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Contrasting Population Structures of the Genes Encoding Ten Leading Vaccine-Candidate Antigens of the Human Malaria Parasite, Plasmodium falciparum


The extensive diversity of Plasmodium falciparum antigens is a major obstacle to a broadly effective malaria vaccine but population genetics has rarely been used to guide vaccine design. We have completed a meta-population genetic analysis of the genes encoding ten leading P. falciparum vaccine antigens, including the pre-erythrocytic antigens csp, trap, lsa1 and glurp; the merozoite antigens eba175, ama1, msp's 1, 3 and 4, and the gametocyte antigen pfs48/45. A total of 4553 antigen sequences were assembled from published data and we estimated the range and distribution of diversity worldwide using traditional population genetics, Bayesian clustering and network analysis. Although a large number of distinct haplotypes were identified for each antigen, they were organized into a limited number of discrete subgroups. While the non-merozoite antigens showed geographically variable levels of diversity and geographic restriction of specific subgroups, the merozoite antigens had high levels of diversity globally, and a worldwide distribution of each subgroup. This shows that the diversity of the non-merozoite antigens is organized by physical or other location-specific barriers to gene flow and that of merozoite antigens by features intrinsic to all populations, one important possibility being the immune response of the human host. We also show that current malaria vaccine formulations are based upon low prevalence haplotypes from a single subgroup and thus may represent only a small proportion of the global parasite population. This study demonstrates significant contrasts in the population structure of P. falciparum vaccine candidates that are consistent with the merozoite antigens being under stronger balancing selection than non-merozoite antigens and suggesting that unique approaches to vaccine design will be required. The results of this study also provide a realistic framework for the diversity of these antigens to be incorporated into the design of next-generation malaria vaccines.


Infection with the protozoan parasite Plasmodium falciparum causes more than 500 million episodes of clinical malaria and two million deaths each year [1]. A broadly effective malaria vaccine would have a significant global health impact on this enormous public health burden. Over the past 40 years, an intensive international effort has led to the development of several antigens from P. falciparum as malaria vaccine candidates. They include surface exposed proteins from morphologically distinct developmental stages of the parasite lifecycle within the human host namely the Circumsporozoite Surface Antigen (CSP), Thrombospondin Related Adhesion Protein (TRAP), Liver Stage Antigen 1 (LSA1), Apical Membrane Antigen 1 (AMA1), Erythrocyte Binding Antigen 175 (EBA175), Merozoite Surface Proteins (MSPs 1–5), Glutamate Rich Protein (GLURP) and Pfs48/45 ([2]; Table 1). Many of these antigens have undergone rigorous developmental and preclinical testing as subunit vaccines [2] but only a few have reached advanced clinical trials (e.g. Phase 2b: CSP (RTS,S); AMA1 (FMP2.1, C1); MSP142 (FMP1); MSP3 (LSP)) [3]. The variable success of candidate malaria vaccines may be due to the high degree of diversity of P. falciparum antigens [4] and a variant-specific immune response [5], [6], particularly as most vaccines are formulated with a single polymorphic variant. There is now increasing recognition that a malaria vaccine may need to contain multiple variants of the target antigen to be effective against an entire parasite population [7].

Table 1. Summary of population genetic data collected for the genes encoding twelve P. falciparum vaccine antigens.

The extent and distribution of genetic diversity of P. falciparum is for the most part associated with transmission intensity and geographic origin [8], [9], but unique patterns of diversity have been observed for P. falciparum antigens. Because many antigens are under immune selection, they are several times more diverse than the neutral loci used in genome wide analyses. This is the case even in low transmission regions [10], [11], [12], suggesting that vaccines will need to represent a large number of variants regardless of the region of deployment. The strong geographic differentiation observed in genome-wide markers [8], [9] is also detectable in genes encoding the sporozoite antigen, csp [13], [14], [15] and to a greater extent, the gametocyte antigen, pfs48/45 [16], raising the possibility that malaria vaccines may need to be tailored for specific regions. However, a lack of geographic differentiation has been observed for blood stage antigens such as ama1 [17], [18], [19], msp3 [20], msp4,5 [21] and S-antigen [22]. Ama1 variants have recently been shown to cluster into six genetically distinct subgroups on the basis of antibody cross-reactivity, with all subgroups being found worldwide. This study illustrated that immune selection may play a role in structuring the diversity of this highly polymorphic antigen. Consequently, a small number of variants from distinct subgroups may give the sought after broad vaccine coverage [18]. To inform the design of next generation malaria vaccines, population genetic studies for each candidate antigen in the spectrum of endemic regions will be essential. Such analyses will help to prioritize candidates, advance our understanding of the geographic distribution of genetic diversity and provide a framework for testing the immunological significance of antigen diversity.

An enormous amount of research has highlighted the extensive diversity of P. falciparum antigens [23], however the majority of studies have focused on just one or two countries per antigen and comparisons among studies have rarely taken place. To facilitate the design of broad-spectrum malaria vaccines, we have summarized the known global range and distribution of genetic diversity of ten leading malaria vaccine antigens for which population-level sequence data was available. We collected sequences from natural populations and laboratory-isolates and completed a population genetic analysis using a variety of traditional and more recently developed clustering tools. By comparative analyses we show evidence that the diversity of non-merozoite antigens is largely structured on the basis of geographic origin while for merozoite antigens, the dominant targets of natural host immunity [24] a relative lack of geographic structure was observed with the majority of diversity being contained within each location. This meta-population genetic analysis of ten leading malaria vaccine candidates provides a framework by which to consider parasite diversity in the design of the next generation of malaria vaccines.


Data summary

More than 4500 sequences with an average length of 0.8 kb were compiled from GenBank and the published literature for the genes encoding twelve antigens that matched the inclusion criteria (Tables 1, S1 and S2). Although msp2 and msp5 matched the criteria we did not complete the population genetic analyses. For msp2, this was due to the majority of sequences being comprised of highly polymorphic repeats (with many gaps) flanked by only short regions of unique sequence. Haplotypes could therefore only be defined on the basis of differing numbers of repeats, resulting in an overestimation of biologically significant diversity. For msp5, there were only five haplotypes and preliminary analyses showed that they were not structured within nor among populations (data not shown), so diversity in this antigen was also unlikely to have major biological significance. Among the remaining ten antigens, the number of nonsynonymous polymorphisms (dN) was several-fold greater than the number of synonymous polymorphisms (dS; Table 1), which is an indication of immune selection in the P. falciparum genome [4]. The population dataset included sequences from the natural parasite populations of between 2 and 13 countries and a minimum of 2 geographical regions (namely Americas (Central and South), Asia Pacific or Africa, Table S1). The median sample size was 31 sequences (range = 8–1368) per country, and each country contained a median number of 8 distinct haplotypes (range = 1–68) (Table 2). Only small sample sizes were available for glurp and pfs48/45 so we caution that the results for these antigens may be biased and thus should be interpreted with care. To focus the analysis on the putative antigenic diversity (i.e. polymorphisms that change protein structure) the nonsynonymous single nucleotide polymorphism (nsSNP) haplotypes were derived for all antigen sequences, except for msp1, for which the majority of the data comprised only a 5 amino acid haplotype (corresponding to polymorphisms found only in the MSP119 domain), so the remaining msp1 DNA sequences were converted to the corresponding amino acid haplotype. It is important to note that haplotypes are simple combinations of nucleotides or amino acids with no particular weight placed upon any position or change, rather all of the following analyses were based on whether each polymorphic site was the same or different.

Table 2. Estimates of diversity for the genes encoding ten P. falciparum vaccine antigens.

Polymorphism and haplotype diversity

Comparing among countries for each antigen, the genes encoding the non-merozoite antigens (csp, trap, lsa1, glurp and pfs48/45; Table 1) showed variation in diversity as measured by the polymorphism (k and ) and haplotype diversity (Hd) statistics. Whereas, the genes encoding each of the merozoite antigens (ama1, eba175 and msp1, msp3 and msp4; Table 1) each showed similar levels of diversity among countries and regions (Table 2). For example, csp was significantly more diverse in African compared to Asia-Pacific countries (P<0.01) while for ama1 there were no significant differences between African and Asia-Pacific countries (P>0.05). Furthermore, for non-merozoite antigens the degree of haplotype diversity was strongly correlated with the amount of polymorphism (ρ = 0.63, P<0.01), whereas for the merozoite antigens, the amount of polymorphism (k and ) varied widely among antigens but Hd was almost always high (Figure 1; Table 2; ρ = 0.13; P>0.05). Therefore, haplotype diversity varied widely among countries and regions for non-merozoite antigens in association with transmission intensity and polymorphism, but was consistently high for the merozoite antigens irrespective of transmission intensity and levels of polymorphism suggesting that the latter are under stronger balancing selection.

Figure 1. The relationship between polymorphism and haplotype diversity of the genes encoding ten P. falciparum vaccine antigens.

Non-merozoite antigens are represented by a solid symbol and merozoite antigens by an open symbol.

Genetic differentiation and gene flow

To determine how the observed diversity was distributed among countries, population structure was first inferred by measuring genetic differentiation among countries both within and among regions. To do this we calculated FST from haplotype-frequencies and pairwise DNA sequence diversity, although only the former was calculated for MSP1 (see Materials and Methods). The haplotype frequency-based statistics are more sensitive for small sample sizes, while the sequence based statistic is a more sensitive method for detecting population structure in highly polymorphic loci [25], (see Text S1 for specific examples). Significant differentiation was identified among regions for all P. falciparum vaccine antigens, albeit to a lesser degree for lsa1, ama1, eba175 and msp4 compared to the other 6 antigens (Table 3A,B). This regional differentiation was accompanied by limited gene flow (Nm) for all antigens except for low-moderate levels for lsa1, ama1 and eba175 and very high for msp4 (Table 3C). Differentiation was also detected within the Americas for csp and msp1 (the only antigens for which we had multiple populations within this region), within the Asia Pacific for csp, trap, ama1 and msp1, and significant but low levels of differentiation within Africa for ama1 (Table 3). For MSP1, the Asia-Pacific countries spanned a broad area. Accordingly, pairwise comparisons identified differentiation between the Pacific and mainland Asian countries (FST = 0.06–0.31; P<0.01). Significant differentiation was also observed in pairwise comparisons of countries from East (Vietnam, Thailand) and West (India, Iran) mainland Asia (FST = 0.09–0.27; P<0.001) with no structure among countries within these subregions (FST = 0, P>0.05; 0.03, P<0.05 respectively). Additional structure was detected among the Pacific island nations (FST = 0.07–0.21; P<0.01). We also observed significant differentiation between local populations such as Vanuatu's islands for csp (Pentecost compared to Gaua: FST = 0.14; P = 0.02; and Malakula: FST = 0.19; P<0.01) and MSP1 (all comparisons, FST = 0.17–0.54; P<0.01) and distant locations of India for MSP1 (FST = 0.32; P<0.001). No such structuring was observed for csp within Brazil or Myanmar, ama1 in PNG or Thailand, msp1 in Vietnam, nor msp4 in Senegal (Table S1).

Table 3. Estimates of genetic differentiation and gene flow for the genes encoding ten P. falciparum vaccine antigens.

Clustering and networks

In the analysis so far, individuals were grouped by geographic location, assuming that geography (or other associated variables e.g. host genetics, vector species) will be the dominant barrier to gene flow. It is possible that these somewhat arbitrary groupings might incorrectly estimate population structure, or fail to identify within population subdivision, although where possible, we measured genetic differentiation within a country as described above (Table S1). To address this, and to identify subgroups of related nsSNP (or for MSP1, amino acid) haplotypes that are genetically and thus potentially antigenically distinct, we also used a Bayesian clustering algorithm [26], [27] and confirmed the results using network analysis (see Materials and Methods). The Bayesian algorithm groups related haplotypes into a predefined number of clusters (K) on the basis of shared allele frequencies. Each haplotype is then assigned a membership coefficient (Q) to each of the clusters with the majority of the haplotypes being assigned to only one cluster at “true” K (Figure 2 A–J) and variability in the data increasing thereafter (Figure S1; [26], [27]). Using this approach we found a small number of distinct clusters for all antigens (Kmean = 4.5, Krange = 3–6). Although admixed haplotypes (<75% membership to any one cluster) were prevalent for trap, ama1, eba175 and msp4 (Figure 2 B, D, E and H), increased estimates of K resulted in even higher proportions of admixed haplotypes (Figure S2) thus confirming that the distribution of the haplotypes was best explained by the K presented in Figure 2 (A–J). Network analysis differs in that it simply shows connectivity among all haplotypes on the basis of shared SNPs and allows for the visualization of recombinant haplotypes that bridge the major subgroups. If haplotypes differed by fewer nsSNPs than the predefined threshold (t), they were connected, and if greater than t they were not. We used a t-value that connected the majority of haplotypes so all relationships could be examined in one network, and for clarity. The results supported the cluster analysis with haplotypes grouping into a small number of tightly connected lobes that corresponded to each of the structure defined subgroups (Figure S3). Bridging connections were predominantly characterized by admixed haplotypes or entire subgroups (e.g. ama1, msp4) as defined by the cluster analysis and suggest that these comprise recombinant haplotypes (Figure S3).

Figure 2. Global population structure of the genes encoding ten P. falciparum vaccine antigens based on Bayesian cluster analysis.

Membership coefficients for A–J) individual nsSNP haplotypes and K–T) the population average for the estimated number of clusters (K, shown on the left of the two histograms). In the latter, countries from different continents are separated by a blank space and organised from east on the left, to west on the right with vaccine haplotypes on the far right hand side. An asterisk denotes countries for which fewer than 8 haplotypes were available that were taken from dataset 2 (Table S2). Dark blue = cluster 1; Red = cluster 2; Green = cluster 3; Purple = cluster 4; Light blue = cluster 5; Orange = cluster 6.

To determine whether the above-defined “subgroups” were geographically restricted, for the Bayesian cluster data we plotted the average Q for each country (Figure 2 K–T), and calculated the average frequency of haplotypes with membership to the predominant subgroup (fm) and the population diversity (Pd) a simple measure of the distribution of clusters that is analogous to the Hd (see above and Materials and Methods). Globally (i.e. comparisons among all countries), the cluster analysis supported the high levels of differentiation among regions for the non-merozoite antigens with a high frequency of haplotypes belonging to one subgroup (fm = 69.5±8%) and low population diversity (Pd = 0.40±0.07), albeit lsa1 showed low to medium frequencies of all clusters in PNG (Pd = 0.80) and Kenya (Pd = 0.74; Figure 2 M; Table S3). In contrast, all of the merozoite antigens showed low to medium frequencies of all clusters in all countries (fm = 45.8±7%) and high population diversity (Pd = 0.64±0.07), although the frequency of each cluster was variable among countries (Figure 2 N–R; Table S3). These variations in frequency were consistent with the moderate geographic differentiation described above. The network analysis further supported these results with the non-merozoite antigen haplotypes being most strongly connected with others originating from the same geographic region (Figure 3 A–C, I, J), whereas for the merozoite antigens, haplotypes from different regions often connected within the same lobes of the network (Figure 3 D–H). These analyses also supported the diversity analyses with (for example) the highly diverse African csp and trap haplotypes being loosely or disconnected from the main network (Figure 3 A, B), whereas geographic origin did not correlate with the connectivity of the ubiquitously diverse merozoite antigen haplotypes to the network (Figure 3 D–H).

Figure 3. Global population structure of the genes encoding ten P. falciparum vaccine antigens based on network analysis.

Networks of nsSNP haplotypes were drawn by first removing multiple copies of each haplotype , leaving only one copy per country for the analysis. Hence, identical haplotypes from different regions, but not within regions were included. Each node (coloured circle) represents a haplotype, shaded by region of origin: Red = Africa, Green = Asia, Blue = Americas. Nodes are tied by edges (black lines) demonstrating that they share a predefined threshold (t) of nsSNPs for csp = 24; trap = 67; lsa1 = 8; ama1 = 48; eba175 = 18; msp1 = 5; msp3 = 63; msp4 = 19; glurp = 18; pfs48/45 = 23. Vaccine haplotypes are shaded in yellow. Haplotypes originating from isolates with unknown origin are shaded in white (unless they were vaccine haplotypes).

Within regions, the significant differentiation detected in the Americas for both csp and MSP1 (Table 3), was supported by the cluster analysis (note that network analysis is not presented at this or any finer resolution). For csp the majority of haplotypes showed membership to one cluster, albeit different clusters for each country (Figure 2K; Table S3). For MSP1, a single cluster was found in Peru (1 haplotype, Table 2) and 4 clusters (7 haplotypes; including that found in Peru) were found in Brazil (Figure 2P; Table S3). In the Asia-Pacific region, for trap the differentiation between Thailand and India was supported by the cluster analysis (Figure 2L; Table S3). Whereas, for csp, lsa1, ama1 and MSP1 varying degrees of support were given to the differentiation observed with the same clusters being found in all Asia Pacific countries albeit at variable frequencies (Figure 2 K, M, N and P). For csp, a single cluster was predominant among all Asia-Pacific countries, with low frequencies of haplotypes belonging to a second cluster. The majority of Iranian haplotypes clustered with the Asia-Pacific cluster and a minor proportion with the African cluster, a structure that is consistent with Iran's central location between these two regions. For lsa1, all clusters were found in PNG, but only 2 in Malaysia. A limited degree of differentiation between the two countries (Table 3), and lower diversity in Malaysia (Table 2) suggests that it shares haplotypes with PNG. For ama1, all clusters were present in each country at variable frequencies and high population diversity (Figure 2N, 3). This is consistent with a previous report [18], albeit our dataset contained 3.6 times the number of haplotypes (Table 1). For MSP1, the differentiation seen among Asia-Pacific subregions was supported by variable frequencies of the four clusters (Figure 2 P; Table S3). The remaining two antigens studied in the Asia Pacific, msp4 and pfs48/45, showed strong similarities in the cluster analysis (Figure 2 R, T) demonstrating a lack of population structure for these antigens in this region. Among African countries, the cluster analysis confirmed a lack of population structure with strong similarities among countries for the four antigens for which multiple African sites were sampled (Figure 2 K, N–P). To identify local population structure, we investigated differences among locations within the same country. The results show that only MSP1 and pfs48/45 were structured among locales within a country (Figure S4).

Prevalence of vaccine haplotypes

The majority of haplotypes upon which current vaccines are based were found to be present, but at extremely low frequencies in the global parasite population, with higher frequencies observed only for lsa1 and MSP1 vaccine haplotypes (Table 4). Because distinct haplotypes may be different by as few as one nsSNP, which is less likely to encode antigenic differences than multiple nsSNPs, the breadth of biologically significant similarities may be underestimated by this analysis. Therefore, we also included vaccine haplotypes in the cluster (Figure 2 K–T) and network analysis (Figure 3) to identify their associated subgroups. MSP1 vaccine haplotypes grouped with three distinct subgroups of the four defined (Figure 2 P; Figure S3 F), each representing the most common haplotypes. For the remaining antigens, the subgroups to which vaccine haplotypes associated were of a limited prevalence in all populations (ama1, eba175, msp3, msp4, glurp, pfs48/45) (Figure 2) or were geographically restricted (csp, trap, lsa1) (Figure 2, 3). All of the laboratory isolates (Table S2) were also included in the cluster and network analysis. This allowed the assignment of these isolates to haplotypes and structure defined clusters, thus providing a framework for experiments to test the biological significance of diversity and identifying the most distinct haplotypes for diversity-covering vaccines (Table S4; [18]).

Table 4. Worldwide prevalence of P. falciparum antigen haplotypes that are components of malaria vaccines.


To provide a rational framework for incorporating diversity into the next generation of malaria vaccines, we have completed a meta-population genetic analysis and thus summarised the known global range and natural distribution of diversity for ten leading malaria vaccine candidates. There are many natural population datasets available from previous studies and there is a strong precedent for comparing multiple datasets for such studies even when only small numbers of samples are available (e.g. [17], [28]). Sample size was a limitation for the population genetic analysis of some antigens and locations, however the majority of natural populations (>70%) were represented by at least 20 sequences. For populations with less than this number of sequences the results should be interpreted with care. Despite these small sample sizes, similar results to other countries from the same region were observed. For example, Indian csp sequences (n = 11) showed a similar pattern of diversity to other Asian countries with larger sample sizes (n = 25–143), as did Thai and Indian pfs48/45 sequences (n = 10 for both). We also used haplotype frequencies to measure differentiation, which has been shown to be more reliable than sequence diversity for smaller sample sizes [25] but results for both statistics were similar for antigens with smaller sample sizes (lsa1, glurp, pfs48/45). The patterns of diversity and geographic population structure observed for these antigens warrant further investigation by deep sampling in each geographic region. Another potential source of bias is the combination of data from different time points and from patients with different clinical status (Table S1). Frequency dependant selection acts on antigens under strong immune selection [29] resulting in changes in allele frequency over time, and thus may exaggerate differentiation or alter cluster frequencies seen among countries within the same region, such as that observed for ama1 and MSP1 in the Asia-Pacific. Clinical samples may also be biased toward particular antigen haplotypes [30], [31], [32], [33]. Nevertheless the differences among countries (particularly evident in the large Asia-Pacific region) appeared to increase with geographic distance, independently of both time and clinical definition (Table S1), so these factors should not change the overall conclusions of this study. A phenomenal amount of additional sampling and sequencing, requiring a vastly inflated budget and a major international consortium would be needed to address these sampling issues. Our strategy, in using population genetic data already generated and freely available has revealed important insights into the overall organization of genetic diversity of vaccine antigens and provides a framework for future studies to improve malaria vaccine design.

By comparing the diversity found in different countries worldwide we demonstrated that csp, and to a lesser extent trap and lsa1 showed similar patterns to that of putatively neutrally evolving microsatellite and SNP markers [8], [34], [35]. The highest levels of diversity were found in Africa where transmission is holoendemic (very high), the lowest in the Americas where it is hypoendemic (low) and moderate levels in the Asia-Pacific where transmission ranges from meso-hyperendemic (medium to high). This suggests that transmission plays a predominant role in the diversification of these non-merozoite antigens, and the similarities to neutral markers suggest that these genes are not under strong balancing selection. Glurp and pfs48/45 also showed similarly variable diversity but there was no apparent trend for higher diversity in Africa compared to other regions and as mentioned above the small sample size for these antigens makes it difficult to draw solid conclusions. For the merozoite antigens, the observation of high levels of haplotype diversity among countries at different ends of transmission spectrum even for antigens with low levels of polymorphism (e.g. eba175, msp4) suggests that recombination generates a number of different haplotypes even where significant functional constraints exist. Together with immunological evidence that blood stage antigens are major targets of natural host immunity [24], this is a strong indication of balancing (e.g. immune) selection. Immune selection favours a low-medium frequency of distinct haplotypes and thus increased probability of newly infecting parasites carrying antigenically distinct haplotypes to those previously encountered by the host. Therefore, if vaccine candidates are prioritized on the basis of low levels of polymorphism, careful consideration must also be given to distribution of haplotypes within natural populations.

A successful malaria vaccine will need to target a large proportion of the parasite population, but it would not be feasible to vaccinate individuals with the large numbers of haplotypes we have described. A single haplotype will have some capacity to elicit cross-reactive responses against those that are genetically similar but the exact amount of polymorphism that defines antigenically different haplotypes is not well understood. Recent work has shown that ama1 haplotypes were organized into six strongly differentiated subgroups by the Bayesian algorithm implemented in the program structure [27], [36]. In this study, evidence from invasion inhibition assays suggested that haplotypes from the same subgroup were antigenically similar and thus able to elicit cross-reactive antibody responses, whilst those from different subgroups were antigenically distinct [18]. Therefore, clustering tools may be useful in defining biologically significant variation in P. falciparum antigens. Our analysis used two different clustering tools to subgroup the compiled haplotypes, namely the Bayesian clustering and network analysis. Our dataset contained a much larger number of ama1 sequences (n = 572, compared to 158 in the previous study [18]), with several additional natural populations, yet did not identify any further subgroups. By completing these analyses for all of the leading vaccine antigens in our study we found as few as three, and no more than six subgroups for any antigen in the worldwide parasite population. This suggests that for all ten of the leading vaccine antigens, it may be feasible to cover diversity by inclusion of a small number of carefully selected haplotypes from each subgroup. However, a large number of admixed haplotypes in the cluster analyses or bridging connections among major lobes in the network analyses indicates recombination occurs among subgroups and that there is potential for the evolution of further antigenically distinct haplotypes. Notably, three of the four antigens for which these putative recombinants were common were merozoite antigens (ama1, eba175 and msp4). A series of experiments now needs to be done for each antigen to verify the immunological relevance of the patterns observed, the haplotypes from each subgroup that will elicit broadly protective immune responses, and to quantify the contribution of each polymorphism to antigenic diversity.

The geographic distribution of the defined diversity must also be a consideration in the design of a broad-spectrum malaria vaccine because significant variation among regions would suggest a need for vaccines to be tailored accordingly. When we investigated the geographic distribution of diversity for each of the ten vaccine antigens we found stark contrasts among antigens from the different developmental stages of the parasite lifecycle. Although tests of genetic differentiation and gene flow among countries suggested among-region structuring of diversity for all antigens, stronger differentiation among countries and/or regions was found for the non-merozoite antigens. The cluster and network analyses supported strong among region structure (and lower within location diversity) for csp, pfs48/45 and glurp and that within regions for trap and lsa1, albeit much weaker geographic structuring for the latter antigen. By contrast, the merozoite antigens generally had lower levels of among and within region differentiation and gene flow, and haplotypes formed subgroups independent of geographic origin with uniformly high levels of within population diversity. These comparative analyses confirm that there are extreme differences in the population structure of different types of antigens and thus may explain why paradoxical estimates of the most recent common ancestor of P. falciparum have been obtained in the past by evolutionary biologists using these markers (reviewed in [37]). Interestingly, the cluster analyses also showed differing frequencies of shared subgroups among countries, which were previously shown to vary over time for ama1 [18]. This may reflect both geographic isolation and natural fluctuations over time as a result of frequency dependent selection or may simply be the result of the variable sample collection mentioned above. For MSP1, strong differentiation and a variable cluster frequency among sub-regions and island nations of the Asia-Pacific suggests that the biogeography of this region constitutes a strong barrier to gene flow. If the subgrouping of haplotypes is immunologically significant, current vaccine formulations may only target parasites carrying haplotypes from the same subgroup, giving those carrying haplotypes from distinct subgroups a selective advantage. To give a greater probability of broad efficacy, a population-specific vaccine strategy incorporating haplotypes representative for the region may be effective for the non-merozoite antigens while a diversity-covering approach may be necessary for the merozoite antigens.

There are a number of possible explanations for the contrasting population structures of P. falciparum antigens. The stronger geographic population structure observed in the non-merozoite compared to the merozoite antigens may at least in part driven by the biology and kinetics of the lifecycle, with shorter, less frequent exposures to human immunity. Therefore, a background of geographic barriers or other location-specific environmental factors will shift the distribution of diversity among populations. This is a possibility for csp and trap which are expressed on the surface of a small number of sporozoites (∼20 parasites) that rapidly migrate to the liver after inoculation into the human host by the mosquito and pfs48/45 which is expressed only in the mosquito stages [38], [39], [40], [41], [42]. Similarly, lsa1 is expressed by liver schizonts but is a strong target of naturally acquired immunity [39], [43], [44], in agreement with the weaker geographic structuring of this antigen. Glurp is unusual because it is expressed in a number of stages exposed to the human immune response including on the sporozoite, liver schizont, merozoite and gametocyte [38] and shows very strong geographic structuring, however it is possible that the small sample size for each population has overemphasised the diversity among locales. Other region-specific factors that may decrease gene flow among P. falciparum populations include human genetic polymorphisms that confer resistance to malaria [45] and adaptation to different anophelene species that transmit P. falciparum worldwide [46]. These “bottlenecks” may lead to population structure in genes expressed during the human or mosquito stages respectively, and in neutral loci as markers of the underlying population biology [8], [35]. For the merozoite antigens the diversity within populations may be high as a result of exposure to the host immune response. These antigens are all exclusively expressed during the merozoite stage except for eba175 and msp4, which are also expressed in the sporozoite [47], [48], [49]. Merozoite exposure is brief (<2 mins), but it occurs repeatedly at a high parasitemia (>10,000 parasites in the first cycle, thereafter increasing exponentially) so there are many opportunities for immune selection. Some diversification of merozoite antigens may be adaptations to polymorphisms in erythrocyte receptors essential for parasite invasion [50]. Finally, antigens from both groups that are expressed in the mosquito stages (i.e. csp, trap, eba175, msp4, glurp and pfs48/45) may be exposed to immune selection by the anophelene vector (e.g. csp [14]). In support of the biological significance of the contrasting population structures observed, balancing selection has been detected in all of the merozoite antigens [51], [52], [53], [54], [55] whereas for the non-merozoite antigens, balancing selection was detected in trap and pfs48/45 [56], [57] but not in csp [57] and lsa1 [58] (glurp has not been investigated). Furthermore, a vaccine-mediated haplotype-specific immune response was detected for recombinant vaccines based upon msp1 [59] and msp2 [6] but not for csp [60], [61] suggesting that different haplotypes are antigenically distinct for the former two antigens. The results of our study are consistent with the structuring of diversity by balancing selection for the merozoite but not for the non-merozoite antigens.

This investigation has revealed a possible framework by which to formulate malaria vaccines with a greater potential for broad protection against the enormous diversity of parasite antigens. It may be possible to tackle the neglected problem of antigen diversity in malaria vaccine design by inclusion of the most prevalent haplotype(s), or a diversity-covering vaccine with inclusion of at least one representative haplotype from each of the defined subgroups of haplotypes. Because they show different population structures, the former approach may be more appropriate for the non-merozoite antigens, and the latter for the merozoite antigens. The haplotype and subgroup classification for a number of laboratory isolates are available in the supporting online material (Table S4) as a first step to guide the selection of such haplotypes, and to help define immunological correlates of protection which are now urgently needed to support these important findings. Nevertheless, if these contrasting population genetic structures of the genes encoding P. falciparum antigens are considered in the design of next generation vaccines, perhaps the best test of biological relevance will be the outcome of the ensuing vaccine trials.

Materials and Methods

Data collection

The P. falciparum antigens selected for the study were key components of malaria vaccines in the late stages of development or in recent trials [2], [3]. To be included in the study, we searched for population data - which we defined as 8 or more sequences from a defined location (e.g. a village or town) - for a minimum of two countries for each antigen. DNA sequences (and amino acid polymorphisms for MSP1) were then obtained for the twelve antigens meeting these criteria, including surface proteins expressed during several different lifecycle stages (Table 1). Sequences were collected from GenBank and further sequences or haplotypes were reconstructed from published data. If only the haplotypes and frequencies were available the appropriate number of copies for each allele was added to the dataset to ensure natural population frequencies (Table S1). Additional sequence data from cultured or field isolates not fitting the above criteria were also collected, including those upon which vaccines have been based (Table S2). These sequences were included in the calculation of the (known) extent of diversity worldwide (Table 1) and in the cluster analyses to maximize the sample number and provide a reference for vaccine development. Tables S1 and S2 contain summary information (e.g. GenBank accession numbers, reference) for each of these dataset. The sequences and haplotypes are available from the authors upon request. For msp1 and msp2 all DNA sequences were translated using TranSeq ( For simple multiple alignments with few gaps, DNA sequences were aligned using Sequencher 4.8 (Gene Codes, Ann Arbor, MI). Amino acid alignments (MSP1 and MSP2) were done using Clustal W [62]. Gaps were removed from all alignments because indels and repeats evolve by different mechanisms to SNPs and may result in false estimates of biologically significant diversity. We also removed invariant sites and synonymous SNPs to simplify the haplotype and focus the analysis only on the putative antigenic diversity. The resultant nonsynonymous SNP (nsSNP) haplotypes or polymorphic amino acid haplotypes (for MSP1 and MSP2) were then used for population genetic analysis.

Population Genetics

Population genetic analyses were first done with the complete dataset (Tables S1 and S2) to investigate the global range of diversity as well as the frequency of haplotypes being used in vaccine development, while the population dataset (Table S1) was used to investigate the range and distribution of diversity within and among the natural P. falciparum populations of individual countries. Population genetic parameters were determined using DnaSP v. 4.20.2 [63]. However, for MSP1 and MSP2 amino acid sequences we used Arlequin v. 3.1.1 [64] because DnaSP only handles DNA sequences. As measures of diversity we defined the polymorphism by counting the total number of synonymous (dS) and number of nonsynonymous (dN) SNPs; and by calculating from nsSNP haplotypes, the number of polymorphic sites (S), the average pairwise number of polymorphisms (k) and from complete DNA sequences (minus any gaps) the nucleotide diversity (), the latter being a proportional measure of polymorphism that can be compared among antigens. Additional measures of diversity calculated included the number of distinct haplotypes (h) (although this is heavily biased by sample size) and the haplotype diversity which is analogous to the heterozygosity (Hd = [n/(n−1)][(1−Σ(fi)2)] where n is the sample size and f is the frequency of the ith allele) and can also be compared among antigens. The Mann-Whitney test was used to compare polymorphism or diversity among regions where at least 3 countries were included per region (or subregion). Spearman's rank correlation coefficient (ρ) was used to measure associations between polymorphism (Π) and diversity (Hd). Statistical analysis was done using SPSS v. 17.

To assess population structure we first estimated the genetic differentiation (i.e. the difference in the average diversity within compared to that among populations) for each antigen by calculating FST from both haplotype frequencies and pairwise sequence diversity. For comparisons among countries or regions for all antigens except MSP1, two transformed FST statistics available in DnaSP, were calculated namely HST which is loosely based on Hd, and KST* based on k. Importantly, both HST and KST* are weighted for variable population size [25]. Significance was tested by comparison with 95% confidence intervals from 1000 permutations [25]. For comparisons among defined natural populations within a country (i.e. >8 sequences in each) we calculated Weir and Cockerhams θ [64] from the pairwise sequence diversity (i.e. analogous to KST*) in Arlequin. For MSP1 we measured the equivalent to HST available in Arlequin software, namely Weir and Cockerhams θ [64] calculated from haplotype frequencies. Significance was tested by the permutation test. Gene flow (Nm) was calculated using the method of Hudson et al. [65]. Population structure was also assessed using the Bayesian clustering algorithm implemented in structure v. 2.2 [27], [36], which assigns individual multi-locus genotypes probabilistically to a user-defined number of clusters (K) [27]. For each set of antigen haplotypes, structure was run 20 times for K = 1–10 for 10,000 Monte Carlo Markov Chain (MCMC) iterations after a burn-in period of 10,000 [66] using the admixture model and correlated allele frequencies. The mean log probability of the data (LnP[D]) and its standard deviation was plotted to predict the optimal value for K. Membership coefficients (Q) were then averaged across individuals within countries and/or regions to reveal any geographic association of the resultant clusters. To quantify the distribution of clusters within a geographically defined region we developed a population diversity statistic, Pd, where Pd = 1−∑(fi)2, where fi is the frequency of the ith cluster (analogous to Hd). A low Pd (<0.5) indicated that the geographically defined population (e.g.. country, village) has parasites with predominant membership to one cluster, and high Pd (>0.5) indicated membership to multiple clusters with low-medium frequencies. We confirmed the cluster analysis by visualizing the relationships between isolates using a transparent network analysis technique which simply connects isolates, represented as nodes within a network, based on shared SNPs. Unlike phylogenetic methods there is no evolutionary model behind network construction, but a simple threshold was used to define where an edge was drawn. For each antigen, this threshold was defined so as best to visualize the relationships between isolates, and in particular the recombinant isolates. The software program R and the ‘network’ package was used to construct and visualize the antigen networks [67], [68].

Supporting Information

Text S1.

Haplotype-frequency vs. sequence based F-statistics and supporting references

(0.13 MB DOC)

Figure S1.

Log probability of the data plots for Bayesian cluster analysis. LnP(D) is shown for nsSNP haplotypes of (A) csp, (B) trap, (C) lsa1, (D) ama1, (E) eba175, (F) msp1, (G) msp3, (H) msp4, (I) glurp and (J) pfs48/45. A plot of the log probability of the data, LnP(D) against all estimates of the number of clusters, K, was used to estimate the true value of K. LnP(D) typically plateaus or continues to increase slightly when true K has been reached (68). The error bars represent the mean value of 20 replicate runs at each K value. For some antigens, LnP(D) did not plateau with increasing K, in which case the lowest value that captured the major structure in the data was chosen (69).

(0.36 MB TIF)

Figure S2.

Bayesian cluster analysis of nsSNP haplotypes for optimum K ± 1. Note the excess of admixed individuals for K+1. Subgroups: Dark blue = 1; Red = 2; Green = 3; Purple = 4; Light blue = POP5; Orange = 6.

(1.48 MB PDF)

Figure S3.

Comparison of Bayesian cluster and network analysis for ten P. falciparum vaccine antigen genes. Subgroups: Dark blue = 1; Red = 2; Green = 3; Purple = 4; Light blue = 5; Orange = 6.

(0.66 MB TIF)

Figure S4.

Local population structure for P. falciparum vaccine antigens based on Bayesian cluster analysis. Comparison of Bayesian cluster and network analysis for ten P. falciparum vaccine antigen genes. Networks (as shown in Figure 3) are shown with individuals shaded by the structure-defined subgroups (as shown in Figure 2). Subgroups: Dark blue = 1; Red = 2; Green = 3; Purple = 4; Light blue = 5; Orange = 6; Admixed haplotypes (those having <75% membership to any one cluster) are shown in white, vaccine haplotypes are shown in yellow.

(0.90 MB TIF)

Table S1.

Population dataset for twelve P. falciparum vaccine antigen genes.

(0.04 MB XLS)

Table S2.

Dataset 2: Sequences from laboratory and other isolates for twelve P. falciparum vaccine antigen genes.

(0.02 MB XLS)

Table S3.

Analysis of structure results for ten P. falciparum vaccine antigens.

(0.02 MB XLS)

Table S4.

Haplotype and cluster membership for laboratory isolates.

(0.03 MB XLS)


We are grateful to all of the researchers whose publically available sequences made this study possible (Tables S1 and S2). We thank B. Crabb for helpful discussion and P. Gilson for critical reading of the manuscript.

Author Contributions

Conceived and designed the experiments: AEB. Performed the experiments: AEB LS. Analyzed the data: AEB LS COB. Wrote the paper: AEB LS JCR. Provided logistical support to the project: JCR.


  1. 1. Snow RW, Guerra CA, Noor AM, Myint HY, Hay SI (2005) The global distribution of clinical episodes of Plasmodium falciparum malaria. Nature 434: 214–217.
  2. 2. Moran M, Guzman J, Ropars A, Jorgensen M, McDonald A, et al. (2007) The Malaria Product Pipeline: Planning for the future. Sydney: The George Institute for International Health.
  3. 3. World Health Organization (2008) Malaria Vaccine Rainbow Table.
  4. 4. Mu J, Awadalla P, Duan J, McGee KM, Keebler J, et al. (2007) Genome-wide variation and identification of vaccine targets in the Plasmodium falciparum genome. Nat Genet 39: 126–130.
  5. 5. Fluck C, Smith T, Beck HP, Irion A, Betuela I, et al. (2004) Strain-specific humoral response to a polymorphic malaria vaccine. Infect Immun 72: 6300–6305.
  6. 6. Genton B, Betuela I, Felger I, Al-Yaman F, Anders RF, et al. (2002) A recombinant blood-stage malaria vaccine reduces Plasmodium falciparum density and exerts selective pressure on parasite populations in a phase 1–2b trial in Papua New Guinea. J Infect Dis 185: 820–827.
  7. 7. Takala SL, Plowe CV (2009) Genetic diversity and malaria vaccine design, testing and efficacy: preventing and overcoming ‘vaccine resistant malaria’. Parasite Immunol 31: 560–573.
  8. 8. Anderson TJ, Haubold B, Williams JT, Estrada-Franco JG, Richardson L, et al. (2000) Microsatellite markers reveal a spectrum of population structures in the malaria parasite Plasmodium falciparum. Mol Biol Evol 17: 1467–1482.
  9. 9. Machado RL, Povoa MM, Calvosa VS, Ferreira MU, Rossit AR, et al. (2004) Genetic structure of Plasmodium falciparum populations in the Brazilian Amazon region. J Infect Dis 190: 1547–1555.
  10. 10. Ferreira MU, Kaneko O, Kimura M, Liu Q, Kawamoto F, et al. (1998) Allelic diversity at the merozoite surface protein-1 (MSP-1) locus in natural Plasmodium falciparum populations: a brief overview. Mem Inst Oswaldo Cruz 93: 631–638.
  11. 11. Ferreira MU, Ribeiro WL, Tonon AP, Kawamoto F, Rich SM (2003) Sequence diversity and evolution of the malaria vaccine candidate merozoite surface protein-1 (MSP-1) of Plasmodium falciparum. Gene 304: 65–75.
  12. 12. Sallenave-Sales S, Daubersies P, Mercereau-Puijalon O, Rahimalala L, Contamin H, et al. (2000) Plasmodium falciparum: a comparative analysis of the genetic diversity in malaria-mesoendemic areas of Brazil and Madagascar. Parasitol Res 86: 692–698.
  13. 13. Jalloh A, van Thien H, Ferreira MU, Ohashi J, Matsuoka H, et al. (2006) Sequence variation in the T-cell epitopes of the Plasmodium falciparum circumsporozoite protein among field isolates is temporally stable: a 5-year longitudinal study in southern Vietnam. J Clin Microbiol 44: 1229–1235.
  14. 14. Kumkhaek C, Phra-Ek K, Renia L, Singhasivanon P, Looareesuwan S, et al. (2005) Are extensive T cell epitope polymorphisms in the Plasmodium falciparum circumsporozoite antigen, a leading sporozoite vaccine candidate, selected by immune pressure? J Immunol 175: 3935–3939.
  15. 15. Kumkhaek C, Phra-ek K, Singhasivanon P, Looareesuwan S, Hirunpetcharat C, et al. (2004) A survey of the Th2R and Th3R allelic variants in the circumsporozoite protein gene of P. falciparum parasites from western Thailand. Southeast Asian J Trop Med Public Health 35: 281–287.
  16. 16. Conway DJ, Machado RL, Singh B, Dessert P, Mikes ZS, et al. (2001) Extreme geographical fixation of variation in the Plasmodium falciparum gamete surface protein gene Pfs48/45 compared with microsatellite loci. Mol Biochem Parasitol 115: 145–156.
  17. 17. Cortes A, Mellombo M, Mueller I, Benet A, Reeder JC, et al. (2003) Geographical structure of diversity and differences between symptomatic and asymptomatic infections for Plasmodium falciparum vaccine candidate AMA1. Infect Immun 71: 1416–1426.
  18. 18. Duan J, Mu J, Thera MA, Joy D, Kosakovsky Pond SL, et al. (2008) Population structure of the genes encoding the polymorphic Plasmodium falciparum apical membrane antigen 1: implications for vaccine design. Proc Natl Acad Sci U S A 105: 7857–7862.
  19. 19. Escalante AA, Grebert HM, Chaiyaroj SC, Magris M, Biswas S, et al. (2001) Polymorphism in the gene encoding the apical membrane antigen-1 (AMA-1) of Plasmodium falciparum. X. Asembo Bay Cohort Project. Mol Biochem Parasitol 113: 279–287.
  20. 20. Benet A, Tavul L, Reeder JC, Cortes A (2004) Diversity of the Plasmodium falciparum vaccine candidate merozoite surface protein 4 (MSP4) in a natural population. Mol Biochem Parasitol 134: 275–280.
  21. 21. Jongwutiwes S, Putaporntip C, Friedman R, Hughes AL (2002) The extent of nucleotide polymorphism is highly variable across a 3-kb region on Plasmodium falciparum chromosome 2. Mol Biol Evol 19: 1585–1590.
  22. 22. Anderson TJ, Day KP (2000) Geographical structure and sequence evolution as inferred from the Plasmodium falciparum S-antigen locus. Mol Biochem Parasitol 106: 321–326.
  23. 23. Genton B, Reed ZH (2007) Asexual blood-stage malaria vaccine development: facing the challenges. Curr Opin Infect Dis 20: 467–475.
  24. 24. Doolan DL, Dobano C, Baird JK (2009) Acquired immunity to malaria. Clin Microbiol Rev 22: 13–36. Table of Contents.
  25. 25. Hudson RR, Boos DD, Kaplan NL (1992) A statistical test for detecting geographic subdivision. Mol Biol Evol 9: 138–151.
  26. 26. Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164: 1567–1587.
  27. 27. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. 155: 945.
  28. 28. Escalante AA, Grebert HM, Isea R, Goldman IF, Basco L, et al. (2002) A study of genetic diversity in the gene encoding the circumsporozoite protein (CSP) of Plasmodium falciparum from different transmission areas–XVI. Asembo Bay Cohort Project. Mol Biochem Parasitol 125: 83–90.
  29. 29. Forsyth KP, Anders RF, Cattani JA, Alpers MP (1989) Small area variation in prevalence of an S-antigen serotype of Plasmodium falciparum in villages of Madang, Papua New Guinea. Am J Trop Med Hyg 40: 344–350.
  30. 30. Amodu OK, Adeyemo AA, Ayoola OO, Gbadegesin RA, Orimadegun AE, et al. (2005) Genetic diversity of the msp-1 locus and symptomatic malaria in south-west Nigeria. Acta Trop 95: 226–232.
  31. 31. Ariey F, Hommel D, Le Scanf C, Duchemin JB, Peneau C, et al. (2001) Association of severe malaria with a specific Plasmodium falciparum genotype in French Guiana. J Infect Dis 184: 237–241.
  32. 32. Ofosu-Okyere A, Mackinnon MJ, Sowa MP, Koram KA, Nkrumah F, et al. (2001) Novel Plasmodium falciparum clones and rising clone multiplicities are associated with the increase in malaria morbidity in Ghanaian children during the transition into the high transmission season. Parasitology 123: 113–123.
  33. 33. Robert F, Ntoumi F, Angel G, Candito D, Rogier C, et al. (1996) Extensive genetic diversity of Plasmodium falciparum isolates collected from patients with severe malaria in Dakar, Senegal. Trans R Soc Trop Med Hyg 90: 704–711.
  34. 34. Joy DA, Feng X, Mu J, Furuya T, Chotivanich K, et al. (2003) Early origin and recent expansion of Plasmodium falciparum. Science 300: 318–321.
  35. 35. Mu J, Awadalla P, Duan J, McGee KM, Joy DA, et al. (2005) Recombination Hotspots and Population Structure in Plasmodium falciparum. PLoS Biol 3: e335.
  36. 36. Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. 164: 1567.
  37. 37. Hartl DL, Volkman SK, Nielsen KM, Barry AE, Day KP, et al. (2002) The paradoxical population genetics of Plasmodium falciparum. Trends Parasitol 18: 266–272.
  38. 38. Borre MB, Dziegiel M, Hogh B, Petersen E, Rieneck K, et al. (1991) Primary structure and localization of a conserved immunogenic Plasmodium falciparum glutamate rich protein (GLURP) expressed in both the preerythrocytic and erythrocytic stages of the vertebrate life cycle. Mol Biochem Parasitol 49: 119–131.
  39. 39. Guerin-Marchand C, Druilhe P, Galey B, Londono A, Patarapotikul J, et al. (1987) A liver-stage-specific antigen of Plasmodium falciparum characterized by gene cloning. Nature 329: 164–167.
  40. 40. Kocken CH, Jansen J, Kaan AM, Beckers PJ, Ponnudurai T, et al. (1993) Cloning and expression of the gene coding for the transmission blocking target antigen Pfs48/45 of Plasmodium falciparum. Mol Biochem Parasitol 61: 59–68.
  41. 41. Rogers WO, Malik A, Mellouk S, Nakamura K, Rogers MD, et al. (1992) Characterization of Plasmodium falciparum sporozoite surface protein 2. Proc Natl Acad Sci U S A 89: 9176–9180.
  42. 42. Yoshida N, Nussenzweig RS, Potocnjak P, Nussenzweig V, Aikawa M (1980) Hybridoma produces protective antibodies directed against the sporozoite stage of malaria parasite. Science 207: 71–73.
  43. 43. Doolan DL, Hoffman SL (2000) The complexity of protective immunity against liver-stage malaria. J Immunol 165: 1453–1462.
  44. 44. Fidock DA, Gras-Masse H, Lepers JP, Brahimi K, Benmohamed L, et al. (1994) Plasmodium falciparum liver stage antigen-1 is well conserved and contains potent B and T cell determinants. J Immunol 153: 190–204.
  45. 45. Wellems TE, Hayton K, Fairhurst RM (2009) The impact of malaria parasitism: from corpuscles to communities. J Clin Invest 119: 2496–2505.
  46. 46. Hume JC, Tunnicliff M, Ranford-Cartwright LC, Day KP (2007) Susceptibility of Anopheles gambiae and Anopheles stephensi to tropical isolates of Plasmodium falciparum. Malar J 6: 139.
  47. 47. Bottius E, BenMohamed L, Brahimi K, Gras H, Lepers JP, et al. (1996) A novel Plasmodium falciparum sporozoite and liver stage antigen (SALSA) defines major B, T helper, and CTL epitopes. J Immunol 156: 2874–2884.
  48. 48. Gruner AC, Brahimi K, Letourneur F, Renia L, Eling W, et al. (2001) Expression of the erythrocyte-binding antigen 175 in sporozoites and in liver stages of Plasmodium falciparum. J Infect Dis 184: 892–897.
  49. 49. Wang L, Menting JG, Stowers A, Charoenvit Y, Sacci JB Jr, et al. (2000) Antigens cross reactive with Plasmodium falciparum merozoite surface protein 4 are found in pre-erythrocytic and sexual stages. Mol Biochem Parasitol 109: 189–194.
  50. 50. Williams TN (2006) Human red blood cell polymorphisms and malaria. Curr Opin Microbiol 9: 388–394.
  51. 51. Baum J, Thomas AW, Conway DJ (2003) Evidence for diversifying selection on erythrocyte-binding antigens of Plasmodium falciparum and P. vivax. Genetics 163: 1327–1336.
  52. 52. Polley SD, Chokejindachai W, Conway DJ (2003) Allele frequency-based analyses robustly map sequence sites under balancing selection in a malaria vaccine candidate antigen. Genetics 165: 555–561.
  53. 53. Polley SD, Conway DJ (2001) Strong diversifying selection on domains of the Plasmodium falciparum apical membrane antigen 1 gene. Genetics 158: 1505–1512.
  54. 54. Polley SD, Tetteh KK, Lloyd JM, Akpogheneta OJ, Greenwood BM, et al. (2007) Plasmodium falciparum merozoite surface protein 3 is a target of allele-specific immunity and alleles are maintained by natural selection. J Infect Dis 195: 279–287.
  55. 55. Polson HE, Conway DJ, Fandeur T, Mercereau-Puijalon O, Longacre S (2005) Gene polymorphism of Plasmodium falciparum merozoite surface proteins 4 and 5. Mol Biochem Parasitol 142: 110–115.
  56. 56. Anthony TG, Polley SD, Vogler AP, Conway DJ (2007) Evidence of non-neutral polymorphism in Plasmodium falciparum gamete surface protein genes Pfs47 and Pfs48/45. Mol Biochem Parasitol 156: 117–123.
  57. 57. Weedall GD, Preston BM, Thomas AW, Sutherland CJ, Conway DJ (2007) Differential evidence of natural selection on two leading sporozoite stage malaria vaccine candidate antigens. Int J Parasitol 37: 77–85.
  58. 58. Hughes MK, Hughes AL (1995) Natural selection on Plasmodium surface proteins. Mol Biochem Parasitol 71: 99–113.
  59. 59. Thera MA, Doumbo OK, Coulibaly D, Diallo DA, Sagara I, et al. (2006) Safety and allele-specific immunogenicity of a malaria vaccine in Malian adults: results of a phase I randomized trial. PLoS Clin Trials 1: e34.
  60. 60. Alloueche A, Milligan P, Conway DJ, Pinder M, Bojang K, et al. (2003) Protective efficacy of the RTS,S/AS02 Plasmodium falciparum malaria vaccine is not strain specific. Am J Trop Med Hyg 68: 97–101.
  61. 61. Alonso PL, Sacarlal J, Aponte JJ, Leach A, Macete E, et al. (2004) Efficacy of the RTS,S/AS02A vaccine against Plasmodium falciparum infection and disease in young African children: randomised controlled trial. Lancet 364: 1411–1420.
  62. 62. Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22: 4673–4680.
  63. 63. Rozas J, Sanchez-DelBarrio JC, Messeguer X, Rozas R (2003) DnaSP, DNA polymorphism analyses by the coalescent and other methods. Bioinformatics 19: 2496–2497.
  64. 64. Excoffier L, Laval G, Schneider S (2005) Arlequin ver. 3.0: An integrated software package for population genetics data analysis. Evolutionary Bioinformatics Online 1: 47–50.
  65. 65. Hudson RR, Slatkin M, Maddison WP (1992) Estimation of levels of gene flow from DNA sequence data. Genetics 132: 583–589.
  66. 66. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14: 2611–2620.
  67. 67. Team RDC (2008) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria.
  68. 68. Butts CT, Handcock MS, Hunter DR (2008) network: Classes for Relational Data. R package version 1.4-1. Irvine, California.