Genome sequencing for detection of pathogenic deep intronic variation: A clinical case report illustrating opportunities and challenges
Bibliographic record
Abstract
Variants in JAM3 have been reported in four families manifesting a severe autosomal recessive disorder characterized by hemorrhagic destruction of the brain, subependymal calcification, and cataracts. We describe a 7-year-old male with a similar presentation found by research-based quad genome sequencing to have two novel splicing variants in trans in JAM3, including one deep intronic variant (NM_032801.4: c.256+1260G>C) not detectable by standard exome sequencing. Targeted sequencing of RNA isolated from transformed lymphoblastoid cell lines confirmed that each of the two variants has a deleterious effect on JAM3 mRNA splicing. The role for genome sequencing as a clinical diagnostic test extends to those patients with phenotypes strongly suggestive of a specific Mendelian disorder, especially when the causal genetic variant(s) are not found by a more targeted approach. Barriers to diagnosis via identification of pathogenic deep intronic variation include lack of laboratory consensus regarding in silico splicing prediction tools and limited access to clinically validated confirmatory RNA experiments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".