PT - JOURNAL ARTICLE AU - Polubriaginof, Fernanda AU - Vanguri, Rami AU - Quinnies, Kayla AU - Belbin, Gillian M. AU - Yahi, Alexandre AU - Salmasian, Hojjat AU - Lorberbaum, Tal AU - Nwankwo, Victor AU - Li, Li AU - Shervey, Mark AU - Glowe, Patricia AU - Ionita-Laza, Iuliana AU - Simmerling, Mary AU - Hripcsak, George AU - Bakken, Suzanne AU - Goldstein, David AU - Kiryluk, Krzysztof AU - Kenny, Eimear E. AU - Dudley, Joel AU - Vawdrey, David K. AU - Tatonetti, Nicholas P. TI - Estimate of disease heritability using 7.4 million familial relationships inferred from electronic health records AID - 10.1101/066068 DP - 2017 Jan 01 TA - bioRxiv PG - 066068 4099 - http://biorxiv.org/content/early/2017/05/24/066068.short 4100 - http://biorxiv.org/content/early/2017/05/24/066068.full 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.