Abstract
BackgroundHaplotypes are important in anti-malarial drug resistance because genes encoding drug resistance may accumulate mutations at several codons in the same gene, each mutation increasing the level of drug resistance and, possibly, reducing the metabolic costs of previous mutation. Patients often have two or more haplotypes in their blood sample which may make it impossible to identify exactly which haplotypes they carry, and hence to measure the type and frequency of resistant haplotypes in the malaria population.ResultsThis study presents two novel statistical methods expectation–maximization (EM) and Markov chain Monte Carlo (MCMC) algorithms to investigate this issue. The performance of the algorithms is evaluated on simulated datasets consisting of patient blood characterized by their multiplicity of infection (MOI) and malaria genotype. The datasets are generated using different resistance allele frequencies (RAF) at each single nucleotide polymorphisms (SNPs) and different limit of detection (LoD) of the SNPs and the MOI. The EM and the MCMC algorithm are validated and appear more accurate, faster and slightly less affected by LoD of the SNPs and the MOI compared to previous related statistical approaches.ConclusionsThe EM and the MCMC algorithms perform well when analysing malaria genetic data obtained from infected human blood samples. The results are robust to genotyping errors caused by LoDs and function well even in the absence of MOI data on individual patients.Electronic supplementary materialThe online version of this article (doi:10.1186/s12936-016-1473-5) contains supplementary material, which is available to authorized users.
Highlights
Haplotypes are important in tracking anti-malarial drug resistance because genes encoding drug resistance may accumulate mutations at several codons in the same gene, each mutation increasing the level of drug resistance and possibly, reducing the metabolic costs of previous mutation
The aim of the present study is to present two novel approaches i.e. Markov chain Monte Carlo (MCMC) and EM algorithms for haplotype reconstruction with known or unknown multiplicity of infection (MOI), and to compare the results to those obtained from the related statistical approaches described above; In addition, quantify the impact of misclassified observed genotype and examine the accuracy of the various method in estimating the population haplotype frequency
The multiplicity of infection (MOI) in each blood sample is generated randomly by the default frequency distributions given by Jaki et al [8] i.e. with “population” MOI frequencies as follows: = 1–4 %, 2–40 %, 3–10 %, 4–10 %, 5–20 %, 6–5 %, 7–6 %, 8–5 % [8]; this reflects a distribution of MOI observed in a relative intense area of malaria transmission
Summary
Haplotypes are important in anti-malarial drug resistance because genes encoding drug resistance may accumulate mutations at several codons in the same gene, each mutation increasing the level of drug resistance and, possibly, reducing the metabolic costs of previous mutation. The presence of multiple clones (each of which is haploid) in a blood sample often makes it impossible to identify what 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. Haplotypes are important in tracking anti-malarial drug resistance because genes encoding drug resistance may accumulate mutations at several codons in the same gene, each mutation increasing the level of drug resistance and possibly, reducing the metabolic costs of previous mutation
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