Robust detection of point mutations involved in multidrug-resistant Mycobacterium tuberculosis in the presence of co-occurrent resistance markers

Date published

2020-12-21

Free to read from

Supervisor/s

Journal Title

Journal ISSN

Volume Title

Publisher

PLOS (Public Library of Science)

Department

Type

Article

ISSN

1553-734X

Format

Citation

Libiseller-Egger J, Phelan J, Campino S, et al., (2020) Robust detection of point mutations involved in multidrug-resistant Mycobacterium tuberculosis in the presence of co-occurrent resistance markers. PLoS Computational Biology, Volume 16, Issue 12, 2020, Article number e1008518

Abstract

Tuberculosis disease is a major global public health concern and the growing prevalence of drug-resistant Mycobacterium tuberculosis is making disease control more difficult. However, the increasing application of whole-genome sequencing as a diagnostic tool is leading to the profiling of drug resistance to inform clinical practice and treatment decision making. Computational approaches for identifying established and novel resistance-conferring mutations in genomic data include genome-wide association study (GWAS) methodologies, tests for convergent evolution and machine learning techniques. These methods may be confounded by extensive co-occurrent resistance, where statistical models for a drug include unrelated mutations known to be causing resistance to other drugs. Here, we introduce a novel ‘cannibalistic’ elimination algorithm (“Hungry, Hungry SNPos”) that attempts to remove these co-occurrent resistant variants. Using an M. tuberculosis genomic dataset for the virulent Beijing strain-type (n=3,574) with phenotypic resistance data across five drugs (isoniazid, rifampicin, ethambutol, pyrazinamide, and streptomycin), we demonstrate that this new approach is considerably more robust than traditional methods and detects resistance-associated variants too rare to be likely picked up by correlation-based techniques like GWAS

Description

Software Description

Software Language

Github

Keywords

Mycobacterium tuberculosis, genome-wide association study

DOI

Rights

Attribution 4.0 International

Relationships

Relationships

Supplements

Funder/s