On the genetic structure of Denmark

This is a guest post by Yorgos Athanasiadis, our postdoc who did the analysis in the two papers discussed below — Thomas

Scandinavian countries present close linguistic, cultural and historical links with each other. Yet, in our recently published paper (1) we found that they can differ considerably in their genetic fine print. Our study centred primarily on Denmark, but also explored genetic patterns in Sweden and Norway thanks to collaborations with international GWAS consortia.

Our data came from a highly successful citizen science project back in 2013|14 targeting high school students from across the Denmark (2). The resulting sample of about 800 students represents a snapshot of a single generation born in the mid-1990’s, allowing us to calibrate more accurately all of our historical estimates.

A total of 36 schools with good coverage of the entire country participated in the project’s outreach activities. Map taken from ref. 2.
A total of 36 schools with good coverage of the entire country participated in the project’s outreach activities. Map taken from ref. 2.

Classical PCA showed no geographic structure in a subset of about 400 students with all four of their grandparents born in Denmark, but there was weak correlation between PC1 and grandparental place of birth (measured as averaged geographic coordinates). Moreover, average pairwise FST between six well-defined geographic regions was extremely low (0.0002), ranking in between England and Scotland (as reported in Nature). Finally, Cheng and Nielsen’s Ohana revealed similar mixture profiles in all six geographic regions from Denmark, with populations in the east presenting slightly higher affinity with Poland (inset in the following Figure).

Results from Ohana For K = 4. The method helped us identify two well-defined geographical clusters (the Iberian in blue and the East European in yellow), as well as two that are more open to interpretations (we call them Central and Nordic European clusters in red and green, respectively).
Results from Ohana For K = 4. The method helped us identify two well-defined geographical clusters (the Iberian in blue and the East European in yellow), as well as two that are more open to interpretations (we call them Central and Nordic European clusters in red and green, respectively).

All the above methods assume to some extent independence between the used genetic markers, i.e. they do not model LD explicitly. To gain more power in our investigations, we also considered IBD-based methods that leverage haplotype sharing between individuals.

The first thing we tried was to paint Danish chromosomes according to a set of Western European donors and to use the similarities/differences in identifying clusters within Denmark. Interestingly, the method failed to detect any meaningful clustering, lumping all individuals into one big cluster, a fact that points out even further the lack of strong genetic structure in Denmark. This lack of structure was also reflected by the very similar mixture profiles produced by this method across all six regions of Denmark.

Clustering and admixture results for the six geographic regions of Denmark based on chromosome-painting methods. Bar plots are best interpreted as mixture profiles, although in some cases historical insights can also be extracted (e.g. Polish admixture in the Southeast of Denmark). Note that Iberians, contrary to the popular belief, do not seem to have left their mark on the genetic makeup of present-day Denmark.
Clustering and admixture results for the six geographic regions of Denmark based on chromosome-painting methods. Bar plots are best interpreted as mixture profiles, although in some cases historical insights can also be extracted (e.g. Polish admixture in the Southeast of Denmark). Note that Iberians, contrary to the popular belief, do not seem to have left their mark on the genetic makeup of present-day Denmark.

The six Danish regions showed highest affinity with a cluster that we call BRI(tish), because it’s mostly made up by British samples, followed by the NOR(wegian) and SWE(dish) clusters. This is not to say that Danes are about 40% made up by British DNA, as some enthusiastic twitters have mentioned. The BRI cluster also includes German, Belgian and Dutch samples, meaning that it might as well be reflecting some other ethnic component; in lack of a better name, we called it BRI. Another interesting fact is that because of the presence of this cluster, haplotype sharing with other Scandinavians was about 40%. Finally, a small Polish component was detected in the South of Denmark, in the regions where history informs us about the presence of Wend settlements from the 10th century on. Co-ancestry curves provided time estimates for an admixing event that involved a Polish-like ancestral population around 1052 AD – a result that is too congruous to ignore!

We used total IBD sharing within Denmark as a proxy for relatedness and found that participants tend to live close to their closest “genomic relatives”. The geographic distance between any two such individuals presented a bimodal distribution enriched for distances up to 50 Km – probably representing individuals living in urban regions. There was also a significant negative relationship between genetic closeness and geographic proximity.

Results from geographic analysis of IBD sharing patterns. In both plots we see that participants tend to live close to their closest genomic relatives. This observation points out that Denmark presents weak structure that is undetectable by methods assuming unlinked markers.
Results from geographic analysis of IBD sharing patterns. In both plots we see that participants tend to live close to their closest genomic relatives. This observation points out that Denmark presents weak structure that is undetectable by methods assuming unlinked markers.

Finally, IBD sharing was also used to study Ne in historical terms, in a manner similar to the PSMC curves. Interestingly, the three Scandinavian countries presented quite different patterns of historical Ne. Sweden and Norway had more inflated recent Ne, compared to Denmark, possibly due to the lack of strong structure in the latter. Indeed, Sweden and Norway are much larger countries and their landscape provides more opportunities for partial genetic isolation contrary to Denmark’s flattened land and good maritime network.

Results from the IBDNe analysis of three Scandinavian countries. We observed quite distinct patterns of Ne change in the last centuries, possibly reflecting different levels of genetic structure, with Denmark presenting the lowest of them all.
Results from the IBDNe analysis of three Scandinavian countries. We observed quite distinct patterns of Ne change in the last centuries, possibly reflecting different levels of genetic structure, with Denmark presenting the lowest of them all.

Our papers stand as an example of how far one can nowadays go with SNP data in order to answer questions of historical relevance. It is tempting to see our results as proving the obvious: that “Danes are Danes”. However, experience from other genomic projects in European countries has shown that the degree of population structure can be surprisingly high, even for areas of the same size or smaller than Denmark (e.g. the Netherlands and Western France).

References
1. Athanasiadis G, et al.: Nationwide genomic study in Denmark reveals remarkable population homogeneity. Genetics 2016.
2. Athanasiadis G, et al.: Spitting for science: Danish high school students commit to a large-scale self-reported genetic study. PLOS ONE 2016;11:e0161822.

Author: Thomas Mailund

My name is Thomas Mailund and I am a research associate professor at the Bioinformatics Research Center, Uni Aarhus. Before this I did a postdoc at the Dept of Statistics, Uni Oxford, and got my PhD from the Dept of Computer Science, Uni Aarhus.

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