SuperDCA for genome-wide epistasis analysis

Research output: Contribution to journalArticleScientific

Researchers

Research units

  • Wellcome Trust Sanger Institute
  • Imperial College London
  • Wellcome Sanger Institute
  • University of Oslo
  • KTH Royal Institute of Technology
  • Chinese Academy of Sciences
  • University of Helsinki

Abstract

The potential for genome-wide modeling of epistasis has recently surfaced given the possibility of sequencing densely sampled populations and the emerging families of statistical interaction models. Direct coupling analysis (DCA) has earlier been shown to yield valuable predictions for single protein structures, and has recently been extended to genome-wide analysis of bacteria, identifying novel interactions in the co-evolution between resistance, virulence and core genome elements. However, earlier computational DCA methods have not been scalable to enable model fitting simultaneously to 10000-100000 polymorphisms, representing the amount of core genomic variation observed in analyses of many bacterial species. Here we introduce a novel inference method (SuperDCA) which employs a new scoring principle, efficient parallelization, optimization and filtering on phylogenetic information to achieve scalability for up to 100000 polymorphisms. Using two large population samples of Streptococcus pneumoniae, we demonstrate the ability of SuperDCA to make additional significant biological findings about this major human pathogen. We also show that our method can uncover signals of selection that are not detectable by genome-wide association analysis, even though our analysis does not require phenotypic measurements. SuperDCA thus holds considerable potential in building understanding about numerous organisms at a systems biological level.

Details

Original languageEnglish
JournalbioRxiv
Publication statusPublished - 2017
MoE publication typeNot Eligible

ID: 16833174