Pots, Not People, in Northwest Anatolia?





Anatolia_N and Meso_Anatolia Form a Clade, With Respect To Other Groups


Outgroup Test X Y D Z-Score SNPs
Chimp CHG Anatolia_N Meso_Ana 0.000322 0.792 946889
Chimp Iran_N Anatolia_N Meso_Ana -0.000379 -0.895 751697
Chimp Iron_Gates Anatolia_N Meso_Ana -0.000465 -1.348 945737
Chimp Natufian Anatolia_N Meso_Ana -0.000126 -0.258 454638
Chimp Ukraine_N Anatolia_N Meso_Ana -0.000831 -2.441 877282
Chimp Levant_N Anatolia_N Meso_Ana -0.000047 -0.122 737416
Chimp Hajji_Firuz Anatolia_N Meso_Ana -0.000022 -0.062 879843
Chimp Pponnese_N Anatolia_N Meso_Ana 0.000149 0.435 917602
Chimp Starcevo Anatolia_N Meso_Ana -0.000125 -0.351 880994


F3-ratio test for Potential Admixture


Source 1 Source 2 Target f_3 std. Error Z SNPs
Meso_Ana Iron_Gates Anatolia_N -0.00164 0.000896 -1.831 745983
Meso_Ana Ukraine_N Anatolia_N -0.003474 0.000917 -3.787 671035
Meso_Ana Levant_N Anatolia_N -0.000648 0.001014 -0.639 547417
Meso_Ana CHG Anatolia_N 0.001094 0.001347 0.812 691079
Meso_Ana Iran_N Anatolia_N -0.002526 0.001415 -1.785 556216


qpAdm Model of Anatolia_N Using Meso_Anatolia



numsnps used: 520478

best coefficients: 0.970 0.030

std. errors: 0.017 0.017

fixed pat     wt    dof      chisq       tail prob

   00                0        8        5.839      0.665269


qpGraph Modeling of Anatolia_N






















Of Stone & Blood: The demography of the Megalithic expansions (work in progress)

One subject that has often been overlooked, is the genetic influence of the first megalith builders of Northern France. This location is meeting point between Danubian and Cardial Neolithic groups. Not only that, but also mixture with Mesolithic hunters of the region. This mixture can be seen quite clearly in the mtDNA (Rivollat et al, 2015; LeRoy et al, 2016). The one thing that is also questioned is just how much genetic impact did Megalithism have on the genetic make-up of Northern European farmers of the Middle and Late Neolithic. Is this a movement of people, or “monumental” ideas?


Since early times, the movement of farmers to Britain was seen as a movement from France, from Michelsberg, or related groups (Childe, 1931). To test this idea, I used datasets from Lipson et al, 2017; Olalde et al, 2018; and Mathieson et al, 2018) to look for a relationship using qpGraph.

Not only are the samples from Southern France and England alike, they are basically identical! Both are probably rooted in the Paris Basin, showing Danubian, Cardial, and Mesolithic roots. As  the link is shown below, in Le Roy et al (2016). Also, note the shift towards France, by later Central Farmers of the Middle to Late Neolithic.









Below, is the first graph, showing the genetic ties between England, and Southern France. Both are likely from the Paris Basin group.


D-stats showing France_MN and England_N as a clade

Out Test X Y D Z-score SNPs
Chimp Ust_Ishim France_MN England_N -0.000325 -1.052 890030
Chimp WHG France_MN England_N 0.000041 0.116 888629
Chimp Iberia_EN France_MN England_N -0.000248 -0.923 860108
Chimp LBKAustria France_MN England_N -0.000119 -0.469 887340
Chimp GermanMN France_MN England_N 0.000152 0.514 853634
Chimp Gokhem2 France_MN England_N -0.00022 -0.437 209487
Chimp MN_Iberia France_MN England_N -0.000539 -1.933 830076
Chimp GAC_Poland France_MN England_N 0.000002 0.007 694973
Chimp Czech_MN France_MN England_N -0.000167 -0.558 818848

Graph with England_N alone


German Middle Neolithic Funnelbeaker

This same set-up is the one I used to then find the relationship to later Megalith building cultures. The first one I chose to look at was the Funnelbeaker samples from Germany. The surprising thing from this graph is that the German Middle Neolithic samples (minus the Roessen sample) were almost exactly like the England and French Neolithic samples, with a slight bit more Danubian admixture.



Attempt to create TRB-Germany without admixture from French-related groups

The first graph, is just Germany_MN, uplaced, with the worst-Z being one that asks for Germany_MN and England_N to be closer.


The second graph forces Germany_MN to be attached to LBK


The worst-Z from this graph asks for WHG to be closer to Germany_MN. The following graph includes an edge from WHG to Germany_MN.


As a result of this graph, the worst-Z now asks for Germany_MN and Iberia_EN to be closer. Due to this, a migration edge from Iberia_EN is added to the next graph.


This last graph has many issues. Not only the zero drift edges, but a migration edge of 0% from Iberia to Germany and a worst-Z still not resolved. It seems that deviating from the desire to connect Germany_MN and England_N caused the poor outcome.

Iberian Middle Neolithic at La Mina

The second group that I looked at was the Middle Neolithic group from La Mina, in Northern Spain. Once again, the Middle Neolithic population was almost identical to the group with roots in France. Specifically, those that moved to Southern France.


Swedish Funnelbeaker

While Gokhem2 does not have as much coverage as I would like, I thought that they would also be good to check, to make sure they are also rooted in the same group. As suspected, she was also almost exactly like the English and French samples.



Globular Amphora in Poland

Since Globular Amphora is supposed to come out of the TRB group in Northern Germany, I thought that I would also take a look at them and see if they are also closely related to the farmers from France. They also turned out to have a great deal of their ancestry from this group.



So, the picture that is emerging is that at one time, groups from Iberia, to Britain, up to Scandinavia, and all the way to the steppes of Ukraine were rooted in the French Middle Neolithic.

D-stats suggestive of Cardial gene-flow

Out Test X Y D Z-score SNPs
Chimp England_N Iberia_EN LBKAustria -0.00065 -3.053 922319
Chimp France_MN Iberia_EN LBKAustria -0.00089 -3.519 877516
Chimp GermanMN Iberia_EN LBKAustria 0.000082 0.311 927867
Chimp Gokhem2 Iberia_EN LBKAustria 0.000096 0.253 232081
Chimp MN_Iberia Iberia_EN LBKAustria -0.001163 -4.727 888929
Chimp GAC Iberia_EN LBKAustria 0.000024 0.103 923159

African Asymmetry

Now, Chimp does have its shortfalls, such as the branch-shortening effect, and ancient attraction, but it is basically symmetrical in relationship to each of these samples. However, the same can not be said for Africans. That is the reason that I have not included them in these graphs. Now, with them, the graphs really don’t change, but you can have the need for edges from farmers to Africans. Farmers, specifically those from Europe, are much closer to basically all Africans, when put against ENA, hunter-gatherers, and more basal sources, such as Natufians.

This is reflected in graphs, and also D-stats. Now, how this is so is the big question. We may see farmer movement taking R1b-V88 and Iberian ancestry as far as South_Africa_2000BP, or something such as ascertainment bias. The fact that Malawi_Hora_Holocene_8100BP is also showing this is quite puzzling and leaning me more towards some kind of bias. The likelihood of a migration of something like a Dzudzuana to SE Africa that early could be possible, but without the actual sample, that is hard to test.

Out Test X Y D Z-Score SNPs
Chimp Mbuti_DG Iberia_EN LBK -0.000074 -0.522 982851
Chimp Mbuti_DG Iberia_EN Natufian -0.000816 -2.636 474711
Chimp Mbuti_DG Iberia_EN GanjDareh -0.000459 -1.691 787482
Chimp Mbuti_DG Iberia_EN IBM 0.000146 0.595 964435
Chimp Mbuti_DG Iberia_EN Van2900BP -0.000726 -2.156 432540
Chimp Mbuti_DG Iberia_EN Iron_Gates -0.000646 -3.658 983550
Chimp Mbuti_DG Iberia_EN EHG -0.00045 -1.947 943281
Chimp Mbuti_DG Iberia_EN SteppeEBA -0.000218 -1.259 981530
Chimp Mota Iberia_EN LBK -0.00013 -0.672 982461
Chimp Mota Iberia_EN Natufian -0.001552 -3.748 474581
Chimp Mota Iberia_EN GanjDareh -0.000572 -1.566 787235
Chimp Mota Iberia_EN IBM -0.000134 -0.4 964105
Chimp Mota Iberia_EN Van2900BP -0.000879 -1.906 432413
Chimp Mota Iberia_EN Iron_Gates -0.000761 -3.119 983154
Chimp Mota Iberia_EN EHG -0.0009 -2.745 942951
Chimp Mota Iberia_EN SteppeEBA -0.00039 -1.64 981147
Chimp S_A2000BP Iberia_EN LBK -0.000432 -2.587 931815
Chimp S_A2000BP Iberia_EN Natufian -0.000884 -2.562 455408
Chimp S_A2000BP Iberia_EN GanjDareh -0.000862 -2.975 750479
Chimp S_A2000BP Iberia_EN IBM -0.000536 -1.999 914899
Chimp S_A2000BP Iberia_EN Van2900BP -0.000479 -1.33 414977
Chimp S_A2000BP Iberia_EN Iron_Gates -0.00093 -4.843 932493
Chimp S_A2000BP Iberia_EN EHG -0.000834 -3.332 894713
Chimp S_A2000BP Iberia_EN Steppe_EBA -0.000556 -2.95 930610
Chimp Malawi_HH Iberia_EN LBK -0.000165 -0.79 575904
Chimp Malawi_HH Iberia_EN Natufian -0.000828 -1.801 320660
Chimp Malawi_HH Iberia_EN GanjDareh -0.000506 -1.312 505066
Chimp Malawi_HH Iberia_EN IBM -0.000526 -1.589 571516
Chimp Malawi_HH Iberia_EN Van2900BP -0.000141 -0.311 310516
Chimp Malawi_HH Iberia_EN Iron_Gates -0.000669 -2.642 576018
Chimp Malawi_HH Iberia_EN EHG -0.000607 -1.87 563496
Chimp Malawi_HH Iberia_EN SteppeEBA -0.000593 -2.408 575632
Chimp Yoruba Iberia_EN LBK -0.000133 -0.871 897516
Chimp Yoruba Iberia_EN Natufian -0.000948 -2.915 432020
Chimp Yoruba Iberia_EN GanjDareh -0.00057 -1.919 718598
Chimp Yoruba Iberia_EN IBM 0.001219 4.714 881191
Chimp Yoruba Iberia_EN Van2900BP -0.000978 -2.637 394707
Chimp Yoruba Iberia_EN Iron_Gates -0.000762 -4.026 898020
Chimp Yoruba Iberia_EN EHG -0.000876 -3.353 862918
Chimp Yoruba Iberia_EN SteppeEBA -0.000375 -1.995 896410

Testing with qpGraph also always resulted in gene-flow from a source like Iberia_EN, and sometimes also from Iberomaurusians (IBM). Mbuti is not bad, in that it is nearly perfectly aligned with Danubian and Cardial sources, the problem lies in the HG stats, where Mbuti does want to be closer to farmers than hunters, and the flow is always to Africa. Testing with flow both to and from Iberian farmers illustrates the same with all Africans in a tree. Here is a tree with South_Africa_2000BP as an example:




This asymmetry makes me wary of including them in analysis that includes both hunters and various types of farmers. I also feel adding too many more admixture edges will be distracting and admixture from Eurasians to Africans does need more ancient samples to resolve whether these are all legit or not.

Trying to create a graph without Chimp does lose a more neutral source for sorting out this farmer and hunter ancestry in Northern Europe, although it does seem minimal, judging by this basic tree involving England_N and France_MN. It seems the most affected is the actual Cardial ancestry in the samples. They become less Cardial than Danubian, in the tree not involving TRB samples, but actually are now closer to what they were with the other farmers involved in the tree.

However, moving onto Germany_MN, we see that Chimp would make the fit better, decreasing the worst Z that seems to want more Cardial influence to England_N.



Including Mbuti into the Analyses

As expected, adding an African to a graph, when they are not symmetrically related, created a graph that becomes overly complicated and does not match other statistical methods. This includes haplotype analysis of an Irish farmer that shared more ancestry with Cardial than Danubian farmers.


With this output, it seems safest to avoid using Africans in analysis that is Eurasian specific and stick to using Chimp as a more neutral outgroup. No African can be a purely symmetrical outgroup and can drastically affect the quality of the graph and qpAdm output.
With all of these results, it appears that not only is Funnelbeaker and Globular Amphora share a good amount of ideas regarding pottery and burial, but actual developed as a mix of Atlantic farmers, mixing with local farmer and hunter groups, adopting some of their culture and genes as well.
With the results from the graphs, D-stats, and also parental markers, it seems that it is quite probably that farmers from France are responsible for the spread of Megalithism. Not only do graphs prefer a closeness of farmers from France, England, and Germany, but there are new markers of “Western” origin in Germany Middle Neolithic farmers. This includes many shared Y-DNA markers, including I2a1b, and shared mtDNA that are not in Danubian farmers, such as U3, H3, H4, and others. Also notable, is the f3(England_N LBK_Austria Germany_MN -0.000433 -0.382 353272), suggesting gene-flow from France to Germany does work.
While more sampling across France will help, it does seem that there is a shared ancestry  between England, France, and Germany that has more to it than shared Danubian ancestry.
Gordon Childe, V. (1931). The Continental Affinities of British Neolithic Pottery. Archaeological Journal, 88(1), 37–66. doi:10.1080/00665983.1931.10853568

Melie Le Roy et al (2016)Distinct ancestries for similar funerary practices? A GIS analysiscomparing funerary, osteological and aDNA data from the Middle Neolithic necropolis Gurgy “Les Noisats” (Yonne, France) http://dx.doi.org/10.1016/j.jas.2016.07.003

Mark Lipson et al (2017) Parallel paleogenomic transects reveal complex genetic history of early European farmers. Nature volume 551, pages 368–372 (16 November 2017) https://www.nature.com/articles/nature24476#supplementary-information
Inigo Olalde et al (2018) The Beaker phenomenon and the genomic transformation of Northwest Europe. Nature volume 555, pages 190–196 (08 March 2018) https://www.nature.com/articles/nature25738
Maïté Rivollat et al., When the Waves of European Neolithization Met: First Paleogenetic Evidence from Early Farmers in the Southern Paris Basin. PLoS ONE 2015. Open accessLINK [doi:10.1371/journal.pone.0125521]
Nicole Sheridan (2013) Early Neolithic habitation structures in Britain and Ireland: A matter of circumstance and context. https://www.researchgate.net/publication/302214298_Early_Neolithic_Habitation_Structures_in_Britain_and_Ireland_a_Matter_of_Circumstance_and_Context











Chi-square Tail-prob Andronovo Okunevo Han
17.866 0.0222543 0.721 0.279 NA
std error 0.02 0.02 NA
Chi-square Tail-prob Andronovo Okunevo Han
9.613 0.211584 0.766 0.178 0.056
std error 0.026 0.04 0.019
Chi-square Tail-prob Andronovo Shamanka_BA
5.1 0.746845 0.814 0.186
std error 0.016 0.016


Chi-square Tail-prob Sintashta Shamanka_EN Afanasievo
6.196 0.625314 0.686 0.189 0.125
std error 0.069 0.014 0.07


Chi-square Tail-prob Sintashta Shamanka_BA Afanasievo
7.951 0.633621 0.541 0.178 0.281
std error 0.081 0.017 0.081




Chi-square Tail-prob Andronovo Okunevo Han
12.248 0.140492 0.741 0.259 NA
std error 0.028 0.028 NA
Chi-square Tail-prob Andronovo Okunevo Han
6.036 0.535555 0.784 0.151 0.064
std error 0.032 0.051 0.025
Chi-square Tail-prob Andronovo Shamanka_BA
5.318 0.723087 0.846 0.154
std error ..022 0.022





art by Johnny Shumate



Chi-square Tail-prob Karsuk Okunevo
5.291 0.726104 0.933 0.067
std error 0.037 0.037
Chi-square Tail-prob Karasuk Shamanka_BA
7.201 0.515133 0.967 0.033
std error 0.021 0.021





Chi-square Tail-prob Karsuk Okunevo Han
16.037 0.0247822 0.313 0.34 0.347
std error 0.036 0.05 0.023
Chi-square Tail-prob Karasuk ShamankaBA Han
1.761 0.971876 0.43 0.43 0.14
std error 0.028 0.08 0.061



Chi-square Tail-prob Karasuk Okunevo
116.899 1.45E-21 0.41 0.59
std error 0.089 0.089
Chi-square Tail-prob Karasuk BMAC Han
6.075 0.531047 0.568 0.099 0.333
std error 0.045 0.042 0.019


Tian-Shan Saka

Chi-square Tail-prob Karasuk Okunevo BMAC Han
10.628 0.10059 0.574 0.134 0.21 0.082
std error 0.06 0.047 0.023 0.018
Chi-square Tail-prob Karasuk ShamankaBA BMAC
10.703 0.152108 0.618 0.173 0.209
std error 0.04 0.017 0.033




Chi-square Tail-prob Karasuk Okunevo Han BMAC
6.786 .341095 0.429 0.284 0.180 0.107
std error 0.06 0.051 0.02 0.034
Chi-square Tail-prob Karasuk Shamanka_BA BMAC
2.488 .927977 0.526 0.372 0.102
std error 0.044 0.019 0.036


Scythian_Samara (Steppe_IA)

Chi-square Tail-prob Karasuk Armenia_EBA
20.194 0.00962638 0.923 0.077
std error 0.048 0.048
Chi-square Tail-prob Karasuk BMAC
15.104 0.0571488 0.863 0.137
std error 0.042 0.042
Chi-square Tail-prob Karasuk BMAC West_Siberia
9.936 0.192223 0.769 0.166 0.065
std error 0.067 0.045 0.037
Chi-square Tail-prob Karasuk BMAC Botai
8.261 0.310125 0.674 0.236 0.089
std error 0.108 0.068 0.056
Chi-square Tail-prob Mezhovskaya BMAC
12.765 0.120182 0.913 0.087
std error 0.05 0.05
Chi-square Tail-prob Tagar BMAC
17.493 0.0253636 0.841 0.159
std error 0.042 0.042

Hungarian Scythian

Chi-square Tail-prob Karasuk Hungary_BA
13.624 0.0921081 0.355 0.645
std error 0.035 0.035
Chi-square Tail-prob Karasuk Balkan_BA
13.99 0.0820368 0.247 0.753
std error 0.037 0.037
Chi-square Tail-prob Scythian_Samara Hungary_BA
18.514 0.0176836 0.314 0.686
std error 0.029 0.029
Chi-square Tail-prob Mezhovskaya Hungary_BA
16.258 0.0388319 0.339 0.661
std error 0.043 0.043




Bagley, R. Shang Archaeology; The Northern Zone. (1999) http://s155239215.onlinehome.us/turkic/btn_Archeology/Zhou/CambridgeZhouChouArcheologyNorthEn.htm


Chechushkov et al., Early horse bridle with cheekpieces as a marker of social change: An experimental and statistical study, Journal of Archaeological Science, Volume 97, September 2018, Pages 125-136, https://doi.org/10.1016/j.jas.2018.07.012

Chernykh, The Formation of the Eurasian “Steppe Belt” of Stockbreeding Cultures. http://www.academia.edu/22557016/FORMATION_OF_THE_EURASIAN_STEPPE_BELT_OF_STOCKBREEDING_CULTURES.BY_E.N._Chernykh





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

“Okunev Culture”. The Great Soviet Encyclopedia. 1979


Another look at South Asian aDNA

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


SGDP chisq tail prob SIS1 Irula Sintashta Dali_EBA Arm_EBA
w Kostenki 2.872 0.82475 0.208 0.623 0.1 0.069 NA
std error 0.038 0.035 0.036 0.035 NA
w/o Kostenki 2.031 0.844866 0.212 0.62 0.103 0.065 NA
std error 0.037 0.034 0.036 0.035 NA
w Kostenki 1.868 0.760109 0.167 0.609 0.065 0.097 0.063
std error 0.071 0.034 0.063 0.051 0.08
w/o Kostenki 2.599 0.761475 0.178 0.61 0.057 0.097 0.058
std error 0.063 0.035 0.065 0.046 0.081
Tiwari chisq tail prob SIS1 Irula Sintashta Dali_EBA Arm_EBA
w Kostenki 8.292 0.217452 0.138 0.583 0.208 0.071 NA
std error 0.025 0.021 0.023 0.02 NA
w/o Kostenki 7.332 0.19711 0.139 0.577 0.211 0.073 NA
std error 0.025 0.02 0.023 0.02 NA
w Kostenki 5.855 0.320606 0.089 0.579 0.154 0.099 0.08
std error 0.04 0.021 0.039 0.026 0.049
w/o Kostenki 5.351 0.253112 0.096 0.574 0.162 0.097 0.071
std error 0.039 0.02 0.039 0.026 0.049
TN chisq tail prob SIS1 Irula Sintashta Dali_EBA Arm_EBA
w Kostenki 0.925 0.988309 0.156 0.656 0.113 0.074 NA
std error 0.043 0.039 0.04 0.04 NA
w/o Kostenki 0.557 0.989882 0.168 0.643 0.117 0.072 NA
std error 0.042 0.037 0.038 0.039 NA
w Kostenki 0.861 0.973004 0.145 0.653 0.097 0.086 0.019
std error 0.079 0.039 0.073 0.049 0.095
w/o Kostenki 0.0624 0.960366 0.191 0.638 0.13 0.068 -0.027
std error 0.079 0.038 0.072 0.049 0.093
UP chisq tail prob SIS1 Irula Sintashta Dali_EBA Arm_EBA
w Kostenki 7.561 0.272087 0.147 0.598 0.181 0.075 NA
std error 0.032 0.028 0.031 0.031 NA
w/o Kostenki 5.636 0.343262 0.151 0.59 0.188 0.071 NA
std error 0.031 0.027 0.029 0.029 NA
w Kostenki 7.338 0.196693 0.11 0.599 0.144 0.094 0.054
std error 0.066 0.028 0.059 0.039 0.081
w/o Kostenki 5.866 0.209375 0.136 0.59 0.171 0.08 0.022
std error 0.061 0.027 0.054 0.037 0.073


Dzh1 = Dzharkutan1_BA, Late BMAC

Steppe_E = Steppe_MLBA_East

SGDP chisq tail prob Irula Dzh1 Sintashta Steppe_E Dali_EBA
w Kostenki 6.954 0.433685 0.681 0.203 0.116 NA NA
std error 0.023 0.034 0.028 NA NA
6.294 0.505837 0.678 0.198 NA 0.124 NA
std error 0.023 0.034 NA 0.028 NA
3.787 0.705485 0.629 0.225 NA 0.075 0.07
std error 0.029 0.036 NA 0.037 0.033

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…

Aligrama chisq tail prob Irula Dzh1 Steppe_E Dali_EBA
12.238 0.0568697 0.49 0.355 0.091 0.063
std error 0.03 0.036 0.034 0.03
Butkara_IA chisq tail prob Irula Dzh1 Steppe_E Dali_EBA
5.148 0.524941 0.404 0.489 0.03 0.077
std error 0.029 0.034 0.034 0.031
Pak_IA_Ali chisq tail prob Irula Dzh1 Steppe_E Dali_EBA
8.014 0.237064 0.431 0.419 0.087 0.063
std error 0.042 0.053 0.05 0.043
S_Sharif_IA chisq tail prob Irula Dzh1 Steppe_E Dali_EBA
8.433 0.208056 0.437 0.364 0.141 0.059
std error 0.018 0.023 0.022 0.018
SPGT chisq tail prob Irula Dzh1 Steppe_E Dali_EBA
11.378 0.0773638 0.316 0.503 0.113 0.069
std error 0.014 0.018 0.018 0.015

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.

Aligrama chisq tail prob Irula BMAC Steppe_East WSiberia_N
15.054 0.0198432 0.5 0.359 0.083 0.058
std error 0.027 0.036 0.034 0.022
Butkara_IA chisq tail prob Irula BMAC Steppe_East WSiberia_N
8.929 0.177591 0.411 0.477 0.052 0.06
std error 0.025 0.033 0.034 0.022
Pak_IA_Ali chisq tail prob Irula BMAC Steppe_East WSiberia_N
6.755 0.344126 0.438 0.402 0.104 0.055
std error 0.037 0.053 0.05 0.032
S_Sharif_IA chisq tail prob Irula BMAC Steppe_East WSiberia_N
10.458 0.106644 0.442 0.368 0.144 0.046
std error 0.015 0.022 0.022 0.013
SPGT chisq tail prob Irula BMAC Steppe_East WSiberia_N
8.626 0.195754 0.313 0.509 0.102 0.076
std error 0.011 0.016 0.015 0.009


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

SIS1 chisquare Tail-prob Ganj_Dareh W_Siberia
46.394 7.33E-08 0.953 0.047
std error 0.02 0.02
SIS1 chisquare Tail-prob Ganj_Dareh Anatolia W_Siberia
2.715 0.84368 0.775 0.148 0.076
std error 0.03 0.021 0.019
SIS1 chisquare Tail-prob Ganj_Dareh Anatolia W_Siberia Onge
5.606 0.346473 0.716 0.172 0.095 0.017
std error 0.051 0.027 0.022 0.031
SIS1 chisquare Tail-prob Ganj_Dareh Anatolia W_Siberia Irula
  2.275 0.80994 0.738 0.155 0.073 0.034
std error 0.051 0.022 0.02 0.041
SIS2 chisquare Tail-prob Ganj_Dareh W_Siberia
31.452 5.13E-05 0.837 0.163
std error 0.021 0.021
SIS2 chisquare Tail-prob Ganj_Dareh Anatolia W_Siberia
31.336 2.19E-05 0.842 -0.005 0.163
    std error 0.036 0.023 0.022
SIS2 chisquare Tail-prob Ganj_Dareh Anatolia W_Siberia Onge
7.09 0.214035 0.663 0.042 0.15 0.145
std error 0.057 0.031 0.024 0.034
SIS2 chisquare Tail-prob Ganj_Dareh Anatolia W_Siberia Irula
9.597 0.0874816 0.61 0.031 0.128 0.231
std error 0.058 0.023 0.021 0.049
SIS2 chisquare Tail-prob Ganj_Dareh W_Siberia Irula
11.044 0.0870409 0.662 0.124 0.214
std error 0.043 0.022 0.047
SIS2 chisquare Tail-prob SIS1 Irula
20.805 0.00407046 0.661 0.339
std error 0.046 0.046
SIS2 chisquare Tail-prob SIS1 W_Siberia Irula
12.337 0.0548625 0.636 0.074 0.29
    std error 0.045 0.025 0.049
SIS2 chisquare Tail-prob Sarazm_EN Irula
7.525 0.376375 0.707 0.293
std error 0.043 0.043
SIS3 chisquare Tail-prob Ganj_Dareh W_Siberia
270.628 0 0.835 0.165
    std error 0.023 0.023
SIS3 chisquare Tail-prob Ganj_Dareh W_Siberia Onge
  14.444 0.0250507 0.494 0.089 0.417
std error 0.031 0.024 0.03
SIS3 chisquare Tail-prob Ganj_Dareh Anatolia W_Siberia Onge
10.019 0.074706 0.401 0.063 0.097 0.439
std error 0.052 0.028 0.023 0.032
SIS3 chisquare Tail-prob Ganj_Dareh Anatolia W_Siberia Irula
6.653 0.247741 0.229 0.004 0.039 0.727
std error 0.058 0.024 0.02 0.048
SIS3 chisquare Tail-prob Ganj_Dareh W_Siberia Irula
6.47 0.372669 0.231 0.035 0.734
std error 0.042 0.02 0.047
SIS3 chisquare Tail-prob SIS1 Irula
5.423 0.608532 0.23 0.77
std error 0.041 0.041
SIS3 chisquare Tail-prob Geoksiur Irula
5.274 0.626561 0.213 0.787
std error 0.038 0.038








European Farmers Part I; Mediterranean vs Danubian

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:

Farmer simple

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.

simple Farmer2

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

simple Farmer3

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.

simple Farmer5

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.

simple Farmer6

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.

simple Farmer7

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.


Out Test Pop1 Pop2 D-stat Z-score SNPs
Mbuti_DG Cardial_EN LBK_Austria Starcevo -0.000114 -0.537 899799
Mbuti_DG Cardial_EN LBK_EN Starcevo 0.000076 0.398 903127
Mbuti_DG Cardial_EN LBK_Austria Koros_EN -0.000287 -0.959 892981
Mbuti_DG Cardial_EN LBK_EN Koros_EN -0.000035 -0.119 895930


Source Source Target f_3 std. Err Z SNPs
Koros_EN LaBrana Iberia_EN -0.002397 0.001668 -1.437 403414
Koros_EN Iron_Gates Iberia_EN -0.000355 0.001208 -0.294 598713
Koros_EN French_HG Iberia_EN -0.001492 0.001914 -0.779 127825
Cardial_EN LaBrana Iberia_EN -0.001178 0.001525 -0.773 410098
Cardial_EN Iron_Gates Iberia_EN 0.001799 0.001092 1.648 585125
Cardial_EN French_HG Iberia_EN -0.000595 0.001713 -0.348 131631
Koros_EN LaBrana LBK_EN -0.005487 0.00118 -4.65 580503
Koros_EN Iron_Gates LBK_EN -0.005153 0.000779 -6.615 755300
Koros_EN French_HG LBK_EN -0.003943 0.001382 -2.854 175316
Cardial_EN LaBrana LBK_EN -0.002165 0.001022 -2.118 555955
Cardial_EN Iron_Gates LBK_EN -0.001198 0.000649 -1.847 711734
Cardial_EN French_HG LBK_EN -0.001455 0.001165 -1.249 171888
Starcevo LaBrana LBK_EN -0.00003 0.000841 -0.035 600458
Starcevo Iron_Gates LBK_EN 0.000585 0.000568 1.031 770549
Starcevo French_HG LBK_EN -0.000667 0.001019 -0.654 180239
Koros_EN Iron_Gates LBK_Austria -0.005504 0.000912 -6.033 715204
Koros_EN Iron_Gates Cardial_EN 0.002384 0.001571 1.517 553269
Koros_EN Iron_Gates Iberia_EN -0.000355 0.001208 -0.294 598713
Starcevo Iberia_EN LBK_Austria 0.000357 0.000672 0.53 615071
Starcevo Iberia_EN LBK_EN -0.000208 0.000516 -0.403 665936
Starcevo Cardial_EN LBK_Austria 0.00095 0.00067 1.419 581722
Starcevo Cardial_EN LBK_EN 0.001295 0.000578 2.24 626360
Starcevo Iron_Gates LBK_Austria 0.000502 0.000715 0.702 729333
Starcevo Iron_Gates LBK_EN 0.000585 0.000568 1.031 770549
Koros_EN Iberia_EN LBK_EN -0.000175 0.000696 -0.251 647053
Koros_EN Iberia_EN Starcevo 0.000738 0.001072 0.689 496318
Koros_EN Cardial_EN LBK_EN 0.000021 0.000789 0.026 609865
Koros_EN Cardial_EN Starcevo -0.000584 0.001115 -0.524 470485



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)


The first farmers, with a focus on Anatolia

Background on the Sites and Samples









Outgroup Test Pop1 Pop2 f4 Z-score SNPs
Mbuti_DG Ust_Ishim Boncuklu Anatolia_N1 -0.000282 -0.628 1081252
Chimp Ust_Ishim Boncuklu Anatolia_N1 -0.000416 -0.876 1036168
Mbuti_DG Natufian Boncuklu Anatolia_N1 0.00148 2.878 494327
Chimp Natufian Boncuklu Anatolia_N1 0.001535 2.843 472808
Mbuti_DG Levant_N Boncuklu Anatolia_N1 0.000891 2.171 804093
Chimp Levant_N Boncuklu Anatolia_N1 0.000694 1.556 769663
Mbuti_DG Tepe_Abdul Boncuklu Anatolia_N1 -0.000556 -1.448 1003898
Chimp Tepe_Abdul Boncuklu Anatolia_N1 -0.000645 -1.559 962310
Mbuti_DG Wezmeh Boncuklu Anatolia_N1 -0.00025 -0.562 1082448
Chimp Wezmeh Boncuklu Anatolia_N1 -0.000415 -0.882 1037321
Mbuti_DG CHG Boncuklu Anatolia_N1 -0.000361 -0.867 1083101
Chimp CHG Boncuklu Anatolia_N1 -0.000445 -1.031 1037961
Mbuti_DG Iron_Gates Boncuklu Anatolia_N1 -0.000623 -1.69 1076193
Chimp Iron_Gates Boncuklu Anatolia_N1 -0.000712 -1.803 1031346
Mbuti_DG EHG Boncuklu Anatolia_N1 -0.000393 -0.989 995924
Chimp EHG Boncuklu Anatolia_N1 -0.000544 -1.281 954789
Mbuti_DG W_Siberia Boncuklu Anatolia_N1 0.000176 0.404 818559
Chimp W_Siberia Boncuklu Anatolia_N1 0.000181 0.397 783377
Mbuti_DG Ami Boncuklu Anatolia_N1 -0.001048 -1.936 734487
Chimp Ami Boncuklu Anatolia_N1 -0.001122 -1.945 702374



Pop1 Pop2 Target f3 std error Z-score SNPs
Boncuklu Tepe_Abdul Anatolia_N1 0.004 0.002013 1.987 494742
Boncuklu Wezmeh Anatolia_N1 0.002506 0.002218 1.13 486870
Boncuklu Levant_N Anatolia_N1 -0.000089 0.001916 -0.047 382114
Boncuklu Tepecik Anatolia_N1 0.004217 0.00194 2.174 361064
Boncuklu Natufian Anatolia_N1 -0.001456 0.00235 -0.619 222845

qpAdm Models


 probability   Natufian  Tepe_Abdul  Kostenki14    std error    std error    std error
0.236 0.232 0.449 0.32 0.142 0.107 0.074



   probability    Boncuklu    Levant_N    std error    std error
0.815 0.687 0.313 0.064 0.064

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

probability Boncuklu Natufian std error std error
0.302 0.771 0.229 0.071 0.071

qpGraph Models


6/4/18 New Update: Simple qpGraphs








































