bioRxivGeneticsPreprint
Estimate of disease heritability using 7.4 million familial relationships inferred from electronic health records
Fernanda C. G. PolubriaginofiD, Rami Sesha VanguriiD, Kayla Quinnies, Gillian M. BelbiniD, Alexandre Yahi, Hojjat Salmasian, Tal LorberbaumiD, Victor Nwankwo, Li LiiD, Mark Shervey, Patricia Glowe, Iuliana Ionita-Laza, Mary C. SimmerlingiD, George Hripcsak, Suzanne Bakken HenryiD, David B. GoldsteiniD, Krzysztof KirylukiD, Eimear E. KennyiD, Joel T. DudleyiD, David K. Vawdrey, Nicholas P. TatonettiiD
Abstract
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.\n\nOne Sentence SummaryWe demonstrate that next-of-kin information can be used to identify familial relationships in the EHR, providing unique opportunities for precision medicine studies.
- organism: human
- assay: electronic health records
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 4
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