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Hi-C Identifies Complex Genomic Rearrangements and TAD-Shuffling in Developmental Diseases.


ABSTRACT: Genome-wide analysis methods, such as array comparative genomic hybridization (CGH) and whole-genome sequencing (WGS), have greatly advanced the identification of structural variants (SVs) in the human genome. However, even with standard high-throughput sequencing techniques, complex rearrangements with multiple breakpoints are often difficult to resolve, and predicting their effects on gene expression and phenotype remains a challenge. Here, we address these problems by using high-throughput chromosome conformation capture (Hi-C) generated from cultured cells of nine individuals with developmental disorders (DDs). Three individuals had previously been identified as harboring duplications at the SOX9 locus and six had been identified with translocations. Hi-C resolved the positions of the duplications and was instructive in interpreting their distinct pathogenic effects, including the formation of new topologically associating domains (neo-TADs). Hi-C was very sensitive in detecting translocations, and it revealed previously unrecognized complex rearrangements at the breakpoints. In several cases, we observed the formation of fused-TADs promoting ectopic enhancer-promoter interactions that were likely to be involved in the disease pathology. In summary, we show that Hi-C is a sensible method for the detection of complex SVs in a clinical setting. The results help interpret the possible pathogenic effects of the SVs in individuals with DDs.

SUBMITTER: Melo US 

PROVIDER: S-EPMC7273525 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Genome-wide analysis methods, such as array comparative genomic hybridization (CGH) and whole-genome sequencing (WGS), have greatly advanced the identification of structural variants (SVs) in the human genome. However, even with standard high-throughput sequencing techniques, complex rearrangements with multiple breakpoints are often difficult to resolve, and predicting their effects on gene expression and phenotype remains a challenge. Here, we address these problems by using high-throughput ch  ...[more]

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