Pan-Genome-Wide Association Study of Serotype 19A Pneumococci Identifies Disease-Associated Genes

Abstract

Despite the widespread implementation of pneumococcal vaccines, hypervirulent Streptococcus pneumoniae serotype 19A is endemic worldwide. It is still unclear whether specific genetic elements contribute to complex pathogenicity of serotype 19A isolates. We performed a large-scale pan-genome-wide association study (pan-GWAS) of 1,292 serotype 19A isolates sampled from patients with invasive disease and asymptomatic carriers. To address the underlying disease-associated genotypes, a comprehensive analysis using three methods (Scoary, a linear mixed model, and random forest) was performed to compare disease and carriage isolates to identify genes consistently associated with disease phenotype. By using three pan-GWAS methods, we found consensus on statistically significant associations between genotypes and disease phenotypes (disease or carriage), with a subset of 30 consistently significant disease-associated genes. The results of functional annotation revealed that these disease-associated genes had diverse predicted functions, including those that participated in mobile genetic elements, antibiotic resistance, virulence, and cellular metabolism. Our findings suggest the multifactorial pathogenicity nature of this hypervirulent serotype and provide important evidence for the design of novel protein-based vaccines to prevent and control pneumococcal disease. IMPORTANCE It is important to understand the genetic and pathogenic characteristics of S. pneumoniae serotype 19A, which may provide important information for the prevention and treatment of pneumococcal disease. This global large-sample pan-GWAS study has identified a subset of 30 consistently significant disease-associated genes that are involved in mobile genetic elements, antibiotic resistance, virulence, and cellular metabolism. These findings suggest the multifactorial pathogenicity nature of hypervirulent S. pneumoniae serotype 19A isolates and provide implications for the design of novel protein-based vaccines.

Keywords: Streptococcus pneumoniae; bacterial genomics; carriage; genome-wide association study; invasive pneumococcal disease; pathogenicity; pneumococcus.

FIG 1

Characteristics of S. pneumoniae serotype 19A isolates used in this study and pan-genome analysis of S. pneumoniae 19A genomes, constructed using Roary. (a) Distribution of pneumococcal serotype 19A isolates from disease and carriage by the age of the individuals; (b) line graph showing the distribution of sampling years of disease and carriage pneumococcal serotype 19A isolates; (c) pan-genomic visualization based on the phylogenetic tree and the matrix of genotypes (presence or absence); (d) gene accumulation curve contrasting the number of new genes and unique genes; (e) gene accumulation curve contrasting the number of total genes and conserved genes.

FIG 2

Phylogenetic tree of 1,292 S. pneumoniae serotype 19A isolates based on core genes (a) and core SNPs (b). The colored stripes at the tips of the tree indicate isolate metadata: isolate source, sequence types, and continents.

FIG 3

pan-GWAS analyses for identifying disease-associated genotypes using the Scoary, LMM, and random forest methods. Manhattan plots show statistical significance (−log₁₀) of the genes using Scoary (a) and LMM (c). Volcano plots show the relationship between statistical significance (−log₁₀) and the effect size in terms of the log-transformed (base 2) carriage-to-disease odds ratio for the genetic variants using Scoary (b) and LMM (d). The horizontal lines represent the pan-WGAS statistical significance threshold based on the Bonferroni adjustment. The importance score for the 100 top-ranked genes (e) and ROC curves (f) for random forest with the 100 top-ranked genes are shown.

FIG 4

Visualization of the disease-associated genes identified by three methods (Scoary, LMM, and RF) shown as intersecting sets (a) and a Venn diagram (b).

FIG 5

Risk assessment plot for the disease-associated genes. Change in risk score for the gene when a disease-associated gene is present (y axis) compared to absent (x axis). A point above the diagonal implies that the risk score is increased when the gene is present.