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Friday, April 25, 2014

Low genomic diversity among ancient Swedish hunter-gatherers (+ no East Asian admix for La Brana1 nor MA1)


Here's an Admixture graph from a paper published in Science today, which focuses on ancient genomes from Mesolithic and Neolithic Sweden.


I think it sums up very succinctly some of stuff we've discussed at length on these blogs in recent months. Note that it's the Neolithic and post-Neolithic European farmers, Gökhem2 and Oetzi the Iceman, respectively, who appear to be mixtures of two very distinct Eurasian clades; one shifted towards Sub-Saharan Africa, and the other basically identical to that of European hunter-gatherers.

On the other hand, hunter-gatherers La Brana1 and MA1, from Mesolithic Iberia and Upper Paleolithic Siberia, respectively, are each derived entirely from two closely related North Eurasian hunter-gatherer clades, while Ajvide58, a hunter-gather from early Neolithic Gotland, appears to be a mixture between these clades. Note also that Anzick-1 and Saqqaq are significantly East Eurasian (pink clade), but none of the ancient North Eurasians show this influence.

Moreover, one of the main findings of this study is that the Swedish hunter-gatherers had extremely low conditional nucleotide, or genomic, diversity (0.18122), much lower than the ancient Swedish farmers (0.20134), modern Europeans (from 0.20754 among the British to 0.21269 among Spaniards) and even modern Han Chinese (0.19442), who are known to be amongst the least genetically diverse human groups. When the Scandinavian hunter-gatherers are combined with La Brana1, the figure goes up to just 0.19744. Refer to Table S16. in the freely available supplementary material PDF for more details (see here). This quote explaining the results is from the main article:

The distinct features of the two Neolithic Scandinavian groups; non-symmetric gene-flow into farmers, low level of diversity among hunter-gatherers and strong differentiation between groups have important implications for our understanding of the demographic histories of these groups. The greater diversity in the farmer population may have been influenced by gene-flow from hunter-gatherers. However, the low level of genetic diversity in Neolithic hunter-gatherers likely has a demo-graphic explanation, similar to the Iberian Mesolithic individual (8). Although we cannot exclude that this low diversity is a feature restricted to the Gotland island hunter-gatherer population, we note that this may be due to the fact that their ancestors resided in ice-free refugia in Eu-rope during the Last Glacial Maximum (LGM), potentially causing population bottlenecks. Climatic changes and occasional population crashes, likely affected the population sizes of hunter-gatherers (24, 25).

The PCA from the paper underlines how extremely closely related La Brana1 was to the hunter-gatherers from up north. That's despite the fact that he was from Iberia and his Y-chromosome belonged to haplogroup C6, while all of the successfully tested Y-chromosomes from among the Northern European hunter-gatherers to haplogroup I.


However, it's interesting to note that just like Motala3, Motala12 and Loschbour from the Lazaridis et al. preprint, Ajvide58 belonged to sub-haplogroup I2a1, which is presently much less common across Northwestern Europe, especially Sweden, than I1. It actually peaks today in the Balkans and Eastern Europe, and in fact the Loschbour I2a1b sequence is most closely related to that of a Russian from the HGDP dataset, sampled in the Kargopol district in the northwest of the country.

By the way, it warms my heart to see the following two figures in the supplementary material, in which the authors note that the sharp genetic differentiation between Ajvide58 and Gökhem2 is reflected in their varying affinities to modern Northwestern and Southwestern Eurasians. That's because two years ago I wrote a blog entry based on an ADMIXTURE run suggesting that modern West Eurasians descend from two main ancient stocks: the Northwest Eurasians and Southwest Eurasians (see here). Looking back at that effort, I certainly rambled on and didn't quite hit the nail on the head, but the general idea wasn't too far off from what we're now finding out about our deep ancestry thanks to ancient DNA and more sophisticated analysis methods.


Interestingly, the Northern European sample with the lowest affinity to Ajvide58 are the Saami from Norrbotten, Sweden. This is most likely due to post-Mesolithic Siberian ancestry among the Saami, which is essentially East Asian admixture and, as per above, not found among the prehistoric North Eurasian hunter-gatherers or European farmers.

I haven't yet had a chance to look in detail at the inferred pigmentation traits of the ancient Swedes. But Gökhem2 carried the derived allele for SLC24A5, which means she probably had fair skin. On the other hand, Ajvide58 was ancestral at this locus, meaning he was probably dark skinned, much like La Brana1. However, as far as I can make out right now, tables S9 and S10 in the supplementary material suggest that both Gökhem2 and Ajvide58 might have been blue eyed and fair haired.

Citation...

Skoglund, Malmstrom et al., Genomic Diversity and Admixture Differs for Stone-Age Scandinavian Foragers and Farmers, Published Online April 24 2014, Science DOI: 10.1126/science.1253448.

See also...

Ancient human genomes suggest (more than) three ancestral populations for present-day Europeans

Another look at the Lazaridis et al. ancient genomes preprint

Mesolithic genome from Spain reveals markers for blue eyes, dark skin and Y-haplogroup C6

Tuesday, April 22, 2014

Tuscan-like farmer from late Neolithic Iberia


I found this Principal Component Analysis (PCA) in a thesis published recently by Uppsala University (see here). It features a 4,000 year-old human sample from the archeological site of El Portalón, near the city of Burgos in northern Spain. The thesis refers to this individual as a late Neolithic farmer, which is probably another way of saying that his remains date back to the Chalcolithic or Copper Age. Indeed, I'm pretty sure that El Portalón is a Chalcolithic site.

Interestingly, he doesn't cluster with modern Basques, like the Swedish Neolithic farmer Gok4, nor with modern Sardinians, like Oetzi the Iceman. He's actually closest to modern Tuscans from Italy. This might be an artefact of the low resolution of the data (only 66,476,944 bp of DNA sequences), but if not, then it could be a signal of population movements to Iberia from somewhere in the east during the Copper Age.

Needless to say, it's a shame we don't know this guy's Y-haplogroup, because it's now generally believed that haplogroup R1b made its appearance in Western Europe during the Copper Age, and that it arrived there from somewhere in the east. By the way, his mtDNA belongs to haplogroup U5b1b, which is actually a marker typical of Western and Central European hunter-gatherers.

Citation...

Daskalaki, E. 2014. Archaeological Genetics - Approaching Human History through DNA Analysis. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1101. 61 pp. Uppsala: Acta Universitatis Upsaliensis

See also...

The story of R1b: it's complicated

Thursday, April 3, 2014

The really old Europe is mostly in Eastern Europe


A new version of the Lazaridis et al. ancient genomes preprint has just appeared at arXiv (see here). It includes several new Principal Component Analysis (PCA) maps, TreeMix graphs, a ChromoPainter/fineSTRUCTURE co-ancestry matrix, and an updated ADMIXTURE analysis. The revised text underlines the relatively close genetic relationship between indigenous European hunter-gatherers and present-day Eastern Europeans:


The co-ancestry matrix (Fig. S19.3) confirms the ability of this method to meaningfully cluster individuals. We highlight two clusters: Stuttgart joins all Sardinian individuals in cluster A and Loschbour joins a cluster B that encompasses all Belarusian, Ukrainian, Mordovian, Russian, Estonian, Finnish, and Lithuanian individuals. These results confirm Sardinia as a refuge area where ancestry related to Early European Farmers has been best preserved, and also the greater persistence of WHG-related ancestry in present-day Eastern European populations. The latter finding suggests that West European Hunter-Gatherers (so-named because of the prevalence of Loschbour and La Braña) or populations related to them have contributed to the ancestry of present-day Eastern European groups. Additional research is needed to determine the distribution of WHG-related populations in ancient Europe.


Fig. S10.5 suggests that the main axis of differentiation in Europe when the subcontinent is considered as a whole may tend to Northeastern Europe rather than SSE/NNW (8). This is consistent with our analysis of ancestry proportions in European populations (Fig. 2B, Extended Data Table 3) which indicate a cline of reduced EEF (and increasing WHG) ancestry along that direction.

Citation...

Iosif Lazaridis, Nick Patterson, Alissa Mittnik, et al., Ancient human genomes suggest three ancestral populations for present-day Europeans, arXiv, April 2, 2014, arXiv:1312.6639v2

Wednesday, March 26, 2014

4 Ancestors Oracle results for Anzick-1, La Brana-1 and MA-1


It's unlikely you'll ever see "oracle" results like these in a formal study on ancient genomes, which I think is a shame, because they're really fascinating.

The important thing to keep in mind is that it's probably wrong in most cases to think of present-day populations as ancestral to people who lived 7,000, 13,000 and especially 24,000 years ago. Indeed, it's more logical to assume that the affinities shown in these results are mostly due to the ancient individuals or their close relatives contributing DNA to our genomes.

Then again, it is possible, for instance, that a population very similar to present-day Oceanians was living somewhere on mainland Asia during the Upper Palaeolithic, and that's why La Brana-1 shows signals of ancestry from Melanesia and Malaya. Interestingly, he belonged to Y-chromosome haplogroup C, which today reaches extreme frequencies in Oceania and East Asia.

The 4 Ancestors Oracle was designed by Alexandr Burnashev, and used here by me to interpret ancestry proportions from the Eurogenes K15 test (see spreadsheet here). Both tools are available at GEDmatch for free for people with genotype data from 23andMe and similar companies.

Anzick-1

Least-squares method.

Using 1 population approximation:
1 Pima @ 3.005527
2 Mayan @ 6.593102
3 Karitiana @ 9.749507
4 North_Amerindian @ 27.037993
5 East_Greenlander @ 63.458136
6 West_Greenlander @ 64.974008
7 MA-1 @ 82.206699
8 Chukchi @ 87.490051
9 Uygur @ 95.358376
10 Afghan_Hazara @ 95.536961
11 Uzbeki @ 95.618405
12 Tatar @ 95.739181
13 Afghan_Turkmen @ 95.860959
14 Hazara @ 96.290938
15 Shors @ 96.554106
16 Koryak @ 96.779903
17 Nogay @ 97.015998
18 Tadjik @ 97.344164
19 Afghan_Tadjik @ 97.569548
20 Turkmen @ 97.722246

Using 2 populations approximation:
1 Pima+Pima @ 3.005527
2 Karitiana+Pima @ 4.012735
3 Karitiana+Mayan @ 4.197319
4 Mayan+Pima @ 4.469513
5 Mayan+Mayan @ 6.593102
6 Karitiana+North_Amerindian @ 9.158761
7 Karitiana+Karitiana @ 9.749507
8 North_Amerindian+Pima @ 14.755839
9 Mayan+North_Amerindian @ 15.292709
10 North_Amerindian+North_Amerindian @ 27.037993
11 East_Greenlander+Karitiana @ 27.204899
12 Karitiana+West_Greenlander @ 27.7474
13 East_Greenlander+Pima @ 32.903069
14 East_Greenlander+Mayan @ 33.276658
15 Pima+West_Greenlander @ 33.664672
16 Mayan+West_Greenlander @ 34.254995
17 Karitiana+MA-1 @ 37.085534
18 Chukchi+Karitiana @ 39.241032
19 MA-1+Pima @ 42.043159
20 MA-1+Mayan @ 43.172013

Using 3 populations approximation:
1 50% Pima +25% Karitiana +25% Pima @ 1.892714
2 50% Pima +25% Karitiana +25% Mayan @ 2.264604
3 50% Karitiana +25% Karitiana +25% North_Amerindian @ 2.678692
4 50% Mayan +25% Karitiana +25% Pima @ 3.132374
5 50% Karitiana +25% North_Amerindian +25% Pima @ 3.451868
6 50% Karitiana +25% Mayan +25% North_Amerindian @ 3.521602
7 50% Pima +25% Mayan +25% Pima @ 3.596698
8 50% Karitiana +25% Mayan +25% Pima @ 3.910387
9 50% Karitiana +25% Pima +25% Pima @ 4.012735
10 50% Karitiana +25% Mayan +25% Mayan @ 4.197319
11 50% Mayan +25% Karitiana +25% Mayan @ 4.199092
12 50% Mayan +25% Pima +25% Pima @ 4.469513
13 50% Mayan +25% Mayan +25% Pima @ 5.491278
14 50% Pima +25% Karitiana +25% North_Amerindian @ 5.884377
15 50% Karitiana +25% Karitiana +25% Mayan @ 6.589713
16 50% Mayan +25% Karitiana +25% North_Amerindian @ 6.591639
17 50% Karitiana +25% Karitiana +25% Pima @ 6.825849
18 50% Pima +25% North_Amerindian +25% Pima @ 8.677955
19 50% Pima +25% Mayan +25% North_Amerindian @ 8.972578
20 50% Karitiana +25% North_Amerindian +25% North_Amerindian @ 9.158761

Using 4 populations approximation:
1 Karitiana+Pima+Pima+Pima @ 1.892714
2 Karitiana+Mayan+Pima+Pima @ 2.264604
3 Karitiana+Karitiana+Karitiana+North_Amerindian @ 2.678692
4 Pima+Pima+Pima+Pima @ 3.005527
5 Karitiana+Mayan+Mayan+Pima @ 3.132374
6 Karitiana+Karitiana+North_Amerindian+Pima @ 3.451868
7 Karitiana+Karitiana+Mayan+North_Amerindian @ 3.521602
8 Mayan+Pima+Pima+Pima @ 3.596698
9 Karitiana+Karitiana+Mayan+Pima @ 3.910387
10 Karitiana+Karitiana+Pima+Pima @ 4.012735
11 Karitiana+Karitiana+Mayan+Mayan @ 4.197319
12 Karitiana+Mayan+Mayan+Mayan @ 4.199092
13 Mayan+Mayan+Pima+Pima @ 4.469513
14 Mayan+Mayan+Mayan+Pima @ 5.491278

15 Karitiana+North_Amerindian+Pima+Pima @ 5.884377
16 Karitiana+Mayan+North_Amerindian+Pima @ 6.121206
17 Karitiana+Karitiana+Karitiana+Mayan @ 6.589713
18 Karitiana+Mayan+Mayan+North_Amerindian @ 6.591639
19 Mayan+Mayan+Mayan+Mayan @ 6.593102
20 Karitiana+Karitiana+Karitiana+Pima @ 6.825849
21 North_Amerindian+Pima+Pima+Pima @ 8.677955
22 Mayan+North_Amerindian+Pima+Pima @ 8.972578
23 Karitiana+Karitiana+North_Amerindian+North_Amerindian @ 9.158761
24 Karitiana+Karitiana+Karitiana+West_Greenlander @ 9.240173
25 East_Greenlander+Karitiana+Karitiana+Karitiana @ 9.346544
26 Mayan+Mayan+North_Amerindian+Pima @ 9.425745
27 Karitiana+Karitiana+Karitiana+Karitiana @ 9.749507
28 Mayan+Mayan+Mayan+North_Amerindian @ 10.01596
29 Karitiana+North_Amerindian+North_Amerindian+Pima @ 11.908971
30 East_Greenlander+Karitiana+Karitiana+Pima @ 12.010881
31 Karitiana+Mayan+North_Amerindian+North_Amerindian @ 12.081559
32 East_Greenlander+Karitiana+Karitiana+Mayan @ 12.120046
33 Karitiana+Karitiana+Pima+West_Greenlander @ 12.145197
34 Karitiana+Karitiana+Mayan+West_Greenlander @ 12.408976
35 North_Amerindian+North_Amerindian+Pima+Pima @ 14.755839
36 East_Greenlander+Karitiana+Pima+Pima @ 14.803215
37 Karitiana+Karitiana+Karitiana+MA-1 @ 14.913242
38 East_Greenlander+Karitiana+Mayan+Pima @ 14.970851
39 Mayan+North_Amerindian+North_Amerindian+Pima @ 14.974386
40 Karitiana+Pima+Pima+West_Greenlander @ 15.086462

Gaussian method.

Using 1 population approximation:
1 Pima @ 3.434735
2 Mayan @ 4.70517
3 North_Amerindian @ 5.773016
4 Karitiana @ 9.72238
5 East_Greenlander @ 9.995653
6 West_Greenlander @ 10.907357
7 Chukchi @ 14.282297
8 MA-1 @ 14.734353
9 Koryak @ 17.138626
10 Ket @ 24.236645
11 Selkup @ 27.603109
12 Shors @ 27.857241
13 Hakas @ 33.049508
14 Altaian @ 35.82071
15 Mari @ 37.216176
16 Chuvash @ 39.8589
17 Tuvinian @ 39.946418
18 Uygur @ 39.9803
19 Evens @ 40.918238
20 Afghan_Hazara @ 42.705673

Using 2 populations approximation:
1 Karitiana+Pima @ 2.865464
2 Pima+Pima @ 3.434735
3 Karitiana+North_Amerindian @ 3.438239
4 Karitiana+Mayan @ 4.069781
5 Mayan+Pima @ 4.189114
6 North_Amerindian+Pima @ 4.579623
7 Mayan+Mayan @ 4.70517
8 Mayan+North_Amerindian @ 5.181026
9 East_Greenlander+Karitiana @ 5.481781
10 North_Amerindian+North_Amerindian @ 5.773016
11 Karitiana+West_Greenlander @ 6.135233
12 East_Greenlander+Pima @ 6.481362
13 Chukchi+Karitiana @ 6.764147
14 East_Greenlander+Mayan @ 6.922655
15 Pima+West_Greenlander @ 6.970825
16 Karitiana+Koryak @ 7.325513
17 Mayan+West_Greenlander @ 7.352754
18 Karitiana+MA-1 @ 7.524793
19 East_Greenlander+North_Amerindian @ 7.732776
20 Chukchi+Pima @ 7.73597

Using 3 populations approximation:
1 50% Karitiana +25% Karitiana +25% North_Amerindian @ 2.344693
2 50% Karitiana +25% North_Amerindian +25% Pima @ 2.847556
3 50% Karitiana +25% Pima +25% Pima @ 2.865464
4 50% Pima +25% Karitiana +25% Pima @ 3.007575
5 50% Karitiana +25% East_Greenlander +25% Karitiana @ 3.118962
6 50% Pima +25% Karitiana +25% North_Amerindian @ 3.432938
7 50% Karitiana +25% North_Amerindian +25% North_Amerindian @ 3.438239
8 50% Karitiana +25% Mayan +25% North_Amerindian @ 3.455284
9 50% Karitiana +25% Mayan +25% Pima @ 3.508374
10 50% Pima +25% Karitiana +25% Mayan @ 3.54801
11 50% Karitiana +25% Karitiana +25% West_Greenlander @ 3.684447
12 50% Karitiana +25% Karitiana +25% Pima @ 3.766195
13 50% Karitiana +25% East_Greenlander +25% Pima @ 3.829455
14 50% Karitiana +25% Chukchi +25% Karitiana @ 3.853179
15 50% Pima +25% Mayan +25% Pima @ 3.876181
16 50% Mayan +25% Karitiana +25% Pima @ 3.935784
17 50% Pima +25% North_Amerindian +25% Pima @ 3.977392
18 50% North_Amerindian +25% Karitiana +25% Pima @ 4.044734
19 50% Karitiana +25% Mayan +25% Mayan @ 4.069781
20 50% Mayan +25% Pima +25% Pima @ 4.189114

Using 4 populations approximation:
1 Karitiana+Karitiana+Karitiana+North_Amerindian @ 2.344693
2 Karitiana+Karitiana+North_Amerindian+Pima @ 2.847556
3 Karitiana+Karitiana+Pima+Pima @ 2.865464
4 Karitiana+Pima+Pima+Pima @ 3.007575
5 East_Greenlander+Karitiana+Karitiana+Karitiana @ 3.118962
6 Karitiana+North_Amerindian+Pima+Pima @ 3.432938
7 Pima+Pima+Pima+Pima @ 3.434735
8 Karitiana+Karitiana+North_Amerindian+North_Amerindian @ 3.438239
9 Karitiana+Karitiana+Mayan+North_Amerindian @ 3.455284
10 Karitiana+Karitiana+Mayan+Pima @ 3.508374
11 Karitiana+Mayan+Pima+Pima @ 3.54801
12 Karitiana+Karitiana+Karitiana+West_Greenlander @ 3.684447
13 Karitiana+Karitiana+Karitiana+Pima @ 3.766195
14 East_Greenlander+Karitiana+Karitiana+Pima @ 3.829455
15 Chukchi+Karitiana+Karitiana+Karitiana @ 3.853179
16 Mayan+Pima+Pima+Pima @ 3.876181
17 Karitiana+Mayan+North_Amerindian+Pima @ 3.908241
18 Karitiana+Mayan+Mayan+Pima @ 3.935784
19 North_Amerindian+Pima+Pima+Pima @ 3.977392
20 Karitiana+North_Amerindian+North_Amerindian+Pima @ 4.044734
21 Karitiana+Karitiana+Mayan+Mayan @ 4.069781
22 Mayan+Mayan+Pima+Pima @ 4.189114
23 Karitiana+Karitiana+Karitiana+Koryak @ 4.189486
24 Karitiana+Mayan+Mayan+North_Amerindian @ 4.228106
25 Karitiana+Karitiana+Pima+West_Greenlander @ 4.256633
26 East_Greenlander+Karitiana+Karitiana+Mayan @ 4.279896
27 Karitiana+Mayan+Mayan+Mayan @ 4.296297
28 Mayan+North_Amerindian+Pima+Pima @ 4.366474
29 East_Greenlander+Karitiana+Pima+Pima @ 4.416364
30 Mayan+Mayan+Mayan+Pima @ 4.453012
31 Karitiana+Mayan+North_Amerindian+North_Amerindian @ 4.464436
32 East_Greenlander+Karitiana+Karitiana+North_Amerindian @ 4.474178
33 Chukchi+Karitiana+Karitiana+Pima @ 4.497774
34 Karitiana+Karitiana+Karitiana+Mayan @ 4.562844
35 North_Amerindian+North_Amerindian+Pima+Pima @ 4.579623
36 Karitiana+Karitiana+Mayan+West_Greenlander @ 4.620526
37 Mayan+Mayan+North_Amerindian+Pima @ 4.638639
38 Karitiana+North_Amerindian+North_Amerindian+North_Amerindian @ 4.661835
39 Mayan+Mayan+Mayan+Mayan @ 4.70517
40 Karitiana+Karitiana+Karitiana+Ket @ 4.747046


La Brana-1

Least-squares method.

Using 1 population approximation:
1 Southwest_Finnish @ 13.121294
2 Finnish @ 13.131219
3 Polish @ 14.081928
4 Estonian @ 14.134514
5 South_Polish @ 14.771163
6 East_Finnish @ 15.763002
7 Russian_Smolensk @ 15.92973
8 Ukrainian @ 15.932621
9 Ukrainian_Lviv @ 16.024086
10 Belorussian @ 16.06542
11 Estonian_Polish @ 16.676242
12 Southwest_Russian @ 17.164691
13 East_German @ 17.206296
14 Hungarian @ 17.719063
15 Ukrainian_Belgorod @ 17.914931
16 Austrian @ 18.079076
17 Croatian @ 18.390911
18 Lithuanian @ 18.431995
19 North_Swedish @ 18.533636
20 Kargopol_Russian @ 18.940305

Using 2 populations approximation:
1 Lithuanian+North_German @ 11.932813
2 Belorussian+Southwest_Finnish @ 12.085714
3 Belorussian+North_Swedish @ 12.098491
4 Russian_Smolensk+Southwest_Finnish @ 12.147254
5 Polish+Southwest_Finnish @ 12.156833
6 Danish+Lithuanian @ 12.201767
7 North_Swedish+Russian_Smolensk @ 12.219192
8 Finnish+Polish @ 12.252393
9 Estonian+Southwest_Finnish @ 12.259661
10 Lithuanian+North_Swedish @ 12.264153
11 Estonian_Polish+North_Swedish @ 12.277182
12 Finnish+Russian_Smolensk @ 12.37369
13 Irish+Lithuanian @ 12.378891
14 Estonian_Polish+Southwest_Finnish @ 12.401639
15 South_Polish+Southwest_Finnish @ 12.408348
16 Belorussian+North_German @ 12.435995
17 Belorussian+Finnish @ 12.451844
18 Lithuanian+North_Dutch @ 12.454881
19 Lithuanian+West_Scottish @ 12.489829
20 Lithuanian+Southwest_Finnish @ 12.540049

Using 3 populations approximation:
1 50% Belorussian +25% Finnish +25% Irish @ 11.449209
2 50% Lithuanian +25% Finnish +25% Irish @ 11.473003
3 50% Belorussian +25% Finnish +25% West_Scottish @ 11.513279
4 50% Lithuanian +25% Finnish +25% West_Scottish @ 11.516098
5 50% Lithuanian +25% Irish +25% Southwest_Finnish @ 11.554782
6 50% Lithuanian +25% Finnish +25% Southeast_English @ 11.574213
7 50% Belorussian +25% Finnish +25% North_German @ 11.58659
8 50% Lithuanian +25% Southwest_Finnish +25% West_Scottish @ 11.592957
9 50% Belorussian +25% Danish +25% Finnish @ 11.624585
10 50% Belorussian +25% Finnish +25% Southeast_English @ 11.632877
11 50% Estonian +25% Irish +25% Russian_Smolensk @ 11.647657
12 50% Belorussian +25% Finnish +25% North_Dutch @ 11.649033
13 50% Lithuanian +25% Uygur +25% West_Scottish @ 11.65513
14 50% Lithuanian +25% Southeast_English +25% Southwest_Finnish @ 11.65643
15 50% Lithuanian +25% Finnish +25% Orcadian @ 11.656453
16 50% Estonian_Polish +25% Finnish +25% Irish @ 11.666348
17 50% Estonian +25% Belorussian +25% Irish @ 11.666855
18 50% Belorussian +25% Finnish +25% Orcadian @ 11.686005
19 50% Belorussian +25% Irish +25% Southwest_Finnish @ 11.692952
20 50% Finnish +25% Lithuanian +25% North_German @ 11.694072

Using 4 populations approximation:
1 Belorussian+Finnish+Irish+Lithuanian @ 11.388626
2 Belorussian+Finnish+Lithuanian+West_Scottish @ 11.442542
3 Finnish+Irish+Lithuanian+Russian_Smolensk @ 11.445114
4 Belorussian+Belorussian+Finnish+Irish @ 11.449209
5 Estonian_Polish+Finnish+Irish+Lithuanian @ 11.461631
6 Finnish+Irish+Lithuanian+Lithuanian @ 11.473003
7 Finnish+Lithuanian+Russian_Smolensk+West_Scottish @ 11.492353
8 Belorussian+Belorussian+Finnish+West_Scottish @ 11.513279
9 Finnish+Lithuanian+Lithuanian+West_Scottish @ 11.516098
10 Estonian_Polish+Finnish+Lithuanian+West_Scottish @ 11.518031
11 Belorussian+Finnish+Lithuanian+Southeast_English @ 11.531992
12 Belorussian+Estonian_Polish+Finnish+Irish @ 11.543334
13 Belorussian+Irish+Lithuanian+Southwest_Finnish @ 11.552609
14 Irish+Lithuanian+Lithuanian+Southwest_Finnish @ 11.554782
15 Finnish+Lithuanian+Lithuanian+Southeast_English @ 11.574213
16 Belorussian+Finnish+Irish+Russian_Smolensk @ 11.57985
17 Finnish+Irish+Lithuanian+Southwest_Russian @ 11.582457
18 Belorussian+Finnish+Lithuanian+North_German @ 11.584609
19 Belorussian+Belorussian+Finnish+North_German @ 11.58659
20 Estonian+Finnish+Irish+Lithuanian @ 11.588694
21 Lithuanian+Lithuanian+Southwest_Finnish+West_Scottish @ 11.592957
22 Belorussian+Estonian+Finnish+North_German @ 11.598763
23 Belorussian+Finnish+Lithuanian+Orcadian @ 11.600066
24 Belorussian+Lithuanian+Southwest_Finnish+West_Scottish @ 11.601151
25 Belorussian+Danish+Finnish+Lithuanian @ 11.605389
26 Belorussian+Estonian_Polish+Finnish+West_Scottish @ 11.609689
27 Finnish+Lithuanian+Russian_Smolensk+Southeast_English @ 11.616126
28 Belorussian+Estonian+Finnish+Irish @ 11.617753
29 Estonian_Polish+Finnish+Lithuanian+Southeast_English @ 11.618373
30 Belorussian+Belorussian+Danish+Finnish @ 11.624585
31 Estonian+Finnish+Lithuanian+North_German @ 11.627321
32 Danish+Finnish+Lithuanian+Russian_Smolensk @ 11.630575
33 Estonian_Polish+Irish+Lithuanian+Southwest_Finnish @ 11.631229
34 Finnish+Irish+Lithuanian+Polish @ 11.631785
35 Belorussian+Belorussian+Finnish+Southeast_English @ 11.632877
36 Belorussian+Finnish+Russian_Smolensk+West_Scottish @ 11.636868
37 Finnish+Lithuanian+North_German+Russian_Smolensk @ 11.636912
38 Belorussian+Lithuanian+North_German+Southwest_Finnish @ 11.64435
39 Finnish+Lithuanian+Southwest_Russian+West_Scottish @ 11.64488
40 Irish+Lithuanian+Russian_Smolensk+Southwest_Finnish @ 11.645191

Gaussian method.

Using 1 population approximation:
1 Finnish @ 12.13225
2 East_Finnish @ 13.30436
3 North_Swedish @ 14.062415
4 Tatar @ 14.300577
5 Chuvash @ 15.336729
6 Ukrainian @ 16.653447
7 Kargopol_Russian @ 17.57561
8 Ukrainian_Lviv @ 17.838462
9 Moldavian @ 18.490696
10 Erzya @ 18.598911
11 Swedish @ 18.764247
12 Mari @ 18.957703
13 Nogay @ 18.966288
14 Ukrainian_Belgorod @ 19.915172
15 Southwest_Finnish @ 20.191279
16 Spanish_Cataluna @ 20.520598
17 West_Norwegian @ 20.702668
18 Chechen @ 20.984133
19 Ashkenazi @ 21.177091
20 Southwest_Russian @ 21.287124

Using 2 populations approximation:
1 Estonian+NAN_Melanesian @ 11.665381
2 Estonian+Malay @ 11.695397
3 NAN_Melanesian+Southwest_Finnish @ 12.063626
4 Cambodian+Estonian @ 12.076704
5 Estonian+Uygur @ 12.076736
6 North_Swedish+Tatar @ 12.095857
7 Malay+Southwest_Finnish @ 12.101402
8 Finnish+Finnish @ 12.13225
9 Finnish+Tatar @ 12.136373
10 Estonian+Nogay @ 12.191935
11 Estonian+Tibeto-Burman_Burmese @ 12.225743
12 Afghan_Turkmen+Estonian @ 12.232561
13 Estonian+Yizu @ 12.269345
14 Chuvash+Finnish @ 12.306072
15 Finnish+NAN_Melanesian @ 12.322344
16 Chuvash+North_Swedish @ 12.325109
17 Lithuanian+NAN_Melanesian @ 12.340578
18 Finnish+Malay @ 12.347274
19 Austroasiatic_Ho+Estonian @ 12.366024
20 East_Finnish+Tatar @ 12.402369

Using 3 populations approximation:
1 50% Estonian +25% Estonian +25% NAN_Melanesian @ 7.519412
2 50% Estonian +25% NAN_Melanesian +25% Southwest_Finnish @ 7.594293
3 50% Estonian +25% NAN_Melanesian +25% West_Norwegian @ 7.641618
4 50% Estonian +25% Estonian +25% Malay @ 7.682039
5 50% Estonian +25% NAN_Melanesian +25% North_Swedish @ 7.68724
6 50% Estonian +25% Finnish +25% NAN_Melanesian @ 7.739943
7 50% Estonian +25% NAN_Melanesian +25% Norwegian @ 7.745554
8 50% Southwest_Finnish +25% Estonian +25% NAN_Melanesian @ 7.788532
9 50% Estonian +25% NAN_Melanesian +25% West_Scottish @ 7.789055
10 50% Estonian +25% NAN_Melanesian +25% Swedish @ 7.78974
11 50% Estonian +25% Malay +25% Southwest_Finnish @ 7.798226
12 50% Estonian +25% Irish +25% NAN_Melanesian @ 7.813374
13 50% Estonian +25% Lithuanian +25% NAN_Melanesian @ 7.820487
14 50% Estonian +25% Malay +25% North_Swedish @ 7.832748
15 50% Estonian +25% Danish +25% NAN_Melanesian @ 7.849902
16 50% Southwest_Finnish +25% Lithuanian +25% NAN_Melanesian @ 7.868194
17 50% Estonian +25% Malay +25% West_Norwegian @ 7.873654
18 50% Estonian +25% NAN_Melanesian +25% North_Dutch @ 7.878452
19 50% Estonian +25% NAN_Melanesian +25% Orcadian @ 7.881948
20 50% Estonian +25% East_Finnish +25% NAN_Melanesian @ 7.885834

Using 4 populations approximation:
1 Estonian+Estonian+Estonian+NAN_Melanesian @ 7.519412
2 Estonian+Estonian+NAN_Melanesian+Southwest_Finnish @ 7.594293
3 Estonian+Estonian+NAN_Melanesian+West_Norwegian @ 7.641618
4 Estonian+Estonian+Estonian+Malay @ 7.682039
5 Estonian+Estonian+NAN_Melanesian+North_Swedish @ 7.68724
6 Estonian+Lithuanian+NAN_Melanesian+West_Norwegian @ 7.717353
7 Estonian+Estonian+Finnish+NAN_Melanesian @ 7.739943
8 Estonian+Estonian+NAN_Melanesian+Norwegian @ 7.745554
9 Estonian+Lithuanian+NAN_Melanesian+Southwest_Finnish @ 7.782141
10 Estonian+NAN_Melanesian+Southwest_Finnish+Southwest_Finnish @ 7.788532
11 Estonian+Estonian+NAN_Melanesian+West_Scottish @ 7.789055
12 Estonian+Estonian+NAN_Melanesian+Swedish @ 7.78974
13 Estonian+Estonian+Malay+Southwest_Finnish @ 7.798226
14 Estonian+Lithuanian+NAN_Melanesian+North_Swedish @ 7.798828
15 Estonian+Estonian+Irish+NAN_Melanesian @ 7.813374
16 Estonian+Estonian+Lithuanian+NAN_Melanesian @ 7.820487
17 Estonian+Lithuanian+NAN_Melanesian+Norwegian @ 7.829443
18 Estonian+Estonian+Malay+North_Swedish @ 7.832748
19 Estonian+Lithuanian+NAN_Melanesian+West_Scottish @ 7.835346
20 Danish+Estonian+Estonian+NAN_Melanesian @ 7.849902
21 Estonian+Irish+Lithuanian+NAN_Melanesian @ 7.867791
22 Lithuanian+NAN_Melanesian+Southwest_Finnish+Southwest_Finnish @ 7.868194
23 Estonian+Estonian+Malay+West_Norwegian @ 7.873654
24 Estonian+Lithuanian+NAN_Melanesian+Swedish @ 7.877051
25 Estonian+Estonian+NAN_Melanesian+North_Dutch @ 7.878452
26 Estonian+Finnish+NAN_Melanesian+Southwest_Finnish @ 7.880836
27 Estonian+Estonian+NAN_Melanesian+Orcadian @ 7.881948
28 East_Finnish+Estonian+Estonian+NAN_Melanesian @ 7.885834
29 Lithuanian+Lithuanian+NAN_Melanesian+West_Norwegian @ 7.89676
30 Danish+Estonian+Lithuanian+NAN_Melanesian @ 7.907441
31 Estonian+Estonian+Malay+Norwegian @ 7.911522
32 Estonian+Lithuanian+NAN_Melanesian+Orcadian @ 7.918518
33 Estonian+Estonian+Finnish+Malay @ 7.91857
34 Lithuanian+NAN_Melanesian+Southwest_Finnish+West_Norwegian @ 7.926443
35 Estonian+Lithuanian+NAN_Melanesian+North_Dutch @ 7.931129
36 Estonian+NAN_Melanesian+North_Swedish+Southwest_Finnish @ 7.932949
37 Lithuanian+NAN_Melanesian+North_Swedish+Southwest_Finnish @ 7.934087
38 Estonian+Estonian_Polish+NAN_Melanesian+West_Norwegian @ 7.938131
39 Estonian+Finnish+Lithuanian+NAN_Melanesian @ 7.938899
40 Belorussian+Estonian+NAN_Melanesian+West_Norwegian @ 7.945243


MA-1

Least-squares method.

Using 1 population approximation:
1 Mari @ 38.628222
2 Chuvash @ 38.677879
3 Burusho @ 40.956032
4 Tatar @ 42.703663
5 Punjabi_Jat @ 43.467174
6 Pathan @ 43.680458
7 Tadjik @ 44.231328
8 Afghan_Uzbeki @ 44.674129
9 Kalash @ 45.127315
10 Afghan_Tadjik @ 45.252617
11 Erzya @ 45.425704
12 Afghan_Pashtun @ 45.502676
13 Kargopol_Russian @ 45.815053
14 Uzbeki @ 46.172607
15 Afghan_Hazara @ 46.880737
16 Brahmin_UP @ 46.986478
17 Afghan_Turkmen @ 46.999402
18 Sindhi @ 47.85567
19 East_Finnish @ 48.694793
20 Hazara @ 48.993168

Using 2 populations approximation:
1 Brahmin_UP+Mari @ 26.092786
2 Kshatriya+Mari @ 26.496655
3 Bangladeshi+Mari @ 26.683673
4 Brahmin_UP+Chuvash @ 26.794254
5 Gujarati+Mari @ 27.074151
6 Chuvash+Kshatriya @ 27.085486
7 Bangladeshi+Chuvash @ 27.241915
8 Kanjar+Mari @ 27.244439
9 Chuvash+Kanjar @ 27.606985
10 Dharkar+Mari @ 27.658244
11 Mari+Uttar_Pradesh @ 27.695378
12 Chuvash+Gujarati @ 27.70266
13 Mari+Punjabi_Jat @ 27.967695
14 Chuvash+Uttar_Pradesh @ 27.984982
15 Mari+North_Kannadi @ 28.009527
16 Chuvash+Dharkar @ 28.063921
17 Kol+Mari @ 28.094145
18 Chuvash+North_Kannadi @ 28.183158
19 Chuvash+Kol @ 28.379488
20 Dusadh+Mari @ 28.588956

Using 3 populations approximation:
1 50% Mari +25% Anzick-1 +25% Sakilli @ 19.015569
2 50% Mari +25% Mayan +25% Sakilli @ 19.065849
3 50% Mari +25% Anzick-1 +25% Chamar @ 19.081669
4 50% Mari +25% Chamar +25% Mayan @ 19.104924
5 50% Mari +25% Anzick-1 +25% North_Kannadi @ 19.116955
6 50% Mari +25% Mayan +25% North_Kannadi @ 19.133136
7 50% Mari +25% Pima +25% Sakilli @ 19.244908
8 50% Mari +25% Anzick-1 +25% Piramalai @ 19.24858
9 50% Mari +25% Mayan +25% Piramalai @ 19.2825
10 50% Mari +25% Chamar +25% Pima @ 19.304215
11 50% Mari +25% North_Kannadi +25% Pima @ 19.324581
12 50% Mari +25% Pima +25% Piramalai @ 19.470905
13 50% Mari +25% Anzick-1 +25% Kol @ 19.507868
14 50% Mari +25% Anzick-1 +25% Dusadh @ 19.532324
15 50% Mari +25% Kol +25% Mayan @ 19.540734
16 50% Mari +25% Anzick-1 +25% Kanjar @ 19.541315
17 50% Mari +25% Karitiana +25% Sakilli @ 19.561078
18 50% Mari +25% Kanjar +25% Mayan @ 19.562043
19 50% Mari +25% Anzick-1 +25% Uttar_Pradesh @ 19.569267
20 50% Mari +25% Dusadh +25% Mayan @ 19.572752

Using 4 populations approximation:
1 Anzick-1+Mari+Mari+Sakilli @ 19.015569
2 Mari+Mari+Mayan+Sakilli @ 19.065849
3 Anzick-1+Chamar+Mari+Mari @ 19.081669
4 Chamar+Mari+Mari+Mayan @ 19.104924
5 Anzick-1+Mari+Mari+North_Kannadi @ 19.116955
6 Mari+Mari+Mayan+North_Kannadi @ 19.133136
7 Mari+Mari+Pima+Sakilli @ 19.244908
8 Anzick-1+Mari+Mari+Piramalai @ 19.24858
9 Anzick-1+Chuvash+Mari+Sakilli @ 19.270827
10 Mari+Mari+Mayan+Piramalai @ 19.2825
11 Chamar+Mari+Mari+Pima @ 19.304215
12 Anzick-1+Chamar+Chuvash+Mari @ 19.306828
13 Mari+Mari+North_Kannadi+Pima @ 19.324581
14 Anzick-1+Chuvash+Mari+North_Kannadi @ 19.372215
15 Chuvash+Mari+Mayan+Sakilli @ 19.393876
16 Chamar+Chuvash+Mari+Mayan @ 19.403211
17 Chuvash+Mari+Mayan+North_Kannadi @ 19.461362
18 Mari+Mari+Pima+Piramalai @ 19.470905
19 Chuvash+Mari+Pima+Sakilli @ 19.49982
20 Anzick-1+Kol+Mari+Mari @ 19.507868
21 Anzick-1+Chuvash+Mari+Piramalai @ 19.520419
22 Chamar+Chuvash+Mari+Pima @ 19.529462
23 Anzick-1+Dusadh+Mari+Mari @ 19.532324
24 Kol+Mari+Mari+Mayan @ 19.540734
25 Anzick-1+Kanjar+Mari+Mari @ 19.541315
26 Karitiana+Mari+Mari+Sakilli @ 19.561078
27 Kanjar+Mari+Mari+Mayan @ 19.562043
28 Anzick-1+Mari+Mari+Uttar_Pradesh @ 19.569267
29 Dusadh+Mari+Mari+Mayan @ 19.572752
30 Chuvash+Mari+North_Kannadi+Pima @ 19.579781
31 Mari+Mari+Mayan+Uttar_Pradesh @ 19.60021
32 Chuvash+Mari+Mayan+Piramalai @ 19.626428
33 Karitiana+Mari+Mari+North_Kannadi @ 19.635717
34 Chamar+Karitiana+Mari+Mari @ 19.638863
35 Kol+Mari+Mari+Pima @ 19.713214
36 Anzick-1+Chuvash+Chuvash+Sakilli @ 19.722979
37 Anzick-1+Chamar+Chuvash+Chuvash @ 19.729557
38 Kanjar+Mari+Mari+Pima @ 19.733243
39 Chuvash+Mari+Pima+Piramalai @ 19.742307
40 Dusadh+Mari+Mari+Pima @ 19.743616

Gaussian method.

Using 1 population approximation:
1 Burusho @ 18.907245
2 Shors @ 19.587376
3 Mari @ 20.775166
4 Ket @ 21.279087
5 Uygur @ 21.499367
6 Afghan_Hazara @ 21.832142
7 Kalash @ 22.029123
8 Pathan @ 22.054061
9 Hazara @ 22.055767
10 Hakas @ 22.500277
11 Brahmin_UP @ 22.554816
12 Tadjik @ 22.578244
13 Afghan_Pashtun @ 23.341735
14 Uzbeki @ 23.55565
15 West_Greenlander @ 23.583757
16 Afghan_Tadjik @ 23.646924
17 Afghan_Turkmen @ 24.129874
18 Kshatriya @ 24.554648
19 Altaian @ 24.695106
20 Tabassaran @ 25.101222

Using 2 populations approximation:
1 Burusho+West_Greenlander @ 13.003878
2 Brahmin_UP+West_Greenlander @ 13.123778
3 Kshatriya+West_Greenlander @ 13.420561
4 Kalash+West_Greenlander @ 13.52133
5 Punjabi_Jat+West_Greenlander @ 13.556967
6 Gujarati+West_Greenlander @ 13.645164
7 Kanjar+West_Greenlander @ 13.67808
8 Bangladeshi+West_Greenlander @ 13.748737
9 Pathan+West_Greenlander @ 13.968909
10 North_Kannadi+West_Greenlander @ 14.11898
11 Brahmin_UP+Ket @ 14.244349
12 Sindhi+West_Greenlander @ 14.309141
13 Afghan_Pashtun+West_Greenlander @ 14.330957
14 Ket+Kshatriya @ 14.48723
15 Uttar_Pradesh+West_Greenlander @ 14.490282
16 Burusho+Ket @ 14.547424
17 Burusho+East_Greenlander @ 14.558551
18 East_Greenlander+Kalash @ 14.570226
19 Kanjar+Ket @ 14.65779
20 Dharkar+West_Greenlander @ 14.718221

Using 3 populations approximation:
1 50% Mari +25% Anzick-1 +25% Sakilli @ 8.893804
2 50% Mari +25% Anzick-1 +25% Austroasiatic_Ho @ 8.956881
3 50% Mari +25% Anzick-1 +25% Chamar @ 8.962893
4 50% Mari +25% Anzick-1 +25% North_Kannadi @ 8.967219
5 50% Mari +25% Anzick-1 +25% Piramalai @ 8.995534
6 50% Mari +25% Karitiana +25% Sakilli @ 9.00039
7 50% Mari +25% Austroasiatic_Ho +25% Karitiana @ 9.003715
8 50% Mari +25% Pima +25% Sakilli @ 9.027798
9 50% Mari +25% Austroasiatic_Ho +25% Pima @ 9.070877
10 50% Mari +25% Karitiana +25% North_Kannadi @ 9.116706
11 50% Mari +25% North_Kannadi +25% Pima @ 9.122314
12 50% Mari +25% Karitiana +25% Piramalai @ 9.123265
13 50% Mari +25% Chamar +25% Pima @ 9.13065
14 50% Mari +25% Chamar +25% Karitiana @ 9.138673
15 50% Mari +25% Pima +25% Piramalai @ 9.139344
16 50% Mari +25% Mayan +25% Sakilli @ 9.232749
17 50% Mari +25% Anzick-1 +25% Kanjar @ 9.24072
18 50% Mari +25% Anzick-1 +25% Dusadh @ 9.250495
19 50% Mari +25% Anzick-1 +25% Chenchu @ 9.263556
20 50% Mari +25% Anzick-1 +25% Kurumba @ 9.275127

Using 4 populations approximation:
1 Anzick-1+Mari+Mari+Sakilli @ 8.893804
2 Anzick-1+Austroasiatic_Ho+Mari+Mari @ 8.956881
3 Anzick-1+Chamar+Mari+Mari @ 8.962893
4 Anzick-1+Mari+Mari+North_Kannadi @ 8.967219
5 Anzick-1+Mari+Mari+Piramalai @ 8.995534
6 Karitiana+Mari+Mari+Sakilli @ 9.00039
7 Austroasiatic_Ho+Karitiana+Mari+Mari @ 9.003715
8 Mari+Mari+Pima+Sakilli @ 9.027798
9 Austroasiatic_Ho+Mari+Mari+Pima @ 9.070877
10 Karitiana+Mari+Mari+North_Kannadi @ 9.116706
11 Mari+Mari+North_Kannadi+Pima @ 9.122314
12 Karitiana+Mari+Mari+Piramalai @ 9.123265
13 Chamar+Mari+Mari+Pima @ 9.13065
14 Chamar+Karitiana+Mari+Mari @ 9.138673
15 Mari+Mari+Pima+Piramalai @ 9.139344
16 Anzick-1+Chuvash+Mari+Sakilli @ 9.161714
17 Anzick-1+Chamar+Chuvash+Mari @ 9.219074
18 Anzick-1+Chuvash+Mari+North_Kannadi @ 9.231996
19 Mari+Mari+Mayan+Sakilli @ 9.232749
20 Anzick-1+Kanjar+Mari+Mari @ 9.24072
21 Anzick-1+Dusadh+Mari+Mari @ 9.250495
22 Anzick-1+Chuvash+Mari+Piramalai @ 9.258041
23 Anzick-1+Chenchu+Mari+Mari @ 9.263556
24 Anzick-1+Kurumba+Mari+Mari @ 9.275127
25 Chuvash+Karitiana+Mari+Sakilli @ 9.276357
26 Chuvash+Mari+Pima+Sakilli @ 9.288998
27 Anzick-1+Austroasiatic_Ho+Chuvash+Mari @ 9.326092
28 Austroasiatic_Ho+Mari+Mari+Mayan @ 9.329769
29 Austroasiatic_Ho+Mari+Mari+North_Amerindian @ 9.331319
30 Chamar+Mari+Mari+Mayan @ 9.33404
31 Mari+Mari+Mayan+North_Kannadi @ 9.33799
32 Mari+Mari+Mayan+Piramalai @ 9.369549
33 Mari+Mari+North_Amerindian+Sakilli @ 9.370443
34 Anzick-1+Mari+Mari+Uttar_Pradesh @ 9.373816
35 Chamar+Chuvash+Mari+Pima @ 9.373935
36 Chuvash+Mari+North_Kannadi+Pima @ 9.375032
37 Chuvash+Karitiana+Mari+North_Kannadi @ 9.38308
38 Austroasiatic_Ho+Chuvash+Karitiana+Mari @ 9.392049
39 Chamar+Chuvash+Karitiana+Mari @ 9.393342
40 Dusadh+Mari+Mari+Pima @ 9.393689

Citations...

Olalde et al., Derived immune and ancestral pigmentation alleles in a 7,000-year-old Mesolithic European, Nature (2014), doi:10.1038/nature12960

Raghavan et al., Upper Palaeolithic Siberian genome reveals dual ancestry of Native Americans, Nature, (2013), Published online 20 November 2013, doi:10.1038/nature12736

Rasmussen et al., The genome of a Late Pleistocene human from a Clovis burial site in western Montana, Nature, (2013), Published online 12 February 2014, doi:10.1038/nature13025

See also...

PCA of five ancient genomes

Monday, March 10, 2014

Extreme positive selection for light skin, hair and eyes on the Pontic-Caspian steppe...or not


Unusually strong positive selection over the past 5,000 years, rather than population replacement or even admixture, is responsible for the high frequencies of light skin, hair and eyes among present-day Eastern Europeans, according to a new paper by Wilde et al. at PNAS.

The authors were able to infer pigmentation traits from ancient DNA for 63 Eneolithic and Bronze Age samples, mostly from Kurgan mounds from the Pontic-Caspian steppe of Ukraine and surrounds. The results suggest that the ancient individuals were overall much darker than present-day Ukrainians, who, nevertheless, appear to be their direct descendants based on mitochondrial DNA (mtDNA) sequences. Quoting the paper:

To this end we compared the 60 mtDNA HVR1 sequences obtained from our ancient sample to 246 homologous modern sequences (29–31) from the same geographic region and found low genetic differentiation (FST = 0.00551; P = 0.0663) (32). Coalescent simulations based on the mtDNA data, accommodating uncertainty in the ancient sample age, failed to reject population continuity under a wide range of assumed ancestral population size combinations (Fig. 1).

Conversely, continuity between early central European farmers and modern Europeans has been rejected in a previous study (33). However, the Eneolithic and Bronze Age sequences presented here are ∼500–2,000 y younger than the early Neolithic and belong to lineages identified both in early farmers and late hunter–gatherers from central Europe (33).

...

In sum, a combination of selective pressures associated with living in northern latitudes, the adoption of an agriculturalist diet, and assortative mating may sufficiently explain the observed change from a darker phenotype during the Eneolithic/Early Bronze age to a generally lighter one in modern Eastern Europeans, although other selective factors cannot be discounted. The selection coefficients inferred directly from serially sampled data at these pigmentation loci range from 2 to 10% and are among the strongest signals of recent selection in humans.

Well, either this is indeed a remarkable finding, or something's not quite right. I think it's the latter.

The argument for genetic continuity from the Eneolithic/Bronze Age to the present on the Pontic-Caspian steppe based on mtDNA sequences is actually very weak. The results could simply mean that the ancient samples shared deep maternal ancestry with modern Ukrainians and most other Europeans.

Indeed, we know for a fact that much of the Pontic-Caspian steppe was occupied by Turkic groups of Asian origin from the early Middle Ages until only a couple of hundred years ago. They were eventually cleared out by Tsarist Russia, and mainly replaced by East Slavic settlers from just northwest of the steppe. This process might not be easy to see by comparing low resolution mtDNA data, even between European populations separated by 5,000 years, but it's likely to be obvious when looking at full mtDNA genomes, high-density genome-wide data, and/or Y-chromosome haplogroups.

Surprisingly, the article doesn't mention Keyser et al. 2009, a very important study which showed that a sample of Kurgan nomads from Bronze and Iron Age South Siberia had frequencies of light hair and eyes comparable to those of present-day Northern and Eastern Europeans (see here). Also worth noting is that the most common Y-chromosome haplogroup among these individuals was R1a, which is today the most frequent haplogroup in Eastern Europe, including Ukraine.

What this suggests to me is that the Kurgan cultural horizon was not genetically homogeneous. I suspect that Kurgan groups closer to the Balkans carried significantly higher levels of Near Eastern Neolithic farmer ancestry, and were thus much darker than those in the more temperate northerly regions. However, it seems that at some point, the Neolithic farmer DNA was diluted enough by continuous movements of light pigmented groups from the north and east, possibly made up mostly of males, that there was a major shift in pigmentation traits from Near Eastern-like to North European-like across most of Eastern Europe. This scenario actually fits very nicely with the latest on the genetic origins of Europeans (see here).

We won't know what really happened until we see at least a few complete ancient genomes from Eastern Europe. But for now, I'd have to suspend my disbelief to accept that present-day Eastern Europeans are, by and large, descendants of these exceedingly brunet prehistoric people of the Pontic-Caspian steppe.

Citation...

Wilde et al., Direct evidence for positive selection of skin, hair, and eye pigmentation in Europeans during the last 5,000 y, PNAS, Published online before print on March 10, 2014, DO:I10.1073/pnas.1316513111

See also...

PCA of ancient European mtDNA

Saturday, March 8, 2014

Ancient North Eurasian (ANE) admixture across Asia


Studies of ancient genomes usually feature unsupervised analyses with the ADMIXTURE software. These are very informative, but only if interpreted in the right context and with caution, because they attempt to fit the ancient samples, often thousands of years old, into ancestral clusters mostly derived from present-day populations. That's like putting the cart before the horse.

So I thought I'd try a different approach, in the hope of achieving more straightforward results, and run ADMIXTURE in supervised mode, with the 24,000 year-old MA-1 or Mal'ta boy genome from South Siberia as one of the reference samples. After a lot of tweaking of the dataset, the experiment seems to have worked, because the cluster created from the ancient genome is basically identical to the MA-1-derived Ancient North Eurasian (ANE) component recently described in the Lazaridis et al. preprint.

Note also that the ANE in my analysis peaks among the Karitiana Indians at around 43%. This is very much in line with a TreeMix graph in Raghavan et al., which shows a Karitiana individual with 41.6% (plus or minus 3.4%) admixture from a clade ancestral to MA-1 (see image here).

Nevertheless, there are clearly some issues with this test. For instance, many South Asians show unexpectedly high levels of Sub-Saharan admixture (in particular, the Austroasiatic samples from India score around 6-7%, which has never been reported before). I'd say this is because they carry genetic variation indigenous to South Asia that doesn't fit well into any of the four ancestral components. The Eastern non-African (ENA) cluster, based on Han Chinese samples, captures most of this diversity, but some of it appears to be siphoned off into the other three clusters. I think the only way to really solve this problem is to include pre-Neolithic genomes from South Asia in the analysis.

By the way, I used 53K SNPs at read depth x2 or more, but varying the quality of SNPs from read depth x1 to x3 doesn't change the results very much.


Key: red = Ancient North Eurasian (ANE); green = Middle Eastern (ME); aqua = Eastern non-African (ENA); purple = Sub-Saharan African (SSA). ANE K=4 ADMIXTURE Test spreadsheet

Update 10/03/2014: To try and improve the results, I increased the number of markers to 116K, made the Mbuti and Biaka pygmies the Sub-Saharan references, and in fact removed all of the other Sub-Saharan samples (see the spreadsheet here).

The Sub-Saharan noise among the South Asians did fall slightly, but remained at around 6% among the Austroasiatic and South Indians. I think this confirms my hunch that indigenous South Asian ancestry is not a good fit for any of the four ancestral clusters in this analysis.

Update 16/03/2014: I attempted a supervised ADMIXTURE run using La Brana-1 and MA-1 as separate references in the hope of creating two distinct Mammoth Steppe hunter-gatherer clusters (western and eastern, respectively). But this proved impossible, either because these ancient samples are too similar, and/or I can only get 26K SNPs to overlap between them and the rest of my dataset.

So I tried the next best thing, and ran two supervised analyses with the roles of La Brana-1 and MA-1 reversed. In other words, first La Brana-1 was the Mammoth Steppe reference, and then MA-1. Here are the results:

Mammoth Steppe Test with La Brana-1 as a reference

Mammoth Steppe Test with MA-1 as a reference

I have no idea whether there's anything useful there? But as far as I can see, the Mammoth Steppe ancestry proportions for the Abkhasians, Lezgins, Chechens and Druze are very close to their ANE ancestry proportions reported in the Lazaridis et al. preprint. Moreover, the levels of Mammoth Steppe ancestry across Europe appear to show a very solid correlation with the levels of overall hunter-gatherer related ancestry throughout Europe reported in the same paper.

But what troubles me is that the Karitiana Indians are only around 27% Mammoth Steppe in both runs, when they ought to be around 40%. Obviously some of their ANE is being lumped into their ENA.

Update 19/03/2014: In my latest analysis, MA-1 clusters between Eastern Europe and the Northeast Caucasus, while La Brana-1 in the Baltic region, as expected. See here.


Citations...

Raghavan et al., Upper Palaeolithic Siberian genome reveals dual ancestry of Native Americans, Nature, (2013), Published online 20 November 2013, doi:10.1038/nature12736

Iosif Lazaridis, Nick Patterson, Alissa Mittnik, et al., Ancient human genomes suggest three ancestral populations for present-day Europeans, bioRxiv, Posted December 23, 2013, doi: 10.1101/001552

See also...

PCA of five ancient genomes

First genome of an Upper Paleolithic human

Ancient human genomes suggest (more than) three ancestral populations for present-day Europeans

Thursday, February 27, 2014

Khazar shmazar


Human Biology recently posted several open access manuscripts dealing with the topic of Jewish origins (see submissions from 2013 here). One of these preprints is essentially a rebuttal to an Eran Elhaik paper from a couple of years ago, which argued that a substantial part of Ashkenazi Jewish ancestry was derived from within the Khazar Empire. The leading author of the new preprint is Doron M. Behar, but thirty people in all, many of them well known scientists, have put their names on it. Here's the abstract:

The origin and history of the Ashkenazi Jewish population have long been of great interest, and advances in high-throughput genetic analysis have recently provided a new approach for investigating these topics. We and others have argued on the basis of genome-wide data that the Ashkenazi Jewish population derives its ancestry from a combination of sources tracing to both Europe and the Middle East. It has been claimed, however, through a reanalysis of some of our data, that a large part of the ancestry of the Ashkenazi population originates with the Khazars, a Turkic-speaking group that lived to the north of the Caucasus region ~1,000 years ago. Because the Khazar population has left no obvious modern descendants that could enable a clear test for a contribution to Ashkenazi Jewish ancestry, the Khazar hypothesis has been difficult to examine using genetics. Furthermore, because only limited genetic data have been available from the Caucasus region, and because these data have been concentrated in populations that are genetically close to populations from the Middle East, the attribution of any signal of Ashkenazi-Caucasus genetic similarity to Khazar ancestry rather than shared ancestral Middle Eastern ancestry has been problematic. Here, through integration of genotypes on newly collected samples with data from several of our past studies, we have assembled the largest data set available to date for assessment of Ashkenazi Jewish genetic origins. This data set contains genome-wide single-nucleotide polymorphisms in 1,774 samples from 106 Jewish and non- Jewish populations that span the possible regions of potential Ashkenazi ancestry: Europe, the Middle East, and the region historically associated with the Khazar Khaganate. The data set includes 261 samples from 15 populations from the Caucasus region and the region directly to its north, samples that have not previously been included alongside Ashkenazi Jewish samples in genomic studies. Employing a variety of standard techniques for the analysis of populationgenetic structure, we find that Ashkenazi Jews share the greatest genetic ancestry with other Jewish populations, and among non-Jewish populations, with groups from Europe and the Middle East. No particular similarity of Ashkenazi Jews with populations from the Caucasus is evident, particularly with the populations that most closely represent the Khazar region. Thus, analysis of Ashkenazi Jews together with a large sample from the region of the Khazar Khaganate corroborates the earlier results that Ashkenazi Jews derive their ancestry primarily from populations of the Middle East and Europe, that they possess considerable shared ancestry with other Jewish populations, and that there is no indication of a significant genetic contribution either from within or from north of the Caucasus region.

I'm really not sure what to make of all of this attention that the Khazar hypothesis is still getting? It's been obvious for a while now that in terms of genetic structure Ashkenazi Jews are basically a group of East Mediterranean origin. But Elhaik's paper did get a fair bit of media coverage, so I suppose after that a rebuttal was to be expected.

In any case, I'm not complaining. This paper includes a very interesting genotype dataset of many previously unpublished samples, which I tested last week with PCA (see here).

Citations...

Behar, Doron M.; Metspalu, Mait; Baran, Yael; Kopelman, Naama M.; Yunusbayev, Bayazit; Gladstein, Ariella; Tzur, Shay; Sahakyan, Havhannes; Bahmanimehr, Ardeshir; Yepiskoposyan, Levon; Tambets, Kristiina; Khusnutdinova, Elza K.; Kusniarevich, Aljona; Balanovsky, Oleg; Balanovsky, Elena; Kovacevic, Lejla; Marjanovic, Damir; Mihailov, Evelin; Kouvatsi, Anastasia; Traintaphyllidis, Costas; King, Roy J.; Semino, Ornella; Torroni, Anotonio; Hammer, Michael F.; Metspalu, Ene; Skorecki, Karl; Rosset, Saharon; Halperin, Eran; Villems, Richard; and Rosenberg, Noah A., No Evidence from Genome-Wide Data of a Khazar Origin for the Ashkenazi Jews (2013). Human Biology Open Access Pre-Prints. Paper 41.

Elhaik E. The missing link of Jewish European Ancestry: contrasting the Rhineland and Khazarian hypotheses. Genome Biol Evol. 2012. doi:10.1093/gbe/evs119, Advance Access publication December 14, 2012.

See also...

Near Eastern origin of Ashkenazi Levite R1a