Assessing the diagnostic impact of blood transcriptome profiling in a pediatric cohort previously assessed by genome sequencing
Bibliographic record
Abstract
ABSTRACT Despite advances in diagnostic testing and genome sequencing, the majority of individuals with rare genetic disorders remain undiagnosed. As a complement to genome sequencing, transcriptional profiling can provide insight into the functional consequences of DNA variants on RNA transcript expression and structure. Here we assessed the utility of blood derived RNA-seq in a well-studied, but still mostly undiagnosed, cohort of individuals who enrolled in the SickKids Genome Clinic study. This cohort was established to benchmark the ability of genome sequencing technologies to diagnose genetic diseases and has been subjected to multiple analyses. We used RNA-seq to profile whole blood RNA expression from all probands for whom a blood sample was available (n=134). Our RNA-centric analysis included differential gene expression, alternative splicing, and allele specific expression. In one third of the diagnosed individuals (20/61), RNA-seq provided additional evidence supporting the pathogenicity of the variant found by prior DNA-based analyses. In 2/61 cases, RNA-seq changed the GS-derived genetic diagnosis ( EPG5 to LZTR1 in an individual with a Noonan syndrome-like disorder) and discovered an additional relevant gene ( CEP120 in addition to SON in an individual with ZTTK syndrome). In ∼7% (5/73) of the undiagnosed participants, RNA-seq provided at least one plausible, potentially diagnostic candidate gene. This study illustrates the benefits and limitations of using whole-blood RNA profiling to support existing molecular diagnoses and reveal candidate molecular mechanisms underlying undiagnosed genetic disease.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".