4.7 Article

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

Journal

BIOINFORMATICS
Volume 37, Issue 21, Pages 3966-3968

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btab419

Keywords

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Funding

  1. National Institute of General Medical Sciences [1R01GM114128, 1R01GM130715]
  2. National Human Genome Research Institute [1U01HG006389]

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The use of Electronic Health Records (EHR) has increased rapidly in the past few decades. A new stand-alone application, E-Pedigrees, has been developed to automatically construct family pedigrees using two validated family prediction algorithms. This software is fast, easy to use, compatible with different EHR data sources, and provides accurate results for downstream analyses.
Motivation: 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. Results: 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.

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