1. A molecular barcode and web-based data analysis tool to identify imported Plasmodium vivax malaria
- Author
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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 Assefa, Awab 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, Pearson, Richard D [0000-0002-7386-3566], Sutanto, Edwin [0000-0002-2997-4648], Marfurt, Jutta [0000-0002-3846-0825], Echeverry, Diego F [0000-0003-0301-4478], Tobón-Castaño, Alberto [0000-0002-1671-2649], Grigg, Matthew J [0000-0001-9914-8352], Alam, Mohammad S [0000-0001-8330-5499], Khan, Wasif A [0000-0002-7650-8068], Ley, Benedikt [0000-0002-5734-0845], Mueller, Ivo [0000-0001-6554-6889], Lacerda, Marcus VG [0000-0003-3374-9985], Pereira, Dhelio B [0000-0002-7761-5498], Espino, Fe EJ [0000-0003-1690-1711], Koh, Gavin [0000-0002-7336-1566], Rayner, Julian C [0000-0002-9835-1014], Drury, Eleanor [0000-0002-9887-6961], Miotto, Olivo [0000-0001-8060-6771], Nosten, Francois [0000-0002-7951-0745], Kwiatkowski, Dominic P [0000-0002-5023-0176], Price, Ric N [0000-0003-2000-2874], Auburn, Sarah [0000-0002-4638-536X], and Apollo - University of Cambridge Repository
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Likelihood Functions ,Internet ,Molecular medicine ,45 ,Epidemiology ,692/308/2056 ,article ,Medicine (miscellaneous) ,631/114/1305 ,General Biochemistry, Genetics and Molecular Biology ,692/4017 ,Malaria ,Genetics research ,Machine learning ,Malaria, Vivax ,692/699/255/1629 ,Humans ,PLASMODIUM ,692/308/174 ,Plasmodium vivax ,General Agricultural and Biological Sciences - Abstract
Funder: HT was supported by a Charles Darwin University International PhD Scholarship (CDIPS), Funder: Malaysian Ministry of Health (BP00500420), 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.
- Published
- 2022