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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-04

AUTHORS

Yi-Fei Huang, Brad Gulko, Adam Siepel

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. More... »

PAGES

618-624

References to SciGraph publications

  • 2013-07. Genome-wide inference of natural selection on human transcription factor binding sites in NATURE GENETICS
  • 2014-12. Analysis of nascent RNA identifies a unified architecture of initiation regions at mammalian promoters and enhancers in NATURE GENETICS
  • 1986-10. Learning representations by back-propagating errors in NATURE
  • 2012-09. An integrated encyclopedia of DNA elements in the human genome in NATURE
  • 2015-08. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning in NATURE BIOTECHNOLOGY
  • 2011-10. A high-resolution map of human evolutionary constraint using 29 mammals in NATURE
  • 2015-10. A global reference for human genetic variation in NATURE
  • 2014-03. An atlas of active enhancers across human cell types and tissues in NATURE
  • 2016-02. A spectral approach integrating functional genomic annotations for coding and noncoding variants in NATURE GENETICS
  • 2013-08. An atlas of over 90,000 conserved noncoding sequences provides insight into crucifer regulatory regions in NATURE GENETICS
  • 2015-03. A method for calculating probabilities of fitness consequences for point mutations across the human genome in NATURE GENETICS
  • 2014-03. A general framework for estimating the relative pathogenicity of human genetic variants in NATURE GENETICS
  • 2007-07. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project in NATURE
  • 2015-12. The Ensembl Regulatory Build in GENOME BIOLOGY
  • 2012-09. Analysis of variation at transcription factor binding sites in Drosophila and humans in GENOME BIOLOGY
  • 2015-02. Integrative analysis of 111 reference human epigenomes in NATURE
  • 2014-03. Functional annotation of noncoding sequence variants in NATURE METHODS
  • 2002-12. Initial sequencing and comparative analysis of the mouse genome in NATURE
  • 2015-12. De novo assembly of bacterial transcriptomes from RNA-seq data in GENOME BIOLOGY
  • 2015-10. Predicting effects of noncoding variants with deep learning-based sequence model in NATURE METHODS
  • 2014-01. The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine in HUMAN GENETICS
  • 2014-10. FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer in GENOME BIOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/ng.3810

    DOI

    http://dx.doi.org/10.1038/ng.3810

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1084129150

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/28288115


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