<i>ANKRD11</i> pathogenic variants and 16q24.3 microdeletions share an altered DNA methylation signature in patients with KBG syndrome
Bibliographic record
Abstract
Pathogenic variants in ANKRD11 or microdeletions at 16q24.3 are the cause of KBG syndrome (KBGS), a neurodevelopmental syndrome characterized by intellectual disability, dental and skeletal anomalies, and characteristic facies. The ANKRD11 gene encodes the ankyrin repeat-containing protein 11A transcriptional regulator, which is expressed in the brain and implicated in neural development. Syndromic conditions caused by pathogenic variants in epigenetic regulatory genes show unique patterns of DNA methylation (DNAm) in peripheral blood, termed DNAm signatures. Given ANKRD11's role in chromatin modification, we tested whether pathogenic ANKRD11 variants underlying KBGS are associated with a DNAm signature. We profiled whole-blood DNAm in 21 individuals with ANKRD11 variants, 2 individuals with microdeletions at 16q24.3 and 28 typically developing individuals, using Illumina's Infinium EPIC array. We identified 95 differentially methylated CpG sites that distinguished individuals with KBGS and pathogenic variants in ANKRD11 (n = 14) from typically developing controls (n = 28). This DNAm signature was then validated in an independent cohort of seven individuals with KBGS and pathogenic ANKRD11 variants. We generated a machine learning model from the KBGS DNAm signature and classified the DNAm profiles of four individuals with variants of uncertain significance (VUS) in ANKRD11. We identified an intermediate classification score for an inherited missense variant transmitted from a clinically unaffected mother to her affected child. In conclusion, we show that the DNAm profiles of two individuals with 16q24.3 microdeletions were indistinguishable from the DNAm profiles of individuals with pathogenic variants in ANKRD11, and we demonstrate the diagnostic utility of the new KBGS signature by classifying the DNAm profiles of individuals with VUS in ANKRD11.
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 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) | 0.001 | 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".