Genomic characteristics of miscarriage copy number variants
Bibliographic record
Abstract
Studies of copy number variants (CNVs) in miscarriages are rare in comparison to post-natal cases with developmental abnormalities. The overall characteristics of miscarriage CNVs (size, gene content and function) are therefore largely unexplored. Our goal was to assess and compare the characteristics of CNVs identified in 101 euploid miscarriages from four high-resolution array studies that documented both common miscarriage CNVs (i.e. CNVs found in controls from the Database of Genomic Variants, DGV) and rare miscarriage CNVs (not reported in DGV). Our miscarriage analysis included 24 rare CNVs with 93 genes, and 372 common CNVs (merged into 119 common CNV regions; CNVRs) with 354 genes. The rare and common CNVs were comparable in size (median size of ∼ 0.16 and 0.14 Mb, respectively); however, rare CNVs showed a significantly higher gene density, with 56 genes/Mb in rare and 24 genes/Mb in common CNVs (P = 0.03). Rare CNVs also had two times more genes with mouse knock-out models which were reported for 42% of rare and 19% of common CNV genes. No specific pathway enrichment was noted for 24 rare CNV genes, but common CNV genes showed significant enrichment in genes from immune-response related pathways and pregnancy/reproduction-related biological processes. Our analysis of CNVs from euploid miscarriages suggests that both rare and common CNVs could have a role in miscarriage by impacting pregnancy-related genes or pathways. Cataloguing of all CNVs and detailed description of their characteristics (e.g. gene content, genomic breakpoints) is desirable in the future for better understanding of their relevance to pregnancy loss.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".