First step towards a consensus strategy for multi-locus diagnostic testing of imprinting disorders
Bibliographic record
Abstract
BACKGROUND: Imprinting disorders, which affect growth, development, metabolism and neoplasia risk, are caused by genetic or epigenetic changes to genes that are expressed from only one parental allele. Disease may result from changes in coding sequences, copy number changes, uniparental disomy or imprinting defects. Some imprinting disorders are clinically heterogeneous, some are associated with more than one imprinted locus, and some patients have alterations affecting multiple loci. Most imprinting disorders are diagnosed by stepwise analysis of gene dosage and methylation of single loci, but some laboratories assay a panel of loci associated with different imprinting disorders. We looked into the experience of several laboratories using single-locus and/or multi-locus diagnostic testing to explore how different testing strategies affect diagnostic outcomes and whether multi-locus testing has the potential to increase the diagnostic efficiency or reveal unforeseen diagnoses. RESULTS: We collected data from 11 laboratories in seven countries, involving 16,364 individuals and eight imprinting disorders. Among the 4721 individuals tested for the growth restriction disorder Silver-Russell syndrome, 731 had changes on chromosomes 7 and 11 classically associated with the disorder, but 115 had unexpected diagnoses that involved atypical molecular changes, imprinted loci on chromosomes other than 7 or 11 or multi-locus imprinting disorder. In a similar way, the molecular changes detected in Beckwith-Wiedemann syndrome and other imprinting disorders depended on the testing strategies employed by the different laboratories. CONCLUSIONS: Based on our findings, we discuss how multi-locus testing might optimise diagnosis for patients with classical and less familiar clinical imprinting disorders. Additionally, our compiled data reflect the daily life experiences of diagnostic laboratories, with a lower diagnostic yield than in clinically well-characterised cohorts, and illustrate the need for systematising clinical and molecular data.
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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 | 0.191 | 0.164 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.017 | 0.018 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".