Research Article

The human splicing code reveals new insights into the genetic determinants of disease

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Science  18 Dec 2014:
1254806
DOI: 10.1126/science.1254806

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Abstract

To facilitate precision medicine and whole genome annotation, we developed a machine learning technique that scores how strongly genetic variants affect RNA splicing, whose alteration contributes to many diseases. Analysis of over 650,000 intronic and exonic variants reveals widespread patterns of mutation-driven aberrant splicing. Intronic disease mutations alter splicing nine times more often than common variants, and missense exonic disease mutations that least impact protein function are five times more likely to alter splicing than others. Tens of thousands of disease-causing mutations are detected, including those involved in cancers and spinal muscular atrophy. Examination of intronic and exonic variants found using whole genome sequencing of individuals with autism reveals mis-spliced genes with neurodevelopmental phenotypes. Our approach provides evidence for causal variants and should enable new discoveries in precision medicine.

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