compGWAS: a new GWAS tool allows revelation of the genetic architecture and risk stratification for the versatile pathogen Streptococcus pyogenes

Background Gene inactivations caused by loss-of-function mutations and regulatory changes caused by insertions/deletions (InDels) are common genetic factors closely related to phenotypic diversity or pathogenic versatility of many bacterial species. However, these genetic factors were usually ignored by the computational approach of genome-wide association studies (GWAS). It prevents the full understanding of the contributions of genetic variants to phenotypic diversity or the roles in shaping genetic architecture of bacterial species of diverse phenotypes. Group A Streptococcus pyogenes (GAS) is one of the most versatile pathogens causing a variety of primary diseases, as well as disease progressions, complications, and sequelae and is a promising species to do investigations in this regard. Methods By using GAS as a paradigm, we developed a new GWAS tool, compGWAS, to comprehensively identify phenotype-associated genetic variants that include not only SNPs, but also InDels and gene inactivations. The genetic architecture of GAS phenotypes was revealed by considering all these types of variants. A GWAS polygenic score (GPS) model was developed through integration of all types of associated variants for phenotype stratification. Results By leveraging this newly developed tool, we constructed a relationship network between 1,361 variants linked with 783 genes and eight GAS phenotypes. The network shows a high level of polygenicity of the GAS phenotypes (ranging from 6 to 148 genes) and pleiotropicity of the causal genes (as many as eight phenotypes). Further investigation revealed a unique genetic architecture of GAS phenotypes as a combination of many low-effect common variants and a small proportion of high-effect low-frequency variants with gene inactivations being predominant. By adding gene inactivations and InDels, the proportion of explained phenotypic variance increased by 7%-16%, resulting in a total explained variance as high as 50%. The high explained variance allowed us to construct a GPS model with high discriminatory capabilities in GAS phenotype stratification with the AUC > 80% in the validation dataset. Conclusions Our work provides a novel tool and analysis framework for investigating phenotypic effects and genetic characteristics of InDels and gene inactivations previously ignored. Our study has implications for understanding genetic architecture of versatile pathogens like GAS.