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Sci Transl Med. 2019 Apr 24;11(489). pii: eaat6177. doi: 10.1126/scitranslmed.aat6177.

Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation.

Author information

1
Rady Children's Institute for Genomic Medicine, San Diego, CA 92123, USA.
2
Department of Pediatrics, University of California San Diego, San Diego, CA 92093, USA.
3
Department of Pediatrics, University of Washington, Seattle, WA 98195, USA.
4
Diploid, 3001 Leuven, Belgium.
5
Clinithink Ltd., London N1 6DR, UK.
6
Codified Genomics, LLC, Houston, TX 77033, USA.
7
Rady Children's Hospital, San Diego, CA 92123, USA.
8
Alexion Pharmaceuticals Inc., New Haven, CT 06510, USA.
9
University of Kansas School of Medicine, Kansas City, MO 66160, USA.
10
Department of Neurosciences, University of California San Diego, San Diego, CA 92093, USA.
11
Tessella, Needham, MA 02494, USA.
12
Illumina Inc., San Diego, CA 92122, USA.
13
Fabric Genomics Inc., Oakland, CA 94612, USA.
14
Rady Children's Institute for Genomic Medicine, San Diego, CA 92123, USA. skingsmore@rchsd.org.

Abstract

By informing timely targeted treatments, rapid whole-genome sequencing can improve the outcomes of seriously ill children with genetic diseases, particularly infants in neonatal and pediatric intensive care units (ICUs). The need for highly qualified professionals to decipher results, however, precludes widespread implementation. We describe a platform for population-scale, provisional diagnosis of genetic diseases with automated phenotyping and interpretation. Genome sequencing was expedited by bead-based genome library preparation directly from blood samples and sequencing of paired 100-nt reads in 15.5 hours. Clinical natural language processing (CNLP) automatically extracted children's deep phenomes from electronic health records with 80% precision and 93% recall. In 101 children with 105 genetic diseases, a mean of 4.3 CNLP-extracted phenotypic features matched the expected phenotypic features of those diseases, compared with a match of 0.9 phenotypic features used in manual interpretation. We automated provisional diagnosis by combining the ranking of the similarity of a patient's CNLP phenome with respect to the expected phenotypic features of all genetic diseases, together with the ranking of the pathogenicity of all of the patient's genomic variants. Automated, retrospective diagnoses concurred well with expert manual interpretation (97% recall and 99% precision in 95 children with 97 genetic diseases). Prospectively, our platform correctly diagnosed three of seven seriously ill ICU infants (100% precision and recall) with a mean time saving of 22:19 hours. In each case, the diagnosis affected treatment. Genome sequencing with automated phenotyping and interpretation in a median of 20:10 hours may increase adoption in ICUs and, thereby, timely implementation of precise treatments.

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