PT  - JOURNAL ARTICLE
AU  - Polubriaginof, Fernanda
AU  - Vanguri, Rami
AU  - Quinnies, Kayla
AU  - Belbin, Gillian
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
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.