Category Archives: Europe

Dutch: single or dual population?

A recent study deals with the autosomal structure (or lack of it) of the population of the Netherlands.
Oscar Lao et al., Clinal distribution of human genomic diversity across the Netherlands despite archaeological evidence for genetic discontinuities in Dutch population history. Investigative Genetics 2013. Open accessLINK [doi:10.1186/2041-2223-4-9]
They studied the autosomal DNA of almost 1000 anonymous male donors from the Netherlands. Interestingly the lowest cross-validation value was at K=1, what indicates that the Dutch (Frisians included) are a very homogeneous population, that the most accurate result of their splitting into several components produced only one such component.

Supp. fig. 3-A

K=2 and K=3 however produce similarly low scores, however the researchers preferred to study K=5, which makes a shallow valley between its neighboring values. Probably not the best idea but nevertheless the overall result is similar to what they get at K=3.

Supp. Fig. 3b (ADMIXTURE clustering)

K=2 is very intriguing because only a few scattered individuals fall totally (just two) or partly within the second cluster. These individuals persist in their distinctiveness through the whole series. I wonder if they are people with non-European ancestry (no way to know because they are anonymous donors and as far as I could discern ancestry information was not requested from them).
K=3 is what I would consider the most usable K-level, with similar cross-validation score to the lowest one (K=1) and displaying two widely represented clusters (plus the anomalous one mentioned before). However the authors preferred to work on K=5, which, luckily enough, is quite similar to K=3 in the essentials, also showing two basic components (yellow and pink):

Figure 4 Admixture analysis of the Dutch samples. A) Pie chart map of the genome-wide ancestry assignment in the 54 Dutch subpopulations estimated with 10 independent runs by ADMIXTURE [26] using K = 5 assumed parental populations. B) Individual ancestry estimated by ADMIXTURE using K = 5. C) Ternary plot of subpopulations using the three.

If we ignore the ubiquitous orange component and the minor ones, we can appreciate that the country has two distinct areas:
  1. Southern area (dominated by the pink component): including Zeeland, North Brabant, Limburg, South Holland, much of North Holland and, counterintuively, Western Overjissel.
  2. Northern area (dominated by the yellow component): including Friesland, Gröningen, Drenthe and the eastern areas of Gelderland and Overjissel.
  3. Transitional area: Utrecht and parts of Gelderland and North Holland.

Frisian language today
(CC by ArnoldPlaton)
The authors go to great lengths to try to explain this structure but they do not seem to reach any strong conclusion. I’m not any expert in Dutch history but a tentative explanation may be that, roughly, the yellow-dominated areas correspond more strongly to the areas of Low German/Frisian presence and/or some of their prehistoric precursors (often prehistoric cultures of Low Germany tended to be distinct to those further South).

Low Saxon area (NL)
(CC by Gebruker:Grönneger 1)
While Dutch and the related Limburgish dialect are part of the wider Low Franconian category (descending from Frankish Germanic and historically spoken around the Rhine), most of the yellow-dominated regions belong to distinct historical language areas: Frisian and Low German, which are both believed to derive (together with English) from the same ancestral Ingaevonic branch of West Germanic. This historical and prehistorical duality may well explain the modern genetic duality in its fundamentals, if not the genetic boundary in detail.

Your take in any case.

Approx. Germanic dialectal areas some 2000 years ago
Red: North Sea Germanic (Ingaevonic)
Orange: Wesser-Rhine Germanic (Istvaeonic)
full legend
(CC by Hayden120)


Autosomal genetics of the Roma People

Some more information on the genetics of the Roma People.
Prija Moorjani et al., Reconstructing Roma History from Genome-Wide Data. PLoS ONE 2013. Open accessLINK [doi:10.1371/journal.pone.0058633]


The Roma people, living throughout Europe and West Asia, are a diverse population linked by the Romani language and culture. Previous linguistic and genetic studies have suggested that the Roma migrated into Europe from South Asia about 1,000–1,500 years ago. Genetic inferences about Roma history have mostly focused on the Y chromosome and mitochondrial DNA. To explore what additional information can be learned from genome-wide data, we analyzed data from six Roma groups that we genotyped at hundreds of thousands of single nucleotide polymorphisms (SNPs). We estimate that the Roma harbor about 80% West Eurasian ancestry–derived from a combination of European and South Asian sources–and that the date of admixture of South Asian and European ancestry was about 850 years before present. We provide evidence for Eastern Europe being a major source of European ancestry, and North-west India being a major source of the South Asian ancestry in the Roma. By computing allele sharing as a measure of linkage disequilibrium, we estimate that the migration of Roma out of the Indian subcontinent was accompanied by a severe founder event, which appears to have been followed by a major demographic expansion after the arrival in Europe.

The claim of “80%” West Eurasian ancestry seems quite exaggerated on light of the ADMIXTURE data, where at least 40% is clearly of South Asian origin (maybe somewhat more as NW South Asians display some West Eurasian admixture). I guess that they are just speculating on the ANI/ASI (North-South Indian) issue and attributing ANI to a West Eurasian gene pool, what is most confusing to say the least.

Figure 1. Relationship of Roma with other worldwide populations.
(click to expand)

It is true in any case that FST distances are significantly higher with Gujarati Indians (GIH) than with Europeans (CEU, TSI), the former at 0.026, while the latter at only 0.016.
Whatever the case I’d focus on the Fig 1(a) ADMIXTURE graph, because in the (b) one the appearance of European affinity among many South Asians (in my understanding, a 50,000 years-old affinity highlighted only for lack of sufficient K-depth, K=3 only!) is only a factor of confusion. Following this criterion, Roma appear to be some 60% West Eurasian and 40% NW Indian.
Something I really miss in this paper is a more detailed comparison not just with South Asians (more K-depth please!) but also with West Asians, totally absent from the study.
See also: Romani mtDNA

Autosomal DNA of NE Europeans

A paper of some interest is available these days at the Public Library of Science:
Andrey V. Kruhnin et al., A Genome-Wide Analysis of Populations from European Russia Reveals a New Pole of Genetic Diversity in Northern Europe. PLoS ONE 2013. Open accessLINK [doi:10.1371/journal.pone.0058552]


Several studies examined the fine-scale structure of human genetic variation in Europe. However, the European sets analyzed represent mainly northern, western, central, and southern Europe. Here, we report an analysis of approximately 166,000 single nucleotide polymorphisms in populations from eastern (northeastern) Europe: four Russian populations from European Russia, and three populations from the northernmost Finno-Ugric ethnicities (Veps and two contrast groups of Komi people). These were compared with several reference European samples, including Finns, Estonians, Latvians, Poles, Czechs, Germans, and Italians. The results obtained demonstrated genetic heterogeneity of populations living in the region studied. Russians from the central part of European Russia (Tver, Murom, and Kursk) exhibited similarities with populations from central–eastern Europe, and were distant from Russian sample from the northern Russia (Mezen district, Archangelsk region). Komi samples, especially Izhemski Komi, were significantly different from all other populations studied. These can be considered as a second pole of genetic diversity in northern Europe (in addition to the pole, occupied by Finns), as they had a distinct ancestry component. Russians from Mezen and the Finnic-speaking Veps were positioned between the two poles, but differed from each other in the proportions of Komi and Finnic ancestries. In general, our data provides a more complete genetic map of Europe accounting for the diversity in its most eastern (northeastern) populations.

I’m not too sure of how to analyze this paper because, on one side, there’s some missing data, especially in regards to the ADMIXTURE analysis (FST distances between components) and then for some reason the Chinese control was totally removed from further analysis as well, making very difficult for example to estimate if and how much East Asian admixture exists in these NE European populations. Then on the other side, nearly all Finno-Ugrian peoples (as well as the Mezen Russians, genetically Finno-Ugrian as well) are highly endogamous peoples, what almost invariably distorts ADMIXTURE analysis by creating many localized components of dubious relevance.
The ADMIXTURE analysis was presented, as often happens quite incorrectly, for values under the cross-validation optimum, which in this case is at least known: K=6 and K=7 (very similar lowest values):

Figure 4. ADMIXTURE clustering of individuals from the populations studied.
Results obtained at K = 2 to 5 are shown. Each individual is represented by a vertical line composed of colored segments, in which each segment represents the proportion of an individual’s ancestry derived from one of the K ancestral populations. Individuals are grouped by population (labeled on the bottom of the graph). In addition to populations used in principal component analysis, a Chinese sample (Han Chinese from Beijing [22]) was included. The results at K = 5 are also accompanied by average ancestral proportions by population (*). Population designations are the same as in Figure 1.
[From fig. 1:] Key: Komi_Izh – Izhemski Komi, Komi_Pr – Priluzski Komi, Rus_Tv – Russians from Tver, Rus_Ku – Russians from Kursk, Rus_Mu – Russians from Murom, Rus_Me – Russians from Mezen, Finns_He – Finns from Helsinki, Finns_Ku – Finns from Kuusamo, Rus_HGDP – Russians from the Human Genome Diversity Panel.
At least in the supplemental materials we find the missing K-values:

Figure S4. Results of ADMIXTURE clustering at K = 6 to 8. The number of populations and their order are the same as at Figure 4.
[Note: per fig. S5, the optimal K-values are K=6 and K=7]

Something that may call your attention is the relatively high value of the Chinese component in Italians (Tuscans, judging on the locator map). This anomalous effect (unheard of in other studies) may well be caused because a West Asian control is clearly missing and Italians have relatively high West Asian affinity, being otherwise relatively isolated within this Northern European sample. 
Notice also how every single endogamous Finno-Ugric population forms their own cluster: a generic Finno-Ugrian component at K=3 (red), a distinction between the Komi and the Finnic component at K=4 (red and purple), then at K=5 we get a mini-break with a more general North/South Europe distinction showing up (yellow and blue components), but at “optimal” K=6 and K=7, we still see other localized components forming: first Komi_Pr (brown) and then the Vepsian one (grey). So out of seven “optimal” components (K=7), four are local corresponding to highly endogamous populations. 
But I’m running a bit ahead of myself, admittedly. The endogamy index is analyzed as ROH values: nROH for the mean and cROH for the average:

Table 2. Summary of ROH statistics of 16 European populations.

We can see here that large and relatively cosmopolitan populations like Germans and Italians have low ROH values. Czechs and Central Russians come next, with Poles already showing a bit higher endogamy index. Latvians and Estonians are still relatively low but Northern Finno-Ugrian peoples (including Mezen Russians) deviate a lot, with values (at the non-asterisk columns) that are at best almost double than those for Estonians and, at worst, six times higher.
So in this particular case, and quite exceptionally, I’d say that K=2 or K=3 are the most realistic K-values, in spite of scoring quite poor in the cross-validation test. Of course that the N-S European distinction shown at K=5 is also real and not caused by any “effect” but otherwise the clusters showing up correspond to extreme drift caused by isolation and endogamy and therefore only tell us about that peculiarity of the European Far North. 
K=2 is surely the most informative level for East Asian genetic influence, except for  the already mentioned Italian anomaly (which may also affect to lesser extent Central Europeans). However because this study is so limited in this aspect, I’d encourage the development of more informative studies, which could for example ponder the FST distances between components, always informative, and/or use other population sampling strategies that better capture this aspect.
After all this is a study focused on Russia, even if that way it has also produced some valuable information for much of NE Europe.
Figure 3. Principal component
analysis of the combined autosomal genotypic data of individuals from
Russia and seven European countries (Finnland, Estonia, Latvia, Poland,
Czech Republic, Germany [5] and Italia [22]).

first two PCs are shown. The color legend for the predefined population
labels is indicated within the plot. Population designations are the
same as in Figure 1.

Appendix: Finno-Ugrian peoples/languages map by Marting/Nug (anti-copyright):


Y-DNA of Moldovans

Moldova is the easternmost generally recognized sovereign state in Europe speaking a Romance language. Achieving its independence from the Soviet Union in 1991, Moldova saw part of its territory segregated in the mostly unrecognized Republic of Transistria (multiethnic, under military control of Russia). There was some talk about joining the related Republic of Romania but this plan seems to have been abandoned for now.

Alexander Varzari et al., Paleo-Balkan and Slavic Contributions to the Genetic Pool of Moldavians: Insights from the Y Chromosome. PLoS ONE 2013. Open accessLINK [doi:10.1371/journal.pone.0053731]


Moldova has a rich historical and cultural heritage, which may be reflected in the current genetic makeup of its population. To date, no comprehensive studies exist about the population genetic structure of modern Moldavians. To bridge this gap with respect to paternal lineages, we analyzed 37 binary and 17 multiallelic (STRs) polymorphisms on the non-recombining portion of the Y chromosome in 125 Moldavian males. In addition, 53 Ukrainians from eastern Moldova and 54 Romanians from the neighboring eastern Romania were typed using the same set of markers. In Moldavians, 19 Y chromosome haplogroups were identified, the most common being I-M423 (20.8%), R-M17* (17.6%), R-M458 (12.8%), E-v13 (8.8%), R-M269* and R-M412* (both 7.2%). In Romanians, 14 haplogroups were found including I-M423 (40.7%), R-M17* (16.7%), R-M405 (7.4%), E-v13 and R-M412* (both 5.6%). In Ukrainians, 13 haplogroups were identified including R-M17 (34.0%), I-M423 (20.8%), R-M269* (9.4%), N-M178, R-M458 and R-M73 (each 5.7%). Our results show that a significant majority of the Moldavian paternal gene pool belongs to eastern/central European and Balkan/eastern Mediterranean Y lineages. Phylogenetic and AMOVA analyses based on Y-STR loci also revealed that Moldavians are close to both eastern/central European and Balkan-Carpathian populations. The data correlate well with historical accounts and geographical location of the region and thus allow to hypothesize that extant Moldavian paternal genetic lineages arose from extensive recent admixture between genetically autochthonous populations of the Balkan-Carpathian zone and neighboring Slavic groups.

Most interesting is without doubt the list of haplogroups:

Table 2 – Kharahasani is located to the South and Sofia to the North, the Romanian and Ukranian samples are both from nearby regions (Romanian Moldavia and Transistrian Ukranians).

My notes (see ISOGG for nomenclature):
  • The high diversity of haplogroup I (also in nearby Romania and Ukraine), including I1-M253, I2a1b-M423 and I2a2-M223 is consistent with the wider region being, arguably, ancestral to this lineage. However “Low Germanic” I2b does not show up, as doesn’t “West Mediterranean” I2a1a nor Anatolian-Caucasian I2a*.
  • Among Neolithic-specific inputs, which are particularly important in the Balcans, Moldovans show notable (13%) presence of E1b1b1a1-M78 variants, especially the well studied E1b1b1a1b-V13, related now even by ancient DNA to European Neolithic flows. They also have some (2%) E1b1b1b2a-M123, an Eastern and NE African lineage found at low frequencies in Southern Europe.
  • Another clearly Neolithic lineage is G2a-P15, found among Moldovans only at very low frequencies (more common in the context of the Mediterranean Neolithic it seems).
  • Not yet documented by aDNA but also likely Neolithic in Europe is haplogroup J, found in Europe mostly as J2 (originating in Highland West Asia) but also, mostly in the Balcans, as J1 (originating maybe in Palestine). Moldovans show both at low frequencies (4% each).
  • Almost all the rest belongs to the largest European clade, R, mostly its Eastern variant R1a1a-M17 (30%). Western R1b1a2-M269 makes up 16%. 
  • Minor clades are H (Romani), T (South and West Asian, with extensions into East Africa and, thinly, in Europe), N (NE European and North Asian) and Q (probably from West Asia).
In the PC analysis (fig. 2, not shown) Moldovans appear intermediate between Balcanic and Central-Eastern European populations but rather leaning towards the latter.
In spite of their historical and ethno-linguistic connection Romanians and Moldavians do not appear to be particularly related in the genetic aspect:

The genetic relationship between Moldavians and Romanians deserves
special attention, since these two groups speak practically the same
language and share many cultural features. It is reasonable to assume
that Moldavians and Romanians inherited genetic lineages, shared with
other Balkan populations, from Vlachs who, in turn, received them from
Paleo-Balkan tribes. However, Moldavians and Romanians do not form a
cluster that would have separated them from the neighboring populations.
Indeed, in the space of multi-dimensional scaling based on the RST
distances between STR haplotypes, Romanian populations appeared
scattered among the Balkan populations and did not cluster with the
Moldavians (Figure 3). According to the AMOVA analysis, the degree of within-group
differentiation among Moldavian and Romanian populations was
significantly greater than genetic differences between either Romanians
or Moldavians and the group comprised of the Balkan populations (Table 3). Moldavians and Romanians also appear dissimilar on the diagram of binary lineages (PC plot, Figure 2).
Thus, sharing nearly the same language is not accompanied by specific
genetic similarity between Moldavians and Romanians. Furthermore,
Italian populations that share the Romance/Latin language with
Moldavians and Romanians, show little genetic similarity with them.
These results agree with previous genetic studies suggesting that the
genetic landscape of southeast Europe had been formed long before the
modern linguistic/ethnic landscape was shaped [16], [48].

Instead the genetic affinities of Moldovans lean strongly towards their Slavic neighbors from Eastern and Central Europe:

In contrast to Romanians and most other Balkan populations, Moldavians
show a clear genetic similarity to western and eastern Slavs. This is
strongly implied by haplogroup R-M17, which dominates the paternal
lineages of the Slavs and is broadly represented in Moldavians. (…)
The noteworthy domination of R-M17 chromosomes in Moldavians compared to
Romanians is due to the R-M458 subclade. Haplogroup R-M458 likely has
its roots in western/northern Poland, where it has its greatest modern
concentration and microsatellite diversity [49].

This supports my impression of R1a1a1b1a1-M458 being not spread by Slavic migrations (it is very rare among Balcanic Slavs but has an notable presence in Greek Macedonia instead) but much earlier, plausibly by Indoeuropean migrations which had a major sub-center in Poland in the Chalcolithic period.

Posted by on January 19, 2013 in Europe, European origins, Moldova, population genetics, Y-DNA


Mitochondrial DNA of some Slavic peoples

With emphasis on haplogroup H5 and H6.
Marta Mielnik-Sikorska et al., The History of Slavs Inferred from Complete Mitochondrial Genome Sequences. PLoS ONE 2013. Open accessLINK [doi:10.1371/journal.pone.0054360]

Most interesting is maybe table 1 (right), which lists two Polish populations (Kashubia, at the Baltic coast, and Podhale: the Carpathian piedmont), Ukrainians and Czechs.
We can see here that the most common lineages among these Slavs are not different from other European populations, namely H*, H1, U5a, U5b and also the, arguably Neolithic, lineages J1 and T2. I find relevant in this sense that there is a significant amount of T(xT1,T2) among Kashubian Polish especially.
Another point of interest is the minor presence of North and Central Asian lineages A, C, D and G, for which the authors present an elaborate rationale:

… we were able to pinpoint some lineages which could possibly reflect the relatively recent contacts of Slavs with nomadic Altaic peoples (C4a1a, G2a, D5a2a1a1).

They also suggest that the L2a1l2a, found among the Polish, is of Ashkenazi Jewish origin. L1b1a8 found in Polish and Russians belongs to the wider L1b1a, recently argued to be European-specific.
Another point of notice may be the rare HV0(xV) found at significant frequencies among Ukranians (4.5%).
But the authors make a particular effort to discern within haplogroups H5 and H6, which they find of particular interest. H5 might be (with doubts) of Italian origin and they consider its coalescence age (on the dubious molecular clock estimate methods) as clearly pre-Neolithic.
Based on these speculative methods they argue that several Slavic-specific clades within H5 may be contemporary in origin with U4a2, common in Central and Eastern Europe. They consider both to be roughly from Early Neolithic times.

Figure 1. Complete mtDNA phylogenetic tree of haplogroup H5.
Green: Polish, Czech, Slovaks, Ukrainians and Russians
Red: German, Dutch and Austrians
Yellow: Italians and Spaniards
Blue: Irish, British, Danes and Finns
Magenta: Tunisia
→ Black: Levant
Grey: USA
White: unknown geography
If they are correct in their interpretation of the tempo of H5, the hypothesis of H sublineages migrating Northwards, from Southern to Central Europe, within Neolithic would seem to gain some support.
However H5 is less common in the Czech Republic and Austria than in Poland or Ukraine and the Neolithic colonization of Poland should have gone via the Czech Republic and, previously, Austria. Of course we cannot reject upfront a founder effect specific to Poland but what about Ukraine, which was almost totally oblivious to the Balcano-Danubian Neolithic phenomenon?
The other focus is H6, which is found almost only among Ukranian and Czechs of the four target populations. Generally speaking H6a and the most rare H6c are European, while H6b is Central and West Asian. In spite of its extreme rarity, the authors detected H6c in three individuals (one Czech, one Pole and one Slovak), all non-Slavic H6c are from Central-West or NW Europe (or from unknown locations). This seems to define H6c as a rare Northern-Central European haplogroup (excluding Eastern Europe apparently).

The remaining H6 samples sequenced in our study belong to different H6a subclusters being identified as singletons (H6a1a*) or as members of subclusters H6a1a4, H6a1a9 and H6a1b3. Subcluster H6a1a9 is novel, comprising of two haplotypes found in Russians and Ukrainians. Subcluster H6a1b3 is also interesting because it contains, except for European individuals of unknown origin, a founder haplotype of Czech origin and two Polish haplotypes.

Figure 2. Complete mtDNA phylogenetic tree of haplogroup H6 (legend as above).


Eye color, face shape and perception of trustworthiness

An old popular Galician song said:

Ollos verdes son traidores…
azules son mentireiros,
os negros e acastañados son firmes e verdadeiros.


Green eyes are treacherous…
blue ones are deceitful,
the black and brown ones are loyal and truthful.

Just word of a silly mariner song? Intriguingly science confirms now, in a way, part of this perception (at least for blue and brown eyes).
But notice please that it is the precisely the perception what is being confirmed: people seem to perceive blue eyes in general as somewhat less trustworthy. The study says nothing about people with blue eyes being untrustworthy in fact, just that we tend to distrust them more than people with brown eyes.

Karel Kleisner et al., Trustworthy-Looking Face Meets Brown Eyes. PLoS ONE 2013. Open access → LINK [doi:10.1371/journal.pone.0053285]


We tested whether eye color influences perception of trustworthiness.
Facial photographs of 40 female and 40 male students were rated for
perceived trustworthiness. Eye color had a significant effect, the
brown-eyed faces being perceived as more trustworthy than the blue-eyed
ones. Geometric morphometrics, however, revealed significant
correlations between eye color and face shape. Thus, face shape likewise
had a significant effect on perceived trustworthiness but only for male
faces, the effect for female faces not being significant. To determine
whether perception of trustworthiness was being influenced primarily by
eye color or by face shape, we recolored the eyes on the same male
facial photos and repeated the test procedure. Eye color now had no
effect on perceived trustworthiness. We concluded that although the
brown-eyed faces were perceived as more trustworthy than the blue-eyed
ones, it was not brown eye color per se that caused the stronger perception of trustworthiness but rather the facial features associated with brown eyes.

So the authors conclude that it is not eye color but associated face shape what drives untrustworthiness because the phenotype associated with blue eyes is more angular, less rounded, at least for males:

Figure 2. Shape changes associated with eye color and perceived trustworthiness.
spline visualizations of the way face shape correlates with eye color
(a–f) and trustworthiness (g–i). Generated face shapes of blue-eyed
woman (a) and brown-eyed woman (c) compared to average female face (b).
Generated face shapes of blue-eyed man (d) and brown-eyed man (f)
compared to average male face (e). Generated face shapes of
untrustworthy-looking man (g) and trustworthy-looking (i) man compared
to average male face (h). The TPS grids of perceived trustworthiness for
women are not shown because shape analysis did not meet statistical
significance. The generated facial images (a–f) were magnified 3x for
better readability.

They claim that they found no correlation with facial shape for women but I find in the image above almost exactly the same pattern for men and women and not only what they detected: notably the blue eyed people (both genders) and the less trusted men all have in my opinion:
  • Smaller eyes
  • More serious (defiant, analytic, unsympathetic) expression
  • Proportionally broader face or at least jaws
In general the faces to the left look significantly colder, less empathic, a perception that blue eyes can only enhance.
The authors ponder if there is a phenotype linkage disequilibrium associating face and eye color, what seems plausible. But then go on speculating about sexual selection and what not. 
In this sense Razib has an interesting critical analysis questioning if selection is behind the blue eye incomplete sweep in West Eurasia or Europe. If I understand him correctly he seems to suggest, never clearly naming it, that blue eye may have been favored because of the associated skin pigmentation trait, a key adaptive value in the dark winters of Europe and very especially the northern half of it.

Update: is this a peculiarity of Central Europe or the Czech Republic?

A reader sent me an email in which it was questioned if this association is peculiar of the Czech Republic, where the study was performed, and can’t be extended for example to Britain. Examples of soft-faced blue-eyed Britons mentioned were Hugh Grant and Alec Baldwin (I’m not sure if Baldwin is such a good counter-example but Grant is for sure one such case). 

I find it a very good criticism and hope that entices debate.

See also: Causes of skin and hair color variance in Europeans remain undetermined.


Posted by on January 10, 2013 in Anthropometry, Europe, pigmentation, psychology, West Eurasia


Genetic isolates from Friuli

Just a quick mention of this paper on selected rare populations of Friuli because I totally fail to see the angle of interest in this paper, yet, together with other data may be of interest for European population genetics… potentially.
Tõnu Esko et al., Genetic characterization of northeastern Italian population isolates in the context of broader European genetic diversity. European Journal of Human Genertics, 2012. Open accessLINK [doi: 10.1038/ejhg.2012.229]


Population genetic studies on European populations have highlighted Italy as one of genetically most diverse regions. This is possibly due to the country’s complex demographic history and large variability in terrain throughout the territory. This is the reason why Italy is enriched for population isolates, Sardinia being the best-known example. As the population isolates have a great potential in disease-causing genetic variants identification, we aimed to genetically characterize a region from northeastern Italy, which is known for isolated communities. Total of 1310 samples, collected from six geographically isolated villages, were genotyped at >145 000 single-nucleotide polymorphism positions. Newly genotyped data were analyzed jointly with the available genome-wide data sets of individuals of European descent, including several population isolates. Despite the linguistic differences and geographical isolation the village populations still show the greatest genetic similarity to other Italian samples. The genetic isolation and small effective population size of the village populations is manifested by higher levels of genomic homozygosity and elevated linkage disequilibrium. These estimates become even more striking when the detected substructure is taken into account. The observed level of genetic isolation in Friuli-Venezia Giulia region is more extreme according to several measures of isolation compared with Sardinians, French Basques and northern Finns, thus proving the status of an isolate.

Fig. 2
Model-based mapping convergence with SPA. Label position indicates the (a) specific PC1 and PC2 coordinate values for each individual and (b) the mean PC1 and PC2 coordinate values for each population. For (a, b),
the colors have a following meaning: (1) dark blue color: a homogeneous
fraction of the FVG population; a blue color: more general fraction of
the FVG population; a red color: other Italian samples; a violet color:
Basques; an orange color: Slovenians; and green color: all other
populations. For (a, b), the following population abbreviation
labels are used: AT, Austrians; BA, French Basques; BG, Bulgarians; BO,
Borbera; CA, Carlantino; CL, Clauzetto; CH, Swiss; CZ, Czechs; GR,
Germans; ER, Erto; ES, Spaniards; FR, French; HU, Hungarians; IL,
Illegio; IT, Italians; JW_A, Ashkenazy Jews; JW_S, Sephardic Jews; OR,
Orcadians; RE, Resia; RO, Romanians; SA, Sardinians; SA_, Sauris; SMC,
San Martino del Carso; SI, Slovenians; TU, Tuscans. The extra
abbreviations: N, northern; S, southern; I, a more homogeneous
sub-population; G, a more general sub-population.


Posted by on December 21, 2012 in autosomal DNA, Europe, Italy, West Eurasia


Romani autosomal genetics

French Gitanes (Roma)
CC by Fiore S. Barbato
If a few days ago I mentioned the study by Rai et al. of Romani Y-DNA, which locate their origins with great certainty in the NW reaches of the Indian subcontinent, specifically among the lower castes, now I must echo this other study, still in pre-publication stage, which deals with the autosomal genetics of the same European minority.
Priya Moorjani et al., Reconstructing Roma history from genome-wide data. arXiv 2012. Freely accessibleLINK [ref. arXiv:1212.1696]
The authors studied the nuclear genome of 27 Romani individuals from six populations of four European states: Hungary (three different populations), Romania, Slovakia and Spain. 
A reasonable complaint at this stage could be that the size of the sample is small and very specially too concentrated in a very specific area: the Middle and Lower Danube region. But, well, let’s assume that is not too important. 
The authors appear to confirm the NW Indian ancestral affinities of the Roma, however it seems obvious that they have been heavily admixed with Europeans since their migration a thousand years ago. 
The tests performed on this regard find greater affinity to Romanians than other Europeans but no other Balcanic nor West Asian peoples were tested for, so some question marks remain open. Certainly it is a bit puzzling that with all the worldwide comparisons performed in this paper not a single West Asian population was included. 
There are hence some shortcomings in the sampling and analysis strategy (why to compare with tropical Africans but not with Iranians, Turks, Egyptians or Arabs?) but the study still deserves a mention. 
Principal component analysis:

STRUCTURE  analysis:


Causes of skin and hair color variance in Europeans remain undetermined

Portuguese & N. Irish

Our ability to predict pigmentation traits from genetic loci remains limited but this new paper adds some honest research on the matter:

Sophie I. Candille et al., Genome-Wide Association Studies of Quantitatively Measured Skin, Hair, and Eye Pigmentation in Four European Populations. PLoS ONE, 2012. Open access ··> LINK [doi:10.1371/journal.pone.0048294]
One of the findings is that women have darker skin shades than men in Europe (but not among peoples with dark skin from several continents, where men are darker). Another unstated but curiously counterintuitive finding is that Portuguese (from Porto) have on average the same skin tone as Polish (from Warsaw) do:

Table 1. Skin, hair, and eye pigmentation by sex and country.

However for hair and eye color, Polish have lighter shades, approaching the Irish (Dublin) extreme values, while Portuguese approach Italians (Rome) in hair color and show darker eyes on average than anybody else among the sampled populations.
Another curiosity of the survey is that Irish women show significantly lighter hair shades than Irish men, a phenomenon not appreciable elsewhere.
The authors found that, in general:

… in this European sample, pigmentation phenotypes are mainly stratified by country, whereas height is mainly stratified by sex.

They also found that:

Skin and eye pigmentation are correlated in Ireland. Hair and eye pigmentation are correlated in Portugal. Skin and hair pigmentation are correlated in Poland and Italy (Table S2).

What I find rather curious and suggestive of complex genetic influences affecting more than just one pigmentation trait at the same time. But which ones? And why do they seem to operate differently in different populations?
The GWAS analysis found these loci as significant:

Table 2. GWAS, replication, and combined association results for all signals with p-value<10−5 in the GWAS.

Apparently neither the SCIN nor the FLNB genes have been related with pigmentation before. Therefore the authors applied a strong test of reliability (replication in the table), correcting for geographical structure, which actually discarded all loci except the already known ones for eye color in relation to OCA2/HERC2, which were: rs1667394, rs8039195, rs1635168, rs16950987, and rs8028689.
However further analysis showed that rs1667394 is in linkage disequilibrium (LD) with rs12913832 (OCA2), which is the actual culprit of blue eyes (a well known SNP that explains some 45% of the eye color variance among Dutch).
In regard to the failure to detect markers of skin and hair color variance, they conclude that:

The fact that we did not detect reproducible associations with skin or hair color suggests that, unlike eye color, skin and hair pigmentation variation in Europe are not determined by major loci.

Furthermore, genes that have been shown to contribute to skin color variance in South Asians (rs1426654 SLC24A5, rs16891982 SLC45A2, and rs1042602 TYR) or in African-European admixed populations (rs1426654 in SLC24A5 again), fail to show any importance in intra-European variance for this trait. However rs1426654 is fixated in Northern Europeans (CEU), so it cannot show any variation.
Other SNPs (rs16891982 and r183671 in SLC45A2, which are in LD) may contribute to skin pigmentation, however the pattern mentioned (in which Italians and Portuguese are contrasted with Polish and Irish) rather reminds me of the variation for hair and eye color instead.
They also mention that rs885479 in MC1R has not provided any clear association in previous studies but that they did find some association with skin color, however they did not practice the replication test for this SNP.
In the end not much new other than some cold water but an straightforward study for the record.

See also:


Lactose tolerance favors obesity

While the lactose tolerance allele may have some positive health effects, notably because milk is one of the few good dietary sources of calcium, it seems to correlate also with some negative effects, namely obesity.
Ricardo Almon et al., Association of the European Lactase Persistence Variant (LCT-13910 C>T Polymorphism) with Obesity in the Canary Islands. PLoS ONE 2012. Open access ··> LINK [doi:10.1371/journal.pone.0043978]
Canary Islands, in spite of its subtropical geography, is one of the regions of the European Union where milk is most consumed, at levels comparable to Scandinavia. 
Although there is a strong correlation between being lactose tolerant and milk consumption it is not fully clear yet if it is excess milk consumption what makes people obese or an unknown collateral effect of the European lactase persistence allele.
Interestingly the correlation, very strong, is only found for obesity and not for being overweight:

Fig. 1 – BMI classification by LCT genotypes (LP: n = 330; LNP: n = 221)