The role of climate and out-of-Africa migration in the frequencies of risk alleles for 21 human diseases

Abstract

Background: Demography and environmental adaptation can affect the global distribution of genetic variants and possibly the distribution of disease. Population heterozygosity of single nucleotide polymorphisms has been shown to decrease strongly with distance from Africa and this has been attributed to the effect of serial founding events during the migration of humans out of Africa. Additionally, population allele frequencies have been shown to change due to environmental adaptation. Here, we investigate the relationship of Out-of-Africa migration and climatic variables to the distribution of risk alleles for 21 diseases.

Results: For each disease, we computed the regression of average heterozygosity and average allele frequency of the risk alleles with distance from Africa and 9 environmental variables. We compared these regressions to a null distribution created by regressing statistics for SNPs not associated with disease on distance from Africa and these environmental variables. Additionally, we used Bayenv 2.0 to assess the signal of environmental adaptation associated with individual risk SNPs. For those SNPs in HGDP and HapMap that are risk alleles for type 2 diabetes, we cannot reject that their distribution is as expected from Out-of-Africa migration. However, the allelic statistics for many other diseases correlate more closely with environmental variables than would be expected from the serial founder effect and show signals of environmental adaptation. We report strong environmental interactions with several autoimmune diseases, and note a particularly strong interaction between asthma and summer humidity. Additionally, we identified several risk genes with strong environmental associations.

Conclusions: For most diseases, migration does not explain the distribution of risk alleles and the worldwide pattern of allele frequencies for some diseases may be better explained by environmental associations, which suggests that some selection has acted on these diseases.

Fig. 1

Regression of average heterozygosity of type 2 diabetes risk alleles on distance from Africa. Each point represents the average heterozygosity for one of the 61 populations studied in this paper

Fig. 2

Null Distributions. Blue histograms represent the binned R² values for each of 10,000 sets of resampled SNPs regressed on an environmental variable. Each resampled set contains random SNPs that match the number of risk alleles and global allele frequency of the risk alleles for that disease. Red lines indicate values of R², adjusted as in Methods, with 0.45 for type 2 diabetes on distance from Africa (a) and 0.03 for celiac disease on longitude (b). Before adjustment, the R² values were 0.69 for type 2 diabetes on distance from Africa and 0.13 for celiac disease on longitude. The null distributions for these two diseases are different because each null distribution is created using resampled sets that are matched for number and global allele frequency of the risk alleles. Our analysis included 15 risk alleles for type 2 diabetes and 26 for celiac disease

Fig. 3

P-values for average heterozygosity regressed on environment. P-values are calculated by comparing the R² of the disease risk allele heterozygosities to the null distribution created using 10,000 resampled sets of SNPs matched for number of and global allele frequency of disease risk SNPs. * Indicates significance after adjustment for an FDR of 0.2. + Indicates significance after Bonferroni correction (see “Null Distributions” section in Methods)

Fig. 4

P-values for average risk allele frequency regressed on environment. P-values are calculated by comparing the R² of the disease risk allele frequencies to a null distribution created using 10,000 resampled sets of SNPs matched for number of and global allele frequency of disease risk SNPs. * Indicates significance after adjustment for an FDR of 0.2. + Indicates significance after Bonferroni correction (see “Null Distributions” section in Methods)

Fig. 5

Enrichment of disease risk SNPs in the 0.05 empirical tail in Bayenv. Enrichment is indicated by color. Permutations were carried out to determine whether the number of SNPs with low p-values was more than expected given the total number of risk SNPs for each disease. (see “Enrichment of SNPs with low Bayenv p-values” section in Methods) A star indicates significance at p<0.05 after Bonferroni correction