Design and implementation of multiplexed amplicon sequencing panels to serve genomic epidemiology of infectious disease: A malaria case study. (3rd May 2022)

• Record Type: Journal Article
• Authors: LaVerriere, Emily; Schwabl, Philipp; Carrasquilla, Manuela; Taylor, Aimee R.; Johnson, Zachary M.; Shieh, Meg; Panchal, Ruchit; Straub, Timothy J.; Kuzma, Rebecca; Watson, Sean; Buckee, Caroline O.; Andrade, Carolina M.; Portugal, Silvia; Crompton, Peter D.; Traore, Boubacar; Rayner, Julian C.; Corredor, Vladimir; James, Kashana; Cox, Horace; Early, Angela M.; MacInnis, Bronwyn L.; Neafsey, Daniel E.
• Is Part Of: Molecular ecology resources. Volume 22:Number 6(2022)
• Page Start: 2285
• Page End: 2303
• Publication Date: 2022-05-03
• Subjects: amplicon sequencing -- epidemiology -- genome -- genotyping -- malaria -- relatedness; Molecular ecology -- Periodicals; 572.8
• DOI: 10.1111/1755-0998.13622
• Languages: English
• ISSNs: 1755-098X

Abstract

Abstract: Multiplexed PCR amplicon sequencing (AmpSeq) is an increasingly popular application for cost‐effective monitoring of threatened species and managed wildlife populations, and shows strong potential for the genomic epidemiology of infectious disease. AmpSeq data from infectious microbes can inform disease control in multiple ways, such as by measuring drug resistance marker prevalence, distinguishing imported from local cases, and determining the effectiveness of therapeutics. We describe the design and comparative evaluation of two new AmpSeq assays for Plasmodium falciparum malaria parasites: a four‐locus panel ("4CAST") composed of highly diverse antigens, and a 129‐locus panel ("AMPLseq") composed of drug resistance markers, highly diverse loci for inferring relatedness, and a locus to detect Plasmodium vivax co‐infection. We explore the performance of each panel in various public health use cases with in silico simulations as well as empirical experiments. The 4CAST panel appears highly suitable for evaluating the number of distinct parasite strains within samples (complexity of infection), showing strong performance across a wide range of parasitaemia levels without a DNA pre‐amplification step. For relatedness inference, the larger AMPLseq panel performs similarly to two existing panels of comparable size, despite differences in the data and approach used for designing each panel. Finally, we describe an R package (paneljudge) that facilitates the design and comparative evaluation of genetic panels for relatedness estimation, and we provide general guidance on the design and implementation of AmpSeq panels for the genomic epidemiology of infectious disease.