Mutational signature analyses in multi-child families reveal sources of age-related increases in human germline mutations
Bibliographic record
Abstract
Whole-genome sequencing studies of parent-offspring trios have provided valuable insights into the potential impact of de novo mutations (DNMs) on human health and disease. However, the molecular mechanisms that drive DNMs are unclear. Studies with multi-child families can provide important insight into the causes of inter-family variability in DNM rates but they are highly limited. We characterized 2479 de novo single nucleotide variants (SNVs) in 13 multi-child families of Mexican-American ethnicity. We observed a strong paternal age effect on validated de novo SNVs with extensive inter-family variability in the yearly rate of increase. Children of older fathers showed more C > T transitions at CpG sites than children from younger fathers. Validated SNVs were examined against one cancer (COSMIC) and two non-cancer (human germline and CRISPR-Cas 9 knockout of human DNA repair genes) mutational signature databases. These analyses suggest that inaccurate DNA mismatch repair during repair initiation and excision processes, along with DNA damage and replication errors, are major sources of human germline de novo SNVs. Our findings provide important information for understanding the potential sources of human germline de novo SNVs and the critical role of DNA mismatch repair in their genesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".