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May 16, 2011

The genetic complexity of prehistoric Sweden

Thanks to the fact that northern Europe is cool and archaeological research is rather well developed in the region due to quirks of history, there are lots of findings from ancient DNA which are answering long-standing questions. In particular Scandinavia is of special interest in regards to the transition of Europeans from a hunter-gatherer lifestyle to an agricultural one. We know that hunting and gathering as dominant modes of economic production persisted relatively late in European history in this region, up to ~5,000 years before the present. From my cursory reading of the material on the spread of agriculture in northern Europe one dynamic which seems clear is that the rate of expansion was not always constant, and that at the northern fringes in particular social or ecological frontiers served to demarcate the limits to the expansion of farming groups, which often originated from the south and east. Additionally, on the maritime fringes of the North Sea and Baltic there seem to have been relatively dense agglomerations of hunter-gatherers which resisted or coexisted with farming populations for long periods of time (perhaps they were more accurately termed fisher-gatherers!).

This is where Anna Linderholm’s research comes into the picture. I’ve ...

November 3, 2010

The genetic heritage of Europe’s north

Filed under: Culture,Finland,Genetics,Historical Genetics,History,Sweden — Razib Khan @ 1:43 pm

If you haven’t, you should keep an eye on Dienekes‘  Dodecad Ancestry Project (RSS). The pilot phase of data collection is over, and the first population level statistics are now coming out. Of particular interest to me is a new analysis of various northern European ethnicities just published.

The samples used in this analysis are:

- 25 HapMap-3 White Americans. These are the Mormons of predominantly Northwest European heritage

- 5 Dodecad Project Finns

- 25 HGDP-CEPH Russians from Vologda, in north-central European Russia

- 12 Dodecad Project continental Germanics (Scandinavians and Germans)

- 10 Behar et al. (2010) Lithuanians

- 9 Behar et al. (2010) Belorussians

- 3 Dodecad Project Northern Slavs

Below are two visualizations of the genetic structure. First, an MDS. And second, a bar plot of ancestral quanta derived from ADMIXTURE. I’ve added some clarifying labels.



Remember that the data you input into these analyses shape the nature of the outcomes to some extent. All these populations are very genetically close when scaled to average worldwide inter-population genetic variation. So what Dienekes is smoking out here are subtle differences between relatively close groups.

The first clear result supports previous research using uniparental markers: the ethnogenesis of the “Great Russians” involved both demographic expansion, and, cultural assimilation. The process on the southern and eastern frontiers is well documented, because it continued into the early modern period via a series of private wars of expansion. Turkic and Ugric groups were defeated by “Cossacks”, and often themselves integrated into the Cossack population as it expanded further into Siberia and the Steppe. Lenin’s paternal grandmother for example is often claimed to have been a Kalmyk, a branch of the Dzungar Mongol Confederacy which had settled in the lower Volga region. Whatever the truth, Lenin’s father clearly had an Asiatic cast to his features. The ancestral quanta estimates always seem to show that Russians, though not other Slavs further to the west, seem to average around ~5% or so “eastern” ancestry (by analogy, this is about the amount of African ancestry in the typical Levantine Arab).

But the expansion into the Finnic north is less well documented. To some extent the process of Russification began far earlier, as even Kievan Rus at the turn of the first millennium has been claimed to have had Finnic elements (the Rus were Swedes, but they probably picked up Finns in their warbands as they swept south, in addition to the numerous indigenous Finnic groups in northeast Europe). Additionally, unlikely the Muslim Turks these Finnic groups were often small-scale societies without international connections or affiliation with any “higher civilization” which could serve as an oppositional ideology to Orthodox Russian culture. The wide geographic expanse of the Russian ethnos means that one must be exceedingly sensitive to sample representativeness. Readers of Russian or Finnish origin are often aware of which localities in northern Russia were only recently Slavicized, and so express caution in comments as to utilization of those samples as representatives of Slavs more generally.

The second peculiarity are the “Germans” who affiliate with the Finns in the MDS, and contribute to the Finnish element among the Germans. Dienekes says: “without revealing any information, I’ll just say that this is contributed primarily by 3 Dodecad Project members who deviate towards Finns and whose ADMIXTURE analysis shows a higher than expected Northeast Asian component. Their outlier status is also visible in the MDS plot.” By “Northeast Asian” he presumably means one of the 10 ancestral components he’d found in earlier analyses. Without any more information I assume there’s a high probability that these are simply Germanized part-Sami. Much of northern Scandinavia was inhabited by Sami down to the early modern period. For example, the Sami were ethnically cleansed and assimilated across the north half of what is today Sweden as late as the 1600s and 1700s. Though I haven’t done the requisite reading, I wouldn’t be surprised if this was just a function of more advanced farming techniques as well as hardy New World crops such as potatoes which pushed the possible limits of Swedish settlement north.

Finally, there’s a clear Finnic component in the results. As Dienekes noted this Finnic component itself may be a composite of East and West Eurasian elements, just as the South Asian component in Eurasia may be a composite of “Ancient North Indians” and “Ancient South Indians.” One thing to remember about the Finnic component is there’s evidence for a fair amount of genetic variation within Finland. Representativeness is probably key here, just as it is for Russians. Ethnic Finnish individuals with ancestry along the southern and western coasts probably have more affinity with Germanic populations than Karelians.

For many decades there have been arguments as to the provenance of the Finns. Specifically, are they outsiders to Norden who arrived from the east, bringing with them their language? Or are they are indigenous vis-a-vis Germanic speakers? The past is complex, so a simple model is going to shave off a lot of the detail, but I suspect that the truth is closer to the second. It seems that the Finnic groups, or at least their languages, have an ultimate origin in Central Eurasia after the last Ice Age. But they are possibly a circumpolar population which expanded north and practiced hunter-gatherer lifestyles following the ice sheets. Over time agriculturalists expanded north and squeezed them on the margin, but I believe there were natural ecological limits to the practice of techniques derived from Middle Eastern crops. Though northern Finns adopted some agricultural techniques, there was enough of a slowdown of the spread agriculture by Indo-European speakers and their precursors that they managed to hold their own in the north. In much of European Russia, and later in pre-19th century Finland, we see plenty of evidence of language-switching from Finnic to Indo-European (in Finland nationalism resulted in a back-switch over the past 150 years). If the Malthusian pre-modern age had persisted for another two or three centuries I would not be surprised if Finnic languages were totally absorbed by Russian and Scandinavian Indo-European dialects. As it is, 19th century language based nationalism stopped the process of elite culture assimilation, and in some cases reversed it (many elite Finland Swedes abandoned Swedish language and identity in the 19th and early 20th centuries).

Addendum: The picture I present above is simple, and I don’t believe it captures a lot of what happened. For example, from my reading there was a pause of about 1,000 years in the expansion of agriculture once it reached the Kattegat between Denmark and Sweden. I suspect that these long pauses were a function of ecology and geography, as they’re often just too long to be determined by social-political inertia. Additionally, it seems unlikely to me that the first agriculturalists in Europe were Indo-European speakers. Rather, that is possibly a subsequent linguistic overlay, especially in the western regions of Europe.

September 27, 2010

American family values: where even the dull can dream!

ResearchBlogging.orgOne of the issues when talking about the effect of environment and genes on behavioral and social outcomes is that the entanglements are so complicated. People of various political and ideological commitments tend to see the complications as problems for the other side, and yet are often supremely confident of the likely efficacy of their predictions based on models which they shouldn’t even been too sure of. That is why cross-cultural studies are essential. Just as psychology has overly relied on the WEIRD nature of data sets, so it is that those interested in social science need to see if their models are robust across cultures (I’m looking at you economists!).

That is why this ScienceDaily headline, Family, Culture Affect Whether Intelligence Leads to Education, grabbed my attention. The original paper is Family Background Buys an Education in Minnesota but Not in Sweden:

Educational attainment, the highest degree or level of schooling obtained, is associated with important life outcomes, at both the individual level and the group level. Because of this, and because education is expensive, the allocation of education across society is an important social issue. A dynamic quantitative environmental-genetic model can help document the effects of social allocation patterns. We used this model to compare the moderating effect of general intelligence on the environmental and genetic factors that influence educational attainment in Sweden and the U.S. state of Minnesota. Patterns of genetic influence on educational outcomes were similar in these two regions, but patterns of shared environmental influence differed markedly. In Sweden, shared environmental influence on educational attainment was particularly important for people of high intelligence, whereas in Minnesota, shared environmental influences on educational attainment were particularly important for people of low intelligence. This difference may be the result of differing access to education: state-supported access (on the basis of ability) to a uniform higher-education system in Sweden versus family-supported access to a more diverse higher-education system in the United States.

Minnesota is to some extent the Scandinavia of America, so the cross-cultural difference is particularly notable. You wouldn’t be surprised for example by big differences between Mississippi and Sweden. But looking at a comparison between the Upper Midwest and Scandinavia is closer to seeing the impact of national culture and policy differences on populations which were originally very similar.

Their methodology was simple, though as with much of this sort of behavior genetic work the statistical analysis can be somewhat labored. In both Sweden and Minnesota you had samples of dizygotic and monozygotic twins which give you a way to compare the effect of genes on variation in life outcomes. Sweden has large data sets from male conscription for behavior genetics analysis. They compared this with the Minnesota Twin Family Study data set.

Since the topline results are pretty straightforward, I thought I’d give you some statistics. Table 1 has raw correlations. Note that they converted educational attainment into a seven-point scale, less than 9 years of education to completion of doctoral studies.


You see the expected drop off in correlation between identical and fraternal twins. Identical twins share more genetic identity than fraternal twins, so they’re going to be more identical on a host of metrics aside from appearance. Those are just raw correlation values of traits though across categories of twins. The core intent of the paper was to explore the relationship between genes, family environment, and other environmental factors, and educational attainment. To do this they constructed a model. Below you see estimates of the variance in the trait explained by variance in genes, shared environment (family), non-shared environment (basically “noise” or error, or it could be something like peer group), from Sweden to Minnesota, and, at three intelligence levels. Two standard deviations below the norm is borderline retarded, ~2.5% of the population or so, and two standard deviations above the norm is at Mensa level.


It’s interesting that as you move up the IQ scale the genetic variation explains more and more of variance the educational attainment. Someone with an IQ of 130 is likely to be university educated. But there are many who are not. Why? The way I interpret these results is that if you are that intelligent and do not manage to complete university you may have heritable dispositions of personality which result in you not completing university. If, for example, you come from a family which is very intelligent, but is low on conscientiousness, then there may be a high likelihood that you just won’t complete university because you can’t be bothered to focus. Or, you may have personality characteristics so that you don’t want to complete university. A second major finding here is that Sweden and the USA go in totally different directions when it comes to the sub-average and dull in prediction of years of education. Why? The explanation in the paper seems plausible: Sweden strongly constrains higher education supply, but makes it available to those with proven academic attainments at a nominal price. Family encouragement and connections don’t matter as much, if you can’t pass the university entrance examination you can’t pass it. In contrast in the USA if you’re dull, but come from a more educated or wealthier family, you can find some university or institution of higher education which you can matriculate in. Supply is more flexible to meet the demand. I actually know of a woman who is strongly suspected to be retarded by her friends. I have been told she actually tested in the retarded range in elementary school but was taken out of that  track because her family demanded it (she’s the product of a later conception, and her family made their money in real estate, not through professional advancement). Over the years she has enrolled in various community colleges, but never to my knowledge has she completed a degree. If she had not had family connections there is a high probability she wouldn’t have completed high school. As it is, she can check off “some college” on demographic surveys despite likely be functionally retarded.

The next table is a bit more confusing. It shows you the correlations between the effects of the variable on education and intelligence. In other words, does a change in X have the same directional effect on Y and Z, and what is the extent of the correspondence between the effect on Y and Z.


Shared environment had almost the same effect on intelligence and education, while genetics had a more modest simultaneous effect. Not too surprising that non-shared environment didn’t have a strong correlation in effect across the traits, the authors note that much of this is going to noise in the model, and so not systematically biased in any direction. Though the confidence intervals here are a little strange. I’m not going to get into the details of the model, because frankly I’m not going to replicate the analysis with their data myself. That’s why I wanted to present raw correlations first. That’s pretty straightforward. Estimates of variances out of models with a set of parameters is less so. Here’s an interesting point from the correlations in the last table:

The patterns of genetic correlations in the two samples differed. In Sweden, genetic correlation was steadily in excess of .50 across the range of intelligence, indicating a genetically influenced direct effect of intelligence on educational attainment that was weaker than the shared environmental effect on educational attainment. In the MTFS [Minnesota] population, however, genetic correlation was in excess of .50 when level of intelligence was low, but was halved at higher levels of intelligence. This indicated that genetic influences on intelligence tended to limit educational attainment when the level of intelligence was low, but not when the level of intelligence was average or high.

Now let me jump to conclusion:

This finding indicates that genetic influences common to intelligence and educational attainment may have been more effective in limiting educational attainment in individuals with low levels of intelligence than in encouraging educational attainment in those with high levels of intelligence. As in Sweden, shared environmental influences on intelligence and educational attainment were completely linked, indicating a direct contribution from shared environmental influences on intelligence to educational attainment. The decrease in shared environmental variance with higher intelligence, however, indicated that shared environmental influences were more effective in encouraging educational attainment in higher-intelligence individuals than in limiting educational attainment in lower-intelligence individuals. In other words, in populations in which shared environmental influences such as family history and values encouraged high levels of educational attainment, individuals were able to surmount limitations in intelligence.

Our analysis does not permit the conclusion that these differences in educational systems cause the differences in environmental and genetic influences on educational attainment observed in this study, but it is reasonable to hypothesize that this is likely. In particular, the greater expense of higher education and greater subjectivity of admission standards in the United States compared with Sweden may partially explain the very different patterns of shared environmental influences in the two population samples. Regardless of the causes underlying the differences we observed, the results of our study make clear that the degrees of environmental and genetic influences can vary substantially between groups with different circumstances, and even within such groups. Our results also suggest that the ways in which social systems are organized may have implications for how and to what extent environmental and genetic influences on behavior will be expressed.

This discussion about the role of environment, genes, and culture, on various outcomes should not hinge on one paper. But, these sorts of results are often not widely disseminated among the intellectual classes. One aspect of the American educational system in contrast to some other nations is that not-too-brights have university degrees. Education has long been a project for social engineering in the USA, going back to Horace Mann. Legacies, underrepresented minorities, the poor, those with particular talents, etc., are all part of the decentralized system of university admissions in the United States. In contrast, in nations such as Sweden or Japan there is a more centralized and universal set of criteria. This results is more perfect sorting by the metrics of interest without considerations of social engineering. I know that Sweden has traditionally had a small aristocratic class, while the Japanese aristocracy were basically abolished after World War II. Additionally, both are relatively homogeneous societies so considerations of racial representativeness are not operative. Or weren’t until recently in the case of Sweden. But consider one reality: if such a system is perfectly meritocratic over time if the traits being evaluated are heritable then you will have genetic stratification and reduction of social mobility assuming assortative mating at university.

Currently there is some handwringing by the elites about the fact that so few poor kids get admitted to Ivy League universities. I think there’s a simple way to change this: get rid of the implicit Asian quotas. After all, there was a lot of socioeconomic diversity after the Ivy League universities got rid of their Jewish quotas, but the children of the Jews who didn’t have to go to CUNY and went to Harvard are well off themselves. But more socioeconomic mobility through removing the implicit Asian quota would cause other difficulties, as elite private universities need their slots for both legacies as well as underrepresented minorities for purposes of social engineering/fostering diversity/encouraging donations. Additionally, just as with the Jews the welter of mobility in one generation of the children of Asian immigrants would settle into quiescence in the next if the traits which enable university admission are genetically or culturally heritable.

Citation: Johnson W, Deary IJ, Silventoinen K, Tynelius P, & Rasmussen F (2010). Family background buys an education in Minnesota but not in Sweden. Psychological science : a journal of the American Psychological Society / APS, 21 (9), 1266-73 PMID: 20679521

September 21, 2010

Swedes are not sexist or nativist

A party, the Sweden Democrats, is about to enter the Swedish parliamanent which is described in this way in Wikipedia:

The party has its origins in the nationalist movement Bevara Sverige Svenskt (”Keep Sweden Swedish”)…During the mid 1990s, the party leader Mikael Jansson strove to make the party more respectable, modelling it after other “euronationalist” parties, most prominently the French National Front. This policy continues to be followed by the present leader Jimmie Åkesson. This effort included ousting openly extremist members.

Yes. More respectable by modeling itself on the National Front. Here’s a bit about the organization which eventually grew into the Sweden Democrats:

Bevara Sverige Svenskt (”Keep Sweden Swedish”) was a Swedish nationalist movement based in Stockholm and is a slogan used by various Swedish nationalist parties. The stated objective of the BSS movement, and the aim of the slogan, was to initiate a debate in order to reduce immigration from non-European countries and repatriate non-ethnic Swedes.

The Swedes, and the world, are shocked. Should they be? From what I can tell the Social Democratic Party of Sweden no longer has a hegemonic grip on Sweden’s politics. But the core working class base of such coalitions is shrinking because of economic restructuring throughout the developed world, with the remnants often defecting to Right-populism. Today Gunnar Mydral would have to look to writing a book about his own nation, which has about the same foreign born proportion as the USA (though that is a touch deceptive as many of these are other Scandinavians or Finns).

This prompted me to look in the World Values Survey. Specifically, the last wave which started around 2005. One thing you notice in the survey is that Swedes are very politically correct, even compared to their Nordic neighbors. I have read that the ecological awareness imputed to Native Americans in part because of the Noble Savage idea has actually resulted in a real shift and striving by many Native Americans to actually implement those ideals. Sometimes I wonder if the Swedes are so “progressive” and “forward thinking” in surveys because everyone always pats them on the back for being progressive and forward thinking. Sweden sure is the least sexist and nativist nation in the WVS.

There are two questions which ask about job preference in times of scarcity. First, “Employers should give priority to (nation) people than immigrants,” and second, “Men should have more right to a job than women.” There are three responses: agree, disagree, neither. Let’s code agree = 1, disagree = -1, and neither as 0. Weight by proportion and get an index of “nativism” and “sexism” within the population. If you get a score of -1, that would mean everyone was nativist or sexist. If you get 0, that would indicate perfect balance. 0.5, a touch on the nativist or sexist side. The plot below has sexism on the x-axis, and nativism on the y-axis.


Though I think racism is more taboo than sexism internationally (if Saudis explicitly treated blacks in their nation as they do women there would be a natural boycott. One of the reasons the Saudis banned slavery in 1960 had to do with protests which they kept encountering in the civilized world). But sexism is more taboo than nativism (I think there are important reasons for the rank order, but that’s not a matter for this post). The correlation between nativism and sexism is ~0.76, so variation in sexism explains 58% of the variation in nativism. As you can see Sweden is a definite outlier.

Note: don’t attach too much normative baggage to my use of the terms “sexist” and “nativist.” They seemed compact and communicated the underlying sentiments.

Here are the raw values:

Men should get preference in jobs over women
Country Agree Disagree Neither
Sweden 2.10% 94.10% 3.80%
Andorra 4.40% 89.90% 5.70%
Ethiopia 6.00% 85.60% 8.40%
Norway 6.50% 88.60% 5.00%
United States 6.80% 66.40% 26.80%
New Zealand 8.00% 72.60% 19.40%
Finland 9.60% 81.50% 8.80%
Netherlands 12.50% 81.40% 6.20%
Serbia 12.50% 63.10% 24.30%
Slovenia 13.60% 73.50% 13.00%
Australia 13.90% 64.70% 21.40%
Canada 14.30% 77.90% 7.80%
Great Britain 16.20% 76.10% 7.70%
Spain 17.40% 76.00% 6.60%
Peru 17.70% 72.80% 9.50%
Germany 17.80% 66.80% 15.40%
France 18.10% 73.80% 8.10%
Guatemala 19.10% 72.30% 8.60%
Hong Kong 21.60% 44.20% 34.30%
Uruguay 21.90% 69.30% 8.90%
Italy 22.00% 59.20% 18.80%
Switzerland 22.10% 62.90% 15.00%
Brazil 22.30% 64.10% 13.60%
Bulgaria 24.20% 52.60% 23.20%
Mexico 25.30% 67.60% 7.00%
Trinidad 25.30% 65.70% 8.90%
Rwanda 25.30% 64.20% 10.50%
Japan 27.10% 17.90% 55.00%
Argentina 27.70% 60.00% 12.30%
Chile 30.20% 46.30% 23.50%
Poland 30.80% 51.00% 18.20%
Thailand 32.30% 40.60% 27.20%
Ukraine 32.50% 44.70% 22.80%
Zambia 33.60% 51.50% 15.00%
Romania 35.20% 40.90% 23.90%
South Korea 36.50% 26.40% 37.10%
Cyprus 36.50% 46.40% 17.10%
Russia 36.60% 43.70% 19.70%
South Africa 37.10% 49.50% 13.40%
Moldova 38.10% 39.00% 22.90%
Viet Nam 40.80% 37.70% 21.50%
China 42.30% 32.70% 25.10%
Taiwan 43.60% 36.00% 20.40%
Malaysia 49.00% 15.20% 35.70%
Morocco 50.80% 33.20% 16.00%
India 51.40% 20.50% 28.10%
Burkina Faso 52.30% 34.80% 12.90%
Georgia 52.50% 26.10% 21.40%
Turkey 53.30% 29.80% 16.90%
Ghana 53.60% 37.40% 8.90%
Indonesia 55.40% 36.20% 8.40%
Mali 62.40% 22.80% 14.80%
Iran 69.40% 16.50% 14.10%
Jordan 88.20% 7.90% 3.90%
Egypt 89.10% 4.30% 6.60%

Natives should get preference in jobs over immigrants
Sweden 11.80% 79.90% 8.30%
Andorra 29.80% 53.20% 17.00%
Norway 34.70% 57.30% 8.00%
Netherlands 40.10% 49.80% 10.20%
Canada 40.90% 46.10% 13.10%
Australia 41.60% 36.40% 21.90%
France 42.10% 46.40% 11.50%
Serbia 44.70% 28.80% 26.40%
Switzerland 48.00% 35.50% 16.50%
New Zealand 51.90% 29.30% 18.80%
Great Britain 52.90% 36.40% 10.60%
Ethiopia 54.70% 29.30% 16.00%
Finland 54.90% 30.80% 14.30%
United States 55.40% 20.00% 24.60%
Germany 55.70% 27.90% 16.40%
Spain 57.70% 34.20% 8.10%
Thailand 61.20% 16.80% 22.10%
Japan 62.70% 6.10% 31.20%
Italy 63.90% 19.10% 17.00%
Turkey 64.40% 23.20% 12.40%
Romania 65.10% 14.60% 20.30%
China 66.00% 13.70% 20.40%
Ukraine 69.90% 16.20% 13.90%
Burkina Faso 71.70% 18.80% 9.50%
Argentina 71.90% 17.40% 10.70%
Hong Kong 72.30% 3.80% 23.90%
Uruguay 72.50% 21.30% 6.30%
Rwanda 72.60% 18.00% 9.40%
Slovenia 73.70% 15.00% 11.30%
Viet Nam 74.30% 10.80% 14.90%
Mexico 74.80% 19.60% 5.60%
India 75.20% 6.10% 18.70%
Moldova 75.50% 8.50% 15.90%
Bulgaria 76.60% 14.70% 8.70%
Zambia 77.00% 11.40% 11.60%
South Africa 78.30% 11.00% 10.70%
Cyprus 78.60% 12.20% 9.20%
South Korea 78.90% 2.40% 18.70%
Guatemala 79.60% 10.20% 10.30%
Chile 79.80% 7.50% 12.70%
Brazil 81.40% 9.50% 9.10%
Russia 81.40% 9.00% 9.60%
Poland 81.60% 8.40% 10.00%
Peru 82.20% 12.50% 5.30%
Mali 83.80% 7.10% 9.10%
Trinidad 84.00% 10.80% 5.30%
Morocco 84.90% 5.60% 9.50%
Ghana 85.20% 8.60% 6.10%
Malaysia 86.10% 2.10% 11.80%
Georgia 87.00% 4.50% 8.60%
Indonesia 87.40% 5.50% 7.10%
Iran 89.00% 5.40% 5.60%
Taiwan 91.00% 3.80% 5.20%
Egypt 97.90% 0.20% 1.90%
Jordan 98.50% 0.80% 0.70%

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