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Pathogenic variants that alter protein code often disrupt splicing.

Rachel SoemediKamil J CyganChristy L RhineJing WangCharlston BulacanJohn YangPinar Bayrak-ToydemirJamie McDonaldWilliam G Fairbrother
Published in: Nature genetics (2017)
The lack of tools to identify causative variants from sequencing data greatly limits the promise of precision medicine. Previous studies suggest that one-third of disease-associated alleles alter splicing. We discovered that the alleles causing splicing defects cluster in disease-associated genes (for example, haploinsufficient genes). We analyzed 4,964 published disease-causing exonic mutations using a massively parallel splicing assay (MaPSy), which showed an 81% concordance rate with splicing in patient tissue. Approximately 10% of exonic mutations altered splicing, mostly by disrupting multiple stages of spliceosome assembly. We present a large-scale characterization of exonic splicing mutations using a new technology that facilitates variant classification and keeps pace with variant discovery.
Keyphrases
  • high throughput
  • copy number
  • randomized controlled trial
  • small molecule
  • big data
  • deep learning
  • dna methylation