This paper extends previous genome-wide studies which found structure within continental (mainly European) populations, by showing that PCA shows structure in all continental groups, and in more than the first two principal components that usually correspond with geography.
An interesting table from the paper shows the number of significant principal components within each continent:
Notice the large number of principal components in the Middle East and Central Asia, suggesting a very significant differentiation in these regions, despite the small number of tested populations, and underscoring the need for more comprehensive sampling.
In the case of the Middle East, where only Afroasiatic populations were sampled, this is even more remarkable; further study of Indo-European, Turkic, and Caucasian speakers from the region will no doubt reveal further differences among them. As for Central Asia, the large number of significant principal components is related to both the inter-racial difference between Caucasoids and Mongoloids in this contact zone, as well as intra-racial differences.
The supplementary material (pdfs) has plots for the significant components of the seven continental regions. Progress will now occur by extending population sampling to unexamined populations and by full-genome sequencing which will allow us to detect finer-level distinctions even in fairly homogeneous regions of the world such as Europe.
American Journal of Human Genetics doi:10.1016/j.ajhg.2009.04.015
Genome-wide Insights into the Patterns and Determinants of Fine-Scale Population Structure in Humans
Shameek Biswas et al.
Studying genomic patterns of human population structure provides important insights into human evolutionary history and the relationship among populations, and it has significant practical implications for disease-gene mapping. Here we describe a principal component (PC)-based approach to studying intracontinental population structure in humans, identify the underlying markers mediating the observed patterns of fine-scale population structure, and infer the predominating evolutionary forces shaping local population structure. We applied this methodology to a data set of 650K SNPs genotyped in 944 unrelated individuals from 52 populations and demonstrate that, although typical PC analyses focus on the top axes of variation, substantial information about population structure is contained in lower-ranked PCs. We identified 18 significant PCs, some of which distinguish individual populations. In addition to visually representing sample clusters in PC biplots, we estimated the set of all SNPs significantly correlated with each of the most informative axes of variation. These polymorphisms, unlike ancestry-informative markers (AIMs), constitute a much larger set of loci that drive genomic signatures of population structure. The genome-wide distribution of these significantly correlated markers can largely be accounted for by the stochastic effects of genetic drift, although significant clustering does occur in genomic regions that have been previously implicated as targets of recent adaptive evolution.