<i>KIF1A</i> novel frameshift variant p.(Ser887Profs*64) exhibits clinical heterogeneity in a Pakistani family with hereditary sensory and autonomic neuropathy type IIC
Bibliographic record
Abstract
Background: Hereditary sensory and autonomic neuropathies (HSANs) are rare heterogeneous group of neurological disorders caused by peripheral nerve deterioration. The HSANs sub-clinical classes have clinical and genetic overlap which often lead to misdiagnosis. In the present study a Pakistani family with five affected members suffering from severe neuropathy were genetically analyzed to identify the disease causative element in the family.Methods: Genome wide high-density single nucleotide polymorphism (SNP) microarray analysis was carried out followed by whole exome sequencing of the affected proband and another affected sibling. Shared homozygous regions in all severely affected members were identified through homozygosity mapping approach.Results: The largest homozygous region of 14.1 Mb shared by the five severely affected members of the family was identified on chromosome 2. Subsequent exome sequencing identified a novel single nucleotide deletion c.2658del; p.(Ser887Profs*64) in KIF1A. Segregation analysis revealed that this mutation was homozygous in all five affected individuals of the family with severe clinical manifestation, while members of the family that were heterozygous carriers shared abnormal skin features (scaly skin) only with the homozygous affected members.Conclusions: A novel frameshift mutation p.(Ser887Profs*64) in KIF1A is the potential cause of severe HSANIIC in a Pakistani family along with incomplete penetrance in mutation carriers. We demonstrate that using a combination of different techniques not only strengthens the gene finding approach but also helps in proper sub-clinical characterization along with identification of mutated alleles exhibiting incomplete penetrance leading to intrafamilial clinical variability in HSAN group of inherited diseases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".