Skip Navigation

Associations between α+-Thalassemia and Plasmodium falciparum Malarial Infection in Northeastern Tanzania

  1. Anders Enevold1,
  2. Michael Alifrangis1,
  3. Juan J. Sanchez2,
  4. Ilona Carneiro3,
  5. Cally Roper3,
  6. Claus Børsting2,
  7. John Lusingu1,4,
  8. Lasse S. Vestergaard1,
  9. Martha M. Lemnge4,
  10. Niels Morling2,
  11. Eleanor Riley3 and
  12. Chris J. Drakeley3,5
  1. 1 Centre for Medical Parasitology, Institute for International Health, Immunology, and Microbiology, Copenhagen, Denmark
  2. 2 Department of Forensic Genetics, Institute of Forensic Medicine, University of Copenhagen, Copenhagen, Denmark
  3. 3 Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
  4. 4 National Institute for Medical Research, Tanga Medical Research Centre, Tanga, Tanzania
  5. 5 Joint Malaria Programme, Moshi, Tanzania
  1. Reprints or correspondence: Anders Enevold, Institute for International Health, Immunology, and Microbiology, CSS, Øster Farimagsgade 5, Bldg. 22+23, PO Box 2099, 1014 Copenhagen K, Denmark (enevold{at}cmp.dk).

  1. Presented in part: 4th MIM Pan-African Malaria Conference, Yaoundé, Cameroon, 13-18 November 2005 (poster 590B).

 
Next Section

Abstract

Background. The 2 most common hemoglobinopathies, sickle cell trait and α+-thalassemia, confer partial resistance to fatal forms of malaria, but the molecular basis for this protection is still not understood. Examination of the relationship between these traits and malaria transmission intensity may provide insights into the protection afforded.

Methods. The distribution of the 2 traits was assessed among children resident in 13 villages in the Eastern Arc Mountains in Tanzania, where Plasmodium falciparum transmission intensity is closely correlated with altitude. Associations between the prevalence of the 2 traits and malariometric indices were investigated by logistic regression. Short tandem repeat (STR) microsatellite allele frequencies were used to assess population substructuring.

Results. The frequency of α+-thalassemia ranged from 10%–25% in high-altitude villages (>1200 m) to 45%–55% in low-altitude villages (<600 m). The carriage rate of α+-thalassemia decreased by ~12% per 100-m increase in altitude ( P < .001 ) and was ~50% lower among those with patent parasitemia than among uninfected individuals (P = .014 ). The prevalence of the sickle cell trait was lower than that of α+-thalassemia (range, 0%-14%) and was significantly associated with village altitude only ( P = .011 ). STR allele frequencies were similar in all villages.

Conclusions. In this malaria-endemic region of Tanzania, α+-thalassemia is common and clearly associated with P. falciparum transmission intensity. There was no evidence of population substructuring, and the results are suggestive of selection of the α3.7 allele by malaria.

Epidemiological studies of genetic disorders can provide important clues to gene function and disease etiology. This is particularly true for malaria, which is a powerful force in the selection of human genetic variation [1]. The 2 most common hemoglobinopathies, α +-thalassemia and sickle cell disease, are genetic disorders affecting millions of people in tropical and subtropical countries. In 1949, Haldane proposed that the high prevalence of these hemoglobinopathies in malaria-endemic areas reflected a balanced polymorphism in which the deleterious consequences of homozygosity are offset by the protection from severe Plasmodium falciparum malaria conferred by the heterozygous state [2]. This hypothesis was later supported by Allison’s observations on the sickle cell trait and malaria severity [3, 4], although evidence for balancing selection for a +thalassemia is less clear-cut. Although each trait is thought to be independently associated with protection from malaria, a negative epistatic interaction between them has been reported recently [5]. Despite many decades of research, the molecular basis for the protection against malaria afforded by either trait remains obscure.

The sickle allele (HbS) is distributed throughout malaria-endemic countries. Although the homozygote state (sickle cell disease [HbSS]) has fatal consequences for the carrier, the heterozygote state (HbAS) is often phenotypically normal and is associated with reduced susceptibility to malaria [4]. The sickle cell trait (HbAS) is found at high frequencies in sub-Saharan Africa (15%-30%) with large differences in distribution both between and within countries, reflecting variations in malaria transmission intensity (reviewed in [6-8]). HbC (in West Africa) and HbE (in Asia) polymorphisms also confer resistance to malaria, although, for HbC at least, resistance is most evident in homozygotes [9]. All 3 disorders are caused by different single amino acid substitutions in the β-hemoglobin gene.

The α-thalassemias are caused by deletion of 1 or both copies of the α-globin gene on a single chromosome. α+-Thalassemia, in which 1 copy of the gene is lost, is typically caused by 1 of 4 deletions-α3.7I, α3.7II, α3.7IIIor α4.2-that are found patchily across malarious regions of the world [10]; α+ homozygotes (−α−α) are moderately anaemic, whereas α+ heterozygotes (−ααα) are phenotypically normal. α+-Thalassemia is believed to be the most common single-gene disorder in the world [10], with α4.2heterozygote frequencies reaching up to 80% in parts of India [11] and Nepal [12] and 60% in Melanesia [13]. In Africa, the most common α+-thalassemia deletion, α3.7, reaches gene frequencies up to 32% in Ghana [14] and 50% in Kenya [15]. Strong altitude-and latitude-dependent clines in the prevalence of both α+-thalassemia and P. falciparum in Melanesia [13, 16] are suggestive of selection of α+-thalassemia by malarial infection.

Although these hemoglobinopathies are generally found in populations living in, or derived from, malaria-endemic areas, it is not clear why some polymorphisms are prevalent in some malaria-endemic areas and absent in others. The distribution may be explained by population migration or by introduction or eradication of malaria, disturbing the previously existing equilibrium. Alternatively, as recently shown in Kenya [5], negative epistatic interactions between different malaria-protective traits may lead to accumulation of resistance alleles at some loci at the expense of others.

In an attempt to further understand the relationship between hemoglobinopathies and resistance to malaria, we investigated the distribution of α+-thalassemia and the sickle cell trait in children living in villages on Kilimanjaro and in the Eastern Arc Mountains of northeastern Tanzania. The area is malaria endemic, and we have previously shown that malaria transmission intensity is closely correlated with altitude, with zero to moderate malaria transmission in the highlands and intense malaria transmission in lowland villages [17]; thus, the range of transmission intensities in the study area encompasses the entire spectrum of transmission intensities found across Africa [18]. Importantly, there are substantial and stable populations resident at the different altitudes. We have attempted to relate the distribution of these important hemoglobinopathies to variation in P. falciparum transmission.

Previous SectionNext Section

Methods

Samples. Cross-sectional surveys were conducted during the short rainy season in October and November 2001 in villages in 2 regions, Kilimanjaro and Tanga, in northeastern Tanzania; villages were arranged in 3 altitude transects (100–1800 m). The predominant ethnic groups are the Wachagga, Wapare, and Wasambaa in transects in Kilimanjaro and the Pare and Usambara mountains, respectively (figure 1 and table 1; for details, see Drakeley et al. [17]). Complete data were obtained from 816 children <5 years of age living in 13 villages. Basic demographic, anthropometric, and clinical data were collected. A fingerprick blood sample was collected for malaria blood film analysis, for measurement of hemoglobin concentration (Hemocue) and serological parameters and for genetic analysis. Written, informed consent was obtained from the parent or guardian of each child, and ethical clearance was obtained from the institutional review boards of the National Institute of Medical Research of Tanzania and the London School of Hygiene and Tropical Medicine.

Figure 1.
View larger version:
Figure 1.

Map of the study villages. The contour lines represent 400-m altitude bands. The Kilimanjaro region includes Mokala, Machame Aleni, Ikuini, Kileo (Kilimanjaro transect), Bwambo, Mpinji, Lower Goha, and Kadando (South Pare transect); the Tanga region includes Kwadoe, Funta, Tamota, Mgila (West Usambara transect), and Mgome (coastal plain).

DNA extraction and screening methods. Pellets of whole blood cells were spotted onto filter paper (LKB) and dried. DNA was extracted from filter papers in 96-well-plate format using Chelex 100 (Bio-Rad), as described elsewhere [20]. β-Hemoglobin genotypes (A, S, and C alleles) were determined by a simple high-throughput method using polymerase chain reaction (PCR) with sequence-specific oligonucleotide probes and ELISA-based technology [21]. The African a-globin deletion, α3.7, was detected by PCR, as described elsewhere [22].

Short tandem repeat (STR) typing. Fifteen autosomal STR sequences (CSF1PO, D13S317, D16S539, D18S51, D19S433, D21S11, D2S1338, D3S1358, D5S818, D7S820, D8S1179, FGA, TH01, TPOX, and vWA) [23] were typed twice by 2 independent PCRs in samples from children from 4 villages by use of the AmpFlSTR Identifiler PCR Amplification Kit (Applied Biosystems). Within each village, all samples contributing to deviations from Hardy-Weinberg expectation were also tested with the PowerPlex 16 Kit (Promega), to check for null alleles. A total of 0.5 μL of Chelex 100-purified DNA was amplified as recommended by the manufacturer in a reaction of 10 μL. Data were analyzed using GeneScan (version 3.7) and GenoTyper (version 3.7; both from Applied Biosystems). The minimum peak height was set to 100 relative fluorescence units for all dyes.

Statistical and comparative data analysis. Before analysis, hemoglobin concentrations were adjusted for altitude by use of the conversion method of Dirren et al. [24], and parasite densities were log transformed. Only the first child attending the survey from each family was included in the analysis; siblings were identified by address, paternal and family name, and date of birth.

Data were analyzed using Stata(version 9.0; StataCorp). Logistic regression analyses were done to describe the distribution of α+-thalassemia, the sickle cell trait, and mild anemia as the dependent variables, with altitude, parasite prevalence, geographical region, and either the sickle cell trait (for the a +thalassemia analysis) or α+-thalassemia (for the sickle cell analysis) as independent variables. Hardy-Weinberg proportions and Fisher’s exact tests [25, 26] for associations between the alleles of the 15 autosomal STRs were calculated using GENEPOP (version 3.4) [27]. To test for possible genetic variation between the populations in the 4 villages, the Arlequin program (version 2.000; available at: http:\lgb.unige.ch) was used to conduct analysis of the molecular variance (AMOVA) and to determine mean pairwise differences. Association of alleles across loci (linkage disequilibrium [LD]) was estimated using Fisher’s exact test [26].

Previous SectionNext Section

Results

Prevalence of α+-thalassemia and sickle cell trait. Genotype and allele frequencies for the sickle cell trait and α+-thalassemia were calculated for 660 children (i.e., after exclusion of siblings) and are shown in table 1. The overall allele frequency for the α3.7 deletion was 0.158, ranging from 0.018 (156) in Machame Aleni (a high-altitude, low-malaria-prevalence village) to 0.324 (33102) in Mgome, the village at the lowest altitude and with the highest prevalence of malarial parasitemia. Overall, ∼29% of those tested were heterozygous for α+-thalassemia, and 1.2% were α+-thalassemia homozygotes. Over the entire sample, allele frequencies showed a slight deviation from Hardy-Weinberg equilibrium, with the proportion of α3.7 homozygotes being somewhat less than predicted (1.2% observed and 2.5% expected; x2=3.2 with Yates’s correction; P = .07) and the proportion of heterozygotes higher than expected.

The overall frequency of the HbS allele (0.027) was almost 6-fold lower than that of the α3.7 allele, ranging from 0.00 in 2 villages to a maximum of 0.078 in Mgome. No HbSS homozygotes were detected, nor was the HbC allele present in any sample.

Associations between malariometric indices and prevalence of hemoglobinopathies. The relationship between village-level frequencies of a +-thalassemia and HbAS, altitude, and parasite prevalence are shown in figure 2. The prevalence of α+-thalassemia decreased steadily with increasing altitude and decreasing parasite prevalence; this was the case for the sample as a whole (figure 2A and 2B ) but was also the case within each of the 3 independent altitude transects (table 1).

Figure 2.
View larger version:
Figure 2.

Associations between altitude, parasite prevalence, and carriage rates of the α3.7 allele and the HbS allele. A, Relationship between rates of carriage of α3.7 or HbS and village altitude. Regression coefficients were as follows: for α3.7 prevalence and altitude, β = −0.014, SE(β) =0.003, and P = .001; for HbAS prevalence and altitude, β =0.006, SE(β) = 0.002, and P = .004. B, Relationship between rates of carriage of α3.7 or HbS and village prevalence of malaria parasites. Regression coefficients were as follows: for α3.7 prevalence and parasite prevalence, β = 0.29, SE(β) = 0.07, and P = .002; for HbAS prevalence and parasite prevalence, β = 0.14 SE(β) = 0.03 and P = .001. C, Relationship between parasite prevalence and observed(O) frequency of α3.7 and HbAS heterozygotes. Regression coefficients were as follows: for α3.7 Oheterozygote frequency ratio and parasite prevalence, β = 0.21 SE(β) = 0.09 and P = .033 for HbAS Oheterozygote frequency ratio and parasite prevalence, β = 0.08 SE(β) = 0.01, and P = .0003. In each graph, black circles show village rates of carriage of α3.7 with their fitted regression line, and white circles show village rates of carriage of HbS with their fitted regression line. Only 1 sibling was sampled from each household, to give a total no. analyzed of 660.

Table 1.
View larger version:
Table 1.

Genotype and allele frequencies for α3.7 and HbS for children <5 years of age, by study village.

In univariate logistic regression models, α+-thalassemia was negatively associated with village altitude, decreasing by ∼12% per 100-m increase in altitude (odds ratio [OR], 0.88 [95% confidence interval {CI}, 0.84-0.93]; P < .001 ) and was ∼50% lower in those with patent parasitemia than those without (OR,

0.48 [95% CI, 0.27-0.86]; P = .014 ). a +-Thalassemia carriage rates were also 2-fold higher in the Tanga region than in the Kilimanjaro region (OR, 1.96 [95% CI, 1.24-3.11]; P = .004). The sickle cell trait showed a statistically significant negative association with village altitude only (OR, 0.92 [95% CI, 0.86- 0.97]; P = .011 ). No interaction between the 2 traits was apparent (OR, 0.91 [95% CI, 0.48-1.92]; P p .79).

After adjustment for village, altitude, region, and carriage of the sickle cell trait, parasite prevalence was significantly negatively associated with the carriage of α3.7 (OR, 0.44 [95% CI, 0.24–0.81]; P = .009) and village altitude (OR, 0.76 [95% CI, 0.69–0.84]; P = .0001 ) and was 5-fold higher in the Tanga region than in the Kilimanjaro region (OR, 4.6 [95% CI, 1.83- 11.6]; P = .001 ). In a similar multivariable logistic regression analysis for mild anemia (hemoglobin concentration <11g), there was a 2-fold increased risk of mild anemia with carriage of α3.7 (OR, 2.10 [95% CI, 1.4- 3.1]; P < .001 ) and residence in the Tanga region (OR, 1.94 [95% CI, 1.36–2.79]; P < .001 ); conversely, there was a 6% reduction in the risk of mild anemia with every 100-m increase in altitude (OR, 0.94 [95% CI, 0.91- 0.99]; P = .021 ). Neither parasite prevalence nor the sickle cell trait was significantly associated with mild anemia in this regression model ( P > .05).

The proportion of α3.7 and HbAS heterozygotes was greater than expected in the majority of villages. This excess of heterozygotes was positively associated with parasite prevalence for both hemoglobinopathies (figure 2C ).

Assessment of autosomal STR loci genetically independent of the α-globin gene cluster. Because associations between allele frequency, village altitude, and malaria transmission intensity might theoretically be confounded by population sub-structuring (e.g., as a result of founder effects andmigration across the altitudes), DNA from 222 children in 4 different villages were typed for allelic variation in 15 autosomal STRs that are located in different chromosomes and are independent of the a-globin gene cluster. Two of the highest situated villages, Kwadoe and Bwambo, and 2 of the lowest situated villages, Mgila and Kadando, were chosen to give a reasonable representation of the population as a whole (table 2). Complete consistency between genotypes was observed using 2 validated STR kits [23], suggesting that no null alleles (resulting from mutations in the primer annealing sites) were present. LD tests for pairs of STRs demonstrated no significant deviation from expectations ( P > .05 after sequential Bonferroni correction [29]), as was expected given the independent segregation of these loci. Some deviations from Hardy-Weinberg equilibrium were observed, but none remained significant after sequential Bonferroni correction for the number of loci analyzed. The genetic diversity was high in all 4 villages; the mean heterozygosity of the 15 loci ranged from 0.77 for Bwambo to 0.82 for Kadondo (table 2).

Table 2.
View larger version:
Table 2.

Autosomal short tandem repeat (STR) analysis of 4 Tanzanian villages.

AMOVA showed that genetic variation between the populations in the 4 villages was low (0.86% of the total variation), whereas most (99.14%) of the genetic variation was attributable to differences among individuals within villages (table 2). The STR data were grouped into transects, and AMOVA determined that just 0.16% of the STR variation was due to allelic differences among transects (indicative of differences between ethnic groups) but that only 0.70% was due to allelic differences among villages located at different altitudes within transects. The significance of the variance components was assessed by comparison of the observed values with the distribution of 1000 permutations obtained by randomization under the null hypothesis of no population structure. Accordingly, F ST: (the genetic distance or proportion of STR variance in genotype frequencies between populations [30]) between the transects was <0.009 ( P < .001 ) for South Pare and <0.006 ( P < .02) for West Usambara (also confirmed by use of R ST [the sum of squared differences method]), and the genetic identity among transects was high, ranging from 0.986 to 0.997. A low degree of intergroup variability was observed when STR data were combined by altitude ( F CT = −0.001; P = .34).

On the basis of the average number of migrants per generation (4.6%) (calculated using the private allele method and corrected for sample size [31]) and the average frequency of STR alleles found in only 1 population (1.4%), the gene flow among the 4 villages appeared to be relatively high. Furthermore, there were no private “valley” or “mountain” alleles. Importantly, when individuals (from all villages pooled together) were grouped according to α+-thalassemia status, no significant differences in STR genotypes were observed between α+-thalassemia carriers and noncarriers ( x 2 = 41.44 ; df = 30; P = .08) (table 2).

Previous SectionNext Section

Discussion

In the present study, we observed a strong positive correlation at the population level between the prevalence of α+-thalassemia and malaria transmission intensity. Similarly, we observed a significant negative association between carriage of the α3.7 allele and the risk of being parasitemic at the individual level. These results support the hypothesis that the prevalence of α+-thalassemia is associated with malaria endemicity in sub-Saharan Africa and complements the findings of studies conducted previously in Sardinia [32] and Melanesia [13]. Our findings are likely to be generalizable to much of Africa, because our study area comprises villages with a very wide range of parasite prevalence (0%-85% in children <5 years old) and a correspondingly wide range of α+-thalassemia prevalences (10%-56%). Furthermore, the same association among α+-thalassemia prevalence, altitude, and malaria endemicity was observed in 3 groups of villages in 3 different mountain ranges that were separated by some 350 km, were populated by diverse ethnic groups, and had different ecological factors defining malaria transmission intensity [17, 33]. Thus, the principle finding of the present study has been replicated in 3 independent surveys.

We are confident that the cline in α+-thalassemia and HbS frequencies with altitude reflects stable differences in malaria transmission at different altitudes. Although parasite prevalence can be affected by the impact of short-term meteorological effects on mosquito numbers and longevity-which we believe explain differences in parasite prevalence between the Tanga and Kilimanjaro regions at the time of the survey-correlations between altitude and malaria transmission intensity in our study area have been confirmed using both standard entomological techniques [34] and a novel serological approach [35]. Furthermore, patterns of clinical malaria in the study area are entirely consistent with the entomologic, serologic, and genetic data [36]. Our observation that α+-thalassemia is associated with mild anemia is in line with those of other studies [15]. Moreover, our observation that α+-thalassemia carriers are less likely to be infected with malarial parasites than non-carriers suggests that the effect of hemoglobin genotype on anemia is direct rather than being the indirect result of the impact of genotype on malarial infection. Thus, α+-thalassemia and other hemoglobinopathies may directly contribute to the high burden of anemia seen in many African settings [37] and may contribute to the persistence of mild anemia even in the presence of effective malaria control measures [38].

We observed consistent deviations from Hardy-Weinberg expectations, with a consistent excess of heterozygotes across all transects. This is indicative of a heterozygote advantage operating in populations exposed to high levels of malaria transmission, as classically described for HbAS. However, a review of available data suggests that this may also represent transitional selection and may reflect more recent settlement of our study area [39]. Further investigations of the nature of the selection operating at the α-hemoglobin locus are planned. Historical and linguistic studies show that all of the populations in the study area derive from the in-migration of southern Cushitic peoples 4000–5000 years ago [40]. The founder populations of each ethnic group are thus likely to have shared a common genetic heritage. This assumption is supported by our analysis of 15 unlinked, highly polymorphic loci, which suggested that the study populations are effectively panmictic, with no genetic substructuring by altitude, ethnic group, or location. Although the present analysis was restricted to 4 of 13 villages, 99.2% of the total genetic diversity in these samples is found within any 1 village, indicating moderate to high levels of gene flow among villages. We did not find evidence of linkage disequilibrium between any pair of neutral STRs that would suggest that the association with altitude was due to migration rather than selection. Furthermore, the association between allele frequency and altitude was seen in 3 independent transects comprising 3 separate ethnic groups; it is most unlikely that the migration pattern of each of these ethnic groups would have led, by chance, to the same very skewed distribution of α+-thalassemia and sickle cell alleles. It is also unlikely that the prevalence of hemoglobinopathies is directly influenced by altitude, because the physiological effects of decreasing oxygen concentration are relatively minor within this altitude range [24], and there is no evidence of reduced fitness of children with α+-thalassemia associated with oxygen deprivation [13]. Taken together, these data demonstrate that malaria provides a strong selective force favoring the persistence and spread of α+-thalassemia and reveal carriage rates of α+-thalassemia as a further correlate of malaria transmission intensity within our study area.

Although the overall frequencies of the α3.7 allele (0.16) and the HbS allele (0.05) were lower in our study area than those recently described only 300 km away on the Kenyan coast (α+thalassemia range, 0.41–0.50; HbAS, 0.15 [5, 15]), allele frequencies in the highest transmission village on the Tanzanian coastal plain (Mgome) were more similar to those in coastal Kenya. The somewhat lower frequencies of hemoglobinopathies (despite equivalent or higher levels of malaria transmission) might be explained by more recent settlement of the mountainous areas of East Africa, compared with coastal regions. It is also possible, as suggested by Williams et al. [5], that a negative interaction between HbS and a +-thalassemia-such that when the 2 traits are inherited together the malaria-protective effect of either trait is lost-mitigates against high levels of either trait accumulating in sites of exceptionally high malaria transmission. Initial attempts to model these interactions are generally supportive of this hypothesis (A. Ghani [London School of Hygiene and Tropical Medicine, London, United Kingdom], unpublished preliminary data).

In conclusion, the present study has provided important information about the way in which selective forces imposed by malaria have shaped the human genome. The steep cline in α+-thalassemia prevalence with altitude within an otherwise genetically similar population provides a remarkable example of how protective genes can be selected even between populations living in very close proximity. Our study also confirms that altitude reflects a historic and stable proxy for malaria endemicity in this area and provides us with a unique opportunity to use altitude-dependent clines in allele frequency to identify other genetic traits that confer resistance to malaria.

Previous SectionNext Section

Acknowledgments

This study was conducted under the auspices of the Joint Malaria Programme (JMP), a collaboration between the National Institute for Medical Research, Kilimanjaro Christian Medical College, the London School of Hygiene and Tropical Medicine, and the Centre for Medical Parasitology, University of Copenhagen. We thank the JMP field teams for collecting the original data and Dr. Azra Ghani for her advice, and we acknowledge the continued support of Profs. I. C. Bygbjerg, T. G. Theander, A. Kitua, and J. Shao.

Previous SectionNext Section

Footnotes

  • Potential conflicts of interest: none reported.

  • Financial support: UK Medical Research Council (grant G9901439 to the study); Danish International Development Agency, Danish Ministry of Foreign Affairs (grant 91203 to A.E.); Wellcome Trust Research Training Fellowship (grant 063516 to C.J.D.).

  • Received December 22, 2006.
  • Accepted March 9, 2007.
Previous Section
 

References

  1. ↵
    1. Kwiatkowski DP
    . How malaria has affected the human genome and what human genetics can teach us about malaria. Am J Hum Genet 2005;77:171-92.
    CrossRefMedlineWeb of Science
  2. ↵
    1. Haldane JBS
    . The rate of mutation of human genes. Proceedings of the 8th International Congress on Genetics, Hereditus (supplement). 1949;35:267-73.
  3. ↵
    1. Allison AC
    . Protection afforded by sickle-cell trait against subtertian malarial infection. BMJ 1954;1:290-4.
    FREE Full Text
  4. ↵
    1. Allison AC
    . Polymorphism and natural selection in human populations. Cold Spring Harb Symp Quant Biol 1964;29:137-49.
    Abstract/FREE Full Text
  5. ↵
    1. Williams TN,
    2. Mwangi TW,
    3. Wambua S,
    4. et al
    . Negative epistasis between the malaria-protective effects of alpha+-thalassemia and the sickle cell trait. Nat Genet 2005;37:1253-7.
    CrossRefMedlineWeb of Science
  6. ↵
    1. Allison AC
    . The distribution of the sickle-cell trait in East Africa and elsewhere, and its apparent relationship to the incidence of subtertian malaria. Trans R Soc Trop Med Hyg 1954;48:312-8.
    CrossRefMedlineWeb of Science
    1. Livingstone FB
    . Frequencies of hemoglobin variants. New York: Oxford University Press, 1985.
  8. ↵
    1. Weatherall DJ,
    2. Clegg JB
    . Inherited haemoglobin disorders: an increasing global health problem. Bull World Health Organ 2001;79:704-12.
    MedlineWeb of Science
  9. ↵
    1. Modiano D,
    2. Luoni G,
    3. Sirima BS,
    4. et al
    . Haemoglobin C protects against clinical Plasmodium falciparum malaria. Nature 2001;414:305-8.
    CrossRefMedline
  10. ↵
    1. Weatherall DJ,
    2. Clegg JB
    . The thalassaemia syndromes. Oxford: Blackwell Scientific Publications, 2001.
    1. Fodde R,
    2. Losekoot M,
    3. Vandenbroek MH,
    4. et al
    . Prevalence and molecular heterogeneity of alfa+ thalassemia in 2 tribal populations from Andhra-Pradesh, India. Hum Genet 1988;80:157-60.
    CrossRefMedlineWeb of Science
  12. ↵
    1. Modiano G,
    2. Morpurgo G,
    3. Terrenato L,
    4. et al
    . Protection against malaria morbidity: near-fixation of the alpha-thalassemia gene in a nepalese population. Am J Hum Genet 1991;48:390-7.
    Medline
  13. ↵
    1. Flint J,
    2. Hill AVS,
    3. Bowden DK,
    4. et al
    . High-frequencies of alpha-thalassemia are the result of natural-selection by malaria. Nature 1986;321:744-50.
    CrossRefMedline
  14. ↵
    1. Mockenhaupt FP,
    2. Ehrhardt S,
    3. Gellert S,
    4. et al
    . Alpha+-thalassemia protects African children from severe malaria. Blood 2004;104:2003-6.
    Abstract/FREE Full Text
  15. ↵
    1. Williams TN,
    2. Wambua S,
    3. Uyoga S,
    4. et al
    . Both heterozygous and homozygous alpha+ thalassemias protect against severe and fatal Plasmodium falciparum malaria on the coast of Kenya. Blood 2005;106:368-71.
    Abstract/FREE Full Text
  16. ↵
    1. Allen SJ,
    2. O'Donnell A,
    3. Alexander NDE,
    4. et al
    . Alpha+-thalassemia protects children against disease caused by other infections as well as malaria. Proc Natl Acad Sci USA 1997;94:14736-41.
    Abstract/FREE Full Text
  17. ↵
    1. Drakeley CJ,
    2. Carneiro I,
    3. Reyburn H,
    4. et al
    . Altitude-dependent and -independent variations in Plasmodium falciparum prevalence in northeastern Tanzania. J Infect Dis 2005;191:1589-98.
    Abstract/FREE Full Text
  18. ↵
    1. Hay SI,
    2. Rogers DJ,
    3. Toomer JF,
    4. Snow RW
    . Annual Plasmodium falciparum entomological inoculation rates (EIR) across Africa: literature survey, internet access, and review. Trans R Soc Trop Med Hyg 2000;94:113-27.
    CrossRefMedlineWeb of Science
    1. Lusingu JPA,
    2. Vestergaard LS,
    3. Mmbando BP,
    4. et al
    . Malaria morbidity and immunity among residents of villages with different Plasmodium falciparum transmission intensity in North-Eastern Tanzania. Malar J 2004;3:26.
    CrossRefMedline
  20. ↵
    1. Pearce RJ,
    2. Drakeley C,
    3. Chandramohan D,
    4. Mosha F,
    5. Roper C
    . Molecular determination of point mutation haplotypes in the dihydrofolate reductase and dihydropteroate synthase of Plasmodium falciparum in three districts of northern Tanzania. Antimicrob Agents Chemother 2003;47:1347-54.
    Abstract/FREE Full Text
  21. ↵
    1. Enevold A,
    2. Vestergaard LS,
    3. Lusingu J,
    4. et al
    . Rapid screening for glucose-6-phosphate dehydrogenase deficiency and haemoglobin polymorphisms in Africa by a simple high-throughput SSOP-ELISA method. Malar J 2005;4:61.
    CrossRefMedline
  22. ↵
    1. Liu YT,
    2. Old JM,
    3. Miles K,
    4. Fisher CA,
    5. Weatherall DJ,
    6. Clegg JB
    . Rapid detection of alpha-thalassaemia deletions and alpha-globin gene triplication by multiplex polymerase chain reactions. Br J Haematol 2000;108:295-9.
    CrossRefMedlineWeb of Science
  23. ↵
    1. Collins PJ,
    2. Hennessy LK,
    3. Leibelt CS,
    4. Roby RK,
    5. Reeder DJ,
    6. Foxall PA
    . Developmental validation of a single-tube amplification of the 13 CODIS STR loci, D2S1338, D19S433, and amelogenin: the AmpFlSTR Identifier PCR amplification kit. J Forensic Sci 2004;49:1265-77.
    MedlineWeb of Science
  24. ↵
    1. Dirren H,
    2. Logman MHGM,
    3. Barclay DV,
    4. Freire WB
    . Altitude correction for hemoglobin. Eur J Clin Nutr 1994;48:625-32.
    MedlineWeb of Science
  25. ↵
    1. Guo SW,
    2. Thompson EA
    . Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics 1992;48:361-72.
    CrossRefMedlineWeb of Science
  26. ↵
    1. Zaykin D,
    2. Zhivotovsky L,
    3. Weir BS
    . Exact tests for association between alleles at arbitrary numbers of loci. Genetica 1995;96:169-78.
    CrossRefMedlineWeb of Science
  27. ↵
    1. Raymond M,
    2. Rousset F
    . Genepop (version-1.2)-population-genetics software for exact tests and ecumenicism. J Hered 1995;86:248-9.
    FREE Full Text
    1. Weir BS,
    2. Cockerham CC
    . Estimating F-statistics for the analysis of population-structure. Evolution 1984;38:1358-70.
    CrossRefWeb of Science
  29. ↵
    1. Rice WR
    . Analyzing tables of statistical tests. Evolution 1989;43:223-5.
    CrossRefWeb of Science
  30. ↵
    1. Wright S
    . Evolution and the genetics of populations: variability within and among natural populations. Chicago: University of Chicago Press, 1978.
  31. ↵
    1. Barton NH,
    2. Slatkin M
    . A quasi-equilibrium theory of the distribution of rare alleles in a subdivided population. Heredity 1986;56:409-15.
    CrossRefMedlineWeb of Science
  32. ↵
    1. Siniscalco M,
    2. Bernini L,
    3. Motulsky AG,
    4. Latte B
    . Favism and thalassaemia in Sardinia and their relationship to malaria. Nature 1961;190:1179.
    CrossRefWeb of Science
  33. ↵
    1. Bodker R,
    2. Akida J,
    3. Shayo D,
    4. et al
    . Relationship between altitude and intensity of malaria transmission in the Usambara Mountains, Tanzania. J Med Entomol 2003;40:706-17.
    MedlineWeb of Science
  34. ↵
    1. Beier JC,
    2. Killeen GF,
    3. Githure JI
    . Short report: entomologic inoculation rates and Plasmodium falciparum malaria prevalence in Africa. Am J Trop Med Hyg 1999;61:109-13.
    Abstract
  35. ↵
    1. Drakeley CJ,
    2. Corran PH,
    3. Coleman PG,
    4. et al
    . Estimating medium-and long-term trends in malaria transmission by using serological markers of malaria exposure. Proc Natl Acad Sci USA 2005;102:5108-13.
    Abstract/FREE Full Text
  36. ↵
    1. Reyburn H,
    2. Mbatia R,
    3. Drakeley C,
    4. et al
    . Association of transmission intensity and age with clinical manifestations and case fatality of severe Plasmodium falciparum malaria. JAMA 2005;293:1461-70.
    Abstract/FREE Full Text
  37. ↵
    1. Schellenberg D,
    2. Menendez C,
    3. Aponte JJ,
    4. et al
    . Intermittent preventive antimalarial treatment for Tanzanian infants: follow-up to age 2 years of a randomised, placebo-controlled trial. Lancet 2005;365:1481-3.
    CrossRefMedlineWeb of Science
  38. ↵
    1. Schellenberg D,
    2. Schellenberg JRMA,
    3. Mushi A,
    4. et al
    . The silent burden of anaernia in Tanzanian children: a community-based study. Bull World Health Organ 2003;81:581-90.
    MedlineWeb of Science
  39. ↵
    1. Flint J,
    2. Harding RM,
    3. Boyce AJ,
    4. Clegg JB
    . The population genetics of the haemoglobinopathies. Baillieres Clin Haematol 1998;11:1-51.
    CrossRefMedlineWeb of Science
  40. ↵
    1. Ehret C
    . The civilizations of Africa: a history to 1800. May 2002 ed. Charlottesville: University of Virginia Press, 2002.
| Table of Contents Journal Home Page

This Article

  1. J Infect Dis. (2007) 196 (3): 451-459. doi: 10.1086/519390
  1. AbstractFree
  2. » Full Text (HTML)Free
  3. Full Text (PDF)Free

Classifications

Share

  1. Email this article

Navigate This Article

Current Issue

  1. March 1, 2012 205 (5)
  1. Journal of Infectious Diseases
  1. Alert me to new issues

Most