Geography is a better determinant of human genetic differentiation than ethnicity

Abstract

Individuals differ genetically in their susceptibility to particular diseases and their response to drugs. However, personalized treatments are difficult to develop, because disease susceptibility and drug response generally have poorly characterized genetic architecture. It is thus tempting to use the ethnicity of patients to capture some of the variation in allele frequencies at the genes underlying a clinical trait. The success of such a strategy depends on whether human populations can be accurately classified into discrete genetic ethnic groups. Despite the heated discussions and controversies surrounding this issue, there has been essentially no attempt so far to quantify the relative power of ethnic groups and geography at predicting the proportion of shared alleles between human populations. Here, we present the first such quantification using a dataset of 51 populations typed at 377 autosomal microsatellite markers, and show that pair-wise geographic distances across landmasses constitute a far better predictor than ethnicity. Allele-sharing between human populations worldwide decays smoothly with increasing physical distance. We discuss the relevance of these patterns for the expected distribution of variants of medical interest. The distribution patterns of gene coding for simple traits are expected to be highly heterogeneous, as most such genes experienced strong natural selection. However, variants involved in complex traits are expected to behave essentially neutrally, and we expect them to fit closely our predictions based on microsatellites. We conclude that the use of ethnicity alone will often be inadequate as a basis for medical treatment.

Figure 1

Illustration of the shortest routes through landmasses and specified land bridges. For clarity, we limit ourselves to the routes connecting a single population against all others. We chose the Adygei, an ethnic group of the Russian Caucasus as focal population (larger dot at the centre of the web of routes)

Figure 2

Relationship between pair-wise allele sharing (-log(AS)) and geographic distance for all possible pairs of populations in the dataset. -log(AS) increases significantly with distance (R=0.77, p<0.001).

Figure 3

Boxplot of ranked genetic dissimilarity corrected by geographic distance (i.e. residuals). Pairwise comparisons were grouped as “Between” ethnic groups or within ethnic group (individual groups shown in the plot). There are significant differences among the four ethnic groups with Eurasia and Eastern Asia being characterised by higher homogeneity (ANOSIM R=0.51, p=0.001). Boxes represent the median and interquartile range (IQR); whiskers extend to the most extreme data points up to 1.5 times the IQR; open dots represent outliers outside this range.

Figure 4

Proportion of variance explained (R²) in allele sharing between all pairs of populations by geographic distance along landmasses (Geo), ethnicity (Ethnic), ethnicity after geography has been accounted for (Ethnic. Geo) and Geography after ethnicity was taken into account (Geo. Ethnic). The x-axis represents the cumulative pooling of alleles below a certain overall frequency in the 51 populations analysed.