Variable expressivity and mutation databases: The androgen receptor gene mutations database
Bibliographic record
Abstract
For over 50 years genetics has presumed that variations in phenotypic expression have, for the most part, been the result of alterations in genotype. The importance and value of mutation databases has been based on the premise that the same gene or allelic variation in a specific gene that has been proven to determine a specific phenotype, will always produce the same phenotype. However, recent evidence has shown that so called "simple" Mendelian disorders or monogenic traits are often far from simple, exhibiting phenotypic variation (variable expressivity) that cannot be explained solely by a gene or allelic alteration. The AR gene mutations database now lists 25 cases where different degrees of androgen insensitivity are caused by identical mutations in the androgen receptor gene. In five of these cases the phenotypic variability is due to somatic mosaicism, that is, somatic mutations that occur in only certain cells of androgen-sensitive tissue. Recently, a number of other cases of variable expressivity have also been linked to somatic mosaicism. The impact of variable expressivity due to somatic mutations and mosaicism on mutation databases is discussed. In particular, the effect of an organism exhibiting genetic heterogeneity within its tissues, and the possibility of an organism's genotype changing over its lifetime, are considered to have important implications for mutation databases in the future.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".