signals, and these signals are of shorter length (indicating higher resolution fine-mapping capability) and drive association of larger regions detected by other methods.<\/jats:p>","DOI":"10.1093\/bib\/bbab497","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T08:21:12Z","timestamp":1635841272000},"source":"Crossref","is-referenced-by-count":6,"title":["eSCAN: scan regulatory regions for aggregate association testing using whole-genome sequencing data"],"prefix":"10.1093","volume":"23","author":[{"given":"Yingxi","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Statistics and Data Science, Yale University, New Haven, CT, 06511, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8324-2803","authenticated-orcid":false,"given":"Quan","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA"}]},{"given":"Le","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA"}]},{"given":"Jai G","family":"Broome","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Washington, Seattle, WA 98195, USA"},{"name":"Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA 98195, USA"}]},{"given":"Adolfo","family":"Correa","sequence":"additional","affiliation":[{"name":"Department of Medicine and Population Health Science, University of Mississippi Medical Center, Jackson, MS, 39216, USA"}]},{"given":"Alexander","family":"Reiner","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, University of Washington, Seattle, WA, 98195, USA"},{"name":"Fred Hutchinson Cancer Research Center, University of Washington, Seattle, WA, 98195, USA"}]},{"name":"NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium","sequence":"additional","affiliation":[]},{"given":"Laura M","family":"Raffield","sequence":"additional","affiliation":[{"name":"Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA"}]},{"given":"Yuchen","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Biocontrol, School of Ecology, Sun Yat-sen University, 510275 Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9275-4189","authenticated-orcid":false,"given":"Yun","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA"},{"name":"Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA"},{"name":"Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, 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