Chromosomal regions containing high-density and ambiguously mapped putative single nucleotide polymorphisms (SNPs) correlate with segmental duplications in the human genome
Bibliographic record
Abstract
We have explored the National Center for Biotechnology Information (NCBI) single nucleotide polymorphisms (SNPs) database for a correlation between the density of putative SNPs, as well as SNPs that map to different chromosomal locations (ambiguously mapped SNPs), and segmental duplications of DNA in chromosome regions involved in genomic disorders. A high density of SNPs (14.4 and 12.4 SNPs per kb) was detected in the low copy repeats (LCRs) responsible for the chromosome 17p12 duplication and deletion that cause peripheral neuropathies. None of the SNPs at the PMP22 gene were ambiguously mapped, but 93% of the SNPs at LCRs mapped on both LCR copies, indicating that they are in fact variants in paralogous sequences. Similarly, a high SNP density was found in the LCR regions flanking the neurofibromatosis type 1 (NF1) gene, with 80% of SNPs mapping on both LCR copies. A high density of SNPs was found within LCR sequences involved in the deletions that mediate contiguous gene syndromes on chromosomes 7q11, 15q11-q13 and 22q11. We have analyzed the whole sequence of chromosome 22, which contains 14% of ambiguously mapped SNPs, and have found a good correlation between these SNPs and segmental duplications detected by BLAST analysis. We have identified several segments of ambiguously mapped SNPs, four corresponding to LCRs involved in the chromosome 22q11 microdeletion syndromes. Our data indicate that most SNPs in LCR segments are in fact paralogous sequence variants (PSVs), and suggest that a significant proportion of the SNPs in the NCBI database correspond to PSVs within segmental duplications of the human genome sequence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".