Abstract
BackgroundMalaria 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.ResultsThe 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.ConclusionsThe 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.
Highlights
Malaria patients can have two or more haplotypes in their blood sample making it challenging to iden‐ tify which haplotypes they carry
The multiplicity of infection (MOI) in each blood sample generated randomly by the default frequency distributions given by Jaki et al [19] with true MOI frequencies is as follows: 1–4%, 2–40%, 3–10%, 4–10%, 5–20%, 6–5%, 7–6%, 8–5%, this reflects a distribution of MOI observed in a relative intense area of malaria transmission
This approach was used to generate genetic datasets, assumed: 100 blood samples per-dataset, diallelic Single Nucleotide Polymorphisms (SNPs) either resistant or sensitive resistance allele frequencies (RAF) at each codon ranging from 1%, to 50%, and linkage equilibrium (LE) between all SNPs and MOI markers. 1000 datasets were generated and analysed assuming differing limit of detection (LoD): 0.0/0.0, 0.1/0.05, 0.2/0.1, 0.3/0.15 where the first number is LoDSNP and the second is LoDMOI
Summary
Malaria patients can have two or more haplotypes in their blood sample making it challenging to iden‐ tify which haplotypes they carry. The presence of multiple clones each of which is haploid in a blood sample makes it difficult to identify which multiple SNPs haplotypes are present in each patient. This makes estimating the frequencies of haplotypes in the malaria population from human blood samples a challenging computational task. This method works on the principle that consider
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