Exome sequencing identifies mutations in <i><scp>KIF14</scp></i> as a novel cause of an autosomal recessive lethal fetal ciliopathy phenotype
Bibliographic record
Abstract
Gene discovery using massively parallel sequencing has focused on phenotypes diagnosed postnatally such as well-characterized syndromes or intellectual disability, but is rarely reported for fetal disorders. We used family-based whole-exome sequencing in order to identify causal variants for a recurrent pattern of an undescribed lethal fetal congenital anomaly syndrome. The clinical signs included intrauterine growth restriction (IUGR), severe microcephaly, renal cystic dysplasia/agenesis and complex brain and genitourinary malformations. The phenotype was compatible with a ciliopathy, but not diagnostic of any known condition. We hypothesized biallelic disruption of a gene leading to a defect related to the primary cilium. We identified novel autosomal recessive truncating mutations in KIF14 that segregated with the phenotype. Mice with autosomal recessive mutations in the same gene have recently been shown to have a strikingly similar phenotype. Genotype-phenotype correlations indicate that the function of KIF14 in cell division and cytokinesis can be linked to a role in primary cilia, supported by previous cellular and model organism studies of proteins that interact with KIF14. We describe the first human phenotype, a novel lethal ciliary disorder, associated with biallelic inactivating mutations in KIF14. KIF14 may also be considered a candidate gene for allelic viable ciliary and/or microcephaly phenotypes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".