Autosomal dominant cerebellar ataxia, deafness and narcolepsy: genes and variants

Autosomal dominant cerebellar ataxia, deafness and narcolepsy is linked to 1 analyzed protein (DNMT1). 5 DNA variants are known to cause it; 27 more are uncertain, and 0 of those already look disease-causing on computable evidence.

Last updated 2026-09-30. Research information, not medical advice.

Genes linked to Autosomal dominant cerebellar ataxia, deafness and narcolepsy

Where Autosomal dominant cerebellar ataxia, deafness and narcolepsy variants cluster

Known disease-causing variants in Autosomal dominant cerebellar ataxia, deafness and narcolepsy

VariantPositionProtein partClinical label
DNMT1 Y495C495RFTSDisease-causing (★★)
DNMT1 A554V554Interaction with the PRC2/EED-EZH2 complexDisease-causing (★★)
DNMT1 G589A589Interaction with the PRC2/EED-EZH2 complexDisease-causing (★)
DNMT1 L592R592Interaction with the PRC2/EED-EZH2 complexDisease-causing (★)
DNMT1 E1531D1531SAM-dependent MTase C5-typeDisease-causing (★)

Diseases related to Autosomal dominant cerebellar ataxia, deafness and narcolepsy

Frequently asked questions

Which genes are linked to Autosomal dominant cerebellar ataxia, deafness and narcolepsy?

In CATVariant, Autosomal dominant cerebellar ataxia, deafness and narcolepsy is linked to 1 analyzed protein: DNMT1 (DNA (cytosine-5)-methyltransferase 1).

How many genetic variants are linked to Autosomal dominant cerebellar ataxia, deafness and narcolepsy?

34 variants: 5 are classified as disease-causing (pathogenic or likely pathogenic) in ClinVar and 27 are of uncertain significance or have conflicting reports.

Which uncertain variants in Autosomal dominant cerebellar ataxia, deafness and narcolepsy look disease-causing?

None of the uncertain variants currently reaches the likely-pathogenic range on computable evidence alone.

About this data

Variant–disease links come from ClinVar, Open Targets and UniProt, pooled from the latest public CATVariant analysis of each human protein. Evidence scores use the ACMG/AMP Bayesian points scale with computable criteria only (position among known disease variants, rarity in gnomAD, calibrated predictors, deep mutational scanning); there is no family or patient data, so they prioritise variants for expert review and never classify them.

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