Hotspots of Large Rare Deletions in the Human Genome
Bibliographic record
Abstract
Background: We have examined the genomic distribution of large rare autosomal deletions in a sample of 440 parentparent-child trios from the Quebec founder population (QFP) which was recruited for a study of Attention Deficit Hyperactivity Disorder. Methodology/Principal Findings: DNA isolated from blood was genotyped on Illumina Hap300 arrays. PennCNV combined with visual evaluation of images generated by the Beadstudio program was used to determine deletion boundary definition of sufficient precision to discern independent events, with near-perfect concordance between parent and child in about 98 % of the 399 events detected in the offspring; the remaining 7 deletions were considered de novo. We defined several genomic regions of very high deletion frequency (‘hotspots’), usually of 0.4–0.6 Mb in length where independent rare deletions were found at frequencies of up to 100 fold higher than the average for the genome as a whole. Five of the 7 de novo deletions were in these hotspots. The same hotspots were also observed in three other studies on members of the QFP, those with schizophrenia, with endometriosis and those from a longevity cohort. Conclusions/Significance: Nine of the 13 hotspots carry one gene (7 of which are very long), while the rest contain no known genes. All nine genes have been implicated in disease. The patterns of exon deletions support the proposed roles for some of these genes in human disease, such as NRXN1 and PARKIN, and suggest limited roles or no role at all, for others,
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".