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] | ||
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 | [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 | ||
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] |