Megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome 2: genes and variants
Megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome 2 is linked to 3 analyzed proteins (AKT3, CCND2 and PIK3R2). 21 DNA variants are known to cause it; 51 more are uncertain, and 2 of those already look disease-causing on computable evidence.
Last updated 2026-09-30. Research information, not medical advice.
Also known as: megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome 1; megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome 3
Genes linked to Megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome 2
AKT3: RAC-gamma serine/threonine-protein kinase
It is especially important for growth and survival signaling in the developing brain. Activating mosaic or germline variants can cause megalencephaly and cortical malformation syndromes, while loss-of-function variants can be associated with microcephaly and neurodevelopmental impairment.
8 disease-causing and 33 uncertain variants in AKT3 are linked to Megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome 2.
CCND2: G1/S-specific cyclin-D2
It promotes G1-to-S cell-cycle progression through activation of CDK4 and CDK6 and is important in proliferating neural and endocrine tissues. Activating germline variants can cause megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome, while overexpression occurs in several cancers.
7 disease-causing and 3 uncertain variants in CCND2 are linked to Megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome 2.
PIK3R2: Phosphatidylinositol 3-kinase regulatory subunit beta
6 disease-causing and 15 uncertain variants in PIK3R2 are linked to Megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome 2.
Where Megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome 2 variants cluster
- PIK3R2 SH2 1 (positions 330–425): 5 of 6 disease-causing changes, 6.3× more than its size predicts.
Known disease-causing variants in Megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome 2
| Variant | Position | Protein part | Clinical label |
|---|---|---|---|
| CCND2 T280N | 280 | Disease-causing (★★) | |
| CCND2 P281S | 281 | Disease-causing (★★) | |
| AKT3 D322N | 322 | Protein kinase | Disease-causing (★★) |
| CCND2 T280A | 280 | Disease-causing (★★) | |
| CCND2 T280P | 280 | Disease-causing (★★) | |
| CCND2 V284E | 284 | Disease-causing (★★) | |
| PIK3R2 K376E | 376 | SH2 1 | Disease-causing (★★) |
| CCND2 P281L | 281 | Disease-causing (★) | |
| AKT3 D322Y | 322 | Protein kinase | Disease-causing (★) |
| AKT3 W79C | 79 | PH | Disease-causing (★) |
| AKT3 K180E | 180 | Protein kinase | Disease-causing (★) |
| AKT3 V183D | 183 | Protein kinase | Disease-causing (★) |
| PIK3R2 W330G | 330 | SH2 1 | Disease-causing (★) |
| PIK3R2 F352L | 352 | SH2 1 | Disease-causing (★) |
| PIK3R2 N561D | 561 | Disease-causing (★) | |
| AKT3 N229S | 229 | Protein kinase | Disease-causing (★) |
| CCND2 T280I | 280 | Disease-causing | |
| AKT3 N321K | 321 | Protein kinase | Disease-causing |
| AKT3 L77H | 77 | PH | Disease-causing |
| PIK3R2 L401P | 401 | SH2 1 | Disease-causing |
| PIK3R2 G385R | 385 | SH2 1 | Disease-causing |
Uncertain variants in Megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome 2 that look disease-causing
| Variant | Position | Protein part | Clinical label | Evidence |
|---|---|---|---|---|
| CCND2 P281R | 281 | Conflicting reports (★) | +7: 7 other pathogenic changes within 3 positions; P281L at the same position is pathogenic; seen in 2e-06 of gnomAD DNA copies; REVEL 0.908 | |
| AKT3 V183A | 183 | Protein kinase | Uncertain (★) | +6: 2 other pathogenic changes within 3 positions; V183D at the same position is pathogenic; not seen in the gnomAD population database; AlphaMissense 0.98 |
Which prediction tools work for Megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome 2
How often each tool ranks a disease-causing variant above a harmless one (AUROC × 100).
- PolyPhen-2: 92 out of 100 (learned from overlapping clinical labels, so this is optimistic)
- CATVariant: 88 out of 100 (learned from overlapping clinical labels, so this is optimistic)
- SIFT: 84 out of 100
- MetaLR: 69 out of 100 (learned from overlapping clinical labels, so this is optimistic)
Diseases related to Megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome 2
- Overgrowth syndrome and/or cerebral malformations due to abnormalities in MTOR pathway genes, also linked to AKT3 and PIK3R2
- Multiple myeloma, also linked to PIK3R2
- Diabetes mellitus, also linked to CCND2
- Lennox-Gastaut syndrome, also linked to CCND2
Frequently asked questions
Which genes are linked to Megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome 2?
In CATVariant, Megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome 2 is linked to 3 analyzed proteins: AKT3 (RAC-gamma serine/threonine-protein kinase), CCND2 (G1/S-specific cyclin-D2) and PIK3R2 (Phosphatidylinositol 3-kinase regulatory subunit beta).
How many genetic variants are linked to Megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome 2?
91 variants: 21 are classified as disease-causing (pathogenic or likely pathogenic) in ClinVar and 51 are of uncertain significance or have conflicting reports.
Which uncertain variants in Megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome 2 look disease-causing?
2 uncertain variants reach the likely-pathogenic range of the ACMG/AMP points scale on computable evidence, for example CCND2 P281R and AKT3 V183A. These are leads for expert review, not diagnoses.
Which variant effect predictor works best for Megalencephaly-polymicrogyria-polydactyly-hydrocephalus syndrome 2?
Among tools not trained on clinical labels, SIFT separates this disease's known disease-causing variants from harmless ones best (AUROC 0.84, based on 20 disease-causing and 48 harmless variants).
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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