Skip to main content

Monarch teaches at the International Summer School for Rare Disease Registries

Last week, I had the pleasure of teaching at the National Centre for Rare Diseases hosted by the Istituto Superiore di Sanità and Dr. Domenica Taruscio. This rare disease registry course is in its second year, and is focused on exposing the maintainers of rare disease registries various aspects of registry planning and management. I was very impressed with the specific way in which this course was run. The week started with a discussion of the different types of registries (aims, study design, data sources), management sustainability, and clinical outcomes analysis. This was followed by an innovative collaborative learning exercise in the afternoon, where the participants were broken up into three groups. The collaborative learning focused on positive interdependence, individual accountability, face-to-face interaction, group processing and exercise of small-group interpersonal skills - all skills needed to realize a quality registry resource in addition to simply being a quality pedagogical approach. Each group had a different rare disease scenario that they had to develop methods and strategies against using what they had learned in the morning session. On each of the following mornings for the rest of the week, they would learn new content such as reference standards and catalogues, coding of rare disease, omics links with biobanks, epidemiologic analyses and confounders, sample stratification, patient unique identifiers, quality assurance methods, data reporting and dissemination and informed consent. Each afternoon, they would then apply these themes to their ongoing scenarios such that the scenarios developed into robust full-fledged registry plans by the end of the week. The teamwork was amazing, as was the instructor engagement throughout the process.

We capped the week off with a Monarch presentation on "The application of the Human Phenotype Ontology" (HPO), where we discussed why rare disease phenotyping needs something more than standard clinical coding systems can provide. Many rare disease phenotypes are sprinkled throughout the literature and clinical notes in completely non-computable ways. The HPO was designed to address this problem and provide a structure on which to perform bioinformatics analyses. Phenotype comparisons can be between patients and known diseases, as shown in our recent paper where we used the HPO to help diagnose undiagnosed patients. Phenotype comparisons can also be across species as well, to aid candidate prioritization in tools such as Exomiser. We also discussed the Global Alliance for Genomics and Health Matchmaker exchange, and how the HPO was being used to identify cohorts in tools such as PhenomeCentral. Finally, we ended with a summary of tools being developed by Monarch to support quality assurance of phenotype data to aid clinicians during the course of their phenotyping. We believe that the efforts that Monarch is making to define an exchange standard for rare disease phenotyping will be of great value to the rare disease registry communities and are looking forward to working with them further on their data publication.

Popular posts from this blog

How to annotate a patient's phenotypic profile

How to annotate a patient's phenotypic profile using PhenoTips and the Human Phenotype Ontology PurposeWe have observed that performance of computational search algorithms within and across species improves if a comprehensive list of phenotypic features is recorded. It is helpful if the person annotating thinks of the set of annotations as a query against all known phenotype profiles. Therefore, the set of phenotypes chosen for the annotation must be as specific as possible, and represent the most salient and important observable phenotypes. Towards this end, Monarch has been asked to provide guidance on how to create a quality patient profile using the Human Phenotype Ontology (HPO). Below we detail our annotation guidelines for use in the PhenoTips application, our partner organization. 

The guidelines can also be considered more generically so as to be applicable to any annotation effort using HPO or even using other phenotype ontologies. The annotations should be limited to th…

Why the Human Phenotype Ontology?

We've often been asked, why should we use the Human Phenotype Ontology to describe patient phenotypes, rather than a more widely-used clinical vocabulary such as ICD or SNOMED? Here are the answers to some of these frequently asked questions:

1. We should use what other big NIH projects, like ClinVar, are using.

ClinVar is using HPO terms to describe phenotypes. This is done in collaboration with MedGen, which has imported HPO terms. Here is an example:

There are now many bioinformatics tools that use the HPO to empower exome diagnostics. The Monarch team has published two of these recently

1) Exomiser (Robinson et al., 2014 Genome Res.) => For discovering new disease genes via model organism data, several successful use cases at UDP and elsewhere

2) PhenIX (Zemojtel et al., 2014 Science Translational Medicine) => For clinical diagnostics of “difficult” cases. This paper was on Russ Altman's year in review at AMIA this year.

Also, a num…

Finally, a medical terminology that patients, doctors, and machines can all understand.

By Nicole Vasilevsky, Mark Engelstad, Erin Foster, Julie McMurry, Chris Mungall, Peter Robinson, Sebastian Köhler, Melissa Haendel
For many patients with rare and undiagnosed diseases, getting an accurate diagnosis, or even finding the appropriate experts is a long and winding road. To accelerate and facilitate this process, we developed a medical vocabulary (“HPO”) which is comprised of 12,000 terms that doctors can use to codify the precise and distinct observations about patients and their conditions. The HPO is structured in a way that enables machines to intelligently compare a patient’s profile with what scientists worldwide have already uncovered about diseases and their genetic causes.
Until now, most of the HPO labels and synonyms were composed of clinical terms unfamiliar to patients. For example, a patient may know they are ‘color-blind’, but may not be familiar with the clinical term ‘Dyschromatopsia’. This is why we developed a layer of 5,000 corresponding terms that can b…