Biology subjects

Simpson, V.

Publications and source records attributed to Simpson, V..

3 recordsLinked to original sources

bioRxiv2024

A complex Plasmodium falciparum cryptotype circulating at low frequency across the African continent

The population structure of the malaria parasite Plasmodium falciparum can reveal underlying demographic and adaptive evolutionary processes. Here, we analyse population structure in 4,376 P. falciparum genomes from 21 countries across Africa. We identified a strongly differentiated cluster of parasites, comprising [~]1.2% of samples analysed, geographically distributed over 13 countries across the continent. Members of this cluster, named AF1, carry a genetic background consisting of a large number of highly differentiated variants, rarely observed outside this cluster, at a multitude of genomic loci distributed across most chromosomes. At these loci, the AF1 haplotypes appear to have common ancestry, irrespective of the sampling location; outside the shared loci, however, AF1 members are genetically similar to their sympatric parasites. AF1 parasites sharing up to 23 genomic co-inherited regions were found in all major regions of Africa, at locations over 7,000 km apart. We coined the term cryptotype to describe a complex common background which is geographically widespread, but concealed by genomic regions of local origin. Most AF1 differentiated variants are functionally related, comprising structural variations and single nucleotide polymorphisms in components of the MSP1 complex and several other genes involved in interactions with red blood cells, including invasion and erythrocyte antigen export. We propose that AF1 parasites have adapted to some as yet unidentified evolutionary niche, by acquiring a complex compendium of interacting variants that rarely circulate separately in Africa. As the cryptotype spread across the continent, it appears to have been maintained mostly intact in spite of recombination events, suggesting a selective advantage. It is possible that other cryptotypes circulate in Africa, and new analysis methods may be needed to identify them.

genomics↗

bioRxiv2019

Genome variation and population structure among 1,142 mosquitoes of the African malaria vector species Anopheles gambiae and Anopheles coluzzii

Mosquito control remains a central pillar of efforts to reduce malaria burden in sub-Saharan Africa. However, insecticide resistance is entrenched in malaria vector populations, and countries with high malaria burden face a daunting challenge to sustain malaria control with a limited set of surveillance and intervention tools. Here we report on the second phase of a project to build an open resource of high quality data on genome variation among natural populations of the major African malaria vector species Anopheles gambiae and Anopheles coluzzii. We analysed whole genomes of 1,142 individual mosquitoes sampled from the wild in 13 African countries, and a further 234 individuals comprising parents and progeny of 11 lab crosses. The data resource includes high confidence single nucleotide polymorphism (SNP) calls at 57 million variable sites, genome-wide copy number variation (CNV) calls, and haplotypes phased at biallelic SNPs. We used these data to analyse genetic population structure, and characterise genetic diversity within and between populations. We also illustrate the utility of these data by investigating species differences in isolation by distance, genetic variation within proposed gene drive target sequences, and patterns of resistance to pyrethroid insecticides. This data resource provides a foundation for developing new operational systems for molecular surveillance, and for accelerating research and development of new vector control tools.

genomics↗

bioRxiv2019

A molecular barcode and online tool to identify and map imported infection with Plasmodium vivax

Imported cases present a considerable challenge to the elimination of malaria. Traditionally, patient travel history has been used to identify imported cases, but the long-latency liver stages confound this approach in Plasmodium vivax. Molecular tools to identify and map imported cases offer a more robust approach, that can be combined with drug resistance and other surveillance markers in high-throughput, population-based genotyping frameworks. Using a machine learning approach incorporating hierarchical FST (HFST) and decision tree (DT) analysis applied to 831 P. vivax genomes from 20 countries, we identified a 28-Single Nucleotide Polymorphism (SNP) barcode with high capacity to predict the country of origin. The Matthews correlation coefficient (MCC), which provides a measure of the quality of the classifications, ranging from -1 (total disagreement) to 1 (perfect prediction), exceeded 0.9 in 15 countries in cross-validation evaluations. When combined with an existing 37-SNP P. vivax barcode, the 65-SNP panel exhibits MCC scores exceeding 0.9 in 17 countries with up to 30% missing data. As a secondary objective, several genes were identified with moderate MCC scores (median MCC range from 0.54-0.68), amenable as markers for rapid testing using low-throughput genotyping approaches. A likelihood-based classifier framework was established, that supports analysis of missing data and polyclonal infections. To facilitate investigator-lead analyses, the likelihood framework is provided as a web-based, open-access platform (vivaxGEN-geo) to support the analysis and interpretation of data produced either at the 28-SNP core or full 65-SNP barcode. These tools can be used by malaria control programs to identify the main reservoirs of infection so that resources can be focused to where they are needed most.

molecular biology↗