Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data

Yi Fei Huang, Brad Gulko, Adam Siepel

Research output: Contribution to journalArticle

89 Scopus citations

Abstract

Many genetic variants that influence phenotypes of interest are located outside of protein-coding genes, yet existing methods for identifying such variants have poor predictive power. Here we introduce a new computational method, called LINSIGHT, that substantially improves the prediction of noncoding nucleotide sites at which mutations are likely to have deleterious fitness consequences, and which, therefore, are likely to be phenotypically important. LINSIGHT combines a generalized linear model for functional genomic data with a probabilistic model of molecular evolution. The method is fast and highly scalable, enabling it to exploit the 'big data' available in modern genomics. We show that LINSIGHT outperforms the best available methods in identifying human noncoding variants associated with inherited diseases. In addition, we apply LINSIGHT to an atlas of human enhancers and show that the fitness consequences at enhancers depend on cell type, tissue specificity, and constraints at associated promoters.

Original languageEnglish (US)
Pages (from-to)618-624
Number of pages7
JournalNature Genetics
Volume49
Issue number4
DOIs
StatePublished - Mar 30 2017

All Science Journal Classification (ASJC) codes

  • Genetics

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