Last Updated: 17/09/2024
Developing and refining methods of analysing malaria genetic data obtained from infected human blood samples
Objectives
The spread of drug-resistant malaria is a potent threat to human health in much of the developing world. Resistance can be tracked and quantified provided we know the mutations responsible for resistance (true in many cases) and these data form the basis for many key genetic surveillance programmes aimed at ensuring the continued provision of effective drugs.
The requisite malaria genetic data can, in principle, be obtained from analysing infected human blood samples but people are often simultaneously infected by several malaria clones and this makes it impossible to clearly identify the genetic composition of any single clone.
This program of research aims to develop and refine suitable methodologies that can overcome these problems of data interpretation allowing good quality genetic data on the frequency of drug resistance to inform public health policy. These methods of analysis also have more general application in understanding the genetic epidemiology of malaria transmission.
Liverpool School of Tropical Medicine (LSTM), United Kingdom
The easiest method of obtaining malaria genetic data from natural transmission settings is by genotyping infected human blood samples. There are two major methodological issues in analysing these data. Firstly, we can only identify alleles as being present or absent in the blood sample so that, for example, if a blood sample contains 5 malaria clones and both wildtype and mutant alleles are detected at a locus, it is impossible to discern if that human infection contains 1, 2, 3 or 4 mutant clones; this precludes estimating population allele frequencies by simple counting.
The second problem is that we may detect several alleles at different genetic positions in the same blood sample but we cannot always recover the individual malaria multi-locus haplotypes. These issues become extremely important when trying to assess the level of drug resistance in the malaria populations but have more general application in understanding the genetic epidemiology of malaria transmission.
We have developed maximum likelihood (ML) methods to infer allele and haplotype frequencies from infected blood samples but these need to be updated to reflect different assumptions about malaria genetic epidemiology (for example, the possibility of co-transmission of genetically-related clones), to reflect differences in data structure (for example, the number of clones in a human blood sample, the MOI, may not be reported), and to reflect differing assay sensitivities and the presence of genotyping errors in the field datasets. We will address these issues by extending the ML approach and developing a parallel Bayesian approach.
May 2013 — May 2016
$526,205
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