Disease Heritability Inferred from Familial Relationships Reported in Medical Records

Fernanda C G Polubriaginof; Rami Vanguri; Kayla Quinnies; Gillian M Belbin; Alexandre Yahi; Hojjat Salmasian; Tal Lorberbaum; Victor Nwankwo; Li Li; Mark M 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

doi:10.1016/j.cell.2018.04.032

Disease Heritability Inferred from Familial Relationships Reported in Medical Records

Cell. 2018 Jun 14;173(7):1692-1704.e11. doi: 10.1016/j.cell.2018.04.032. Epub 2018 May 17.

Authors

Fernanda C G Polubriaginof¹, Rami Vanguri¹, Kayla Quinnies², Gillian M Belbin³, Alexandre Yahi¹, Hojjat Salmasian⁴, Tal Lorberbaum⁵, Victor Nwankwo¹, Li Li³, Mark M Shervey³, Patricia Glowe³, Iuliana Ionita-Laza⁶, Mary Simmerling⁷, George Hripcsak⁸, Suzanne Bakken⁹, David Goldstein¹⁰, Krzysztof Kiryluk¹¹, Eimear E Kenny³, Joel Dudley³, David K Vawdrey⁴, Nicholas P Tatonetti¹²

Affiliations

¹ Department of Biomedical Informatics, Columbia University, New York, NY, USA.
² Department of Biomedical Informatics, Columbia University, New York, NY, USA; Institute for Genomic Medicine, Columbia University, New York, NY, USA.
³ Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY, USA.
⁴ Department of Biomedical Informatics, Columbia University, New York, NY, USA; Value Institute, NewYork-Presbyterian Hospital, New York, NY, USA.
⁵ Department of Biomedical Informatics, Columbia University, New York, NY, USA; Department of Physiology and Cellular Biophysics, Columbia University, New York, NY, USA.
⁶ Mailman School of Public Health, Columbia University, New York, NY, USA; Department of Biostatistics, Columbia University, New York, NY, USA.
⁷ Department of Medicine, Weill Cornell Medicine, Cornell University, New York, NY, USA; Quality and Patient Safety, NewYork-Presbyterian Hospital, New York, NY, USA.
⁸ Department of Biomedical Informatics, Columbia University, New York, NY, USA; Medical Informatics Services, NewYork-Presbyterian Hospital, New York, NY; Observational Health Data Sciences and Informatics (OHDSI), New York, NY, USA.
⁹ Department of Biomedical Informatics, Columbia University, New York, NY, USA; School of Nursing, Columbia University, New York, NY, USA; Data Science Institute, Columbia University, New York, NY, USA.
¹⁰ Institute for Genomic Medicine, Columbia University, New York, NY, USA.
¹¹ Department of Medicine, Columbia University, New York, NY, USA.
¹² Department of Biomedical Informatics, Columbia University, New York, NY, USA; Institute for Genomic Medicine, Columbia University, New York, NY, USA; Observational Health Data Sciences and Informatics (OHDSI), New York, NY, USA; Department of Medicine, Columbia University, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA; Data Science Institute, Columbia University, New York, NY, USA. Electronic address: nick.tatonetti@columbia.edu.

Abstract

Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a 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 7.4 million 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 the literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a validation of the use of EHRs for genetics and disease research.

Keywords: data mining; disease heritability; electronic health record; familial relationships; family history; genetics; observational databases.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, U.S. Gov't, P.H.S.

MeSH terms

Algorithms
Databases, Factual
Electronic Health Records*
Family Relations
Genetic Diseases, Inborn / genetics*
Genetic Diseases, Inborn / pathology
Genotype
Humans
Pedigree
Phenotype
Quantitative Trait, Heritable

Abstract

Publication types

MeSH terms

Grants and funding