Identification of Mutation Combinations in Genome-Wide Association Studies: Application for Mycobacterium tuberculosis

Abstract

In genome-wide association studies, combinations of single nucleotide polymorphisms are considered to be more effective than individual mutations in linking genes to traits. Clearly, finding the most relevant combinations from tens of thousands of these mutations associated with a trait is a complicated combinatorial problem. To achieve the higher prediction performance, improve computational efficiency and results interpretation, we proposed three algorithms for searching combinations of individual mutations and applied these algorithms to 3178 samples of Mycobacterium tuberculosis strains for predicting their drug resistance to 20 drugs. The single nucleotide polymorphisms associated with drug resistance were identified in the Mycobacterium tuberculosis genome using the single-marker test, and the combinations of individual mutations were searched using the multimarker test. The data were compared with those predicted by the widely recognized Mykrobe and TB-profiler software. Comparative analysis of the results obtained showed that, excepting for ofloxacin, the combinations of individual mutations found by our algorithms for the second-line drugs have some advantages in prediction accuracy.