1. Epidemiology and Global Health
  2. Microbiology and Infectious Disease
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Genetic surveillance in the Greater Mekong subregion and South Asia to support malaria control and elimination

  1. Christopher G Jacob
  2. Nguyen Thuy-Nhien
  3. Mayfong Mayxay
  4. Richard J Maude
  5. Huynh Hong Quang
  6. Bouasy Hongvanthong
  7. Viengxay Vanisaveth
  8. Thang Ngo Duc
  9. Huy Rekol
  10. Rob van der Pluijm
  11. Lorenz von Seidlein
  12. Rick Fairhurst
  13. François Nosten
  14. Md Amir Hossain
  15. Naomi Park
  16. Scott Goodwin
  17. Pascal Ringwald
  18. Keobouphaphone Chindavongsa
  19. Paul Newton
  20. Elizabeth Ashley
  21. Sonexay Phalivong
  22. Rapeephan Maude
  23. Rithea Leang
  24. Cheah Huch
  25. Le Thanh Dong
  26. Kim-Tuyen Nguyen
  27. Tran Minh Nhat
  28. Tran Tinh Hien
  29. Hoa Nguyen
  30. Nicole Zdrojewski
  31. Sara Canavati
  32. Abdullah Abu Sayeed
  33. Didar Uddin
  34. Caroline Buckee
  35. Caterina I Fanello
  36. Marie Onyamboko
  37. Thomas Peto
  38. Rupam Tripura
  39. Chanaki Amaratunga
  40. Aung Myint Thu
  41. Gilles Delmas
  42. Jordi Landier
  43. Daniel M Parker
  44. Nguyen Hoang Chau
  45. Dysoley Lek
  46. Seila Suon
  47. James Callery
  48. Podjanee Jittamala
  49. Borimas Hanboonkunupakarn
  50. Sasithon Pukrittayakamee
  51. Aung Pyae Phyo
  52. Frank Smithuis
  53. Khin Lin
  54. Myo Thant
  55. Tin Maung Hlaing
  56. Parthasarathi Satpathi
  57. Sanghamitra Satpathi
  58. Prativa K Behera
  59. Amar Tripura
  60. Subrata Baidya
  61. Neena Valecha
  62. Anupkumar R Anvikar
  63. Akhter Ul Islam
  64. Abul Faiz
  65. Chanon Kunasol
  66. Eleanor Drury
  67. Mihir Kekre
  68. Mozam Ali
  69. Katie Love
  70. Shavanthi Rajatileka
  71. Anna E Jeffreys
  72. Kate Rowlands
  73. Christina S Hubbart
  74. Mehul Dhorda
  75. Ranitha Vongpromek
  76. Namfon Kotanan
  77. Phrutsamon Wongnak
  78. Jacob Almagro Garcia
  79. Richard D Pearson
  80. Cristina V Ariani
  81. Thanat Chookajorn
  82. Cinzia Malangone
  83. T Nguyen
  84. Jim Stalker
  85. Ben Jeffery
  86. Jonathan Keatley
  87. Kimberly J Johnson
  88. Dawn Muddyman
  89. Xin Hui S Chan
  90. John Sillitoe
  91. Roberto Amato
  92. Victoria Simpson
  93. Sonia Gonçalves
  94. Kirk Rockett
  95. Nicholas P Day
  96. Arjen M Dondorp
  97. Dominic P Kwiatkowski
  98. Olivo Miotto  Is a corresponding author
  1. Wellcome Sanger Institute, United Kingdom
  2. Oxford University Clinical Research Unit, Viet Nam
  3. Lao-Oxford-Mahosot Hospital-Wellcome Research Unit (LOMWRU), Microbiology Laboratory, Mahosot Hospital, Lao People's Democratic Republic
  4. Institute of Research and Education Development (IRED), University of Health Sciences, Ministry of Health, Lao People's Democratic Republic
  5. Centre for Tropical Medicine and Global Health, University of Oxford, United Kingdom
  6. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Thailand
  7. Harvard TH Chan School of Public Health, Harvard University, United States
  8. Institute of Malariology, Parasitology and Entomology (IMPE-QN), Viet Nam
  9. Centre of Malariology, Parasitology, and Entomology, Lao People's Democratic Republic
  10. National Institute of Malariology, Parasitology and Entomology (NIMPE), Viet Nam
  11. National Center for Parasitology, Entomology, and Malaria Control, Cambodia
  12. National Institute of Allergy and Infectious Diseases, National Institutes of Health, United States
  13. Shoklo Malaria Research Unit, Thailand
  14. Chittagong Medical College Hospital, Bangladesh
  15. World Health Organization, Switzerland
  16. Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Thailand
  17. Institute of Malariology, Parasitology and Entomology (IMPEHCM), Viet Nam
  18. Vysnova Partners Inc, Viet Nam
  19. Kinshasa School of Public Health, University of Kinshasa, Democratic Republic of the Congo
  20. Aix-Marseille Université, INSERM, IRD, SESSTIM, Aix Marseille Institute of Public Health, ISSPAM, France
  21. Susan and Henry Samueli College of Health Sciences, University of California, Irvine, United States
  22. Faculty of Tropical Medicine, Mahidol University, Thailand
  23. The Royal Society of Thailand, Thailand
  24. Myanmar-Oxford Clinical Research Unit, Myanmar
  25. Department of Medical Research, Myanmar
  26. Defence Services Medical Research Centre, Myanmar
  27. Midnapore Medical College, India
  28. Ispat General Hospital, India
  29. Agartala Medical College, India
  30. National Institute of Malaria Research, Indian Council of Medical Research, India
  31. Ramu Upazila Health Complex, Bangladesh
  32. Malaria Research Group and Dev Care Foundation, Bangladesh
  33. Wellcome Trust Centre for Human Genetics, University of Oxford, United Kingdom
  34. Worldwide Antimalarial Resistance Network (WWARN), Asia Regional Centre, Thailand
  35. MRC Centre for Genomics and Global Health, Big Data Institute, Oxford University, United Kingdom
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Cite this article as: eLife 2021;10:e62997 doi: 10.7554/eLife.62997

Abstract

Background:

National Malaria Control Programmes (NMCPs) currently make limited use of parasite genetic data. We have developed GenRe-Mekong, a platform for genetic surveillance of malaria in the Greater Mekong Subregion (GMS) that enables NMCPs to implement large-scale surveillance projects by integrating simple sample collection procedures in routine public health procedures.

Methods:

Samples from symptomatic patients are processed by SpotMalaria, a high-throughput system that produces a comprehensive set of genotypes comprising several drug resistance markers, species markers and a genomic barcode. GenRe-Mekong delivers Genetic Report Cards, a compendium of genotypes and phenotype predictions used to map prevalence of resistance to multiple drugs.

Results:

GenRe-Mekong has worked with NMCPs and research projects in eight countries, processing 9623 samples from clinical cases. Monitoring resistance markers has been valuable for tracking the rapid spread of parasites resistant to the dihydroartemisinin-piperaquine combination therapy. In Vietnam and Laos, GenRe-Mekong data have provided novel knowledge about the spread of these resistant strains into previously unaffected provinces, informing decision-making by NMCPs.

Conclusions:

GenRe-Mekong provides detailed knowledge about drug resistance at a local level, and facilitates data sharing at a regional level, enabling cross-border resistance monitoring and providing the public health community with valuable insights. The project provides a rich open data resource to benefit the entire malaria community.

Funding:

The GenRe-Mekong project is funded by the Bill and Melinda Gates Foundation (OPP11188166, OPP1204268). Genotyping and sequencing were funded by the Wellcome Trust (098051, 206194, 203141, 090770, 204911, 106698/B/14/Z) and Medical Research Council (G0600718). A proportion of samples were collected with the support of the UK Department for International Development (201900, M006212), and Intramural Research Program of the National Institute of Allergy and Infectious Diseases.

Introduction

In low-income countries, particularly in sub-Saharan Africa, malaria continues to be a major cause of mortality, and intense efforts are underway to eliminate Plasmodium falciparum parasites, which cause the most severe form of the disease. However, P. falciparum has shown a remarkable ability to develop resistance to antimalarials, rendering therapies ineffective and frustrating control and elimination efforts. This problem is most acutely felt in the Greater Mekong Subregion (GMS), a region that has repeatedly been the origin of drug-resistant strains (Dondorp et al., 2009; Noedl et al., 2008; Plowe, 2009; Roper et al., 2004; Mita et al., 2011) and in neighboring countries including Bangladesh and India, where resistance could be imported. The GMS is a region of relatively low endemicity, with entomological inoculation rates 2–3 orders of magnitude lower than in Africa, where the vast majority of cases occur (Chaumeau et al., 2018; Hay et al., 2000). Infections are most common amongst individuals who work in or live near forests in remote rural parts of the region (Cui et al., 2012). Since infections are infrequent, a high proportion of individuals in this region are immunologically naïve, and develop symptoms that require treatment when infected. This results in high parasite exposure to drugs, which may be a major evolutionary driving force for the emergence of genetic factors that confer resistance to frontline therapies (Escalante et al., 2009). In the past, drug resistance alleles emerged in the GMS and subsequently spread to Africa multiple times, rolling back progress against the disease at the cost of many lives (Mita et al., 2009; Trape et al., 1998). Currently, global malaria control and elimination strategies depend on the efficacy of artemisinin combination therapies (ACTs) which are the frontline therapy of choice worldwide. Hence, in view of the emergence in the GMS of parasite strains resistant to artemisinin (Dondorp et al., 2009; Ashley et al., 2014; MalariaGEN Plasmodium falciparum Community Project, 2016) and its ACT partner drug piperaquine, (Amaratunga et al., 2016; van der Pluijm et al., 2019; Leang et al., 2015; Spring et al., 2015) the elimination of P. falciparum from this region has become a global health priority.

Elimination from the GMS presents significant challenges and, to ensure the most effective outcomes, NCMPs have to evaluate multiple changing factors: efficacy of frontline treatments, available alternatives, routes of spread, location of transmission hubs, importation of cases, and so on. In these assessments, NMCPs make extensive use of clinical and epidemiological data, such as those from routine clinical reporting and therapy efficacy studies. Parasite genetic data is less frequently available, and typically restricted to single genetic variants (Ménard et al., 2016), or small numbers of sites where quality sample collection protocols could be executed (Lim et al., 2013). However, routine mapping of a broad set resistance markers can keep NMCPs abreast of the spread of resistance strains, and help them predict changes in drug efficacy and assess alternative therapies, especially if dense geographical coverage allows mapping of resistance at province or district level. The increased affordability of high-throughput sequencing technologies now offers new opportunities for delivering such knowledge to public health, supporting the optimization of interventions where resources are limited (Nagar et al., 2019). Cost-effective implementation of genomic technologies, aimed at supporting public health decision-making, can make important contributions to malaria elimination (Desmond-Hellmann, 2016).

Here, we describe GenRe-Mekong, a genetic surveillance project conceived to provide public health experts in the GMS with timely and actionable knowledge, to support their decision-making in malaria elimination efforts. GenRe-Mekong analyzes small dried blood spots samples, which are easy to collect at public health facilities from patients with symptomatic malaria, and uses high-throughput technologies to extract large amounts of parasite genetic information from each sample. The results are captured in Genetic Report Cards (GRCs), datasets regularly delivered to NMCPs to keep them abreast of rapid epidemiological changes in the parasite population. The underlying technological platform is designed for low sample processing costs, promoting large-scale genetic epidemiology surveys with dense geographical coverage and large sample sizes.

To date, GenRe-Mekong has worked with NMCPs in Cambodia, Vietnam, Lao PDR (Laos), Thailand, and Bangladesh and has supported large-scale multisite research and elimination projects across the region (van der Pluijm et al., 2019; von Seidlein et al., 2019; Chang et al., 2019; Landier et al., 2018). The project has processed 9623 samples from eight countries, delivering data to the 12 studies that submitted samples. In its initial phase, GenRe-Mekong has focused on applications relevant to the urgent problem of drug resistance. To facilitate integration into NMCP decision-making workflows, our analysis pipelines translate genotypes into predictions of drug resistance phenotypes, and present these as maps which are easily interpreted by public health officials with no prior training in genetics. In Laos and Vietnam, where GenRe-Mekong is implemented in dozens of public health facilities in endemic provinces, results from GenRe-Mekong have been used by NMCPs in assessments of frontline therapy options and resource allocation to combat drug resistance.

GenRe-Mekong protects individual patient privacy, while encouraging aggregation and sharing of standardized data across national borders to answer regional questions about epidemiology, gene flow, and parasite evolution (Hamilton et al., 2019). Aggregated data from multiple studies within GenRe-Mekong have powered large-scale genetic and clinical studies of resistance to dihydroartemisinin-piperaquine (DHA-PPQ), revealing a regional cross-border spread of specific strains (van der Pluijm et al., 2019; Hamilton et al., 2019). To power such high-resolution genetic epidemiology analyses of population structure and gene flow, GenRe-Mekong conducts whole-genome sequencing of selected high-quality samples, contributing to the open-access MalariaGEN Parasite Observatory (http://www.malariagen.net/resource/26) (Pearson et al., 2019). In this article, we summarize some key results from GenRe-Mekong, highlighting how they are used by public health officers to improve interventions. The data used in this paper are openly available, together with detailed methods documentation and details of partner studies, at http://www.malariagen.net/resource/29.

Materials and methods

Additional detailed documentation on the methods used in this study is available from the article’s Resource Page, at https://www.malariagen.net/resource/29.

Sample collection

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GenRe-Mekong samples were collected and contributed by independent studies with different goals, geographical coverage, and sampling strategies. Studies were managed by a local partner, such as a NMCP or a research organization, and often supported by a local technical partner. Most sampling sites were district or subdistrict health centres or provincial hospitals, selected by the local partner according to their public health or research needs. Each site was assigned a code, and its geographical coordinates recorded to support result mapping. GenRe-Mekong uses a common genetic surveillance study protocol covering the entire GMS, which can be locally adapted; this protocol was used for NMCP surveillance projects, after obtaining approval by a relevant local ethics review board and by the Oxford University Tropical Research Ethics Committee (OxTREC). Research studies included in their own protocol provisions for sample collection procedures, informed consent, patient privacy protection, and data sharing compatible with those in the GenRe-Mekong protocol, and obtained ethical approval from both a relevant local ethics review board, and their relevant institutional research ethics committee.

Samples were collected from patients of all ages diagnosed with P. falciparum malaria (including patients co-infected by other Plasmodium species) confirmed by positive rapid diagnostic test or blood smear microscopy. Participation in the study required written informed consent by patient, parent/guardian, or legally authorised representative (plus patient assent wherever required by national regulations), with the exception of Laos, where the Ministry of Health classified GenRe-Mekong as a surveillance activity for national benefit, requiring no additional informed consent. After obtaining consent, and before administering treatment, three 20 μL dried blood spots (DBS) on filter paper were obtained from each patient through finger-prick. GenRe-Mekong supplied study sites with kits containing all necessary materials, including strips of Whatman 31ET CHR filter paper, disposable lancet, 20 μl micropipette, cotton swab, alcohol pad, and plastic bag with silica gel for DBS storage. Scannable barcode stickers with unique identifiers were applied on the filter paper, the sample manifest where the collection date was recorded, and the site records. Samples were identified by means of these anonymous barcodes, and no patient-identifying information or clinical data were collected by GenRe-Mekong.

A number of participating studies also collected an optional anonymous questionnaire, to capture location of abode and work, occupation and travel history of the previous 2 months. These data are intended for in-depth epidemiological studies, such as analyses of the contribution of travel to gene flow (Chang et al., 2019). Data from these questionnaires were stored in a separate system, and linked to genetic data by means of the tracking barcodes. They were not used in the present work.

Sample preparation and genotyping

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DBS samples were received and stored either at the Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam, or at the MORU/WWARN molecular laboratory, Bangkok, Thailand. Samples were registered and tracked in a secure bespoke online database, where location and date of collection were recorded. DNA was extracted from samples using high-throughput robotic equipment (Qiagen QIAsymphony) according to manufacturer’s instructions. Extracted DNA was plated and shipped to the MalariaGEN Laboratory at the Wellcome Sanger Institute (WSI), Hinxton, UK, for genotyping and whole genome sequencing. Parasite DNA was amplified by applying selective whole genome amplification (sWGA) as previously described (Oyola et al., 2016).

Genotyping was performed by the SpotMalaria platform, described in the separate document ‘SpotMalaria platform - Technical Notes and Methods’ available from the Resource Page, which includes the complete list of genotyped variants and the details of the genotyping procedures for these variants. Briefly, the first version of SpotMalaria used multiplexed mass spectrometry arrays on the Agena MassArray system for typing most SNPs, and capillary sequencing for the artemisinin resistance domains of the kelch13 gene. This was eventually replaced by an amplicon sequencing method, using Illumina sequencing of specific genome segments amplified by PCR reaction. The two implementations genotype a common set of variants, each iteration extending or improving on previous versions. Amplicon sequencing also offers greater portability, since it can be deployed on smaller sequencers in country-based laboratories.

Genetic Report Cards generation

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For each sample, genotypes were called for each variant analysed by SpotMalaria, and further processed to determine commonly recognized haplotypes associated with drug resistance (e.g. in genes crt, dhfr, dhps). Genetic barcodes were constructed by concatenating 101 SNP alleles. The generated genotypes, combined with sample metadata, were returned in tabular form to those partners who had submitted the samples along with explanatory documentation for the interpretation of the reports.

The genotypes generated were used to classify samples by their predicted resistance to different drugs. The prediction rules were based on the available data and current knowledge of resistance markers and are detailed in the separate document ‘Mapping genetic markers to resistance status classification’ available from the Resource Page. For each drug, samples were classified as ‘sensitive’, ‘resistant’, ‘undetermined’, or ‘missing’– the latter identifying samples that failed to produce a valid genotype for the classification. Heterozygous samples, that is those containing genomes carrying both sensitive and resistant alleles, were classified as undetermined, due to lack of evidence for the drug resistance phenotype of such mixed infections.

In order to minimize the impact of call missingness, we also applied a set of imputation rules that predict missing alleles in the crt, dhfr, and dhps genes, based on statistically significant association with alleles at other positions. Associations were tested (using the threshold p < 0.05 by Fisher's exact test) using over 7000 samples in the MalariaGEN Pf Community Project Version 6 (Pearson et al., 2019). The rules for imputations were applied before phenotype prediction rules. They are detailed in the separate document ‘Imputation of genotypes for markers of drug resistance’ available from the Resource Page.

Data aggregation and mapping of drug resistance

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To estimate the frequency of resistant parasites for a given drug, we selected samples at the desired level of geographical aggregation (e.g. province/state or district), based on sampling location. After removing samples with missing and undetermined phenotype predictions for the desired drug, we counted the individuals predicted to be resistant (nr) and sensitive (ns), giving a total aggregation sample size N=nr+ns. Resistant parasite frequency was then computed as fr=nr/N. Maps of resistance frequency were produced using Tableau Desktop 2020.1.8 (RRID:SCR_013994, http://www.tableau.com/). To indicate levels of resistance, markers were colored with a custom green-orange-red palette. Pie chart markers, used to represent allele proportions, were also derived from the same set of N aggregated samples.

Population structure analysis

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Pairwise genetic distances between parasites were estimated by comparing genetic barcodes. To reduce error due to missingness, we first eliminated samples with more than 50% missing barcode genotypes; then we removed SNPs with missing calls in >20% of the remaining samples; and finally discarded samples with >25% missingness in the remaining SNPs. This produced a dataset of 87-SNP barcodes for 7490 samples from which genetic distances were estimated. For each sample s, we assigned a within-sample non-reference frequency gs at each position carrying a valid genotype, as follows: gs=0 if the sample carried the reference allele, gs=one if it carried the alternative allele, gs=0.5 if both alleles were present. The distance between two samples at that position was then estimated by: d = g1(1 g2) + g2(1 g1) where g1 and g2 are the gs values for the two samples. The pairwise distance was estimated as the mean of d across all positions where d could be computed (i.e. where neither of the two samples had a missing call). Neighbour-joining trees (NJTs) were then produced using the nj implementation in the R package ape (RRID:SCR_017343) on R v4.0.2 (RRID:SCR_001905, http://www.r-project.org/) from square distance matrices.

Results

Collaborations, site selection, and sample collections

As of August 2019, GenRe-Mekong has partnered with NMCPs in five countries to conduct large-scale genetic surveillance (Vietnam, Laos), smaller-scale pilot projects (Cambodia, Thailand), and epidemiological surveys (Bangladesh). GenRe-Mekong also worked with large-scale research projects investigating drug efficacy and malaria risk, or piloting elimination interventions. A total of 9623 samples from eight countries have been processed in this period (Figure 1—figure supplement 1). The majority of samples (n=6905, 72%) were collected in GMS countries (Vietnam, Laos, Cambodia, Thailand, Myanmar), but GenRe-Mekong also supported projects submitting samples from Bangladesh, India, and DR Congo (Supplementary file 2). The vast majority of processed samples were collected prospectively, under partnership agreements with GenRe-Mekong (n=9002, 93.5%); two research projects submitted retrospective samples collected in the period 2012–2015 (n=621, 6.5%, Figure 1—figure supplement 1). Approximately 59% of samples (n=5716) were submitted by NMCP partnerships, whose contribution increased over time as surveillance projects ramped up (43.4% in 2016, vs. 94.6% in 2018, Figure 1—figure supplement 2). Details of the partnerships, the nature of the studies conducted and the number of processed samples are given in Table 1.

Table 1
Participating studies in GenRe-Mekong.

For each study, we list the NMCP and Research partners involved, the type of study, the geographical region covered and the number of collection sites. In the last two columns, we show the total number of samples submitted, and the number included in the final set of quality-filtered samples used in epidemiology analyses.

NMCP partnerResearch / technical partnerStudy typeRegions surveyedSitesSubmitted
samples
Filtered samples
Center for Malaria Parasitology and Entomology of Lao PDR (CMPE)Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit (LOMWRU), VientianeGenetic SurveillanceSouth Laos
(five provinces)
5115551387
Institute of Malariology, Parasitology, and Entomology Quy Nhon (IMPE-QN), VietnamOxford University Clinical Research Unit (OUCRU), Ho Chi Minh CityGenetic SurveillanceCentral Vietnam
(seven provinces)
5116321492
National Institute of Malariology, Parasitology, and Entomology (NIMPE), VietnamVysnova Partners, Mahidol-Oxford Research Unit (MORU)Epidemiological StudySouth Vietnam
(three provinces)
19292265
National Center for Parasitology, Entomology, and Malaria Control (CNM), CambodiaGenetic SurveillanceNortheast Cambodia
(two provinces)
19182174
Bangladesh National Malaria Control ProgrammeMahidol-Oxford Research Unit (MORU)Epidemiological StudyBangladesh
(Chittagong Division)
5520551575
-Mahidol-Oxford Research Unit (MORU)Clinical Efficacy StudyCambodia, Vietnam, Thailand, Lao PDR, Myanmar, Bangladesh, India, DR Congo1718751123
-National Institutes of Health (NIH)Clinical Efficacy StudyCambodia3592502
-Oxford University Clinical Research Unit (OUCRU)Epidemiological StudySouth Vietnam4184175
-Mahidol-Oxford Research Unit (MORU)Elimination StudyWest Cambodia16932
-Mahidol-Oxford Research Unit (MORU)Epidemiological StudyNortheast Thailand78760
-Shoklo Malaria Research Unit (SMRU)Clinical Efficacy StudyThailand
(Tak province)
42928
-Shoklo Malaria Research Unit (SMRU)Elimination StudyMyanmar (Kayin State)511071813
Total96237626

Partnerships with NMCPs are often supported through collaborations with local malaria research groups, which provide support in implementing sample collections, and assist in the interpretation of results. To facilitate implementation in public health infrastructures, GenRe-Mekong provides template study protocols and associated documents; standardized kits of collection materials and documentation; and training for field and health centre staff. Study protocols are adapted to harmonize with local practices, and then approved by both a local ethical review board and the Oxford Tropical Research Ethics Committee (OxTREC). Informed consent forms and participant information sheets are translated to the local language(s), and public health facility staff are trained to execute sample collection procedures. Collection sites are mostly district-level or subdistrict-level health facilities, selected by NMCPs to cover the most informative endemic areas, often based on reported prevalence (Figure 1). Research studies and elimination projects included in their study protocol a sample collection procedure compatible with the standard GenRe-Mekong procedure, and sites were selected based on the study’s requirements.

Figure 1 with 2 supplements see all
Map of GenRe-Mekong sample collection sites in Asia.

Sites markers are colored by country. One site in Kinshasa (DR Congo) not shown.

Sample processing and genotyping

GenRe-Mekong samples consist of dried blood spots (DBSs) on filter paper. DNA extracted from the samples was selectively amplified (Oyola et al., 2016) to increase the proportion of parasite DNA and reduce human DNA contamination before genotyping (see Materials and methods). The production of genetic report cards involves genotyping different types of variants: single nucleotide polymorphisms (SNPs), copy number variations and sequences of gene domains. These operations were performed by SpotMalaria, the genotyping platform underpinning GenRe-Mekong, whose implementation evolved during the course of the project; details of the methods used in different versions are provided in the Supplementary Materials. In the initial phase, SpotMalaria used a mixture of technologies: capillary sequencing of the kelch13 gene to detect SNPs associated with artemisinin resistance (Ashley et al., 2014; Ariey et al., 2014); and high-throughput mass spectrometry to genotype SNP variants. This was later replaced with an amplicon sequencing process, based on short-read deep sequencing of specific portions of the parasite genome, supporting a high degree of multiplexing (see Materials and methods). A total of 3473 samples (36%) were processed by the amplicon sequencing platform, which delivered a higher genotyping success rate than the earlier process (94% vs 82% mean success rate for genetic barcode positions).

The vast majority of samples were taken from malaria patients upon admission (92%, n=8866). The remainder were from recurrent clinical episodes, or collected as part of post-admission time series to study infection dynamics (n=757, 7.9%), and were excluded from epidemiological analyses in order to minimize biases and avoid duplicates. Genotypes at mitochondrial positions provided confirmation of the infecting parasite species: P. falciparum (Pf), P. vivax (Pv), P. knowlesi (Pk), P. malariae (Pm), and P. ovale (Po). All five species were detected in our dataset: non-Pf parasites were found in 8.8% of samples (n=745 out of 8486 samples for which species could be determined). A proportion of samples (n=414, 4.9%) only tested positive for non-Pf species, possibly due to misdiagnosis or extremely low Pf parasitaemia, and were excluded from epidemiological analyses. Pv was the most commonly detected non-Pf species (317 Pf/Pv mixed infections, and 405 Pv-only infections), followed by Pk (11 Pf/Pk and 6 Pk-only infections), while Pm and Po were detected in three and two samples, respectively.

Genetic barcodes

GenRe-Mekong produces a genetic barcode for each sample to enable analyses of relatedness, diversity, multiplicity of infection and population structure. Genetic barcodes are constructed by concatenating the alleles at 101 SNPs distributed across all nuclear chromosomes (see Materials and methods), chosen on the basis of their geographically widespread variability and their power to recapitulate genetic distance. Genetic barcodes can be used to detect loss of diversity due to demographic effects, (Daniels et al., 2015) or to compare parasites from the same patient to distinguish recrudescences from reinfections (Felger et al., 2020). They can also produce estimates of genetic distance, which may not be sufficiently accurate for detailed inferences, but are useful for visualizing macroscopic population-level features. For example, a neighbor-joining tree derived from these genetic distance estimates (Figure 2) clearly separates parasites from the Thai-Myanmar border region from those circulating along the Thai-Cambodian border, consistent with findings from WGS analyses (Miotto et al., 2015). Hence, while genetic barcodes produce lower resolution results than WGS data, they could be used for rapid low-cost detection of candidate imported parasites, to be further analysed using higher-definition approaches. We used genetic barcode results to discard 827 samples that failed to produce barcodes due to low Pf DNA content. This yielded a final set of 7626 Pf samples, corresponding to 90.2% of all Pf-containing samples taken upon admission, which provided the data used for epidemiological analyses.

Neighbor-joining tree using barcode data to show genetic differentiation between parasites in the Thai-Myanmar and Thai-Cambodian border regions.

The tree was derived from a matrix distance matrix, computed by comparing the genetic barcodes of samples. The branch length separating each pair of parasites represents the amount of genetic differentiation between them: individuals separated by shorter branches are more similar to each other. Samples from provinces/states of Myanmar, Thailand, and Cambodia near to the borders were included. Each circular marker represents a sample, colored by the province/state of origin.

Survey of drug resistance mutations

GenRe-Mekong produces genotypes covering a broad range of known variants associated to drug resistance (Table 2) to support assessment of the spread and risk of drug resistance. The interpretation of these genetic markers in phenotypic terms requires extensive knowledge of relevant literature, which is often outside the domain of expertise of public health officers. To bridge this gap, we use genotypes to derive predicted phenotypes based on a set of rules derived from peer-reviewed publications (see Materials and methods and formal rules definitions available from the article’s Resource Page). These rules predict samples as resistant or sensitive to a particular drug or treatment, or undetermined. Since our procedures do not include the measurement of clinical or in vitro phenotypes, we are only able to predict a drug resistant phenotype based on known associations of certain markers with resistance to certain drugs. Although we report a large catalogue of variations which have been associated with resistance, we do not use all variations to predict resistance. Rather, our predictive rules are conservative and only use markers that have been strongly characterized and validated in published literature and shown to play a crucial role in clinical or in vitro resistance. These critical variants include single nucleotide polymorphisms (SNPs) in genes kelch13 (resistance to artemisinin), (Ariey et al., 2014) crt (chloroquine), dhfr (pyrimethamine), dhps (sulfadoxine), as well as an amplification breakpoint sequence in plasmepsin2/3 (marker of resistance to piperaquine) (Amato et al., 2017). In addition, we report several additional variants found in drug resistance backgrounds but not used to predict resistance, such as mutations in mdr1 (linked to resistance to multiple drugs), components of the predisposing ART-R background arps10, ferredoxin, mdr2 (Miotto et al., 2015), and the exo marker associated with resistance to piperaquine (Amato et al., 2017). Several samples had missing genotype calls which were required for phenotype prediction; therefore, we also devised a number of rules for imputation of missing genotypes based on information from linked alleles. These imputation rules (see Materials and methods) are based on an analysis of allele associations using data from over 7000 samples in the MalariaGEN Pf Community Project (Pearson et al., 2019) and are applied prior to phenotype prediction rules. Phenotypic predictions allow simple estimations of the proportions of resistant parasites at the population level, which can be readily tabulated and mapped for use in public health decision-making. By aggregating sample data at various geographic levels (site, district, province, region, country), GenRe-Mekong delivers to NMCPs maps that capture the current drug resistance landscape, and can be compared to detect changes over time. Most GenRe-Mekong maps use intuitive ‘traffic light’ color schemes, in which red signifies presence of resistance, and green its absence. Below, we illustrate some results at regional level for the GMS and nearby countries, which are also summarized in Table 3.

Table 2
Drug resistance-related SNPs genotyped by GenRe-Mekong (excludes kelch13).
ChromosomePositionGene IdGene DescriptionMutationReferenceAlternate
Pf3D7_04_v3748239PF3D7_0417200dhfr
(bifunctional dihydrofolate reductase-thymidylate synthase)
N51IAT
Pf3D7_04_v3748262C59R/YTC
Pf3D7_04_v3748263C59R/YGA
Pf3D7_04_v3748410S108N/TGAC
Pf3D7_04_v3748577I164LAT
Pf3D7_05_v3958145PF3D7_052300mdr1
(multidrug resistance protein 1)
N86YAT
Pf3D7_05_v3958440Y184FAT
Pf3D7_05_v3961625D1246YGT
Pf3D7_07_v3403623PF3D7_0709000crt
(chloroquine resistance transporter)
N75D/ETA
Pf3D7_07_v3403625K76TAC
Pf3D7_07_v3405362N326SAG
Pf3D7_07_v3405600I356TTC
Pf3D7_08_v3549681PF3D7_0810800dhps
(dihydropteroate synthetase)
S436A/Y/F/GTGC
Pf3D7_08_v3549682S436A/Y/F/GCTAG
Pf3D7_08_v3549685A437GGC
Pf3D7_08_v3549993K540E/NAGT
Pf3D7_08_v3549995K540E/NATG
Pf3D7_08_v3550117A581GCG
Pf3D7_08_v3550212A613S/TGTA
Pf3D7_13_v3748395PF3D7_1318100fd (ferredoxin)D193YCA
Pf3D7_13_v32504560PF3D7_1362500exo (exonuclease)E415GAG
Pf3D7_14_v3-PF3D7_1408000 and PF3D7_1408100pm23 (plasmepsin 2 and plasmepsin 3)Breakpoint--
Pf3D7_14_v31956225PF3D7_1447900mdr2
(multidrug resistance protein 2)
T484IGA
Pf3D7_14_v32481070PF3D7_1460900arps10
(apicoplast ribosomal protein S10)
V127MGA
Pf3D7_14_v32481073D128Y/HGTC
Table 3
Frequencies of resistant parasites in provinces/states/divisions surveyed, for different antimalarials.
CountryProvince, State, or DivisionART-RPPQ-RDHA-PPQ-RCQ-RPYR-RSD-RSP-RSP-R (IPTp)
IndiaOdisha0%0%0%18%57%6%1%0%
West Bengal0%0%0%47%71%14%5%0%
Tripura0%0%0%85%100%99%55%0%
BangladeshChittagong0%0%0%97%100%87%46%16%
MyanmarRakhine0%0%0%71%100%100%51%26%
Bago1%0%0%88%100%100%91%74%
Mandalay29%0%0%96%98%98%29%24%
Kayin54%2%0%100%100%56%73%27%
ThailandTak61%-0%100%100%96%100%88%
Sisakhet100%90%90%100%100%100%100%100%
Ubon Ratchathani80%75%56%100%100%85%100%17%
CambodiaPailin93%97%90%100%100%100%100%56%
Battambang100%100%100%100%100%88%100%29%
Pursat88%98%67%100%100%92%98%44%
Preah Vihear61%100%11%100%100%94%98%21%
Steung Treng93%75%70%100%100%97%100%0%
Ratanakiri49%79%42%99%100%76%90%5%
LaosChampasak66%75%56%100%100%88%94%12%
Attapeu46%43%31%100%100%82%100%18%
Sekong26%6%0%100%100%91%74%5%
Salavan17%2%1%89%97%28%38%1%
Savannakhet10%1%0%87%96%21%41%2%
VietnamBinh Phuoc92%93%83%100%100%100%100%14%
Dak Nong94%92%88%100%100%97%96%22%
Dak Lak96%90%86%100%100%100%99%15%
Gia Lai84%83%76%99%100%98%95%4%
Khanh Hoa22%5%2%95%100%97%74%2%
Ninh Thuan13%18%0%28%100%98%75%0%
Quang Tri16%9%0%75%76%59%26%5%
Congo PDRKinshasa0%0%0%58%98%72%88%0%

The spread of artemisinin resistance (ART-R) is an urgent concern in the GMS. We estimated frequencies of predicted ART-R parasites based on the presence of nonsynonymous mutations in the kelch13 gene, as listed by the World Health Organization, 2018. The resulting map indicates that ART-R has reached very high levels in the lower Mekong region (Cambodia, northeastern Thailand, southern Laos, and Vietnam), nearing fixation in Cambodia and around its borders, with the exception of very few provinces of Laos and the Vietnam coast (Figure 3A). Predicted ART-R frequencies decline to the west of this region: no samples in this study were predicted to be ART-R in India and Bangladesh, thus showing no evidence of spread beyond the GMS, or of local emergence of resistant parasite populations. An analysis of the distribution of kelch13 ART-R alleles (Figure 3—figure supplement 1, Supplementary file 3) reveals a marked difference between the lower Mekong region, where the kelch13 C580Y mutation is the dominant allele, and the region comprising Myanmar and western Thailand, where a wide variety of non-synonymous kelch13 variants are found, and C580Y is not dominant. This reflects a recent increase of C580Y mutant prevalence in Cambodia and neighboring regions, resulting from the rapid spread of the KEL1/PLA1 strain of multidrug-resistant parasites (Hamilton et al., 2019; Amato et al., 2018). This hard selection sweep has replaced a variety of ART-R alleles previously present in that region, resulting from multiple soft sweeps (Miotto et al., 2015; Miotto et al., 2013); this process has not occurred along the Thai-Myanmar border, where allele diversity is still very pronounced. The spread of DHA-PPQ resistant (DHA-PPQ-R) strains in the lower Mekong region is confirmed when we map the frequency of plasmepsin2/3 amplifications conferring piperaquine resistance (PPQ-R, Figure 3—figure supplement 2), which occur where C580Y is most prevalent. Mapping the combined presence of C580Y and plasmepsin2/3 amplification shows that parasites carrying both markers are confined to a well-defined area of the lower Mekong region, and these resistant strains have not made their way into provinces of Laos and Vietnam where ART-R and PPQ-R alleles circulate separately (Figure 3B). Over time, GenRe-Mekong will continue to track across the region the spread of strains carrying drug resistance mutations.

Figure 3 with 5 supplements see all
Map of the spread of (A) artemisinin resistance (ART-R) and (B) dihydroartemisinin-piperaquine resistance (DHA-PPQ-R) in Asian countries.

Marker text and color indicate the proportion of sample classified as resistant in each province/state/division surveyed. A total of 6762 samples were included in (A) and 3395 samples in (B), after excluding samples with undetermined phenotype prediction. The results are summarized in Table 3.

Figure 3—source data 1

Proportions of parasites predicted to be resistant to artemisinin and to the DHA-PPQ combination therapy in each province/state/division.

https://cdn.elifesciences.org/articles/62997/elife-62997-fig3-data1-v1.xlsx

Resistant populations can revert to sensitive haplotypes after drugs are discontinued, as was the case for chloroquine-resistant parasites in East Africa (Laufer et al., 2006; Frosch et al., 2014). To help detect similar trends in the GMS, GenRe-Mekong reports on markers of resistance to previous frontline antimalarials that have been discontinued because of reduced efficacy. The resulting data show that, decades after the replacement of chloroquine as frontline therapy, the frequency of parasites predicted to be resistant (CQ-R) remains exceptionally high across the GMS (Figure 3—figure supplement 3). The reasons for such sustained levels of resistance are unclear; the continued use of chloroquine as frontline treatment for P. vivax malaria, and the low diversity associated with the extremely high prevalence of resistant haplotypes could be major contributing factors. Similarly, we found high levels of the dhfr and dhps markers associated with resistance to sulfadoxine-pyrimethamine (SP, Figure 3—figure supplements 4 and 5). It is unclear why resistance to SP is so widespread, several years after discontinuing this therapy in the GMS, although similar results have been seen in Malawi (Artimovich et al., 2015). Again, very low haplotype diversity may be an obstacle to reversion, and it is also possible that compensatory changes have minimized the fitness impact of resistant mutations over time, diminishing the pressure to revert. It is interesting that predicted resistance is lowest in India, where SP is still used with artesunate as the frontline ACT (Directorate of National Vector Borne Disease Control Programme DGoHS and Government of India, 2013).

Case study: Vietnam

In Vietnam, sample collections were carried out by two NMCP institutes (IMPE-QN and NIMPE), covering approximately 70 sites in seven provinces. Genetic report cards were delivered to public health officials over two malaria seasons (Figure 4), communicating new findings for malaria control. Prior to this surveillance activity, evidence of artemisinin resistance had been found in the provinces of Binh Phuoc, Gia Lai, Dak Nong, Khanh Hoa, and Ninh Thuan province (World Health Organization, 2017). GenRe-Mekong data confirmed the presence of parasites carrying ART-R markers in these provinces, and showed that the province of Dak Lak also has extremely high levels of predicted ART-R (Figure 4—figure supplement 1). Furthermore, our data showed that nearly all ART-R parasites collected near the border with were also predicted to be PPQ-R, in that they carried both the kelch13 C580Y mutation (Figure 4—figure supplement 2) and plasmepsin2/3 amplification (Hamilton et al., 2019; Amato et al., 2018). C580Y parasites were also found in the coastal provinces of Ninh Thuan, Khanh Hoa and Quang Tri, but they did not carry the PPQ-R marker; it is therefore likely the kelch13 mutations were introduced by an earlier sweep of ART-R parasites. Several parasites in Khang Hoa carried the kelch13 P553L mutation, previously associated with an ART-R founder population in Binh Phuoc province (Miotto et al., 2015; Takala-Harrison et al., 2015), supporting the hypothesis they belong to an earlier sweep (Figure 4—figure supplement 2). 

Data from consecutive seasons offers a view of the dynamics of drug resistance spread. In the 2018/2019 season, there was a marked increase in the number of cases in the Krong Pa district of Gia Lai province (Figure 4). In 2017/2018, this district accounted for 15% of cases in the three central provinces that border with Cambodia (n=96 of 656); the following season, this increased to 64% (n=341 of 529, p<10−15). In the same timeframe, predicted DHA-PPQ-R parasites in Krong Pa rose from 65% (n=40 of 62) to 98% (n=298 of 305, p<10−14). These results suggest that an outbreak occurred in this district in 2018/2019, underpinned by strong selection of a genetic background able to survive the frontline ACT DHA-PPQ.

Figure 4 with 2 supplements see all
Longitudinal sample counts and proportions of DHA-PPQ-R parasites in three provinces of Central Vietnam.

The same geographical area (Gia Lai, Dak Lak, and Dak Nong provinces) is shown for two malaria seasons: 2017/18 (12 months from May 2017, n=523) and 2018/2019 (the following 12 months, n=455). Districts are represented by markers whose size is proportional to the number of samples, and whose color indicates the frequency of samples carrying both the kelch13 C580Y mutation and the plasmepsin2/3 amplification, and thus predicted to be DHA-PPQ-R. Marker labels show district name, resistant parasite frequency, and sample count.

Figure 4—source data 1

Proportions of samples predicted to be resistant to DHA-PPQ in districts of Vietnam, in the seasones 2017/18 and 2018/19.

https://cdn.elifesciences.org/articles/62997/elife-62997-fig4-data1-v1.xlsx

Case study: Laos

The Lao NMCP implemented genetic surveillance in five provinces of southern Laos, at over 50 public health facilities. Artemisinin-resistant parasites were found in all five provinces, at frequencies higher in districts bordering Thailand and Cambodia (Figure 5A). The kelch13 C580Y mutation was found in four of the five provinces, and was the most common ART-R allele (Figure 5—figure supplement 1). However, parasites carrying both C580Y and the plasmepsin2–3 amplification were restricted to the two southernmost provinces (Champasak and Attapeu, referred to as ‘Lower Zone’, Figure 5B), and completely absent from Savannakhet and Salavan provinces (‘Upper Zone’) where C580Y parasites lack the PPQ-R amplification. In other words, it appears that DHA-PPQ-R parasites, possibly imported from Cambodia or Thailand, have migrated into the Lower Zone but not the Upper Zone, where a different population of ART-R parasites circulates.

Figure 5 with 2 supplements see all
Proportions of ART-R and KEL1/PLA1 parasites in southern Laos districts.

Districts in five provinces of southern Laos are represented by markers whose color and label indicates the frequency of samples classified as ART-R (A) and as DHA-PPQ-R, i.e. possessing markers of resistance to both artemisinin and piperaquine (B). Only districts with more than 10 samples with valid genotypes are shown. In panel (B), a dashed line denotes a hypothetical demarcation line between a Lower Zone, where DHA-PPQ-R strains have spread, and an Upper Zone, where they are absent and ART-R parasites belong to different strains.

Figure 5—source data 1

Counts and proportions of samples predicted to be resistant to artemisinin and DHA-PPQ in districts of Laos.

https://cdn.elifesciences.org/articles/62997/elife-62997-fig5-data1-v1.xlsx

Given the very recent aggressive spread of DHA-PPQ-R strains, it is likely that ART-R parasites in the Upper Zone are remnants of an earlier sweep which may also have spread from the south, as suggested by the higher frequency in Salavan province than in Savannakhet. To confirm the presence of distinct ART-R populations, we used genetic barcodes to construct a tree that recapitulates population structure in Laos (Figure 5—figure supplement 2), which clearly separates Upper Zone and Lower Zone parasites. In this tree, DHA-PPQ-R parasites form a large, tight cluster clearly separated from the kelch13 wild-type samples from the Upper Zone. The Upper Zone C580Y mutants cluster separately from both these groups, and appear more similar to some C580Y mutants from the Lower Zone which do not carry the PPQ-R amplification, corroborating the hypothesis that Upper Zone mutants migrated from the South. It is likely that the northward spread of DHA-PPQ-R strains has been contained by the use of artemether-lumefantrine in Laos, which diminishes the survival advantage of resistance to piperaquine. However, the spread of DHA-PPQ-R parasites across the Lower Zone, probably displacing previous ART-R strains, suggests that they are well-adapted and highly competitive even in the absence of pressure from piperaquine.

Release of genetic report card data

GenRe-Mekong’s primary data outputs are Genetic Report Cards, delivered as spreadsheets comprising sample metadata (time and place of collection), drug resistance genotypes and phenotype predictions, detected species and genetic barcodes. As soon as sample processing is complete, GRCs are returned to the stakeholders of the studies that contributed the samples, which typically include the NMCP and local scientific partners. Detailed analyses of GRC data may also be conducted by the GenRe-Mekong analysis team and local partners, and their results reported to the NMCP. On a regular basis, GRC data from all studies will be aggregated and released to public access, to benefit the research and public health community. The public releases are detailed by sample, and comprise all genetic data and their derivatives such as phenotype predictions. The first public release is currently available from the article’s Resource Page at https://www.malariagen.net/resource/29.

Discussion

GenRe-Mekong provides a genetic surveillance platform suitable for endemic regions of low- and middle-income countries, which delivers to NMCPs detailed knowledge about the genetic epidemiology of malaria parasites, to support decision-making. Pilot studies have been conducted in all GMS countries, with the Vietnam and Laos NMCPs having implemented GenRe-Mekong on a long-term basis. GenRe-Mekong has multiple features that facilitate NMCP engagement: a sample collection procedure that easily integrates with standard medical facility workflows; standardized protocols and training to support implementation; clear presentation of results, including translation to phenotype predictions, to provide intuitive understanding and rapid communication; and support by our regional analysis team and local partners to deliver and discuss findings. GenRe-Mekong has also worked closely with research projects, contributing to their analyses of the genotyping data and supporting publication of key findings. The genetic data produced were valuable for a wide range of research applications, such as clinical studies of drug efficacy (van der Pluijm et al., 2019), evaluation of elimination interventions (Landier et al., 2018), and epidemiological investigation of malaria importation (Chang et al., 2019).

Collaborations with public health organizations have rapidly translated into real impact for malaria control, especially where GenRe-Mekong has been implemented over multiple seasons. Genetic surveillance results were used by the Vietnam NMCP and Ministry of Health in reviews of national drug policy, leading to the replacement of DHA-PPQ with artesunate-pyronaridine as frontline therapy in four provinces. These included the province of Dak Lak, where an early report by GenRe-Mekong in 2018 was the first evidence of ART-R, confirmed by treatment failure data from in vivo therapy efficacy studies (TES) in 2019. In addition, our report of a DHA-PPQ-R outbreak in Gia Lai province has alerted authorities to the need to review the use of DHA-PPQ in that province. In Laos, authorities have been equally responsive, using GenRe-Mekong reports in their review of frontline therapy choices: the Ministry of Health opted against adopting the DHA-PPQ ACT based on our evidence of the expansion of resistant strains in the Lower Zone of southern Laos. The impact has not been limited to the national level: data shared by surveillance and research projects participating in GenRe-Mekong has powered regional large-scale epidemiological analyses in the GMS and beyond, revealing patterns of spread and evolution of multidrug-resistant malaria (van der Pluijm et al., 2019). By combining results from areas populated by multidrug resistant strains with those from countries where these strains could potentially spread, such as Bangladesh and India, GenRe-Mekong maps support risk assessment and preparedness. GenRe-Mekong will continue to encourage public data sharing to increase the value of genetic data generated, while respecting patient anonymity and giving recognition to those who contributed to the project.

A major advantage of genetic surveillance, compared to more costly clinical studies, is the potential for dense coverage across all endemic areas, which can identify important spatial heterogeneities across the territory. For example, DHA-PPQ was adopted as frontline therapy in Thailand based on the drug’s efficacy in the western provinces; genetic data about the rise in prevalence of DHA-PPQ-R strains in the northeast of the country would probably have led to a different recommendation, had that information been available. Similarly, our data suggests that a single efficacy study in Savannakhet province could have convinced authorities that DHA-PPQ was suitable for Laos, with potential disastrous effects in the southernmost provinces. The extensive coverage provided by GenRe-Mekong routine surveillance allowed a more balanced evaluation of resistant strains prevalence across all endemic provinces. In addition to dense coverage, genetic surveillance should also feature systematic and continued sampling over time, to support the detection of epidemiological changes, and also to allow prevalence comparisons between region, which is most meaningful when collection periods are matched.

The SpotMalaria genotyping platform is designed for extensibility, and has been expanded twice in the course of the project: to test for the newly discovered marker for the plasmepsin2/3 amplification (Amato et al., 2017) and to add new mutations in crt which are associated to higher levels of piperaquine resistance in KEL1/PLA1 parasite (Hamilton et al., 2019; Ross et al., 2018; Agrawal et al., 2017). Such improvements will continue as new markers are identified, and new techniques developed. However, there are newer drugs such as pyronaridine, and established drugs such as lumefantrine and amodiaquine, for which clinical drug resistance markers are yet to be identified. GenRe-Mekong will support the identification of new markers in practical ways, by performing WGS on selected surveillance samples, and contributing these data to public repositories to study epidemiological effects, such as reductions in diversity, increases in cases and founder populations, (Miotto et al., 2013) and to identify genomic regions under selection that may lead to discovering new markers. As the project develops, Genetic Report Cards will be expanded, to address new public health use cases, including those not directly related to drug resistance. For example, genetic barcodes and WGS data can be used to detect imported cases; to distinguish recrudescences from reinfections; and to measure connectedness between sites, and routes of spread (Chang et al., 2019).

GenRe-Mekong was conceived as a versatile and extensible platform that can be easily integrated in a wide range of endemic settings, at relatively low cost to allow extensive geographical coverage. These properties demand trade-offs, imposing certain limitations on the platform. First, we work with small-volume DBS samples, which makes sample collection easy to integrate in routine public health operations; however, low blood volumes mean low genotyping success rates from sub-microscopic infection, and thus GenRe-Mekong only processes samples from cases confirmed by microscopy or rapid diagnostic test (RDT). Second, we focus on genotypes that can be obtained from our high-throughput amplicon sequencing platform, allowing us to contain costs and manpower requirements. In some cases, we have to relax this restriction: for example, mdr1 copy numbers currently cannot be reliably estimated from amplicon sequencing, because of the requirement for selective DNA amplification. Because of the importance of this genotype, we currently use an additional qPCR assay, but it is desirable to find innovative solutions that keep laboratory processes streamlined. Third, while our genetic barcodes can support useful analyses of populations, we plan to improve their resolution by including amplicons containing multiple highly polymorphic SNPs, which may be more informative of identity by descent.

In the future, the integration of genetic surveillance data in public health decision-making processes will be a major focus for GenRe-Mekong, to be addressed in several ways. First, we will make available online platforms for selecting, visualizing and retrieving genetic epidemiology data, which will provide customized views of the data. Second, we will integrate with public health information systems, such as NMCPs’ dashboards, at both national and international level. This includes sharing GenRe-Mekong data through the World Health Organization’s data visualization platform, Malaria Threats Map (http://apps.who.int/malaria/maps/threats/). Third, we will provide training and support to expand in-country expertise, developing local capacity to evaluate drug resistance data and other outputs that GenRe-Mekong will deliver in the future. Finally, we will promote in-country implementations of the SpotMalaria amplicon sequencing platform that underpins the system, to enable faster turnaround times and long-term self-sufficiency. As the adoption cycle continues, we envisage that a growing global network of public health experts will leverage on genetic surveillance to maximize the impact of their interventions, and accelerate progress toward malaria elimination.

Data availability

The data used in this paper, including all genotypes, sample metadata and resulting phenotype predictions, are openly available together with detailed methods documentation and details of partner studies, from the article's Resource Page, at http://www.malariagen.net/resource/29.

The following data sets were generated
    1. Jacob CG
    2. Miotto O
    (2021) MalariaGEN
    ID 29. Genetic surveillance in the Greater Mekong Subregion and South Asia to support malaria control and elimination: about the data.

References

  1. Report
    1. Directorate of National Vector Borne Disease Control Programme DGoHS
    2. Government of India
    (2013)
    National Drug Policy on Malaria
    Ministry of Health and Family Welfare.
    1. Plowe CV
    (2009) The evolution of drug-resistant malaria
    Transactions of the Royal Society of Tropical Medicine and Hygiene 103:S11–S14.
    https://doi.org/10.1016/j.trstmh.2008.11.002
  2. Book
    1. World Health Organization
    (2017)
    Update on Artemisinin Resistance
    Geneva: World Health Organization.
  3. Report
    1. World Health Organization
    (2018)
    Status Report on Artemisinin Resistance and ACT Efficacy
    Geneva: World Health Organization.

Decision letter

  1. Daniel E Neafsey
    Reviewing Editor; Broad Institute of MIT and Harvard, United States
  2. Dominique Soldati-Favre
    Senior Editor; University of Geneva, Switzerland
  3. Daniel E Neafsey
    Reviewer; Broad Institute of MIT and Harvard, United States
  4. Didier Ménard
    Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This work demonstrates the value of malaria genetic surveillance conducted on a large scale. The GenRe-Mekong dataset illuminates heterogeneity in the distribution of drug resistance alleles due to patterns of parasite spread within and across national borders, and underscores the value of data sharing to obtain a regional perspective. While some topics are deserving of deeper treatment, the reviewers accept the authors' perspective that a single manuscript cannot provide a thorough treatment to every dimension of a dataset of this scale.

Decision letter after peer review:

Thank you for submitting your article "Genetic surveillance in the Greater Mekong Subregion and South Asia to support malaria control and elimination" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Daniel E Neafsey as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Dominique Soldati-Favre as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Didier Ménard (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.

Summary:

This manuscript describes a study (GenRe-Mekong) of 9,623 samples (7,626 ultimately retained for analysis) from malaria-infected patients deriving from eight countries in the Greater Mekong Subregion (GMS). Case studies in particular countries (eg Vietnam, Laos) offer detailed perspective on how resistance alleles may influence outbreaks and case counts locally. This manuscript demonstrates the potential value of systematic application of genomic epidemiological approaches to malaria on a large geographic scale in a region of the world where management of drug resistance is essential for local disease control and elimination efforts. All reviewers agreed this manuscript was well-written, and the work will be of significant interest. Given the large scale of the Genre-Mekong effort, certain aspects of the analysis seem under-developed. The authors may be planning follow up publications to more deeply explore some aspects of the data, but certain features warrant further profiling in the present manuscript. The impact of the manuscript could be enhanced through attention to details that would provide deeper perspective on the data and its relevance to public health. A combined set of suggested revisions from all three reviewers is presented below.

Essential revisions:

1) Given the public health profile of this manuscript, deeper commentary on local actionable decisions that could be informed by the present data should be included.

2) Greater background on the state of malaria in the GMS during the sampling period from the perspective of traditional epidemiological measures and clinical treatment failures would make the paper more accessible to a Broad audience not familiar with malaria in this region.

3) Need for more precise and disciplined language to distinguish 'predicted resistance' from genotypes vs. resistance as measured clinically.

4) More thorough interpretation of the patterns of association between known and candidate resistance markers in this large dataset could be helpful for determining whether any new insights are possible.

5) In general, the issue of distinguishing validated vs. non-validated/candidate resistance mutations needs to be more thoroughly addressed. Some SPOTmalaria markers are validated (evidenced by gene editing) and some are not at all (e.g. exo and arps10, ferredoxin, mdr2, see Table SM1). This need to be clearly stated in the main text. The choice of some molecular markers, which are not validated, has been decided by the authors rather than via recommendation by international organizations. Similarly for Pv molecular markers, it should be stated that none of them have been validated (Table SM2).

6) Attention to additional informative dimensions of the data, such as heterozygosity rate to inform complexity of infection (COI), and interpretation of the spatial and temporal patterns of COI, would further demonstrate the value to public health.

7) It seems that the p-value of an association in the 7K samples used to define imputation rules may give overconfidence in an association, as it is only as representative as the training set. Put another way, there is concern that this might cause regression to the mean, where samples are imputed as having a particular common combination of mutations when in fact this particular sample may simply be different from what we have seen before. In this situation imputation may preclude further investigation of the true genotypes. Perhaps one way around this would be to quantify the degree to which each result relies on imputed values, thereby giving more confidence in more complete results and highlighting areas where more data is needed. The overall degree of imputation in the data (proportion imputed sites) should also be reported in the manuscript.

https://doi.org/10.7554/eLife.62997.sa1

Author response

Essential revisions:

1) Given the public health profile of this manuscript, deeper commentary on local actionable decisions that could be informed by the present data should be included.

We have provided some evidence in the Discussion section, for Vietnam:

“Collaborations with public health organizations have rapidly translated into real impact for malaria control, especially where GenRe-Mekong has been implemented over multiple seasons. Genetic surveillance results were used by the Vietnam NMCP and Ministry of Health in reviews of national drug policy, leading to the replacement of DHA-PPQ with artesunate-pyronaridine as frontline therapy in four provinces. These included the province of Dak Lak, where an early report by GenRe-Mekong in 2018 was the first evidence of ART-R, confirmed by treatment failure data from in vivo therapy efficacy studies (TES) in 2019. In addition, our report of a KEL1/PLA1 outbreak in Gia Lai province has alerted authorities to the need to review the use of DHA-PPQ in that province.”

and also for Laos:

“In Laos, authorities have been equally responsive, using GenRe-Mekong reports in their review of frontline therapy choices: the Ministry of Health opted against adopting the DHA-PPQ ACT based on our evidence of the expansion of resistant strains in the Lower Zone of southern Laos.”

Given the space available, this seemed like adequate coverage of the impact on local decision-making. Perhaps the reviewer meant that we should indicate at the start of the paper the types of application of drug resistance surveillance which would benefit NMCPs. We addressed this by modifying the Introduction section as follows:

“Parasite genetic data is less frequently available, and typically restricted to single genetic variants, or small numbers of sites where quality sample collection protocols could be executed. However, routine mapping of a broad set resistance markers can keep NMCPs abreast of the spread of resistance strains, and help them predict changes in drug efficacy and assess alternative therapies, especially if dense geographical coverage allows mapping of resistance at province or district level. The increased affordability of high-throughput sequencing technologies now offers new opportunities for delivering such knowledge to public health, supporting the optimization of interventions where resources are limited.”

2) Greater background on the state of malaria in the GMS during the sampling period from the perspective of traditional epidemiological measures and clinical treatment failures would make the paper more accessible to a Broad audience not familiar with malaria in this region.

We have expanded the Introduction section with the following:

“This problem is most acutely felt in the Greater Mekong Subregion (GMS), a region that has repeatedly been the origin of drug resistant strains, and in neighbouring countries including Bangladesh and India, where resistance could be imported. The GMS is a region of relatively low endemicity, with entomological inoculation rates 2-3 orders of magnitude lower than in Africa, where the vast majority of cases occur. [Chaumeau et al., Wellcome Open Res 2018; 3: 109, Hay et al., Trans Royal Soc Trop Med Hyg 2000; 94: 113-27.] Infections are most common amongst individuals who work in or live near forests, in remote rural parts of the region. [Cui et al., Acta Trop 2012; 121: 227-39] Since infections are infrequent, a high proportion of individuals in this region are immunologically naïve, and develop symptoms that require treatment when infected. This results in high parasite exposure to drugs, which may be a major evolutionary driving force for the emergence of genetic factors that confer resistance to frontline therapies. [Escalante et al., Trends Parasitol 2009; 25: 557-63] In the past, drug resistance alleles emerged in the GMS and subsequently spread to Africa multiple times, rolling back progress against the disease at the cost of many lives. Currently, global malaria control and elimination strategies depend on the efficacy of artemisinin combination therapies (ACTs) which are the frontline therapy of choice worldwide. Hence, in view of the emergence in the GMS of parasite strains resistant to artemisinin and its ACT partner drug piperaquine, the elimination of P. falciparum from this region has become a global health priority.”

3) Need for more precise and disciplined language to distinguish 'predicted resistance' from genotypes vs. resistance as measured clinically.

We have reviewed the whole manuscript to identify all mentions of resistance where there may have been such ambiguities. We made many changes throughout the paper to evidence the predictive nature of the phenotypes (too numerous to list here explicitly). We also modified the “Survey of drug resistance mutations” section of the Results to clarify:

“To bridge this gap, we use genotypes to derive predicted phenotypes based on a set of rules derived from peer-reviewed publications (see Materials and Methods and formal rules definitions available from the article’s Resource Page). These rules predict samples as resistant or sensitive to a particular drug or treatment, or undetermined. Since our procedures do not include the measurement of clinical or in vitro phenotypes, we are only able to predict a drug resistant phenotype based on known associations of certain markers with resistance to certain drugs. Although we report a large catalogue of variations which have been associated with resistance, we do not use all variations to predict resistance. Rather, our predictive rules are conservative and only use markers that have been strongly characterized and validated in published literature and shown to play a crucial role in clinical or in vitro resistance. These critical variants include single nucleotide polymorphisms (SNPs) in genes kelch13 (resistance to artemisinin), crt (chloroquine), dhfr (pyrimethamine), dhps (sulfadoxine), as well as an amplification breakpoint sequence in plasmepsin2/3 (marker of resistance to piperaquine). In addition, we report several additional variants found in drug resistance backgrounds but not used to predict resistance, such as mutations in mdr1 (linked to resistance to multiple drugs), components of the predisposing ART-R background arps10, ferredoxin, mdr2, and the exo marker associated with resistance to piperaquine.”

4) More thorough interpretation of the patterns of association between known and candidate resistance markers in this large dataset could be helpful for determining whether any new insights are possible.

This is just one of the many interesting questions that we hope the research community will explore using this dataset. However, it is not within the scope of this paper, which is focussed on the application of genotyping to public health. We had to set some boundaries to the present analysis, and unavoidably, leave some space for future work.

5) In general, the issue of distinguishing validated vs. non-validated/candidate resistance mutations needs to be more thoroughly addressed. Some SPOTmalaria markers are validated (evidenced by gene editing) and some are not at all (e.g. exo and arps10, ferredoxin, mdr2, see Table SM1). This need to be clearly stated in the main text. The choice of some molecular markers, which are not validated, has been decided by the authors rather than via recommendation by international organizations. Similarly for Pv molecular markers, it should be stated that none of them have been validated (Table SM2).

This is a fair comment, and we had assumed that it had been addressed by supplying as supplementary material a whole document detailing explicit rules for predicting drug resistant phenotypes from these markers. In these rules, it is clear that markers such as arps10, ferredoxin and mdr2 are not used to predict artemisinin resistance; we genotype and supply them, however, because they have been associated with resistance, and may be (or become) of interest. In the main text, we have now changed Section “Survey of drug resistance mutations” of the main text as follows:

“GenRe-Mekong produces genotypes covering a broad range of known variants associated to drug resistance (Table 2) to support assessment of the spread and risk of drug resistance. [...] Although we report a large catalogue of variations which have been associated with resistance, we do not use all variations to predict resistance. Rather, our predictive rules are conservative and only use markers that have been strongly characterized and validated in published literature and shown to play a crucial role in clinical or in vitro resistance. [...] In addition, we report several additional variants found in drug resistance backgrounds but not used to predict resistance, such as mutations in mdr1 (linked to resistance to multiple drugs), components of the predisposing ART-R background arps10, ferredoxin, mdr2 (Miotto et al., 2015), and the exo marker associated with resistance to piperaquine (Amato et al., 2017).”

It appears to us that the document “The SpotMalaria platform – Technical Notes and Methods” provided in the Supplementary Materials has ample treatment of the roles described in the literature for the markers we genotype, both for Pf and for Pv. Still, we added an introductory paragraph before Tables SM1 and SM2 (Page 4 of that document) as follows:

“All genotyped markers associated with drug resistance are listed in Tables SM1 and SM2 for P. falciparum and P. vivax, respectively. A broad spectrum of markers is covered, obtained from scientific literature and public health guidelines, including variants that are validated experimentally, as well as markers associated with resistance but not validated. The prediction of resistant phenotypes requires contextual information about the role of the variant alleles. In the following sections, background information and references are provided, and a separate document formally details the rules which are used to predict drug resistance phenotypes from some of these variants.”

The fact that the Pv markers are just putatively associated with resistance is discussed in the relevant paragraphs of that document.

6) Attention to additional informative dimensions of the data, such as heterozygosity rate to inform complexity of infection (COI), and interpretation of the spatial and temporal patterns of COI, would further demonstrate the value to public health.

This is a reasonable comment, and we can only agree. However, we are still working on the methods of estimation of sample heterozygosity, FWS and COI. At present, we do not feel ready to publish those results, but did not feel we should hold up the present publication for this reason. We will publish an update when that work is complete.

7) It seems that the p-value of an association in the 7K samples used to define imputation rules may give overconfidence in an association, as it is only as representative as the training set. Put another way, there is concern that this might cause regression to the mean, where samples are imputed as having a particular common combination of mutations when in fact this particular sample may simply be different from what we have seen before. In this situation imputation may preclude further investigation of the true genotypes. Perhaps one way around this would be to quantify the degree to which each result relies on imputed values, thereby giving more confidence in more complete results and highlighting areas where more data is needed. The overall degree of imputation in the data (proportion imputed sites) should also be reported in the manuscript.

This is a valid point, but it is useful to frame it in context.

First, we should highlight that imputation rules (see Supplementary materials) are only specified for three genes that possess variants that correlate with mutations conferring drug resistance: crt (resistance to chloroquine), dhfr (pyrimethamine) and dhps (sulfadoxine). No imputation was applied when estimating the prevalence of resistance to artemisinin or piperaquine, which are the most prominent results presented.

Second, the three genes in question have established some common haplotypes in several geographical regions, which have been in circulation for several decades. The high frequency of these haplotypes gives us confidence that they provide reliable imputation variants.

That said, we did compute some statistics to estimate the validity of our imputation rules, by testing whether each imputation rule would predict the correct allele in samples that met the conditions for triggering the rule, but did not require imputation (i.e. the allele to be imputed was present and thus could be compared for agreement). These tests were performed on 21,272 SpotMalaria samples, including all samples in the GenRe-Mekong study and several others from other studies using SpotMalaria. We found the following:

– For all rules (a total of 70,596 triggered cases), we found agreement in 99.1% of cases.

– When stratifying by gene, agreement was >99.9% for the crt and dhfr genes, while dhps (involved in sulfadoxine resistance) had 96.7% agreement.

– When considering only rules that affect the phenotype prediction, agreement was still >99.9% for the crt and dhfr genes, while dhps agreement was 95.4%.

– Even if we take imputation rule disagreements to represent a rule error rate, this does not equate to a phenotype prediction error rate, since rules are only triggered for a minority of samples.

The worst-performing rule for dhps had a disagreement rate of 8.9%, and was used to impute 303 samples out of 21,272 (i.e. 1.4% of samples). Thus, we estimate it could introduce phenotype prediction error of 0.089 0.014, i.e. around 0.1%. The second-worst rule had a disagreement rate of 3.2% and was used on 102 samples, introducing an estimated phenotype prediction error of around 0.02%.

After reviewing the above results, such minimal effects on phenotype predictions were deemed acceptable.

https://doi.org/10.7554/eLife.62997.sa2

Article and author information

Author details

  1. Christopher G Jacob

    Wellcome Sanger Institute, Hinxton, United Kingdom
    Contribution
    Resources, Data curation, Software, Formal analysis, Validation, Investigation, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  2. Nguyen Thuy-Nhien

    Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
    Contribution
    Supervision, Investigation, Project administration
    Competing interests
    No competing interests declared
  3. Mayfong Mayxay

    1. Lao-Oxford-Mahosot Hospital-Wellcome Research Unit (LOMWRU), Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao People's Democratic Republic
    2. Institute of Research and Education Development (IRED), University of Health Sciences, Ministry of Health, Vientiane, Lao People's Democratic Republic
    3. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  4. Richard J Maude

    1. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    2. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    3. Harvard TH Chan School of Public Health, Harvard University, Boston, United States
    Contribution
    Supervision, Investigation, Methodology, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5355-0562
  5. Huynh Hong Quang

    Institute of Malariology, Parasitology and Entomology (IMPE-QN), Quy Nhon, Viet Nam
    Contribution
    Supervision, Investigation, Methodology, Project administration
    Competing interests
    No competing interests declared
  6. Bouasy Hongvanthong

    Centre of Malariology, Parasitology, and Entomology, Vientiane, Lao People's Democratic Republic
    Contribution
    Investigation, Project administration
    Competing interests
    No competing interests declared
  7. Viengxay Vanisaveth

    Centre of Malariology, Parasitology, and Entomology, Vientiane, Lao People's Democratic Republic
    Contribution
    Investigation, Project administration
    Competing interests
    No competing interests declared
  8. Thang Ngo Duc

    National Institute of Malariology, Parasitology and Entomology (NIMPE), Hanoi, Viet Nam
    Contribution
    Investigation, Project administration
    Competing interests
    No competing interests declared
  9. Huy Rekol

    National Center for Parasitology, Entomology, and Malaria Control, Phnom Penh, Cambodia
    Contribution
    Investigation, Project administration
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    No competing interests declared
  10. Rob van der Pluijm

    1. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    2. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
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    Supervision, Investigation, Project administration
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    No competing interests declared
  11. Lorenz von Seidlein

    1. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    2. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    Contribution
    Supervision, Investigation
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    No competing interests declared
  12. Rick Fairhurst

    National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, United States
    Present address
    AstraZeneca, Gaithersburg, United States
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    Supervision, Investigation
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    No competing interests declared
  13. François Nosten

    1. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    2. Shoklo Malaria Research Unit, Mae Sot, Thailand
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    Supervision, Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7951-0745
  14. Md Amir Hossain

    Chittagong Medical College Hospital, Chittagong, Bangladesh
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    Supervision, Investigation
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  15. Naomi Park

    Wellcome Sanger Institute, Hinxton, United Kingdom
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    Investigation, Methodology
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    No competing interests declared
  16. Scott Goodwin

    Wellcome Sanger Institute, Hinxton, United Kingdom
    Contribution
    Methodology
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    No competing interests declared
  17. Pascal Ringwald

    World Health Organization, Geneva, Switzerland
    Contribution
    Investigation, Methodology
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  18. Keobouphaphone Chindavongsa

    Centre of Malariology, Parasitology, and Entomology, Vientiane, Lao People's Democratic Republic
    Contribution
    Supervision, Investigation
    Competing interests
    No competing interests declared
  19. Paul Newton

    1. Lao-Oxford-Mahosot Hospital-Wellcome Research Unit (LOMWRU), Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao People's Democratic Republic
    2. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    3. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    Contribution
    Supervision, Investigation
    Competing interests
    No competing interests declared
  20. Elizabeth Ashley

    1. Lao-Oxford-Mahosot Hospital-Wellcome Research Unit (LOMWRU), Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao People's Democratic Republic
    2. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    Contribution
    Supervision, Investigation
    Competing interests
    No competing interests declared
  21. Sonexay Phalivong

    Lao-Oxford-Mahosot Hospital-Wellcome Research Unit (LOMWRU), Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao People's Democratic Republic
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    Supervision, Investigation, Project administration
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    No competing interests declared
  22. Rapeephan Maude

    1. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    2. Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
    Contribution
    Investigation
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    No competing interests declared
  23. Rithea Leang

    National Center for Parasitology, Entomology, and Malaria Control, Phnom Penh, Cambodia
    Contribution
    Investigation, Project administration
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    No competing interests declared
  24. Cheah Huch

    National Center for Parasitology, Entomology, and Malaria Control, Phnom Penh, Cambodia
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    No competing interests declared
  25. Le Thanh Dong

    Institute of Malariology, Parasitology and Entomology (IMPEHCM), Ho Chi Minh City, Viet Nam
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    No competing interests declared
  26. Kim-Tuyen Nguyen

    Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
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  27. Tran Minh Nhat

    Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
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    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9500-8341
  28. Tran Tinh Hien

    Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
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  29. Hoa Nguyen

    Vysnova Partners Inc, Hanoi, Viet Nam
    Contribution
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    is an employee of Vysnova Partners Inc
  30. Nicole Zdrojewski

    Vysnova Partners Inc, Hanoi, Viet Nam
    Contribution
    Supervision, Investigation
    Competing interests
    is an employee of Vysnova Partners Inc
  31. Sara Canavati

    Vysnova Partners Inc, Hanoi, Viet Nam
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    is an employee of Vysnova Partners Inc
  32. Abdullah Abu Sayeed

    Chittagong Medical College Hospital, Chittagong, Bangladesh
    Contribution
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    No competing interests declared
  33. Didar Uddin

    Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    Contribution
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    No competing interests declared
  34. Caroline Buckee

    Harvard TH Chan School of Public Health, Harvard University, Boston, United States
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    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8386-5899
  35. Caterina I Fanello

    1. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    2. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    Contribution
    Supervision, Investigation
    Competing interests
    No competing interests declared
  36. Marie Onyamboko

    Kinshasa School of Public Health, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  37. Thomas Peto

    1. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    2. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  38. Rupam Tripura

    1. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    2. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  39. Chanaki Amaratunga

    National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, United States
    Present address
    1. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    2. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  40. Aung Myint Thu

    1. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    2. Shoklo Malaria Research Unit, Mae Sot, Thailand
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  41. Gilles Delmas

    1. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    2. Shoklo Malaria Research Unit, Mae Sot, Thailand
    Contribution
    Supervision, Investigation
    Competing interests
    No competing interests declared
  42. Jordi Landier

    1. Shoklo Malaria Research Unit, Mae Sot, Thailand
    2. Aix-Marseille Université, INSERM, IRD, SESSTIM, Aix Marseille Institute of Public Health, ISSPAM, Marseille, France
    Contribution
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    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8619-9775
  43. Daniel M Parker

    1. Shoklo Malaria Research Unit, Mae Sot, Thailand
    2. Susan and Henry Samueli College of Health Sciences, University of California, Irvine, Irvine, United States
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
  44. Nguyen Hoang Chau

    Oxford University Clinical Research Unit, Ho Chi Minh City, Viet Nam
    Contribution
    Investigation
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    No competing interests declared
  45. Dysoley Lek

    National Center for Parasitology, Entomology, and Malaria Control, Phnom Penh, Cambodia
    Contribution
    Supervision, Investigation
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    No competing interests declared
  46. Seila Suon

    National Center for Parasitology, Entomology, and Malaria Control, Phnom Penh, Cambodia
    Contribution
    Investigation
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    No competing interests declared
  47. James Callery

    1. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    2. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
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    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3218-2166
  48. Podjanee Jittamala

    Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
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  49. Borimas Hanboonkunupakarn

    Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
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    No competing interests declared
  50. Sasithon Pukrittayakamee

    1. Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
    2. The Royal Society of Thailand, Bangkok, Thailand
    Contribution
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    No competing interests declared
  51. Aung Pyae Phyo

    1. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    2. Myanmar-Oxford Clinical Research Unit, Yangon, Myanmar
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  52. Frank Smithuis

    1. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    2. Myanmar-Oxford Clinical Research Unit, Yangon, Myanmar
    Contribution
    Supervision, Investigation
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    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4704-9915
  53. Khin Lin

    Department of Medical Research, Pyin Oo Lwin, Myanmar
    Contribution
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    No competing interests declared
  54. Myo Thant

    Defence Services Medical Research Centre, Yangon, Myanmar
    Contribution
    Investigation
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    No competing interests declared
  55. Tin Maung Hlaing

    Defence Services Medical Research Centre, Yangon, Myanmar
    Contribution
    Supervision, Investigation
    Competing interests
    No competing interests declared
  56. Parthasarathi Satpathi

    Midnapore Medical College, Midnapur, India
    Contribution
    Supervision, Investigation
    Competing interests
    No competing interests declared
  57. Sanghamitra Satpathi

    Ispat General Hospital, Rourkela, India
    Contribution
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    No competing interests declared
  58. Prativa K Behera

    Ispat General Hospital, Rourkela, India
    Contribution
    Investigation
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    No competing interests declared
  59. Amar Tripura

    Agartala Medical College, Agartala, India
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  60. Subrata Baidya

    Agartala Medical College, Agartala, India
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  61. Neena Valecha

    National Institute of Malaria Research, Indian Council of Medical Research, New Delhi, India
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  62. Anupkumar R Anvikar

    National Institute of Malaria Research, Indian Council of Medical Research, New Delhi, India
    Contribution
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    Competing interests
    No competing interests declared
  63. Akhter Ul Islam

    Ramu Upazila Health Complex, Cox's Bazar, Bangladesh
    Contribution
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    Competing interests
    No competing interests declared
  64. Abul Faiz

    Malaria Research Group and Dev Care Foundation, Dhaka, Bangladesh
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  65. Chanon Kunasol

    Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    Contribution
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    Competing interests
    No competing interests declared
  66. Eleanor Drury

    Wellcome Sanger Institute, Hinxton, United Kingdom
    Contribution
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    Competing interests
    No competing interests declared
  67. Mihir Kekre

    Wellcome Sanger Institute, Hinxton, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  68. Mozam Ali

    Wellcome Sanger Institute, Hinxton, United Kingdom
    Contribution
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    Competing interests
    No competing interests declared
  69. Katie Love

    Wellcome Sanger Institute, Hinxton, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  70. Shavanthi Rajatileka

    Wellcome Sanger Institute, Hinxton, United Kingdom
    Contribution
    Supervision, Investigation, Methodology, Project administration
    Competing interests
    No competing interests declared
  71. Anna E Jeffreys

    Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
    Contribution
    Supervision, Investigation, Methodology, Project administration
    Competing interests
    No competing interests declared
  72. Kate Rowlands

    Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  73. Christina S Hubbart

    Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
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    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9576-9581
  74. Mehul Dhorda

    1. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    2. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    3. Worldwide Antimalarial Resistance Network (WWARN), Asia Regional Centre, Bangkok, Thailand
    Contribution
    Supervision, Investigation, Methodology, Project administration
    Competing interests
    No competing interests declared
  75. Ranitha Vongpromek

    1. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    2. Worldwide Antimalarial Resistance Network (WWARN), Asia Regional Centre, Bangkok, Thailand
    Contribution
    Data curation, Supervision, Validation, Investigation, Methodology, Project administration
    Competing interests
    No competing interests declared
  76. Namfon Kotanan

    Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
    Contribution
    Data curation, Validation, Investigation, Methodology
    Competing interests
    No competing interests declared
  77. Phrutsamon Wongnak

    Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    Contribution
    Data curation, Formal analysis, Investigation, Methodology
    Competing interests
    No competing interests declared
  78. Jacob Almagro Garcia

    MRC Centre for Genomics and Global Health, Big Data Institute, Oxford University, Oxford, United Kingdom
    Contribution
    Data curation, Software, Formal analysis, Investigation, Methodology
    Competing interests
    No competing interests declared
  79. Richard D Pearson

    1. Wellcome Sanger Institute, Hinxton, United Kingdom
    2. MRC Centre for Genomics and Global Health, Big Data Institute, Oxford University, Oxford, United Kingdom
    Contribution
    Data curation, Software, Formal analysis, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7386-3566
  80. Cristina V Ariani

    Wellcome Sanger Institute, Hinxton, United Kingdom
    Contribution
    Data curation, Formal analysis
    Competing interests
    No competing interests declared
  81. Thanat Chookajorn

    Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
    Contribution
    Data curation, Supervision, Project administration
    Competing interests
    No competing interests declared
  82. Cinzia Malangone

    Wellcome Sanger Institute, Hinxton, United Kingdom
    Contribution
    Software, Supervision, Project administration
    Competing interests
    No competing interests declared
  83. T Nguyen

    Wellcome Sanger Institute, Hinxton, United Kingdom
    Contribution
    Software
    Competing interests
    No competing interests declared
  84. Jim Stalker

    Wellcome Sanger Institute, Hinxton, United Kingdom
    Contribution
    Software, Supervision
    Competing interests
    No competing interests declared
  85. Ben Jeffery

    MRC Centre for Genomics and Global Health, Big Data Institute, Oxford University, Oxford, United Kingdom
    Contribution
    Software, Supervision, Visualization
    Competing interests
    No competing interests declared
  86. Jonathan Keatley

    Wellcome Sanger Institute, Hinxton, United Kingdom
    Contribution
    Software, Supervision, Visualization
    Competing interests
    No competing interests declared
  87. Kimberly J Johnson

    1. Wellcome Sanger Institute, Hinxton, United Kingdom
    2. MRC Centre for Genomics and Global Health, Big Data Institute, Oxford University, Oxford, United Kingdom
    Contribution
    Software, Visualization, Project administration
    Competing interests
    No competing interests declared
  88. Dawn Muddyman

    Wellcome Sanger Institute, Hinxton, United Kingdom
    Contribution
    Software, Project administration
    Competing interests
    No competing interests declared
  89. Xin Hui S Chan

    1. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    2. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    Contribution
    Investigation, Project administration
    Competing interests
    No competing interests declared
  90. John Sillitoe

    Wellcome Sanger Institute, Hinxton, United Kingdom
    Contribution
    Resources, Supervision, Investigation, Project administration
    Competing interests
    No competing interests declared
  91. Roberto Amato

    Wellcome Sanger Institute, Hinxton, United Kingdom
    Contribution
    Resources, Software, Supervision, Validation, Investigation, Project administration
    Competing interests
    No competing interests declared
  92. Victoria Simpson

    1. Wellcome Sanger Institute, Hinxton, United Kingdom
    2. MRC Centre for Genomics and Global Health, Big Data Institute, Oxford University, Oxford, United Kingdom
    Contribution
    Conceptualization, Resources, Software, Supervision, Validation, Investigation, Project administration
    Competing interests
    No competing interests declared
  93. Sonia Gonçalves

    Wellcome Sanger Institute, Hinxton, United Kingdom
    Contribution
    Conceptualization, Software, Supervision, Validation, Investigation, Methodology, Project administration
    Competing interests
    No competing interests declared
  94. Kirk Rockett

    1. Wellcome Sanger Institute, Hinxton, United Kingdom
    2. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
    Contribution
    Conceptualization, Formal analysis, Supervision, Validation, Investigation, Methodology, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6369-9299
  95. Nicholas P Day

    1. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    2. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    Contribution
    Resources, Formal analysis, Supervision, Validation, Investigation, Methodology, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2309-1171
  96. Arjen M Dondorp

    1. Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    2. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    Contribution
    Resources, Formal analysis, Supervision, Investigation, Methodology, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5190-2395
  97. Dominic P Kwiatkowski

    1. Wellcome Sanger Institute, Hinxton, United Kingdom
    2. MRC Centre for Genomics and Global Health, Big Data Institute, Oxford University, Oxford, United Kingdom
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  98. Olivo Miotto

    1. Wellcome Sanger Institute, Hinxton, United Kingdom
    2. Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    3. MRC Centre for Genomics and Global Health, Big Data Institute, Oxford University, Oxford, United Kingdom
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    olivo@tropmedres.ac
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8060-6771

Funding

Bill and Melinda Gates Foundation (OPP11188166)

  • Dominic P Kwiatkowski
  • Olivo Miotto

Bill and Melinda Gates Foundation (OPP1204268)

  • Olivo Miotto

Wellcome Trust (098051)

  • Dominic P Kwiatkowski

Wellcome Trust (206194)

  • Dominic P Kwiatkowski

Wellcome Trust (203141)

  • Dominic P Kwiatkowski

Wellcome Trust (090770)

  • Dominic P Kwiatkowski

Wellcome Trust (204911)

  • Dominic P Kwiatkowski

Wellcome Trust (106698/B/14/Z)

  • Dominic P Kwiatkowski

Medical Research Council (G0600718)

  • Dominic P Kwiatkowski

Department for International Development, UK Government (201900)

  • Arjen M Dondorp

Department for International Development, UK Government (M006212)

  • Arjen M Dondorp

The funders had no role in study design, data collection, data analysis, data interpretation, or report writing. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication

Acknowledgements

We are grateful all patients and health workers who participated in sample collections. This study used data from the MalariaGEN Pf3k Project and Plasmodium falciparum Community Project. We thank the staff of Wellcome Sanger Institute Sample Logistics, Sequencing, and Informatics facilities for their contribution; in particular, we are grateful to the Wellcome Sanger Institute DNA Pipelines Informatics team for supporting the development of the methods used in this work. We thank the many collaborators who contributed to the GenRe-Mekong Project, and especially: Pannapat Masingboon, Narisa Thongmee, Zoë Doran, Salwaluk Panapipat, Ipsita Sinha, Rapeephan Maude, Vilasinee Yuwaree, Tran Minh Nhat, Hoang Hai Phuc, Ro Mah Huan, Nguyen Minh Nhat, Tran Van Don. PR is a staff member of the World Health Organization; PR alone is responsible for the views expressed in this publication and they do not necessarily represent the decisions, policy or views of the World Health Organization. This work was supported, in whole or in part, by the Bill and Melinda Gates Foundation [OPP11188166, OPP1204268]. Under the grant conditions of the Foundation, a Creative Commons Attribution 4.0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission.

Ethics

Human subjects: For each country where the surveillance project was implemented in collaboration with public health authorities, we submitted a common GenRe-Mekong protocol, and obtained approval by a relevant local ethics review board and by the Oxford University Tropical Research Ethics Committee (OxTREC). In all countries we obtain informed consent from each malaria patient providing a sample, except for Laos, where the Ministry of Health has classified the project as routine surveillance for the benefit of the country, and removed the requirement for consent. Collaborating research studies included in their own protocol provisions for sample collection procedures and informed consent, compatible with those in the GenRe-Mekong protocol, and obtained ethical approval from both a relevant local ethics review board, and their relevant institutional research ethics committee.

Senior Editor

  1. Dominique Soldati-Favre, University of Geneva, Switzerland

Reviewing Editor

  1. Daniel E Neafsey, Broad Institute of MIT and Harvard, United States

Reviewers

  1. Daniel E Neafsey, Broad Institute of MIT and Harvard, United States
  2. Didier Ménard

Publication history

  1. Preprint posted: July 25, 2020 (view preprint)
  2. Received: September 10, 2020
  3. Accepted: June 30, 2021
  4. Version of Record published: August 10, 2021 (version 1)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

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  1. Further reading

Further reading

    1. Ecology
    2. Epidemiology and Global Health
    Morgan P Kain et al.
    Research Article Updated

    Identifying the key vector and host species that drive the transmission of zoonotic pathogens is notoriously difficult but critical for disease control. We present a nested approach for quantifying the importance of host and vectors that integrates species’ physiological competence with their ecological traits. We apply this framework to a medically important arbovirus, Ross River virus (RRV), in Brisbane, Australia. We find that vertebrate hosts with high physiological competence are not the most important for community transmission; interactions between hosts and vectors largely underpin the importance of host species. For vectors, physiological competence is highly important. Our results identify primary and secondary vectors of RRV and suggest two potential transmission cycles in Brisbane: an enzootic cycle involving birds and an urban cycle involving humans. The framework accounts for uncertainty from each fitted statistical model in estimates of species’ contributions to transmission and has has direct application to other zoonotic pathogens.

    1. Epidemiology and Global Health
    2. Genetics and Genomics
    Mohd Anisul et al.
    Research Article Updated

    Background:

    The virus SARS-CoV-2 can exploit biological vulnerabilities (e.g. host proteins) in susceptible hosts that predispose to the development of severe COVID-19.

    Methods:

    To identify host proteins that may contribute to the risk of severe COVID-19, we undertook proteome-wide genetic colocalisation tests, and polygenic (pan) and cis-Mendelian randomisation analyses leveraging publicly available protein and COVID-19 datasets.

    Results:

    Our analytic approach identified several known targets (e.g. ABO, OAS1), but also nominated new proteins such as soluble Fas (colocalisation probability >0.9, p=1 × 10-4), implicating Fas-mediated apoptosis as a potential target for COVID-19 risk. The polygenic (pan) and cis-Mendelian randomisation analyses showed consistent associations of genetically predicted ABO protein with several COVID-19 phenotypes. The ABO signal is highly pleiotropic, and a look-up of proteins associated with the ABO signal revealed that the strongest association was with soluble CD209. We demonstrated experimentally that CD209 directly interacts with the spike protein of SARS-CoV-2, suggesting a mechanism that could explain the ABO association with COVID-19.

    Conclusions:

    Our work provides a prioritised list of host targets potentially exploited by SARS-CoV-2 and is a precursor for further research on CD209 and FAS as therapeutically tractable targets for COVID-19.

    Funding:

    MAK, JSc, JH, AB, DO, MC, EMM, MG, ID were funded by Open Targets. J.Z. and T.R.G were funded by the UK Medical Research Council Integrative Epidemiology Unit (MC_UU_00011/4). JSh and GJW were funded by the Wellcome Trust Grant 206194. This research was funded in part by the Wellcome Trust [Grant 206194]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.