BACKGROUND

Patients with rare diseases may be the best source of information on their phenotypes (their physical features). The premise of our study is that “self-phenotyping” will be an accurate source of patient data and will empower patients, which may be particularly beneficial to the rare disease population. ClinGen, an NIH-funded resource, developed a patient self-phenotyping survey, GenomeConnect (https://www.genomeconnect.org/), which asks patient-friendly questions that are mapped to a small subset of Human Phenotype Ontology (HPO) terms (https://hpo.jax.org/app/). An alternative method for self-phenotyping is for patients to generate HPO terms for their condition directly. Because most HPO terms are medical and often unfamiliar to patients, the Monarch Initiative translated approximately a third of the HPO terms into layperson language (the other terms were too clinical to have a lay-friendly translation). This layperson version of HPO is a much larger set of HPO terms than those mapped to the GenomeConnect survey. However, the layperson HPO has not yet been incorporated into patient-centered applications, and neither the GenomeConnect survey nor the layperson HPO has been validated to effectively inform diagnosis in patients with rare diseases.

OBJECTIVES

The overall objective was to understand how best to support patients providing descriptions of their own phenotypes. The specific objectives were to (1) develop a layperson HPO-based tool for patients to use for self-phenotyping (Phenotypr); (2) validate the GenomeConnect-mapped HPO profiles and layperson HPO profiles computationally through the Monarch Initiative's algorithms (https://monarchinitiative.org/) to determine their diagnostic utility relative to gold-standard clinical profiles; and (3) test the GenomeConnect survey and Phenotypr tool in participants with diagnosed genetic diseases to determine which was better at identifying the clinical diagnosis and which survey participants preferred.

METHODS

To evaluate the diagnostic utility of the layperson HPO and GenomeConnect survey, we created derived disease profiles for every known Mendelian disease in the Monarch corpus (∼7600 diseases, encoded using the Mondo disease terminology). These disease profiles were subsets of the gold-standard Monarch profiles and were based on the GenomeConnect survey mappings to HPO and the layperson HPO subset of HPO. We compared each of the derived profiles against the corpus of Monarch gold-standard profiles and determined the degree to which the given disease was detected as the closest match. We offered enrollment to participants aged ≥18 years, or the parent/guardian of a child aged <18 years, who were diagnosed with a genetic disease. We enrolled participants from the Boston Children's Hospital (BCH) genetics clinic, the BCH Down syndrome program, GenomeConnect, a 16p research registry, and the BCH Manton Center for Orphan Diseases Research. Participants were randomly assigned to the GenomeConnect survey, Phenotypr survey (the layperson HPO-based tool that we developed), or both. We compared the derived HPO phenotypic profile from each patient with the corresponding Phenotypr survey HPO profile and/or the GenomeConnect survey HPO profile (depending on which survey they completed, or if they completed both), as well as against the clinical-grade, gold-standard HPO profiles developed by Monarch. Multiple semantic similarity methods were used to determine which survey generated HPO terms that best matched the derived HPO profile and clinical gold-standard profiles. Our primary end point was a comparison of the similarity scores between the patient-derived HPO profiles from the GenomeConnect or Phenotypr survey and the simulated HPO profiles overall to see which survey, GenomeConnect or Phenotypr, yielded higher similarity scores on average and a tighter distribution compared with the simulated HPO profiles. We conducted qualitative interviews with a subset of participants to determine which survey participants preferred.

RESULTS

We developed Phenotypr, a layperson HPO-based software application, which uses an autocompletion format (the user starts typing and a menu of options appears starting with the typed letters). We adapted the preexisting GenomeConnect survey, which uses a multiple-choice format, for the study. For 7344 known Mendelian diseases in the Monarch corpus, the diagnostic power (ie, the capability of returning the correct diagnosis) of layperson HPO profiles was greater than that of the GenomeConnect HPO profiles. This was measured by simulating patient responses for each disease, running them through a disease classifier, and plotting a receiving operating characteristic (ROC) curve (eg, the true-positive rate vs the false-positive rate). The layperson subset had an area under the ROC curve (AUROC) of 0.95, and the GenomeConnect subset had an AUROC of 0.91, suggesting that the layperson subset can better distinguish the correct disease. We offered enrollment to 1061 individuals and enrolled 282 (26.5%). Participants completing the GenomeConnect survey generally had a higher median similarity score to their derived disease profile than did those completing the Phenotypr survey; therefore, the GenomeConnect survey was more accurate. However, the Phenotypr survey had a tighter distribution of scores for respondents who completed both surveys and was therefore more precise. We conducted 17 qualitative interviews, and participants generally preferred the GenomeConnect multiple-choice format over the autocomplete Phenotypr format.

CONCLUSIONS

Both the GenomeConnect and Phenotypr surveys were useful in obtaining phenotype data directly from patients. The GenomeConnect survey was more accurate, and the Phenotypr survey was more precise. The use of the layperson HPO, developed as Phenotypr (objective A), could effectively inform diagnosis if performed accurately by patients (objective B). Phenotypr has a large vocabulary of layperson HPO terms and uses an autocomplete method. GenomeConnect maps to tenfold-fewer HPO terms and uses a multiple-choice format. Future work will involve taking the strengths of each mode of survey to develop a combined approach to patient self-phenotyping (objective C).

LIMITATIONS

The number of individuals with any given diagnosis was small, making comparisons between the 2 surveys for any given diagnosis challenging. In addition, the overall variety of types of diseases (eg, the heterogeneity of phenotypic profiles) was limited. These tools should be tested in larger and more-diverse populations and with patients with unknown diagnoses.