E-Pedigrees: a large-scale automatic family pedigree prediction application.


Journal

Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944

Informations de publication

Date de publication:
05 11 2021
Historique:
received: 21 01 2021
revised: 30 04 2021
medline: 13 4 2023
pubmed: 5 6 2021
entrez: 4 6 2021
Statut: ppublish

Résumé

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. 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' basic demographic information and/or emergency contact data to infer high-accuracy parent-child 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. The Python 3.3+ version E-Pedigrees application is freely available on: https://github.com/xiayuan-huang/E-pedigrees.

Identifiants

pubmed: 34086863
pii: 6292080
doi: 10.1093/bioinformatics/btab419
pmc: PMC8570807
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

3966-3968

Subventions

Organisme : NIGMS NIH HHS
ID : R01 GM130715
Pays : United States
Organisme : NLM NIH HHS
ID : T15 LM007079
Pays : United States
Organisme : NHGRI NIH HHS
ID : U01 HG006389
Pays : United States

Informations de copyright

© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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Auteurs

Xiayuan Huang (X)

Department of Biostatistics & Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA.

Nicholas Tatonetti (N)

Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.

Katie LaRow (K)

Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA.

Brooke Delgoffee (B)

Office of Research Computing and Analytics, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA.

John Mayer (J)

Office of Research Computing and Analytics, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA.

David Page (D)

Department of Biostatistics & Bioinformatics, Duke University, Durham, NC 27710, USA.

Scott J Hebbring (SJ)

Center for Precision Medicine Research, Marshfield Clinic Research Foundation, Marshfield, WI 54449, USA.

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Classifications MeSH