RT Journal Article
SR Electronic
T1 Estimate of disease heritability using 7.4 million familial relationships inferred from electronic health records
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 066068
DO 10.1101/066068
A1 Fernanda Polubriaginof
A1 Rami Vanguri
A1 Kayla Quinnies
A1 Gillian M. Belbin
A1 Alexandre Yahi
A1 Hojjat Salmasian
A1 Tal Lorberbaum
A1 Victor Nwankwo
A1 Li Li
A1 Mark Shervey
A1 Patricia Glowe
A1 Iuliana Ionita-Laza
A1 Mary Simmerling
A1 George Hripcsak
A1 Suzanne Bakken
A1 David Goldstein
A1 Krzysztof Kiryluk
A1 Eimear E. Kenny
A1 Joel Dudley
A1 David K. Vawdrey
A1 Nicholas P. Tatonetti
YR 2017
UL http://biorxiv.org/content/early/2017/05/24/066068.abstract
AB Heritability is essential for understanding the biological causes of disease, but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHR) passively capture a wide range of clinically relevant data and provide a novel 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 millions of 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 literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a novel validation of the use of EHRs for genetics and disease research.One Sentence Summary We demonstrate that next-of-kin information can be used to identify familial relationships in the EHR, providing unique opportunities for precision medicine studies.