Standard apologies that I have had not the marginal time to blog much, but I thought it was important that I least note that Dr. Peter Ralph and Dr. Graham Coop’s paper on identity-by-descent segments and European populations and history is out in its final form in PLoS Biology, The Geography of Recent Genetic Ancestry across Europe. I’ve been familiar with the outlines of these results for about a year now, and to be frank I am still digesting them. The media hype will come and go, with true but to some extent trivial headlines that “all Europeans are related,” but the consequences of these sorts of genetic inquiries into the relatedness of populations are going to be long lasting. At least they should be.
But before I go on about that, if you find the paper itself a bit daunting (though the main body of the text strikes me as eminently readable for a piece of statistical genetics), see Carl Zimmer’s condensation. With this sort of result there is liable to be confusion, so note that Graham Coop has been posting comments on Carl’s blog (and elsewhere, and you can always send him a note on Twitter). Additionally he has a very readable FAQ out. Dr. Coop told me on Twitter that there would even be updates tomorrow as well! In particular one aspect of the paper which I noticed is that most relatively short, but detectable segments (~10 cM), between any two individuals in many nationalities is not going to be evidence of recent genealogical affinities, but deeper historical process.
The Pith: You’re Asian. Yes, you!
A conclusion to an important paper, Nick Patterson, Priya Moorjani, Yontao Luo, Swapan Mallick, Nadin Rohland, Yiping Zhan, Teri Genschoreck, Teresa Webster, and David Reich:
In particular, we have presented evidence suggesting that the genetic history of Europe from around 5000 B.C. includes:
1. The arrival of Neolithic farmers probably from the Middle East.
2. Nearly complete replacement of the indigenous Mesolithic southern European populations by Neolithic migrants, and admixture between the Neolithic farmers and the indigenous Europeans in the north.
3. Substantial population movement into Spain occurring around the same time as the archaeologically attested Bell-Beaker phenomenon (HARRISON, 1980).
4. Subsequent mating between peoples of neighboring regions, resulting in isolation-by-distance (LAO et al., 2008; NOVEMBRE et al., 2008). This tended to smooth out population structure that existed 4,000 years ago.
Further, the populations of Sardinia and the Basque country today have been substantially less influenced by these events.
It’s in Genetics, Ancient Admixture in Human History. Reading through it I can see why it wasn’t published in Nature or Science: methods are of the essence. The authors review five population genetic statistics of phylogenetic and evolutionary genetic import, before moving onto the novel results. These statistics, which measure the possibility of admixture, the extent of admixture, and the date of admixture, are often presented, but nested into supplements, in previous papers by the same group. On the one hand this removes from view the engines which are driving the science. On the other hand I have always appreciated that a benefit of this injustice to the methods which make insight possible is that those without academic access can actually bite into the meat of the researcher’s mode of thought.
I did read through the methods. Twice. I’ve encountered all the statistics before, and I’ve read how they were generated, but I’ll be honest and admit that I haven’t internalized them. That has to end now, because the authors have finally released a software package which implements the statistics, ADMIXTOOLS. I plan to use it in the near future, and it is generally best if you understand the underlying mechanisms of a software package if you are at the bleeding end of analytics. I will review the technical points in more detail in future posts, more for my own edification than yours. But for the moment I’ll be a bit more cursory. Four of the tests use comparisons of allele frequencies along explicit phylogenetic trees. That’s so general as to be uninformative as a description, but I think it’s accurate to the best of my knowledge. In the basics the tests are seeing if a model fits the data (as opposed to TreeMix, which finds the best model out of a range to fit the data). The last method, rolloff, infers the timing of an admixture event based upon the decay of linkage disequilibrium. In short, admixture between two very distinct populations has the concrete result of producing striking genomic correlations. Over time these correlations dissipate due to recombination. The magnitude of dissipation can allow one to gauge the time in the past when the original admixture occurred.
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:
I decided to take the Dodecad ADMIXTURE results at K = 10, and redo some of the bar plots, as well as some scatter plots relating the different ancestral components by population. Don’t try to pick out fine-grained details, see what jumps out in a gestalt fashion. I removed most of the non-European populations to focus on Western Europeans, with a few outgroups for reference.
Here’s a table of the correlations (I bolded the ones I thought were interesting):
|W Asian||NW African||S Europe||NE Asian||SW Asian||E Asian||N European||W African||E African||S Asian|
In the age of 500,000 SNP studies of genetic variation across dozens of populations obviously we’re a bit beyond lists of ABO blood frequencies. There’s no real way that a conventional human is going to be able to discern patterns of correlated allele frequency variations which point to between population genetic differences on this scale of marker density. So you rely on techniques which extract the general patterns out of the data, and present them to you in a human-comprehensible format. But, there’s an unfortunate tendency for humans to imbue the products of technique with a particular authority which they always should not have.
The History and Geography of Human Genes is arguably the most important historical genetics work of the past generation. It has surely influenced many within the field of genetics, and because of its voluminous elegant visual displays of genetic data it is also a primary source for those outside of genetics to make sense of phylogenetic relations between human populations. And yet one aspect of this great work which never caught on was the utilization of “synthetic maps” to visualize components of genetic variation between populations. This may have been fortuitous, a few years ago a paper was published, Interpreting principal components analyses of spatial population genetic variation, which suggested that the gradients you see on the map above may be artifacts:
Nearly 30 years ago, Cavalli-Sforza et al. pioneered the use of principal component analysis (PCA) in population genetics and used PCA to produce maps summarizing human genetic variation across continental regions. They interpreted gradient and wave patterns in these maps as signatures of specific migration events. These interpretations have been controversial, but influential, and the use of PCA has become widespread in analysis of population genetics data. However, the behavior of PCA for genetic data showing continuous spatial variation, such as might exist within human continental groups, has been less well characterized. Here, we find that gradients and waves observed in Cavalli-Sforza et al.’s maps resemble sinusoidal mathematical artifacts that arise generally when PCA is applied to spatial data, implying that the patterns do not necessarily reflect specific migration events. Our findings aid interpretation of PCA results and suggest how PCA can help correct for continuous population structure in association studies.
A paper earlier this year took the earlier work further and used a series of simulations to show how the nature of the gradients varied. In light of recent preoccupations the results are of interest. Principal Component Analysis under Population Genetic Models of Range Expansion and Admixture:
One of the more popular posts on this weblog (going by StumbleUpon and search engine referrers) focuses on genetic variation in Europe as a function of geography. In some ways the results are common sense; populations closer to each other are more genetically related. Why not? Historically people have married their neighbors and so gene flow is often well modeled as isolation by distance. The scientific rationale for these studies is to smoke out population stratification in medical genetics research programs which attempt to find associations between genes and particular diseases. By population stratification I mean the fact that different populations will naturally have different gene frequencies, and if those populations exhibit different frequencies of the disease/trait under investigation then one may have to deal with spurious correlations. If, for example, your study population includes many people of African and European descent, presumably cautious researchers would immediately by aware of this problem and attempt to take it into account. But what about populations which are genetically closer, or whose genetic difference may not be so well manifest in physical characteristics which might clue you in to the issue of stratification?
That’s why the sorts of results which might seem common sense in the aggregate are useful. One can ask questions as to the genetic closeness of Irish and English, or Irish and Spanish, in a rigorous sense. In the United States research programs which are constrained to white cases and controls may hide population stratification because of the ethnic diversity of the American population. A primary motivation for studies of Jewish genetics are the cluster of “Jewish diseases” which are common within that population. In our age it is fashionable to focus on what binds us together as a species, but genetic differences matter a great deal. Ask the parents of multiracial children who require bone marrow transplants.
A new paper in Human Heredity examines a large sample of five European populations, and goes over the between population allele frequency differences with a fine tooth comb. Genetic Differences between Five European Populations:
A few years ago you started seeing the crest of studies which basically took several hundred individuals (or thousands) from a range of locations, and then extracted out the two largest components of genetic variation from the hundreds of thousands of variants. The clusters which fell out of the genetic data, with each point being an individual’s position, were transposed onto a geographical map. The figure to the left (from this paper) has been widely circulated. You don’t have to be a deep thinker to understand why things shake out this way; people are more closely related to those near than those far because gene flow ties populations together, and its power decreases as a function of distance.
Of course the world isn’t flat, and history perturbs regularities. Jews for example often don’t shake out where they “should” geographically, because of their historical mobility contingent upon random and often capricious geopolitical or social pressures. The Hazara of Afghanistan have their ethnogenesis in the melange of peoples who were thrown together after the Mongol conquest of Central Asia and Iran in the 13th century, and the subsequent collapse of the Ilkhan dynasty. Though the Hazara have mixed with their Persian, Tajik and Pashtun neighbors, they still retain a strong stamp of Mongolian ancestry which means that they are at some remove on the “genetic map” from their geographical neighbors.
A few months ago I blogged a paper in PLoS Biology which suggested that a common Y chromosomal haplogroup, in fact the most common in Europe and at modal frequency along the Atlantic fringe, is not pre-Neolithic. Rather their analysis of the data implied that the European variants were derived from an Anatolian variant. The implication was that a haplogroup which had previously been diagnostic of “Paleolithicness,” so to speak, of a particular population may in fact be an indication of the proportion of Neolithic Middle Eastern ancestry. The most interesting case were the Basques, who have a high frequency of this haplogroup, and are often conceived of as “ur-Europeans,” Paleolithic descendants of the Cro-Magnons in the most romantic tellings. I was somewhat primed to accept this finding because of confusing results from ancient DNA extraction which implies a lot of turnover in maternal lineages, the mtDNA. My logic being that if the mtDNA exhibited rupture, then the Y lineages should too, as demographic revolutions are more likely to occur among men.
But perhaps not. A new paper in PLoS ONE takes full aim at the paper I blogged above. It is in short a purported refutation of the main finding of the previous paper, and a reinstatement of what had been the orthodoxy (note the citations to previous papers). A Comparison of Y-Chromosome Variation in Sardinia and Anatolia Is More Consistent with Cultural Rather than Demic Diffusion of Agriculture: