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Facioscapulohumeral muscular dystrophy (FSHD) is characterized by incomplete penetrance and intra-familial clinical variability. The disease has been associated with the genetic and epigenetic features of the D4Z4 repetitive elements at 4q35. Recently, D4Z4 hypomethylation has been proposed as a reliable marker in the FSHD diagnosis. We exploited the Italian Registry for FSHD, in which FSHD families are classified using the Clinical Comprehensive Evaluation Form (CCEF). A total of 122 index cases showing a classical FSHD phenotype (CCEF, category A) and 110 relatives were selected to test with the receiver operating characteristic (ROC) curve, the diagnostic and predictive value of D4Z4 methylation. Moreover, we performed DNA methylation analysis in selected large families with reduced penetrance characterized by the co-presence of subjects carriers of one D4Z4 reduced allele with no signs of disease or presenting the classic FSHD clinical phenotype. We observed a wide variability in the D4Z4 methylation levels among index cases revealing no association with clinical manifestation or disease severity. By extending the analysis to family members, we revealed the low predictive value of D4Z4 methylation in detecting the affected condition. In view of the variability in D4Z4 methylation profiles observed in our large cohort, we conclude that D4Z4 methylation does not mirror the clinical expression of FSHD. We recommend that measurement of this epigenetic mark must be interpreted with caution in clinical practice.
Pubmed ID: 32290091
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THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
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