Journal of Hematology & Thromboembolic Diseases
Open Access

Abstract

Using Bayesian Models to Locate Mutations for HBV Drug Resistance

Gadareth Higgs, Zhixiang Lin, Valeria Cento, Valentina Svicher, Shilpa Hattangadi and Jing Zhang

Introduction: The Hepatitis B virus (HBV) is very common, and has been difficult to treat, mainly because of the high mutation rate of the polymerase gene of its reverse transcriptase. The aim of our study was to use Bayesian statistics to determine the positions of mutations within the HBV genome.

Material and methods: The sequence data was derived from 73-treatment naïve and 215 treatment failures, of various drugs, from patient data provided by collaborators at the University of Tor Vergata. The Metropolis-Hastings algorithm was applied to the data to determine the mutation locations that correlate with drug resistance.

Results: For amino acid positions 80-250, nineteen positions were shown to have mutated in the treatment failure group. Fifteen of the nineteen positions were in the D genotype of HBV, while the other four were within the A genotype originating from the drug lamivudine (LMV). For amino acid positions 250-344, sixteen positions were mutated with seven of the sixteen originating from LMV in the D genotype. Four mutations originated from LMV in the A genotype.

Conclusion: This research identified previously unknown mutation positions and confirmed positions identified in previous research. Collaborators at the University of Rome, Tor Vergata, have validated the mutated positions experimentally with a 454-pyrosequencer. It is hoped that knowledge of these mutations would lead to improved treatment options. Also, with increased availability of genomic data, future research can be done on a larger HBV dataset and for other diseases.