Journal
NATURE GENETICS
Volume 49, Issue 6, Pages 848-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/ng.3837
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Funding
- Center for Computational Molecular Biology (CCMB), Brown University
- Graduate Research Fellowship from National Science Foundation (NSF)
- US National Institutes of Health (NIH) [R01GM095612, R01GM105681, R21HG007905]
- SFARI award [342705]
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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.
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