AbstractIn many species a fundamental feature of genetic diversity is that genetic similarity decays with geographic distance; however, this relationship is often complex, and may vary across space and time. Methods to uncover and visualize such relationships have widespread use for analyses in molecular ecology, conservation genetics, evolutionary genetics, and human genetics. While several frameworks exist, a promising approach is to infer maps of how migration rates vary across geographic space. Such maps could, in principle, be estimated across time to reveal the full complexity of population histories. Here, we take a step in this direction: we present a method to infer maps of population sizes and migration rates associated with different time periods from a matrix of genetic similarity between every pair of individuals. Specifically, genetic similarity is measured by counting the number of long segments of haplotype sharing (also known as identity-by-descent tracts). By varying the length of these segments we obtain parameter estimates associated with different time periods. Using simulations, we show that the method can reveal time-varying migration rates and population sizes, including changes that are not detectable when using a similar method that ignores haplotypic structure. We apply the method to a dataset of contemporary European individuals (POPRES), and provide an integrated analysis of recent population structure and growth over the last ∼3,000 years in Europe.
That’s interesting, I suppose, but what is really needed from population genetics is two things. First, global assays of genetic kinship. Second, application of genetic structure and genetic integration (e.g., Gillet and Gregorious) to human genetic data. These things are consistently not being done. Is it because they are viewed as uninteresting to the field, or is it because the findings would be politically unpalatable to the field?
Author summaryWe introduce a novel statistical method to infer migration rates and population sizes across space in recent time periods. Our approach builds upon the previously developed EEMS method, which infers effective migration rates under a dense lattice. Similarly, we infer demographic parameters under a lattice and use a (Voronoi) prior to regularize parameters of the model. However, our method differs from EEMS in a few key respects. First, we use the coalescent model parameterized by migration rates and population sizes while EEMS uses a resistance model. As another key difference, our method uses haplotype data while EEMS uses the average genetic distance. A consequence of using haplotype data is that our method can separately estimate migration rates and population sizes, which in essence is done by using a recombination rate map to calibrate the decay of haplotypes over time. An additional useful feature of haplotype data is that, by varying the lengths analyzed, we can infer demography associated with different recent time periods. We call our method MAPS for estimating Migration And Population-size Surfaces. To illustrate MAPS on real data, we analyze a genome-wide SNP dataset on 2224 individuals of European ancestry.
I’m not going to judge the validity of this approach without more data; however, any cursory look at current population genetic studies illustrates how the “testing companies” are behind the cutting edge of methodology.
Largely speaking, the spatial variation in inferred dispersal rates and population densities is remarkably consistent across the different time scales (Fig 4). In the MAPS dispersal surfaces, several regions with consistently low estimated dispersal rates coincide with geographic features that would be expected to reduce gene flow, including the English Channel, Adriatic Sea and the Alps.
In general, geographic barriers have historically impeded (but obviously not abrogated) gene flow.
In addition we see consistently high dispersal across the region between the UK and Norway, which may reflect the known genetic effects of the Viking expansion .
See more on this below.
These features are consistent with visual inspection of the raw lPSC sharing data (S4b Fig). The MAPS population density surfaces consistently show lowest density in Ireland, Switzerland, Iberia, and the southwest region of the Balkans. This is consistent with samples within each of these areas having among the highest PSC segment sharing (S4a Fig). The MAPS inferred country population sizes are also highly correlated with estimated current census population sizes from  and  (S5 Fig) which can be mainly attributed to the fact that lPSC segments are highly informative of current census population sizes (Fig 5).
We do note the lower estimated dispersal rates between Portugal and Spain compared to the rest of Europe in the analyses of longer PSC segments (5-10 and > 10cM), and the higher estimated dispersal rates through the Baltic Sea (> 10cM segments), possibly reflecting changing gene flow in these regions in recent history.
I’m not sure what to make of that Iberian data. I’m not aware of any significant geographical barrier there, so is that an example of political barriers affecting gene flow? The data of this paper call into question “testing companies” using generalized “Iberian” or “British/Irish” ancestral categories.
Our estimates of dispersal distances and population density from the POPRES data are among the first such estimates using a spatial model for Europe (though see ). The features observed in the dispersal and population density surfaces are in principle discernible by careful inspection of the numbers of shared PSC segments between pairs of countries (e.g. using average pairwise numbers of shared segments, S4b Fig, as in ). For example, high connectivity across the North Sea is reflected in the raw PSC calls: samples from the British Isles share a relatively high number of PSC segments with those from Sweden (S4b Fig).
This is consistent with what is mentioned above, compatible with the historically known gene flow from Scandinavia to the British Isles, particularly England, during the Viking age.
Also the low estimated dispersal between Switzerland and Italy is consistent with Swiss samples sharing relatively few PSC segments with Italians given their close proximity (S4b Fig).
The Alps being one of the geographical barriers mentioned above. This of course is not compatible with Der Movement dogma of Northern Italians being “Celto-Germanic Nordics.”
However, identifying interesting patterns directly from the PSC segment sharing data is not straightforward, and one goal of MAPS (and EEMS) is to produce visualizations that point to patterns in the data that suggest deviations from simple isolation by distance.
The inferred population size surfaces for the POPRES data show a general increase in sizes through time, with small fluctuations across geography; In our results, the smallest inferred population sizes are in the Balkans and Eastern Europe more generally. This is in agreement with the signal seen previously ; however, taken at face value, our results suggest that high PSC sharing in these regions may be due more to consistently low population densities than to historical expansions (such as the Slavic or Hunnic expansions).
Relative population density may be a driver of genetic history, and one ignored by Der Movement in lieu of more colorful stories about expansions and admixture.
The roles of migration, admixture and acculturation in the European transition to farming have been debated for over 100 years. Genome-wide ancient DNA studies indicate predominantly Aegean ancestry for continental Neolithic farmers, but also variable admixture with local Mesolithic hunter-gatherers. Neolithic cultures first appear in Britain circa 4000 BC, a millennium after they appeared in adjacent areas of continental Europe. The pattern and process of this delayed British Neolithic transition remain unclear. We assembled genome-wide data from 6 Mesolithic and 67 Neolithic individuals found in Britain, dating 8500-2500 BC. Our analyses reveal persistent genetic affinities between Mesolithic British and Western European hunter-gatherers. We find overwhelming support for agriculture being introduced to Britain by incoming continental farmers, with small, geographically structured levels of hunter-gatherer ancestry. Unlike other European Neolithic populations, we detect no resurgence of hunter-gatherer ancestry at any time during the Neolithic in Britain. Genetic affinities with Iberian Neolithic individuals indicate that British Neolithic people were mostly descended from Aegean farmers who followed the Mediterranean route of dispersal. We also infer considerable variation in pigmentation levels in Europe by circa 6000 BC.
Contra Duchesne, ancestry deriving from Neolithic farmers is not restricted to Southern Europe; it is just much more concentrated there.