Hello, all! Greetings from your new Monarch blogger! I’m Lilly, and I will be taking over this blog space to keep you updated about exciting developments being done at the Monarch Initiative. I recently completed my PhD in neuroscience from Oregon Health & Science University, and I am very excited to work with the Monarch team to further study the relationships between genes, phenotypes, and diseases. During my PhD, I studied how the brain’s innate immune system responds after an injury. Interesting fact: our brain’s immune system is separated from the rest of our body by the blood brain barrier. While white blood cells (leukocytes) patrol our bodies for disease and foreign objects, glial cells defend the brain. Glial cells also have various other functions, including creating myelin (insulation for neurons), and glial cell malfunction can lead to numerous diseases, such as Multiple Sclerosis . I researched how glial cells react to brain injury by performing experiment...

Genomics, electronic health records, participant-provided data, sensors, and mobile health technologies can all contribute to personalized medicine. However, we currently cannot achieve statistical correlations amongst these almost unlimited number of parameters that will be collected by the PMI and the depth of mechanistic understanding that will be required for treatment stratification and the development of novel, targeted therapies. The promise of personalized medicine requires deep knowledge of the relationships between genotype, phenotype, and environmental variables - but we simply don’t have enough data. For example, in the ExAC database there are 3,230 genes with near-complete depletion of predicted protein-truncating variants, where 72% of these genes having no currently established human disease phenotype. If we look across organisms, we see that of these 2311 genes with unknown causal phenotypes/diseases, 88% have an associated phenotype in an ortholog, with 56% having or...

Monarch's Phenogrid  phenotype comparison tool is now available for phenotype profile comparisons on the International Mouse Phenotype Consortium ( mousephenotype.org ) site. Visitors to the IMPC site can find Phenogrid comparisons on disease pages such as the entry for  Pfeiffer Syndrome .  Under the mouse models, you will see a plus ("+") symbol at the end of each row: Clicking on that plus sign will reveal the Phenogrid widget showing mouse strains  with phenotype profiles similar to those of the gene in question. For Pfeiffer, the grid will reveal multiple variants of Fgfr1, illustrating differences in phenotypes seen across these strains.  Mousing over the cells will show the details of the match between the given phenotype and the model: Mousing over the model label will lead to the display of a dialog box with listings of the specific genotype of the model and relevant phenotypes, all of which can be clicked to access  pages with additional ...

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...

Peter Robinson from Charite - Universitätsmedizin Berlin, received the prestigious European Rare Disease Organization (EURODIS) Scientific Award on “ Rare Disease Day ”, February 23, 2016. EURODIS is a non-governmental organization that provides an alliance for patients with rare diseases in Europe and beyond. This Scientific Award recognizes his scientific excellence and his support of the patient community through the work he does with the Human Phenotype Ontology and the Monarch Initiative , the world’s largest gene-phenotype knowledgebase. Dr. Robinson attended the award ceremony in Brussels, Belgium, where he received the award from the distinguished guest, HRH Princess Astrid of Belgium. Only 8 awards are given, and there were 350 nominations this year. We at Monarch could not be more proud!

Monarch is co-hosting a NIH Symposium titled “Linking Disease Model Phenotypes to Human Conditions” on September 10-11, 2015 at the Fishers Lane Auditorium, NIH, Rockville, MD.  The purpose of the meeting is to convene a colloquium on the current status of Phenomics and its role in closing the gap that exists between biomedical research and clinical medical practice. The wealth of whole organism, cellular, and molecular data generated in the research laboratory must be translated into clinically relevant knowledge that enables the physician to make the best possible treatment decisions. Phenomics is gaining momentum due to the availability of the complete genomes for many organisms as well as higher throughput methods to genetically modify model organism genomes and observe and record phenotypes. Disease models comprise some of the most important tools of biomedical research. The efficacy of the use of disease models is based upon the principles of evolutionary conservatio...

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. ...