Choanal atresia-athelia-hypothyroidism-delayed puberty-short stature syndrome: genes and variants

Choanal atresia-athelia-hypothyroidism-delayed puberty-short stature syndrome is linked to 1 analyzed protein (KMT2D). 6 DNA variants are known to cause it; 354 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 Choanal atresia-athelia-hypothyroidism-delayed puberty-short stature syndrome

Known disease-causing variants in Choanal atresia-athelia-hypothyroidism-delayed puberty-short stature syndrome

VariantPositionProtein partClinical label
KMT2D A3541P3541Disease-causing (★★)
KMT2D L3528V3528Disease-causing (★)
KMT2D E3569G3569Coiled coilDisease-causing (★)
KMT2D R5030L5030C2HC pre-PHD-type 2Disease-causing (★)
KMT2D L3542P3542Disease-causing
KMT2D G3553V3553Disease-causing

Same protein, different disease

Diseases related to Choanal atresia-athelia-hypothyroidism-delayed puberty-short stature syndrome

Frequently asked questions

Which genes are linked to Choanal atresia-athelia-hypothyroidism-delayed puberty-short stature syndrome?

In CATVariant, Choanal atresia-athelia-hypothyroidism-delayed puberty-short stature syndrome is linked to 1 analyzed protein: KMT2D (Histone-lysine N-methyltransferase 2D).

How many genetic variants are linked to Choanal atresia-athelia-hypothyroidism-delayed puberty-short stature syndrome?

387 variants: 6 are classified as disease-causing (pathogenic or likely pathogenic) in ClinVar and 354 are of uncertain significance or have conflicting reports.

Which uncertain variants in Choanal atresia-athelia-hypothyroidism-delayed puberty-short stature syndrome 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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