Novel genotyping approaches to easily detect genomic admixture between the major Afrotropical malaria vector species, Anopheles coluzzii and An. gambiae

The two most efficient and most recently radiated Afrotropical vectors of human malaria - Anopheles coluzzii and An.gambiae - are identified by single-locus diagnostic PCR assays based on species-specific markers in a 4Mb region on chromosome-X centromere. Inherently, these diagnostic assays cannot detect interspecific autosomal admixture shown to be extensive at the westernmost and easternmost extremes of the species range. The main aim of this study was to develop novel, easy-to-implement tools for genotyping An.coluzzii and An.gambiae-specific ancestral informative markers (AIMs) identified from the Anopheles gambiae 1000 genomes (Ag1000G) project. First, we took advantage of this large set of data in order to develop a multilocus approach to genotype 26 AIMs on all chromosome arms valid across the species range. Second, we tested the multilocus assay on samples from Guinea Bissau, The Gambia and Senegal, three countries spanning the westernmost hybridization zone, where conventional species diagnostic is problematic due to the putative presence of a novel "hybrid form". The multilocus assay was able to capture patterns of admixture reflecting those revealed by the whole set of AIMs and provided new original data on interspecific admixture in the region. Third, we developed an easy-to-use, cost-effective PCR approach for genotyping two AIMs on chromosome-3 among those included in the multilocus approach, opening the possibility for advanced identification of species and of admixed specimens during routine large scale entomological surveys, particularly, but not exclusively, at the extremes of the range, where WGS data highlighted unexpected autosomal admixture

Human candidate gene polymorphisms and risk of severe malaria in children in Kilifi, Kenya: a case-control association study

Background: Human genetic factors are important determinants of malaria risk. We investigated associations between multiple candidate polymorphisms—many related to the structure or function of red blood cells—and risk for severe Plasmodium falciparum malaria and its specific phenotypes, including cerebral malaria, severe malaria anaemia, and respiratory distress. Methods: We did a case-control study in Kilifi County, Kenya. We recruited as cases children presenting with severe malaria to the high-dependency ward of Kilifi County Hospital. We included as controls infants born in the local community between Aug 1, 2006, and Sept 30, 2010, who were part of a genetics study. We tested for associations between a range of candidate malaria-protective genes and risk for severe malaria and its specific phenotypes. We used a permutation approach to account for multiple comparisons between polymorphisms and severe malaria. We judged p values less than 0·005 significant for the primary analysis of the association between candidate genes and severe malaria. Findings: Between June 11, 1995, and June 12, 2008, 2244 children with severe malaria were recruited to the study, and 3949 infants were included as controls. Overall, 263 (12%) of 2244 children with severe malaria died in hospital, including 196 (16%) of 1233 with cerebral malaria. We investigated 121 polymorphisms in 70 candidate severe malaria-associated genes. We found significant associations between risk for severe malaria overall and polymorphisms in 15 genes or locations, of which most were related to red blood cells: ABO, ATP2B4, ARL14, CD40LG, FREM3, INPP4B, G6PD, HBA (both HBA1 and HBA2), HBB, IL10, LPHN2 (also known as ADGRL2), LOC727982, RPS6KL1, CAND1, and GNAS. Combined, these genetic associations accounted for 5·2% of the variance in risk for developing severe malaria among individuals in the general population. We confirmed established associations between severe malaria and sickle-cell trait (odds ratio [OR] 0·15, 95% CI 0·11–0·20; p=2·61 × 10−58), blood group O (0·74, 0·66–0·82; p=6·26 × 10−8), and –α3·7-thalassaemia (0·83, 0·76–0·90; p=2·06 × 10−6). We also found strong associations between overall risk of severe malaria and polymorphisms in both ATP2B4 (OR 0·76, 95% CI 0·63–0·92; p=0·001) and FREM3 (0·64, 0·53–0·79; p=3·18 × 10−14). The association with FREM3 could be accounted for by linkage disequilibrium with a complex structural mutation within the glycophorin gene region (comprising GYPA, GYPB, and GYPE) that encodes for the rare Dantu blood group antigen. Heterozygosity for Dantu was associated with risk for severe malaria (OR 0·57, 95% CI 0·49–0·68; p=3·22 × 10−11), as was homozygosity (0·26, 0·11–0·62; p=0·002). Interpretation: Both ATP2B4 and the Dantu blood group antigen are associated with the structure and function of red blood cells. ATP2B4 codes for plasma membrane calcium-transporting ATPase 4 (the major calcium pump on red blood cells) and the glycophorins are ligands for parasites to invade red blood cells. Future work should aim at uncovering the mechanisms by which these polymorphisms can result in severe malaria protection and investigate the implications of these associations for wider health. Funding: Wellcome Trust, UK Medical Research Council, European Union, and Foundation for the National Institutes of Health as part of the Bill & Melinda Gates Grand Challenges in Global Health Initiative

THE REAL McCOIL: A method for the concurrent estimation of the complexity of infection and SNP allele frequency for malaria parasites.

As many malaria-endemic countries move towards elimination of Plasmodium falciparum, the most virulent human malaria parasite, effective tools for monitoring malaria epidemiology are urgent priorities. P. falciparum population genetic approaches offer promising tools for understanding transmission and spread of the disease, but a high prevalence of multi-clone or polygenomic infections can render estimation of even the most basic parameters, such as allele frequencies, challenging. A previous method, COIL, was developed to estimate complexity of infection (COI) from single nucleotide polymorphism (SNP) data, but relies on monogenomic infections to estimate allele frequencies or requires external allele frequency data which may not available. Estimates limited to monogenomic infections may not be representative, however, and when the average COI is high, they can be difficult or impossible to obtain. Therefore, we developed THE REAL McCOIL, Turning HEterozygous SNP data into Robust Estimates of ALelle frequency, via Markov chain Monte Carlo, and Complexity Of Infection using Likelihood, to incorporate polygenomic samples and simultaneously estimate allele frequency and COI. This approach was tested via simulations then applied to SNP data from cross-sectional surveys performed in three Ugandan sites with varying malaria transmission. We show that THE REAL McCOIL consistently outperforms COIL on simulated data, particularly when most infections are polygenomic. Using field data we show that, unlike with COIL, we can distinguish epidemiologically relevant differences in COI between and within these sites. Surprisingly, for example, we estimated high average COI in a peri-urban subregion with lower transmission intensity, suggesting that many of these cases were imported from surrounding regions with higher transmission intensity. THE REAL McCOIL therefore provides a robust tool for understanding the molecular epidemiology of malaria across transmission settings

Evaluating the performance of malaria genomics for inferring changes in transmission intensity using transmission modelling

AbstractAdvances in genetic sequencing and accompanying methodological approaches have resulted in pathogen genetics being used in the control of infectious diseases. To utilise these methodologies for malaria we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment. Here we develop an individual-based transmission model to simulate malaria parasite genetics parameterised using estimated relationships between complexity of infection and age from 5 regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterise the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The best performing model successfully predicted malaria prevalence with mean absolute error of 0.055, suggesting genetic tools could be used for monitoring the impact of malaria interventions.</jats:p

A high throughput multi-locus insecticide resistance marker panel for tracking resistance emergence and spread in Anopheles gambiae

The spread of resistance to insecticides in disease-carrying mosquitoes poses a threat to the effectiveness of control programmes, which rely largely on insecticide-based interventions. Monitoring mosquito populations is essential, but obtaining phenotypic measurements of resistance is laborious and error-prone. High-throughput genotyping offers the prospect of quick and repeatable estimates of resistance, while also allowing resistance markers to be tracked and studied. To demonstrate the potential of highly-mulitplexed genotypic screening for measuring resistance-association of mutations and tracking their spread, we developed a panel of 28 known or putative resistance markers in the major malaria vector Anopheles gambiae, which we used to screen mosquitoes from a wide swathe of Sub-Saharan Africa (Burkina Faso, Ghana, Democratic Republic of Congo (DRC) and Kenya). We found resistance association in four markers, including a novel mutation in the detoxification gene Gste2 (Gste2-119V). We also identified a duplication in Gste2 combining a resistance-associated mutation with its wild-type counterpart, potentially alleviating the costs of resistance. Finally, we describe the distribution of the multiple origins of kdr resistance, finding unprecedented diversity in the DRC. This panel represents the first step towards a quantitative genotypic model of insecticide resistance that can be used to predict resistance status in An. gambiae

Geographic-genetic analysis of Plasmodium falciparum parasite populations from surveys of primary school children in Western Kenya.

BACKGROUND: Malaria control, and finally malaria elimination, requires the identification and targeting of residual foci or hotspots of transmission. However, the level of parasite mixing within and between geographical locations is likely to impact the effectiveness and durability of control interventions and thus should be taken into consideration when developing control programs. METHODS: In order to determine the geographic-genetic patterns of Plasmodium falciparum parasite populations at a sub-national level in Kenya, we used the Sequenom platform to genotype 111 genome-wide distributed single nucleotide polymorphic (SNP) positions in 2486 isolates collected from children in 95 primary schools in western Kenya. We analysed these parasite genotypes for genetic structure using principal component analysis and assessed local and global clustering using statistical measures of spatial autocorrelation. We further examined the region for spatial barriers to parasite movement as well as directionality in the patterns of parasite movement. RESULTS: We found no evidence of population structure and little evidence of spatial autocorrelation of parasite genotypes (correlation coefficients <0.03 among parasite pairs in distance classes of 1km, 2km and 5km; p value<0.01). An analysis of the geographical distribution of allele frequencies showed weak evidence of variation in distribution of alleles, with clusters representing a higher than expected number of samples with the major allele being identified for 5 SNPs. Furthermore, we found no evidence of the existence of spatial barriers to parasite movement within the region, but observed directional movement of parasites among schools in two separate sections of the region studied. CONCLUSIONS: Our findings illustrate a pattern of high parasite mixing within the study region. If this mixing is due to rapid gene flow, then "one-off" targeted interventions may not be currently effective at the sub-national scale in Western Kenya, due to the high parasite movement that is likely to lead to re-introduction of infection from surrounding regions. However repeated targeted interventions may reduce transmission in the surrounding regions

Malaria Host Candidate Genes Validated by Association With Current, Recent, and Historical Measures of Transmission Intensity

Background: Human malaria susceptibility is determined by multiple genetic factors. It is unclear, however, which genetic variants remain important over time. Methods: Genetic associations of 175 high-quality polymorphisms within several malaria candidate genes were examined in a sample of 8096 individuals from northeast Tanzania using altitude, seroconversion rates, and parasite rates as proxies of historical, recent, and current malaria transmission intensity. A principal component analysis was used to derive 2 alternative measures of overall malaria propensity of a location across different time scales. Results: Common red blood cell polymorphisms (ie, hemoglobin S, glucose-6-phosphate dehydrogenase, and α-thalassemia) were the only ones to be associated with all 3 measures of transmission intensity and the first principal component. Moderate associations were found between some immune response genes (ie, IL3 and IL13) and parasite rates, but these could not be reproduced using the alternative measures of malaria propensity. Conclusions: We have demonstrated the potential of using altitude and seroconversion rate as measures of malaria transmission capturing medium- to long-term time scales to detect genetic associations that are likely to persist over time. These measures also have the advantage of minimizing the deleterious effects of random factors affecting parasite rates on the respective association signals

Evaluating the Performance of Malaria Genetics for Inferring Changes in Transmission Intensity Using Transmission Modeling.

Substantial progress has been made globally to control malaria, however there is a growing need for innovative new tools to ensure continued progress. One approach is to harness genetic sequencing and accompanying methodological approaches as have been used in the control of other infectious diseases. However, to utilize these methodologies for malaria, we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment, which all impact the level of genetic diversity and relatedness of malaria parasites. We develop an individual-based transmission model to simulate malaria parasite genetics parameterized using estimated relationships between complexity of infection and age from five regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterize the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The model predicted malaria prevalence with a mean absolute error of 0.055. Different assumptions about the availability of sample metadata were considered, with the most accurate predictions of malaria prevalence made when the clinical status and age of sampled individuals is known. Parasite genetics may provide a novel surveillance tool for estimating the prevalence of malaria in areas in which prevalence surveys are not feasible. However, the findings presented here reinforce the need for patient metadata to be recorded and made available within all future attempts to use parasite genetics for surveillance

Resistance to pirimiphos-methyl in West African Anopheles is spreading via duplication and introgression of the Ace1 locus

Publisher Copyright: © 2021 Grau-Bové et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Vector population control using insecticides is a key element of current strategies to prevent malaria transmission in Africa. The introduction of effective insecticides, such as the organophosphate pirimiphos-methyl, is essential to overcome the recurrent emergence of resistance driven by the highly diverse Anopheles genomes. Here, we use a population genomic approach to investigate the basis of pirimiphos-methyl resistance in the major malaria vectors Anopheles gambiae and A. coluzzii. A combination of copy number variation and a single non-synonymous substitution in the acetylcholinesterase gene, Ace1, provides the key resistance diagnostic in an A. coluzzii population from Côte d’Ivoire that we used for sequence-based association mapping, with replication in other West African populations. The Ace1 substitution and duplications occur on a unique resistance haplotype that evolved in A. gambiae and introgressed into A. coluzzii, and is now common in West Africa primarily due to selection imposed by other organophosphate or carbamate insecticides. Our findings highlight the predictive value of this complex resistance haplotype for phenotypic resistance and clarify its evolutionary history, providing tools to for molecular surveillance of the current and future effectiveness of pirimiphos-methyl based interventions.publishersversionpublishe