Use of a Comprehensive 66‐Gene Cholestasis Sequencing Panel in 2171 Cholestatic Infants, Children, and Young Adults
Bibliographic record
Abstract
OBJECTIVES: Cholestasis is caused by a wide variety of etiologies, often genetic in origin. Broad overlap in clinical presentations, particularly in newborns, renders prioritizing diagnostic investigations challenging. In this setting, a timely, comprehensive assessment using a multigene panel by a clinical diagnostic laboratory would likely prove useful. We summarize initial findings from a testing program designed to discover genetic causes of cholestasis. METHODS: A neonatal/adult sequencing panel containing 66 genes (originally 57; nine added March 2017) relevant to cholestasis was used. A broad range of eligible patients were enrolled with current/history of cholestasis without an identified cause, or unexplained chronic liver disease. DNA sequencing utilized a custom-designed capture library, and variants were classified and reported as benign, likely benign, variant of unknown significance (VOUS), likely pathogenic (LP), or pathogenic (P), according to the clinical interpretation workflow at EGL Genetics (Tucker, GA). RESULTS: A total of 2433 samples were submitted between February 2016 and December 2017; 2171 results were reported. Median turnaround time was 21 days. Results from the 2171 subjects (57% <1 year old) included 583 P variants, 79 LP variants, and 3117 VOUS; 166 P/LP variants and 415 VOUS were novel. The panel's overall diagnostic yield was 12% (n = 265/2171) representing 32 genes. The top five genetic diagnoses for the group, in order: JAG1 + NOTCH2 (Alagille syndrome), ABCB11, SERPINA1, ABCB4, and POLG. CONCLUSIONS: These findings support the utility of comprehensive rapid multigene testing in diagnosing cholestasis and highlight the evolving understanding of genetic variants contributing to the pathogenesis of cholestasis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".