Greenberg dysplasia: genes and variants
Greenberg dysplasia is linked to 1 analyzed protein (LBR). 4 DNA variants are known to cause it; 38 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 Greenberg dysplasia
LBR: Delta(14)-sterol reductase LBR
An inner nuclear-membrane protein with sterol-reductase activity in the cholesterol-biosynthesis pathway. It also contributes to nuclear-envelope organization and myeloid-cell maturation, and LBR variants are associated with Pelger-Huet anomaly and skeletal dysplasia.
4 disease-causing and 38 uncertain variants in LBR are linked to Greenberg dysplasia.
Known disease-causing variants in Greenberg dysplasia
| Variant | Position | Protein part | Clinical label |
|---|---|---|---|
| LBR N547D | 547 | Disease-causing (★★) | |
| LBR R583Q | 583 | Disease-causing (★) | |
| LBR R586H | 586 | Disease-causing | |
| LBR D460G | 460 | Transmembrane | Disease-causing |
Diseases related to Greenberg dysplasia
- Connective tissue disorder, also linked to LBR
- Pelger-Huët anomaly, also linked to LBR
- Regressive spondylometaphyseal dysplasia, also linked to LBR
- Reynolds syndrome, also linked to LBR
Frequently asked questions
Which genes are linked to Greenberg dysplasia?
In CATVariant, Greenberg dysplasia is linked to 1 analyzed protein: LBR (Delta(14)-sterol reductase LBR).
How many genetic variants are linked to Greenberg dysplasia?
54 variants: 4 are classified as disease-causing (pathogenic or likely pathogenic) in ClinVar and 38 are of uncertain significance or have conflicting reports.
Which uncertain variants in Greenberg dysplasia 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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