1. BAFopathies' DNA methylation epi-signatures demonstrate diagnostic utility and functional continuum of Coffin-Siris and Nicolaides-Baraitser syndromes.
- Author
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Aref-Eshghi E, Bend EG, Hood RL, Schenkel LC, Carere DA, Chakrabarti R, Nagamani SCS, Cheung SW, Campeau PM, Prasad C, Siu VM, Brady L, Tarnopolsky MA, Callen DJ, Innes AM, White SM, Meschino WS, Shuen AY, Paré G, Bulman DE, Ainsworth PJ, Lin H, Rodenhiser DI, Hennekam RC, Boycott KM, Schwartz CE, and Sadikovic B
- Subjects
- Abnormalities, Multiple diagnosis, Chromatin Assembly and Disassembly, DNA Helicases genetics, DNA-Binding Proteins genetics, Epigenesis, Genetic, Epigenomics, Face abnormalities, Facies, Foot Deformities, Congenital diagnosis, Foot Deformities, Congenital genetics, Hand Deformities, Congenital diagnosis, Hand Deformities, Congenital genetics, Humans, Hypotrichosis diagnosis, Hypotrichosis genetics, Intellectual Disability diagnosis, Intellectual Disability genetics, Micrognathism diagnosis, Micrognathism genetics, Mutation, Neck abnormalities, Nuclear Proteins genetics, SMARCB1 Protein genetics, Syndrome, Abnormalities, Multiple genetics, Chromosomal Proteins, Non-Histone genetics, DNA Methylation, Transcription Factors genetics
- Abstract
Coffin-Siris and Nicolaides-Baraitser syndromes (CSS and NCBRS) are Mendelian disorders caused by mutations in subunits of the BAF chromatin remodeling complex. We report overlapping peripheral blood DNA methylation epi-signatures in individuals with various subtypes of CSS (ARID1B, SMARCB1, and SMARCA4) and NCBRS (SMARCA2). We demonstrate that the degree of similarity in the epi-signatures of some CSS subtypes and NCBRS can be greater than that within CSS, indicating a link in the functional basis of the two syndromes. We show that chromosome 6q25 microdeletion syndrome, harboring ARID1B deletions, exhibits a similar CSS/NCBRS methylation profile. Specificity of this epi-signature was confirmed across a wide range of neurodevelopmental conditions including other chromatin remodeling and epigenetic machinery disorders. We demonstrate that a machine-learning model trained on this DNA methylation profile can resolve ambiguous clinical cases, reclassify those with variants of unknown significance, and identify previously undiagnosed subjects through targeted population screening.
- Published
- 2018
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