Genome-wide epistasis and co-selection study using mutual information

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

Tutkijat

  • Johan Pensar
  • Santeri Puranen

  • Brian Arnold
  • Neil MacAlasdair
  • Juri Kuronen
  • Gerry Tonkin-Hill
  • Maiju Pesonen

  • Yingying Xu

  • Aleksi Sipola
  • Leonor Sánchez-Busó
  • John A. Lees
  • Claire Chewapreecha
  • Stephen D. Bentley
  • Simon R. Harris
  • Julian Parkhill
  • Nicholas J. Croucher
  • Jukka Corander

Organisaatiot

  • University of Helsinki
  • Harvard University
  • University of Oslo
  • New York University
  • University of Cambridge
  • King Mongkut's University of Technology Thonburi
  • Imperial College London

Kuvaus

Covariance-based discovery of polymorphisms under co-selective pressure or epistasis has received considerable recent attention in population genomics. Both statistical modeling of the population level covariation of alleles across the chromosome and model-free testing of dependencies between pairs of polymorphisms have been shown to successfully uncover patterns of selection in bacterial populations. Here we introduce a model-free method, SpydrPick, whose computational efficiency enables analysis at the scale of pan-genomes of many bacteria. SpydrPick incorporates an efficient correction for population structure, which adjusts for the phylogenetic signal in the data without requiring an explicit phylogenetic tree. We also introduce a new type of visualization of the results similar to the Manhattan plots used in genome-wide association studies, which enables rapid exploration of the identified signals of co-evolution. Simulations demonstrate the usefulness of our method and give some insight to when this type of analysis is most likely to be successful. Application of the method to large population genomic datasets of two major human pathogens, Streptococcus pneumoniae and Neisseria meningitidis, revealed both previously identified and novel putative targets of co-selection related to virulence and antibiotic resistance, highlighting the potential of this approach to drive molecular discoveries, even in the absence of phenotypic data.

Yksityiskohdat

AlkuperäiskieliEnglanti
Artikkelie112
Sivut1-14
JulkaisuNucleic Acids Research
Vuosikerta47
Numero18
TilaJulkaistu - 10 lokakuuta 2019
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

ID: 37477852