Journal Article

Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes

Välimäki, Niko, Chewapreecha, Claire, Lees, John A., Marttinen, Pekka, Honkela, Antti, Tong, Steven Y. C., Croucher, Nicholas J., Davies, Mark R., Parkhill, Julian, Corander, Jukka, Harris, Simon R., Vehkala, Minna, Steer, Andrew C., Bentley, Stephen D.

2016

Overview

Bacterial genomes vary extensively in terms of both gene content and gene sequence. This plasticity hampers the use of traditional SNP-based methods for identifying all genetic associations with phenotypic variation. Here we introduce a computationally scalable and widely applicable statistical method (SEER) for the identification of sequence elements that are significantly enriched in a phenotype of interest. SEER is applicable to tens of thousands of genomes by counting variable-length k-mers using a distributed string-mining algorithm. Robust options are provided for association analysis that also correct for the clonal population structure of bacteria. Using large collections of genomes of the major human pathogens Streptococcus pneumoniae and Streptococcus pyogenes , SEER identifies relevant previously characterized resistance determinants for several antibiotics and discovers potential novel factors related to the invasiveness of S. pyogenes . We thus demonstrate that our method can answer important biologically and medically relevant questions. Plasticity and clonal population structure in bacterial genomes can hinder traditional SNP-based genetic association studies. Here, Corander and colleagues present a method to identify variable-length sequence elements enriched in a phenotype of interest, and demonstrate its use in human pathogens.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 631/114/2415 / 631/208/205/2138 / 631/208/325/2482 / Antibiotics / Bacteria / Computer science / Computer Simulation / DNA, Bacterial - genetics / Genome, Bacterial / Genome-Wide Association Study / Genomes / Genomics / Genotype & phenotype / Humanities and Social Sciences / Information technology / Methods / Models, Genetic / multidisciplinary / Mutation / Nucleic Acid Amplification Techniques / Phenotypic variations / Population structure / Science / Science (multidisciplinary) / Statistical analysis / Statistical methods / Streptococcus infections / Streptococcus pneumoniae - genetics / Streptococcus pyogenes - genetics