Delineation of a KDM2B-related neurodevelopmental disorder and its associated DNA methylation signature
Bibliographic record
Abstract
PURPOSE: Pathogenic variants in genes involved in the epigenetic machinery are an emerging cause of neurodevelopment disorders (NDDs). Lysine-demethylase 2B (KDM2B) encodes an epigenetic regulator and mouse models suggest an important role during development. We set out to determine whether KDM2B variants are associated with NDD. METHODS: Through international collaborations, we collected data on individuals with heterozygous KDM2B variants. We applied methylation arrays on peripheral blood DNA samples to determine a KDM2B associated epigenetic signature. RESULTS: We recruited a total of 27 individuals with heterozygous variants in KDM2B. We present evidence, including a shared epigenetic signature, to support a pathogenic classification of 15 KDM2B variants and identify the CxxC domain as a mutational hotspot. Both loss-of-function and CxxC-domain missense variants present with a specific subepisignature. Moreover, the KDM2B episignature was identified in the context of a dual molecular diagnosis in multiple individuals. Our efforts resulted in a cohort of 21 individuals with heterozygous (likely) pathogenic variants. Individuals in this cohort present with developmental delay and/or intellectual disability; autism; attention deficit disorder/attention deficit hyperactivity disorder; congenital organ anomalies mainly of the heart, eyes, and urogenital system; and subtle facial dysmorphism. CONCLUSION: Pathogenic heterozygous variants in KDM2B are associated with NDD and a specific epigenetic signature detectable in peripheral blood.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".