Regression for accurate and sensitive grading of mutations diagnostic of antibiotic resistance in Mycobacterium tuberculosis

Rapid genotype-based drug susceptibility testing for the Mycobacterium tuberculosis complex (MTBC) relies on a comprehensive knowledgebase of the genetic determinants of resistance. We built a catalog of resistance-associated mutations in MTBC using a novel regression-based approach and benchmarked it against the 2 ^nd edition of the World Health Organization mutation catalog. We trained multivariate logistic regression models on over 50,000 MTBC isolates to associate binary resistance phenotypes for 15 antitubercular drugs with variants extracted from candidate resistance genes. Regression detects 452/457 (99%) resistance-associated variants identified using the existing method ( a.k.a, SOLO method) and grades 218 (29%) more total variants than SOLO. The regression-based catalog achieves higher sensitivity on average (+3.2 percentage points, pp) than SOLO with smaller average decreases in specificity (−1.0 pp) and positive predictive value (−1.8 pp). The regression pipeline also detects isoniazid resistance compensatory mutations in ahpC and variants linked to bedaquiline and aminoglycoside hypersusceptibility. These results inform the continued development of targeted next generation sequencing, whole genome sequencing, and other commercial molecular assays for diagnosing resistance in MTBC. In addition to grading genetic variants by their associations with phenotype, regression models could potentially provide an accurate and scalable method of predicting antibiotic resistance from bacterial genetic profiles.