| Literature DB >> 29779949 |
Fernanda C G Polubriaginof1, Rami Vanguri1, Kayla Quinnies2, Gillian M Belbin3, Alexandre Yahi1, Hojjat Salmasian4, Tal Lorberbaum5, Victor Nwankwo1, Li Li3, Mark M Shervey3, Patricia Glowe3, Iuliana Ionita-Laza6, Mary Simmerling7, George Hripcsak8, Suzanne Bakken9, David Goldstein10, Krzysztof Kiryluk11, Eimear E Kenny3, Joel Dudley3, David K Vawdrey4, Nicholas P Tatonetti12.
Abstract
Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified 7.4 million familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with the literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a validation of the use of EHRs for genetics and disease research.Entities:
Keywords: data mining; disease heritability; electronic health record; familial relationships; family history; genetics; observational databases
Mesh:
Year: 2018 PMID: 29779949 PMCID: PMC6015747 DOI: 10.1016/j.cell.2018.04.032
Source DB: PubMed Journal: Cell ISSN: 0092-8674 Impact factor: 41.582