NIH Research Festival
In silico drug resistance prediction using tuberculosis (TB) whole genome sequencing (WGS) data is becoming increasingly important as diagnosis of TB moves towards direct sequencing of patient samples. Using the TB Genomic Analysis Portal (G-AP) (https://gap.tbportals.niaid.nih.gov/GenomicsPortal/), we conducted a GWAS analysis using 60 drug sensitive patients as controls and 84 extensively drug resistant (XDR) patients as cases, and a detailed in silico drug resistance analysis using a sample from an XDR patient was conducted using the G-AP- integrated tools TB Profiler, Mykrobe Predictor, and KvarQ. The GWAS analysis identified two highly significant missense variants, katG S315T (p-value 1.1e-34) and rpoB S450W (p-value 4.1e-23). Investigation of an individual sample from a patient who died from XDR TB using the three available in silico drug resistance prediction algorithms consistently predicted resistance to isoniazid, rifampicin,streptomycin, ethambutol and floroquinolones. Resistance predictions for pyrazinimide were not consistent with both TB Profiler and KvarQ predicting resistance and Mykrobe Predictor predicting sensitivity. Overall, we found that G-AP is an effective tool for the analysis of TB WGS data by demonstrating the use of the system to identify significant variants associated with drug resistance and to perform in silico drug resistance prediction for a patient with XDR TB. This tool is an important resource for both TB research and clinical practice.
Scientific Focus Area: Genetics and Genomics
This page was last updated on Friday, March 26, 2021