A systematic analysis of mitochondrial aminoacyl tRNA synthetase variants in a rare disease cohort
Bibliographic record
Abstract
Mitochondrial aminoacyl-tRNA synthetases (mt-aaRSs) are a group of proteins encoded by nuclear DNA that play a crucial role in mitochondrial protein synthesis. Mitochondrial diseases caused by mt-aaRS variants are phenotypically heterogenous but often present with significant neurological features such as childhood-onset encephalopathy and seizures. As such, these conditions are a diagnostic challenge. We present an approach that systematically quantifies phenotypic similarity of individuals with an mt-aaRS variant to published cases, to aid variant interpretation, in RD-Connect-a large Europe-wide rare disease cohort. Across 98 individuals with a mt-aaRS gene of interest, we prioritised 38 individuals with 63 variants following bioinformatic and manual analyses. We additionally reviewed Exomiser prioritisation using a pre-defined gene list for neurological disorders within the RD-Connect Genome-Phenome Analysis Platform (GPAP). We were able to generate likely diagnoses in 11 individuals and VUS findings in 13 individuals, following careful phenotype similarity analysis using a phenotype-genotype dataset generated from 234 published individuals. Four of these 24 individuals did not have an Exomiser-ranked gene variant in the GPAP. Therefore, this approach, using individual-level curated phenotype-genotype data to support variant interpretation, can highlight potentially significant variants that may not be captured by current pipelines. This workflow can be replicated in other heterogeneous rare diseases to support clinical practice.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".