Realization of a Standard of Care for Rare Diseases Using Patient-Engaged Phenotyping [Methods Study], United States, 2018-2020 (ICPSR 39716)

Version Date: Mar 11, 2026

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Principal Investigator(s):
Ingrid A. Holm, Children's Hospital (Boston, Mass)

https://doi.org/10.3886/ICPSR39716.v1

Version V1

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Summary

To diagnose rare genetic conditions, doctors look at patients' genetic data and a phenotypic profile. A phenotypic profile is a record of all the physical traits of a condition. It uses a list of standard terms called Human Phenotype Ontology, or HPO. Doctors and clinic staff do a thorough exam with the patient to create the profile. The exam takes a long time and often more than one visit.

Patients may be able to create phenotypic profiles themselves using surveys. These surveys may take less time than clinic visits. But it is unclear whether patient surveys can provide enough details to correctly identify conditions.

In this project, the research team tested two surveys:

  • Phenotypr. This survey asks patients to describe their symptoms and then matches the descriptions to plain language HPO or clinical HPO terms.
  • GenomeConnect. This survey uses multiple choice questions to asks patients about their health and symptoms.

Citation

Holm, Ingrid A. Realization of a Standard of Care for Rare Diseases Using Patient-Engaged Phenotyping [Methods Study], United States, 2018-2020. Inter-university Consortium for Political and Social Research [distributor], 2026-03-11. https://doi.org/10.3886/ICPSR39716.v1

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Funding

Patient-Centered Outcomes Research Institute (PCORI) (ME-1511-33184)

Subject Terms

Geographic Coverage

Distributor(s)

Inter-university Consortium for Political and Social Research
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Time Period(s)

2018 -- 2020
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Study Purpose

To validate two self-phenotyping surveys for generating phenotypic profiles for patients with rare genetic conditions

Study Design

In the simulation study, researchers validated two web-based self-phenotyping surveys:

  • Phenotypr. Researchers developed this survey, which asks patients to describe all their symptoms and then matches the descriptions to layperson HPO or standard HPO terms.
  • GenomeConnect. Researchers adapted this preexisting survey for the web. It uses multiple choice questions to ask patients about their issues or symptoms for one body system at a time.

To validate the surveys, researchers compared the similarity of the phenotypic profiles generated by each survey to Monarch phenotypic profiles, a collection of published phenotypic profiles for each known genetic disease. First, for each survey, researchers simulated profiles of every known genetic condition in the Monarch collection based on how a patient with that condition might answer the survey's questions. Researchers simulated 20 patient profiles for each of the 7,344 genetic conditions, resulting in 146,880 profiles per survey. Researchers analyzed the clinical utility of the GenomeConnect and Phenotypr simulated profiles. They used a Bayesian ontology query algorithm to generate receiver operating characteristic curves for published Monarch phenotypic profiles and simulated GenomeConnect and Phenotypr phenotypic profiles. The curves measured how well each survey differentiated between conditions. Analyses showed that the Monarch (area under the curve [AUC] = 0.985), Phenotypr (AUC = 0.957), and GenomeConnect (AUC = 0.913) phenotypic profiles all had good diagnostic power.

Then researchers randomly assigned 282 patients with rare genetic conditions to complete one or both surveys. Patients represented 257 different rare genetic conditions. Both the GenomeConnect and Phenotypr surveys were useful in collecting phenotype data directly from patients.

Compared with profiles from patients completing the Phenotypr survey, profiles from patients completing the GenomeConnect survey were more concordant with the simulated profiles; as such, GenomeConnect was more accurate. Among patients who completed both surveys, Phenotypr had a tighter distribution of similarity scores than GenomeConnect; as such, Phenotypr was more precise.

Researchers also interviewed 17 patients who took both surveys. They preferred GenomeConnect over Phenotypr. Patients, clinicians, and a genetic counselor helped design the study and provided input throughout.

Universe

Patients with rare genetic conditions

Data Source

7,344 Human Phenotype Ontology (HPO) Monarch reference phenotypic profiles; survey data from 282 patients with rare genetic conditions; qualitative interviews with 17 patients who took both surveys

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Original Release Date

2026-03-11

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Notes

  • The public-use data files in this collection are available for access by the general public. Access does not require affiliation with an ICPSR member institution.

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