PT  - JOURNAL ARTICLE
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
TI  - Estimate of disease heritability using 4.7 million familial relationships inferred from electronic health records
AID  - 10.1101/066068
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 066068
4099  - http://biorxiv.org/content/early/2016/07/28/066068.short
4100  - http://biorxiv.org/content/early/2016/07/28/066068.full
AB  - 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.