{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:15Z","timestamp":1772138055822,"version":"3.50.1"},"reference-count":56,"publisher":"Oxford University Press (OUP)","issue":"24","license":[{"start":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T00:00:00Z","timestamp":1666396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R35GM142701"],"award-info":[{"award-number":["R35GM142701"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,12,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Understanding the functional consequence of genetic variants, especially the non-coding ones, is important but particularly challenging. Genome-wide association studies (GWAS) or quantitative trait locus analyses may be subject to limited statistical power and linkage disequilibrium, and thus are less optimal to pinpoint the causal variants. Moreover, most existing machine-learning approaches, which exploit the functional annotations to interpret and prioritize putative causal variants, cannot accommodate the heterogeneity of personal genetic variations and traits in a population study, targeting a specific disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>By leveraging paired whole-genome sequencing data and epigenetic functional assays in a population study, we propose a multi-modal deep learning framework to predict genome-wide quantitative epigenetic signals by considering both personal genetic variations and traits. The proposed approach can further evaluate the functional consequence of non-coding variants on an individual level by quantifying the allelic difference of predicted epigenetic signals. By applying the approach to the ROSMAP cohort studying Alzheimer\u2019s disease (AD), we demonstrate that the proposed approach can accurately predict quantitative genome-wide epigenetic signals and in key genomic regions of AD causal genes, learn canonical motifs reported to regulate gene expression of AD causal genes, improve the partitioning heritability analysis and prioritize putative causal variants in a GWAS risk locus. Finally, we release the proposed deep learning model as a stand-alone Python toolkit and a web server.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>https:\/\/github.com\/lichen-lab\/DeepPerVar.<\/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\/btac696","type":"journal-article","created":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T09:51:42Z","timestamp":1666259502000},"page":"5340-5351","source":"Crossref","is-referenced-by-count":12,"title":["DeepPerVar: a multi-modal deep learning framework for functional interpretation of genetic variants in personal genome"],"prefix":"10.1093","volume":"38","author":[{"given":"Ye","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Software Engineering, Auburn University , Auburn, AL 36849, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9372-5606","authenticated-orcid":false,"given":"Li","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Florida , Gainesville, FL 32603, USA"}]}],"member":"286","published-online":{"date-parts":[[2022,10,22]]},"reference":[{"key":"2022121418421647700_btac696-B1","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1038\/nn.4156","article-title":"The psychencode project","volume":"18","author":"Akbarian","year":"2015","journal-title":"Nat. Neurosci"},{"key":"2022121418421647700_btac696-B2","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1038\/nbt.3300","article-title":"Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning","volume":"33","author":"Alipanahi","year":"2015","journal-title":"Nat. Biotechnol"},{"key":"2022121418421647700_btac696-B3","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1038\/s41592-021-01252-x","article-title":"Effective gene expression prediction from sequence by integrating long-range interactions","volume":"18","author":"Avsec","year":"2021","journal-title":"Nat. Methods"},{"key":"2022121418421647700_btac696-B4","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1038\/cr.2011.22","article-title":"Regulation of chromatin by histone modifications","volume":"21","author":"Bannister","year":"2011","journal-title":"Cell Res"},{"key":"2022121418421647700_btac696-B5","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1093\/bioinformatics\/btv741","article-title":"traseR: an R package for performing trait-associated SNP enrichment analysis in genomic intervals","volume":"32","author":"Chen","year":"2016","journal-title":"Bioinformatics"},{"key":"2022121418421647700_btac696-B6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-016-1112-z","article-title":"DIVAN: accurate identification of non-coding disease-specific risk variants using multi-omics profiles","volume":"17","author":"Chen","year":"2016","journal-title":"Genome Biol"},{"key":"2022121418421647700_btac696-B7","doi-asserted-by":"crossref","first-page":"1573","DOI":"10.1093\/bioinformatics\/bty872","article-title":"TIVAN: tissue-specific cis-eQTL single nucleotide variant annotation and prediction","volume":"35","author":"Chen","year":"2019","journal-title":"Bioinformatics"},{"key":"2022121418421647700_btac696-B8","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1093\/nar\/26.1.285","article-title":"The human gene mutation database","volume":"26","author":"Cooper","year":"1998","journal-title":"Nucleic Acids Res"},{"key":"2022121418421647700_btac696-B9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41398-019-0592-5","article-title":"Examining the association between genetic liability for schizophrenia and psychotic symptoms in Alzheimer\u2019s disease","volume":"9","author":"Creese","year":"2019","journal-title":"Transl. Psychiatry"},{"key":"2022121418421647700_btac696-B10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2018.142","article-title":"A multi-omic atlas of the human frontal cortex for aging and Alzheimer\u2019s disease research","volume":"5","author":"De Jager","year":"2018","journal-title":"Sci. Data"},{"key":"2022121418421647700_btac696-B11","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1038\/nmeth.2238","article-title":"The encode project","volume":"9","author":"de Souza","year":"2012","journal-title":"Nat. Methods"},{"key":"2022121418421647700_btac696-B12","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1038\/ng.3404","article-title":"Partitioning heritability by functional annotation using genome-wide association summary statistics","volume":"47","author":"Finucane","year":"2015","journal-title":"Nat. Genet"},{"key":"2022121418421647700_btac696-B13","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1186\/s13059-014-0480-5","article-title":"FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer","volume":"15","author":"Fu","year":"2014","journal-title":"Genome Biol"},{"key":"2022121418421647700_btac696-B14","doi-asserted-by":"crossref","first-page":"R24","DOI":"10.1186\/gb-2007-8-2-r24","article-title":"Quantifying similarity between motifs","volume":"8","author":"Gupta","year":"2007","journal-title":"Genome Biol"},{"key":"2022121418421647700_btac696-B15","doi-asserted-by":"crossref","first-page":"9362","DOI":"10.1073\/pnas.0903103106","article-title":"Potential etiologic and functional implications of genome-wide association loci for human diseases and traits","volume":"106","author":"Hindorff","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2022121418421647700_btac696-B16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-019-0183-6","article-title":"CommonMind Consortium provides transcriptomic and epigenomic data for Schizophrenia and Bipolar Disorder","volume":"6","author":"Hoffman","year":"2019","journal-title":"Sci. Data"},{"key":"2022121418421647700_btac696-B17","doi-asserted-by":"crossref","first-page":"10597","DOI":"10.1093\/nar\/gkz808","article-title":"Functional interpretation of genetic variants using deep learning predicts impact on chromatin accessibility and histone modification","volume":"47","author":"Hoffman","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2022121418421647700_btac696-B18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-016-1030-0","article-title":"An epigenetic clock analysis of race\/ethnicity, sex, and coronary heart disease","volume":"17","author":"Horvath","year":"2016","journal-title":"Genome Biol"},{"key":"2022121418421647700_btac696-B19","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1038\/ng.3810","article-title":"Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data","volume":"49","author":"Huang","year":"2017","journal-title":"Nat. Genet"},{"key":"2022121418421647700_btac696-B20","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.3390\/cells8091034","article-title":"H3K4me3, H3K9ac, H3K27ac, H3K27me3 and H3K9me3 histone tags suggest distinct regulatory evolution of open and condensed chromatin landmarks","volume":"8","author":"Igolkina","year":"2019","journal-title":"Cells"},{"key":"2022121418421647700_btac696-B21","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1016\/j.ajhg.2013.04.015","article-title":"Sequence kernel association tests for the combined effect of rare and common variants","volume":"92","author":"Ionita-Laza","year":"2013","journal-title":"Am. J. Hum. Genet"},{"key":"2022121418421647700_btac696-B22","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1038\/ng.3477","article-title":"A spectral approach integrating functional genomic annotations for coding and noncoding variants","volume":"48","author":"Ionita-Laza","year":"2016","journal-title":"Nat. Genet"},{"key":"2022121418421647700_btac696-B23","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1038\/s41588-018-0311-9","article-title":"Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer\u2019s disease risk","volume":"51","author":"Jansen","year":"2019","journal-title":"Nat. Genet"},{"key":"2022121418421647700_btac696-B24","doi-asserted-by":"crossref","first-page":"e1004722","DOI":"10.1371\/journal.pgen.1004722","article-title":"Integrating functional data to prioritize causal variants in statistical fine-mapping studies","volume":"10","author":"Kichaev","year":"2014","journal-title":"PLoS Genet"},{"key":"2022121418421647700_btac696-B25","author":"Kingma","year":"2015"},{"key":"2022121418421647700_btac696-B26","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1038\/ng.2892","article-title":"A general framework for estimating the relative pathogenicity of human genetic variants","volume":"46","author":"Kircher","year":"2014","journal-title":"Nat. Genet"},{"key":"2022121418421647700_btac696-B27","doi-asserted-by":"crossref","first-page":"e1008500","DOI":"10.1371\/journal.pgen.1008500","article-title":"Use of &gt;100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic\/Latino populations","volume":"15","author":"Kowalski","year":"2019","journal-title":"PLoS Genet"},{"key":"2022121418421647700_btac696-B28","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1038\/nature14248","article-title":"Integrative analysis of 111 reference human epigenomes","volume":"518","author":"Kundaje","year":"2015","journal-title":"Nature"},{"key":"2022121418421647700_btac696-B29","doi-asserted-by":"crossref","first-page":"D862","DOI":"10.1093\/nar\/gkv1222","article-title":"ClinVar: public archive of interpretations of clinically relevant variants","volume":"44","author":"Landrum","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2022121418421647700_btac696-B30","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1016\/j.cell.2019.11.020","article-title":"Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders","volume":"179","author":"Lee","year":"2019","journal-title":"Cell"},{"key":"2022121418421647700_btac696-B31","doi-asserted-by":"crossref","first-page":"2987","DOI":"10.1093\/bioinformatics\/btr509","article-title":"A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data","volume":"27","author":"Li","year":"2011","journal-title":"Bioinformatics"},{"key":"2022121418421647700_btac696-B32","doi-asserted-by":"crossref","first-page":"21364","DOI":"10.1073\/pnas.1922703117","article-title":"A method for scoring the cell type-specific impacts of noncoding variants in personal genomes","volume":"117","author":"Li","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2022121418421647700_btac696-B33","first-page":"1","article-title":"Biological relevance of computationally predicted pathogenicity of noncoding variants","volume":"10","author":"Liu","year":"2019","journal-title":"Nat. Commun"},{"key":"2022121418421647700_btac696-B34","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1101\/gr.107524.110","article-title":"The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data","volume":"20","author":"McKenna","year":"2010","journal-title":"Genome Res"},{"key":"2022121418421647700_btac696-B35","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.biopsych.2013.08.020","article-title":"Psychosis in Alzheimer\u2019s disease","volume":"75","author":"Murray","year":"2014","journal-title":"Biol. Psychiatry"},{"key":"2022121418421647700_btac696-B36","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1002\/ajmg.b.32761","article-title":"A screen of 1,049 schizophrenia and 30 Alzheimer\u2019s-associated variants for regulatory potential","volume":"183","author":"Myint","year":"2020","journal-title":"Am. J. Med. Genet. B Neuropsychiatr. Genet"},{"key":"2022121418421647700_btac696-B37","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1038\/s41588-020-0696-0","article-title":"An integrated multi-omics approach identifies epigenetic alterations associated with Alzheimer\u2019s disease","volume":"52","author":"Nativio","year":"2020","journal-title":"Nat. Genet"},{"key":"2022121418421647700_btac696-B38","author":"Paszke","year":"2017"},{"key":"2022121418421647700_btac696-B39","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1016\/j.ajhg.2014.03.004","article-title":"Joint analysis of functional genomic data and genome-wide association studies of 18 human traits","volume":"94","author":"Pickrell","year":"2014","journal-title":"Am. J. Hum. Genet"},{"key":"2022121418421647700_btac696-B40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1086\/321275","article-title":"Linkage disequilibrium in humans: models and data","volume":"69","author":"Pritchard","year":"2001","journal-title":"Am. J. Hum. Genet"},{"key":"2022121418421647700_btac696-B41","doi-asserted-by":"crossref","first-page":"e107","DOI":"10.1093\/nar\/gkw226","article-title":"DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences","volume":"44","author":"Quang","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2022121418421647700_btac696-B42","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1038\/nmeth.2832","article-title":"Functional annotation of noncoding sequence variants","volume":"11","author":"Ritchie","year":"2014","journal-title":"Nat. Methods"},{"key":"2022121418421647700_btac696-B43","first-page":"1","article-title":"SuRFing the genomics wave: an R package for prioritising SNPs by functionality","volume":"6","author":"Ryan","year":"2014","journal-title":"Genome Med"},{"key":"2022121418421647700_btac696-B44","doi-asserted-by":"crossref","first-page":"R111","DOI":"10.1093\/hmg\/ddv260","article-title":"Strategies for fine-mapping complex traits","volume":"24","author":"Spain","year":"2015","journal-title":"Hum. Mol. Genet"},{"key":"2022121418421647700_btac696-B45","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res"},{"key":"2022121418421647700_btac696-B46","doi-asserted-by":"crossref","first-page":"e1001779","DOI":"10.1371\/journal.pmed.1001779","article-title":"UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age","volume":"12","author":"Sudlow","year":"2015","journal-title":"PLoS Med"},{"key":"2022121418421647700_btac696-B47","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1038\/s41586-022-04394-w","article-title":"Genetic associations of protein-coding variants in human disease","volume":"603","author":"Sun","year":"2022","journal-title":"Nature"},{"key":"2022121418421647700_btac696-B48","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1126\/science.1262110","article-title":"The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans","volume":"348","author":"The GTEx Consortium","year":"2015","journal-title":"Science"},{"key":"2022121418421647700_btac696-B49","doi-asserted-by":"crossref","first-page":"2383","DOI":"10.1093\/hmg\/9.16.2383","article-title":"Transcriptional regulation of Alzheimer\u2019s disease genes: implications for susceptibility","volume":"9","author":"Theuns","year":"2000","journal-title":"Hum. Mol. Genet"},{"key":"2022121418421647700_btac696-B50","first-page":"A68","article-title":"The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge","volume":"19","author":"Tomczak","year":"2015","journal-title":"Contemp. Oncol. (Pozn)"},{"key":"2022121418421647700_btac696-B51","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.ajhg.2017.06.005","article-title":"10 years of GWAS discovery: biology, function, and translation","volume":"101","author":"Visscher","year":"2017","journal-title":"Am. J. Hum. Genet"},{"key":"2022121418421647700_btac696-B52","doi-asserted-by":"crossref","first-page":"3645","DOI":"10.1093\/bioinformatics\/btx469","article-title":"ggseqlogo: a versatile R package for drawing sequence logos","volume":"33","author":"Wagih","year":"2017","journal-title":"Bioinformatics"},{"key":"2022121418421647700_btac696-B53","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1016\/j.cell.2014.08.009","article-title":"Determination and inference of eukaryotic transcription factor sequence specificity","volume":"158","author":"Weirauch","year":"2014","journal-title":"Cell"},{"key":"2022121418421647700_btac696-B54","doi-asserted-by":"crossref","first-page":"R137","DOI":"10.1186\/gb-2008-9-9-r137","article-title":"Model-based analysis of ChiP-Seq (MACS)","volume":"9","author":"Zhang","year":"2008","journal-title":"Genome Biol"},{"key":"2022121418421647700_btac696-B55","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1038\/nmeth.3547","article-title":"Predicting effects of noncoding variants with deep learning\u2013based sequence model","volume":"12","author":"Zhou","year":"2015","journal-title":"Nat. Methods"},{"key":"2022121418421647700_btac696-B56","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1038\/s41588-018-0160-6","article-title":"Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk","volume":"50","author":"Zhou","year":"2018","journal-title":"Nat. Genet"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btac696\/46908399\/btac696.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/24\/5340\/47887054\/btac696.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/24\/5340\/47887054\/btac696.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T03:43:19Z","timestamp":1678333399000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/38\/24\/5340\/6769889"}},"subtitle":[],"editor":[{"given":"Can","family":"Alkan","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,10,22]]},"references-count":56,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,10,22]]},"published-print":{"date-parts":[[2022,12,13]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btac696","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2022.04.10.487809","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,12,15]]},"published":{"date-parts":[[2022,10,22]]}}}