Table 3.

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

Method	Details of approach	Key recent studies and advances achieved in identifying drug-resistance-associated genetic variants	Availability	Reference(s)
bugwas	Uses linear mixed models with a correction for population stratification. Uses SNPs identified through mapping to a reference	Applied 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]	https://github.com/sgearle/bugwas	[99, 100]
SEER	Uses logistic and linear regression with a correction for population stratification. Uses SNPs identified through mapping to a reference	Initially applied to Streptococcus. To date, has not been applied to M. tuberculosis	https://github.com/johnlees/seer/wiki	[101]
treeWAS	Uses a phylogenetic test to identify convergent evolution using kmers, which can detect both individual variants and gene presence or absence agnostic of a reference	Initially applied to Neisseria meningitidis. Has not yet been applied to M. tuberculosis	https://github.com/caitiecollins/treeWAS	[102, 103]
phyC	Uses phylogenetic tests to identify convergent evolution, using SNPs identified through mapping to a reference	Identified 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]	https://bitbucket.org/rpetit3/visa-gwas	[7, 59, 102]

Abbreviation: GWAS genome-wide association study, SNP single nucleotide polymorphism