2021
DOI: 10.1111/mec.16029
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The impact of indoor residual spraying on Plasmodium falciparum microsatellite variation in an area of high seasonal malaria transmission in Ghana, West Africa

Abstract: Here, we report the first population genetic study to examine the impact of indoor residual spraying (IRS) on Plasmodium falciparum in humans. This study was conducted in an area of high seasonal malaria transmission in Bongo District, Ghana. IRS was implemented during the dry season (November–May) in three consecutive years between 2013 and 2015 to reduce transmission and attempt to bottleneck the parasite population in humans towards lower diversity with greater linkage disequilibrium. The study was done aga… Show more

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Cited by 8 publications
(13 citation statements)
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References 99 publications
(158 reference statements)
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“…Of the remaining monoclonal samples that were able to be analysed, our results from SNP barcodes did not reflect diversity, similarity and structure estimates as found in other studies using a higher magnitude of genome-wide SNPs ( Mobegi et al, 2014 ; Daniels et al, 2015 ; Amambua-Ngwa et al, 2019 ; Moser et al, 2020 ; Verity et al, 2020 ; MalariaGEN et al, 2021 ), putatively neutral microsatellites ( Anderson et al, 2000 ; Mobegi et al, 2012 ; Duffy et al, 2017 ; Argyropoulos et al, 2021 ) and antigenic markers ( Ruybal-Pesántez et al, 2017b ; Day et al, 2017 ; Rorick et al, 2018 ). One possible explanation for these observed discrepancies is the “ascertainment bias” phenomenon, where polymorphisms that were discovered in few samples or locations can result in a deviation from an expected allele frequency distribution ( Kuhner et al, 2000 ; Wakeley et al, 2001 ; Helyar et al, 2011 ).…”
Section: Discussioncontrasting
confidence: 67%
See 3 more Smart Citations
“…Of the remaining monoclonal samples that were able to be analysed, our results from SNP barcodes did not reflect diversity, similarity and structure estimates as found in other studies using a higher magnitude of genome-wide SNPs ( Mobegi et al, 2014 ; Daniels et al, 2015 ; Amambua-Ngwa et al, 2019 ; Moser et al, 2020 ; Verity et al, 2020 ; MalariaGEN et al, 2021 ), putatively neutral microsatellites ( Anderson et al, 2000 ; Mobegi et al, 2012 ; Duffy et al, 2017 ; Argyropoulos et al, 2021 ) and antigenic markers ( Ruybal-Pesántez et al, 2017b ; Day et al, 2017 ; Rorick et al, 2018 ). One possible explanation for these observed discrepancies is the “ascertainment bias” phenomenon, where polymorphisms that were discovered in few samples or locations can result in a deviation from an expected allele frequency distribution ( Kuhner et al, 2000 ; Wakeley et al, 2001 ; Helyar et al, 2011 ).…”
Section: Discussioncontrasting
confidence: 67%
“…The answer likely lies in the use of polymorphic markers such as putatively neutral markers that permit the inclusion of “dominant” infections (e.g., short tandem repeats (STRs) or microsatellites) ( Anderson et al, 1999 ; Tessema et al, 2020 ). For example, microsatellites were able to resolve global P. falciparum structure with only 12 markers ( Anderson et al, 2000 ), while 9-10 microsatellite markers were able to give realistic assessments of these measures related to both long-lasting insecticidal net (LLIN) ( Kattenberg et al, 2019 ) and indoor residual spraying (IRS) ( Argyropoulos et al, 2021 ) interventions, respectively, in moderate-to-high transmission settings. With respect to neutral variation, STR loci are more useful to detect recent population expansions than SNPs as they accumulate new mutations at a faster rate, are multiallelic often in excess of 10 alleles, and have more private alleles; thus they remain the most informative putatively neutral markers in population genetic studies across many organisms ( Ellegren, 2004 ; Selkoe and Toonen, 2006 ; Guichoux et al, 2011 ), including in P. falciparum and P. vivax genomes across various geographic populations ( Han et al, 2022 ).…”
Section: Discussionmentioning
confidence: 99%
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“…Plasmodium falciparum genetic data can complement existing epidemiological surveillance strategies to inform transmission dynamics and patterns. For example, malaria genomics has been used to understand changes in transmission intensity (1) including following the implementation of interventions (2)(3)(4), to monitor the emergence and spread of drug (5)(6)(7) and diagnostic resistance (8,9), and to identify patterns of connectivity between parasite populations impacting control efforts (10)(11)(12)(13). Aside from genomic applications associated with phenotypic signatures in the parasite populations, such as drug or diagnostic resistance, many applications of malaria genomics rely on identifying levels of parasite relatedness (14,15).…”
Section: Introductionmentioning
confidence: 99%