Evaluating selection at intermediate scales within genes provides robust identification of genes under positive selection in M. tuberculosis clinical isolates.

Multiple studies have reported genes in the M. tuberculosis (Mtb) genome that are under diversifying selection, based on genetic variants among Mtb clinical isolates. These might reflect adaptions to selection pressures associated with modern clinical treatment of TB. Many, but not all, of these genes under selection are related to drug resistance. Most of these studies have evaluated selection at the gene-level. However, positive selection can be evaluated on different scales, including individual sites (codons) and local regions within an ORF. In this paper, we use GenomegaMap, a Bayesian method for calculating selection, to evaluate selection of genes in the Mtb genome at all three levels. We present evidence that the intermediate analysis (windows of codons) yields the most credible list of candidate genes under selection (excluding PPE and PE_PGRS genes, which are predicted less reliably due to frequent sequencing errors). A further advantage of this approach is that it identifies specific regions within proteins that are under selective pressure, which is useful for structural and functional interpretation. In an analysis of two separate collections of Mtb clinical isolates (from Moldova; and a globally-representative set), we observed 53 and 173 significant genes under selection, with 36 % overlap. The lists of genes under selection include many drug-resistance genes, as well as other genes that have previously been reported to be under selection (resR, phoR). The specific regions under selection identified within drug-resistance genes are shown to correspond to protein structural features known to be involved in resistance, supporting accuracy of the method. Positive selection in several ESX-1-related genes was also observed, suggesting adaptation to immune pressure.