ChoruMM: a versatile multi-components mixed model for bacterial-GWAS

Genome-wide Association Studies (GWAS) have been central to studying the genetics of complex human outcomes, and there is now tremendous interest in implementing GWAS-like approaches to study pathogenic bacteria. A variety of methods have been proposed to address the complex linkage structure of bacterial genomes, however, some questions remain about to optimize the genetic modelling of bacteria to decipher causal variations from correlated ones. Here we examined the genetic structure underlying whole-genome sequencing data from 3,824 Listeria monocytogenes strains, and demonstrate that the standard human genetics model, commonly assumed by existing bacterial GWAS methods, is inadequate for studying such highly structured organisms. We leverage these results to develop ChoruMM, a robust and powerful approach that consists of a multi-component linear mixed model, where components are inferred from a hierarchical clustering of the bacteria genetic relatedness matrix. Our ChoruMM approach also includes post-processing and visualization tools that address the pervasive long-range correlation observed in bacteria genome and allow to assess the type I error rate calibration.