## Abstract

Multilocus genotyping of microbial pathogens has revealed a range of population structures, with some bacteria showing extensive recombination and others showing almost complete clonality. The population structure of the protozoan parasite Plasmodium falciparum has been harder to evaluate, since most studies have used a limited number of antigen-encoding loci that are known to be under strong selection. We describe length variation at 12 microsatellite loci in 465 infections collected from 9 locations worldwide. These data reveal dramatic differences in parasite population structure in different locations. Strong linkage disequilibrium (LD) was observed in six of nine populations. Significant LD occurred in all locations with prevalence <1% and in only two of five of the populations from regions with higher transmission intensities. Where present, LD results largely from the presence of identical multilocus genotypes within populations, suggesting high levels of self-fertilization in populations with low levels of transmission. We also observed dramatic variation in diversity and geographical differentiation in different regions. Mean heterozygosities in South American countries (0.3–0.4) were less than half those observed in African locations (0.76–0.8), with intermediate heterozygosities in the Southeast Asia/Pacific samples (0.51–0.65). Furthermore, variation was distributed among locations in South America (FST = 0.364) and within locations in Africa (FST = 0.007). The intraspecific patterns of diversity and genetic differentiation observed in P. falciparum are strikingly similar to those seen in interspecific comparisons of plants and animals with differing levels of outcrossing, suggesting that similar processes may be involved. The differences observed may also reflect the recent colonization of non-African populations from an African source, and the relative influences of epidemiology and population history are difficult to disentangle. These data reveal a range of population structures within a single pathogen species and suggest intimate links between patterns of epidemiology and genetic structure in this organism.

## Introduction

Multilocus genotyping has been used extensively to investigate the genetic structure of bacterial pathogens in the past 20 years. This approach superseded genetic typing systems utilizing surface antigens and provided many fundamental insights into the spread of epidemics, the extent of recombination, and the degree of population differentiation in bacterial pathogens (Caugant et al. 1986<$REFLINK> ; Maynard-Smith et al. 1993<$REFLINK> ; Haubold et al. 1998<$REFLINK> ; McGee, Koornhof, and Caugant 1998<$REFLINK> ; Feil et al. 1999<$REFLINK> ; Souza et al. 1999<$REFLINK> ). In particular, this approach demonstrated that while some bacteria, such as Neisseria gonnhorrea, show high levels of recombination, others, such as Escherichia coli and Salmonella, have a predominantly clonal population structure. Classical multilocus approaches have not been widely applied to the protozoan pathogen Plasmodium falciparum, the causative agent of the most pathogenic of the human malarias. In fact, previous work on the molecular population genetics of this parasite have utilized a limited number of surface antigen loci, such as merozoite surface antigens and circumsporozoite surface proteins (Paul et al. 1995<$REFLINK> ; Babiker et al. 1997<$REFLINK> ). These proteins form the basis for a number of candidate malaria vaccines, and there is abundant evidence from patterns of substitution that these proteins are under strong natural selection (Hughes 1992<$REFLINK> ; Hughes and Hughes 1995<$REFLINK> ). Interpretation of population structure using data derived from these loci is problematic, since it is not clear whether the patterns observed reflect population history or natural selection. As a consequence, there is currently uncertainty about many aspects of the population genetics of P. falciparum.

Plasmodium falciparum is a hermaphroditic protozoan, with haploid asexual replication in the human host and a brief diploid sexual phase in the mosquito vector. Haploid parasites divide mitotically in the human host, and some cells differentiate into male and female stages. Male and female gametes fuse in the mosquito host to form a short-lived diploid zygote. Meiotic division then gives rise to haploid cells that develop into infective sporozoites, which migrate to the mosquito salivary glands and infect humans during mosquito blood-feeding. Fusion of male and female gametes from the same clone (selfing) results in no effective recombination, while fusion of gametes from different clones (outcrossing) may result in recombination. While Plasmodium has a well-established sexual phase in its life cycle and genetic crosses have been performed (Walker-Jonah et al. 1992<$REFLINK> ; Su et al. 1999<$REFLINK> ), there is ongoing discussion about the level of effective recombination in natural malaria populations (Rich et al. 1998<$REFLINK> ; Conway et al. 1999<$REFLINK> ). Rich et al. (1998)<$REFLINK> used substitution patterns in the circumsporozoite antigen to argue that P. falciparum populations are predominantly clonal (Rich, Hudson, and Ayala 1997<$REFLINK> ). This conclusion was based on the absence of any decay in linkage disequilibrium (LD) with distance across the locus studied. This claim was clearly refuted by Conway et al. (1999)<$REFLINK> , who showed a rapid decay in LD with physical distance along a chromosome and argued for high levels of recombination, at least in African locations. However, the situation is by no means clear: while some authors have observed no evidence for LD between physically unlinked antigen loci (Babiker et al. 1994<$REFLINK> ; Paul et al. 1995<$REFLINK> ), others have reported strong LD (Abderrazak et al. 1999<$REFLINK> ). Other aspects of population structure are also debated. Data on levels of geographical genetic differentiation among parasite populations are conflicting, with two antigen loci (MSP-1 and MSP-2) indicating low levels of global genetic structure (FST < 0.2) (Conway 1997<$REFLINK> ) and a third locus, the gametocyte surface antigen pfs48/45, suggesting strong subdivision (FST > 0.7) (Drakeley et al. 1996<$REFLINK> ). Once again, interpretation of these data is hampered by the use of small numbers of strongly selected loci.

### Population History

Table 4 shows pairwise measurements of (δμ)2 between populations from South America and Africa and corresponding estimates of divergence time. Using the point estimate of microsatellite mutation rate, divergence times range from 385 to 1,101 years with a mean of 665. These estimates may be substantially altered by inaccuracies in the mutation rate estimate or by variation in mutation rate across loci. Using the upper and lower confidence intervals on the mutation rate, estimated divergence times range from 305 to 1,559 years.

## Discussion

The microsatellite data reveal a spectrum of population structures within a single pathogen species. Strong LD, low genetic diversity, and high levels of geographical variation are observed in regions of low transmission, while random association among loci, high genetic diversity, and minimal geographical differentiation are observed in regions of Africa and Papua New Guinea, where transmission is intense. In the following paragraphs, we first discuss possible explanations for these patterns. First, we explore the reasons for the differences in levels of LD among populations. Second, we evaluate the importance of population history and disease ecology in determining the patterns of diversity and geographical structuring observed.

Significant deviations from random association among loci were observed in six of nine parasite populations using both the complete data set and the reduced data set from which multiple infections were removed. Maynard-Smith et al. (1993)<$REFLINK> have described a simple framework for evaluating the population structure of microbial pathogens. They distinguish between “clonal” organisms, such as Salmonella and E. coli, in which levels of recombination are insufficient to break down clonal lineages, and “epidemic” population structures of organisms such as Neisseria meningitidis, in which LD results from temporal expansion of particular clones in an otherwise sexual population. Epidemic population structures can be identified by treating multiply represented genotypes as single individuals and remeasuring LD. This procedure restores linkage equilibrium to four of the six malaria populations investigated; LD remained in populations from Zimbabwe and Bolivia. Hence, the P. falciparum populations studied here range from epidemic in low-transmission areas to panmixia in high-transmission areas. In Bolivia, LD remains even when only unique genotypes are included in the data set. Two explanations are conceivable. The rate of recombination may be sufficiently low relative to mutation, such that LD is maintained. Alternatively, the populations may result from admixture with a genetically divergent parasite population, and insufficient time has passed for recombination to homogenize these two populations. We note that parasite populations in South America show strong differentiation over relatively small geographical distances, so admixture of populations may occur frequently. LD also remains in Zimbabwe, even when unique genotypes are analyzed. This is surprising, given that we observe very high levels of multiclone infection in this region, suggesting relatively high levels of transmission. The Zimbabwe sample was collected from people visiting two different clinics in Mutare and Mutasa. These samples showed no significant genetic differentiation and were therefore analyzed together. Moreover, significant LD was observed in both populations, even when only unique haplotypes were analyzed (Mutare: n = 32, ISA = 0.0167, P = 0.0058; Mutasa: n = 24, ISA = 0.0158, P = 0.0461), suggesting that combining different populations did not generate the observed LD. The simplest explanation for the observed association between transmission intensity and LD is that P. falciparum utilizes a mixed mating system in which inbreeding predominates in low-transmission areas, while higher levels of outbreeding occur in regions with higher transmission. This may occur, since people are rarely superinfected with more than one parasite clone in low-transmission regions. As a result, unrelated parasites rarely co-occur in the same mosquito blood meal. Conversely, multiple-clone infections are frequent where transmission is intense. Consequently, mosquitoes frequently ingest unrelated parasites, leading to higher levels of outbreeding (Babiker et al. 1994<$REFLINK> ; Paul et al. 1995<$REFLINK> ). To further investigate the relationship between LD and transmission, we compared two indicators of transmission intensity (prevalence and proportion of infections containing multiple clones) with ISA, which measures the strength of LD (fig. 5 ). In general, parasites from regions with low prevalence or low levels of multiple infection show higher levels of ISA than those from regions with high prevalence or with high levels of multiple infections. This relationship should be viewed with some caution. Hudson (1994)<$REFLINK> has shown that ISA is not directly comparable between populations when Ne varies. A theoretical framework to allow interpretation of microsatellite-derived ISA values in terms of levels of recombination would be extremely useful. Such a model does exist for markers evolving by IAM (Hudson 1994<$REFLINK> ). However, for most microsatellite data, this mutation model is likely to be inappropriate. How frequently does outcrossing occur in populations of P. falciparum? Inbreeding coefficients have previously been measured in P. falciparum by genotyping oocysts dissected from mosquitoes. This stage of the P. falciparum life cycle contains the haploid products of meiosis and can be used to measure diploid genotypes and heterozygote deficits. This approach has been used in both Papua New Guinea and Tanzania, and inbreeding coefficients of 0.9 and 0.3, respectively, have been reported (Babiker et al. 1994<$REFLINK> ; Paul et al. 1995<$REFLINK> ). However, the prevalence of infection in mosquitoes from low-transmission regions (often <1/1,000) makes this method impractical, since literally thousands of mosquitoes would need to be dissected. Furthermore, recent reanalyses of the data from Papua New Guinea have suggested the presence of nonamplifying alleles, which may lead to overestimation of inbreeding coefficients using this method (Anderson et al. 2000a<$REFLINK> ). Estimating inbreeding from blood stage data is less straightforward, and the simplest estimates may be the most informative. In Colombia, only 3 of 30 (10%) infections contained multiple clones. Therefore, outcrossing is unlikely to occur in >10% of infected mosquitoes in this region. Similarly, the observation of the same multilocus genotype in blood samples collected 4 years apart is informative. If we assume a generation time of 2 months, this parasite genotype has been transmitted through 24 generations without change due to recombination.

Levels of LD may have important consequences for a number of aspects of P. falciparum biology. In particular, the rate at which recombination breaks down association between genes may influence the persistence of clonal genotypes (Paul et al. 1995<$REFLINK> ; Hastings and Wedgewood-Oppenheim 1997<$REFLINK> ), the maintenance of antigenically distinct “strains” (Gupta et al. 1996<$REFLINK> ; Hastings and Wedgewood-Oppenheim 1997<$REFLINK> ), sex ratio (Read et al. 1992<$REFLINK> ; Dye and Godfray 1993<$REFLINK> ), and the spread of drug resistance (Dye and Williams 1997<$REFLINK> ; Hastings 1997<$REFLINK> ; Hastings and Mackinnon 1998<$REFLINK> ). The extensive LD observed has important practical consequences for malaria research, a major goal of which is to locate parasite genes underlying important phenotypes such as pathogenicity and resistance to drugs. Two resources—the sequence data emerging from the malaria genome project (Gardner et al. 1998<$REFLINK> ) and a dense microsatellite map, with markers every 30–50 kb (Su and Wellems 1996<$REFLINK> ; Su et al. 1999<$REFLINK> )—should simplify the location of important genes in P. falciparum. However, the high recombination rate (1 cM = 15–30 kb) observed in a genetic cross (Walker-Jonah et al. 1992<$REFLINK> ) and the recent demonstration that LD is rarely detected between markers separated by >1 kb in African populations (Conway et al. 1999<$REFLINK> ) may discourage researchers from using LD in natural populations as a mapping tool. In populations with high levels of inbreeding, the “effective” recombination rate will be considerably reduced. In such populations, it should be possible to locate genes encoding important parasite traits using relatively low densities of marker loci (Noorberg 2000<$REFLINK> ). This approach is likely to be particularly effective for genes involved in drug resistance, since the mutations involved have occurred recently (White 1992<$REFLINK> ), allowing little time for LD between marker and trait loci to have been broken down (fig. 6 ). For example, with 1% recombination, LD may be maintained between markers spaced 5 cM apart for 2,750 generations, which is equivalent to >400 years if we assume a 2-month generation time for P. falciparum. Thus, for recently evolved traits (<50 years ago) genome screens using 200–400 markers spaced at 75–150-kb intervals are likely to be successful. In comparison, in regions with 50% outcrossing, all traces of LD between loci will be lost in <60 generations (≅10 years), and marker densities one or two orders of magnitude higher would be necessary. Empirical data provide encouraging support for this mapping approach: LD is observed for >60 kb on either side of the putative chloroquine resistance locus (Su et al. 1997<$REFLINK> ). ### Population Differentiation and Diversity We observe dramatic differences in both genetic diversity and genetic differentiation in different regions. Sequencing studies of antigen-encoding loci have shown lower levels of variation in antigen-encoding genes (Yoshida et al. 1990<$REFLINK> ; Anderson and Day 2000<$REFLINK> ) in South American locations. Since these loci encode antigens exposed to the immune system, it is uncertain whether the patterns observed indicate different regimes of immune selection in different regions, or whether these patterns reflect population history. The microsatellite data clarify this issue. Patterns of diversity are remarkably consistent across loci. At each of the 12 loci examined, diversity is lower in the three South American locations than in the three African populations, with intermediate levels in the populations from Papua New Guinea and Thailand. The loci were selected for use in this study on the basis of patterns of variation in 12 laboratory isolates originally isolated from worldwide locations. Therefore, the trivial explanation of ascertainment bias (Ellegren, Primmer, and Sheldon 1995<$REFLINK> ) is unlikely to explain the differences observed. Differences in contemporary patterns of disease ecology and/or in population history are more likely explanations. These explanations are evaluated in the following paragraphs.

The differences in diversity may result from differences in effective population size and levels of inbreeding in low- and high-transmission regions. The intraspecific patterns of diversity, genetic differentiation, and LD observed in P. falciparum show a striking similarity to interspecific patterns of variation observed in plants (Schoen and Brown 1991<$REFLINK> ; Awadalla and Ritland 1997<$REFLINK> ) and animals (Jarne 1995<$REFLINK> ) with differing levels of inbreeding. Outbred species typically show higher levels of genetic variation and lower levels of genetic differentiation than inbred species. The interplay between mating system, diversity, and differentiation is complex. Three factors are likely to result in the reduced levels of genetic variation observed in inbred populations of P. falciparum. First, Ne is halved in situations of complete inbreeding relative to complete outbreeding (Pollak 1987<$REFLINK> ). This alone cannot account for the variation in diversity observed in P. falciparum, since Ne is reduced 9–23-fold in South American populations relative to African populations (table 2 ). Second, LD generated by selfing will increase the size of genomic regions involved in selective events, since “hitchhiking” either with deleterious sites (background selection) (Charlesworth, Morgan, and Charlesworth 1993<$REFLINK> ) or with sites under positive selection (selective sweeps) (Hedrick 1980<$REFLINK> ) will remove variation in the vicinity of the sites under selection. The size of genomic regions affected will be greatest in geographical regions in which strong LD is observed. Third, the effect of LD and inbreeding on diversity are likely to be compounded by the fact that both numbers of infected hosts and numbers of clones per individual are generally higher in areas of high transmission than in areas of low transmission. The reduced effective size of parasite populations in low-transmission areas may also explain the increased levels of genetic differentiation in regions such as South America, since allele frequencies may change rapidly in small populations owing to increased levels of genetic drift. If this explanation is correct, then we might expect to see similar numbers of alleles in both South America and Africa if sufficient populations are sampled. The fact that variation is distributed among populations in South America, while variation is distributed within populations in African locations, may give an illusion of reduced variation in parasites from the New World when the number of populations sampled is limited.

There is some supporting evidence for explanations involving disease ecology from two recent studies in which malaria parasites from isolated epidemics were genotyped for antigen-encoding loci. Arez et al. (1999) observed no genetic variation at loci in a malaria epidemic on Cabo Verde, while Laserson et al. (1999) observed no genetic variation at two antigen loci in an epidemic among Yanomani Indians in the Venezuelan Amazon. These papers suggest the importance of recent founder events associated with epidemic malaria in generating low-diversity parasite populations. Patterns of allelic distribution also provide some support for this explanation. In South American locations, the distribution of allele frequencies is flat, while in African countries the distributions are L-shaped (fig. 7 ). Furthermore, in two locations, Bolivia and Brazil, the modal allele frequency range is in one of the intermediate allele frequency classes (40%–50% for Bolivia and 10%–20% for Colombia). Such “mode shifts,” indicating a loss of rare alleles, are commonly observed in recently bottlenecked populations and appear to be indicative of populations that are not at mutation drift equilibrium (Maruyama and Fuerst 1985<$REFLINK> ; Luikart et al. 1998<$REFLINK> ).

Population history may also explain or contribute to the patterns of diversity and geographical differentiation observed. The high diversity in Africa may reflect the fact that this was the source for parasite populations in other parts of the world. Multiple colonization events could also explain the strong geographical structuring of South American populations if different events have led to the establishment of populations in different regions of the continent. It is thought that malaria was introduced into South America ≅500 years ago with the arrival of Europeans and that subsequent reintroduction from Africa occurred in the course of the slave trade. Pairwise measurements of Goldstein's (δμ)2 distances (Goldstein et al. 1995<$REFLINK> ) between South American and African malaria populations range from 0.773 to 2.101, consistent with a split between South American and non–South American populations between 385 and 1,101 years ago (mean = 665 years ago), assuming a generation time of 2 months for P. falciparum. These figures are consistent with the historical scenario but should be viewed with caution. The range is extremely large, particularly when we consider that error around our estimate of mutation rate or variation among loci in mutation rate has not been incorporated. When confidence intervals around the mutation rate are used, mean divergence times range from as short as 131 years to as long as 2,488 years. Furthermore, (δμ)2 is accurate only if populations compared are in mutation drift equilibrium, which seems unlikely for parasite populations in regions of unstable epidemiology. Indeed, allelic distributions in both Bolivia and Colombia strongly suggest that these populations are not at mutation drift equilibrium (fig. 7 ). It will prove extremely difficult to determine the extent to which the low genetic diversity in South America reflects contemporary patterns of genetic structure and epidemiology, as argued above, or whether bottlenecks resulting from recent colonization events are responsible. Regardless of the causes, the dramatic differences in genetic diversity, population differentiation, and LD in different locations have important consequences for our understanding of P. falciparum biology. In parasite populations with low microsatellite diversity, we would also expect to see reduced diversity in antigen-encoding loci (Ferreira et al. 1998<$REFLINK> ) and a smaller repertoire of variant surface antigens. Hence, under a model of genotype-specific immunity (Gupta et al. 1994<$REFLINK> ), we might expect effective immunity to malaria to be generated following a relatively small number of infective mosquito bites in low-transmission regions. Second, in regions with low levels of recombination, multilocus genotypes may be maintained through multiple generations. In this situation, it should be possible to track the spread of multilocus genotypes within communities, as is done for bacterial haplotypes. Furthermore, comparison of infection characteristics of multiply represented haplotypes can be used to investigate which aspects of P. falciparum virulence (or other traits) are a product of parasite genetics rather than host factors. Jeffrey Long, Reviewing Editor 1 Keywords: Plasmodium falciparum, linkage disequilibrium heterozygosity population structure infinite-alleles model stepwise mutation model 2 Address for correspondence and reprints: Timothy J. C. Anderson, Department of Genetics, Southwest Foundation for Biomedical Research, 7620 NW Loop 410, P.O. Box 760549, San Antonio, Texas 78245-0549. E-mail: tanderso@darwin.sfbr.org. Table 1 Patterns of Diversity in Nine Malaria Populations Table 2 Estimates of Effective Sizes (Ne) of Plasmodium falciparum Populations Table 3 Multilocus Linkage Disequilibrium in Nine Malaria Populations Table 4 Genetic Distances (({δ}{μ})2) and Inferred Separation Times Between South American and African Populations appendix Allele Frequencies and Sample Sizes (n) for 12 Microsatellite Loci from 9 Populations of Plasmodium falciparum appendix Continued appendix Continued Fig. 1.—Population differentiation in Plasmodium falciparum. The numbers marked on the arrows describe levels of genetic differentiation measured using coancestry coefficients (𝛉) between parasite populations. Ranges of values of 𝛉 are shown in comparisons between groups of populations from different geographical regions. Bootstrapping revealed significant (P < 0.001) differentiation between populations in all but 1 of the 36 pairwise comparisons. No significant differentiation was observed between the Uganda and Congo populations. Furthermore, no significant differentiation was observed between samples collected from Porto Velho, Brazil, in successive years or among samples collected from Mutasa and Mutare in Zimbabwe. Populations from Brazil and Zimbabwe are therefore treated as single populations in all analyses Fig. 1.—Population differentiation in Plasmodium falciparum. The numbers marked on the arrows describe levels of genetic differentiation measured using coancestry coefficients (𝛉) between parasite populations. Ranges of values of 𝛉 are shown in comparisons between groups of populations from different geographical regions. Bootstrapping revealed significant (P < 0.001) differentiation between populations in all but 1 of the 36 pairwise comparisons. No significant differentiation was observed between the Uganda and Congo populations. Furthermore, no significant differentiation was observed between samples collected from Porto Velho, Brazil, in successive years or among samples collected from Mutasa and Mutare in Zimbabwe. Populations from Brazil and Zimbabwe are therefore treated as single populations in all analyses Fig. 2.—Unrooted neighbor-joining tree showing the relationships between the nine parasite populations. Bootstrap support (1,000 replications) for the nodes are shown, while abbreviated population names are shown at the branch tips: Congo (CON), Uganda (UGA), Zimbabwe (ZIM), Colombia (COL), Bolivia (BOL), Brazil (BRA), Mebat (MEB), Buksak (BUK), and Thailand (THA). The tree shown is based on Nei's (1978) distance; trees based on allele sharing and chord distances give identical topologies Fig. 2.—Unrooted neighbor-joining tree showing the relationships between the nine parasite populations. Bootstrap support (1,000 replications) for the nodes are shown, while abbreviated population names are shown at the branch tips: Congo (CON), Uganda (UGA), Zimbabwe (ZIM), Colombia (COL), Bolivia (BOL), Brazil (BRA), Mebat (MEB), Buksak (BUK), and Thailand (THA). The tree shown is based on Nei's (1978) distance; trees based on allele sharing and chord distances give identical topologies Fig. 3.—Midpoint rooted neighbor-joining tree showing the relationships between Plasmodium falciparum haplotypes from nine different locations (see Materials and Methods for details of distance measure). Fifteen randomly chosen haplotypes from each location are included, while branches are colored to show the origin of the parasites. Terminal branches of zero length mark identical haplotypes. The numbers in square brackets describe the proportion of parasites from a particular country that are found together in one cluster in the tree Fig. 3.—Midpoint rooted neighbor-joining tree showing the relationships between Plasmodium falciparum haplotypes from nine different locations (see Materials and Methods for details of distance measure). Fifteen randomly chosen haplotypes from each location are included, while branches are colored to show the origin of the parasites. Terminal branches of zero length mark identical haplotypes. The numbers in square brackets describe the proportion of parasites from a particular country that are found together in one cluster in the tree Fig. 4.—Summary of patterns of multiple infection in Plasmodium falciparum–infected blood samples from nine locations. A, The proportion of infections containing more than one parasite clone. B, The estimated mean number of clones per sample; the error bars represent standard deviations of the estimates derived from the 12 loci. The criteria used to score numbers of alleles per locus are described, and the statistical methods used to estimate clonal carriage are summarized in the Materials and Methods section. Data from Buksak were excluded from this analysis, since radioactive, rather than fluorescent, detection of alleles was used (see Materials and Methods). As such, the results are not directly comparable Fig. 4.—Summary of patterns of multiple infection in Plasmodium falciparum–infected blood samples from nine locations. A, The proportion of infections containing more than one parasite clone. B, The estimated mean number of clones per sample; the error bars represent standard deviations of the estimates derived from the 12 loci. The criteria used to score numbers of alleles per locus are described, and the statistical methods used to estimate clonal carriage are summarized in the Materials and Methods section. Data from Buksak were excluded from this analysis, since radioactive, rather than fluorescent, detection of alleles was used (see Materials and Methods). As such, the results are not directly comparable Fig. 5.—The relationship between transmission intensity and linkage disequilibrium. Two surrogate measures of transmission intensity, (A) infection (B) prevalence and proportion of people carrying multiple infection, were used to assess levels of parasite transmission. Linkage disequilibrium was assessed using the statistic ISA. Methods used to estimate these parameters are described in the text Fig. 5.—The relationship between transmission intensity and linkage disequilibrium. Two surrogate measures of transmission intensity, (A) infection (B) prevalence and proportion of people carrying multiple infection, were used to assess levels of parasite transmission. Linkage disequilibrium was assessed using the statistic ISA. Methods used to estimate these parameters are described in the text Fig. 6.—The probability of observing no recombination between a marker locus and a trait locus separated by 5 cM (≈75–150 kb in Plasmodium falciparum) in populations with differing levels of recombination and inbreeding. The frequency of chromosomes bearing the marker allele and the gene of interest is assumed to be 0.5, while for the case of a map distance of 5 cM, the rate of recombination (c) is 0.0025. The probability that there has been no recombination between marker and trait locus after t generations is given by P = (1 − c′)t, where c′ is the effective rate of recombination (Lynch and Walsh 1997<$REFLINK> ). The effective rate of recombination is given by c′ = c(1 − F), where c is the recombination rate and F is the inbreeding coefficient (Conway et al. 1999<$REFLINK> ) Fig. 6.—The probability of observing no recombination between a marker locus and a trait locus separated by 5 cM (≈75–150 kb in Plasmodium falciparum) in populations with differing levels of recombination and inbreeding. The frequency of chromosomes bearing the marker allele and the gene of interest is assumed to be 0.5, while for the case of a map distance of 5 cM, the rate of recombination (c) is 0.0025. The probability that there has been no recombination between marker and trait locus after t generations is given by P = (1 − c′)t, where c′ is the effective rate of recombination (Lynch and Walsh 1997<$REFLINK> ). The effective rate of recombination is given by c′ = c(1 − F), where c is the recombination rate and F is the inbreeding coefficient (Conway et al. 1999<\$REFLINK> )

Fig. 7.—Allele frequency distributions for each of the nine populations studied. Only polymorphic loci are included

Fig. 7.—Allele frequency distributions for each of the nine populations studied. Only polymorphic loci are included

We thank the following people for assistance in obtaining samples and epidemiological data: Mauro Toledo Marrelli, Moses Lagog, Andres Ruiz Linares, Carlos Muskus, and Chris Plowe and Renato Gusmao. This work was funded by the UNDP/World Bank/WHO Special Program for Research and Training in Tropical Diseases, the Bolivian Ministry of Health, the Pan American Health Organization (grant HDP-HD-G-USA-1035), and the Wellcome Trust. Comments from Andres Ruiz Linares, Xin-Zhuan Su, David Conway, and April Hopstetter greatly improved the manuscript.

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