Figures
Abstract
Background
Merozoites of Plasmodium falciparum invade through several pathways using different RBC receptors. Field isolates appear to use a greater variability of these receptors than laboratory isolates. Brazilian field isolates were shown to mostly utilize glycophorin A-independent invasion pathways via glycophorin B (GPB) and/or other receptors. The Brazilian population exhibits extensive polymorphism in blood group antigens, however, no studies have been done to relate the prevalence of the antigens that function as receptors for P. falciparum and the ability of the parasite to invade. Our study aimed to establish whether variation in the GYPB*S/s alleles influences susceptibility to infection with P. falciparum in the admixed population of Brazil.
Methods
Two groups of Brazilian Amazonians from Porto Velho were studied: P. falciparum infected individuals (cases); and uninfected individuals who were born and/or have lived in the same endemic region for over ten years, were exposed to infection but have not had malaria over the study period (controls). The GPB Ss phenotype and GYPB*S/s alleles were determined by standard methods. Sixty two Ancestry Informative Markers were genotyped on each individual to estimate admixture and control its potential effect on the association between frequency of GYPB*S and malaria infection.
Results
GYPB*S is associated with host susceptibility to infection with P. falciparum; GYPB*S/GYPB*S and GYPB*S/GYPB*s were significantly more prevalent in the in the P. falciparum infected individuals than in the controls (69.87% vs. 49.75%; P<0.02). Moreover, population genetics tests applied on the GYPB exon sequencing data suggest that natural selection shaped the observed pattern of nucleotide diversity.
Citation: Tarazona-Santos E, Castilho L, Amaral DRT, Costa DC, Furlani NG, Zuccherato LW, et al. (2011) Population Genetics of GYPB and Association Study between GYPB*S/s Polymorphism and Susceptibility to P. falciparum Infection in the Brazilian Amazon. PLoS ONE 6(1): e16123. https://doi.org/10.1371/journal.pone.0016123
Editor: Georges Snounou, Université Pierre et Marie Curie, France
Received: July 29, 2010; Accepted: December 14, 2010; Published: January 24, 2011
Copyright: © 2011 Tarazona-Santos et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This project has been funded in part with federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health number 5R03TW007349-3 and 1R56AI069547-01A2 to SL. This research was also supported in part by the intramural funding of the New York Blood Center to SL and by funding from Brazilian National Research Council (CNPq) to LC, ETS and MM; São Paulo State Research Agency (FAPESP) to LC, DRTA, DCC, NGF, ARBR, RLDM; Minas Gerais State Research Agency (FAPEMIG) to ETS, Brazilian Ministry of Education (CAPES) to LWZ. The Hematology and Hemotherapy Center - Hemocentro UNICAMP, forms part of the National Institute of Science and Technology of Blood, Brazil (INCT do Sangue -CNPq / MCT / FAPESP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Specificity of invasion of Plasmodium falciparum merozoites into human red blood cells (RBCs) was the first indication that malaria parasites possess ligands that recognize and interact exclusively with receptors on the surface of the host RBCs [1]. The specificity of malaria parasites for RBC depends on a number of ligand-receptor interactions that are dynamic in P. falciparum and provide a greater flexibility to the parasite to overcome the variability in host RBCs and to evade immune responses. The P. falciparum merozoites invade through several pathways using different RBC receptors. Receptors include the glycophorin A, B and C (GPA, GPB and GPC), Band 3 and others (Receptors Y, E, Z and X), whose molecular identity has not yet been determined [2]. Field isolates have shown even greater variability than the laboratory strains in which the known invasion pathways have been defined [3]–[9]. Our initial studies of field isolates from Mato Grosso, Brazil have revealed that these parasites differ from common laboratory strains, and mostly utilized GPA-independent invasion pathways – the so-called alternative invasion pathways via receptors such as GPB, Receptor Y, Receptor Z and/or others [2].
The genetic polymorphisms of glycophorins or other known receptors play an important role in the resistance to the invasion of erythrocytes by P. falciparum [10]–[19]. In hyperendemic malaria regions of Papua New Guinea deletions in Band 3 (SLC4A1Δ27) or in GPC (Ge phenotype, GYPCΔex3) are highly prevalent and these RBC phenotypes confer selective advantage on morbidity [13]–[19]. In vitro invasion of RBCs that have GPC deficiency is significantly reduced but not eliminated [10], [11], [20]–[23].
The polymorphisms identified so far in the two other known P. falciparum glycophorin receptors, GPA (GenBank M60707) and GPB (GenBank M60708), have been shown to be only mildly associated with the efficiency of P. falciparum invasion and they conferred only partial protection against invasion of RBCs [24], [25]. For example, the invasion of P. falciparum malaria of En(a−) (lack of GPA) and S−s−U− (lack of GPB) RBCs is significantly reduced but not eliminated [10], [11], [20]–[23], presumably because the parasites can use one or more of the other RBC receptors for invasion.
The GYPA and GYPB genes code for Type I membrane RBC proteins that carry antigens of the MNS blood group system. GYPA has two codominant allelic forms, which determine the M or N antigens at the N-terminus of GPA (1SSTTG5 for M and 1LSTTE5 for N). GPB is identical to GPAN for the first 26 amino acids and, thus, also encodes the N antigen at the N-terminus. GYPB also has two codominant alleles: GYPB*S and GYPB*s corresponding to S and s antigens, respectively, on the RBC surface. The Ss antigens are defined by an amino acid change at position 29 [Met(S)/Thr(s)] of GPB and are displayed as the S+s−; S−s+ or S+s+ phenotypes [26]. In addition to their sequence homology, GYPA and GYPB recombination and gene conversion hotspots have been identified, generating on the RBC surface many different hybrid GYP gene products that encode GPB bearing low-prevalence antigens like He and Dantu [27]. The GP.Dantu glycophorin is specified by a hybrid gene whose N-terminal sequence is encoded by the GYPB gene and C-terminal sequence by the GYPA gene. In this hybrid, the genomic breakpoint is located in its intron 4, a composite of GYPB and GYPA [28]. The hallmark of GPB.He antigen is the change of the N-terminal sequence (from 1LSTTE5 for N to 1WSTSG5 for He) that abolishes the expression of the common N antigen. GP.He isoform of GPB may or may not carry the S or the s antigen depending on whether it associates with mutations that affect the splicing of exon 4 [29]. Dantu invariably expresses s, albeit weakly. The Dantu variant is prevalent in Africans; 4% vs. 0% in Europeans [30], [31]. The in vitro invasion of GP.Dantu RBCs by P. falciparum is severely compromised [30], [32].
Some other GYPB nucleotide changes are known to influence the expression of S or s antigens on the RBCs [27], [33]. The GPB U antigen is defined by aa 33–39 [34]. Thus, a deletion of GYPB exons 2–5 results in S−s−U− phenotype; absence of GPB on the RBCs. Although, the GPB U− RBCs are also S−s−, approximately 16% of S−s− RBCs are U+ (S−s−U+var phenotype) and encoded by a hybrid glycophorin gene [29]. Of these, ∼23% are associated with a variant GPB that usually expresses the He antigen, albeit variably [35]–[37]. Nucleotide changes in or around GYPB exon 5 were suggested to be its molecular origin [29]. Notably, a higher prevalence of the S−s−U− phenotype is found in Africa (2–8%); among the pygmies (20%) [38]–[40], up to 37% of West Africans and ∼1% of African Americans [41]. Africans also have a higher prevalence of the Henshaw phenotype (S−s−U+var or GP.He phenotype) [27]. The prevalence of GP.He in African Americans is 3% and up to 7% in people of African origin in Natal, Brazil [27]. The existence of these GPB variants in people of African origin has led to the speculation that these variants may have been selected as a result of the relative resistance that they confer against P. falciparum malaria. Recently, a new GYPB-A-B recombinant allele (Morobe allele) was found in a highly endemic area in Papua New Guinea [42], but its precise protective effect has not been yet characterized. In South and SE Asia significantly higher prevalence of antigens carried by GPA/GPB hybrids, such as the MUT, MINY, HIL, Hop, Sta, Mur and Mia antigens, are present (0.68–15% vs. 0% in Caucasian), some of which also affect the expression of the GPB S antigen on RBC surface [27]. All these recombinant variants, largely described by blood group scholars, are consistent with the results of a genome wide survey for Copy Number Polymorphisms (CNPs) in the human genome developed using Comparative Genome Hybridization [43], which identified the GYPB locus as a CNP in African populations. A recent study have identified the P. falciparum ligand for GPB, which raised the possibility that mutations in the gene encoding Glycophorin B in malaria endemic areas could affect susceptibility to malaria through the inability of the ligand to bind to the varied receptor [44].
The Brazilian population exhibits extensive polymorphism in blood group antigens. Although many of these are well documented [45]–[47], no studies have been done to specifically relate the frequencies of defined polymorphic blood group antigens that function as receptors for P. falciparum and the ability of the parasite to invade them. An uncontrolled GPB phenotype-based study of four different ethnic groups in Colombia, suggested an association between the GPB S−s+ variant and a greater resistance to malaria (P. vivax and/or P. falciparum) in people of African origin [48]. A study by Beiguelman et al. [49] in a rural area of Rondônia was not able to substantiate the studies in Colombia [48] as they did not find any significant associations between GPB SS, Ss or ss phenotypes and Plasmodium infection status. However, preliminary studies in four other endemic regions of the Brazilian Amazon including Porto Velho of Rondônia also observed higher frequencies of the GPB S+s+ phenotype among P. falciparum malaria patients from Belém and Rio Branco, while higher frequencies of the GPB S−s+ phenotype were found in uninfected blood donors from Belém, Porto Velho, and Rio Branco [50]. The discrepancy between the two studies in Rondônia was attributed to potential differences of the populations studied.
These preliminary studies pointed to a potential association between GPB S+ carriers and their P. falciparum infection status, although the significance of these results was impacted by the lack of control for ethnicity, a potential confounding factor. Therefore, our present study in Porto Velho, Rondônia was aimed to further establish whether molecular variation in the GYPB gene, particularly the one that generates the GYPB*S/s alleles, influences host susceptibility to infection with P. falciparum, taking into account the possible confounding factor of ethnicity. In addition, we interpreted our results in the context of the coding nucleotide diversity and haplotype structure of GYPB, which might also influence the prevalence of P. falciparum infections in this population.
Materials and Methods
Ethics Statement
The research reported here was approved by the IRB of each of the collaborating institutions Hemocentro, UNICAMP, State of Sao Paulo; the Faculdade de Medicina, São José do Rio Preto, State of Sao Paulo; and the Universidade Federal do Rio de Janeiro. The collaborating institutions are registered with the OHRP (FWA00007713; FWA00000377; and FWA00003452, respectively) and their IRB are also registered. The overall protocol was also approved by the IRB from the New York Blood Center (Protocol 415-05). A written informed consent was obtained from all adults, as well as from the parents or legal guardians of minors who participated in the present study.
Study population
Two groups of individuals have been recruited for this study over the period of 2006–2007 in Porto Velho, Rondônia: 1) P. falciparum infected individuals; and 2) uninfected individuals who were born and/or have lived in the same endemic region for over ten years, were exposed to infection but have not had malaria in the past or over the 2–3 year study period. All consenting individuals have been interviewed and information regarding their gender, age, date of birth, place of birth, mother's name and maiden surname, ethnic origin of their parent and their grandparents, length of residence in their present locality, history of exposure and/or number of malaria episodes in the last 10 years, and past treatment for malaria were recorded.
Recruitment of P. falciparum infected individuals (N = 83) was done in the local healthcare clinic, Centro de Pesquisa em Medicina Tropical (CEPEM), Porto Velho – Rondônia. The consented individuals were of ranging age (18–62) and a female/male ratio of 27/56 (Table 1). The blood samples of the consented infected individuals were analyzed for P. falciparum parasitemia using Giemsa stained thick blood smears. The density of parasitemia in the infected individuals was recorded and expressed as the number of asexual P. falciparum per microliter of blood assuming a leukocyte count of 8000/µl. All patients with any symptoms of malaria and/or microscopically confirmed infections were given standard and appropriate treatment. The P. falciparum treatments involved artemether-lumefantrine (Coartem®) or quinine-doxycycline therapy, the first-line anti-malarial therapies recommended by the Brazilian Ministry of Health, which are superior to other available and affordable treatments. DNA prepared from whole blood samples was later analyzed by PCR for parasite species specific genotyping using established protocols [51] to confirm their infection status with the malaria parasites. It appeared that only 32.3% of all the P. falciparum infected individuals had mono-infection with P. falciparum; the majority of the individuals had mixed infection with P. vivax (66.3%) or had a triple infection with P. vivax and P. malariae (1.4%).
Active recruitment of the uninfected individuals who were born and/or have lived in the same endemic region for over ten years and have reported that they never had an episode of malaria although their family members or neighbors had it (markers for exposure) was done in the villages around Porto Velho (Ouro Preto do Oeste, Guajará-Mirim, Ji-Paraná and Candeias do Jamari). Those who met the inclusion criteria and consented to participate were tested on site with the OptiMal® (DiaMed AG, Switzerland), a rapid malaria diagnosis test, to validate their non-infection status. Only those that were negative by the OptiMal® kit were bled. To further verify the non-infection status of the individuals in this study group, the DNA extracted from their whole blood sample was later analyzed by PCR for parasite species specific genotyping to identify those that might have asymptomatic malaria infections or to confirm their malaria infection free status using established protocols [51]. It appeared that 30 of the individuals were PCR positive for malaria DNA; 27 of them had P. vivax specific DNA and 7 had a mixed P. vivax and P. falciparum DNA. These individuals were therefore excluded from the uninfected control group but also were not included in the infected group. The final control uninfected group constituted of 199 individuals; 18–56 years of age and a female/male ratio of 97/102. A follow up of these individuals 2 years after the start of the study confirmed that they still had no episodes of malaria in the past or over the study period.
Phenotyping for GPB Ss blood group antigens
The presence of GPB Ss antigens on the surface of the RBCs was detected by the hemagglutination test using specific gel cards (Diamed AG, Morat, Switzerland) and appropriate commercial antibodies. The testing was done using fresh blood from all individuals from both study groups at the Blood Bank of Porto Velho (Centro de Hemoterapia e Hematologia de Rondônia- Fundação Hemeron/Hemotereapy and Hematology Center of the Rondônia State – HEMERON Foundation) on the same day of the blood collection.
Genotyping for GYPB*S/s
DNA samples of each individual were prepared from frozen blood samples. Genomic DNA was isolated by a whole blood DNA extraction kit (QIAmp, Qiagen, Valencia, CA) according to the manufacturer's instructions. The DNA solutions were analyzed for quality by agarose gel electrophoresis. The GYPB* S/s genotyping was performed using one or more of the following assays:
Allele-specific PCR.
Allele-specific PCR (AS-PCR) for the GYPB*S/s alleles were performed in all samples. The sequences of primer combinations and control primers that amplified an unrelated gene (human growth hormone gene) were previously published [29]. AS-PCR was carried out under the following conditions: 1× PCR buffer, 1.5 mmol/L MgCl2, 0.2 mmol/L dNTP mix, 100 ng of sense and antisense primers, 100 ng of control primers, 2.5 U Taq DNA polymerase and ∼50 ng of genomic DNA per 50 µl of total volume. The amplification was performed using a 35-cycles protocol, with an annealing temperature of 62°C.
GYPB Exon 5 combination AS/PCR-RFLP assay.
To determine if the S allele was silenced, genomic DNA samples from S−s+ samples genotyped as GYPB*S/s were amplified with the GPB4/5, GPBIVS5 and GPB5T primers [29], using a combination AS/PCR-RFLP assay to determine whether GYPB is present or absent and to distinguish the variant GYPB genes products in S−s+ (GYPB*S silent gene) individuals. The PCR products were digested with EcoRI overnight at 37°C. The uncut and digested products were analyzed on a 10% polyacrylamide gel.
BeadChip DNA analysis.
For some of the P. falciparum infected and controls samples for which there was discordance between the two genotyping methods described above and the phenotyping by hemagglutination were re-tested for the GYPB*S/s genotypes using a DNA array, BeadChip™ Human Erythrocyte Antigen (“HEA”), containing specific probes directed to polymorphic sites in RHCE, FY (including FY-GATA and FY265), DO (including HY and JO), CO, DI, SC, GYPA, GYPB (including markers permitting the identification of U-negative and U-variant types), LU, KEL, JK, LW and one mutation associated with hemoglobinopathies (HgbS) (BioArray Solutions, Warren, NJ, USA). The HEA BeadChip assay was performed in accordance with a previously described protocol [52], [53].
GYPB exon sequencing of GYPB.
Specific PCR amplification of GYPB is difficult because of its high homology with the two distinct glycophorin genes, GYPA and GYPE. We therefore designed the GYPB-specific primers using the following procedure: we aligned human GYPA (GenBank m60707), GYPB (GenBank m60708), and GYPE (GenBank m29609) sequences and identified sites that are variable and specific for GYPB gene segments encompassing exons 2, 4, 5 and 6. The GYPB-specific primers had to be at their 3′ terminals sites 100% specific for their own sequences. After PCR and sequencing, we verified the identity of the PCR amplicon by verifying the presence of GYPB specific sites. Primers used for PCR amplification contained a M13F or M13R tails (Table 2) and their PCR products included the respective exon and part of its flanking intron regions. PCR amplification was done using 10 ng of genomic DNA, 200 nM of each primer and Platinum PCR SuperMix (Invitrogen, Carlsbad, CA, US) in final volume of 50 µL. The cycling profile used was 2 minutes at 94°C, 45 cycles of 30 seconds at 94°C, 45 seconds at annealing temperature, 2 minutes at 72°C and a final extension of 5 minutes at 72°C. PCR products were purified and bi-directionally sequenced using M13 forward or M13 reverse primers and Applied Biosystem technology (Genewiz, NJ, US). Sequences were analyzed using phred-phrap-consed software [54] and the pipeline described by Machado et al. [55], using the Genbank sequence NC_000004.11 as reference.
Genotyping a GYPB tagSNP using Taqman real time PCR assay.
To complement the sequencing data and obtain a better coverage of the GYPB haplotype structure, we identified SNPs from the HapMap (June 2009) database that were not included in the re-sequenced regions (for example, intronic SNPs), were common (MAF>0.10) and were polymorphic in at least one of the HapMap population. We analyzed the pattern of linkage disequilibrium across GYPB from HapMap data and selected two tag-SNPs (that can be used as surrogate for untested SNPs, due to the pattern of linkage disequilibrium): rs4835511 and rs9685167. We genotyped them by TaqMan assays (Applied Biosystems, Palo Alto, CA, US); rs9685167 genotyping did not work properly, while rs4835511 did using 10 ng of genomic DNA, TaqMan® Genotyping Assay 20× and TaqMan® Genotyping Master Mix (Applied Biosystems®, Foster City, CA, US) in final volume of 10 µL.
Ancestry Informative Markers genotyping
We genotyped 62 Ancestry Informative Markers (AIMs) in the DNA samples of all cases and controls. The first set of AIMs consisted of 14 SNPs reported and genotyped in two multiplex reactions as in Da Silva et al. [56] The second set of AIMs included 48 INDELs reported and genotyped in three multiplex reactions as in Santos et al. [57].
Statistical and population genetics analyses
We used the Fisher exact test to assess the Hardy-Weinberg equilibrium. To measure association between S/s genotypes and malaria infection we used the haplotype score test by Schaid et al. [58] and Lake et al. [59] implemented in the software Haplostats v.1.4, assuming dominance for the S allele and when necessary, including age, gender, and African, European or Native American ancestry (see below) as covariates. Although Haplostats has been developed keeping in mind haplotype analyses, the association test (analogous to Fisher exact tests) is also suitable for single SNPs.
Individual European, African and Native American ancestry were estimated using the Bayesian clustering algorithms developed by Pritchard and implemented in the program STRUCTURE v2.3.2 [60], [61]. We assumed that three parental populations (K = 3 clusters) contributed to the genome of the admixed individuals. STRUCTURE estimates individual admixture conditioning on Hardy-Weinberg and linkage equilibrium on each of the K = 3 clusters, that represent the parental populations. We run the program using a length of burn-in Period of 100 000 and 10 0000 repetitions of MCMC after burning. We used prior population information for individuals from the parental populations to assist clustering (USEPOPINFO = 1) and assumed the admixture model for individuals from the admixed populations, inferring the alpha parameter for each population. We also used the parameters GENSBACK = 2 and MIGRPRIOR = 0.05. Moreover, we assumed that allele frequencies were correlated (i.e. similar across parental populations) and that the parental populations show different levels of differentiation (FST, with prior mean of 0.01 and standard deviation of 0.05). The admixture in each group (cases and controls) is calculated by Structure as the average of the admixture for each individual within the group. We performed these analyses with the two sets of data we produced for this study: (1) the 48 INDELS previously used by Santos et al. [57], using the ancestral populations reported in that publication, and (2) using 62 AIMs (those in Santos et al. [57] and the 14 SNPs reported by da Silva et al. [56], using the parental populations from the latter publication. Both measurements of ancestry were highly correlated (Spearman correlation coefficients: 0.82, 0.89 and 0.92 for African, European and Native American admixture, respectively, with P always<0.01).
We inferred haplotypes considering SNPs with a Minor Allele Frequency (MAF) ≥0.05, using the method by Stephens and Scheet [62]. The recombination parameter ρ was also calculated for each population by using the method of Li and Stephens [63]. These inferences were performed by the software Phase v.2.1.1., using 10.000 iterations, thinning intervals of 100 and burn in of 1000. Linkage disequilibrium (LD) was estimated by r2 for SNPs with MAF≥0.05 in at least one population [64] and its significance assessed by LOD scores, using software Haploview v.3.2 [65].
For the analyses of the sequencing data, we assessed intra-population variability using the following estimators of the θ parameter based on the infinite-site-model of mutations: π, the per-site mean number of pair-wise differences between sequences [66], and by θw, based on the number of segregating sites [67]. To investigate if the observed patterns of variability in the studied Brazilian population is consistent with the neutral model of evolution, we used the statistical tests of Tajima's D [68], Fu and Li's D* and Fu and Li 's F* [69] on the re-sequencing data, testing these statistics against the standard null hypothesis of neutrality (no natural selection) under constant population size.
Results and Discussion
GYPB*S/s frequencies in the control and P. falciparum infected populations
Two groups of individuals were studied; the uninfected group (N = 199) and the P. falciparum infected group (N = 83). Host DNA from these individuals was used for GYPB*S/s genotyping by AS-PCR, PCR-RFLP and/or DNA array analysis. Specific PCR amplification of GYPB*S/GYPB*s is difficult because of the high homology between GYPA, GYPB and GYPE. To have accurate results we have chosen two different methods for GYPB*S/GYPB*s genotyping: the allele-specific AS-PCR, an “in house” method, and the HEA (i.e. Human Erythrocyte Antigen) BeadChip, a microarray method commercially available. All samples were analyzed by both methods. When the genotype results in an individual were inconsistent using both methods, we excluded these individuals from further analyses, even thought the HEA BeadChip is known to be more accurate than the AS-PCR [52], [53]. Only 3 individuals had mismatching genotypes and were not included in the association studies. Genomic DNA samples from individuals phenotyped S−s+ but genotyped as GYPB*S/s were analyzed using a combination AS/PCR-RFLP assay [29], [70] in order to determine if the S allele was silenced (GYPB*S silent gene).
When the genotype distributions of GYPB*S/s alleles in the two study groups were compared, the differences in the frequencies of the GYPB*S/GYPB*S and GYPB*S/GYPB*s vs. GYPB*s/GYPB*s genotypes between the P. falciparum infected individuals (cases) and the uninfected individuals (control) were significant (69.87% vs. 49.75% of GYPB*S/GYPB*S and GYPB*S/GYPB*s and 30.1% vs. 50.25% of GYPB*s/GYPB*s, respectively; P<0.02, Odds Ratio = 1.55 with 95%CI = [1.02, 2.38]) (Table 1). In these analyses, we assumed that the presence of the GYPB*S allele is a dominant risk factor for susceptibility to infection (i.e., regardless if it is a homozygote or heterozygote), as it results in the phenotypic expression of GPB S+ on the surface of the RBCs. Intriguingly, only the infected individuals do not fit the Hardy-Weinberg expectation; with an excess of the heterozygous GYPB*S/GYPB*s genotype (66.27%, Table 1) as compared to what was expected (46.66%).
We observed a discordance of 5% between Ss phenotyping (performed in the field) and genotyping, which is compatible with previous studies [70]. Noteworthy, the results of phenotypes match those of genotypes, both for the excess of S+s+ or GYPB*S/GYPB*s heterozygous individuals among cases and for the association of the presence of the GPB S+ or GYPB*S putatively dominant allele with their infection status. Although GYPB, GYPA and GYPE genotyping is associated with technical difficulties due to their extensive sequence homology, the high concordance of our phenotype and genotype results and the significance of the association even when phenotypes are analyzed, suggest that our genotyping results are robust.
Interestingly, we have found a higher frequency of heterozygous GYPB*S/s genotypes among the P. falciparum infected individuals (66.27% in cases versus 42.21% in controls). Considering that heterozygous are more frequent that the homozygous S+s−, it seems that the amount of GPB S receptor molecules doesn't influence the susceptibility to P. falciparum infection. If we make an analogy to the Duffy blood group (FY), the receptor for P. vivax, and susceptibility of P. vivax malaria, different studies [20], [71], [72], including one in the Amazonian region of Brazil [73], have demonstrated that individuals with the FYA/FYB genotype have higher susceptibility to malaria infection. The Fy gene has two antigens (Fya and Fyb) that are encoded by the co-dominant alleles FYA and FYB, located on chromosome 1. The corresponding anti-Fya and anti-Fyb antibodies define four different phenotypes; Fy(a+b+), Fy(a+b−), Fy(a−b+) and Fy(a−b−). The FYA and FYB alleles differ by one nucleotide change in exon 2 encoding glycine in Fya or aspartic acid in Fyb at residue 42 [73]. In the Brazilian study [73], the authors reported a larger number of malaria episodes among patients with the heterozygote (FY*A/FY*B) genotype than the homozygote (FY*A/FY*A or FY*B/FY*B) genotypes. Individuals homozygous for FYA or FYB alleles expressed a lower quantity of the Duffy antigen, which is required for the P. vivax invasion, than those who were heterozygotes. Apart from the different levels of the expression, the specific conformation of the Fya and Fyb antigens may also determine differences in the susceptibility to infection. Nevertheless, it was concluded that one of the possible consequences of differential susceptibility to P. vivax malaria could be modifications in allelic frequencies of FY*A and FY*B in populations exposed to P. vivax, the most prevalent malaria species in the Brazilian Amazon region.
Our study has verified for the first time that molecular variation in the GYPB gene, particularly in the GYPB*S/s alleles, influenced host susceptibility to infection with P. falciparum in Porto Velho, Rondônia. In this study we took into consideration the possible confounding factor of ethnicity by performing association analyses adjusted for admixture, as well as for age and gender (Table 1). An association between other human receptor polymorphisms and variations in the parasite ligands of P. falciparum that modulate susceptibility to malaria, was also demonstrated [25], [74]–[76].
GYPB*S/s allele frequencies vary across human populations; the GYPB*S allele (supposedly associated with infection) is less common in East Asians (∼10%) than in Sub-Saharan Africans (>25%) and Europeans (∼30–40%). Therefore, if the cases have more European ancestry than controls; a false positive result may emerge due to the association of any variant more common in Europeans, and thus the association results we got might not be at all related with susceptibility to infection conferred by the GPB Ss blood group antigens. To avoid a false positive result, we also measured the association among GPB Ss variants and infection by controlling the effect of admixture. Because we ascertained that African, European and Native American admixture do not differ among cases and controls (Figure 1 and Table 1), we exclude the possibility that our result is a spurious association. Controlling for admixture is essential in genetic epidemiology studies performed in Latin American populations, where large inter-individual differences in admixture are the rule [27].
Individual European, African and Native American ancestry were inferred from 60 ancestry informative markers in cases (magenta) and controls (yellow). Admixture was inferred by comparison with individuals from the putative parental populations: Europeans (red), African/African American (green) and Native Americans (blue). Admixture was estimated using the software Structure and average admixture over cases and controls is shown in Table 1.
We hypothesize that this Met29Thr polymorphism might be associated with changes in the structure of the GPB molecule that is used by P. falciparum to enter the RBCs. By having a Thr residue (GPB s+) on the RBCs instead of a Met residue (GPB S+), GPB s+ gains potentially a new site for O-glycosylation, which can likely alter its conformation and thus influence the efficiency of invasion by the parasites and ultimately, susceptibility to infection. Alternatively, this polymorphism may also affect dimerization of GPB molecules with other GPB or with GPA molecules [77].
Population genetics of GYPB and inferences about natural selection
In principle, the GYPB*S/s alleles might not be the only functional GPB variants that modify P. falciparum invasion efficiencies in the Brazilian population studied. To understand the relationships between the observed association and the haplotype structure of GYPB in this population, we sequenced exons 2, 4, 5 and 6 of GYPB and their flanking regions, for a total of 1492 bp in sub-samples of cases and controls, matching the proportion of SS, Ss and ss genotypes observed in the total number of cases and controls studied. These sequences are publicly available under the GenBank accession numbers HQ639948–HQ640229. This sub-sample includes 41 cases (2 SS, 26 Ss, 13 ss) and 100 controls (8 SS, 42 Ss and 50 ss). We also used HapMap data available in June 2009 to explore the pattern of linkage disequilibrium across GYPB and selected the tag-SNP rs4835511 to be genotyped in the GYPB sequenced individuals using TaqMan (Applied Biosystem) assay.
Table 3 shows the common GYPB haplotypes (i.e. a combination of alleles on the same chromosome) based on eight common SNPs across the sequenced region and the tag-SNP rs4835511. Haplotypes are sorted on the basis of their S/s (rs7683365) allele, and Figure 2 shows that the S/s SNP is in linkage disequilibrium with some common silent polymorphisms in exon 4 and its adjacent introns, but not with all the common SNPs reported in the GYPB sequenced regions. In particular, there is no linkage disequilibrium between the S/s alleles (rs7683365) and the non-synonymous common SNP rs1132783 (Ser/Thr) in exon 5. Since this polymorphism is predicted by PolyPhen [78] as benign, we assumed that its role in determining susceptibility to malaria infection is minor in respect to the S/s allele; SNP rs1132783 is located in the transmembrane domain of the protein, thus unlikely to have an effect on the host-parasite interaction. The site-specific PolyPhen algorithm (http://genetics.bwh.harvard.edu/pph/), which uses protein structure and/or sequence conservation information from each gene to predict whether a nonsynonymous mutation is “benign,” “possibly damaging,” or “probably damaging”, was shown to be the best predictor of the fitness effects of nonsynonymous mutations in a study analyzing a large polymorphism data set from 301 human genes [79].
Linkage disequilibrium among common SNPs in GYPB was estimated in both study groups: the controls (a) and in the cases, malaria infected individuals from Brazilian Amazon (b). Underlined SNPs are non-synonymous substitutions: rs7683365 is the SNP determining S/s antigens; rs1132783 is a Ser/Thr polymorphism (see Table 3).
The pattern of nucleotide diversity of GYPB revealed by the sequencing data is also informative about the role of the S/s alleles in susceptibility to malaria infection, because it allows inferences about the action of natural selection driven by malaria during the human evolutionary history [80]. Inferences about the action of natural selection have two implications. First, variants on genes inferred to be under selection have contributed to determine phenotype variability and perhaps, differential susceptibility to diseases such as malaria. Second, by definition of natural selection, these variants have been associated with relatively different reproductive efficiencies (i.e. fitness) of their carriers, and therefore, they have biomedical relevance. In particular for malaria, Ayodo et al. [81] have evidenced that combining information from association studies and evolutionary inferences about natural selection increases the probability of identifying susceptibility genes of malaria infection.
Analysis of GYPB nucleotide diversity in the Brazilian population studied (Table 4) reveals that: (a) GYPB shows a level of diversity that is similar to the most variable loci (ABO, SERPINA5) observed in African populations [82]; the most diverse continental human population. This is due in part to the genomic structure of the glycophorins' region, that encodes the GYPA, GYPB and GYPE highly homologous genes, and which might be affected by high rates of gene conversion among these loci. The observed high diversity may also be due the admixed nature of the Brazilian population, whose diversity reflects the combining effect of the parental population's diversity. (b) When the diversity of GYPB is measured separately for haplotypes carrying S or s alleles, diversity is consistently lower across cases and controls for the s haplotypes (associated with resistance to infection) than for the S haplotypes (associated with infection), notwithstanding the higher frequency of the s allele, that being the most common in humans, is expected to be the ancestral one, a condition typically associated with higher nucleotide diversity.
The pattern of genetic diversity on a specific genomic region depends both on the demographic history of populations, as well as on locus specific evolutionary factors such as mutation, recombination and natural selection. Almost 60 years ago, Haldane [83] proposed the so-called “Malaria Hypothesis” - that malaria might act as a selective force on human populations. Since then, several studies have tested and verified this hypothesis in the human host [81] and the malaria parasites [84], [85]. To infer if malaria-driven natural selection has shaped the diversity of GYPB, we used statistical tests of the null hypothesis of neutrality: that the GYPB pattern of diversity may be explained considering only the demographic history of the studied population and mutation and recombination patterns of GYPB, without the need to invoke the action of other factors such as natural selection. These statistical tests [68], [69] (Tajima's D and Fu-Li's D* and F*) are based on the proportion of rare and common polymorphisms expected in a population under neutrality, and this proportion is informative about natural selection. Table 4 shows that Tajima's and Fu-Li's statistical tests are negative and significantly different from 0, which is indicative of an excess of rare alleles in respect to neutral expectations. This result is consistent with the following scenario involving the action of natural selection (i.e. a selective sweep): a beneficial substitution (putatively the s allele) rapidly increases in frequency (i.e. incomplete sweep) carrying its associated haplotype through a hitchhiking effect. This process, driven by natural selection, is too rapid for recombination to shuffle the surrounding haplotype. As a consequence, it is expected that the nucleotide diversity (π) of the haplotypes carrying the beneficial allele (s in this case) is lower than the alternative allele (S), as observed in our data (Table 4). During a selective sweep, rare substitutions become common both because: the rise in frequency of the s haplotype; other substitutions associated with S become rare; and new (and therefore rare) substitutions appear in the expanding positively selected haplotype. The negative values observed for the neutrality tests for GYPB is consistent with this scenario (Table 4).
In the case of the Brazilian admixed population, the observed excess of rare alleles in GYPB can not be a consequence of admixture, which generally results in a reduced proportion of rare alleles in respect to neutral expectations (and therefore, to positive values of statistical tests such as D, D* and F*) [69]. Instead, we are likely observing the signature of a selective sweep that occurred during the last thousands of years of the human evolution in malaria affected regions mainly in Southern Europe and more likely Africa [86], where the ancestors of our studied Brazilian population settled (see Table 1 for the predominant European admixture proportion). Although our interpretation may be true and consistent with the results of the present association study, an unambiguous inference about the action of natural selection would require a comparison of GYPB diversity with a set of other loci not affected by natural selection, that would allow to obtain a more realistic null neutral distribution of neutrality statistics (D, D*, F*) that incorporate the specific demographic history of the studied population. On the other hand, the lack of evidence of the action of natural selection on African or European populations on genomic screenings of signatures of natural selection [87]–[89] may be due to the difficulties in genotyping/sequencing of the glycophorins' genomic regions.
Altogether, our results suggest that the S domain on GPB is important for its binding to the specific ligand of the P. falciparum parasite, EBL-1, which was recently identified and characterized [44]. RBCs carrying glycophorin B but not RBCs lacking glycophorin B (S−s−U−) were shown to adsorb the native EBL-1 from P. falciparum culture supernatants. Future studies are needed to demonstrate whether EBL-1 binds differentially to GPB S−s+ vs. S+s+ RBCs, and thus indirectly substantiate at a molecular level our observation of association studies at the population level and our population genetics inferences about the action of natural selection. Performing similar studies in other regions of the Amazons and other endemic regions of the world is also needed to further substantiate our observations; including both association studies with larger sample sizes and with a population genetics approach that include sequencing and genotyping at a higher resolution. Because P. falciparum shows also high population structure, in particular in the Amazon Region [90], it is also important to understand the extent of variability in host-parasite interaction and their co-evolution. The statistical association between the presence of the S allele and infection supports also the hypothesis that P. falciparum parasites in the Brazilian Amazon regions utilize GPB as a key receptor for invasion, and consequently individuals who carry distinct GYPB gene variants, which might facilitate erythrocyte invasion, will be more susceptible to P. falciparum infection. Thus, our results reinforce the need of studies focusing on in vitro invasion assays using erythrocytes with diverse GYPB genotypes and P. falciparum strains from different origin to establish the role of the GPB receptor for P. falciparum parasites of this region.
Acknowledgments
We thank Dr. Cheng-Han Huang and Yogi Vedvyas for designing and optimizing the GYPB primers sets, Professor Luiz Hildebrando Pereira da Silva for allowing us to use the facilities at Centro de Pesquisa em Medicina Tropical (CEPEM) and Elieth Mesquita for helping in the recruitment of P. falciparum infected individuals and blood collections at health clinic at CEPEM. We also thank the Managing Director of the Blood Bank of Porto Velho (Centro de Hemoterapia e Hematologia de Rondônia- Fundação Hemeron// Hemotereapy and Hematology Center of the Rondônia State – HEMERON Foundation) for helping us process the blood samples collected from uninfected individuals. We also thank Maria Clara da Silva and Marilia Scliar for sharing information about genotyping and sequencing protocols.
Author Contributions
Conceived and designed the experiments: SL LC ETS RLM ARR MGZ SEBS. Performed the experiments: LC DRTA DCC NGF SEBS RLM. Analyzed the data: SL LC ETS RLM ARR DRTA DCC NGF LWZ MM SEBS. Contributed reagents/materials/analysis tools: ETS SEBS. Wrote the paper: SL ETS LC RLM MR.
References
- 1. McGhee RB (1953) The infection by Plasmodium lophurae of duck erythrocytes in the chicken embryo. J Exp Med 97(6): 773–82.
- 2. Cowman AF, Crabb BS (2006) Invasion of red blood cells by malaria parasites. Cell 124(4): 755–66.
- 3. Okoyeh JN, Pillai CR, Chitnis CE (1999) Plasmodium falciparum field isolates commonly use erythrocyte invasion pathways that are independent of sialic acid residues of glycophorin A. Infect Immun 67(11): 5784–91.
- 4. Baum J, Pinder M, Conway DJ (2003) Erythrocyte invasion phenotypes of Plasmodium falciparum in The Gambia. Infect Immun 71(4): 1856–63.
- 5. Baum J, Thomas AW, Conway DJ (2003) Evidence for diversifying selection on erythrocyte-binding antigens of Plasmodium falciparum and P. vivax. Genetics 163(4): 1327–36.
- 6. Lobo CA, de Frazao K, Rodriguez M, Reid M, Zalis M, et al. (2004) Invasion profiles of Brazilian field isolates of Plasmodium falciparum: phenotypic and genotypic analyses. Infect Immun 72(10): 5886–91.
- 7. Bei AK, Membi CD, Rayner JC, Mubi M, Ngasala B, et al. (2007) Variant merozoite protein expression is associated with erythrocyte invasion phenotypes in Plasmodium falciparum isolates from Tanzania. Mol Biochem Parasitol 153(1): 66–71.
- 8. Deans AM, Nery S, Conway DJ, Kai O, Marsh K, et al. (2007) Invasion Pathways and Malaria Severity in Kenyan Plasmodium falciparum Clinical Isolates. Infect Immun 75(6): 3014–20.
- 9. Jennings CV, Ahouidi AD, Zilversmit M, Bei AK, Rayner J, et al. (2007) Molecular analysis of erythrocyte invasion in Plasmodium falciparum isolates from Senegal. Infect Immun 75(7): 3531–8.
- 10. Pasvol G, Wainscoat JS, Weatherall DJ (1982) Erythrocytes deficiency in glycophorin resist invasion by the malarial parasite Plasmodium falciparum. Nature 297(5861): 64–6.
- 11. Pasvol G, Jungery M, Weatherall DJ, Parsons SF, Anstee DJ, et al. (1982) Glycophorin as a possible receptor for Plasmodium falciparum. Lancet 2(8305): 947–50.
- 12. Hadley TJ (1986) Invasion of erythrocytes by malaria parasites: a cellular and molecular overview. Annu Rev Microbiol 40: 451–77.
- 13. Booth PB, Albrey JA, Whittaker J, Sanger R (1970) Gerbich blood group system: a useful genetic marker in certain Melanesians of Papua and New Guinea. Nature 228(270): 462.
- 14. Booth PB, McLoughlin K, Hornabrook RW, Macgregor A, Malcolm LA (1972) The Gerbich blood group system in New Guinea. II. The Morobe District and North Papuan Coast. Hum Biol Oceania 1(4): 259–66.
- 15. Macgregor A, Booth PB (1973) A second example of anti-Ge1, and some observations on Gerbich subgroups. Vox Sang 25(5): 474–8.
- 16. Booth PB, Tills D, Warlow A, Kopec AC, Mourant AE, et al. (1982) Red cell antigen, serum protein and red cell enzyme polymorphisms in Karkar Islanders and inhabitants of the adjacent North Coast of New Guinea. Hum Hered 32(6): 385–403.
- 17. Serjeantson SW, White BS, Bhatia K, Trent RJ (1994) A 3.5 kb deletion in the glycophorin C gene accounts for the Gerbich-negative blood group in Melanesians. Immunol Cell Biol 72(1): 23–7.
- 18. Patel SS, King CL, Mgone CS, Kazura JW, Zimmerman PA (2004) Glycophorin C (Gerbich antigen blood group) and band 3 polymorphisms in two malaria holoendemic regions of Papua New Guinea. Am J Hematol 75(1): 1–5.
- 19. Allen SJ, O'Donnell A, Alexander ND, Mgone CS, Peto TE, et al. (1999) Prevention of cerebral malaria in children in Papua New Guinea by southeast Asian ovalocytosis band 3. Am J Trop Med Hyg 60(6): 1056–60.
- 20. Miller LH, Haynes JD, McAuliffe FM, Shiroishi T, Durocher JR, et al. (1977) Evidence for differences in erythrocyte surface receptors for the malarial parasites, Plasmodium falciparum and Plasmodium knowlesi. J Exp Med 146(1): 277–81.
- 21. Facer CA (1983) Merozoites of P. falciparum require glycophorin for invasion into red cells. Bull Soc Pathol Exot Filiales 76(5): 463–9.
- 22. Facer CA (1983) Erythrocyte sialoglycoproteins and Plasmodium falciparum invasion. Trans R Soc Trop Med Hyg 77(4): 524–30.
- 23. Pasvol G (1984) Receptors on red cells for Plasmodium falciparum and their interaction with merozoites. Philos Trans R Soc Lond B Biol Sci 307(1131): 189–200.
- 24. Weatherall DJ (1996) Host genetics and infectious disease. Parasitology 112: SupplS23–9.
- 25. Miller LH (1994) Impact of malaria on genetic polymorphism and genetic diseases in Africans and African Americans. Proc Natl Acad Sci U S A 91(7): 2415–9.
- 26. Wang HY, Tang H, Shen CK, Wu CI (2003) Rapidly evolving genes in human. I. The glycophorins and their possible role in evading malaria parasites. Mol Biol Evol 20(11): 1795–804.
- 27.
Reid ME, Lomas-Francis C (2004) The Blood Group Antigen. 2nd ed. FactBook Seriers. Elsevier, Academic Press.
- 28. Huang CH, Blumenfeld OO (1988) Characterization of a genomic hybrid specifying the human erythrocyte antigen Dantu: Dantu gene is duplicated and linked to a delta glycophorin gene deletion. Proc Natl Acad Sci U S A 85(24): 9640–4.
- 29. Storry JR, Reid ME, Fetics S, Huang CH (2003) Mutations in GYPB exon 5 drive the S−s−U+(var) phenotype in persons of African descent: implications for transfusion. Transfusion 43(12): 1738–47.
- 30. Unger P, Procter JL, Moulds JJ, Moulds M, Blanchard D, et al. (1987) The Dantu erythrocyte phenotype of the NE variety. II. Serology, immunochemistry, genetics, and frequency. Blut 55(1): 33–43.
- 31. Contreras M, Green C, Humphreys J, Tippett P, Daniels G, et al. (1984) Serology and genetics of an MNSs-associated antigen Dantu. Vox Sang 46(6): 377–86.
- 32. Field SP, Hempelmann E, Mendelow BV, Fleming AF (1994) Glycophorin variants and Plasmodium falciparum: protective effect of the Dantu phenotype in vitro. Hum Genet 93(2): 148–50.
- 33. Storry JR, Reid ME, MacLennan S, Lubenko A, Nortman P (2001) The low-incidence MNS antigens M(v), s(D), and Mit arise from single amino acid substitutions on GPB. Transfusion 41(2): 269–75.
- 34. Dahr W, Moulds JJ (1987) High-frequency antigens of human erythrocyte membrane sialoglycoproteins, IV. Molecular properties of the U antigen. Biol Chem Hoppe Seyler 368(6): 659–67.
- 35. Blumenfeld OO, Huang CH (1997) Molecular genetics of glycophorin MNS variants. Transfus Clin Biol 4(4): 357–65.
- 36. Reid ME, Storry JR, Ralph H, Blumenfeld OO, Huang CH (1996) Expression and quantitative variation of the low-incidence blood group antigen He on some S−s−red cells. Transfusion 36(8): 719–24.
- 37. Huang CH, Blumenfeld OO, Reid ME, Chen Y, Daniels GL, et al. (1997) Alternative splicing of a novel glycophorin allele GPHe(GL) generates two protein isoforms in the human erythrocyte membrane. Blood 90(1): 391–7.
- 38. Fraser GR, Giblett ER, Motulsky AG (1966) Population genetic studies in the Congo. 3. Blood groups (ABO, MNSs, Rh, Jsa). Am J Hum Genet 18(6): 546–52.
- 39. Vos GH, Moores P, Lowe RF (1971) A comparative study between the S−s−U−phenotype found in Central African Negroes and the S−s+U−penotype in Rhnull individuals. S Afr J Med Sci 36(1): 1–6.
- 40. Lowe RF, Moores PP (1972) S−s−U−red cell factor in Africans of Rhodesia, Malawi, Mozambique and Natal. Hum Hered 22(4): 344–50.
- 41.
Issitt PD, Anstee DJ (1998) Applied blood group serology. 4th ed, ed. N.C. Durham. Montgomery Scientific Publications.
- 42.
Patel SS, Katsnelson S, McNamara D, Reeder JC, Kazura JW, et al. (2004) Multiple Erythrocyte Polymorphisms Including a Glycophorin B Variant in a malaria endemic area of Papua New Guinea. ASTMH Abstract #913 2004.
- 43. Conrad DF, Pinto D, Redon R, Feuk L, Gokcumen O, et al. Origins and functional impact of copy number variation in the human genome. Nature 464(7289): 704–12.
- 44. Mayer DC, Cofie J, Jiang L, Hartl DL, Tracy E, et al. (2009) Glycophorin B is the erythrocyte receptor of Plasmodium falciparum erythrocyte-binding ligand, EBL-1. Proc Natl Acad Sci U S A 106(13): 5348–52.
- 45. Baleotti W Jr, Rios M, Reid ME, Fabron A Jr, Pellegrino J Jr, et al. (2003) A novel DI*A allele without the Band 3-Memphis mutation in Amazonian Indians. Vox Sang 84(4): 326–30.
- 46. Castilho L, Rios M, Pellegrino J Jr, Saad ST, Costa FF, et al. (2004) A Novel FY Allele in Brazilians. Vox Sang 87(3): 190–5.
- 47. Baleotti W Jr, Rios M, Reid ME, Hashmi G, Fabron A Jr, et al. (2006) Dombrock gene analysis in Brazilian people reveals novel alleles. Vox Sang 91(1): 81–7.
- 48. Montoya F, Restrepo M, Montoya AE, Rojas W (1994) Blood groups and malaria. Rev Inst Med Trop Sao Paulo 36(1): 33–8.
- 49. Beiguelman B, Alves FP, Moura MM, Engracia V, Nunes AC, et al. (2003) The association of genetic markers and malaria infection in the Brazilian Western Amazonian region. Mem Inst Oswaldo Cruz 98(4): 455–60.
- 50. Cavasini CE, De Mattos LC, Alves RT, Couto AA, Calvosa VS, et al. (2006) Frequencies of ABO, MNSs, and Duffy phenotypes among blood donors and malaria patients from four Brazilian Amazon areas. Hum Biol 78(2): 215–9.
- 51. Kimura M, Kaneko O, Liu Q, Zhou M, Kawamoto F, et al. (1997) Identification of the four species of human malaria parasites by nested PCR that targets variant sequences in the small subunit rRNA gene. Parasitology International 46: 91–95.
- 52. Hashmi G, Shariff T, Seul M, Vissavajjhala P, Hue-Roye K, et al. (2005) A flexible array format for large-scale, rapid blood group DNA typing. Transfusion 45(5): 680–8.
- 53. Hashmi G, Shariff T, Zhang Y, Cristobal J, Chau C, et al. (2007) Determination of 24 minor red blood cell antigens for more than 2000 blood donors by high-throughput DNA analysis. Transfusion 47(4): 736–47.
- 54. Montgomery KT, Iartchouck O, Li L, Loomis S, Obourn V, et al. (2008) PolyPhred analysis software for mutation detection from fluorescence-based sequence data. Curr Protoc Hum Genet Chapter 7: Unit 7 16.
- 55. Machado M, Magalhães WCS, Sene A, Araújo B, Faria-Campos A, et al. (2010) Phred-Phrap-Consed-Polyphred to analyses tools: a pipeline to facilitate population genetics re-sequencing studies. Investigative Genetics. In Press.
- 56. da Silva MC, Zuccherato LW, Soares-Souza GB, Vieira ZM, Cabrera L, et al. (2010) Development of two flexible multiplex mini-sequencing panels of ancestry informative SNPs for admixture studies in Latin Americans and their application to populations of the state of Minas Gerais in Brazil. Genetics and Molecular Research 9(4): 2069–85.
- 57. Santos NP, Ribeiro-Rodrigues EM, Ribeiro-Dos-Santos AK, Pereira R, Gusmao L, et al. (2010) Assessing individual interethnic admixture and population substructure using a 48-insertion-deletion (INSEL) ancestry-informative marker (AIM) panel. Hum Mutat 31(2): 184–90.
- 58. Schaid DJ, Rowland CM, Tines DE, Jacobson RM, Poland GA (2002) Score tests for association between traits and haplotypes when linkage phase is ambiguous. Am J Hum Genet 70(2): 425–34.
- 59. Lake SL, Lyon H, Tantisira K, Silverman EK, Weiss ST, et al. (2003) Estimation and tests of haplotype-environment interaction when linkage phase is ambiguous. Hum Hered 55(1): 56–65.
- 60. Pritchard JK, Stephens M, Rosenberg NA, Donnelly P (2000) Association mapping in structured populations. Am J Hum Genet 67(1): 170–81.
- 61. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155(2): 945–59.
- 62. Stephens M, Scheet P (2005) Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation. Am J Hum Genet 76(3): 449–62.
- 63. Li N, Stephens M (2003) Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data. Genetics 165(4): 2213–33.
- 64. Hill WG, Robertson AR (1968) Linkage disequilibrium in finite populations. Theor Appl Genet 38: 226–231.
- 65. Barrett JC, Fry B, Maller J, Daly MJ (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21(2): 263–5.
- 66. Tajima F (1983) Evolutionary relationship of DNA sequences in finite populations. Genetics 105(2): 437–60.
- 67. Watterson GA (1975) On the number of segregating sites in genetical models without recombination. Theor Popul Biol 7(2): 256–76.
- 68. Tajima F (1989) Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123(3): 585–95.
- 69. Fu YX, Li WH (1993) Statistical tests of neutrality of mutations. Genetics 133(3): 693–709.
- 70. Omoto R, Reid ME, Castilho L (2008) Molecular analyses of GYPB in African Brazilians. Immunohematology 24(4): 148–53.
- 71. Miller LH, Mason SJ, Dvorak JA, McGinniss MH, Rothman IK (1975) Erythrocyte receptors for (Plasmodium knowlesi) malaria: Duffy blood group determinants. Science 189(4202): 561–3.
- 72. Michon P, Woolley I, Wood EM, Kastens W, Zimmerman PA, et al. (2001) Duffy-null promoter heterozygosity reduces DARC expression and abrogates adhesion of the P. vivax ligand required for blood-stage infection. FEBS Lett 495(1–2): 111–4.
- 73. Cavasini CE, de Mattos LC, Couto AA, Couto VS, Gollino Y, et al. (2007) Duffy blood group gene polymorphisms among malaria vivax patients in four areas of the Brazilian Amazon region. Malar J 6: 167.
- 74. Lell B, May J, Schmidt-Ott RJ, Lehman LG, Luckner D, et al. (1999) The role of red blood cell polymorphisms in resistance and susceptibility to malaria. Clin Infect Dis 28(4): 794–9.
- 75. Carter R, Mendis KN (2002) Evolutionary and historical aspects of the burden of malaria. Clin Microbiol Rev 15(4): 564–94.
- 76. Zimmerman PA, Patel SS, Maier AG, Bockarie MJ, Kazura JW (2003) Erythrocyte polymorphisms and malaria parasite invasion in Papua New Guinea. Trends Parasitol 19(6): 250–2.
- 77.
Dahr W (1986) Recent Advances in Blood Group Biochemistry. Vengelen-Tyler V, Judd WJ, editors. Arlington, VA: American Association of Blood Banks. pp. 23–65.
- 78. Ramensky V, Bork P, Sunyaev S (2002) Human non-synonymous SNPs: server and survey. Nucleic Acids Res 30(17): 3894–900.
- 79. Williamson SH, Hernandez R, Fledel-Alon A, Zhu L, Nielsen R, et al. (2005) Simultaneous inference of selection and population growth from patterns of variation in the human genome. Proc Natl Acad Sci U S A 102(22): 7882–7.
- 80. Sabeti PC (2008) Natural selection: Uncovering mechanisms of evolutionary adaptation to infectious disease. Nature Education 1(1).
- 81. Ayodo G, Price AL, Keinan A, Ajwang A, Otieno MF, et al. (2007) Combining evidence of natural selection with association analysis increases power to detect malaria-resistance variants. Am J Hum Genet 81(2): 234–42.
- 82. Akey JM, Eberle MA, Rieder MJ, Carlson CS, Shriver MD, et al. (2004) Population history and natural selection shape patterns of genetic variation in 132 genes. PLoS Biol 2(10): e286.
- 83. Haldane JBS (1949) Disease and evolution. Ricerca Science Supplement 19: 3–10.
- 84. Escalante AA, Barrio E, Ayala FJ (1995) Evolutionary origin of human and primate malarias: evidence from the circumsporozoite protein gene. Mol Biol Evol 12(4): 616–26.
- 85. Neafsey DE, Schaffner SF, Volkman SK, Park D, Montgomery P, et al. (2008) Genome-wide SNP genotyping highlights the role of natural selection in Plasmodium falciparum population divergence. Genome Biol 9(12): R171.
- 86. Campbell MC, Tishkoff SA (2008) African genetic diversity: implications for human demographic history, modern human origins, and complex disease mapping. Annu Rev Genomics Hum Genet 9: 403–33.
- 87. Grossman SR, Shylakhter I, Karlsson EK, Byrne EH, Morales S, et al. (2010) A composite of multiple signals distinguishes causal variants in regions of positive selection. Science 327(5967): 883–6.
- 88. Altshuler DM, Gibbs RA, Peltonen L, Dermitzakis E, et al. International HapMap 3 Consortium (2010) Integrating common and rare genetic variation in diverse human populations. Nature 467(7311): 52–8.
- 89. Durbin RM, Abecasis GR, Altshuler DL, Auton A, et al. 1000 Genomes Project Consortium (2010) A map of human genome variation from population-scale sequencing. Nature 467(7319): 1061–73.
- 90. Machado RLD, Povoa MM, Calvosa VSP, Ferreira MU, Rossit ARB, et al. (2004) Genetic Structure of Plasmodium falciparum populations in the Brazilian Amazon Region. The Journal of Infectious Disease 190(9): 1547–1555.