PT  - JOURNAL ARTICLE
AU  - Trimarsanto, Hidayat
AU  - Amato, Roberto
AU  - Pearson, Richard D
AU  - Sutanto, Edwin
AU  - Noviyanti, Rintis
AU  - Trianty, Leily
AU  - Marfurt, Jutta
AU  - Pava, Zuleima
AU  - Echeverry, Diego F
AU  - Lopera-Mesa, Tatiana M
AU  - Montenegro, Lidia Madeline
AU  - Tobón-Castaño, Alberto
AU  - Grigg, Matthew J
AU  - Barber, Bridget
AU  - William, Timothy
AU  - Anstey, Nicholas M
AU  - Getachew, Sisay
AU  - Petros, Beyene
AU  - Aseffa, Abraham
AU  - Assefa, Ashenafi
AU  - Rahim, Awab Ghulam
AU  - Chau, Nguyen Hoang
AU  - Hien, Tran Tinh
AU  - Alam, Mohammad Shafiul
AU  - Khan, Wasif A
AU  - Ley, Benedikt
AU  - Thriemer, Kamala
AU  - Wangchuck, Sonam
AU  - Hamedi, Yaghoob
AU  - Adam, Ishag
AU  - Liu, Yaobao
AU  - Gao, Qi
AU  - Sriprawat, Kanlaya
AU  - Ferreira, Marcelo U
AU  - Barry, Alyssa
AU  - Mueller, Ivo
AU  - Drury, Eleanor
AU  - Goncalves, Sonia
AU  - Simpson, Victoria
AU  - Miotto, Olivo
AU  - Miles, Alistair
AU  - White, Nicholas J
AU  - Nosten, Francois
AU  - Kwiatkowski, Dominic P
AU  - Price, Ric N
AU  - Auburn, Sarah
TI  - A molecular barcode and online tool to identify and map imported infection with <em>Plasmodium vivax</em>
AID  - 10.1101/776781
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 776781
4099  - http://biorxiv.org/content/early/2019/09/24/776781.short
4100  - http://biorxiv.org/content/early/2019/09/24/776781.full
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.