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. 2014 May 8;9(5):e96486.
doi: 10.1371/journal.pone.0096486. eCollection 2014.

A genome wide association study of Plasmodium falciparum susceptibility to 22 antimalarial drugs in Kenya

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A genome wide association study of Plasmodium falciparum susceptibility to 22 antimalarial drugs in Kenya

Jason P Wendler et al. PLoS One. .

Abstract

Background: Drug resistance remains a chief concern for malaria control. In order to determine the genetic markers of drug resistant parasites, we tested the genome-wide associations (GWA) of sequence-based genotypes from 35 Kenyan P. falciparum parasites with the activities of 22 antimalarial drugs.

Methods and principal findings: Parasites isolated from children with acute febrile malaria were adapted to culture, and sensitivity was determined by in vitro growth in the presence of anti-malarial drugs. Parasites were genotyped using whole genome sequencing techniques. Associations between 6250 single nucleotide polymorphisms (SNPs) and resistance to individual anti-malarial agents were determined, with false discovery rate adjustment for multiple hypothesis testing. We identified expected associations in the pfcrt region with chloroquine (CQ) activity, and other novel loci associated with amodiaquine, quinazoline, and quinine activities. Signals for CQ and primaquine (PQ) overlap in and around pfcrt, and interestingly the phenotypes are inversely related for these two drugs. We catalog the variation in dhfr, dhps, mdr1, nhe, and crt, including novel SNPs, and confirm the presence of a dhfr-164L quadruple mutant in coastal Kenya. Mutations implicated in sulfadoxine-pyrimethamine resistance are at or near fixation in this sample set.

Conclusions/significance: Sequence-based GWA studies are powerful tools for phenotypic association tests. Using this approach on falciparum parasites from coastal Kenya we identified known and previously unreported genes associated with phenotypic resistance to anti-malarial drugs, and observe in high-resolution haplotype visualizations a possible signature of an inverse selective relationship between CQ and PQ.

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Conflict of interest statement

Competing Interests: Philip Bejon and Alexis Nzila are PLOS ONE editorial board members. This does not alter the authors' adherence to PLOS ONE Editorial policies and criteria.

Figures

Figure 1
Figure 1. Manhattan plot of genome-wide associations with CQ activities from 35 parasite isolates.
Horizontal axis is genome position, and vertical axis is –log10(p-value). Chromosomes alternate yellow and red, starting from chromosome 1 on the left. Yellow spire on chromosome 7 is in the region of pfcrt.
Figure 2
Figure 2. Manhattan plot of genome-wide associations with PQ activities from 35 parasite isolates.
Horizontal axis is genome position, and vertical axis is –log10(p-value). Chromosomes alternate yellow and red, starting from chromosome 1 on the left. Yellow spire on chromosome 7 is in the region of pfcrt.
Figure 3
Figure 3. Cluster plot of drug correlations.
Red to blue indicates the degree of positive to negative correlation. Significance levels of spearman rank tests are indicated with stars in each box (see legend).
Figure 4
Figure 4. Haplotype plot for pfcrt (MAL7P1.27), sorted by CQ and PQ activities.
Left panel is sorted top to bottom by increasing CQ IC50, and the right panel is sorted by PQ IC50. Each row represents a sample, and each column a potential SNP. Drug activity is shown as increasing green intensity in the far left column of each plot. Blue cells indicate positions matching the reference genome, and red the alternate allele. Mixed infections are represented by blending of red and blue, proportional to the within-sample allele frequencies. White cells indicate missing data. Nonsynonymous SNPs are labeled with the amino acid substitution along the bottom, and with a dot if synonymous.

References

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