1000 human genomes carry widespread signatures of GC biased gene conversion
Bibliographic record
Abstract
BACKGROUND: GC-Biased Gene Conversion (gBGC) is one of the important theories put forward to explain profound long-range non-randomness in nucleotide compositions along mammalian chromosomes. Nucleotide changes due to gBGC are hard to distinguish from regular mutations. Here, we present an algorithm for analysis of millions of known SNPs that detects a subset of so-called "SNP flip-over" events representing recent gBGC nucleotide changes, which occurred in previous generations via non-crossover meiotic recombination. RESULTS: This algorithm has been applied in a large-scale analysis of 1092 sequenced human genomes. Altogether, 56,328 regions on all autosomes have been examined, which revealed 223,955 putative gBGC cases leading to SNP flip-overs. We detected a strong bias (11.7% ± 0.2% excess) in AT- > GC over GC- > AT base pair changes within the entire set of putative gBGC cases. CONCLUSIONS: On average, a human gamete acquires 7 SNP flip-over events, in which one allele is replaced by its complementary allele during the process of meiotic non-crossover recombination. In each meiosis event, on average, gBGC results in replacement of 7 AT base pairs by GC base pairs, while only 6 GC pairs are replaced by AT pairs. Therefore, every human gamete is enriched by one GC pair. Happening over millions of years of evolution, this bias may be a noticeable force in changing the nucleotide composition landscape along chromosomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".