Structural and sequence variants in patients with Silver-Russell syndrome or similar features-Curation of a disease database.
Zeynep TümerJulia Angélica López-HernándezIrène NetchineMiriam ElbrachtKaren GrønskovLene Bjerring GedeJana SachwitzJohan T den DunnenThomas EggermannPublished in: Human mutation (2018)
Silver-Russell syndrome (SRS) is a clinically and molecularly heterogeneous disorder involving prenatal and postnatal growth retardation, and the term SRS-like is broadly used to describe individuals with clinical features resembling SRS. The main molecular subgroups are loss of methylation of the distal imprinting control region (H19/IGF2:IG-DMR) on 11p15.5 (50%) and maternal uniparental disomy of chromosome 7 (5%-10%). Through a comprehensive literature search, we identified 91 patients/families with various structural and small sequence variants, which were suggested as additional molecular defects leading to SRS/SRS-like phenotypes. However, the molecular and phenotypic data of these patients were not standardized and therefore not comparable, rendering difficulties in phenotype-genotype comparisons. To overcome this challenge, we curated a disease database including (epi)genetic phenotypic data of these patients. The clinical features are scored according to the Netchine-Harbison clinical scoring system (NH-CSS), which has recently been accepted as standard by consensus. The structural and sequence variations are reviewed and where necessary redescribed according to recent recommendations. Our study provides a framework for both research and diagnostic purposes through facilitating a standardized comparison of (epi)genotypes with phenotypes of patients with structural/sequence variants.
Keyphrases
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- copy number
- peritoneal dialysis
- prognostic factors
- systematic review
- preterm infants
- gene expression
- genome wide
- big data
- dna methylation
- physical activity
- signaling pathway
- cell proliferation
- case report
- artificial intelligence
- clinical practice
- patient reported
- drug induced
- data analysis