Diagnostic yield and limitations of whole-genome sequencing for hereditary cerebellar ataxia
Bibliographic record
Abstract
Abstract Less than half of the individuals with hereditary cerebellar ataxia receives a genetic diagnosis. Repeat expansions account for disproportionate number of hereditary cerebellar ataxia and have genetically heterogeneous causes. These genetic loci include ATXN1, ATXN2, ATXN3, CACNA1A, ATXN7, ATXN8OS, ATXN10, PPP2R2B, TBP, ATN1, FMR1, BEAN1, NOP56, GLS, THAP11, GAA-FGF14, ZFHX3, FXN and RFC1. This study aims to assess the yield of short-read whole genome sequencing in the molecular diagnosis of hereditary cerebellar ataxia. We recruited 380 patients (351 probands) from a national ataxia centre in United Kingdom. They underwent short-read whole genome sequencing as a part of the 100 000 Genomes Project. Bioinformatic pipeline of whole genome sequencing include variant prioritization in selected virtual gene panels, customized analysis with a focus on repeat expansions, structural variants and recently reported hereditary cerebellar ataxia genes. All potential genetic variants were reviewed in a multidisciplinary team, and further confirmation tests were performed as appropriate. Whole genome sequencing identified causative variants in 115 (33%) out of 351 probands. We established 46 distinct presumptive molecular diagnoses with the most frequent being SPG7 (n = 22), RFC1 (n = 20) and CACNA1A (n = 10). However, it failed to detect any probands with novel ataxia gene GAA-FGF14, which was subsequently identified on polymerase chain reaction screening in 10 unsolved probands. In conclusion, whole genome sequencing is a useful diagnostic test in hereditary cerebellar ataxia patients and can be used to detect repeat expansions, structural and mitochondrial variants. However, identification of complex structural variants and sizing of large repeat expansions remains a challenge and require alternative molecular testing techniques.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".