Phenotypic heterogeneity associated with <scp><i>KIF21A</i></scp>: Two new cases and review of the literature
Bibliographic record
Abstract
Our understanding of genetic and phenotypic heterogeneity associated with the clinical spectrum of rare diseases continues to expand. Thorough phenotypic descriptions and model organism functional studies are valuable tools in dissecting the biology of the disease process. Kinesin genes are well known to be associated with specific disease phenotypes and a subset of kinesin genes, including KIF21A, have been associated with more than one disease. Here we report two patients with KIF21A variants identified by exome sequencing; one with biallelic variants, supporting a novel KIF21A related syndrome with recessive inheritance and the second report of this condition, and another with a heterozygous de novo variant allele representing a phenotypic expansion of the condition described to date. We provide detailed phenotypic information on both families, including a novel neuropathology finding of neuroaxonal dystrophy associated with biallelic variants in KIF21A. Additionally, we studied the dominant variant in Saccharomyces cerevisiae to assess variant pathogenicity and found that this variant appears to impair protein function. KIF21A associated disease has mounting evidence for phenotypic heterogeneity; further patients and study of an allelic series are required to define the phenotypic spectrum and further explore the molecular etiology for each of these conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".