Genetic surveillance in the Greater Mekong subregion and South Asia to support malaria control and elimination

Abstract

Background:National Malaria Control Programmes (NMCPs) currently make limited use of parasite genetic data. We have developed GenRe-Mekong, a platform for genetic surveillance of malaria in the Greater Mekong Subregion (GMS) that enables NMCPs to implement large-scale surveillance projects by integrating simple sample collection procedures in routine public health procedures.Methods:Samples from symptomatic patients are processed by SpotMalaria, a high-throughput system that produces a comprehensive set of genotypes comprising several drug resistance markers, species markers and a genomic barcode. GenRe-Mekong delivers Genetic Report Cards, a compendium of genotypes and phenotype predictions used to map prevalence of resistance to multiple drugs.Results:GenRe-Mekong has worked with NMCPs and research projects in eight countries, processing 9623 samples from clinical cases. Monitoring resistance markers has been valuable for tracking the rapid spread of parasites resistant to the dihydroartemisinin-piperaquine combination therapy. In Vietnam and Laos, GenRe-Mekong data have provided novel knowledge about the spread of these resistant strains into previously unaffected provinces, informing decision-making by NMCPs.Conclusions:GenRe-Mekong provides detailed knowledge about drug resistance at a local level, and facilitates data sharing at a regional level, enabling cross-border resistance monitoring and providing the public health community with valuable insights. The project provides a rich open data resource to benefit the entire malaria community.Funding:The GenRe-Mekong project is funded by the Bill and Melinda Gates Foundation (OPP11188166, OPP1204268). Genotyping and sequencing were funded by the Wellcome Trust (098051, 206194, 203141, 090770, 204911, 106698/B/14/Z) and Medical Research Council (G0600718). A proportion of samples were collected with the support of the UK Department for International Development (201900, M006212), and Intramural Research Program of the National Institute of Allergy and Infectious Diseases.

Resolving drug selection and migration in an inbred South American Plasmodium falciparum population with identity-by-descent analysis

“…Recent efforts in the malaria field have borne several amplicon panels with wide geographic breadth that can quickly and affordably genotype hundreds or thousands of samples 22, 60, 61 . While whole-genome sequencing will remain the bedrock of selection analyses, these methodological advances in targeted sequencing will facilitate the use of IBD analysis with confidence intervals for describing local population structure (as we do here) 62, 63 , measuring connectivity between populations 64 , identifying likely importation event 65 , and tracking changes in transmission 66 .…”

“…While whole-genome sequencing will remain the bedrock of selection analyses, these methodological advances in targeted sequencing will facilitate the use of IBD analysis with confidence intervals for describing local population structure (as we do here) 62, 63 , measuring connectivity between populations 64 , identifying likely importation event 65 , and tracking changes in transmission 66 . These advances in genomic epidemiology are enhancing established malaria surveillance toolkits and enabling responses tailored to individual country’s needs 22, 60, 67, 68 .…”

Spatio-temporal distribution of antimalarial drug resistant gene mutations in a Plasmodium falciparum parasite population from Kilifi, Kenya: A 25-year retrospective study

“…Here, we describe the spatial and temporal distribution of antimalarial drug resistance markers in Plasmodium falciparum parasites collected in 1994-2018 across Kilifi county, Kenya. We also describe the genetic structure of the parasite population using a set of 101 'genetic barcode' SNPs 26 not directly associated with drug resistance, but which are spaced throughout the genome, and analyse whether the specific temporal and spatial patterns observed in the drug resistance markers can be picked up using these genome-wide distributed barcode SNPs.…”

Distinguishing gene flow between malaria parasite populations

“…However, there is currently only limited guidance on the size [ 5 ] and type [ 16 ] of genetic data needed for measuring gene flow between malaria parasite populations, and none specific to the questions of ranking and classification. Most studies of this kind use genetic data of widely varying sizes and types, including both whole genome sequence data and SNP ‘barcode’ data (typically consisting of 24- to 100-SNPs [ 17 , 18 ]), limiting generalizability and comparability between studies. Importantly, population-level summaries of differentiation and relatedness capture genetic signatures resulting from multiple, interconnected processes, including recent prior and ancestral migration, changes in population size over time, and selection.…”