Robust genotyping tool for autosomal recessive type of limb-girdle muscular dystrophies
Bibliographic record
Abstract
BACKGROUND: Limb-girdle muscular dystrophies are characterized by predominant involvement of the shoulder and pelvic girdle and trunk muscle groups. Currently, there are 31 genes implicated in the different forms of limb-girdle muscular dystrophies, which exhibit similar phenotypes and clinical overlap; therefore, advanced molecular techniques are required to achieve differential diagnosis. METHODS: We investigated 26 patients from Latvia and 34 patients from Lithuania with clinical symptoms of limb-girdle muscular dystrophies, along with 565 healthy unrelated controls from general and ethnic populations using our developed test kit based on the Illumina VeraCode GoldenGate genotyping platform, Ion AmpliSeq Inherited Disease Panel and direct sequencing of mutations in calpain 3 (CAPN3), anoctamin 5 (ANO5) and fukutin related protein (FKRP) genes. RESULTS: Analysis revealed a homozygous CAPN3 c.550delA mutation in eight patients and three heterozygous variants in controls: dysferlin (DYSF) c.5028delG, CAPN3 c.2288A > G, and FKRP c.135C > T. Additionally, three mutations within FKRP gene were found: homozygous c.826C > A, and two compound - c.826C > A/c.404_405insT and c.826C > A/c.204_206delCTC mutations, and one mutation within CLCN1 gene - c.2680C > T p.Arg894Ter. ANO5 c.191dupA was not present. CONCLUSIONS: Genetic diagnosis was possible in 12 of 60 patients (20%). The allele frequency of CAPN3 gene mutation c.550delA in Latvia is 0.0016 and in Lithuania - 0.0029. The allele frequencies of CAPN3 gene mutation c.2288A > G and DYSF gene mutation c.4872delG are 0.003.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".