What's Maykop (or Iran) got to do with it? #2

For the past few days I've been trying to copy and also improve on the qpGraph tree in the Wang et al. preprint (see here). I've managed to come up with a new version of my model that not only offers a better statistical fit, but, in my opinion, also a much more sensible solution. For instance, the Eastern Hunter-Gatherer node now shows 73% MA1-related admixture, which, I'd say, makes more sense

Cultural hitchhiking and competition between patrilineal kin groups may have led to the post-Neolithic Y-chromosome bottleneck (Zeng et al. 2018)

A very interesting paper has just appeared at Nature Communications that potentially offers an explanation for the well documented explosions of certain Y-chromosome lineages in the Old World after the Neolithic, such as those that led to most European males today belonging to Y-haplogroups R1a and R1b (LINK). I might have more to say about this paper in the comments below after I've read it a

What's Maykop (or Iran) got to do with it?

I had a go at imitating this qpGraph tree, from the recent Wang et al. preprint on the genetic prehistory of the Caucasus, using the ancient samples that were available to me. I'm very happy with the outcome, because everything makes good sense, more or less. The real populations and singleton individuals, ten in all, are marked in red. The rest of the labels refer to groups inferred from the

More Botai genomes (Jeong et al. 2018 preprint)

Over at bioRxiv at this LINK. Actually, these may or may not be the same Botai genomes that have already been published along with Damgaard et al. 2018 (see comments below for the discussion about that). Here's the abstract. Emphasis is mine:

The indigenous populations of inner Eurasia, a huge geographic region covering the central Eurasian steppe and the northern Eurasian taiga and tundra,

Global25 workshop 2: intra-European variation

Even though the Global25 focuses on world-wide human genetic diversity, it can also reveal a lot of information about genetic substructures within continental regions.

Several of the dimensions, for instance, reflect Balto-Slavic-specific genetic drift. I ensured that this would be the case by running a lot of Slavic groups in the analysis. A useful by-product of this strategy is that the

Global25 workshop 1: that classic West Eurasian plot

In this Global25 workshop I'm going to show how to reproduce, more or less, that classic plot of West Eurasian genetic diversity seen regularly in ancient DNA papers and at this blog (for instance, here). To do this you'll need the datasheet below, which I'll be updating regularly, and the PAST program, which is freely available here.

G25_West_Eurasia_scaled.dat

This is what you'll get if you

Global25 PAST-compatible datasheets

I'm planning to run regular workshops over the next few months on how to get the most out of Global25 data with various programs, and expecially PAST (see here). So if you have Global25 coordinates, please stay tuned.

To that end, I've put together four color-coded, PAST-compatible Global25 datasheets with thousands of present-day and ancient samples, available at the links below:

On the genetic prehistory of the Greater Caucasus (Wang et al. 2018 preprint)

Finally, the focus shifts to the Eneolithic/Bronze Age North Caucasus. In a new manuscript at bioRxiv, Wang et al. present genome-wide SNP data for 45 prehistoric individuals from the region along a 3000-year temporal transect (see here). From the preprint (emphasis is mine):

Based on PCA and ADMIXTURE plots we observe two distinct genetic clusters: one cluster falls with previously published

New PCA featuring Botai horse tamers, Hun and Saka warriors, and many more...

Just in case anyone's wondering how the ancient samples from the two recent archaeogenetic papers by Damgaard et al. (Nature and Science) behave in my two main Principal Component Analyses (PCA), here you go:



The relevant datasheet is available here. Just over 100 of the new samples made into onto this plot, but to keep things simple I only highlighted a few of them. To see the positions of

Hittite era Anatolians in qpAdm

The apparent lack of steppe ancestry in five Hittite-era, perhaps Indo-European-speaking, Anatolians was interpreted in Damgaard et al. 2018 as a major discovery with profound implications for the origin of the Anatolian branch of Indo-European languages.

But I disagree with this assessment, simply because none of these Hittite-era individuals are from royal Hittite, or Nes, burials. Hence,

Graeco-Aryan parallels

The clearly non-local admixture in the geographically and genetically disparate, but Indo-European-speaking, ancient Mycenaeans and present-day North Indian Brahmins is very similar. So similar, in fact, that it could derive from practically the same population in space and time. The most plausible source for this admixture are the Bronze Age herders of the Pontic-Caspian steppe and their

Protohistoric Swat Valley peoples in qpGraph #2

Three options. Just one passes muster; the one with Sintashta. Coincidence? I think not. Who still wants to claim that there's no Sintashta-related steppe stuff in these Iron Age SPGT South Asians? The relevant graph files are available here. Any ideas for better models?







Update 08/05/2018: The reason that I chose Dzharkutan1_BA, from what is now Uzbekistan, as the BMAC proxy in the above

The protohistoric Swat Valley "Indo-Aryans" might not be exactly what we think they are

I need some help interpreting these linear models of ancient and present-day South Asian populations. Overall, the Iron Age groups from the Swat Valley, or SPGT, look like rather obvious outliers. The relevant datasheet is available here.





The reason for this might be significant bidirectional gene flow and/or continuity between Central Asia and the northern parts of South Asia before

Open analysis thread: genetic distance (Fst) matrix focusing on ancient Central and South Asia

I'm hoping that we can learn something new about the genomic prehistory of Eurasia, and especially Central and South Asia, based on this massive new Fst matrix:

Ancient Central and South Asia genetic distance (Fst) matrix

Hint: it's probably easiest to initially explore this format with a program called PAST. Indeed, if you'd like to model fine scale ancestry proportions based on these data, it