Variation in crossover interference levels on individual chromosomes from human males
Bibliographic record
Abstract
Crossovers (COs) generated by homologous recombination ensure the proper segregation of chromosomes during meiosis. COs exhibit interference, which leads to widely spaced COs along chromosomes. Strong positive CO interference has been found in humans. However, little is known about the extent of human CO interference. In this study, variations in CO interference over the entire human genome and among individuals were analyzed by immunofluorescence combined with fluorescence in situ hybridization of testicular biopsies from 10 control men. These methods allow for direct identification of the frequency and location of COs in specific chromosomes of pachytene cells. The strength of CO interference was estimated by fitting the frequency distribution of inter-CO distances to the gamma model. Positive interference among CO on chromosomes was observed in these men, and the strength of inter-arm interference was significantly stronger than that for intra-arm CO. In addition, interference was observed to act across the centromere. Significant inter-individual and inter-chromosomal variations in the levels of interference were found, with smaller chromosomes exhibiting stronger interference. Discontinuous chromosome regions (gaps) and unsynapsed chromosome regions (splits) in chromosome 9 had both cis and trans effects on CO interference levels. This is the first report that the interference level varies significantly across the whole genome and that, at least in the human male, anomalies in chromosome synapsis play an important role in altering CO interference levels.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".