Clines, Clusters, and the Effect of Study Design on the Inference of Human Population Structure

Abstract

Previously, we observed that without using prior information about individual sampling locations, a clustering algorithm applied to multilocus genotypes from worldwide human populations produced genetic clusters largely coincident with major geographic regions. It has been argued, however, that the degree of clustering is diminished by use of samples with greater uniformity in geographic distribution, and that the clusters we identified were a consequence of uneven sampling along genetic clines. Expanding our earlier dataset from 377 to 993 markers, we systematically examine the influence of several study design variables—sample size, number of loci, number of clusters, assumptions about correlations in allele frequencies across populations, and the geographic dispersion of the sample—on the “clusteredness” of individuals. With all other variables held constant, geographic dispersion is seen to have comparatively little effect on the degree of clustering. Examination of the relationship between genetic and geographic distance supports a view in which the clusters arise not as an artifact of the sampling scheme, but from small discontinuous jumps in genetic distance for most population pairs on opposite sides of geographic barriers, in comparison with genetic distance for pairs on the same side. Thus, analysis of the 993-locus dataset corroborates our earlier results: if enough markers are used with a sufficiently large worldwide sample, individuals can be partitioned into genetic clusters that match major geographic subdivisions of the globe, with some individuals from intermediate geographic locations having mixed membership in the clusters that correspond to neighboring regions.

Comparing population structure as inferred from genealogical versus genetic information

“…Conversely, in this study we were able to identify two clusters and assign individuals almost consistently to their geographic origin, despite F st being only 0.8%, probably due to the fact that Latch and colleagues considered lower numbers of markers and individuals. 9,45 We corroborated our result showing that: (a) individuals belonging to clusters are also genealogically related to other cluster members (by correlating membership coefficients inferred from genomic analysis and average kinship with other cluster members; Figure 2); and (b) the higher the average membership coefficient for a cluster, the greater the kinship between cluster members (as shown by the significant increase in average kinship with increasing membership stringency; Supplementary Table 1).…”

How accurate is the current picture of human genetic variation?

“…A general conclusion from these studies was that human genetic variation is greatly influenced by geography, with genetic differentiation between populations increasing with geographic distance and genetic diversity decreasing with distance from Africa (Relethford, 2004;Prugnolle et al, 2005;Ramachandran et al, 2005;Liu et al, 2006). There has also been some debate over the relative importance of the discontinuities in this pattern of isolation by distance (Kittles and Weiss, 2003;Serre and Pääbo, 2004;Manica et al, 2005;Rosenberg et al, 2005;Witherspoon et al, 2006;Handley et al, 2007;Linz et al, 2007). Recent developments in marker discovery will undoubtedly allow this picture to be refined and lead to a muchimproved understanding of human population structure and past settlement history.…”

Comparison of two geo-evolutionary analysis methods using local and cross-border bovine breeds

“…where the influence of five variables was evaluated on the clustering of individuals using markers such as the loci number, sample size, group number, geographical dispersion of the sample and assumptions about the correlation of the studied allelic frequencies, Rosenberg et al (2005) observed that each of these variables had an effect on the clustering. When the geographical dispersion was used as a variable and the other variables as constants, the geographical dispersion had a very small effect on the clustering capacity (Helmer et al 2003).…”