Whole Genome Methylation Profiling to Enhance Diagnostic Yield in Neurodevelopmental Disorders
Bibliographic record
Abstract
Background/Purpose: Epigenetic analysis in clinical diagnostics offers novel insights into complex genetic disorders, particularly neurodevelopmental delay (NDD) with or without congenital anomalies. We implemented a whole genome methylation profiling technique, EpiSign, to detect and characterize epigenetic signatures in patients with unsolved NDD, enabling the reclassification of variants of uncertain significance (VUS). Methods: We used Illumina EPIC v2.0 BeadChips (whole genome methylation pattern analysis) followed by epigenetic signature analysis with EpiSign v5 (LHSC). Results: In our cohort of 60 control cases (including NDD patients with class 4 or 5 variants in NDD-associated genes) we reproduced diagnoses with 93% sensitivity and 100% precision. We analyzed 29 unsolved cases with syndromic and nonsyndromic NDD. Eight cases presented with VUS, and 21 had no disease-associated variant. Four of the eight patients with VUS had gene-specific methylations consistent with the gene in which the VUS was identified. Three of the 21 unsolved patients without a known VUS had episignatures indicative of distinct conditions. A likely pathogenic variant was identified in one of these patients upon re-evaluation. Conclusion: Typical diagnostic resolution rates for NDD range between 20 and 40%. By analyzing epigenetic signatures, we identified disease-associated episignatures in 24% of selected unsolved cases. Targeted re-evaluation of exome data or prior known VUS improved the diagnostic yield of NDD patients. Our findings underscore the role of epigenetic profiling in clinical genetic diagnostics, highlighting its integration as a crucial component of routine evaluations. This approach enhances understanding of NDD and significantly improves clinical management, paving the way for more effective patient care. Publication History Article published online: 08 October 2024 Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".