LSTM Home > LSTM Research > LSTM Online Archive

Markov chain Monte Carlo and expectation maximization approaches for estimation of haplotype frequencies for multiply infected human blood samples

Downloads

Downloads per month over past year

Ken-Dror, Gie and Hastings, Ian ORCID: https://orcid.org/0000-0002-1332-742X (2016) 'Markov chain Monte Carlo and expectation maximization approaches for estimation of haplotype frequencies for multiply infected human blood samples'. Malaria Journal, Vol 15, Issue 1, e430.

[img]
Preview
Text
Pub_Med_Malar_J_2016_15(1)_430.pdf - Published Version
Available under License Creative Commons Attribution.

Download (2MB) | Preview

Abstract

Background

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. 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.
Results

This 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.
Conclusions

The 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.

Item Type: Article
Additional Information: PMCID: PMC4997664
Subjects: W General Medicine. Health Professions > W 26.5 Informatics. Health informatics
QW Microbiology and Immunology > QW 45 Microbial drug resistance. General or not elsewhere classified.
WC Communicable Diseases > WC 20 Research (General)
Faculty: Department: Biological Sciences > Department of Tropical Disease Biology
Digital Object Identifer (DOI): https://doi.org/10.1186/s12936-016-1473-5
Depositing User: Stacy Murtagh
Date Deposited: 07 Sep 2016 12:23
Last Modified: 19 Sep 2019 11:29
URI: https://archive.lstmed.ac.uk/id/eprint/6108

Statistics

View details

Actions (login required)

Edit Item Edit Item