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Genomic analysis of local variation and recent evolution in Plasmodium vivax

Nature Genetics volume 48, pages 959–964 (2016)Cite this article

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Abstract

The widespread distribution and relapsing nature of Plasmodium vivax infection present major challenges for the elimination of malaria. To characterize the genetic diversity of this parasite in individual infections and across the population, we performed deep genome sequencing of >200 clinical samples collected across the Asia-Pacific region and analyzed data on >300,000 SNPs and nine regions of the genome with large copy number variations. Individual infections showed complex patterns of genetic structure, with variation not only in the number of dominant clones but also in their level of relatedness and inbreeding. At the population level, we observed strong signals of recent evolutionary selection both in known drug resistance genes and at new loci, and these varied markedly between geographical locations. These findings demonstrate a dynamic landscape of local evolutionary adaptation in the parasite population and provide a foundation for genomic surveillance to guide effective strategies for control and elimination of P. vivax.

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Figure 1: Defining the accessible genome.
Figure 2: Copy number variation.
Figure 3: Genetic structure of mixed infections.
Figure 4: Parasite population structure.
Figure 5: Population-specific signatures of recent positive selection.

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Acknowledgements

We thank the patients and communities that provided samples for this study, and our many colleagues who supported this work in the field. Sequencing, data analysis and project coordination were funded by the Wellcome Trust (098051, 090770/Z/09/Z), the Medical Research Council (G0600718) and the UK Department for International Development (M006212). A.B. and I.M. acknowledge the Victorian State Government Operational Infrastructure Support and Australian Government National Health and Medical Research Council Independent Medical Research Institutes Infrastructure Support Scheme (NHMRC IRIISS). S.A. and R.N.P. are funded by the Wellcome Trust (Senior Fellowship in Clinical Science awarded to R.N.P., 091625). This study was supported in part by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases, National Institutes of Health.

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Author notes

  1. Roberto Amato and Sarah Auburn: These authors contributed equally to this work.

Authors and Affiliations

  1. Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK

    Richard D Pearson, Roberto Amato, Olivo Miotto, Eleanor Drury, Daniel Mead, Mihir Kekre, Susana Campino, Magnus Manske, Victoria J Cornelius, Bronwyn MacInnis, Kirk A Rockett, Alistair Miles, Julian C Rayner & Dominic P Kwiatkowski

  2. MRC Centre for Genomics and Global Health, Wellcome Trust Centre for Human Genetics, Oxford, UK

    Richard D Pearson, Roberto Amato, Olivo Miotto, Jacob Almagro-Garcia, Christina Hubbart, Lee Hart, Ben Jeffery, Victoria J Cornelius, Kirk A Rockett, Alistair Miles & Dominic P Kwiatkowski

  3. Global and Tropical Health Division, Menzies School of Health Research and Charles Darwin University, Darwin, Northern Territories, Australia

    Sarah Auburn, Jutta Marfurt, Nicholas M Anstey & Ric N Price

  4. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand

    Olivo Miotto & Nicholas J White

  5. National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Maryland, USA

    Chanaki Amaratunga & Rick M Fairhurst

  6. National Centre for Parasitology, Entomology, and Malaria Control, Phnom Penh, Cambodia

    Seila Suon

  7. Sampov Meas Referral Hospital, Pursat, Cambodia

    Sivanna Mao

  8. Eijkman Institute for Molecular Biology, Jakarta, Indonesia

    Rintis Noviyanti & Hidayat Trimarsanto

  9. Infectious Diseases Society Sabah-Menzies School of Health Research Clinical Research Unit and Queen Elizabeth Hospital Clinical Research Centre, Kota Kinabalu, Sabah, Malaysia

    Timothy William

  10. Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam

    Maciej F Boni, Christiane Dolecek & Hien Tinh Tran

  11. Papua New Guinea Institute of Medical Research, Madang, Papua New Guinea

    Pascal Michon, Peter Siba & Livingstone Tavul

  12. Faculty of Medicine and Health Sciences, Divine Word University, Madang, Papua New Guinea

    Pascal Michon

  13. Division of Population Health and Immunity, The Walter and Eliza Hall Institute for Medical Research, Parkville, Victoria, Australia

    Gabrielle Harrison, Alyssa Barry & Ivo Mueller

  14. Department of Medical Biology, University of Melbourne, Parkville, Victoria, Australia

    Gabrielle Harrison, Alyssa Barry & Ivo Mueller

  15. Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil

    Marcelo U Ferreira

  16. Department of Parasitology, Faculty of Medicine, University of Colombo, Colombo, Sri Lanka

    Nadira Karunaweera

  17. Institut Pasteur de Madagascar, Antananarivo, Madagascar

    Milijaona Randrianarivelojosia

  18. Jiangsu Institute of Parasitic Diseases, Key Laboratory of Parasitic Disease Control and Prevention (Ministry of Health), Jiangsu Provincial Key Laboratory of Parasite Molecular Biology, Wuxi, Jiangsu, China

    Qi Gao

  19. Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand

    Francois Nosten

  20. Nuffield Department of Medicine, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK

    Francois Nosten & Ric N Price

Authors
  1. Richard D Pearson
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  2. Roberto Amato
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  3. Sarah Auburn
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Contributions

C.A., S.S., S.M., R.N., H.T., J.M., N.M.A., T.W., M.F.B., C.D., H.T.T., N.J.W., P.M., P.S., L.T., G.H., A.B., I.M., M.U.F., N.K., M.R. and Q.G. carried out field and laboratory work to obtain P. vivax samples for sequencing. C.H., E.D., D.M., M.K., S.C., B.M. and K.A.R. developed and implemented methods for sample processing and sequencing library preparation. R.D.P., L.H., B.J. and M.M. managed data production pipelines. S.A., O.M., V.J.C., B.M., K.A.R., A.M., J.C.R., R.M.F., F.N., R.N.P. and D.P.K. contributed to study design and management. R.D.P., R.A., S.A., O.M., J.A.-G. and D.P.K. performed data analyses. R.D.P., R.A., S.A. and D.P.K. drafted the manuscript, which was reviewed by all authors.

Corresponding author

Correspondence to Dominic P Kwiatkowski.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Features of the core genome and the internal and subtelomeric hypervariable regions.

(a) Several metrics illustrating the genome accessibility properties of core (unshaded) and hypervariable masked (shaded) regions. Black lines show mean mapping quality per variant position on a scale from 40 to 60. The dashed black line shows the 5th percentile of mean mapping quality per 10-kb window (49.16). Blue lines show the mean proportion of missing genotypes per variant position on a scale from 0 to 0.5. The dashed blue line shows the 95th percentile of mean missingness per 10-kb window (0.224). Green lines show the number of variant positions on a scale from 0 to 2,000. Red lines show the mean number of technical replicate discordances per variant position on a scale from 0 to 0.4. All metrics are shown in non-overlapping 10-kb windows. Regions annotated as SubtelomericHypervariable are shaded in red and as InternalHypervariable are shaded in orange. (b) The read mapping properties of Core, InternalHypervariable and SubtelomericHypervariable regions. These analyses are based on a subset of 187 samples with ≥97% genome callable positions. Coverage classifications were defined as follows: no coverage (left set of bars) refers to positions with zero coverage in at least 1/187 samples; low coverage (middle) refers to positions with coverage <5 (uncallable) in at least 1/187 samples; and poor mapping (right) refers to positions where at least 1/187 samples had ≥10% of reads with mapping quality zero (ambiguously aligned reads). The bars are color-coded according to the genomic region as in a.

Supplementary Figure 2 Minor allele frequency (MAF) spectrum.

Estimates based on 237,116 SNPs segregating in 148 samples with low levels of missingness from WTH, WKH and PID.

Supplementary Figure 3 Over-representation of rare alleles.

Observed theta (number of minor allele genotype calls (number of sites × MAF)/accessible genome length)) for different MAF bins. Dark green and light green lines show MAF calculated from fractional genotype calls and majority allele genotype calls, respectively.

Supplementary Figure 4 Patterns of linkage disequilibrium.

Genome-wide values for r2 were calculated between pairs of SNPs over a range of distances and corrected for the inflation caused by population structure and other confounders as described in the Online Methods. Median values of linkage disequilibrium decay over short distances, e.g., r2 falls to <0.1 within 200 bp in western Thailand (green) and western Cambodia (blue) and within 500 bp in Papua Indonesia (red).

Supplementary Figure 5 Genetic structure of individual infections.

Each of the 148 samples from western Thailand (WTH), western Cambodia (WKH) and Papua Indonesia (PID) is displayed. The box on the left displays a histogram of nonreference allele frequency (NRAF) across all heterozygous SNPs. The horizontal axis is NRAF on a scale of 0 to 1. The vertical axis is the number of SNPs on a scale of 0 to 500. The box on the right displays heterozygosity in 20-kb bins across the genome. The horizontal axis represents genomic position, with vertical lines separating the 14 chromosomes. The vertical axis shows the proportion of heterozygous SNPs in a given bin on a scale of 0 to 0.03. The legend for each sample gives its geographical origin, average read depth, FWS, RoH and the inferred number of dominant clones. The line above each of these plots is colored according to the classification in Figure 3a (right). The inferred number of dominant clones is based on these criteria: one dominant clone, FWS ≥ 0.99; two dominant clones, FWS < 0.99 and NRAF histogram is bimodal and symmetric; three or more dominant clones, FWS < 0.99 and NRAF histogram is not bimodal and symmetric. RoH is the proportion of the genome occupied by runs of homozygosity, defined here as the proportion of 100-kb bins for which mean heterozygosity < 0.005. Samples are ordered by RoH. In samples with two dominant clones, the clones are classified as either unrelated (if RoH < 0.1) or related (if RoH > 0.1).

Supplementary Figure 6 ΔK for values of K used in ADMIXTURE analysis of population structure.

The ΔK metric30 evaluates the second-order rate of change of the likelihood function with respect to K and aims to identify the top-level hierarchical structure of the data. Following this metric, we found K = 3 to be the best choice for the number of putative populations.

Supplementary Figure 7 Details of genomic regions demonstrating haplotype-based evidence of selection.

Each plot shows one of the regions from Supplementary Table 8 plus 20 kb of flanking sequence 5′ and 3′. The top four tracks in each plot show log10 (P) values for genome-wide selection scans. The vertical axis shows log10 (P) on a scale of 0 to 15. The vertical lines represent the boundaries of the regions of selection (red lines in Fig. 5). The lower two tracks in each plot show FST scores between different populations. The vertical axis is scaled from 0 to 1.1. Red points represent nonsynonymous SNPs, blue points represent synonymous SNPs and gray points represent noncoding SNPs. The track at the bottom of each plot illustrates the coordinates of all genes within the given region. Gene names are given where these are available (as per the December 2015 version of GeneDB).

Supplementary Figure 8 Distributions of variant filters illustrating thresholds applied in the study.

The horizontal axis shows the percentile for a given variant annotation when ranked from lowest to highest values. The vertical axis shows the mean discordance of technical replicates per variant within each percentile bin. The horizontal line on each plot shows double the mean technical replicate discordance rate per variant across all discovered variable biallelic positions in non-masked regions (0.049). The vertical line(s) on each plot indicates the thresholds used in the study.

Supplementary Figure 9 Neighbor-joining tree using the P01 reference.

This tree was built using the same set of samples as was used in Figure 4, but here we mapped to the P01 reference (www.genedb.org/Homepage/PvivaxP01), rather than the Sal1 reference, before calling SNPs using GATK best practices. The structure of the tree is essentially the same as that in Figure 4, showing that the choice of reference genome makes little difference to the conclusions drawn from analysis of variation in the core genome.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9, Supplementary Tables 1–8 and Supplementary Note. (PDF 3499 kb)

Supplementary Data 1

Gene-level summaries of variation data. The first sheets give aggregate metrics across all 148 samples used for population genetic analyses, and the other three sheets show metrics for WTH, WKH and ID respectively. Summaries are given for both high-quality SNPs (pass) and all discovered SNPs (all). We do not record SNPs or metrics for genes outside the core genome. N/S, nonsynonymous/synonymous ratio; π, nucleotide diversity per base; D, Tajima's D. (XLSX 3864 kb)

Supplementary Data 2

CNV calls. Start and end coordinates, and copy number for all CNV calls longer than 3 kb. (XLSX 54 kb)

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Pearson, R., Amato, R., Auburn, S. et al. Genomic analysis of local variation and recent evolution in Plasmodium vivax. Nat Genet 48, 959–964 (2016). https://doi.org/10.1038/ng.3599

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  • DOI: https://doi.org/10.1038/ng.3599

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