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Sunday, January 17, 2021
A tantalizing link
A new paper at PLoS ONE reports on the first human genomes reliably associated with the Single Grave culture (SGC). They were sequenced from remains in a burial at Gjerrild, Denmark, roughly dating to 2,500 BCE.
Surprisingly, one of the male genomes belongs to Y-haplogroup R1b-V1636, which is an exceedingly rare marker both in ancient and present-day populations.
However, the results do make sense, because the earliest instances of R1b-V1636 are in three Eneolithic males from burial sites on the Pontic-Caspian (PC) steppe in Eastern Europe, which is precisely where one would expect to find the paternal ancestors of the SGC population. The SGC, of course, is the westernmost variant of the Corded Ware culture (CWC), and there's very little doubt nowadays that the CWC had its roots on the PC steppe.
A Copper Age individual from Arslantepe in central Anatolia also belongs to R1b-V1636, which suggests that Northern Europe shared a very specific link with Anatolia via Eastern Europe during a period generally regarded to have been the time of early Indo-European dispersals.
Numerous SGC barrows or kurgans dot the landscape in what are now the Netherlands, northwestern Germany and Denmark. Unfortunately, most SGC human remains have been eaten up by the acidic soils that exist in this area.
Citation: Egfjord AF-H, Margaryan A, Fischer A, Sjögren K-G, Price TD, Johannsen NN, et al. (2021) Genomic Steppe ancestry in skeletons from the Neolithic Single Grave Culture in Denmark. PLoS ONE 16(1): e0244872. https://doi.org/10.1371/journal.pone.0244872
See also...
Maykop ancestry in Copper Age Arslantepe
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252 comments:
«Oldest ‹Older 201 – 252 of 252@ Romulus
Maybe , or perhaps it was just always there
The Dniester - carpathian region is probably where early WHG expanded from
@archie
Agree yes it seems that it will turn out to be that way.
@ Davidski
random haploid, 1240k
https://drive.google.com/file/d/1zo7d6YRvX1UmDbVpRgQLJBOZsoHhPS2N/view?usp=sharing
Nice one!
I'll stick him in the Global25 datasheets later today.
RUS_Krasnoyarsk_BA_scaled:kra001,0.034147,-0.421445,0.150471,-0.002907,-0.14495,-0.082273,0.019741,0.035998,0.02577,0.000364,0.083792,-3e-04,0.015758,-0.048994,-0.04343,-0.029833,0.000652,0.005574,0.004902,-0.012381,0.026453,0.003462,0.001725,0.00723,0.012813
RUS_Krasnoyarsk_BA:kra001,0.003,-0.0415,0.0399,-0.0009,-0.0471,-0.0295,0.0084,0.0156,0.0126,0.0002,0.0516,-0.0002,0.0106,-0.0356,-0.032,-0.0225,0.0005,0.0044,0.0039,-0.0099,0.0212,0.0028,0.0014,0.006,0.0107
@MAtt
Thank you for the references for Southeast Asians.
@ Davidski
Now a test on low coverage data (Greece, shotgun data with trimmed read ends - "wgs_trim5bp.bam"):
https://drive.google.com/file/d/13zn5Jrs1BTPTBqV7OsCQ9lYeasThu3k7/view?usp=sharing
snps num
Kou01 177694
Kou03 134043
Log02 57298
Log04 48734
Mik15 157119
Pta08 47697
The order of the names may be reversed (or even mixed up) as it's not clear in what order they're used on Galaxy when processed at once. I hope that it will be clear when you'll get coordinates. If not, I'll process them separately.
Later today or tomorrow I'll have 3 Wartberg samples.
The Greek samples look pretty good. Their labels appear to be correct.
I'll put them into the G25 datasheets tomorrow.
@ph2ter, no problem, it was pretty easy once I got lucky sticking a sample ID into Google, and then just matched the IDs to populations from the supplements. Some of the names are a bit messy in supplement ("Philippines non-kankaey"?); I guess David or Mike will clean that up when they look at it themselves. Btw, always been a fan of your maps and other stuff.
Couldn't really wait to see the Naga on G25 - it's to me one of the most interesting situations in human history, where you have basically these guys and Northern Han Chinese, who are almost entirely the same population until 9000-6000 years (300-450 generations ago), with very close to a simple split with very little admixture.
Then one of them is the largest ethnic group in the world, with a history of being one of the more sophisticated cultures certainly for 3000 years... And the other is basically this Neolithic culture that lives in the jungle with a small population size and technology and lifestyle, basically pretty much the same as Taiwanese native or Papuan people.
Interesting because it gives a pretty perfect test of what it is that large increases in population size and change in culture do genetically to human groups, with almost no confound of admixture! No other case like it in human genetic history... A study could really get some high coverage Han and Naga genomes and really reconstruct an ancestor and how each had changed. (In Europeans it's tougher because there's no simple split and all the available populations, even Sardinians, largely share the same increases in group size.)
(Then you've also got the Tibetans, again close to the same pop with little admixture and very strong selection for high altitude adaptation!).
I'd hope that the rest of the samples in the Kilinc 2021 paper would be run as well. @Arza unless you want to do this yourself, could you share how you have done kra001? I could take on the rest of the sample-set.
@Davidski
Do you plan on making a post about kra001 in the future now that the sample is in the datasheets? It definitely seems to look like something Uralic or Uralic adjacent to me.
@ Davidski
Lol, that Greek samples weren't low cov. I've pasted missing snps. Non-missing are here:
SampleName TotalSites NonMissingCalls
Kou01 1233013 1055319
Kou03 1233013 1098970
Log02 1233013 1175715
Log04 1233013 1184279
Mik15 1233013 1075894
Pta08 1233013 1185316
https://drive.google.com/file/d/1mOTuUFaSOuTCYa-WMomIZSKwq-rfyQM7/view?usp=sharing
Gjerrild:
SampleName TotalSites NonMissingCalls
Gjerrild_id1 1233013 9829
Gjerrild_id5 1233013 857420
Gjerrild_id8 1233013 29582
Wartberg:
SampleName TotalSites NonMissingCalls
KH150635 1233013 983592
KH150614_KH150615 1233013 987951
KH150630 1233013 981639
Note that for MDS in the case of Gjerrild they've used transversions only.
After a quick test - Gjerrild_id1 looks much more steppe shifted than in the paper, but it may be just a fault of the ultra low snps number. Wartberg seems to be closest to Iberian samples (all three are EEF-rich).
@ Norfern-Ostrobothnian
mpileup + pileupCaller, just as described here:
https://github.com/stschiff/sequenceTools
pileup on 1240k positions, .snp file for pileupCaller taken from v44.3 dataset
I didn't do anything else, but so far I also didn't have to, as I've used bams that were already well prepared.
@Matt
Thanks! The Maori sample which we have in G25 spreadsheet is half-European. I've extracted his Maori part and made similarity map with such simulated G25 values.
On the map are all these new populations from Southeast Asia and Oceania:
https://i.imgur.com/fbVnEkB.png
@Arza
Some decent genotypes of the Welzin warriors would be nice.
@ Davidski
I've just added them to the queue!
12x Russia, 4x Wartberg:
https://drive.google.com/file/d/1x0VnPhmmcHodNqakB8cnstE7d0zKTGiF/view?usp=sharing
@ Davidski
Welzin:
https://drive.google.com/file/d/17Jqyi6GRJvyhkKpQFtY2WEcV925A4Qeu/view?usp=sharing
I've added the extra Wartberg and Siberian samples.
Haven't checked yet whether they're better proxies for Proto-Uralics than kra001, but I'm guessing no.
I'll have a close look at the Welzin genotypes soon and check whether they're better than the ones that are already in the G25.
What else do we need? How about the Mokrin BA genomes?
After accounting the new samples done by Arza, I have 9 to go, mostly big ones. I'd say I am done in a couple hours.
Trans-Baikal_LN samples (Kamenka) look like Nganassan/Yukagir like people?
E.g. - https://imgur.com/a/G01CXNc
Nganassan have possibly slightly more Nganassan drift (obvs as accumulating over time?) and North/West Eurasian admixture?
1) Another note on the Ramber2020 sample set - really fills out the Island Southeast Asian cline between East Asia and Papuans : https://imgur.com/a/DePNV8g
(Although to some extent the cline splits and the Polynesian part really points more towards people of East New Guinea specifically - Baining - which makes sense in that this is the launching point for Oceanian people who then admixed to create Polynesian peoples).
2) Few quick Vahaduo PCA projections of the new ancient European samples that I'm most interested in, with some comparison samples: https://imgur.com/a/qUl8GTu
It looks to confirm the HG rich character of the Wartburgers and that the Wartburgers "point" even more decisively towards the Western varieties of WHG (France HG, Ireland HG, ITA_Grotta_Continenza) than the population of Blatterhohle.
With a quick Vahaduo run with Beakers (GBR and NLD) as Target and Wartburg, GAC and Scotland_Megalithic as the Source populations along with CWC_Early, then some samples do seem to take some Wartburg... See: https://imgur.com/a/mLPj1kX. But I'm not sure it's particularly necessary or parsimonious. (Average distances in model with CWC_Early are: GAC_Poland: 0.0319, Scotland_Megalithic+N: 0.0322, Warburg_MN: 0.04). Doing this model in G25 is obviously sensitive to the amount of EuroHG related ancestry in the Steppe source; when using Afanasievo instead of CWC_Early, the fit is slightly worse, and also Wartburg goes up.
@ Davidski
Re: Russia
I don't know which samples NO is processing right now, so I'm waiting.
Besides that - Khayirgas and Kamenka should be marked as Yakutia and not Trans-Baikal:
https://advances.sciencemag.org/content/advances/7/2/eabc4587/F1.large.jpg
Re: Mokrin
If there won't be any errors I'll have the full dataset tomorrow.
Re: Wartberg
While they're non UGD-treated, they have reads trimmed at both ends (2bp) which should reduce deamination damage greatly. I'll produce full dataset, but first I want to test something. Can you make G25 coords from this dataset with transitions removed?
https://drive.google.com/file/d/18er5V0bVUn0dw1NvzZbjDSASxIatJhpy/view?usp=sharing
@Arza
I have one sample left. When the genotypes were still in .ped format, were the variants using numbers or letters for nucleobases? Does it make a difference for G25?
@ NO
Here is the ped file format described, so you can check if everything is alright:
https://zzz.bwh.harvard.edu/plink/data.shtml#ped
But I don't know how you have ended with a ped.
It's sample.bam + sample.bam + sample.bam + ... (+ 1240k_positions.BED) -> samtools mpileup = samples.pileup (+v44.3.snp) -> pileupCaller = samples.geno/snp/ind -> convertf = samples.bed/bim/fam
Using convertf you go from EIGENSTRAT straight to PACKEDPED:
genotypename: /datasets/wartberg2_.geno
snpname: /datasets/wartberg2_.snp
indivname: /datasets/wartberg2_.ind
outputformat: PACKEDPED
genooutfilename: /datasets/wartberg2_.bed
snpoutfilename: /datasets/wartberg2_.bim
indoutfilename: /datasets/wartberg2_.fam
outputgroup: YES
This should be it: https://www.mediafire.com/file/wzbaf0taikchz5a/kilinc_2021.zip/file
I also did three highest quality samples from key 2020.
https://www.mediafire.com/file/4oc2sxsb6llmhgq/key_2020.zip/file
https://www.researchgate.net/publication/339472244_Emergence_of_human-adapted_Salmonella_enterica_is_linked_to_the_Neolithization_process
ETR001 is a late Roman individual from Chiusi, Siena.
MK3001 is an Iron Age individual from Marinskaja 3 in southern Stavropol krai, around half East Eurasian and hence might be related to Cimmerians.
MUR009 is an Eneolithic individual from Murzikhinsky 2, Tatarstan. Most likely part of the Volga-Kama culture, and is contemporary to BER001.
@Arza
Scaled
Wartberg_tv:KH150189_KH150632_636,0.129758,0.167562,0.103708,0.045866,0.125254,-0.004183,-0.00376,0.008769,0.078946,0.072894,-0.006171,0.015886,-0.011596,-0.003441,0.011401,0.024662,0.022165,-0.005448,-0.007793,0.023136,0.048415,0.000124,-0.016269,-0.074348,0.012933
Wartberg_tv:KH150614_615,0.132035,0.165531,0.089755,0.020672,0.120638,-0.008925,-0.00329,0.006923,0.089377,0.07089,-0.005034,0.010191,-0.010852,-0.003991,0.002579,0.011138,0.009909,-0.011149,-0.005279,0.02151,0.040304,0.006554,-0.022061,-0.06772,0.003712
Wartberg_tv:KH150619,0.129758,0.170609,0.085606,0.019703,0.121561,-0.01004,-0.000235,0.013153,0.084264,0.07581,-0.005846,0.015286,-0.021258,-0.015551,0.012893,0.030363,0.012517,-0.002154,-0.000754,0.017508,0.040678,0.004822,-0.022061,-0.068564,0.009819
Wartberg_tv:KH150627,0.125205,0.160454,0.115399,0.064277,0.128024,0.001116,0.0047,0.025845,0.086309,0.063054,-0.010555,0.002997,-0.007582,-0.017891,0.019001,0.017634,0.002738,-0.00038,-0.000628,0.029889,0.050536,0.00643,-0.027607,-0.080975,0.015208
Wartberg_tv:KH150630,0.126344,0.169593,0.084098,0.005814,0.114791,-0.011992,-0.0094,0.003231,0.094695,0.086015,-0.005846,0.016485,-0.017096,-0.012799,0.001764,0.027048,0.017341,-0.00152,-0.012947,0.012381,0.027452,0.00371,-0.016762,-0.059285,0.011855
Wartberg_tv:KH150635,0.113823,0.171624,0.07995,0.003553,0.105558,-0.017291,-0.011045,0.008538,0.086514,0.082553,0.004547,0.018733,-0.017691,-0.011698,0.003257,0.015646,0.022817,-0.006714,-0.005782,0.016008,0.035562,-0.001113,-0.017871,-0.054466,0.00958
Wartberg_tv:KH150637,0.134311,0.169593,0.078441,0.007429,0.112021,-0.01004,-0.008695,0.007846,0.079969,0.068885,-0.015102,0.009142,-0.011298,-0.00234,0.003529,0.017237,-0.001956,-0.004181,-0.001634,0.016883,0.035063,0.002844,-0.021199,-0.063021,0.007305
Raw
Wartberg_tv:KH150189_KH150632_636,0.0114,0.0165,0.0275,0.0142,0.0407,-0.0015,-0.0016,0.0038,0.0386,0.04,-0.0038,0.0106,-0.0078,-0.0025,0.0084,0.0186,0.017,-0.0043,-0.0062,0.0185,0.0388,0.0001,-0.0132,-0.0617,0.0108
Wartberg_tv:KH150614_615,0.0116,0.0163,0.0238,0.0064,0.0392,-0.0032,-0.0014,0.003,0.0437,0.0389,-0.0031,0.0068,-0.0073,-0.0029,0.0019,0.0084,0.0076,-0.0088,-0.0042,0.0172,0.0323,0.0053,-0.0179,-0.0562,0.0031
Wartberg_tv:KH150619,0.0114,0.0168,0.0227,0.0061,0.0395,-0.0036,-0.0001,0.0057,0.0412,0.0416,-0.0036,0.0102,-0.0143,-0.0113,0.0095,0.0229,0.0096,-0.0017,-0.0006,0.014,0.0326,0.0039,-0.0179,-0.0569,0.0082
Wartberg_tv:KH150627,0.011,0.0158,0.0306,0.0199,0.0416,0.0004,0.002,0.0112,0.0422,0.0346,-0.0065,0.002,-0.0051,-0.013,0.014,0.0133,0.0021,-0.0003,-0.0005,0.0239,0.0405,0.0052,-0.0224,-0.0672,0.0127
Wartberg_tv:KH150630,0.0111,0.0167,0.0223,0.0018,0.0373,-0.0043,-0.004,0.0014,0.0463,0.0472,-0.0036,0.011,-0.0115,-0.0093,0.0013,0.0204,0.0133,-0.0012,-0.0103,0.0099,0.022,0.003,-0.0136,-0.0492,0.0099
Wartberg_tv:KH150635,0.01,0.0169,0.0212,0.0011,0.0343,-0.0062,-0.0047,0.0037,0.0423,0.0453,0.0028,0.0125,-0.0119,-0.0085,0.0024,0.0118,0.0175,-0.0053,-0.0046,0.0128,0.0285,-0.0009,-0.0145,-0.0452,0.008
Wartberg_tv:KH150637,0.0118,0.0167,0.0208,0.0023,0.0364,-0.0036,-0.0037,0.0034,0.0391,0.0378,-0.0093,0.0061,-0.0076,-0.0017,0.0026,0.013,-0.0015,-0.0033,-0.0013,0.0135,0.0281,0.0023,-0.0172,-0.0523,0.0061
@ Davidski
Thanks!
Mokrin:
https://drive.google.com/file/d/1B0-lc3uMC2_EkP07uQZP0dmOajScbRGJ/view?usp=sharing
@Norfern
The dataset you posted has no overlapping markers with the Global25.
Numbers were not fine after all. Well I retained the .ped so this should be easy.
@ Davidski & NO
Allels are encoded as numbers (in bold). Theoretically this should be fixable with a one-liner in bash, but there are either 5 allels encoded or 4 + missing(?). And they are inconsistent with Reich dataset (letters in italics):
1 rs3094315 0.02013 752566 3 1 G A
1 rs12124819 0.020242 776546 0 1 A G
1 rs28765502 0.022137 832918 2 4 T C
1 rs7419119 0.022518 842013 3 4 T G
1 rs950122 0.02272 846864 2 3 G C
1 rs113171913 0.023436 869303 4 2 C T
Currently I'm downloading 230 GB of Wartberg data and if I won't run out of time on a server I'll have them processed later today or tomorrow.
Hope these work, but if not, it might be the chromosomes.
https://www.mediafire.com/file/dkm7mnl6swco3ud/kilinc_2021_b.zip/file
https://www.mediafire.com/file/x4csdxs2i2c6iw6/key_2020_b.zip/file
@ NO
This is .snp file from v44.3 dataset:
rs3094315 1 0.020130 752566 G A
rs12124819 1 0.020242 776546 A G
rs28765502 1 0.022137 832918 T C
For some reason you still have some alleles encoded as missing(?):
1 rs3094315 0.02013 752566 G A
1 rs12124819 0.020242 776546 0 A
1 rs28765502 0.022137 832918 C T
0 stands for missing and the numbers are in alphabetical order (A;C;G;T).
The reason why they are marked as missing is because I produced the packedped from ped files using plink. I am not sure if this should cause any problems, but if they do, I think the .bim file can be replaced.
I believe it should be all fine, plink merely processed that data this way.
http://zzz.bwh.harvard.edu/plink/binary.shtml
@ NO
Yeah, the last one merges correctly.
@ Davidski
Wartberg (all samples):
https://drive.google.com/file/d/11tk-h1p9GMVYDxYS1knmwu48urwEHCjJ/view?usp=sharing
@ Davidski
Rouran (one sample 74046 snps)
https://pubmed.ncbi.nlm.nih.gov/29681138/
Trypillia
https://www.nature.com/articles/s41598-020-61190-0
https://drive.google.com/file/d/1mjUUgxA8trBS4ujS4z5qe14-vbJETEIF/view?usp=sharing
Could you publish the coordinates of Pocrovca1 (64132 snps), even if it doesn't make it into the G25 spreadsheet? Thank you in advance!
@Norfern
I don't know what these are exactly, so if you can test them and put pop labels on the ones that aren't too noisy, that'd be great.
https://drive.google.com/file/d/1vKGA_wkHYdsq0EbIbaUNRR11rVHMcEs4/view?usp=sharing
I'll be trying to tie them to their archeological cultures if possible, would that be fine? I can also do that to the other samples from the study.
Yeah, like in the G25 datasheets.
Country first, then region and/or archeological affinity and period.
And lo we go: https://www.mediafire.com/file/acht7daiep8l132/kilinc+2021+key+2020.txt/file
Dzhylinda1, cta016, Kamenka1,2,3 are relabeled samples from @Arza.
@Arza
https://drive.google.com/file/d/1jiRw0vcQntVX0jplCkfX4aSkIs4wWwLX/view?usp=sharing
https://ru.wikipedia.org/wiki/%D0%94%D1%8E%D0%BA%D1%82%D0%B0%D0%B9%D1%81%D0%BA%D0%B0%D1%8F_%D0%BA%D1%83%D0%BB%D1%8C%D1%82%D1%83%D1%80%D0%B0
yak025 seems to be part of the Duktai culture.
@ Davidski
Thanks!
IMHO all Wartberg samples are fine. Only few of them show minor African shift, but not higher than Mycenaeans IIRC. They pick up MAR_EN and Levant because probably they require something like Iron_Gates but with slightly less ANE. After removing ~half of transitions that may be a result of damage this shift stays.
Rouran and Pocrovca1 are fine for making plots, but probably not for any modelling.
Pocrovca3 and Gordinesti are from Moldova, not Ukraine.
Could you add also these 4 samples?
https://drive.google.com/file/d/1JFnpkQ0WaBM68JNXeS9GTvJyQEj46ckc/view?usp=sharing
They aren't UDG-treated and they aren't trimmed, so damage may not be small. But they won't be used for modelling just like the rest of the UP stuff, so it doesn't matter anyway.
@ Davidski
BK-1653 is rather MUP (middle), not IUP (initial). It's 10 ky younger and unlike other three it's linked with WHG.
The earlier ones on the other hand when compared with IronGates are more related with Siberians, Tianyuan, Andamanese and... GoyetQ116_1.
pop3 = "Iran_GanjDareh_N", "India_Harappan_published", "Iran_Mesolithic_BeltCave_all_published", "Iran_Mesolithic_HotuIIIb", "Iran_ShahrISokhta_BA2", "Iran_ShahrISokhta_BA2_published", "Iran_TepeAbdulHosein_N.SG", "Iran_Wezmeh_N.SG"
pop4 = "Indian_GreatAndaman_100BP.SG"
pop1 pop2 pop3 pop4 est se z p n
1 Chimp… Bacho… Iran_GanjDar… Indian_Gr… 4.21e-3 5.06e-4 8.31 9.40e-17 906680
2 Chimp… Bacho… India_Harapp… Indian_Gr… -8.31e-4 1.85e-3 -0.449 6.54e- 1 28873
3 Chimp… Bacho… Iran_Mesolit… Indian_Gr… 5.57e-3 1.99e-3 2.80 5.09e- 3 27482
4 Chimp… Bacho… Iran_Mesolit… Indian_Gr… 5.26e-3 1.68e-3 3.13 1.73e- 3 34703
5 Chimp… Bacho… Iran_ShahrIS… Indian_Gr… 2.12e-3 6.04e-4 3.51 4.45e- 4 544519
6 Chimp… Bacho… Iran_ShahrIS… Indian_Gr… 2.18e-3 5.06e-4 4.31 1.62e- 5 749290
7 Chimp… Bacho… Iran_TepeAbd… Indian_Gr… 4.39e-3 5.35e-4 8.21 2.28e-16 882150
8 Chimp… Bacho… Iran_Wezmeh_… Indian_Gr… 4.80e-3 5.80e-4 8.28 1.27e-16 943046
Although they're mostly CT and one maybe F IIRC (I need to check them one more time and using all bams) things are getting quite interesting: https://www.yfull.com/tree/P-M1254*/
Any thoughts on the Beaker artifacts in Nerma culture territory that Andrew just blogged about?
@Norfern
I added these samples. The others are a bit noisy, and there are high quality alternatives already in the G25.
ITA_Etruscan_o2:ETR001
RUS_Amur_IA:bla001
RUS_Marinskaya_IA:MK3001
RUS_Southern_Trans-Baikal_Meso:cta016
RUS_Volga-Kama_N:MUR009
RUS_Yakutia_Ymyiakhtakh_LN:Kamenka1
RUS_Yakutia_Ymyiakhtakh_LN:Kamenka3
RUS_Yakutia_Ymyiakhtakh_LN:N4b2
RUS_Yakutia_Ymyiakhtakh_LN:yak021
@ Davidski
No love for Trypillians? And how Tollense looks like? Better, worse, same?
Anyway, Mokrin came out better than I thought.
The Tollense samples are basically the same. So since I already used them in a blog post I don't want to change them.
I haven't had a chance to look closely at the new CT samples. By the way...
Scaled
BGR_Bacho_Kiro_IUP:BB7-240,-0.063741,-0.13405,-0.082967,0.082688,0.016003,-0.008088,0.00235,-0.004615,0.061971,0.022962,0.004222,-0.005845,-0.000743,-0.001514,-0.002036,-0.014717,0.003651,0.009628,-0.009804,0.008379,-0.001622,0.014096,-0.016145,-0.013616,-0.008023
BGR_Bacho_Kiro_IUP:BK-1653,0.027318,0.037575,0.025267,0.074936,0.054779,-0.00753,-0.00611,-0.004154,0.071379,0.028247,0.009094,-0.007343,0.011001,-0.017478,0.017779,0.02254,0.001695,0.007981,-0.001885,0.032516,0.027452,0.016446,-0.013804,-0.047597,0.007185
BGR_Bacho_Kiro_IUP:CC7-335,-0.067156,-0.119832,-0.073539,0.070737,0.005232,-0.001673,-0.016216,0.003231,0.050313,0.026789,0.006171,-0.006594,0.001635,-0.002202,0.000271,-0.0118,-0.002868,0.010262,-0.004274,0.007879,0.001497,0.00272,-0.008627,-0.011086,0.00012
BGR_Bacho_Kiro_IUP:F6-620,-0.076261,-0.142174,-0.087869,0.073321,0.004616,-0.01255,0.000235,-0.004846,0.040496,0.010752,0.001786,-0.007044,0.011447,0.005092,0.004614,-0.009414,-0.001956,0.015583,-0.006662,0.019009,0.004742,0.006925,-0.002342,-0.007471,-0.004071
Raw
BGR_Bacho_Kiro_IUP:BB7-240,-0.0056,-0.0132,-0.022,0.0256,0.0052,-0.0029,0.001,-0.002,0.0303,0.0126,0.0026,-0.0039,-0.0005,-0.0011,-0.0015,-0.0111,0.0028,0.0076,-0.0078,0.0067,-0.0013,0.0114,-0.0131,-0.0113,-0.0067
BGR_Bacho_Kiro_IUP:BK-1653,0.0024,0.0037,0.0067,0.0232,0.0178,-0.0027,-0.0026,-0.0018,0.0349,0.0155,0.0056,-0.0049,0.0074,-0.0127,0.0131,0.017,0.0013,0.0063,-0.0015,0.026,0.022,0.0133,-0.0112,-0.0395,0.006
BGR_Bacho_Kiro_IUP:CC7-335,-0.0059,-0.0118,-0.0195,0.0219,0.0017,-0.0006,-0.0069,0.0014,0.0246,0.0147,0.0038,-0.0044,0.0011,-0.0016,0.0002,-0.0089,-0.0022,0.0081,-0.0034,0.0063,0.0012,0.0022,-0.007,-0.0092,0.0001
BGR_Bacho_Kiro_IUP:F6-620,-0.0067,-0.014,-0.0233,0.0227,0.0015,-0.0045,0.0001,-0.0021,0.0198,0.0059,0.0011,-0.0047,0.0077,0.0037,0.0034,-0.0071,-0.0015,0.0123,-0.0053,0.0152,0.0038,0.0056,-0.0019,-0.0062,-0.0034
@ Davidski
Re: kra001
There is a map of Siberia embedded in G25 (there is even a gap for Baikal):
https://i.postimg.cc/xj7PLsvq/genes-vs-geography.jpg
And yeah, it's one of the best proxies for PU.
Wow. Thanks!
Simply incredible.
Distance to: BGR_Bacho_Kiro_IUP:F6-620
0.05151971 RUS_Ust_Ishim:Ust_Ishim
0.11367636 LAO_Hoabinhian:La368
0.13282182 IND_Great_Andamanese_100BP:Andaman
0.13579528 Jarawa:JW-61
0.13599595 Jarawa:JW-10
0.13624277 Jarawa:JW-7
0.13643960 Onge:ONG-09
0.13708097 Jarawa:JW-32
0.13716452 Jarawa:JW-54
0.13739246 Onge:ONG-08
Distance to: BGR_Bacho_Kiro_IUP:CC7-335
0.04743152 RUS_Ust_Ishim:Ust_Ishim
0.12950727 LAO_Hoabinhian:La368
0.13119893 RUS_Yana_UP:Yana2
0.13435220 RUS_Yana_UP:Yana1
0.14744225 IND_Great_Andamanese_100BP:Andaman
0.15079180 Bahun:Ba25
0.15182895 Jarawa:JW-32
0.15201911 Jarawa:JW-61
0.15240112 Jarawa:JW-10
0.15282423 Jarawa:JW-53
Distance to: BGR_Bacho_Kiro_IUP:BB7-240
0.04687165 RUS_Ust_Ishim:Ust_Ishim
0.11167690 LAO_Hoabinhian:La368
0.12902982 IND_Great_Andamanese_100BP:Andaman
0.13249983 Jarawa:JW-61
0.13303980 Jarawa:JW-53
0.13390945 Jarawa:JW-10
0.13407636 Jarawa:JW-32
0.13425872 Onge:ONG-17
0.13465172 Onge:ONG-24
0.13520816 Onge:ONG-11
Typical Bulgarian AASI. :)
And Europeans, 10000 years later:
Distance to: BGR_Bacho_Kiro_MUP:BK-1653
0.04734968 CZE_Vestonice16:Vestonice16
0.05896718 RUS_Sunghir:Sunghir3
0.06300165 BEL_GoyetQ116-1:Q116-1
0.06518390 RUS_Sunghir:Sunghir2
0.06529963 RUS_Sunghir:Sunghir4
0.07141504 RUS_Sunghir:Sunghir1
0.07999836 RUS_Kostenki14:Kostenki14
0.11776269 BEL_GoyetQ2:GoyetQ2
0.13913036 RUS_Yana_UP:Yana2
0.14464814 RUS_Yana_UP:Yana1
Andamanese, Yana... all P. The plot thickens.
@Arza
I wonder if that AASI might be associated with the Han signal in GoyetQ116-1.
While diet (food production & economy type & intake of fermemted food as milk) & sun exposure dose & skin shade correlates with immunological function (& vitamin deficiencies). Some genes in general can be effective but not activated because of lack of fuel as fe prohormone vitD. Plague following famine, war & economic crisis is logical.
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