Nicholas P Tatonetti | Search bioRxiv

bioRxiv2016

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

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.

Genetics↗

bioRxiv2015

Data science identifies novel drug interactions that prolong the QT interval

Drug-induced prolongation of the QT interval on the electrocardiogram (long QT syndrome, LQTS) can lead to a potentially fatal ventricular arrhythmia called torsades de pointes (TdP). 180 drugs with both cardiac and non-cardiac indications have been found to increase risk for TdP, but drug-drug interactions contributing to LQTS (QT-DDIs) remain poorly characterized. Traditional methods for mining observational healthcare data are poorly equipped to detect QT- DDI signals due to low reporting numbers and a lack of direct evidence for LQTS. In this study we present an integrative data science pipeline that addresses these limitations by identifying latent signals for QT-DDIs in the FDAs Adverse Event Reporting System and retrospectively validating these predictions using electrocardiogram data in electronic health records. We present 26 novel QT-DDIs flagged using this method that warrant further investigation.\n\nKey Points- Drug-induced long QT syndrome (LQTS) can lead to potentially fatal arrhythmias (torsades de pointes, TdP). Drug-drug interactions that prolong the QT interval (QT- DDIs) can be clinically significant but remain poorly characterized.\n- Observational health data (such as adverse event spontaneous reporting systems and electronic health records) offer an opportunity to mine for new QT-DDIs, but when used individually these datasets have a number of limitations that prevent identification of true signals.\n- We present an integrative data science approach that combines mining for latent QT- DDI signals in the FDA Adverse Event Reporting System and retrospective analysis of electrocardiogram lab results in electronic health records at Columbia University Medical Center to identify 26 novel QT-DDIs.

Bioinformatics↗