A molecular barcode and web-based data analysis tool to identify imported Plasmodium vivax malaria

Hidayat Trimarsanto, Roberto Amato, Richard D. Pearson, Edwin Sutanto, Rintis Noviyanti, Leily Trianty, Jutta Marfurt, Zuleima Pava, Diego F. Echeverry, Tatiana M. Lopera-Mesa, Lidia M. Montenegro, Alberto Tobón-Castaño, Matthew J. Grigg, Bridget Barber, Timothy William, Nicholas M. Anstey, Sisay Getachew, Beyene Petros, Abraham Aseffa, Ashenafi AssefaAwab G. Rahim, Nguyen H. Chau, Tran T. Hien, Mohammad S. Alam, Wasif A. Khan, Benedikt Ley, Kamala Thriemer, Sonam Wangchuck, Yaghoob Hamedi, Ishag Adam, Yaobao Liu, Qi Gao, Kanlaya Sriprawat, Marcelo U. Ferreira, Moses Laman, Alyssa Barry, Ivo Mueller, Marcus V. G. Lacerda, Alejandro Llanos-Cuentas, Srivicha Krudsood, Chanthap Lon, Rezika Mohammed, Daniel Yilma, Dhelio B. Pereira, Fe E. J. Espino, Cindy S. Chu, Iván D. Vélez, Chayadol Namaik-larp, Maria F. Villegas, Justin A. Green, Gavin Koh, Julian C. Rayner, Eleanor Drury, Sónia Gonçalves, Victoria Simpson, Olivo Miotto, Alistair Miles, Nicholas J. White, Francois Nosten, Dominic P. Kwiatkowski, Ric N. Price, Sarah Auburn

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Abstract

Traditionally, patient travel history has been used to distinguish imported from autochthonous malaria cases, but the dormant liver stages of Plasmodium vivax confound this approach. Molecular tools offer an alternative method to identify, and map imported cases. Using machine learning approaches incorporating hierarchical fixation index and decision tree analyses applied to 799 P. vivax genomes from 21 countries, we identified 33-SNP, 50-SNP and 55-SNP barcodes (GEO33, GEO50 and GEO55), with high capacity to predict the infection’s country of origin. The Matthews correlation coefficient (MCC) for an existing, commonly applied 38-SNP barcode (BR38) exceeded 0.80 in 62% countries. The GEO panels outperformed BR38, with median MCCs > 0.80 in 90% countries at GEO33, and 95% at GEO50 and GEO55. An online, open-access, likelihood-based classifier framework was established to support data analysis (vivaxGEN-geo). The SNP selection and classifier methods can be readily amended for other use cases to support malaria control programs.
Original languageEnglish
Article number1411
Pages (from-to)1-10
Number of pages10
JournalCommunications Biology
Volume5
Issue number1
DOIs
Publication statusPublished - 23 Dec 2022

Bibliographical note

Funding Information:
We would like to thank the patients who contributed their samples to the study, and the health workers and field teams who assisted with the sample collections. We also thank the staff of the Wellcome Sanger Institute Sample Logistics, Sequencing, and Informatics facilities for their contributions. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. This research was funded in part by the Wellcome Trust (Senior Fellowship in Clinical Science awarded to R.N.P., 200909). The research was also funded in part by the Australian Department of Foreign Affairs and Trade (TDCRRI 72904), the Australian National Health and Medical Research Council (NHMRC) (APP2001083 awarded to S.A.), and the Bill and Melinda Gates Foundation (OPP1164105). H.T. was supported by a Charles Darwin University International PhD Scholarship (CDIPS). The patient sampling and metadata collection was funded by the Asia-Pacific Malaria Elimination Network (108-07), the Malaysian Ministry of Health (BP00500420), and the NHMRC (1037304 and 1045156; Fellowships to N.M.A. [1042072 and 1135820], B.E.B. [1088738] and M.J.G. [1074795]). M.J.G was also supported by a ‘Hot North’ Earth Career Fellowship (1131932). M.U.F is supported by a senior researcher scholarship from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil. The whole genome sequencing component of the study was supported by grants from the Medical Research Council and UK Department for International Development (M006212) and the Wellcome Trust (204911) awarded to D.P.K., and a Wellcome Trust grant (206194/Z/17/Z) awarded to D.P.K. and J.C.R. This work was supported by the Australian Centre for Research Excellence on Malaria Elimination (ACREME), funded by the NHMRC (APP 1134989).

Publisher Copyright:
© 2022, The Author(s).

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