TY - JOUR
T1 - A molecular barcode and web-based data analysis tool to identify imported Plasmodium vivax malaria
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 M.
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 G.
AU - Chau, Nguyen H.
AU - Hien, Tran T.
AU - Alam, Mohammad S.
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 - Laman, Moses
AU - Barry, Alyssa
AU - Mueller, Ivo
AU - Lacerda, Marcus V. G.
AU - Llanos-Cuentas, Alejandro
AU - Krudsood, Srivicha
AU - Lon, Chanthap
AU - Mohammed, Rezika
AU - Yilma, Daniel
AU - Pereira, Dhelio B.
AU - Espino, Fe E. J.
AU - Chu, Cindy S.
AU - Vélez, Iván D.
AU - Namaik-larp, Chayadol
AU - Villegas, Maria F.
AU - Green, Justin A.
AU - Koh, Gavin
AU - Rayner, Julian C.
AU - Drury, Eleanor
AU - Gonçalves, Sónia
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
N1 - 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).
PY - 2022/12/23
Y1 - 2022/12/23
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85144637810&partnerID=8YFLogxK
U2 - 10.1038/s42003-022-04352-2
DO - 10.1038/s42003-022-04352-2
M3 - Article
SN - 2399-3642
VL - 5
SP - 1
EP - 10
JO - Communications Biology
JF - Communications Biology
IS - 1
M1 - 1411
ER -