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Am J Hum Genet. Aug 2007; 81(2): 234–242.
Published online Jun 15, 2007. doi:  10.1086/519221
PMCID: PMC1950820

Combining Evidence of Natural Selection with Association Analysis Increases Power to Detect Malaria-Resistance Variants


Statistical power to detect disease variants can be increased by weighting candidates by their evidence of natural selection. To demonstrate that this theoretical idea works in practice, we performed an association study of 10 putative resistance variants in 471 severe malaria cases and 474 controls from the Luo in Kenya. We replicated associations at HBB (P=.0008) and CD36 (P=.03) but also showed that the same variants are unusually differentiated in frequency between the Luo and Yoruba (who historically have been exposed to malaria) and the Masai and Kikuyu (who have not been exposed). This empirically demonstrates that combining association analysis with evidence of natural selection can increase power to detect risk variants by orders of magnitude—up to P=.000018 for HBB and P=.00043 for CD36.

Malaria infection (MIM 248310) has exerted severe pressure on the human genome within the past 10,000 years,13 and there are more cases today than ever before, with an estimated 300–660 million new episodes of clinical Plasmodium falciparum malaria every year.4 Despite high infection rates, only 1%–2% of patients develop life-threatening complications, such as cerebral malaria and profound anemia,5 so natural selection has likely operated, to a large extent, on severity. In the context of high infection rates, the genetics of host response are likely to play an important role.6 In sub-Saharan Africa, the populations in which malaria is endemic generally have a lower proportion of cases with severe disease.5,7 This suggests that there exist genetic variants that have risen to higher frequency in malaria-endemic populations because they modulate risk of P. falciparum malaria, similar to the case of the Duffy-null variant that protects against P. vivax malaria.8

A handful of genetic variants have already been associated with risk of or protection against severe malaria infection.5 Our first objective in this study was to test variants of β-globin (HbAS9,10), intercellular adhesion molecule (ICAM TT11), CD36 (CD36 GT12), nitric oxide synthase (NOS2A 1659 AA13), tumor necrosis factor (TNF 238 A14 and TNF 308 A1416), Fc γ-receptor IIA (CD32 AA17,18), interferon-α receptor-1 (IFNARI LI168V CC19 and IFNARI 17470 CC19), and Toll-like receptor (TLR420), which had previously been associated with malaria susceptibility. The particular phenotype we focused on was high levels of parasitemia in young children due to malaria infection.

Second, we compared the frequency differentiation in populations in which malaria is endemic and in closely related populations in which it is not endemic, searching for the differences that would be expected if natural selection had affected those alleles in one population but not in the other, because malaria began to affect only one group. Finally, we formally combined the evidence of association from case-control studies with evidence of natural selection in populations that have been exposed to malaria infection. We note that there has been discussion elsewhere of how one could formally combine case-control association studies with statistical weights obtained on the basis of evidence of natural selection.21 Our goal in this study was to empirically demonstrate the power of this approach.

Material and Methods

Human Subjects

We collected 471 severe malaria cases and 474 controls from the Luo ethnic group, a population that speaks a Nilotic language and lives in a malaria-endemic region in western Kenya. All the severe malaria cases were collected from the Bondo District Hospital’s children’s emergency ward or from its outpatient clinic between May 2004 and August 2005. The average age of the cases was 2.6 years (table 1), reflecting our focus on individuals with no previous immunological protection against malaria. The controls were randomly collected from volunteers at nearby secondary schools, with an average age of 16.9 years (table 1). We focused on older controls, because we knew that they had survived to an older age. Thus, the control samples selected for this study may be slightly enriched for variants protecting against severe malaria, which should make it slightly easier to detect associations.

Table 1.
Characteristics of the Populations Included in This Study

For the selection study, we assembled population control samples from the Masai, Kikuyu, and Yoruba ethnic groups. We collected samples from the Masai and Kikuyu from secondary schools in Narok and Nyeri, Kenya, respectively (table 1). The Yoruba samples were from the International Haplotype Map project22; we analyzed data from unrelated men and women, the parents in HapMap mother-father-child trios.

About 2 ml of blood was obtained by venipuncture for all the samples we collected in Kenya. We extracted DNA within 10 h of blood collection, using a Qiagen DNA Blood mini kit, and then stored it at −20°C. All the participants provided informed consent, and, for children, informed consent was obtained from the parents and/or guardians. The study was reviewed and approved by the Harvard Medical School and Kenyatta University ethical review boards and by the Kenyan government.

Clinical Identification of Human Subjects with Severe Malaria

We identified human subjects who had severe malaria according to World Health Organization criteria. Blood smears and Giemsa staining were used to determine the asexual parasite count (parasitemia level). We identified cases as young children with >12 parasites per 200 red blood cells. All cases were also required to have overlapping clinical manifestations at the time of hospitalization, such as respiratory distress, convulsions, prostration, and hyperthermia (>39°C).

Genotyping at Candidate Genetic Variants

We genotyped all human subjects for 13 candidate malaria SNPs, using mass spectrometry (Sequenom).23 We discarded SNPs with minor-allele frequency averaging <5% across the four ethnic groups, leaving 10 SNPs for subsequent analysis (table (table2).2). Although the X-linked G6PD and CD40 genes are important candidates for malaria-resistance genes,3 we excluded them from this study because we wished to focus on autosomal SNPs that we could compare with an empirical panel of autosomal variants in the genome.

Table 2.
Replication Analysis for 10 Genotypes or Alleles Previously Associated with Malaria Susceptibility

As an assessment of genotyping quality, we observed that, for 85 genotypes obtained in duplicate, there were 2 discrepancies, for a discordance rate of 2.4%. After removing samples with <80% genotyping completeness, we found that the average completeness of genotypes was 97.8%. We also compared genotyping of 10 SNPs in Yoruba samples with data from HapMap.22 Of 30 genotypes obtained in duplicate, there was 1 discrepancy (3.33%). All SNPs were in Hardy-Weinberg equilibrium in all the ethnic groups we studied (P>.05).

Genotyping at 1,454 Random SNPs

For the assessment of allele-frequency differentiation at random SNPs, we used the Illumina Bead Lab System to genotype 1,536 random SNPs from the Illumina linkage panel (covering chromosomes 1, 2, 3, and 22) in 45 of the Luo controls, 47 Masai controls, and 37 Kikuyu controls. We also obtained genotypes for these SNPs in 55 Yoruba samples from the HapMap database.22 Of these SNPs, 1,454 passed standard quality checks and had been genotyped in all four populations.

Case-Control Association Analysis

We assessed the statistical significance of allele-frequency differences between Luo cases and Luo controls, using a χ2 test with 1 df. We used a one-tailed test of statistical significance, since our interest was in assessing whether a genotype or allele previously associated with malaria is more common in cases than in controls. We computed odds ratios (ORs) as A=(fcase/1-fcase)/(fcontrol/1-fcontrol), where fcase is the frequency in cases and fcontrol is the frequency in controls. We also computed a 95% CI as the range of ORs that produced a likelihood ratio consistent with the data (P>.05). Specifically, we estimated the SE of the log OR as

equation image

where ncase-ref and ncase-var are the counts of the reference and variant genotypes in cases, and ncontrol-ref and ncontrol-var are the analagous quantities in controls. The 95% CI is quoted as the range (eln(A)-1.65B to eln(A)+1.65B).

Epistasis Testing

To test for possible epistasis between any two SNPs, we used logistic regression. We compared the fit of three models with the data (case-control status for all the Luo samples): (1) genotype at the first SNP, (2) genotype at the second SNP, and (3) genotype at both SNPs.24 We performed a one-tailed test for association with the genotypes previously associated with malaria. We calculated a Wald statistic and assessed significance for the epistatic interaction by a χ2 test with 1 df.

Statistical Test for Natural Selection

The model of allele-frequency differentiation between two populations that we used to test for selection is that the difference in population frequencies at a given polymorphism is normally distributed with mean 0 and variance cp(1−p), where p is the ancestral frequency. This model is similar to that of Nicholson et al.,25 who showed that, for populations with modest genetic divergence times, it is a good approximation for allele-frequency differentiation. Under certain assumptions, the c parameter is expected to equal 2×FST. From a population genetics perspective, c can be viewed as measuring genetic drift between populations.

To estimate c empirically, we used data from the 1,454 randomly chosen markers. For a given pair of populations, we estimated c as the empirical variance of the difference in population frequencies, after normalizing by p(1−p) and accounting for sampling noise, which has variance p(1-p)(1/N1+1/N2), where N1 and N2 are total allele counts for the two populations at a given marker. We approximated the normalization term p(1−p) by setting p equal to the average of observed frequencies of the two populations, and we approximated binomial sampling noise as normally distributed. The same approximations were applied both to our estimation of c and to our subsequent analysis of individual markers. SNPs with average minor-allele frequency <5% for the two populations being compared were omitted from all computations, since the normal approximation becomes less reliable (table 3).

Table 3.
Tests for Differentiating Selection between Malaria-Endemic and -Nonendemic Populations

To test whether an individual marker was more differentiated than expected between two populations, we compared the observed difference in frequency with the expected distribution N[0,p(1-p)(c+1/N1+1/N2)], using the value of c estimated above, and computed a χ2 statistic with 1 df. A feature of this test is that the χ2 statistic has a mean value of 1 across the set of markers used to infer c. The test appropriately handles different sample sizes for candidate markers versus random markers used to infer c. A detailed statistical treatment will appear elsewhere (A.L.P, N.P., and D.R., unpublished data).

Combining Case-Control Association and the Test for Differentiating Selection

The combined test formally evaluates whether the observed data are consistent with the model of no case-control association and no selection. The test is performed by summing the association χ2 statistic and the differentiation χ2 statistic, forming a χ2 statistic with 2 df. We note that the association χ2 statistic used in this test is, by definition, a two-tailed statistic. We computed this sum for each pair of populations, using the same association statistic in each case. When one of the two populations being compared was the Luo population, we used the summed counts of Luo cases and Luo controls in the combined statistics reported in table 4. This generally leads to less significant P values than does using Luo controls only (and so is conservative). Using summed counts of Luo cases and Luo controls is appropriate under the null assumption of no association and ensures that the association statistic and differentiation statistic are independent. However, for the selection-only statistics reported in table 3, we used Luo controls only, since we wished to evaluate the evidence of selection in the control population, without regard to evidence of case-control association.

Table 4.
Formal Combination of Case-Control Association Analysis and Tests of Natural Selection


Case-Control Association

We tested each of the 10 variants for association with malaria, comparing Luo cases with Luo controls. Two of the variants showed nominally statistically significant associations by one-tailed tests that searched for an association with the genotype or allele previously proposed to affect malaria resistance (table 3). We replicated the well-known association in which heterozygotes for the sickle-cell trait HbAS (HbAS T) are protected against severe malaria (P=.0004; OR 0.57 [95% CI 0.41–0.79]) (see the “Material and Methods” section). Although the OR of 0.57 is less strong than that observed in some previous studies,9 it is in the same range as the OR of 0.45 (0.24–0.84), which was observed in another study of young children with a similar phenotype of severe malaria.10 Different case-control studies focus on different phenotypes, and the protection of HbAS against severe malaria is known to vary with age,26 so it is not surprising that the estimated ORs are heterogeneous across studies. We also replicated the association in which heterozygotes for CD36 GT are at increased risk for severe malaria (P<.015; OR 1.50 [95% CI 1.03–2.18]).12

We note in passing that NOSA (rs8078340) gives a nominally significant P value (by a two-tailed test), but the association is in the opposite direction to previous reports (P=.99) (table 2). Our null findings at the other variants do not necessarily mean that they are unassociated; the CIs for the ORs are broad (table 2) and are often consistent with substantial association. We also note that our study included only individuals with parasitemia; we had no power to detect associations that were specific to cerebral malaria, a phenotype that was the focus of some previous studies.13,27,28

Finally, we tested for epistatic interactions between each pair of variants,29 but no pair showed a statistically significant interaction by a Wald test (not shown). We also tested for different strengths of association by sex but found no evidence of this (table A1).

Allele-Frequency Differentiation and Tests for Natural Selection

To test for differentiating natural selection, we compared the frequencies of the putative susceptibility variants between populations in which malaria is endemic and nonendemic (tables (tables33 and andA2).A2). We observed the most-significant frequency differentiation at the two SNPs that also showed the strongest associations (table 3). The sickle-cell allele HbAS T is present at appreciable frequency in the Luo (13%) and Yoruba (11%) but is absent in the malaria-nonendemic Masai and Kikuyu. The CD36 G allele is present at 22% in the Yoruba and at 6% in the Luo but occurs at only ~1% frequency in the populations in which malaria is nonendemic.

To test whether these allele-frequency differences are greater than what could be explained in the absence of selection, we compared them with a panel of 1,454 random SNPs30 for which we obtained genotypes in 45 Luo, 47 Masai, 37 Kikuyu, and 59 Yoruba. (We first assessed whether there was evidence of population substructure in the Luo,31 which could, in principle, confound our case-control tests of association. No structure was detected, indicating that population stratification is not likely to cause false-positive or false-negative results in the association analysis.) We also used the data to assess the genetic relationships among the populations; understanding this is crucial to the tests for differentiating selection.

The genetic differentiation among populations ranges from FST=0.0012 between Masai and Kikuyu (lowest differentiation) to FST=0.021 between Yoruba and Masai (highest differentiation). We found that the Luo and Masai do not cluster genetically, despite the fact that they both speak Nilotic languages, whereas the Masai and Kikuyu are closely related (despite the fact that the Kikuyu speak a Bantu language) (fig. 1). These results show that the linguistic patterns in Kenya do not correlate with the genetic patterns, which is at odds with what has been suggested elsewhere.32 Sampling of more populations should elucidate the relationships between genetic and linguistic groups in East Africa.33

Figure  1.
Principal-components analysis of samples from four different populations genotyped for 1,454 SNPs. The first eigenvector clusters the Yoruba, Luo, Kikuyu, and Masai. This is contrary to the expectation based on linguistics (the Luo and Masai both speak ...

To formally test for differentiating selection, we computed a χ2 statistic for frequency differentiation at each tested SNP, assuming it was drawn from the empirical distribution defined by 1,454 random SNPs (see the “Material and Methods” section and table 3). Allele-frequency differentiation between malaria-endemic and -nonendemic populations is significant at HbAS T (P=.00036 for the most extreme Luo-Kikuyu comparison) and CD36 G (P=.00096 for Yoruba-Kikuyu), with the results significant even after use of a Bonferroni correction for testing 40 comparisons of malaria-endemic and -nonendemic populations at 10 SNPs (this essentially involves multiplying the nominal P values by a factor of 40). By contrast, the eight SNPs that do not give positive case-control association show no evidence of differentiating selection (P>.25 for each SNP and pair of populations after correction for multiple hypotheses tested; P=.39 for the sum of 32 χ2 statistics at these eight SNPs). We further evaluated the robustness of our selection test by computing χ2 statistics for each of the 1,454 random SNPs for each of the four pairs of populations. If the test is robust, we would expect to achieve a χ2 value >3.84, with probability 0.05. Restricting the analysis to SNPs in which the average allele frequency across the two populations tested was at least 5%, we observed a χ2 value >3.84 in 255 (5%) of 5,090 of tests performed. Similarly, only 4 of 5,090 tests produced a P value <.001, and the lowest P value was not statistically significant after correction for 5,090 hypotheses tested (P>.16). These results show that our test for differentiating natural selection is not prone to false-positive results in a large selection of randomly chosen SNPs.

We note that both HbAS T and CD36 G have been identified elsewhere as targets of recent positive natural selection.22,3436 However, the long-range haplotype test used to detect selection at these alleles detects evidence of selection from any cause and thus is not specific to a particular type of selection (e.g., for malaria resistance). The tests of allele-frequency differentiation we present here are much more specific to malaria. By comparing malaria-endemic and -nonendemic populations, we increase the probability that the loci detected as being affected by selection are specifically associated with malaria resistance. Of all the SNPs we tested for population differentiation—1,454 random SNPs and 10 candidates for malaria susceptibility—2 of those that achieve a nominal P value <.001 for at least one pair of populations were among the candidate malaria-resistance SNPs.

Combined Analysis of Case-Control Association and Selection

Finally, we formally combined the evidence of association with the evidence from the selection test (see the “Material and Methods” section). The combined test evaluates whether the observed data are consistent with the model of no case-control association and no selection. Whereas the evidence of association at HbAS T and CD36 G is only moderate by the association analysis alone (see P values in table tableA1),A1), significance is greatly increased when the association and selection evidence is combined: P=.000018–.00029 for HbAS T and P=.00043–.017 for CD36 G, depending on which populations are compared (table 4). These results remain statistically significant after correction for 40 hypotheses tested (P=.00072 for HbAS T and P=.017 for CD36 G).


We performed a case-control association study of malaria resistance in the Luo, an East African population, analyzing 10 previously implicated variants. We replicated associations at HbAS (OR 0.57 [95% CI 0.41–0.79]) and CD36 (OR 1.50 [95% CI 1.05–2.18]). Our OR for CD36 is in agreement with the results published elsewhere.12 Similarly, the OR for HbAS is in agreement with the previously reported longitudinal study in the same population (OR 0.45 [95% CI 0.24–0.84]; P=.0001).10 For HbAS, the protective effect that we observed is smaller than in some previous reports, which is potentially due to the fact that the cases we studied were young (average age 2.6 years) and thus lacked an immune basis for HbAS protection. (Williams and colleagues showed that HbAS has a more protective effect for older individuals.9,10,26) A possible reason why we did not replicate all the previous associations is that, in our study, the phenotype was parasitemia, whereas previous studies sometimes focused on cerebral malaria (table (table2).2). We also show in table 2 that the CIs for the ORs are broad; thus, many of the variants we tested are consistent with an effect on malaria susceptibility, even if we could not reject the hypothesis of no association.

A particularly striking observation is that, at CD36, where we observe significant case-control association and highly significant allele-frequency differentiation, the variant increasing susceptibility actually has higher frequency in malaria-endemic populations. A possible historical explanation is that the selection pressures on this variant may have changed over time because of host-parasite genetic interactions. For example, the variant may have historically reduced susceptibility to malaria, and then, as the parasite evolved to adapt to the human immune system, the allelic association might have reversed. This hypothesis would be consistent with the known temporal and geographical heterogeneity in CD36 binding and pathogenicity.12 For example, genetic variation at the PfEMP1 gene in the malaria parasite has been shown elsewhere to be associated with the pathogenicity,37,38 and parasite PfEMP1 and human CD36 are known to interact.3941 In future studies, it will be interesting to explore whether human variants at CD36 have different interactions with genetically different malaria parasite strains.42

These results finally provide empirical validation for a long-standing idea.21 The idea is that, to increase power in case-control studies, one can combine the evidence of association with that from tests of natural selection. Previous studies have prioritized SNPs by natural selection on the basis of a combination of the alleles being frequent and being surrounded by a long-range haplotype3,22,43; the present study adds to this in several ways. First, we provide a formal χ2 test of statistical significance, which can be combined with a case-control statistic to provide evidence that a SNP is a statistical outlier and, thus, a strong candidate for being associated with malaria. Second, our selection evidence is more specific to our phenotype of interest, since we are comparing frequency variants in populations differentiated by whether malaria has been historically endemic or nonendemic. Tishkoff et al.33 recently applied a similar strategy to the phenotype of lactase persistence. They compared pastoral and nonpastoral populations in East Africa that have been differently exposed to diets including cow's milk. This analysis demonstrated high allele-frequency differences at variants near the lactase gene LCT and simultaneously showed that these highly differentiated variants also conferred the phenotype of lactase persistence.

We conclude that, in future whole-genome association scans, evidence from case-control comparisons can be combined with allele-frequency differentiation between differently exposed populations—and, potentially, other sources of evidence about recent selection22,43—to provide increased sensitivity and power in tests to detect disease-related genetic variants. It has been suggested that the identification of targets of selection may soon become a mainstream approach to finding genetic variants affecting human disease; our results provide empirical validation for this idea.44 In our study, P values for HbAS and CD36 were enhanced by several orders of magnitude with the use of <60 samples from each population analyzed, suggesting that this strategy may be cost effective relative to the number of additional samples needed to obtain a similar increase in power within the conventional case-control paradigm.


We are grateful to the human subjects and their families who participated in this study. We thank the medical officers and nursing staff in Bondo District Hospital, as well as the staff and students of the secondary schools where controls were collected. We are grateful to Pardis Sabeti, for thoughtful comments, and to Gavin McDonald, Alicja Waliszewska, Julie Neubauer, and Christine Schirmer, for technical advice. This work was supported by funds from Harvard Medical School, National Institutes of Health (NIH) grant R21-AI64519, and a Burroughs Wellcome Career Development Award in the Biomedical Sciences to D.R. A.L.P. is supported by a Ruth Kirschstein K-08 award from the NIH. We declare that we have no competing financial interests.

Appendix A

Table A1.

Statistical Tests for Association[Note]

P in Two-Tailed (One-Tailed) Tests of Previously Associated
Reference SNP,
and Sex
Frequency in Luo Controls
No. of Cases/ControlsOR (95% CI)GenotypeAlleleP in Tests
Based on 3×2
Genotype Table
  All25447/454.57 (.41–.79).0008 (.0004).006 (.003).0031
  Female25232/175.55 (.33–.90).02 (.01)
  Male25215/279.59 (.38–.93).02 (.01)
CD36 GT:
  All12456/4571.50 (1.03–2.18).03 (.015).06 (.03).061
  Female12232/1741.40 (.85–2.33)1.19 (.60)
  Male13224/2831.57 (.90–2.74).11 (0.06)
  All7460/455.71 (.42–1.21).20 (.10).89 (.45).22
  Female8235/176.43 (.18–1.00).04 (.02)
  Male7225/2791.05 (.53–2.09).90 (.45)
NOS2A 1659 AA:
 All6450/455.42 (.21–.83).01 (.99).69 (.5).015
 Female6229/179.55 (.22–1.40).21 (.11)
 Male6221/276.28 (.09–.85).02 (.01)
TNF 238 A:
  All9459/4571.00 (.73–1.39).28 (.14).97 (.49).47
  Female8233/1731.33 (.82–2.17).24 (.12)
  Male9226/284.74 (.47–1.16).20 (.10)
CD32 AA:
  All25455/447.95 (.71–1.29).76 (.38).81 (.45).95
  Female24229/1761.06 (.67–1.67).37 (.19)
  Male25226/271.45 (.30–.70).66 (.33)
  All3455/4571.18 (.54–2.07).48 (.76).93 (.47).86
  Female2231/1801.37 (.45–4.17).37 (.19)
  Male3224/277.37 (.11–1.24).66 (.33)
TNF 308 A:
  All9450/4331.13 (.82–1.56).21 (.11).42 (.21).25
  Female6234/1661.33 (.75–2.37).33 (.17)
  Male11216/2671.17 (.78–1.74).45 (.23)
IFNARI 17470 CC:
  All3455/452.85 (.53–1.36).68 (.34).70 (.35).78
  Female2234/1801.73 (.52–5.70).34 (.17)
  Male3221/272.46 (.15–1.43).66 (.33)
  All10407/2991.36 (.86–2.17).20 (.10).067 (.02).20
  Female9201/1231.48 (.70–3.16).67 (.34)
  Male11206/1761.33 (.74–2.40).17 (.09)

Note.— This table is an expansion of table 2. Values in bold are significant.

Table A2.

Allele Counts in Cases and Controls for the 10 Polymorphisms[Note]

Luo Controls
Luo Cases
AlleleReference SNPRefVarRefVarRefVarRefVarRefVar
HbAS Trs33478911981975194020009111
CD36 Grs32119388565888379184220207822
ICAM Trs549168322768823215630169377624
NOS2A 1659 Ars807834071419671318714147162427919
TNF 238 Grs36152583381837811524016933991
CD32 Ars18012744544464534559595921184949
IFNARI LI168V Crs225716776415076214814448168409816
TNF 308 Ars180062979175813871761218915906
IFNARI 17470 Crs101233561628862228012862131718424
TLR4 Grs498679057131755591651316191095

Note.— Ref = reference allele; Var = variant allele.

Web Resource

The URL for data presented herein is as follows:

Online Mendelian Inheritance in Man (OMIM), http://www.ncbi.nlm.nih.gov/Omim/ (for malaria infection)


1. Bamshad M, Wooding SP (2003) Signatures of natural selection in the human genome. Nat Rev Genet 4:99–111 [PubMed] [Cross Ref]10.1038/nrg999
2. Tishkoff SA, Varkonyi R, Cahinhinan N, Abbes S, Argyropoulos G, Destro-Bisol G, Drousiotou A, Dangerfield B, Lefranc G, Loiselet J, et al (2001) Haplotype diversity and linkage disequilibrium at human G6PD: recent origin of alleles that confer malarial resistance. Science 293:455–462 [PubMed] [Cross Ref]10.1126/science.1061573
3. Sabeti PC, Reich DE, Higgins JM, Levine HZ, Richter DJ, Schaffner SF, Gabriel SB, Platko JV, Patterson NJ, McDonald GJ, et al (2002) Detecting recent positive selection in the human genome from haplotype structure. Nature 419:832–837 [PubMed] [Cross Ref]10.1038/nature01140
4. Guerra CA, Snow RW, Hay SI (2006) Defining the global spatial limits of malaria transmission in 2005. Adv Parasitol 62:157–179 [PMC free article] [PubMed]
5. Kwiatkowski DP (2005) How malaria has affected the human genome and what human genetics can teach us about malaria. Am J Hum Genet 77:171–192 [PMC free article] [PubMed]
6. Mackinnon MJ, Mwangi TW, Snow RW, Marsh K, Williams TN (2005) Heritability of malaria in Africa. PLoS Med 2:e340 [PMC free article] [PubMed] [Cross Ref]10.1371/journal.pmed.0020340
7. Clarke SE, Brooker S, Njagi JK, Njau E, Estambale B, Muchiri E, Magnussen P (2004) Malaria morbidity among school children living in two areas of contrasting transmission in western Kenya. Am J Trop Med Hyg 71:732–738 [PubMed]
8. Miller LH, Mason SJ, Clyde DF, McGinniss MH (1976) The resistance factor to Plasmodium vivax in blacks: the Duffy-blood-group genotype, FyFy. N Engl J Med 295:302–304 [PubMed]
9. Modiano D, Luoni G, Sirima BS, Simpore J, Verra F, Konate A, Rastrelli E, Olivieri A, Calissano C, Paganotti GM, et al (2001) Haemoglobin C protects against clinical Plasmodium falciparum malaria. Nature 414:305–308 [PubMed] [Cross Ref]10.1038/35104556
10. Aidoo M, Terlouw DJ, Kolczak MS, McElroy PD, ter Kuile FO, Kariuki S, Nahlen BL, Lal AA, Udhayakumar V (2002) Protective effects of the sickle cell gene against malaria morbidity and mortality. Lancet 359:1311–1312 [PubMed] [Cross Ref]10.1016/S0140-6736(02)08273-9
11. Kun JF, Klabunde J, Lell B, Luckner D, Alpers M, May J, Meyer C, Kremsner PG (1999) Association of the ICAM-1Kilifi mutation with protection against severe malaria in Lambarene, Gabon. Am J Trop Med Hyg 61:776–779 [PubMed]
12. Aitman TJ, Cooper LD, Norsworthy PJ, Wahid FN, Gray JK, Curtis BR, McKeigue PM, Kwiatkowski D, Greenwood BM, Snow RW, et al (2000) Malaria susceptibility and CD36 mutation. Nature 405:1015–1016 [PubMed] [Cross Ref]10.1038/35016636
13. Burgner D, Usen S, Rockett K, Jallow M, Ackerman H, Cervino A, Pinder M, Kwiatkowski DP (2003) Nucleotide and haplotypic diversity of the NOS2A promoter region and its relationship to cerebral malaria. Hum Genet 112:379–386 [PubMed]
14. Knight JC, Udalova I, Hill AV, Greenwood BM, Peshu N, Marsh K, Kwiatkowski D (1999) A polymorphism that affects OCT-1 binding to the TNF promoter region is associated with severe malaria. Nat Genet 22:145–150 [PubMed] [Cross Ref]10.1038/9649
15. McGuire W, Hill AV, Allsopp CE, Greenwood BM, Kwiatkowski D (1994) Variation in the TNF-alpha promoter region associated with susceptibility to cerebral malaria. Nature 371:508–510 [PubMed] [Cross Ref]10.1038/371508a0
16. Flori L, Delahaye NF, Iraqi FA, Hernandez-Valladares M, Fumoux F, Rihet P (2005) TNF as a malaria candidate gene: polymorphism-screening and family-based association analysis of mild malaria attack and parasitemia in Burkina Faso. Genes Immun 6:472–480 [PubMed] [Cross Ref]10.1038/sj.gene.6364231
17. Shi YP, Nahlen BL, Kariuki S, Urdahl KB, McElroy PD, Roberts JM, Lal AA ( 2001) Fcγ receptor IIa (CD32) polymorphism is associated with protection of infants against high-density Plasmodium falciparum infection. VII. Asembo Bay Cohort Project. J Infect Dis 184:107–111 [PubMed]
18. Cooke GS, Aucan C, Walley AJ, Segal S, Greenwood BM, Kwiatkowski DP, Hill AV (2003) Association of Fcgamma receptor IIa (CD32) polymorphism with severe malaria in West Africa. Am J Trop Med Hyg 69:565–568 [PubMed]
19. Aucan C, Walley AJ, Hennig BJ, Fitness J, Frodsham A, Zhang L, Kwiatkowski D, Hill AV (2003) Interferon-alpha receptor-1 (IFNAR1) variants are associated with protection against cerebral malaria in the Gambia. Genes Immun 4:275–282 [PubMed] [Cross Ref]10.1038/sj.gene.6363962
20. Mockenhaupt FP, Cramer JP, Hamann L, Stegemann MS, Eckert J, Oh NR, Otchwemah RN, Dietz E, Ehrhardt S, Schroder NW, et al (2006) Toll-like receptor (TLR) polymorphisms in African children: common TLR-4 variants predispose to severe malaria. Proc Natl Acad Sci USA 103:177–182 [PMC free article] [PubMed] [Cross Ref]10.1073/pnas.0506803102
21. Roeder K, Bacanu SA, Wasserman L, Devlin B (2006) Using linkage genome scans to improve power of association in genome scans. Am J Hum Genet 78:243–252 [PMC free article] [PubMed]
22. The International HapMap Consortium (2005) A haplotype map of the human genome. Nature 437:1299–1320 [PMC free article] [PubMed] [Cross Ref]10.1038/nature04226
23. Tang K, Fu DJ, Julien D, Braun A, Cantor CR, Koster H (1999) Chip-based genotyping by mass spectrometry. Proc Natl Acad Sci USA 96:10016–10020 [PMC free article] [PubMed] [Cross Ref]10.1073/pnas.96.18.10016
24. Hosmer DW, Lemeshow S (1989) Applied logistic regression. Wiley, New York
25. Nicholson G, Smith AV, Jonsson F, Gustafsson O, Stefansson K, Donnelly P (2002) Assessing population differentiation and isolation from single nucleotide polymorphism data. J R Stat Soc 64:695–71510.1111/1467-9868.00357 [Cross Ref]
26. Williams TN, Mwangi TW, Roberts DJ, Alexander ND, Weatherall DJ, Wambua S, Kortok M, Snow RW, Marsh K (2005) An immune basis for malaria protection by the sickle cell trait. PLoS Med 2:e128 [PMC free article] [PubMed] [Cross Ref]10.1371/journal.pmed.0020128
27. Burgner D, Rockett K, Kwiatkowski D (1999) Nitric oxide and infectious diseases. Arch Dis Child 81:185–188 [PMC free article] [PubMed]
28. Xu W, Humphries S, Tomita M, Okuyama T, Matsuki M, Burgner D, Kwiatkowski D, Liu L, Charles IG (2000) Survey of the allelic frequency of a NOS2A promoter microsatellite in human populations: assessment of the NOS2A gene and predisposition to infectious disease. Nitric Oxide 4:379–383 [PubMed] [Cross Ref]10.1006/niox.2000.0290
29. Williams TN, Mwangi TW, Wambua S, Peto TE, Weatherall DJ, Gupta S, Recker M, Penman BS, Uyoga S, Macharia A, et al (2005) Negative epistasis between the malaria-protective effects of α+-thalassemia and the sickle cell trait. Nat Genet 37:1253–1257 [PMC free article] [PubMed] [Cross Ref]10.1038/ng1660
30. Murray SS, Oliphant A, Shen R, McBride C, Steeke RJ, Shannon SG, Rubano T, Kermani BG, Fan JB, Chee MS, et al (2004) A highly informative SNP linkage panel for human genetic studies. Nat Methods 1:113–117 [PubMed] [Cross Ref]10.1038/nmeth712
31. Patterson N, Price AL, Reich D (2006) Population structure and eigenanalysis. PLoS Genet 2:e190 [PMC free article] [PubMed] [Cross Ref]10.1371/journal.pgen.0020190
32. Cavalli-Sforza LL (2005) The human genome diversity project: past, present and future. Nat Rev Genet 6:333–340 [PubMed] [Cross Ref]10.1038/nrg1596
33. Tishkoff SA, Reed FA, Ranciaro A, Voight BF, Babbitt CC, Silverman JS, Powell K, Mortensen HM, Hirbo JB, Osman M, et al (2007) Convergent adaptation of human lactase persistence in Africa and Europe. Nat Genet 39:31–40 [PMC free article] [PubMed] [Cross Ref]10.1038/ng1946
34. Hanchard NA, Rockett KA, Spencer C, Coop G, Pinder M, Jallow M, Kimber M, McVean G, Mott R, Kwiatkowski DP (2006) Screening for recently selected alleles by analysis of human haplotype similarity. Am J Hum Genet 78:153–159 [PMC free article] [PubMed]
35. Sabeti PC, Schaffner SF, Fry B, Lohmueller J, Varilly P, Shamovsky O, Palma A, Mikkelsen TS, Altshuler D, Lander ES (2006) Positive natural selection in the human lineage. Science 312:1614–1620 [PubMed] [Cross Ref]10.1126/science.1124309
36. Moormann AM, Embury PE, Opondo J, Sumba OP, Ouma JH, Kazura JW, John CC (2003) Frequencies of sickle cell trait and glucose-6-phosphate dehydrogenase deficiency differ in highland and nearby lowland malaria-endemic areas of Kenya. Trans R Soc Trop Med Hyg 97:513–514 [PubMed] [Cross Ref]10.1016/S0035-9203(03)80010-X
37. Kraemer SM, Smith JD (2003) Evidence for the importance of genetic structuring to the structural and functional specialization of the Plasmodium falciparum var gene family. Mol Microbiol 50:1527–1538 [PubMed] [Cross Ref]10.1046/j.1365-2958.2003.03814.x
38. Rottmann M, Lavstsen T, Mugasa JP, Kaestli M, Jensen AT, Muller D, Theander T, Beck HP (2006) Differential expression of var gene groups is associated with morbidity caused by Plasmodium falciparum infection in Tanzanian children. Infect Immun 74:3904–3911 [PMC free article] [PubMed] [Cross Ref]10.1128/IAI.02073-05
39. Ndungu FM, Sanni L, Urban B, Stephens R, Newbold CI, Marsh K, Langhorne J (2006) CD4 T cells from malaria-nonexposed individuals respond to the CD36-binding domain of Plasmodium falciparum erythrocyte membrane protein-1 via an MHC class II-TCR-independent pathway. J Immunol 176:5504–5512 [PubMed]
40. Urban BC, Cordery D, Shafi MJ, Bull PC, Newbold CI, Williams TN, Marsh K (2006) The frequency of BDCA3-positive dendritic cells is increased in the peripheral circulation of Kenyan children with severe malaria. Infect Immun 74:6700–6706 [PMC free article] [PubMed] [Cross Ref]10.1128/IAI.00861-06
41. Jeffares DC, Pain A, Berry A, Cox AV, Stalker J, Ingle CE, Thomas A, Quail MA, Siebenthall K, Uhlemann AC, et al (2007) Genome variation and evolution of the malaria parasite Plasmodium falciparum. Nat Genet 39:120–125 [PMC free article] [PubMed] [Cross Ref]10.1038/ng1931
42. Volkman SK, Sabeti PC, DeCaprio D, Neafsey DE, Schaffner SF, Milner DA Jr, Daily JP, Sarr O, Ndiaye D, Ndir O, et al (2007) A genome-wide map of diversity in Plasmodium falciparum. Nat Genet 39:113–119 [PubMed] [Cross Ref]10.1038/ng1930
43. Voight BF, Kudaravalli S, Wen X, Pritchard JK (2006) A map of recent positive selection in the human genome. PLoS Biol 4:e72 [PMC free article] [PubMed] [Cross Ref]10.1371/journal.pbio.0040072
44. Vallender EJ, Lahn BT (2004) Positive selection on the human genome. Hum Mol Genet 13:R245–R254 [PubMed] [Cross Ref]10.1093/hmg/ddh253

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