"Mongoloidness" and pigmentation among Finno-Ugric peoples

Heapost, L. INDEX OF MONGOLOIDNESS AND PIGMENTATION IN K. MARK STUDIES. Papers on Anthropology; 2004, Vol. 13, p18-37.

The paper presents an overview of some descriptive anthropological traits of Finno-Ugrians and their neighbouring peoples (133 local ethnic groups, 13,000 individuals). To compare all the ethnic groups between themselves the index of Mongoloidness (MI) was calculated on the basis of eight traits and the index of pigmentation (PI) on the basis of two traits. The results were compared on a correlation field. Here, two tendencies expressing different directions could be discerned: 1) a grouping where the decrease in MI is accompanied by the increase in PI (most Baltic Finns and Erza Mordvinians, Terjuhans and Finnish Swedes); 2) a grouping, which includes most other Finno-Ugric peoples. Here a tendency can be noticed of both indexes increasing in the same direction. A compact grouping that deserves special attention here includes the ethnic groups with the highest values of MI and PI (most Mari, part of Udmurts, northern and Kola Sami, also one group of Chuvash and Tatars). The comparison of ethnic groups on the basis of these indexes provides a graphic overview of the morphological peculiarities of the peoples which are in one way or another connected with the historical developments of the peoples of different regions.

I'm not convinced "Mongoloid" features among western Uralic speakers actually derive from Mongoloid admixture. Certainly if there had been significant recent admixture we'd expect more "Mongoloid" morphology to be associated with darker pigmentation. More excerpts within:

Another European genetic structure paper

I was alerted to this study through a post by one of gnxp's competent (i.e., most likely white) posters, and I haven't yet read it. The findings appear to be similar to those of the study released earlier this month (I would guess--based on another recent paper by some of the same authors--they even use some of the same samples). But there are a few new data points: samples from Latvia, Russia, Ukraine, Cyprus, and Turkey show up in the plot. Contrary to the speculation of some, Balts and Russians appear to be even more distant than Finns on PC2, rather than intermediate between Finns and other Europeans.

Update: Per the supplementary material, the sample sizes for Finland, Slovakia, and Ukraine are only 1 each, and 6 for Russia.

Nature advance online publication 31 August 2008 | doi:10.1038/nature07331; Received 30 May 2008; Accepted 12 August 2008; Published online 31 August 2008

Genes mirror geography within Europe

John Novembre et al.

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 populations1, 2, 3, 4, 5. 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 testing6; an individual's DNA can be used to infer their geographic origin with surprising accuracy—often to within a few hundred kilometres.

Also of interest, from the gnxp post:

These authors also develop a model that does reasonably well at predicting the country of origin of an individual based on genetics alone.
[ . . .]
The method the authors develop for predicting an individual's country of origin from genetics are only a beginning for this kind of application of genetic data. They note that the SNP chip used in the study only includes common variation, while rare variants are likely to be much more geographically restricted (and thus more informative in this kind of analysis). The limits to the resolution of these sorts of methods are likely to be very fine indeed; the authors note that, even with this panel, they're able to distinguish with some confidence individuals that are from the German, Italian, and French-speaking parts of Switzerland. With full resequencing data, it's likely that even the precise village of origin of an individual will be predictable from genetics alone.

Fine-scale genetic substructure in Finland and Sweden

Compared to the recent Europe-wide genetic structure paper, this paper contains more (and better-characterized with respect to geography) samples from Finland and Sweden, but typed at fewer loci. The authors detect an east-west duality in Finland. They fail to detect substructure within Sweden, though poorer-quality data or the presence of non-European immigrants in their Swedish sample may be confusing the issue. Nonetheless:

The principal component analysis clearly separated the Finnish regions and Eastern and Western counties from the Swedish as well as the Finnish regions and counties from each other (Figure 2C and 2D). Geneland showed three clusters (Figure 3B), roughly corresponding to Sweden, Eastern Finland and Western Finland. Thus, Geneland was able to correctly identify the country of origin of the individuals despite the lower quality of the Swedish data. Interestingly, the county-level PCA (Figure 2D) and Geneland (Figure 3B) placed the Finnish subpopulation of Swedish-speaking Ostrobothnia closest to Sweden. This minority population originates from the 13th century, when Swedish settlers inhabited areas of coastal Finland [34]. Our result is in congruence with earlier studies where intermediate allele frequencies between Finns and Swedes have been observed in the Swedish speaking Finns [35].

Geneland is an algorithm which "in contrast with Structure, assumes that population membership is structured across space":

If this assumption is correct, the power of inferring clusters increases; if the assumption is incorrect, it will lead to a loss of power but generally not to inference of spurious clusters (in the case of weak spatial organization, Geneland tends to perform like Structure in terms of inferred clusters [27]). Besides, in previous studies with similar goals it has been estimated that Structure needs a minimum of 65 to 100 random markers to separate continental groups and that the number of markers rather than samples is the most important parameter determining statistical power [13, 37]. The differences between and within the neighbouring countries studied here are presumably smaller than those between continents and not large enough to be detected by Structure.

The detection of three clusters by Geneland versus one single cluster by Structure can thus be interpreted as an example of increased power in spatially structured populations.

[. . .]

Our results from the Geneland algorithm demonstrate the benefit of including spatial information in clustering individuals according to their genetic similarity, particularly at low levels of differentiation. Although Geneland has successfully clustered individuals into groups with low or moderate FST in ecological studies [44-46], to the best of our knowledge, this is the first time the algorithm has been used for human or SNP data.

The abstract:

Population substructure in Finland and Sweden revealed by the use of spatial coordinates and a small number of unlinked autosomal SNPs

Ulf Hannelius, Elina Salmela, Tuuli Lappalainen, Gilles Guillot, Cecilia M Lindgren, Ulrika von Dobeln, Paivi Lahermo and Juha Kere

BMC Genetics 2008, 9:54doi:10.1186/1471-2156-9-54
Published: 19 August 2008

Abstract (provisional)

Background
Despite several thousands of years of close contacts, there are genetic differences between the neighbouring countries of Finland and Sweden. Within Finland, signs of an east-west duality have been observed, whereas the population structure within Sweden has been suggested to be more subtle. With a fine-scale substructure like this, inferring the cluster membership of individuals requires a large number of markers. However, some studies have suggested that this number could be reduced if the individual spatial coordinates are taken into account in the analysis.

Results
We genotyped 34 unlinked autosomal single nucleotide polymorphisms (SNPs), originally designed for zygosity testing, from 2044 samples from Sweden and 657 samples from Finland, and 30 short tandem repeats (STRs) from 465 Finnish samples. We saw significant population structure within Finland but not between the countries or within Sweden, and isolation by distance within Finland and between the countries. In Sweden, we found a deficit of heterozygotes that we could explain by simulation studies to be due to both a small non-random genotyping error and hidden substructure caused by immigration. Geneland, a model-based Bayesian clustering algorithm, clustered the individuals into groups that corresponded to Sweden and Eastern and Western Finland when spatial coordinates were used, whereas in the absence of spatial information, only one cluster was inferred.

Conclusions
We show that the power to cluster individuals based on their genetic similarity is increased when including information about the spatial coordinates. We also demonstrate the importance of estimating the size and effect of genotyping error in population genetics in order to strengthen the validity of the results.