A biochemically-interpretable machine learning classifier for microbial GWAS

Erol S. Kavvas, Laurence Yang, Jonathan M. Monk, David Heckmann and Bernhard O. Palsson ()
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Erol S. Kavvas: University of California
Laurence Yang: Queen’s University
Jonathan M. Monk: University of California
David Heckmann: University of California
Bernhard O. Palsson: University of California

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: Abstract Current machine learning classifiers have successfully been applied to whole-genome sequencing data to identify genetic determinants of antimicrobial resistance (AMR), but they lack causal interpretation. Here we present a metabolic model-based machine learning classifier, named Metabolic Allele Classifier (MAC), that uses flux balance analysis to estimate the biochemical effects of alleles. We apply the MAC to a dataset of 1595 drug-tested Mycobacterium tuberculosis strains and show that MACs predict AMR phenotypes with accuracy on par with mechanism-agnostic machine learning models (isoniazid AUC = 0.93) while enabling a biochemical interpretation of the genotype-phenotype map. Interpretation of MACs for three antibiotics (pyrazinamide, para-aminosalicylic acid, and isoniazid) recapitulates known AMR mechanisms and suggest a biochemical basis for how the identified alleles cause AMR. Extending flux balance analysis to identify accurate sequence classifiers thus contributes mechanistic insights to GWAS, a field thus far dominated by mechanism-agnostic results.

Date: 2020
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DOI: 10.1038/s41467-020-16310-9

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