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Phenotype risk scores identify patients with unrecognized mendelian disease patterns

  • Lisa Bastarache
  • , Jacob J. Hughey
  • , Scott Hebbring
  • , Joy Marlo
  • , Wanke Zhao
  • , Wanting T. Ho
  • , Sara L. Van Driest
  • , Tracy L. McGregor
  • , Jonathan D. Mosley
  • , Quinn S. Wells
  • , Michael Temple
  • , Andrea H. Ramirez
  • , Robert Carroll
  • , Travis Osterman
  • , Todd Edwards
  • , Douglas Ruderfer
  • , Digna R. Velez Edwards
  • , Rizwan Hamid
  • , Joy Cogan
  • , Andrew Glazer
  • Wei Qi Wei, Qi Ping Feng, Murray Brilliant, Zhizhuang J. Zhao, Nancy J. Cox, Dan M. Roden, Joshua C. Denny

Research output: Contribution to journalArticlepeer-review

157 Scopus citations

Abstract

Genetic association studies often examine features independently, potentially missing subpopulations with multiple phenotypes that share a single cause. We describe an approach that aggregates phenotypes on the basis of patterns described by Mendelian diseases. We mapped the clinical features of 1204 Mendelian diseases into phenotypes captured from the electronic health record (EHR) and summarized this evidence as phenotype risk scores (PheRSs). In an initial validation, PheRS distinguished cases and controls of five Mendelian diseases. Applying PheRS to 21,701 genotyped individuals uncovered 18 associations between rare variants and phenotypes consistent with Mendelian diseases. In 16 patients, the rare genetic variants were associated with severe outcomes such as organ transplants. PheRS can augment rare-variant interpretation and May identify subsets of patients with distinct genetic causes for common diseases.

Original languageEnglish
Pages (from-to)1233-1239
Number of pages7
JournalScience
Volume359
Issue number6381
DOIs
StatePublished - 16 Mar 2018
Externally publishedYes

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