ClinVar variant-disease associations added

We have added ClinVar variant-disease associations into our database and first released into the Monarch Initiative portal in November, 2014. This new data accompanies previously incorporated ClinVar gene-disease associations (without the specificity of the variations). This initially includes 113,543 SNP, SNV, CNV (and other major rearrangements), linked to 13,591 genes and 11,154 diseases and phenotypes. The associations are also coupled to the original submitters and publications where the variations are reported. The data will be updated approximately monthly.

You can read more about our data sources here.

How to annotate a patient's phenotypic profile

How to annotate a patient's phenotypic profile using PhenoTips and the Human Phenotype Ontology Purpose We 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 b...

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: http://www.ncbi.nlm.nih.gov/medgen/504827 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. ...

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 tha...

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