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Record W4403329153 · doi:10.1055/s-0044-1791932

Whole Genome Methylation Profiling to Enhance Diagnostic Yield in Neurodevelopmental Disorders

2024· article· en· W4403329153 on OpenAlexaff
M. Witzel, Angela Risch, M. Auburger, Stephanie Kleinle, Bekim Sadiković, Ariane Hallermayr, Angela Abicht, Teresa Neuhann

Bibliographic record

VenueNeuropediatrics · 2024
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsProfiling (computer programming)MedicineComputational biologyGenomeGenomicsBioinformaticsGeneticsComputer scienceBiologyGene

Abstract

fetched live from OpenAlex

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

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.025
GPT teacher head0.305
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractno

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