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A biochemically-interpretable machine learning classifier for microbial GWAS.

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.

journal_name

Nat Commun

journal_title

Nature communications

authors

Kavvas ES,Yang L,Monk JM,Heckmann D,Palsson BO

doi

10.1038/s41467-020-16310-9

subject

Has Abstract

pub_date

2020-05-22 00:00:00

pages

2580

issue

1

issn

2041-1723

pii

10.1038/s41467-020-16310-9

journal_volume

11

pub_type

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