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Estimate of disease heritability using 4.7 million familial relationships inferred from electronic health records

View ORCID ProfileFernanda Polubriaginof, Kayla Quinnies, Rami Vanguri, Alexandre Yahi, Mary Simmerling, Iuliana Ionita-Laza, Hojjat Salmasian, Suzanne Bakken, George Hripcsak, David Goldstein, Krzysztof Kiryluk, David K Vawdrey, View ORCID ProfileNicholas P Tatonetti
doi: http://dx.doi.org/10.1101/066068
Fernanda Polubriaginof
Columbia University;
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Kayla Quinnies
Columbia University;
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Rami Vanguri
Columbia University;
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Alexandre Yahi
Columbia University;
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Mary Simmerling
Cornell University;
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Iuliana Ionita-Laza
Columbia University;
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Hojjat Salmasian
NewYork-Presbyterian Hospital
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Suzanne Bakken
Columbia University;
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George Hripcsak
Columbia University;
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David Goldstein
Columbia University;
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Krzysztof Kiryluk
Columbia University;
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David K Vawdrey
NewYork-Presbyterian Hospital
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Nicholas P Tatonetti
Columbia University;
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Abstract

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.

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The copyright holder for this preprint is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
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  • Posted July 28, 2016.

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Estimate of disease heritability using 4.7 million familial relationships inferred from electronic health records
Fernanda Polubriaginof, Kayla Quinnies, Rami Vanguri, Alexandre Yahi, Mary Simmerling, Iuliana Ionita-Laza, Hojjat Salmasian, Suzanne Bakken, George Hripcsak, David Goldstein, Krzysztof Kiryluk, David K Vawdrey, Nicholas P Tatonetti
bioRxiv 066068; doi: http://dx.doi.org/10.1101/066068
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Estimate of disease heritability using 4.7 million familial relationships inferred from electronic health records
Fernanda Polubriaginof, Kayla Quinnies, Rami Vanguri, Alexandre Yahi, Mary Simmerling, Iuliana Ionita-Laza, Hojjat Salmasian, Suzanne Bakken, George Hripcsak, David Goldstein, Krzysztof Kiryluk, David K Vawdrey, Nicholas P Tatonetti
bioRxiv 066068; doi: http://dx.doi.org/10.1101/066068

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