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.</p

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 &amp; Melinda Gates Grand Challenges in Global Health Initiative

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

Author Correction: Investigating the drivers of the spatio-temporal patterns of genetic differences between Plasmodium falciparum malaria infections in Kilifi County, Kenya.

An amendment to this paper has been published and can be accessed via a link at the top of the paper

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

Genetic determinants of glucose-6-phosphate dehydrogenase activity in Kenya

Background: The relationship between glucose-6-phosphate dehydrogenase (G6PD) deficiency and clinical phenomena such as primaquine-sensitivity and protection from severe malaria remains poorly defined, with past association studies yielding inconsistent and conflicting results. One possibility is that examination of a single genetic variant might underestimate the presence of true effects in the presence of unrecognized functional allelic diversity. Methods: We systematically examined this possibility in Kenya, conducting a fine-mapping association study of erythrocyte G6PD activity in 1828 Kenyan children across 30 polymorphisms at or around the G6PD locus. Results: We demonstrate a strong functional role for c.202G>A (rs1050828), which accounts for the majority of variance in enzyme activity observed (P=1.5 × 10-200, additive model). Additionally, we identify other common variants that exert smaller, intercorrelated effects independent of c.202G>A, and haplotype analyses suggest that each variant tags one of two haplotype motifs that are opposite in sequence identity and effect direction. We posit that these effects are of biological and possible clinical significance, specifically noting that c.376A>G (rs1050829) augments 202AG heterozygote risk for deficiency trait by two-fold (OR = 2.11 [1.12 - 3.84], P=0.014). Conclusions: Our results suggest that c.202G>A is responsible for the majority of the observed prevalence of G6PD deficiency trait in Kenya, but also identify a novel role for c.376A>G as a genetic modifier which marks a common haplotype that augments the risk conferred to 202AG heterozygotes, suggesting that variation at both loci merits consideration in genetic association studies probing G6PD deficiency-associated clinical phenotypes. </p

Haplotype heterogeneity and low linkage disequilibrium reduce reliable prediction of genotypes for the ‑α3.7I form of α-thalassaemia using genome-wide microarray data [version 1; peer review: 1 approved, 1 approved with reservations]

Background: The -α3.7I-thalassaemia deletion is very common throughout Africa because it protects against malaria. When undertaking studies to investigate human genetic adaptations to malaria or other diseases, it is important to account for any confounding effects of α-thalassaemia to rule out spurious associations. Methods: In this study we have used direct α-thalassaemia genotyping to understand why GWAS data from a large malaria association study in Kilifi Kenya did not identify the α-thalassaemia signal. We then explored the potential use of a number of new approaches to using GWAS data for imputing α-thalassaemia as an alternative to direct genotyping by PCR. Results: We found very low linkage-disequilibrium of the directly typed data with the GWAS SNP markers around α-thalassaemia and across the haemoglobin-alpha (HBA) gene region, which along with a complex haplotype structure, could explain the lack of an association signal from the GWAS SNP data. Some indirect typing methods gave results that were in broad agreement with those derived from direct genotyping and could identify an association signal, but none were sufficiently accurate to allow correct interpretation compared with direct typing, leading to confusing or erroneous results. Conclusions: We conclude that going forwards, direct typing methods such as PCR will still be required to account for α-thalassaemia in GWAS studies

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

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