Genetic drivers and clinical consequences of mosaic chromosomal alterations in 1 million individuals
Bibliographic record
Abstract
Abstract Mosaic chromosomal alterations of the autosomes (aut-mCAs) are large structural somatic mutations which cause clonal hematopoiesis and increase cancer risk. Here, we detected aut-mCAs in 1,252,761 participants across four biobanks. Through integrative analysis of the minimum critical region and inherited genetic variation, we nominated candidate driver genes for each aut-mCA, prioritizing proto-oncogenes within recurrently gained regions and tumor suppressors within recurrently lost regions. We identified three novel inherited risk loci in MAD1L1 , TCL1A , and ATP2A3 that modulate aut-mCA risk and four novel aut-mCA- specific loci. We found specific aut-mCAs are associated with cardiovascular, cerebrovascular, or kidney disease incidence. High-risk aut-mCAs were associated with elevated plasma protein levels of therapeutically actionable targets: NPM1, PARP1, and TACI. Participants with multiple high-risk features such as high clonal fraction, more than one aut-mCA, and abnormal red cell morphology had a 50% cumulative incidence of blood count abnormalities over 2 years. Leveraging inherited variation, we provide evidence supporting a role for specific autosomal mCAs in chronic lymphocytic leukemia development. Together, our findings provide a framework integrating somatic mosaicism, germline genetics, and clinical phenotypes to identify individuals who could benefit from preventative interventions.
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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 | 0.000 | 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) | 0.003 | 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".