A recently-launched web site is able to match patients genetic and phenotype data on rare diseases. The technological breakthrough is uniting the clinicians who are treating them.

Stephanie and Owen Reimer, a Mennonite couple in rural Canada, have been sorely challenged by something they’re doing their best to overcome. Three of their boys died before or shortly after birth from a rare disorder, apparently genetic in origin. Their one child is healthy. They’d like to have another.

Until recently, people like Stephanie and Owen had nothing to go by when planning to get pregnant again. The origin of disorders such as theirs defied definition, and so prenatal testing for the culprit gene was out of the question.

Now, a first-of-its kind web portal is allowing clinicians and geneticists around the world to match unusual symptoms with known mutant genes, and to provide firm counseling to patients in search of answers.

Phenome Central is a collaborative project of Care for Rare Canada and Australia, the US National lnstitute of Health’s Undiagnosed Diseases Program, and RD-Connect, in Europe. Officially launched on February 28, 2014, the web repository compiles anonymized case data on rare diseases, enabling clinicians with only their own case to go by to connect with others who’ve been dealing with something similar.

Pinpointing the Gene

An estimated 350 million people suffer from some 7,000 rare genetic and metabolic disorders, many linked to a single faulty gene. Clinicians might stumble across one of these once in their lifetime, and have no idea what they’re dealing with.

To identify the origin of an apparent genetic disorder, a causative mutant gene has to be pinpointed. With the advent of ultra-rapid or “fast throughput” genome sequencing technologies, our ability to peer into a genome and pick out rogue genes has advanced by leaps and bounds.

Confirming a genetic disorder’s identity also hinges on establishing its key signs and symptoms – its “phenotype.” In the past, phenotypic descriptions have been hindered by the unsystematic and sloppy terminology of attending clinicians.

“We would get one line of text describing it, which was full of abbreviations, full of typos, and really impossible for a computer to understand,” recalls Dr. Michael Brudno, a University of Toronto bioinformatician who set out to compile complementary sets of genotypic and phenotypic data within a single algorithm – Phenome Central.

“As a human I could make it out, but as soon as I tried to develop an algorithm which tries to match the genotype to the phenotype, I need the computer to understand the phenotype. And having a completely nonsensical line of text does not cut it. So what we started doing is trying to figure out how can we enable clinicians to capture phenotypes precisely.”

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