A scalable EHR-based approach for phenotype discovery and variant interpretation for hereditary cancer genes
Justin D Andujar,
Sarah T Bland,
David R Crosslin,
Jennifer A Pacheco,
Kurt D. Christensen,
Emma F Perez,
Carrie L. Blout Zawatsky,
Kathleen A. Leppig,
Patrick M. Sleiman,
Marc S Williams,
Gail P Jarvik,
Robert C Green,
Wendy K Chung,
Ali G Gharavi,
Niall J. Lennon,
Heidi L Rehm,
Richard A. Gibbs,
Josh F. Peterson,
Dan M Roden,
Georgia L. Wiesner,
Joshua C Denny
Posted 24 Mar 2021
medRxiv DOI: 10.1101/2021.03.18.21253763
Posted 24 Mar 2021
Knowledge of the clinical spectrum of rare genetic disorders helps in disease management and variant pathogenicity interpretation. Leveraging electronic health record (EHR)-linked genetic testing data from the eMERGE network, we determined the associations between a set of 23 hereditary cancer genes and 3017 phenotypes in 23544 individuals. This phenome-wide association study replicated 45% (184/406) of known gene-phenotype associations (P = 5.1 x10-125). Meta-analysis with an independent EHR-derived cohort of 3242 patients confirmed 14 novel associations with phenotypes in the neoplastic, genitourinary, digestive, congenital, metabolic, mental and neurologic categories. Phenotype risk scores (PheRS) based on weighted aggregations of EHR phenotypes accurately predicted variant pathogenicity for at least 50% of pathogenic variants for 8/23 genes. We generated a catalog of PheRS for 7800 variants, including 5217 variants of uncertain significance, to provide empirical evidence of potential pathogenicity. This study highlights the potential of EHR data in genomic medicine.
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