We managed to develop the most scalable and powerful methods for analyzing bacterial population structure and performing genome-wide association studies (GWAS). GWAS is a generally utilized approach to discovery of the genetic architecture of traits in any living organism and it operates by associating measured variation in phenotypes with variation in genotypes using large population samples and statistical models. We introduced the first modular software platform for bacterial GWAS (pyseer) and later a new machine learning -based approach to GWAS which increased the statistical power significantly beyond the previous state of the art. The population structure analysis methods developed by SCARABEE scale to a million whole genomes and beyond. We further established the concept of genome-wide epistasis and co-selection study (GWES) which complements GWAS by allowing discovery of genetic architectures from selection traces without direct access to phenotypic measurements. Finally, we introduced and established ELFI (
http://elfi.ai) as the leading software package for likelihood-free inference for interpretable simulator-based models. By making all SCARABEE methods available as open-source software, we ensured maximal opportunities for exploitation, dissemination and further development.