As many of you know when you have two adjacent demes, breeding populations, they often rapidly equilibrate in gene frequencies if they were originally distinct. There are plenty of good concrete examples of this. The Hui of China are Muslims who speak local Chinese dialects. The most probable root of this community goes back to the enormous population of Central Asia Muslims brought by the Mongol Yuan dynasty that ruled ruled China for over a century from the late 1200s to 1300s. Genetic studies of this group that I’ve seen indicate that a high bound estimate for West Eurasian ancestry is ~10%. The other ~90% is interchangeable with the Han Chinese. So let’s assume that the Hui are ~10% West Asian. If you assume that in the year 1400 the Hui were “pure,” you have 24 generations (25 years per generation). The original population of “Central Asian Muslims” were heterogeneous, including Iranians and Turks. But let’s take it granted that they were 50% East Eurasian and 50% West Eurasian in ancestry at the time of their arrival. What would the intermarriage rate per generation have to be so that the Hui are ~10% West Eurasian at t = 24 (24 generations after the beginning of intermarriage assuming 50/50 West vs. East Eurasian splits)? Turns out all you need is a constant 7% intermarriage rate per generation (the Han Chinese population is so large in relation to the Hui that you can model it as infinite in size).
The situation gets even simpler when you have one population which divides into two. For example, imagine that the Serbs and Croats fissioned from a set of unstructured South Slavic tribes which filtered into ancient Illyria ~600 A.D. Soon enough there was a cultural division between the two in terms of religion (Western vs. Eastern Christian) which threw up a population genetic barrier. If you assume that genetically the two groups were totally similar at t = 0, and you separated them perfectly, over time they would diverge due to drift in their allele frequencies. But the reality is that barriers between geographically close groups do not prevent all intermarriage. Even extremely insular groups in a cultural sense such as the Roma of Eastern Europe are clearly heavily admixed with their surrounding populations, as they seem to be no more than ~50% South Asian in total genome content. Going back to the South Slavs, who start out very similar in our putative scenario, how much intermarriage will be necessary for them to not diverge? The issue is not the rate of intermarriage, rather, one migrant per generation across the two demes will be sufficient to equilibrate allele frequencies. On the face of it this seems implausible, but recall that divergence is driven mostly by drifting of genes as well as new variation (whether through other exogenous migratory sources or mutation). Very small populations are subject to a lot of drift, and so diverge rapidly, but only very few migrants are needed to bring it back into alignment, because they are proportionally significant. In contrast, the frequencies of large populations are less buffeted by generation-to-generation sample variance (e.g., 10 tosses of a coin will deviate more from 50/50 proportionally than 100 tosses), requiring less gene flow proportionally to maintain parity.
The image above is adapted from the 2010 paper A Predominantly Neolithic Origin for European Paternal Lineages, and it shows the frequencies of Y chromosomal haplogroup R1b1b2 across Europe. As you can see as you approach the Atlantic the frequency converges upon ~100%. Interestingly the fraction of R1b1b2 is highest among populations such as the Basque and the Welsh. This was taken by some researchers in the late 1990s and early 2000s as evidence that the Welsh adopted a Celtic language, prior to which they spoke a dialect distantly related to Basque. Additionally, the assumption was that the Basques were the ur-Europeans. Descendants of the Paleolithic populations of the continent both biologically and culturally, so that the peculiar aspects of the Basque language were attributed by some to its ancient Stone Age origins.
As indicated by the title the above paper overturned such assumptions, and rather implied that the origin of R1b1b2 haplogroup was in the Near East, and associated with the expansion of Middle Eastern farmers from the eastern Mediterranean toward western Europe ~10,000 years ago. Instead of the high frequency of R1b1b2 being a confident peg for the dominance of Paleolithic rootedness of contemporary Europeans, as well as the spread of farming mostly though cultural diffusion, now it had become a lynch pin for the case that Europe had seen one, and perhaps more than one, demographic revolutions over the past 10,000 years.
This is made very evident in the results from ancient DNA, which are hard to superimpose upon a simplistic model of a two way admixture between a Paleolithic substrate and a Neolithic overlay. Rather, it may be that there were multiple pulses into a European cul-de-sac since the rise of agriculture from different starting points. We need to be careful of overly broad pronouncements at this point, because as they say this is a “developing” area. But, I want to go back to the western European fringe for a moment.
In light of my last post I had to take note when Dienekes today pointed to this new paper in the American Journal of Physical Anthropology, Population history of the Red Sea—genetic exchanges between the Arabian Peninsula and East Africa signaled in the mitochondrial DNA HV1 haplogroup. The authors looked at the relationship of mitochondrial genomes, with a particular emphasis upon Yemen and the Horn of Africa. This sort of genetic data is useful because these mtDNA lineages are passed from mother to daughter to daughter to daughter, and so forth, and are not subject to the confounding effects of recombination. They present the opportunity to generate nice clear trees based on distinct mutational “steps” which define ancestral to descendant relationships. Additionally, using neutral assumptions mtDNA allows one to utilize molecular clock methods to infer the time until the last common ancestor of any two given lineages relatively easily. This is useful when you want to know when a mtDNA haplgroup underwent an expansion at some point in the past (and therefore presumably can serve as a maker for the people who carried those lineages and their past demographic dynamics).
What did they find? Here’s the abstract:
Archaeological studies have revealed cultural connections between the two sides of the Red Sea dating to prehistory. The issue has still not been properly addressed, however, by archaeogenetics. We focus our attention here on the mitochondrial haplogroup HV1 that is present in both the Arabian Peninsula and East Africa. The internal variation of 38 complete mitochondrial DNA sequences (20 of them presented here for the first time) affiliated into this haplogroup testify to its emergence during the late glacial maximum, most probably in the Near East, with subsequent dispersion via population expansions when climatic conditions improved. Detailed phylogeography of HV1 sequences shows that more recent demographic upheavals likely contributed to their spread from West Arabia to East Africa, a finding concordant with archaeological records suggesting intensive maritime trade in the Red Sea from the sixth millennium BC onwards. Closer genetic exchanges are apparent between the Horn of Africa and Yemen, while Egyptian HV1 haplotypes seem to be more similar to the Near Eastern ones.
Much of this is totally concordant with the results we’ve generated from the autosomal genome. Though the autosomal genome is much more difficult when it comes to implementing many of the tricks & techniques of phylogeography outlined above, it does offer up a much more robust and thorough picture of genetic relationships between contemporary populations. Instead of a a distinct and unique line of paternal or maternal ancestry, thousands of autosomal SNPs can allow one t o get a better picture of the nature of the total genome, and the full distribution of ancestors.
The map to the left shows the spatial gradients of the broader haplogroup under consideration, HV1. But what about the branches? Below is an illustration of the phylogenetic network of branches of HV1, with pie-charts denoting the regional weights of a given lineage:
A new paper in Proceedings of the Royal Society dovetails with some posts I’ve put up on the peopling of Japan of late. The paper is Bayesian phylogenetic analysis supports an agricultural origin of Japonic languages:
Languages, like genes, evolve by a process of descent with modification. This striking similarity between biological and linguistic evolution allows us to apply phylogenetic methods to explore how languages, as well as the people who speak them, are related to one another through evolutionary history. Language phylogenies constructed with lexical data have so far revealed population expansions of Austronesian, Indo-European and Bantu speakers. However, how robustly a phylogenetic approach can chart the history of language evolution and what language phylogenies reveal about human prehistory must be investigated more thoroughly on a global scale. Here we report a phylogeny of 59 Japonic languages and dialects. We used this phylogeny to estimate time depth of its root and compared it with the time suggested by an agricultural expansion scenario for Japanese origin. In agreement with the scenario, our results indicate that Japonic languages descended from a common ancestor approximately 2182 years ago. Together with archaeological and biological evidence, our results suggest that the first farmers of Japan had a profound impact on the origins of both people and languages. On a broader level, our results are consistent with a theory that agricultural expansion is the principal factor for shaping global linguistic diversity.
I don’t know the technical details of linguistics to comment, but the alignment between the linguistic model and archeology is pretty impressive to me. There’s a 95% confidence interval which can push the time back to 4,000 years, so there’s some fudge factor too. The basic technique is borrowed from phylogenetics. This is pretty clear when you notice that one of the algorithms seems to be the same one used in the rice genomics paper. Nick Wade covers the paper in The New York Times, so no need for me to give a blow-by-blow in a domain where I don’t have much insight anyway.
The Pith: Over the past 10,000 years a small coterie of farming populations expanded rapidly and replaced hunter-gatherer groups which were once dominant across the landscape. So, the vast majority of the ancestry of modern Europeans can be traced back to farming cultures of the eastern Mediterranean which swept over the west of Eurasia between 10 and 5 thousand years before the before.
Dienekes Pontikos points me to a new paper in PNAS which uses a coalescent model of 400+ mitochondrial DNA lineages to infer the pattern of expansions of populations over the past ~40,000 years. Remember that mtDNA is passed just through the maternal lineage. That means it is not subject to the confounding dynamic of recombination, allowing for easier modeling as a phylogenetic tree. Unlike the autosomal genome there’s no reticulation. Additionally, mtDNA tends to be highly mutable, and many regions have been presumed to be selectively neutral. So they are the perfect molecular clock. There straightforward drawback is that the history of one’s foremothers may not be a good representative of the history of one’s total lineage. Additionally the haploid nature of mtDNA means that genetic drift is far more powerful in buffeting gene frequencies and introduced stochastic fluctuations, which eventually obscure past mutational signals through myriad mutations. Finally, there are serious concerns as to the neutrality of mtDNA…though the authors claim to address that in the methods. I should also add that it also happens to be the case that there is less controversy and more surety as to the calibration of mutational rates of mtDNA than the Y chromosomal lineages of males. Their good for determining temporal patterns of demographic change, and not just tree structures.
Here’s the abstract, Rapid, global demographic expansions after the origins of agriculture:
After linking to Marnie Dunsmore’s blog on the Neolithic expansion, and reading Peter Bellwood’s First Farmers, I’ve been thinking a bit on how we might integrate some models of the rise and spread of agriculture with the new genomic findings. Bellwood’s thesis basically seems to be that the contemporary world pattern of expansive macro-language families (e.g., Indo-European, Sino-Tibetan, Afro-Asiatic, etc.) are shadows of the rapid demographic expansions in prehistory of farmers. In particular, hoe-farmers rapidly pushing into virgin lands. First Farmers was published in 2005, and so it had access mostly to mtDNA and Y chromosomal studies. Today we have a richer data set, from hundreds of thousands of markers per person, to mtDNA and Y chromosomal results from ancient DNA. I would argue that the new findings tend to reinforce the plausibility of Bellwood’s thesis somewhat.
The primary datum I want to enter into the record in this post, which was news to me, is this: the island of Cyprus seems to have been first settled (at least in anything but trivial numbers) by Neolithic populations from mainland Southwest Asia.* In fact, the first farmers in Cyprus perfectly replicated the physical culture of the nearby mainland in toto. This implies that the genetic heritage of modern Cypriots is probably attributable in the whole to expansions of farmers from Southwest Asia. With this in mind let’s look at Dienekes’ Dodecad results at K = 10 for Eurasian populations (I’ve reedited a bit):
A new paper in The New Journal of Physics shows that a relatively simple mathematical model can explain the rate of expansion of agriculture across Europe, Anisotropic dispersion, space competition and the slowdown of the Neolithic transition:
The front speed of the Neolithic (farmer) spread in Europe decreased as it reached Northern latitudes, where the Mesolithic (hunter-gatherer) population density was higher. Here, we describe a reaction–diffusion model with (i) an anisotropic dispersion kernel depending on the Mesolithic population density gradient and (ii) a modified population growth equation. Both effects are related to the space available for the Neolithic population. The model is able to explain the slowdown of the Neolithic front as observed from archaeological data
The paper is open access, so if you want more of this:
Just click through above. Rather, I am curious more about their nice visualization of the archaeological data: