Multivariable regression models improve accuracy and sensitive grading of antibiotic resistance mutations in Mycobacterium tuberculosis.

ABSTRACT: Rapid genotype-based drug susceptibility testing for the Mycobacterium tuberculosis complex (MTBC) relies on a comprehensive knowledgebase of the genetic determinants of resistance. Here we present a catalogue of resistance-associated mutations using a regression-based approach and benchmark it against the 2nd edition of the World Health Organisation (WHO) mutation catalogue. We train multivariate logistic regression models on over 52,000 MTBC isolates to associate binary resistance phenotypes for 15 antitubercular drugs with variants extracted from candidate resistance genes. Regression detects 450/457 (98%) resistance-associated variants identified using the existing method (a.k.a, SOLO method) and grades 221 (29%) more total variants than SOLO. The regression-based catalogue achieves hi

SUBMITTER: Kulkarni SG 

PROVIDER: S-EPMC11876447 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature