Long-read genome sequencing increases genomic yield in congenital heart disease
Bibliographic record
Abstract
ABSTRACT Congenital heart disease (CHD) is the most common birth defect. We performed Illumina short-read genome sequencing (GS) of 1,101 probands, which identified a genetic cause in 16% of cases. We performed PacBio long-read GS in 43 genotype-elusive patients. Paired analysis revealed higher detection with long-read GS of single nucleotide variants, deletions, duplications, and insertions as well as fewer false-positives for indels and inversions. Long-read GS had higher coverage of nine CHD genes, but a low sequencing depth (<10×) in intragenic regions of four of these genes. Long-read GS was better able to resolve complex structural variants and the size of large repeat expansions in 59 known disease-causing regions. This included a complex de novo structural variant upstream of ZEB2 that was only resolved with long-read GS in a patient with extra-cardiac phenotype overlapping Mowat-Wilson syndrome. Long-read GS may provide an option in CHD patients who remain genotype-elusive on short-read GS.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".