Targeted sequence capture and GS-FLX Titanium sequencing of 23 hypertrophic and dilated cardiomyopathy genes: implementation into diagnostics
Bibliographic record
Abstract
BACKGROUND: Genetic evaluation of cardiomyopathies poses a challenge. Multiple genes are involved but no clear genotype-phenotype correlations have been found so far. In the past, genetic evaluation for hypertrophic (HCM) and dilated (DCM) cardiomyopathies was performed by sequential screening of a very limited number of genes. Recent developments in sequencing have increased the throughput, enabling simultaneous screening of multiple genes for multiple patients in a single sequencing run. OBJECTIVE: Development and implementation of a next generation sequencing (NGS) based genetic test as replacement for Sanger sequencing. METHODS AND RESULTS: In order to increase the number of genes that can be screened in a shorter time period, we enriched all exons of 23 of the most relevant HCM and DCM related genes using on-array multiplexed sequence capture followed by massively parallel pyrosequencing on the GS-FLX Titanium. After optimisation of array based sequence capture it was feasible to reliably detect a large panel of known and unknown variants in HCM and DCM patients, whereby the unknown variants could be confirmed by Sanger sequencing. CONCLUSIONS: The rate of detection of (pathogenic) variants in both HCM and DCM patients was increased due to a larger number of genes studied. Array based target enrichment followed by NGS showed the same accuracy as Sanger sequencing. Therefore, NGS is ready for implementation in a diagnostic setting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".