RT Journal Article
SR Electronic
T1 A molecular barcode and online tool to identify and map imported infection with <em>Plasmodium vivax</em>
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 776781
DO 10.1101/776781
A1 Trimarsanto, Hidayat
A1 Amato, Roberto
A1 Pearson, Richard D
A1 Sutanto, Edwin
A1 Noviyanti, Rintis
A1 Trianty, Leily
A1 Marfurt, Jutta
A1 Pava, Zuleima
A1 Echeverry, Diego F
A1 Lopera-Mesa, Tatiana M
A1 Montenegro, Lidia Madeline
A1 Tobón-Castaño, Alberto
A1 Grigg, Matthew J
A1 Barber, Bridget
A1 William, Timothy
A1 Anstey, Nicholas M
A1 Getachew, Sisay
A1 Petros, Beyene
A1 Aseffa, Abraham
A1 Assefa, Ashenafi
A1 Rahim, Awab Ghulam
A1 Chau, Nguyen Hoang
A1 Hien, Tran Tinh
A1 Alam, Mohammad Shafiul
A1 Khan, Wasif A
A1 Ley, Benedikt
A1 Thriemer, Kamala
A1 Wangchuck, Sonam
A1 Hamedi, Yaghoob
A1 Adam, Ishag
A1 Liu, Yaobao
A1 Gao, Qi
A1 Sriprawat, Kanlaya
A1 Ferreira, Marcelo U
A1 Barry, Alyssa
A1 Mueller, Ivo
A1 Drury, Eleanor
A1 Goncalves, Sonia
A1 Simpson, Victoria
A1 Miotto, Olivo
A1 Miles, Alistair
A1 White, Nicholas J
A1 Nosten, Francois
A1 Kwiatkowski, Dominic P
A1 Price, Ric N
A1 Auburn, Sarah
YR 2019
UL http://biorxiv.org/content/early/2019/09/24/776781.abstract
AB 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.