An Update on Reported Variants in the Skeletal Muscle <i>α</i>‐Actin (<i>ACTA1</i>) Gene
Bibliographic record
Abstract
The ACTA1 gene encodes skeletal muscle alpha‐actin, which forms the core of the sarcomeric thin filament in adult skeletal muscle. ACTA1 represents one of six highly conserved actin proteins that have all been associated with human disease. The first 15 pathogenic variants in ACTA1 were reported in 1999, which expanded to 177 in 2009. Here, we update on the now 607 total variants reported in LOVD, HGMD, and ClinVar, which includes 343 reported pathogenic/likely pathogenic (P/LP) variants. We also provide suggested ACTA1‐specific modifications to ACMG variant interpretation guidelines based on our analysis of known variants, gnomAD reports, and pathogenicity in other actin isoforms. Using these criteria, we report a total of 447 P/LP ACTA1 variants. From a clinical perspective, the number of reported ACTA1 disease phenotypes has grown from five to 20, albeit with some overlap. The vast majority (74%) of ACTA1 variants cause nemaline myopathy (NEM), but there are increasing numbers that cause cardiomyopathy and novel phenotypes such as distal myopathy. We highlight challenges associated with identifying genotype–phenotype correlations for ACTA1. Finally, we summarize key animal models and review the current state of preclinical treatments for ACTA1 disease. This update provides important resources and recommendations for the study and interpretation of ACTA1 variants.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".