{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T13:19:49Z","timestamp":1755695989544,"version":"3.37.3"},"reference-count":14,"publisher":"Oxford University Press (OUP)","issue":"21","license":[{"start":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T00:00:00Z","timestamp":1622764800000},"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\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["1R01GM114128","1R01GM130715"],"award-info":[{"award-number":["1R01GM114128","1R01GM130715"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000051","name":"National Human Genome Research Institute","doi-asserted-by":"publisher","award":["1U01HG006389"],"award-info":[{"award-number":["1U01HG006389"]}],"id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The use and functionality of Electronic Health Records (EHR) have increased rapidly in the past few decades. EHRs are becoming an important depository of patient health information and can capture family data. Pedigree analysis is a longstanding and powerful approach that can gain insight into the underlying genetic and environmental factors in human health, but traditional approaches to identifying and recruiting families are low-throughput and labor-intensive. Therefore, high-throughput methods to automatically construct family pedigrees are needed.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We developed a stand-alone application: Electronic Pedigrees, or E-Pedigrees, which combines two validated family prediction algorithms into a single software package for high throughput pedigrees construction. The convenient platform considers patients\u2019 basic demographic information and\/or emergency contact data to infer high-accuracy parent\u2013child relationship. Importantly, E-Pedigrees allows users to layer in additional pedigree data when available and provides options for applying different logical rules to improve accuracy of inferred family relationships. This software is fast and easy to use, is compatible with different EHR data sources, and its output is a standard PED file appropriate for multiple downstream analyses.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The Python 3.3+ version E-Pedigrees application is freely available on: https:\/\/github.com\/xiayuan-huang\/E-pedigrees.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab419","type":"journal-article","created":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T20:38:08Z","timestamp":1622752688000},"page":"3966-3968","source":"Crossref","is-referenced-by-count":6,"title":["E-Pedigrees: a large-scale automatic family pedigree prediction application"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3730-5014","authenticated-orcid":false,"given":"Xiayuan","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Biostatistics & Medical Informatics, University of Wisconsin-Madison , Madison, WI 53706, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas","family":"Tatonetti","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University , New York, NY 10032, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katie","family":"LaRow","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University , New York, NY 10032, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brooke","family":"Delgoffee","sequence":"additional","affiliation":[{"name":"Office of Research Computing and Analytics, Marshfield Clinic Research Foundation , Marshfield, WI 54449, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Mayer","sequence":"additional","affiliation":[{"name":"Office of Research Computing and Analytics, Marshfield Clinic Research Foundation , Marshfield, WI 54449, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Page","sequence":"additional","affiliation":[{"name":"Department of Biostatistics & Bioinformatics, Duke University , Durham, NC 27710, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Scott J","family":"Hebbring","sequence":"additional","affiliation":[{"name":"Center for Precision Medicine Research, Marshfield Clinic Research Foundation , Marshfield, WI 54449, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,6,4]]},"reference":[{"key":"2023051607340577300_btab419-B1","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1101\/gr.142455.112","article-title":"Genotype calling and haplotyping in parent-offspring trios","volume":"23","author":"Chen","year":"2013","journal-title":"Genome Res"},{"key":"2023051607340577300_btab419-B2","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1093\/bioinformatics\/btz942","article-title":"A haplotype-aware de novo assembly of related individuals using pedigree sequence graph","volume":"36","author":"Garg","year":"2020","journal-title":"Bioinformatics"},{"key":"2023051607340577300_btab419-B3","first-page":"198","article-title":"Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review","volume":"24","author":"Goldstein","year":"2017","journal-title":"JAMIA"},{"key":"2023051607340577300_btab419-B5","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.tig.2018.09.007","article-title":"Genomic and phenomic research in the 21st century","volume":"35","author":"Hebbring","year":"2019","journal-title":"Trends Genet"},{"key":"2023051607340577300_btab419-B6","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1093\/bioinformatics\/btx569","article-title":"Electronic health record: an untapped Re-12 13 source for family-based genetic research","volume":"34","author":"Huang","year":"2018","journal-title":"Bioinformatics"},{"key":"2023051607340577300_btab419-B7","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1126\/science.aam9309","article-title":"Quantitative analysis of population-scale family trees","volume":"360","author":"Kaplanis","year":"2018","journal-title":"Science"},{"key":"2023051607340577300_btab419-B9","first-page":"918","article-title":"Harnessing population pedigree data and machine learning methods to identify patterns of familial bladder cancer risk","volume":"29","author":"Leiser","year":"2002","journal-title":"Cancer Epidemiol"},{"key":"2023051607340577300_btab419-B10","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1038\/ng.3766","article-title":"Quantitative analysis of population-scale family trees","volume":"49","author":"Liu","year":"2017","journal-title":"Nat. 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