Blueprint Genetics joins FDNA’s Year of Discovery, uniting clinicians globally with deep learning technology to make rare disease discoveries

Published on February 27, 2017

We are inviting clinicians worldwide to participate in FDNA’s Year of Discovery. Clinicians are invited to submit patient photos, diagnoses, and phenotypes to the HIPAA-compliant Face2Gene CLINIC system for analysis. March is the month for RASopathies. Blueprint will match each RASopathy case uploaded during the month with a $1 donation to related advocacy.
The Face2Gene deep learning technology will de-identify and analyze the shared cases to identify new phenotypes, facial characteristics and genes that are associated with rare diseases—advancing our understanding and hope for the future.

FDNA

How it works?

  1. Register for a free Face2Gene account at Face2Gene.com or through the app on your mobile device
  2. Login
  3. Add a new case
  4. Upload the patient photo
  5. Enter the patient information and phenotypic traits
  6. Confirm the patient’s clinical and/or molecular diagnosis

Get a more detailed overview at www.Face2Gene.com/tutorial

 

In total Blueprint Genetics will be sponsoring three months: March, May and September.

  • March – RASopathies
  • May – Metabolic conditions, including storage disorders
  • September – Craniosynostoses and Craniofacial conditions

Blueprint Genetics is sponsoring a $1 donation to related advocacy groups, up to $2,500 per month. Cases from other rare diseases can be shared as well during 2017 and they will be included in the search for discoveries as resources permit. Find the whole discovery schedule and get involved at FDNA.com/YearOfDiscovery

 

 

 

Blueprint Genetics & FDNA

Like this:

News

Blueprint Genetics is maximising diagnostic yield by adding all clinically actionable non-coding variants into our Panels

Published on September 13, 2017

Gene Panels have revolutionised clinical diagnostic testing. However, for proportion of patients, sequence information restricted to exons and exon-intron boundaries fails to identify the genetic cause of the disease. Disease-causing non-coding variants may include for example deep intronic variants that create cryptic splice sites that result in aberrant mRNA transcripts.

Read more

Subscribe to our newsletter