Sign in
Evolutionary BiologybioRxiv

Genome wide association with quantitative resistance phenotypes in Mycobacterium tuberculosis reveals novel resistance genes and regulatory regions

M. R. Farhat, L. Freschi, R. Calderon, T. Ioerger, M. Snyder, C. J. Meehan, B. de Jong, L. Rigouts, A. Sloutsky, D. Kaur, S. Sunyaev, D. van Soolingen, J. Shendure, J. Sacchettini, M. Murray

Abstract

Drug resistance is threatening attempts at tuberculosis epidemic control. Molecular diagnostics for drug resistance that rely on the detection of resistance-related mutations could expedite patient care and accelerate progress in TB eradication. We performed minimum inhibitory concentration testing for 12 anti-TB drugs together with Illumina whole genome sequencing on 1452 clinical Mycobacterium tuberculosis (MTB) isolates. We then used a linear mixed model to evaluate genome wide associations between mutations in MTB genes or noncoding regions and drug resistance, followed by validation of our findings in an independent dataset of 792 patient isolates. Novel associations at 13 genomic loci were confirmed in the validation set, with 2 involving noncoding regions. We found promoter mutations to have smaller average effects on resistance levels than gene body mutations in genes where both can contribute to resistance. Enabled by a quantitative measure of resistance, we estimated the heritability of the resistance phenotype to 11 anti-TB drugs and identify a lower than expected contribution from known resistance genes. We also report the proportion of variation in resistance levels explained by the novel loci identified here. This study highlights the complexity of the genomic mechanisms associated with the MTB resistance phenotype, including the relatively large number of potentially causative or compensatory loci, and emphasizes the contribution of the noncoding portion of the genome.

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISEarle '21Barilar '24Zein-Eddine '25Mortimer '18Quistrebert '21Morey-Leon '23
86% semantic matchGenomics
Sarah G. Earle, Daniel J. Wilson·bioRxiv·Sep 14, 2021· 3 citations