Whole exome and genome sequencing for Mendelian immune disorders: from molecular diagnostics to new disease variant and gene discovery
Bibliographic record
Abstract
Whole exome and whole genome sequencing are next generation sequencing (NGS) applications that enable investigation of all coding variants (around 20 000) or all variants (around 4 million) in the human genome. They provide an extremely powerful tool for detecting variants with an established implication in Mendelian disorders as well as for discovering new disease variants and genes. The large number of variants generated requires elaborate databases, prediction models, and integrated workflows to identify which variants are more likely to contribute to disease. We discuss the whole exome and whole genome options, review the sequencing platforms and variant calling pipelines available for different variant types, and devote most of the review to how genetic variants can be annotated and prioritized to identify the ones likely contributing to disorder. The application focus will be Mendelian disorders; disorders caused by rare or common variants with a more complex genetic architecture will only be discussed briefly. For variant annotation and interpretation, we will concentrate on smaller variants (substitutions, insertions, and deletions), only briefly reviewing structural and copy number variation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".