Assessing the diagnostic impact of blood transcriptome profiling in a pediatric cohort previously assessed by genome sequencing
Bibliographic record
Abstract
Despite advances in genome sequencing, many individuals with rare genetic disorders remain undiagnosed. Transcriptional profiling via RNA-seq can reveal functional impacts of DNA variants and improve diagnosis. We assessed blood-derived RNA-seq in the largely undiagnosed SickKids Genome Clinic cohort (n = 134), which has been subjected to multiple analyses benchmarking the utility of genome sequencing. Our RNA-centric analysis identifies gene expression outliers, aberrant splicing, and allele-specific expression. In one-third of diagnosed individuals (20/61), RNA-seq reinforced DNA-based findings. In 2/61 cases, RNA-seq revised diagnoses (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). Additionally, ~7% (5/73) of undiagnosed cases had at least one plausible candidate gene identified. This study highlights both the benefits and limitations of whole-blood RNA profiling in refining genetic diagnoses and uncovering novel disease mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".