Abstract
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.
Highlights
Bacterial genomes vary extensively in terms of both gene content and gene sequence
This is partly due to the fact that bacteria reproduce clonally, meaning that a large proportion of the genome is in linkage disequilibrium (LD) with any given trait[2]
sequence element enrichment analysis (SEER) implements and combines three key insights, which we discuss in detail in the methods section: an efficient scan of all possible k-mers with a distributed string mining algorithm, an appropriate alignment-free correction for clonal population structure, and a fast and fully robust association analysis of all counted k-mers
Summary
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. Studies attempting to determine the genetic basis of bacterial traits have traditionally been limited to identifying emerging clones, which are associated with the phenotype of interest, rather than identifying the specific causal genetic elements[1] This is partly due to the fact that bacteria reproduce clonally, meaning that a large proportion of the genome is in linkage disequilibrium (LD) with any given trait[2]. Finding variants which are not fully penetrant for a phenotype (as may be the case for clinically relevant traits such as virulence) requires large numbers of samples[4] and a more general test of association For these reasons, genome-wide association studies (GWAS) for bacterial phenotypes have only recently started to appear[2,5,6,7,8]. The main advantage of k-mers is their ability to capture several different types of variation present across a collection of genomes, including mutations, indels, recombinations, variable promoter architecture and differences in gene content as well as capturing these variations in regions not present in all genomes
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