Back

PvGAP: Development of a globally-applicable, highly-multiplexed microhaplotype amplicon panel for Plasmodium vivax

Hubbard, A.; Solares, E.; Bradley, L.; Jeang, B.; Yewhalaw, D.; Janies, D.; Lo, E.; Yan, G.; Hemming-Schroeder, E.

2025-05-02 infectious diseases
10.1101/2025.04.30.25326751 medRxiv
Show abstract

BackgroundPlasmodium vivax malaria research has yet to fully benefit from the advances in genomic surveillance that have revolutionized P. falciparum epidemiology. Closing this gap is critical because genomic tools are necessary to monitor the spread of drug resistance, classify infections as local or imported, and distinguish reinfection, recrudescence, and relapse. To achieve these objectives, microhaplotype marker panels that allow powerful genotyping of polyclonal infections are needed. MethodsWe designed a Globally-applicable Amplicon Panel for P. vivax (PvGAP), selecting targets based on both genetic diversity and genetic distance from each other to maximize discriminatory capability between geographic regions. We evaluated this panel with field samples from Ethiopia and in silico using whole genomes from the MalariaGEN Pv4 database. ResultsPvGAP has 80 high diversity targets suitable for population genomics and eight targets of specific epidemiological interest, such as putative markers of drug resistance. We demonstrate PvGAP achieves robust amplification with field data and that it provides competitive accuracy for relatedness inference in three disparate geographic regions. ConclusionsPvGAP joins existing P. vivax panels as a cost effective and practical option for genomic epidemiology of this neglected disease. It will support drug resistance surveillance, discrimination of local and imported cases, and it may aid in separating reinfection, recrudescence, and relapse in therapeutic efficacy studies, all critical needs of National Malaria Control Programs.

Published in The Journal of Infectious Diseases (predicted rank #12) · training set

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.