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Disease Heritability Inferred from Familial Relationships Reported in Medical Records
Author(s) -
Fernanda Polubriaginof,
R. Vanguri,
Kayla M. Quinnies,
Gillian M. Belbin,
Alexandre Yahi,
Hojjat Salmasian,
Tal Lorberbaum,
Victor Nwankwo,
Li Li,
Mark Shervey,
Patricia Glowe,
Iuliana IonitaLaza,
Mary Simmerling,
George Hripcsak,
Suzanne Bakken,
David B. Goldstein,
Krzysztof Kiryluk,
Eimear E. Kenny,
Joel T. Dudley,
David K. Vawdrey,
Nicholas P. Tatonetti
Publication year - 2018
Publication title -
cell
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 26.304
H-Index - 776
eISSN - 1097-4172
pISSN - 0092-8674
DOI - 10.1016/j.cell.2018.04.032
Subject(s) - heritability , disease , biology , clinical phenotype , patient privacy , health records , resource (disambiguation) , genetics , phenotype , medicine , computer science , pathology , health care , computer network , gene , economics , economic growth
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.

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