GWAS for quantitative resistance phenotypes in Mycobacterium tuberculosis reveals resistance genes and regulatory regions

Maha R. Farhat (), Luca Freschi, Roger Calderon, Thomas Ioerger, Matthew Snyder, Conor J. Meehan, Bouke de Jong, Leen Rigouts, Alex Sloutsky, Devinder Kaur, Shamil Sunyaev, Dick van Soolingen, Jay Shendure, Jim Sacchettini and Megan Murray
Additional contact information
Maha R. Farhat: Harvard Medical School
Luca Freschi: Harvard Medical School
Roger Calderon: Socios en Salud
Thomas Ioerger: Texas A and M University
Matthew Snyder: University of Washington
Conor J. Meehan: Institute of Tropical Medicine
Bouke de Jong: Institute of Tropical Medicine
Leen Rigouts: Institute of Tropical Medicine
Alex Sloutsky: University of Massachusetts Medical School
Devinder Kaur: University of Massachusetts Medical School
Shamil Sunyaev: Harvard Medical School
Dick van Soolingen: National Institute for Public Health and the Environment (RIVM)
Jay Shendure: University of Washington
Jim Sacchettini: Texas A and M University
Megan Murray: Harvard Medical School

Nature Communications, 2019, vol. 10, issue 1, 1-11

Abstract: Abstract Drug resistance diagnostics that rely on the detection of resistance-related mutations could expedite patient care and TB eradication. We perform minimum inhibitory concentration testing for 12 anti-TB drugs together with Illumina whole-genome sequencing on 1452 clinical Mycobacterium tuberculosis (MTB) isolates. We evaluate genome-wide associations between mutations in MTB genes or non-coding regions and resistance, followed by validation in an independent data set of 792 patient isolates. We confirm associations at 13 non-canonical loci, with two involving non-coding regions. Promoter mutations are measured to have smaller average effects on resistance than gene body mutations. We estimate the heritability of the resistance phenotype to 11 anti-TB drugs and identify a lower than expected contribution from known resistance genes. This study highlights the complexity of the genomic mechanisms associated with the MTB resistance phenotype, including the relatively large number of potentially causal loci, and emphasizes the contribution of the non-coding portion of the genome.

Date: 2019
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DOI: 10.1038/s41467-019-10110-6

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