{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T14:19:22Z","timestamp":1754144362654,"version":"3.41.2"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100001641","name":"GRF","doi-asserted-by":"publisher","award":["14306020","14304521"],"award-info":[{"award-number":["14306020","14304521"]}],"id":[{"id":"10.13039\/100001641","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Grants Council of the Hong Kong Special Administrative Region"},{"name":"People\u2019s Republic of China and Direct Grants"},{"name":"Research Committee of the Chinese University of Hong Kong"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>DNA methylation at cytosine\u2013phosphate\u2013guanine (CpG) sites is one of the most important epigenetic markers. Therefore, epidemiologists are interested in investigating DNA methylation in large cohorts through epigenome-wide association studies (EWAS). However, the observed EWAS data are bulk data with signals aggregated from distinct cell types. Deconvolution of cell-type-specific signals from EWAS data is challenging because phenotypes can affect both cell-type proportions and cell-type-specific methylation levels. Recently, there has been active research on detecting cell-type-specific risk CpG sites for EWAS data. However, existing methods all assume that the methylation levels of different CpG sites are independent and perform association detection for each CpG site separately. Although these methods significantly improve the detection at the aggregated-level\u2014identifying a CpG site as a risk CpG site as long as it is associated with the phenotype in any cell type, they have low power in detecting cell-type-specific associations for EWAS with typical sample sizes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we develop a new method, Fine-scale inference for Differentially Methylated Regions (FineDMR), to borrow strengths of nearby CpG sites to improve the cell-type-specific association detection. Via a Bayesian hierarchical model built upon Gaussian process functional regression, FineDMR takes advantage of the spatial dependencies between CpG sites. FineDMR can provide cell-type-specific association detection as well as output subject-specific and cell-type-specific methylation profiles for each subject. Simulation studies and real data analysis show that FineDMR substantially improves the power in detecting cell-type-specific associations for EWAS data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>FineDMR is freely available at https:\/\/github.com\/JiaRuofan\/Detection-of-Cell-type-specific-DMRs-in-EWAS.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf243","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T13:03:10Z","timestamp":1752584590000},"page":"i502-i512","source":"Crossref","is-referenced-by-count":0,"title":["Detection of cell-type-specific differentially methylated regions in epigenome-wide association studies"],"prefix":"10.1093","volume":"41","author":[{"given":"Ruofan","family":"Jia","sequence":"first","affiliation":[{"name":"Department of Statistics, The Chinese University of Hong Kong , Shatin, NT , Hong Kong SAR,","place":["China"]}]},{"given":"Yingying","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Statistics, The Chinese University of Hong Kong , Shatin, NT , Hong Kong SAR,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"2025071509030173800_btaf243-B1","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1093\/bioinformatics\/btu049","article-title":"Minfi: a flexible and comprehensive bioconductor package for the analysis of infinium DNA methylation microarrays","volume":"30","author":"Aryee","year":"2014","journal-title":"Bioinformatics"},{"key":"2025071509030173800_btaf243-B2","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1038\/s41584-023-00945-1","article-title":"Citrulline immunity in RA: CD8+ T cells enter the scene","volume":"19","author":"Chemin","year":"2023","journal-title":"Nat Rev Rheumatol"},{"key":"2025071509030173800_btaf243-B3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J R Stat Soc Series B (Methodol)"},{"key":"2025071509030173800_btaf243-B4","doi-asserted-by":"crossref","first-page":"e69","DOI":"10.1093\/nar\/gku154","article-title":"A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data","volume":"42","author":"Feng","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2025071509030173800_btaf243-B5","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1093\/bioinformatics\/btt263","article-title":"Detection of significantly differentially methylated regions in targeted bisulfite sequencing data","volume":"29","author":"Hebestreit","year":"2013","journal-title":"Bioinformatics"},{"key":"2025071509030173800_btaf243-B6","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1186\/s12859-016-1140-4","article-title":"Reference-free deconvolution of DNA methylation data and mediation by cell composition effects","volume":"17","author":"Houseman","year":"2016","journal-title":"BMC Bioinformatics"},{"key":"2025071509030173800_btaf243-B7","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1093\/ije\/dyr238","article-title":"Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies","volume":"41","author":"Jaffe","year":"2012","journal-title":"Int J Epidemiol"},{"key":"2025071509030173800_btaf243-B8","doi-asserted-by":"crossref","first-page":"e161","DOI":"10.1371\/journal.pgen.0030161","article-title":"Capturing heterogeneity in gene expression studies by surrogate variable analysis","volume":"3","author":"Leek","year":"2007","journal-title":"PLoS Genet"},{"key":"2025071509030173800_btaf243-B9","doi-asserted-by":"crossref","first-page":"3898","DOI":"10.1093\/bioinformatics\/btz196","article-title":"Dissecting differential signals in high-throughput data from complex tissues","volume":"35","author":"Li","year":"2019","journal-title":"Bioinformatics"},{"key":"2025071509030173800_btaf243-B10","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1038\/nbt.2487","article-title":"Others epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis","volume":"31","author":"Liu","year":"2013","journal-title":"Nat Biotechnol"},{"key":"2025071509030173800_btaf243-B11","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1038\/s41467-019-10864-z","article-title":"Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies","volume":"10","author":"Luo","year":"2019","journal-title":"Nat Commun"},{"key":"2025071509030173800_btaf243-B12","doi-asserted-by":"crossref","first-page":"e1237","DOI":"10.1002\/cti2.1237","article-title":"Others rheumatoid arthritis CD14+ monocytes display metabolic and inflammatory dysfunction, a phenotype that precedes clinical manifestation of disease","volume":"10","author":"McGarry","year":"2021","journal-title":"Clin Transl Immunol"},{"key":"2025071509030173800_btaf243-B13","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1186\/s13059-016-0935-y","article-title":"An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies","volume":"17","author":"McGregor","year":"2016","journal-title":"Genome Biol"},{"key":"2025071509030173800_btaf243-B14","doi-asserted-by":"crossref","first-page":"S53","DOI":"10.1038\/sj.bmt.1703944","article-title":"Hematopoietic stem cell transplantation for severe rheumatoid arthritis","volume":"32","author":"Moore","year":"2003","journal-title":"Bone Marrow Transplant"},{"key":"2025071509030173800_btaf243-B15","doi-asserted-by":"crossref","first-page":"8649","DOI":"10.3390\/ijms25168649","article-title":"Intestinal dysbiosis, tight junction proteins, and inflammation in rheumatoid arthritis patients: a cross-sectional study","volume":"25","author":"Mucientes","year":"2024","journal-title":"Int J Mol Sci"},{"key":"2025071509030173800_btaf243-B16","doi-asserted-by":"crossref","first-page":"e111733","DOI":"10.1371\/journal.pone.0111733","article-title":"Others sexual dimorphism in the human olfactory bulb: females have more neurons and glial cells than males","volume":"9","author":"Oliveira-Pinto","year":"2014","journal-title":"PLoS One"},{"key":"2025071509030173800_btaf243-B17","doi-asserted-by":"crossref","first-page":"2986","DOI":"10.1093\/bioinformatics\/bts545","article-title":"Comb-p: software for combining, analyzing, grouping and correcting spatially correlated P-values","volume":"28","author":"Pedersen","year":"2012","journal-title":"Bioinformatics"},{"key":"2025071509030173800_btaf243-B18","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.jaci.2014.07.053","article-title":"Others genetic ancestry influences asthma susceptibility and lung function among Latinos","volume":"135","author":"Pino-Yanes","year":"2015","journal-title":"J Allergy Clin Immunol"},{"key":"2025071509030173800_btaf243-B19","doi-asserted-by":"crossref","first-page":"3417","DOI":"10.1038\/s41467-019-11052-9","article-title":"Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology","volume":"10","author":"Rahmani","year":"2019","journal-title":"Nat Commun"},{"key":"2025071509030173800_btaf243-B20","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1038\/nmeth.3809","article-title":"Others sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies","volume":"13","author":"Rahmani","year":"2016","journal-title":"Nat Methods"},{"key":"2025071509030173800_btaf243-B21","doi-asserted-by":"crossref","DOI":"10.1007\/b98888","volume-title":"Functional Data Analysis","author":"Ramsay","year":"2005"},{"key":"2025071509030173800_btaf243-B22","doi-asserted-by":"crossref","DOI":"10.1201\/b11038","volume-title":"Gaussian Process Regression Analysis for Functional Data","author":"Shi","year":"2011"},{"key":"2025071509030173800_btaf243-B23","first-page":"2884","article-title":"A-clustering: a novel method for the detection of co-regulated methylation regions, and regions associated with exposure","volume-title":"Bioinformatics","author":"Sofer"},{"volume-title":"Asymptotic Statistics","year":"2000","author":"Vaart","key":"2025071509030173800_btaf243-B24"},{"key":"2025071509030173800_btaf243-B25","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1038\/s41592-018-0213-x","article-title":"Identification of differentially methylated cell types in epigenome-wide association studies","volume":"15","author":"Zheng","year":"2018","journal-title":"Nat Methods"},{"key":"2025071509030173800_btaf243-B26","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1038\/nmeth.2815","article-title":"Epigenome-wide association studies without the need for cell-type composition","volume":"11","author":"Zou","year":"2014","journal-title":"Nat Methods"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/41\/Supplement_1\/i502\/63745409\/btaf243.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/41\/Supplement_1\/i502\/63745409\/btaf243.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T13:03:14Z","timestamp":1752584594000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/41\/Supplement_1\/i502\/8199367"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"references-count":26,"journal-issue":{"issue":"Supplement_1","published-print":{"date-parts":[[2025,7,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaf243","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"type":"print","value":"1367-4803"},{"type":"electronic","value":"1367-4811"}],"subject":[],"published-other":{"date-parts":[[2025,7]]},"published":{"date-parts":[[2025,7,1]]}}}