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

Abstract

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.

Population genetics of malaria resistance in humans

“…Other factors such as population structure and chance, as well as the experimental design or the phenotype(s) of malaria diagnosed in a given study, may impact the conclusions. Efforts to use genomics approaches and genome-wide association analysis have been initiated (Ayodo et al, 2007;Timmann et al, 2007;The Malaria Genomic Epidemiology Network, 2008;Jallow et al, 2009;Eid et al, 2010) and some new regions of potential malaria resistance have been identified.…”

Evolution of the primate trypanolytic factor APOL1

“…Here, we genotyped G1 and G2 in the Yoruba from West Africa (Nigeria) and Luhya, a tribe in Eastern Africa (Kenya) that is genetically very similar to the Yoruba across the genome [fixation index (F st ) = 0.008] (25). We compared frequency differences using a statistical test for differentiation under a neutral model of genetic drift that accounts for random variation in allele frequency (Methods) (26). Of all 1,440,616 markers from HapMap3 that we compared across the genome in the two tribes, none were as differentiated between the two tribes as G1 (P = 5.11 × 10 −7 ) ( Table 2).…”

“…Individuals were genotyped for the G1 and G2 variants using Sequenom iPlex; rs73885319 was used as a proxy for G1 (r 2 between rs73885319 and rs60910145 is nearly one). The East African populations genotyped in this study are described in the work by Ayodo et al (26). The West African populations genotyped in this study have been previously described (41,42), and the samples were originally obtained from participants in a genetic epidemiology study of type 2 diabetes in West Africa (the Africa America Diabetes Mellitus Study) (43).…”

“…To test for statistically significant differentiation of allele frequency in between two populations, we used all 113 Yoruba and 120 Luhya founders from Hapmap3 (44) genotyped on a panel of 1,440,616 SNPs as well as APOL1 coding variants rs73885319 and rs71785313. We assume that the difference in frequencies for a given polymorphism has a mean of zero and variance of cp(1 − p), where p is the ancestral frequency and c = 2 × F st (26). The small sample size in the two analyzed populations requires modeling sampling noise, which has variance of p(1 − p)(1/N 1 + N 2 ), where N 1 and N 2 are the total counts for the alleles for the two populations.…”

Evidence for additive and interaction effects of host genotype and infection in malaria

“…Susceptibility to infection, like any other complex trait, is multifactorial and has a significant heritable component. Genomewide association (GWA) approaches have been extended to mapping the genetic architecture underlying susceptibility to infectious diseases (1)(2)(3)(4)(5), but only hemoglobin mutations and a handful of other loci conferring risk or protection to malaria have been identified (5)(6)(7)(8). There has also been no explicit effort to characterize the effects of host regulatory variation, polygenic inheritance, and genotype-by-infection interactions on malaria phenotypes in vivo.…”