TY - JOUR T1 - Estimate of disease heritability using 7.4 million familial relationships inferred from electronic health records JF - bioRxiv DO - 10.1101/066068 SP - 066068 AU - Fernanda Polubriaginof AU - Rami Vanguri AU - Kayla Quinnies AU - Gillian M. Belbin AU - Alexandre Yahi AU - Hojjat Salmasian AU - Tal Lorberbaum AU - Victor Nwankwo AU - Li Li AU - Mark Shervey AU - Patricia Glowe AU - Iuliana Ionita-Laza AU - Mary Simmerling AU - George Hripcsak AU - Suzanne Bakken AU - David Goldstein AU - Krzysztof Kiryluk AU - Eimear E. Kenny AU - Joel Dudley AU - David K. Vawdrey AU - Nicholas P. Tatonetti Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/05/24/066068.abstract N2 - 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. ER -