Integrative annotation of variants from 1092 humans: application to cancer genomics. Academic Article uri icon

Overview

MeSH

  • Binding Sites
  • Genome, Human
  • Genomics
  • Humans
  • Kruppel-Like Transcription Factors
  • Mutation
  • Polymorphism, Single Nucleotide
  • Population
  • RNA, Untranslated
  • Selection, Genetic

MeSH Major

  • Genetic Variation
  • Molecular Sequence Annotation
  • Neoplasms

abstract

  • Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations ("ultrasensitive") and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, "motif-breakers"). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e.g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to ~90 cancer genomes reveals nearly a hundred candidate noncoding drivers.

authors

publication date

  • October 4, 2013

has subject area

  • Binding Sites
  • Genetic Variation
  • Genome, Human
  • Genomics
  • Humans
  • Kruppel-Like Transcription Factors
  • Molecular Sequence Annotation
  • Mutation
  • Neoplasms
  • Polymorphism, Single Nucleotide
  • Population
  • RNA, Untranslated
  • Selection, Genetic

Research

keywords

  • Journal Article

Identity

Language

  • eng

PubMed Central ID

  • PMC3947637

Digital Object Identifier (DOI)

  • 10.1126/science.1235587

PubMed ID

  • 24092746

Additional Document Info

start page

  • 1235587

volume

  • 342

number

  • 6154