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Estimate of disease heritability using 7.4 million familial relationships inferred from electronic health records

View ORCID ProfileFernanda Polubriaginof, Rami Vanguri, Kayla Quinnies, Gillian M. Belbin, Alexandre Yahi, Hojjat Salmasian, Tal Lorberbaum, Victor Nwankwo, Li Li, Mark Shervey, Patricia Glowe, Iuliana Ionita-Laza, Mary Simmerling, George Hripcsak, Suzanne Bakken, David Goldstein, Krzysztof Kiryluk, Eimear E. Kenny, Joel Dudley, David K. Vawdrey, View ORCID ProfileNicholas P. Tatonetti
doi: https://doi.org/10.1101/066068
Fernanda Polubriaginof
1Department of Biomedical Informatics, Columbia University, New York, NY
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Rami Vanguri
1Department of Biomedical Informatics, Columbia University, New York, NY
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Kayla Quinnies
1Department of Biomedical Informatics, Columbia University, New York, NY
2Institute for Genomic Medicine, Columbia University, New York, NY
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Gillian M. Belbin
3Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY
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Alexandre Yahi
1Department of Biomedical Informatics, Columbia University, New York, NY
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Hojjat Salmasian
1Department of Biomedical Informatics, Columbia University, New York, NY
4Value Institute, NewYork-Presbyterian Hospital, New York, NY
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Tal Lorberbaum
1Department of Biomedical Informatics, Columbia University, New York, NY
5Department of Physiology and Cellular Biophysics, Columbia University, New York, NY
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Victor Nwankwo
1Department of Biomedical Informatics, Columbia University, New York, NY
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Li Li
3Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY
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Mark Shervey
3Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY
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Patricia Glowe
3Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY
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Iuliana Ionita-Laza
6Mailman School of Public Health, Columbia University, New York, NY
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Mary Simmerling
7Department of Medicine, Weill Cornell Medicine, Cornell University, New York, NY
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George Hripcsak
1Department of Biomedical Informatics, Columbia University, New York, NY
8Medical Informatics Services, NewYork-Presbyterian Hospital, New York, NY
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Suzanne Bakken
1Department of Biomedical Informatics, Columbia University, New York, NY
9School of Nursing, Columbia University, New York, NY
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David Goldstein
2Institute for Genomic Medicine, Columbia University, New York, NY
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Krzysztof Kiryluk
10Department of Medicine, Columbia University, New York, NY
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Eimear E. Kenny
3Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY
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Joel Dudley
3Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY
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David K. Vawdrey
1Department of Biomedical Informatics, Columbia University, New York, NY
4Value Institute, NewYork-Presbyterian Hospital, New York, NY
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Nicholas P. Tatonetti
1Department of Biomedical Informatics, Columbia University, New York, NY
2Institute for Genomic Medicine, Columbia University, New York, NY
10Department of Medicine, Columbia University, New York, NY
11Department of Systems Biology, Columbia University, New York, NY
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  • ORCID record for Nicholas P. Tatonetti
  • For correspondence: nick.tatonetti@columbia.edu
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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.

One Sentence Summary We demonstrate that next-of-kin information can be used to identify familial relationships in the EHR, providing unique opportunities for precision medicine studies.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 24, 2017.
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Estimate of disease heritability using 7.4 million familial relationships inferred from electronic health records
Fernanda Polubriaginof, Rami Vanguri, Kayla Quinnies, Gillian M. Belbin, Alexandre Yahi, Hojjat Salmasian, Tal Lorberbaum, Victor Nwankwo, Li Li, Mark Shervey, Patricia Glowe, Iuliana Ionita-Laza, Mary Simmerling, George Hripcsak, Suzanne Bakken, David Goldstein, Krzysztof Kiryluk, Eimear E. Kenny, Joel Dudley, David K. Vawdrey, Nicholas P. Tatonetti
bioRxiv 066068; doi: https://doi.org/10.1101/066068
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Estimate of disease heritability using 7.4 million familial relationships inferred from electronic health records
Fernanda Polubriaginof, Rami Vanguri, Kayla Quinnies, Gillian M. Belbin, Alexandre Yahi, Hojjat Salmasian, Tal Lorberbaum, Victor Nwankwo, Li Li, Mark Shervey, Patricia Glowe, Iuliana Ionita-Laza, Mary Simmerling, George Hripcsak, Suzanne Bakken, David Goldstein, Krzysztof Kiryluk, Eimear E. Kenny, Joel Dudley, David K. Vawdrey, Nicholas P. Tatonetti
bioRxiv 066068; doi: https://doi.org/10.1101/066068

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