FORGE Canada Consortium: Outcomes of a 2-Year National Rare-Disease Gene-Discovery Project
Bibliographic record
Abstract
Inherited monogenic disease has an enormous impact on the well-being of children and their families. Over half of the children living with one of these conditions are without a molecular diagnosis because of the rarity of the disease, the marked clinical heterogeneity, and the reality that there are thousands of rare diseases for which causative mutations have yet to be identified. It is in this context that in 2010 a Canadian consortium was formed to rapidly identify mutations causing a wide spectrum of pediatric-onset rare diseases by using whole-exome sequencing. The FORGE (Finding of Rare Disease Genes) Canada Consortium brought together clinicians and scientists from 21 genetics centers and three science and technology innovation centers from across Canada. From nation-wide requests for proposals, 264 disorders were selected for study from the 371 submitted; disease-causing variants (including in 67 genes not previously associated with human disease; 41 of these have been genetically or functionally validated, and 26 are currently under study) were identified for 146 disorders over a 2-year period. Here, we present our experience with four strategies employed for gene discovery and discuss FORGE's impact in a number of realms, from clinical diagnostics to the broadening of the phenotypic spectrum of many diseases to the biological insight gained into both disease states and normal human development. Lastly, on the basis of this experience, we discuss the way forward for rare-disease genetic discovery both in Canada and internationally.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".