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Important new paper.

See here.

For the most important figure, see here. This figure includes the NEC Turks and the borderline EC/NEC Cypriots - and it’s possible to draw a crude circle around the EC populations, excluding the NECs.

Dienekes writes:

The newer study included a wider sampling of populations, including Cypriots, Turks, and Eastern Slavs among others. Hence, the correspondence with the map of Europe is even stronger than before.

While in the previous study PC2 separated the Finns from the rest, the wider sampling, especially of Eastern Europeans now causes a clearer separation along the east-west axis. Note that PC2 in this study is not the same as PC2 in the previous one, as can be easily seen by e.g., (i) the fact that Portugal and Spain are correctly placed to the west of Great Britain, and (ii) the fact that the Finnish score is about the same as that of Greeks and Yugoslavs in this study. In any case, Finland is represented by a single individual.

This underscores the often-forgotten fact that PCs are calculated from the available data and thus depend on the included populations. The inclusion of Eastern Europeans (esp. Eastern Slavs and Balts) in this study has now made the strong east-west differentiation in Europe, the most salient feature on the second PC. Unfortunately no higher PCs are presented.

Excerpts from the paper, with analysis:

Understanding the genetic structure of human populations is of fundamental interest to medical, forensic and anthropological sciences. Advances in high-throughput genotyping technology have markedly improved our understanding of global patterns of human genetic variation and suggest the potential to use large samples to uncover variation among closely spaced populations. Here we characterize genetic variation in a sample of 3,000 European individuals genotyped at over half a million variable DNA sites in the human genome. Despite low average levels of genetic differentiation among Europeans, we find a close correspondence between genetic and geographic distances; indeed, a geographical map of Europe arises naturally as an efficient two dimensional summary of genetic variation in Europeans. The results emphasize that when mapping the genetic basis of a disease phenotype, spurious associations can arise if genetic structure is not properly accounted for. In addition, the results are relevant to the prospects of genetic ancestry testing; an individual’s DNA can be used to infer their geographic origin with surprising accuracy— often to within a few hundred kilometres.

On a worldwide scale, Europeans as a whole are genetically relatively homogeneous. However, looking more closely, important intra-European differences do exist, and these correlate quite well with geography. Indeed, quite accurate assessments of geographic origin can be obtained from genetic analyses alone.

After removing SNPs with low-quality scores, we applied various stringency criteria to avoid sampling individuals from outside of Europe, to create more even sample sizes across Europe, to exclude individuals with grandparental ancestry from more than location, and to avoid potential complications of SNPs in high linkage disequilibrium (see Methods and Supplementary Table 3).

Attempts were made for reasonably accurate sampling.

The resulting figure bears a notable resemblance to a geographic map of Europe (Fig. 1a). Individuals from the same geographic region cluster together and major populations are distinguishable. Geographically adjacent populations typically abut each other, and recognizable geographical features of Europe such as the Iberian peninsula, the Italian peninsula, southeastern Europe, Cyprus and Turkey are apparent. The data reveal structure even among French-, German- and Italian speaking groups within Switzerland (Fig. 1b), and between Ireland and the United Kingdom (Fig. 1a, IE and GB). Within some countries individuals are strongly differentiated along the principal component (PC) axes, suggesting that in some cases the resolution of the genetic data may exceed that of the available geographic information.

Quite narrow distinctions are possible. Resolution using genetics can be quite high. Commercial autosomal genetic testing companies will have quite a lot of explaining to do if they do not significantly increase the accurate/precise distinctions possible with their tests, and soon (acknowledging the differences inherent in clustering and admixture analyses).

When we quantitatively compare the geographic position of countries with their PC-based genetic positions, we observe few prominent differences between the two (Supplementary Fig. 1), and those that exist can be explained either by small sample sizes (for example, Slovakia (SK)) or by the coarseness of our geographic data (a problem for large countries, for example, Russia (RU)); see Supplementary Information for more detail. Our method also identifies a few individuals who exhibit large differences between their genetic and geographic positions (Supplementary Fig. 2). These individuals may have mis-specified ancestral origins or be recent migrants. In addition, although the sample used here is unlikely to include many members of smaller genetically isolated populations that exist within countries (for example, Basque residing in Spain or France, Orcadians in Scotland, or individuals of Jewish ancestry), in rare cases outlying individuals could reflect membership of such groups. For example, a small set of Italian individuals cluster ‘southwest’ of the main Italian cluster and one might speculate they are individuals of insular Italian origin (for example, Sardinia or Sicily).

A reason why individualized autosomal genetic testing is important. Chosen or imposed ethnoracial identities may not precisely match actual genetic content. If there is a question, direct analyses are required.

The direction of the PC1 axis and its relative strength may reflect a special role for this geographic axis in the demographic history of Europeans (as first suggested in ref. 10). PC1 aligns north-northwest/ south-southeast (NNW/SSE, 216 degrees) and accounts for approximately twice the amount of variation as PC2 (0.30% versus 0.15%, first eigenvalue54.09, second eigenvalue52.04).

Again, the N/S split seems the most important. From the standpoint of “demographic history,” the degree of Neolithic input seems a likely explanation.

But:

However, caution is required because the direction and relative strength of the PC axes are affected by factors such as the spatial distribution of samples (results not shown, also see ref. 9).

More robust evidence for the importance of a roughly NNW/SSE axis in Europe is that, in these same data, haplotype diversity decreases from south to north (A.A. et al., submitted).

This is consistent with my hypothesis that correlates decreased social investment with increased genetic heterogeneity. It also underscores once again the importance of individual testing.

Fig. 1 suggests, European DNA samples can be very informative about the geographical origins of their donors. Using a multiple- regression-based assignment approach, one can place 50% of individuals within 310 km of their reported origin and 90% within 700 km of their origin (Fig. 2 and Supplementary Table 4, results based on populations with n.6). Across all populations, 50% of individuals are placed within 540 km of their reported origin, and 90% of individuals within 840 km (Supplementary Fig. 3 and Supplementary Table 4). These numbers exclude individuals who reported mixed grandparental ancestry, who are typically assigned to locations between those expected from their grandparental origins (results not shown). Note that distances of assignments from reported origin may be reduced if finer-scale information on origin were available for each individual.

This is remarkable.

From the EGI standpoint, papers such as this correlate with Salter’s original conception of measuring genetic interests as a function of gene frequencies (using Fst). However, the also important admixture studies - as well as 23andme’s “ancestry painting” - correlates with genetic structure. Both types of analyses are required, and I would like to see papers include both clustering and admixture analyses in the same work.