Signatures of natural selection and ethnic-specific prevalence of NPC1 pathogenic mutations contributing to obesity and Niemann–Pick disease type C1
Bibliographic record
Abstract
While homozygous pathogenic mutations in the NPC1 gene cause Niemann-Pick type C1 disease, heterozygous mutations cause highly-penetrant obesity. We aimed to investigate the prevalence of NPC1 mutations and their signatures of natural selection in 122,678 exome sequenced participants from six ethnic groups in the Genome Aggregation Database. Pathogenic missense coding mutations were identified by in silico tools and the ClinVar database. Signatures of natural selection were assessed by the probability of NPC1 being loss-of-function mutation intolerant and Z-scores of observed/expected synonymous and non-synonymous mutation ratios. There was no evidence of negative selection observed for synonymous, non-synonymous and loss-of-function mutations. However, there were significant ethnic differences in the prevalence of heterozygous pathogenic NPC1 mutations ranging from 0.56% in Ashkenazi Jewish to 3.26% in African/African Americans (5.8-fold difference). Four homozygous carriers of pathogenic NPC1 mutations were also identified, belonging to the South Asian population. In conclusion, NPC1 mutations are consistent with a model of balanced selection, where heterozygotes and homozygotes have higher and lower reproductive fitness, respectively. Therefore, NPC1 heterozygous mutations may account for a substantial and ethnic-dependent percentage of obesity in the general population, while NPC1 homozygous mutations may be frequent in the South Asian populations and warrants more investigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".