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

TitleFast, scalable prediction of deleterious noncoding variants from functional and population genomic data.
Publication TypeJournal Article
Year of Publication2017
AuthorsHuang, Y-F, Gulko, B, Siepel, A
JournalNat Genet
Volume49
Issue4
Pagination618-624
Date Published2017 Apr
ISSN1546-1718
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.

DOI10.1038/ng.3810
Alternate JournalNat. Genet.
PubMed ID28288115
PubMed Central IDPMC5395419
Grant ListR01 GM102192 / GM / NIGMS NIH HHS / United States
UM1 HG008901 / HG / NHGRI NIH HHS / United States