Analysis of stop-gain and frameshift variants in human innate immunity genes

PLoS Comput Biol. 2014 Jul 24;10(7):e1003757. doi: 10.1371/journal.pcbi.1003757. eCollection 2014 Jul.

Abstract

Loss-of-function variants in innate immunity genes are associated with Mendelian disorders in the form of primary immunodeficiencies. Recent resequencing projects report that stop-gains and frameshifts are collectively prevalent in humans and could be responsible for some of the inter-individual variability in innate immune response. Current computational approaches evaluating loss-of-function in genes carrying these variants rely on gene-level characteristics such as evolutionary conservation and functional redundancy across the genome. However, innate immunity genes represent a particular case because they are more likely to be under positive selection and duplicated. To create a ranking of severity that would be applicable to innate immunity genes we evaluated 17,764 stop-gain and 13,915 frameshift variants from the NHLBI Exome Sequencing Project and 1,000 Genomes Project. Sequence-based features such as loss of functional domains, isoform-specific truncation and nonsense-mediated decay were found to correlate with variant allele frequency and validated with gene expression data. We integrated these features in a Bayesian classification scheme and benchmarked its use in predicting pathogenic variants against Online Mendelian Inheritance in Man (OMIM) disease stop-gains and frameshifts. The classification scheme was applied in the assessment of 335 stop-gains and 236 frameshifts affecting 227 interferon-stimulated genes. The sequence-based score ranks variants in innate immunity genes according to their potential to cause disease, and complements existing gene-based pathogenicity scores. Specifically, the sequence-based score improves measurement of functional gene impairment, discriminates across different variants in a given gene and appears particularly useful for analysis of less conserved genes.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem
  • Computational Biology / methods*
  • Databases, Genetic*
  • Humans
  • Immunity, Innate / genetics*
  • Interferons / genetics
  • Interferons / metabolism
  • Mutation / genetics*
  • Virus Diseases / immunology

Substances

  • Interferons

Grants and funding

This work is funded by the Swiss National Science Foundation (grant #CRSII3_147665). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.