TY - JOUR T1 - Estimate of disease heritability using 4.7 million familial relationships inferred from electronic health records JF - bioRxiv DO - 10.1101/066068 SP - 066068 AU - Fernanda Polubriaginof AU - Kayla Quinnies AU - Rami Vanguri AU - Alexandre Yahi AU - Mary Simmerling AU - Iuliana Ionita-Laza AU - Hojjat Salmasian AU - Suzanne Bakken AU - George Hripcsak AU - David Goldstein AU - Krzysztof Kiryluk AU - David K. Vawdrey AU - Nicholas P. Tatonetti Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/07/28/066068.abstract N2 - Heritability is a fundamental characteristic of human disease essential to the development of a biological understanding of the causes of disease. Traditionally, heritability studies are a laborious process of patient recruitment and phenotype ascertainment. Electronic health records (EHR) passively capture a wide range and depth of clinically relevant data and represent a novel resource for studying heritability of many traits and conditions that are not typically accessible. In addition to a wealth of disease phenotypes, nearly every hospital collects and stores next-of-kin information on the emergency contact forms when a patient is admitted. Until now, these data have gone completely unused for research purposes. We introduce a novel algorithm to infer familial relationships using emergency contact information while maintaining privacy. Here we show that EHR data yield accurate estimates of heritability across all available phenotypes using millions familial relationships mined from emergency contact data at two large academic medical centers. Estimates of heritability were consistent between sites and with previously reported estimates. Inconsistencies were indicative of limitations and opportunities unique to EHR research. Critically, these analyses provide a novel validation of the utility of electronic health records in inferences about the biological basis of disease. ER -