Integration of Genome and Epigenetic Testing in the Diagnostic Evaluation of Developmental Delay: Differentiating Börjeson–Forssman–Lehmann (BFLS) and White–Kernohan (WHIKERS) Syndromes
Bibliographic record
Abstract
Background: More than 1500 genes are associated with developmental delay and intellectual disability, with variants in many of these genes contributing to a shared phenotype. The discovery of variants of uncertain significance (VUS) found in these genes during genetic testing can lead to ambiguity and further delay in diagnosis and medical management. Phenotyping, additional genetic testing, and functional studies can all add valuable information to help reclassify these variants. Here we demonstrate the clinical utility of epigenetic signatures in prioritizing variants of uncertain significance in genes associated with developmental delay (DD) and intellectual disability (ID). Methods: Genome sequencing was performed in a male with developmental delay. He was found to have VUSs in both PHF6 and DDB1 genes, linked with Börjeson–Forssman–Lehmann syndrome (BFLS) and White–Kernohan syndrome (WHIKERS), respectively. These two disorders share a similar phenotype but have distinct inheritance patterns and molecular pathogenic mechanisms. DNA methylation profiling (DNAm) of whole blood was performed using the clinically validated EpiSign assay. Results: The proband’s methylation profile demonstrated a strong correlation with the BFLS methylation signature, supporting the PHF6 variant as a likely cause of his neurodevelopmental disorder. Conclusions: Epigenetic testing for disorders with distinct methylation patterns can provide diagnostic utility when a patient presents with variants of uncertain significance in genes associated with developmental delay. Epigenetic signatures can also guide genetic counselling and family planning.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".