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Table 3 Publicly available software packages implementing microbial GWAS methods for identifying drug-resistance-associated genetic variants in bacteria

From: Deciphering drug resistance in Mycobacterium tuberculosis using whole-genome sequencing: progress, promise, and challenges

MethodDetails of approachKey recent studies and advances achieved in identifying drug-resistance-associated genetic variantsAvailabilityReference(s)
bugwasUses linear mixed models with a correction for population stratification. Uses SNPs identified through mapping to a referenceApplied to identify resistance to 17 drugs across 3144 isolates from four diverse species of bacteria, including M. tuberculosis [99]. Confirmed that some major known resistance determinants could be recovered. The method was recently extended in a kmer-based method based on bugwas [100][99, 100]
SEERUses logistic and linear regression with a correction for population stratification. Uses SNPs identified through mapping to a referenceInitially applied to Streptococcus. To date, has not been applied to M. tuberculosis[101]
treeWASUses a phylogenetic test to identify convergent evolution using kmers, which can detect both individual variants and gene presence or absence agnostic of a referenceInitially applied to Neisseria meningitidis. Has not yet been applied to M. tuberculosis[102, 103]
phyCUses phylogenetic tests to identify convergent evolution, using SNPs identified through mapping to a referenceIdentified 39 genomic regions that are potentially involved in resistance, and confirmed a rifampicin-conferring mutation in ponA1 [7]. Used within a mixed-regression framework to detect resistance determinants to 14 drugs in a dataset of 6465 global clinical isolates. Identified new ethionamide-resistance codons in ethA and PAS-resistance mutations in the thyX promoter [59][7, 59, 102]
  1. Abbreviation: GWAS genome-wide association study, SNP single nucleotide polymorphism