Markov chain Monte Carlo Gibbs sampler approach for estimating haplotype frequencies among multiple malaria infected human blood samples

Abstract

Background: Malaria patients can have two or more haplotypes in their blood sample making it challenging to identify which haplotypes they carry. In addition, there are challenges in measuring the type and frequency of resistant haplotypes in populations. This study presents a novel statistical method Gibbs sampler algorithm to investigate this issue. Results: The performance of the algorithm is evaluated on simulated datasets consisting of patient blood samples characterized by their multiplicity of infection (MOI) and malaria genotype. The simulation used different resistance allele frequencies (RAF) at each Single Nucleotide Polymorphisms (SNPs) and different limit of detection (LoD) of the SNPs and the MOI. The Gibbs sampler algorithm presents higher accuracy among high LoD of the SNPs or the MOI, validated, and deals with missing MOI compared to previous related statistical approaches. Conclusions: The Gibbs sampler algorithm provided robust results when faced with genotyping errors caused by LoDs and functioned well even in the absence of MOI data on individual patients.