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. 2017 Jul 26;11(7):e0005816.
doi: 10.1371/journal.pntd.0005816. eCollection 2017 Jul.

Genome-wide analysis of ivermectin response by Onchocerca volvulus reveals that genetic drift and soft selective sweeps contribute to loss of drug sensitivity

Affiliations

Genome-wide analysis of ivermectin response by Onchocerca volvulus reveals that genetic drift and soft selective sweeps contribute to loss of drug sensitivity

Stephen R Doyle et al. PLoS Negl Trop Dis. .

Abstract

Background: Treatment of onchocerciasis using mass ivermectin administration has reduced morbidity and transmission throughout Africa and Central/South America. Mass drug administration is likely to exert selection pressure on parasites, and phenotypic and genetic changes in several Onchocerca volvulus populations from Cameroon and Ghana-exposed to more than a decade of regular ivermectin treatment-have raised concern that sub-optimal responses to ivermectin's anti-fecundity effect are becoming more frequent and may spread.

Methodology/principal findings: Pooled next generation sequencing (Pool-seq) was used to characterise genetic diversity within and between 108 adult female worms differing in ivermectin treatment history and response. Genome-wide analyses revealed genetic variation that significantly differentiated good responder (GR) and sub-optimal responder (SOR) parasites. These variants were not randomly distributed but clustered in ~31 quantitative trait loci (QTLs), with little overlap in putative QTL position and gene content between the two countries. Published candidate ivermectin SOR genes were largely absent in these regions; QTLs differentiating GR and SOR worms were enriched for genes in molecular pathways associated with neurotransmission, development, and stress responses. Finally, single worm genotyping demonstrated that geographic isolation and genetic change over time (in the presence of drug exposure) had a significantly greater role in shaping genetic diversity than the evolution of SOR.

Conclusions/significance: This study is one of the first genome-wide association analyses in a parasitic nematode, and provides insight into the genomics of ivermectin response and population structure of O. volvulus. We argue that ivermectin response is a polygenically-determined quantitative trait (QT) whereby identical or related molecular pathways but not necessarily individual genes are likely to determine the extent of ivermectin response in different parasite populations. Furthermore, we propose that genetic drift rather than genetic selection of SOR is the underlying driver of population differentiation, which has significant implications for the emergence and potential spread of SOR within and between these parasite populations.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Maps of sampling sites in Ghana and Cameroon.
(A) Location of Ghana and Cameroon and distance between sampling sites. (B) and (C) sampling sites in Ghana (blue) and Cameroon (red), respectively. Both maps have been scaled to allow comparison of distance within and between sampling regions of each country. Numbers represent sampling locations as described in Table H in S1 Text.
Fig 2
Fig 2. Analysis of shared genetic variation that differentiates ivermectin good responder (GR) from sub-optimally responding (SOR) Onchocerca volvulus adult worms in both the Cameroon and Ghana population samples.
Genetic differentiation was measured using individual single nucleotide polymorphisms (SNPs) (Fisher’s exact test; A) or 10-kb windows (FST; B). Dotted lines represent statistical cutoff applying the Bonferroni correction for SNPs and genome-wide mean FST + 3 standard deviations (SDs) (Cam: 0.355; Gha: 0.255) for 10-kb windows. Red dots highlight differentiation above genome-wide cutoffs that is shared by both groups. Orange dots represent additional shared differentiation at 2 SDs in the FST analysis (B). Manhattan plots of genome-wide FST describing spatial genetic differentiation between GR and SOR pools for both Cameroon (C) and Ghana (D). Each point represents FST calculated for a non-overlapping 10-kb window. Plots are coloured to differentiate the main genomic scaffolds from unplaced scaffolds and contigs. Dotted lines represent genome-wide mean FST + 3 SDs (dark grey; Cam: 0.355; Gha: 0.255) and FST + 5 SDs (light grey; Cam: 0.508; Gha: 0.351).
Fig 3
Fig 3. Analysis of genetic diversity between ivermectin responder phenotypes and drug-naïve (NTL) worms.
(A) Spearman rank correlation analysis of variant read frequencies from 248,102 SNPs. Values within each square represent the correlation coefficient for each pairwise analysis. (B) Degree of shared variation determined by pairwise comparisons of FST between treatment and response groups, summarised from 9,893 10-kb windows throughout the genome. FST distributions were compared statistically using a two-sample Kolmogorov-Smirnov [KS] test. (C) Variant read frequency spectrum from treatment and response subgroups for Cameroon and Ghana. The variant read frequency was calculated at each of the 248,102 SNP positions, from which the proportion of total variants in 0.05 frequency bins is presented. (D) Analysis of invariant loci per group as a proportion of the total number of variants observed, defined as variant read frequencies < 0.05 (blue) and > 0.95 (red).
Fig 4
Fig 4. Analysis of genetic differentiation among 446 O. volvulus female worms from Ghana and Cameroon individually genotyped at 130 loci distributed throughout the genome.
Multi-dimensional scaling analysis was used to determine the relative genetic similarity between worms. Plots contain the same data, but have been presented to emphasise the degree of genetic differences between countries (A), sampling communities within each country (B), and their treatment exposure and phenotypic response to ivermectin if known (C). Ghanaian sampling sites: Asubende (ASU), Begbomdo (BEG), Jagbenbendo (JAG), Kyingakrom (KYG), New Longoro (NLG), and Wiae (WIA). Cameroonian sampling sites: Nkam valley (NKA07), Mbam valley sampled in 1994 (MBM94) before introduction of annual CDTI in 1994 and sampled in 2007 (MBM07).

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