Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

GenomeDiver: a platform for phenotype-guided medical genomic diagnosis

Abstract

Purpose

Making a diagnosis from clinical genomic sequencing requires well-structured phenotypic data to guide genotype interpretation. A patient’s phenotypic features can be documented using the Human Phenotype Ontology (HPO), generating terms used to prioritize genes potentially causing the patient’s disease. We have developed GenomeDiver to provide a user interface for clinicians that allows more effective collaboration with the clinical diagnostic laboratory, with the goal of improving the success of the diagnostic process.

Methods

GenomeDiver uses genomic data to prompt reverse phenotyping of patients undergoing genetic testing, enriching the amount and quality of structured phenotype data for the diagnostic laboratory, and helping clinicians to explore and flag diseases potentially causing their patient’s presentation.

Results

We show how GenomeDiver communicates the clinician’s informed insights to the diagnostic lab in the form of HPO terms for interpretation of genomic sequencing data. We describe our user-driven design process, the engineering of the software for efficiency, security and portability, and examples of the performance of GenomeDiver using genomic testing data.

Conclusion

GenomeDiver is a first step in a new approach to genomic diagnostics that enhances laboratory–clinician interactions, with the goal of directly engaging clinicians to improve the outcome of genomic diagnostic testing.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: The clinician interface for GenomeDiver.
Fig. 2: Following the categorization of the Human Phenotype Ontology (HPO) terms, a second Exomiser run reprioritizes variants, genes, and associated diseases.

Data availability

The GenomeDiver software is available at https://github.com/GenomeDiver/. The patient genomes and HPO terms used in the user experience trial will be available as part of the Clinical Sequencing Evidence-Generating Research (CSER) data deposition on the Analysis, Visualization, and Informatics Lab-space (ANViL, anvilproject.org).

References

  1. 1.

    Splinter, K. et al. Effect of genetic diagnosis on patients with previously undiagnosed disease. N. Engl. J. Med. 379, 2131–2139 (2018).

    CAS  Article  Google Scholar 

  2. 2.

    Chung, C. C. Y. et al. Rapid whole-exome sequencing facilitates precision medicine in paediatric rare disease patients and reduces healthcare costs. Lancet Regional Health Western Pacific. 1, 100001 (2020).

    Article  Google Scholar 

  3. 3.

    Schofield, D., Rynehart, L., Shresthra, R., White, S. M. & Stark, Z. Long-term economic impacts of exome sequencing for suspected monogenic disorders: diagnosis, management, and reproductive outcomes. Genet. Med. 21, 2586–2593 (2019).

    Article  Google Scholar 

  4. 4.

    Nguengang Wakap, S. et al. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. Eur. J. Hum. Genet. 28, 165–173 (2020).

    Article  Google Scholar 

  5. 5.

    Rehm, H. L. & Fowler, D. M. Keeping up with the genomes: scaling genomic variant interpretation. Genome Med 12, 5 (2019).

    Article  Google Scholar 

  6. 6.

    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 38, e164 (2010).

    Article  Google Scholar 

  7. 7.

    Hu, H. et al. VAAST 2.0: improved variant classification and disease-gene identification using a conservation-controlled amino acid substitution matrix. Genet. Epidemiol. 37, 622–634 (2013).

    Article  Google Scholar 

  8. 8.

    Köhler, S. et al. Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Nucleic Acids Res 47, D1018–D1027 (2019).

    Article  Google Scholar 

  9. 9.

    Pengelly, R. J. et al. Evaluating phenotype-driven approaches for genetic diagnoses from exomes in a clinical setting. Sci. Rep. 7, 13509 (2017).

    Article  Google Scholar 

  10. 10.

    Trujillano, D. et al. Clinical exome sequencing: results from 2819 samples reflecting 1000 families. Eur. J. Hum. Genet. 25, 176–182 (2017).

    CAS  Article  Google Scholar 

  11. 11.

    Thompson, R. et al. Increasing phenotypic annotation improves the diagnostic rate of exome sequencing in a rare neuromuscular disorder. Hum. Mutat. 40, 1797–1812 (2019).

    CAS  Article  Google Scholar 

  12. 12.

    Clark, M. M. et al. Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation. Sci. Transl. Med. 11, eaat6177 (2019).

  13. 13.

    Odgis, J. A. et al. The NYCKidSeq project: study protocol for a randomized controlled trial incorporating genomics into the clinical care of diverse New York City children. Trials 22, 56 (2021).

    Article  Google Scholar 

  14. 14.

    Amendola, L. M. et al. The Clinical Sequencing Evidence-Generating Research Consortium: integrating genomic sequencing in diverse and medically underserved populations. Am. J. Hum. Genet. 103, 319–327 (2018).

    CAS  Article  Google Scholar 

  15. 15.

    Brown, T. Design thinking. Harvard Bus. Rev. 86, 84–92 (2008).

    Google Scholar 

  16. 16.

    Smedley, D. et al. Next-generation diagnostics and disease-gene discovery with the Exomiser. Nat. Protoc. 10, 2004–2015 (2015).

    CAS  Article  Google Scholar 

  17. 17.

    Zook, J. M. et al. Extensive sequencing of seven human genomes to characterize benchmark reference materials. Sci. Data. 3, 160025 (2016).

    CAS  Article  Google Scholar 

  18. 18.

    Greally, M. T. Shprintzen-Goldberg Syndrome. in GeneReviews® (eds Adam, M. P. et al.) (University of Washington, Seattle, 1993).

  19. 19.

    Deisseroth, C. A. et al. ClinPhen extracts and prioritizes patient phenotypes directly from medical records to expedite genetic disease diagnosis. Genet. Med. 21, 1585–1593 (2019).

    Article  Google Scholar 

  20. 20.

    Hsieh, T.-C. et al. PEDIA: prioritization of exome data by image analysis. Genet. Med. 21, 2807–2814 (2019).

    Article  Google Scholar 

  21. 21.

    Robinson, P. N. et al. Interpretable clinical genomics with a likelihood ratio paradigm. Am. J. Hum. Genet. 107, 403–417 (2020).

    CAS  Article  Google Scholar 

  22. 22.

    Groza, T. et al. The Human Phenotype Ontology: semantic unification of common and rare disease. Am. J. Hum. Genet. 97, 111–124 (2015).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

Research reported in this publication was part of the NYCKidSeq project, supported by the National Human Genome Research Institute and National Institute for Minority Heath and Health Disparities of the National Institutes of Health under award number 1U01HG0096108.

Author information

Affiliations

Authors

Contributions

Conceptualization: N.M.P., C.S., K.S., F.B., N.S.A-H., K.B., J.A.O., S.A.S., B.D.G., E.E.K., V.J., T.B., J.M.G. Data curation: V.J. Formal analysis: N.M.P., F.B., C.G., T.B., J.M.G. Funding acquisition: C.R.H., M.W., B.D.G., E.E.K. Investigation: N.M.P., C.S., K.S., F.B., T.B., J.M.G. Methodology: N.M.P., C.S., K.S., F.B., C.G., V.J., T.B., J.M.G. Project administration: N.M.P., F.B., G.B., E.E.K., C.G., V.J., T.B., J.M.G. Resources: C.R.H., M.W., B.D.G., E.E.K., C.G., V.J., T.B., J.M.G. Software: N.M.P., C.S., K.S., F.B., J.M.G. Supervision: G.B., E.E.K., C.G., J.M.G. Validation: N.M.P., C.S., K.S., F.B., N.S.A-H., G.A.D., M.W., B.D.G. Visualization: N.P., C.S., K.S., F.B., J.M.G. Writing—original draft: N.M.P., CS KS C.G., J.M.G. Writing—review & editing: N.M.P., C.S., K.S., F.B., N.S.A-H., GB K.B., G.A.D., J.A.O., S.A.S., C.R.H., M.W., B.D.G., E.E.K., C.G., T.B., J.M.G.

Corresponding author

Correspondence to John M. Greally.

Ethics declarations

Ethics declaration

The NYCKidSeq project (ClinicalTrials.gov Identifier: NCT03738098) was approved by the Institutional Review Boards (IRBs) of Icahn School of Medicine at Mount Sinai and Albert Einstein College of Medicine. Informed consent was obtained from all study participants. All data were de-identified for downstream research, including the GenomeDiver user experience trial.

Competing interests

N.S.A.-H. was previously employed at Regeneron Pharmaceuticals and has received an honorarium from Genentech. E.E.K. has received speaker honoraria from Regeneron Pharmaceuticals and Illumina, Inc. The other authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Pearson, N.M., Stolte, C., Shi, K. et al. GenomeDiver: a platform for phenotype-guided medical genomic diagnosis. Genet Med (2021). https://doi.org/10.1038/s41436-021-01219-5

Download citation

Search

Quick links