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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.

Original publication

DOI

10.1093/nar/gkz656

Type

Journal article

Journal

Nucleic acids research

Publication Date

10/2019

Volume

47

Addresses

Department of Mathematics and Statistics, Helsinki Institute for Information Technology (HIIT), Faculty of Science, University of Helsinki, FI-00014 Helsinki, Finland.

Keywords

Humans, Neisseria meningitidis, Streptococcus pneumoniae, Computational Biology, Genomics, Drug Resistance, Microbial, Virulence, Epistasis, Genetic, Genome, Bacterial, Metagenomics