Author: populationgenomics

With Narasimhan et al (2018), we got our first look at Central, South Central, and South Asian aDNA. Not only did we get to see new steppe samples throughout the Bronze Age, but even from the Chalcolithic, through the Bronze Age in the Turan region, including BMAC. While there certainly looks to be steppe ancestry in South Asia, it has likely been highly inflated with previously available aDNA, and those that did not account for ANE that was already present in the region. The anticipation of the soon to be released Harappan sample(s), the models will only improve further.

This post will be constantly evolving as I add new outputs from qpAdm and qpGraph, so keep checking back in.

What I have noticed using qpAdm is that South Asian Dravidians do wonders as stand-ins for Harappan ancestry. So, we may see that some group greatly resembles them. I have seen that using the Palliyar and Paniya does work well, but the Irula does seem to work best. I don’t know whether that really means anything or the fact that they have more coverage.

The first thing I did was to look for populations to occupy the right pops, or populations which create the most significant D-stats between my left populations, or those set as the populations used in the mixture. Aside from using an African, Mbuti_DG, I found that using Ust-Ishim, Onge, Ami, EHG, Iron_Gates, Anatolia_N, Ganj_Dareh_N, and Karitiana. Kostenki14 is a hit and miss, as it doesn’t always have significant stats involved comparing two populations. This could be due to the age of the sample and not really developing any significant drift that can help differentiate populations in the test. This can lead higher chi-squares and lower tail-probabilities.

For the following, Brahmin_SGDP and Brahmin_Tiwari did have good marker counts, ranging from 170-200K, but the Brahmin_TN and Brahmin_UP sit around 50K, so they should be taken with a grain of salt.

SIS1= Shahr_I_Sokhta_BA1

Arm_EBA= Armenia_EBA

NEW-8-9-18

Dzh1 = Dzharkutan1_BA, Late BMAC

Steppe_E = Steppe_MLBA_East

The above is interesting in that there are whole graves spread around from India to West Asia that are completely late BMAC in character. There seems no possible way for there to not be detectable BMAC ancestry in South Asia, considering the amount of cemeteries and remains. I think the Harappan sample(s) will show that BMAC ancestry is indeed important in South Asia.

Looking at the Swat Valley samples, it gets even more interesting…

Interestingly, there seems to be no need for Andronovo admixture in Butkara, Pakistan_IA_Aligrama, and also the first Aligrama can do okay with just Dali, plus late BMAC. Of course, this all depends on the underlying population being similar to the Irula. Either way though, the Steppe ancestry should really not move. Next, I’ll see how including all BMAC samples affects the output.

Update 8-22-18– Looking at Shahr_I_Sokhta 1,2, and 3.

Narasimhan et al, The Genomic Formation of South and Central Asia, Posted March 31, 2018, doi: https://doi.org/10.1101/292581

The farmers of Europe appear to be a very closely related group, that derives from a potentially singular source. We’ve seen several papers over the last couple years devoted to farmers. Last year brought us Lipson et al. (2017), and Mathieson et al. (2017). These papers brought us many new samples from the Mediterranean, Central Europe, and the Balkans. The datasets from these two papers will be the source that I am working with here.

Firstly, I wanted to look at a simple tree to find a decent fit. That led to the following:

This one was not a bad fit. It just had one zero drift edge towards Iron Gates, which would probably be taken care of if more hunters were included that lacked as much ANE as Iron Gates. While the Peloponnese samples are an outgroup to the other farmers, the Koros samples, from the First Temperate Neolithic, appear to be very close to the ancestral population for both the Balkan and Mediterranean groups. For the purpose of starting here, it seems fine. Next, I wanted to add Iberia EN as an offshoot of the Cardial EN samples from Croatia, just to see if the two of Mediterranean origin really are closely related.

This graph left what looks to be a needed admixture event from a hunter branch related to Iron Gates, to Iberia EN.

This graph actually turned out very nice. Iberia EN was able to branch from the same population as the Croatian Cardial and only needed a little extra HG ancestry. This also removed the zero edge from Iron Gates. For the next run, I am going to place LBK Austria coming off the branch to Starcevo.

The first thing I will try after seeing this worst Z-score is to try an admixture edge from the branch related to Iron_Gates into LBK Austria.

Surprisingly, the admixture from a European hunter did not take care of that worst Z-score. So, I scrapped that and decided to go with the admixture from Croatian Cardial into LBK Austria.

This graph resulted in LBK being a mix of 59% Starcevo and 41% Cardial. Still, we have a worst Z that wants Starcevo to also be closer to Cardial. In this case, I will first try a shared branch opposite of Koros, and if needed, after the HG-related admixture at B3.

While this is not a bad result, we do have a couple zero edges here that I would like to resolve. The admixture from the Cardial branch to LBK has also reduced to 5% in this graph. The worst Z involves the Peloponnese Neolithic and Starcevo, and also Iron Gates and LBK. I first want to try an edge from around Iron Gates to LBK to see how that does.

For this last graph, the A2 node for HG was eliminated since there was a 0 drift edge. All HG admixture now comes off of A1. The extra HG into LBK Austria has now put the worst Z-score around 3, which isn’t too bad. The edges all look good. The surprising part is that LBK comes out nearly 50% Cardial-related. This is interesting because, LBK was seen as just a subset of late Starcevo and potentially some Vinca influence.  Since this is unexpected, I am going to see if there is more shared drift between the two before splitting, after the extra HG admixture coming after splitting with a group related to Koros EN.

Still, we have LBK Austria coming out as nearly half Cardial-related. While these results are interesting, they are not matching with D-stats, f3-ratio, or qpAdm results. There may be something else here that will take more complex graphs to figure out.

Here is another way of looking at it.

This graph makes a little more sense, with the separation of Mediterranean and Danubian groups a little more. The next step will be to separate the two before Koros EN. I will continue working from here for the rest of the post. If you have any more ideas, let me know. I will post updates as I have more.

Here are stats that have me thinking there is nothing here as far as admixture from Croatian Cardial.

D-stats

f3

Resources

Lipson, M. et al. Parallel palaeogenomic transects reveal complex genetic history of early European farmers. Nature 551, 368–372 (2017)

Mathieson, I. et al. The genomic history of Southeastern Europe. Nature 555, 197-208 (2018)

Background on the Sites and Samples

We first learned about the demographics of the first farmers across the Levant and Zagros from Lazaridis et al. (2016). From that work, we saw that there were at least two independent developments towards agriculture, with two distinct populations (Lazaridis et al., 2016). One was located in the Levant (Natufians), while the other was at Ganj Dareh, in Iran (Lazaridis et al., 2016). We learned from the paper that Natufians and Ganj Dareh were both nearly equal in Basal Eurasian, yet Ganj Dareh had a lot of ancestry from a lineage related to ANE, and Natufians had little to none of this ancestry (Lazaridis et al., 2016).  The Levant Neolithic samples from PPNB to PPNC were a mix of something related to Natufians, and another lineage related to Anatolian farmers from Barcin and Mentese (Mathieson et al., 2015; Lazaridis et al., 2016).

Also in 2016, we caught our first glimpse of the Early Neolithic in Central Anatolia (Kilinc et al., 2016), on the Konya Plain. The Boncuklu group was actually a very early Neolithic group that did have small-scale agriculture (Baird et al., 2012), but was not “aceramic” as many believe. There were very early styles of pottery production and limited use of ceramics at this site; some of the earliest in West Asia (Fletcher et al., 2017). The Boncuklu farmers were a group with long runs of homozygosity, comparable to Western Hunter Gatherers (WHG) (Kilinc et al., 2016). Despite this, the Boncuklu samples are actually quite similar to the later farmers at Barcin, Mentese, and early European farmers (Kilinc et al., 2016), pointing to Anatolia as a potentially third place of independent farming production, along with the Levant and Zagros.

Broushaki et al. (2016) also brought us more samples from the Zagros, that clustered with those samples from Ganj Dareh. These samples were from Tepe Abdul Hosein and Wezmeh Cave (Broushaki et al., 2016).

When it came to actually looking at the ancestral breakdown of Anatolians, Lazaridis et al. (2016) came up with a very solid model where Anatolians were a mix of lineages related to Ganj Dareh, Levant Neolithic, and WHG, with mixture proportions of 0.387, 0.339, and 0.274, respectively.

For the purpose of this post, I am going to focus on Boncuklu and their role in the formation of the Anatolian Neolithic. Since Boncuklu capture method involved shotgun data, I am also using shotgun sequenced genomes from Broushaki et al. (2016) to use as my Iranian farmers (Tepe Abdul Hosein), and the Barcin farmers (Bar8, Bar31). Also, Kostenki14 and MA1 are being used as hunters, as these two form two poles of variation, and are important in the formation of WHG and EHG. Lineages related to both are also important to the formation of these farmers.

In the analysis of the Boncuklu and Barcin farmers, Kilinc et al. (2016) stated that diversity from mixing with other similar groups and/or admixture from more southern groups is possible in the transition from Boncuklu to the later Neolithic of Anatolia. Taking a look at various methods of analysis, it does seem quite possible that a more southern source did admix into the Anatolian farmers of Barcin Hoyuk.

D-stats

First, looking at D-stats, there isn’t anything that is very significant, in determining if Barcin or Boncuklu share more with any particular population. There are near significant results for Natufians and less for the Levant Neolithic samples.

f3-ratio

Also, looking at f3, there isn’t a lot to really support much in the way of admixture from Levant or Natufians into the Barcin Hoyuk samples.

qpAdm Models

Switching over to qpAdm, the first thing to check is what Boncuklu is best modeled as. For the right populations the simplest and seemingly best for using Kostenki, Natufians, and Tepe_Abdul_Hosein_N as the left pops turned out to be Mbuti_DG, Ust_Ishim, GoyetQ116-1, Vestonice16, MA1, Ami, Onge, and Karitiana.

While the probability and standard errors are a little high, it is hard to keep them any lower. While adding something like Ganj_Dareh_N or Levant_N will reduce the standard error, the probability also dips significantly.

Moving onto Anatolia_N1 (Bar31, Bar8), the right pops, or outgroups became Mbuti_DG, Ust_Ishim, Kostenki14, MA1, Natufian, Tepe_Abdul_Hosein_N, Ami, Onge, Karitiana.

Also, swapping Natufian and Levant_N between left and right populations:

qpGraph Models

After seeing some decent fits in qpAdm, I tried to use qpGraph and see how easily they could be replicated. To being with, I started with a simple tree that contained just the Natufians and Tepe_Abdul_Hosein_N. The Interestingly, CHG comes out as a mix of Natufian, Tepe_Abdul_Hosein, and MA1. first tree should what would be expected, with Natufians and Tepe_Abdul_Hosein_N having similar amounts of Basal Eurasian, and with Iranian farmers having ancestry from a lineage related to MA1.

6/4/18 New Update: Simple qpGraphs

The purpose of these simple graphs is to avoid a lot of admixture edges and to show the steps in finding the ancestry of Boncuklu, Levant, and Barcin samples. Samples that are not absolutely needed, such as Ust_Ishim, MA1, CHG, and any ENA population will be left out. This also shows the work process in how I find where to look at for admixture events.

The first graph here shows that by branching Boncuklu off of Natufian, there is a significant Z-score with Iranian farmers. The next step will be to branch from Iranians to Boncuklu.

The next output had a worst Z-score that involved Kostenki, meaning that an admixture line from the Kostenki branch will be the next admixture source into Boncuklu.

After adding the admixture edge from near Kostenki, Boncuklu comes out mostly Kostenki-related, with the rest being a near even mix of Tepe Abdul Hosein and Natufian.

This created a pretty solid fit, so, the next step would be to see how the graph works with Adding Levant_N. Since the Levant samples harbor a lot of Natufian-related ancestry, that is where they will first be branched off of.

This output shows that an admixture edge is needed from a node on the Boncuklu line to Levant_N in order to drop the Z-score. A shared drift edge with Boncuklu was needed to move the worst Z-score closer to 2.

For the next run, I added the two shotgun samples from Barcin (Bar8, Bar31) to the tree.This left a worst Z > 3, so, the next step will be an edge from a node related to Natufians into Anatolia_N1.

For the last graph, the edge from a Natufian-related node admixes into the Barcin Hoyuk samples. There remains a worst Z > 2.5, that appears to be asking for Iranian admixture into the Levant_N population. While it isn’t necessary, I did look at adding an admixture edge from the Iranian farmers and it only added 2% admixture. This graph should be sufficient enough for a simple tree that incorporates all of the first farmers.

Below are more complex graphs, with an Eastern non-African source (Onge), Ust-Ishim and also MA-1.

For the next step, I also added CHG to the tree, just to see where it was placed and to add another admixture source in West Asia. As other stats show, CHG was less basal than Iranian farmers, but also shifted towards Natufians, potentially sharing a formation story with Boncuklu.

After achieving a good fit with CHG, I then went on to see just how Boncuklu fit into the picture. As expected, Boncuklu is best fit as a mix of Natufian, Iranian, and a lineage related to Kostenki14, with admixture also from CHG. Trying to do just Iranian or just CHG led to significantly higher Z-scores. Both were needed. It appears there is additional ANE in Boncuklu that is not accounted for in Iranians.

After applying a good fit for Boncuklu, I next wanted to see just how the farmers at Barcin Hoyuk fit into tree. For this, I first created a new simplified tree to use and also co-fit the Levant_N samples to see if the best fit includes additional southern ancestry into the later Northwest Anatolian Neolithic.

I also tried a run with just transversion sites, just to see how that would turn out.

Just as a precaution, I ran another test, with Levant_N receiving admixture from a separate branch related to Boncuklu, that is ancestral to Anatolia_N1, to see if that reduced or eliminates the need for admixture from Levant_N. As it turns out, Anatolia then asked first for admixture from Natufians, rather than Levant_N. So, the question then becomes, was there another population that was related to Levant_N which lacked or had less Iranian farmer ancestry and mixed into the ancestors of the Barcin and European farmers? More samples, specifically from southern and especially southeast Anatolia, and PPNA will provide the answers which are needed.For argument sake, I tried both the Natufian and Levant_N admixture edges to see how they turned out. Having more a higher f4 and f3 with Natufians, rather than Levant, leads me to believe this is possible.

Conclusions

The end result here has the Barcin samples as a mix of Boncuklu and Levant_N (or a more Natufian-like group), in agreement with qpAdm. However, this output is only about two-thirds as much Levantine flow into later Anatolian farmers as qpAdm showed. Transversion sites placed more ancestry related to Iranian farmers into both Boncuklu and Levant_N. Boncuklu became significantly more Iranian than the previous run. The amount of flow from Levant_N into Barcin held pretty steady. While more samples across time and space, with similar capture methods, will help this to be resolved. There aren’t much in the way of D-stats or f3_ratio to suggest there is a lot of gene flow there. qpAdm and qpGraph do provide a strong case of gene-flow into Northwest Anatolia.

In upcoming posts I will look at the formation of European farmers, hunter admixture into the Middle Neolithic, and also at the roots of Epipaleolithic and Mesolithic European hunters.

References

Baird D. The Late Epipaleolithic, Neolithic, and Chalcolithic of the Anatolian Plateau, 13,000–4000 BC. In: Potts D., editor. A Companion to the Archaeology of the Ancient Near East. Wiley-Blackwell; 2012. pp. 431–466.

Broushaki F., Thomas M.G., …, Berger J. Early Neolithic genomes from the eastern Fertile Crescent. Science. 2016; 353: pp. 499-503.

Fletcher A., Baird D., Spataro M. & Fairbairn A. Early ceramics in Anatolia: Implications for the production and use of the earliest pottery. The evidence from Boncuklu Hoyuk. Cambridge Archaeological Journal. 2017; 27 (2): pp. 351-369.

Kilinc G.M., Omrak A., …, Gotherstrom A. The demographic development of the first farmers in Anatolia. Current Biology. 2016;  26(19): pp. 2659–2666.

Lazaridis I., Nadel D., …, Reich D. Genomic insights into the origin on farming in the ancient Near East. Nature. 2016;536: pp. 419-424.

Mathieson I., Lazaridis I., Rohland N., Mallick S., Patterson N., Roodenberg S.A., Harney E., Stewardson K., Fernandes D., Novak M. Genome-wide patterns of selection in 230 ancient Eurasians. Nature. 2015;528:499–503. [PubMed]

Thanks for joining me! Please, leave any comments about the post or feedback on the site, in general. I look forward to discussing these posts with all of you. If you have anything specific that you want me to take a look at, let me know.