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

Trimarsanto, H; Amato, R; Pearson, RD; Sutanto, E; Noviyanti, R; Trianty, L; Marfurt, J; Pava, Z; Echeverry, DF; Lopera-Mesa, TM; Montenegro, LM; Tob&#243;n-Casta&#241;o, A; Grigg, MJ; Barber, B; William, T; Anstey, NM; Getachew, S; Petros, B; Aseffa, A; Assefa, A; Rahim, AG; Chau, NH; Hien, TT; Alam, MS; Khan, WA; Ley, B; Thriemer, K; Wangchuck, S; Hamedi, Y; Adam, I; Liu, Y; Gao, Q; Sriprawat, K; Ferreira, MU; Laman, M; Barry, A; Mueller, I; Lacerda, MVG; Llanos-Cuentas, A; Krudsood, S; Lon, C; Mohammed, R; Yilma, D; Pereira, DB; Espino, FEJ; Chu, CS; V&#233;lez, ID; Namaik-Larp, C; Villegas, MF; Green, JA; Koh, G; Rayner, JC; Drury, E; Gon&#231;alves, S; Simpson, V; Miotto, O; Miles, A; White, NJ; Nosten, F; Kwiatkowski, DP; Price, RN; Auburn, S

Author(s): Trimarsanto, H; Amato, R; Pearson, RD; Sutanto, E; Noviyanti, R; Trianty, L; Marfurt, J; Pava, Z; Echeverry, DF; Lopera-Mesa, TM; Montenegro, LM; Tobón-Castaño, A; Grigg, MJ; Barber, B; William, T; Anstey, NM; Getachew, S; Petros, B; Aseffa, A; Assefa, A; Rahim, AG; Chau, NH; Hien, TT; Alam, MS; Khan, WA; Ley, B; Thriemer, K; Wangchuck, S; Hamedi, Y; Adam, I; Liu, Y; Gao, Q; Sriprawat, K; Ferreira, MU; Laman, M; Barry, A; Mueller, I; Lacerda, MVG; Llanos-Cuentas, A; Krudsood, S; Lon, C; Mohammed, R; Yilma, D; Pereira, DB; Espino, FEJ; Chu, CS; Vélez, ID; Namaik-Larp, C; Villegas, MF; Green, JA; Koh, G; Rayner, JC; Drury, E; Gonçalves, S; Simpson, V; Miotto, O; Miles, A; White, NJ; Nosten, F; Kwiatkowski, DP; Price, RN; Auburn, S;
Details: Publication Year 2022-12-23,Volume 5,Issue #1,Page 1411
Journal Title: Nature Communications
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.
Publisher: NPG
Keywords: Humans; *Malaria, Vivax/diagnosis/genetics; Likelihood Functions; Plasmodium vivax/genetics; Malaria; Internet
Research Division(s): Population Health And Immunity
PubMed ID: 36564617
Publisher's Version: https://doi.org/10.1038/s42003-022-04352-2
Open Access at Publisher's Site: https://doi.org/10.1038/s42003-022-04352-2
Terms of Use/Rights Notice: Refer to copyright notice on published article.
Documents: s42003-022-04352-2.pdf - (2.06MB, application/pdf)

Creation Date: 2023-01-30 02:59:57

Last Modified: 2023-01-30 03:08:57