De novo mutations, genetic mosaicism and human disease
Bibliographic record
Abstract
Mosaicism—the existence of genetically distinct populations of cells in a particular organism—is an important cause of genetic disease. Mosaicism can appear as de novo DNA mutations, epigenetic alterations of DNA, and chromosomal abnormalities. Neurodevelopmental or neuropsychiatric diseases, including autism—often arise by de novo mutations that usually not present in either of the parents. De novo mutations might occur as early as in the parental germline, during embryonic, fetal development, and/or post-natally, through ageing and life. Mutation timing could lead to mutation burden of less than heterozygosity to approaching homozygosity. Developmental timing of somatic mutation attainment will affect the mutation load and distribution throughout the body. In this review, we discuss the timing of de novo mutations, spanning from mutations in the germ lineage (all ages), to post-zygotic, embryonic, fetal, and post-natal events, through aging to death. These factors can determine the tissue specific distribution and load of de novo mutations, which can affect disease. The disease threshold burden of somatic de novo mutations of a particular gene in any tissue will be important to define.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".