Were all Corded Ware groups the same people?

LeBrok

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I have many samples and I can say that their admixture levels are quite varied, but it is hard to get a handle on any visible pattern. For example to look at a level of admixture and tell what area of CW they belong to. It is quite a daunting task. Perhaps they were the same group, same people coming from rather same location, but they were not mixed well together yet. Was this uneven mixing from where they came from, or a result of still uneven mixing with local farmers? Bell Beaker and Unetice samples are here too.

I've lined them up from highest Baloch to smallest.
M224345I0103M224345I0103, R1a M417M669778RISE1 R1b-M343>L754F999956Rise94, R1a M417Z378359PL_N17, R1a-Z280M453254Rise154F999948Rise150, Poland, slask 1750 BCM913021Rise00
Germany Espersted4.5 kyaGermnay Espersted4.5kyaPoland 4.6kyaBA Sweden, Vibi4.55kyaPoland, Gustorzyn3.9kyaUnetice EBA K1a4a1 -3.9kyaPoland, slask 3.75 kya3.5kyaEstonia CW4 kya
Run time12.04Run time9.48Run time2.46Run Time5.95Run time10.7Run Time4.06Run time8.44Run Time8.05
S-Indian- S-Indian- S-Indian- S-Indian- S-Indian- S-Indian- S-Indian- S-Indian-
Baloch20.73Baloch20.65Baloch20.3Baloch16.53Baloch15.97Baloch14.46Baloch14.65Baloch14.27
Caucasian5.6Caucasian5.79Caucasian- Caucasian4.23Caucasian5.67Caucasian4.05Caucasian2.73Caucasian-
NE-Euro56.52NE-Euro57.39NE-Euro61.25NE-Euro54.16NE-Euro58.43NE-Euro53.71NE-Euro53.54NE-Euro59.09
SE-Asian- SE-Asian- SE-Asian- SE-Asian- SE-Asian- SE-Asian- SE-Asian- SE-Asian-
Siberian- Siberian- Siberian- Siberian- Siberian- Siberian- Siberian- Siberian0.8
NE-Asian- NE-Asian- NE-Asian- NE-Asian- NE-Asian- NE-Asian- NE-Asian- NE-Asian-
Papuan- Papuan- Papuan- Papuan- Papuan- Papuan- Papuan0.48Papuan-
American1.92American0.67American3.73American- American0.72American0.96American0.22American-
Beringian0.26Beringian- Beringian5.87Beringian- Beringian- Beringian- Beringian0.49Beringian-
Mediterranean14.53Mediterranean15.38Mediterranean8.85Mediterranean24.72Mediterranean18.91Mediterranean26.58Mediterranean27.12Mediterranean25.26
SW-Asian- SW-Asian- SW-Asian- SW-Asian- SW-Asian- SW-Asian- SW-Asian- SW-Asian-
San- San- San- San- San- San- San- San-
E-African- E-African- E-African- E-African- E-African- E-African0.2E-African0.34E-African-
Pygmy- Pygmy- Pygmy- Pygmy- Pygmy- Pygmy- Pygmy- Pygmy-
W-African0.44W-African0.09W-African- W-African0.37W-African0.3W-African- W-African0.42W-African0.5

T644357RISE563 R1b-U152T253390I0806 Rib-DF27M232268BAM425717 I0116M107790I0118F999941Rise98M324645I0112 M130094RISE61 R1a-M417>Z284>CTS8401
Germany, Osterhofen-AltenmarktBell BeakerGermany, Quedlinburg2431-2150BCIrelandBA Rathlin1 UneticeGermany LNSweden, L Beddinge 563.8kyaBellBeakerDenmark4.5kya
Run time6.24Run time4.05Run time16.74Run time6.22Run time11.67Run time13.93Run time 11.21Run time4.55
S-Indian- S-Indian- S-Indian0.97S-Indian- S-Indian1.28S-Indian0.16S-Indian- S-Indian-
Baloch13.69Baloch13.34Baloch12.89Baloch12.21Baloch11.84Baloch11.56Baloch10.96Baloch9.67
Caucasian0.58Caucasian9.25Caucasian2.14Caucasian2.27Caucasian6.75Caucasian- Caucasian1.59Caucasian4.68
NE-Euro60.74NE-Euro49.82NE-Euro52.72NE-Euro58.33NE-Euro49.39NE-Euro59.95NE-Euro54.14NE-Euro54.85
SE-Asian- SE-Asian- SE-Asian- SE-Asian- SE-Asian- SE-Asian0.17SE-Asian0.06SE-Asian-
Siberian- Siberian- Siberian0.96Siberian- Siberian- Siberian- Siberian- Siberian0.27
NE-Asian- NE-Asian- NE-Asian- NE-Asian- NE-Asian- NE-Asian- NE-Asian- NE-Asian-
Papuan- Papuan- Papuan- Papuan- Papuan- Papuan0.31Papuan0.23Papuan-
American- American- American1.14American0.88American- American- American0.3American-
Beringian- Beringian- Beringian- Beringian- Beringian- Beringian0.11Beringian0.1Beringian-
Mediterranean23.18Mediterranean27.41Mediterranean26.58Mediterranean26.25Mediterranean30.33Mediterranean26.73Mediterranean32.4Mediterranean28.7
SW-Asian- SW-Asian- SW-Asian- SW-Asian- SW-Asian- SW-Asian- SW-Asian- SW-Asian-
San- San- San- San- San- San- San- San-
E-African- E-African- E-African- E-African- E-African- E-African- E-African- E-African-
Pygmy- Pygmy- Pygmy0.13Pygmy0.07Pygmy- Pygmy0.11Pygmy- Pygmy-
W-African1.8W-African0.17W-African2.45W-African- W-African0.37W-African0.87W-African0.19W-African1.82

M370010I0047F999945Rise97M671253Rise71F999954Rise569M239638RISE586M191719RISE431 R1a-M417
UneticeSweden, Fredriksberg, Sweden3.6kyaNordic LN, Rise 71Czech, Prague, Brandisek4kya?Czech RepublicEBAProto Unetic, Poland4.15ya
Run Time4.27Run time5.11Run time4.64Run time6.653.39Run time3.01
S-Indian- S-Indian- S-Indian- S-Indian- S-Indian- S-Indian-
Baloch9.4Baloch9.33Baloch9.29Baloch7.37Baloch7.13Baloch6.9
Caucasian6.83Caucasian- Caucasian10.55Caucasian8.33Caucasian1.52Caucasian-
NE-Euro57.57NE-Euro56.96NE-Euro51.89NE-Euro57.8NE-Euro56.14NE-Euro61.32
SE-Asian- SE-Asian- SE-Asian- SE-Asian- SE-Asian- SE-Asian-
Siberian- Siberian- Siberian- Siberian- Siberian- Siberian-
NE-Asian- NE-Asian- NE-Asian- NE-Asian- NE-Asian- NE-Asian-
Papuan- Papuan- Papuan- Papuan- Papuan- Papuan-
American- American- American- American- American- American1.92
Beringian- Beringian- Beringian- Beringian- Beringian- Beringian-
Mediterranean25.97Mediterranean32.93Mediterranean27.34Mediterranean26.28Mediterranean34.73Mediterranean28.8
SW-Asian- SW-Asian0.59SW-Asian- SW-Asian- SW-Asian- SW-Asian-
San- San- San0.22San- San- San-
E-African- E-African- E-African- E-African- E-African- E-African-
Pygmy- Pygmy- Pygmy- Pygmy- Pygmy- Pygmy-
W-African0.23W-African0.18W-African0.7W-African0.23W-African0.49W-African1.05
 
Here as a little chart:
CW chart.JPG
All the admixtures keep rather steady level, within 10 percent.

Dropping Baloch level correlates inversely with rising Med. What does that mean?

Probably Baloch and Med admixtures compete for exactly same genes/markers? Not much more to it.
 
Here are 4 Bronze Age Hungarians, from Baden Culture. They are quite distinct from Corded Ware guys. There is more EEF farmer in them. Much higher level of Med and Caucasian, low Baloch and lower Euro. Maybe even a bit different h-g mix too, containing much less steppe/Baloch, more WHG like.

M631469 RISE349F999933BR2, J-M67M681225BR1M974598 RISE374 and 373
Hungary MBA [2034-1748 BC] T2b3 -Hungary, Ludas-Varjú-dűlő,3.3kyaEBA HungaryMaros Hungary [1866-1619 BC] T2 G2a-P287>P15>PF3178
Run time3.16Run time15.13Run time10.55Run time5.26
S-Indian- S-Indian- S-Indian- S-Indian-
Baloch5.64Baloch3.15Baloch- Baloch-
Caucasian13.81Caucasian14.73Caucasian5.45Caucasian18.58
NE-Euro38.22NE-Euro46.18NE-Euro56.15NE-Euro39.65
SE-Asian- SE-Asian0.2SE-Asian0.49SE-Asian-
Siberian- Siberian- Siberian- Siberian-
NE-Asian- NE-Asian- NE-Asian- NE-Asian-
Papuan- Papuan0.18Papuan- Papuan-
American- American- American- American-
Beringian- Beringian- Beringian- Beringian-
Mediterranean34.63Mediterranean31.73Mediterranean34.48Mediterranean40.09
SW-Asian3.94SW-Asian3.33SW-Asian3.1SW-Asian0.98
San- San- San- San0.17
E-African- E-African- E-African- E-African-
Pygmy- Pygmy- Pygmy- Pygmy0.15
W-African3.75W-African0.48W-African0.3W-African0.39
 
They seem to be the same, differing in about 10% Caucasian.
 
Ancestral Composition for German CW

The first 3 guys have the highest Baloch and look a bit like "outliers". Actually, two first guys, as the third one is of very low quality genome and I'm positive it is misplaced. The first two are from same time period and from Germany. I think they contain more Yamnaya than others.
I managed to create models for them. Pretty good, except baloch is 2 % lower for a perfect model. Even American admixture matches the right level, and American admixture points to strong Steppe input.
This is 3 way composition. First is Yamnaya, second is EEF farmer, 3rd is WHG, above them are the proportions used. 55/15/30 percent respectively.
4th row is the modeled CW from Germany in bold, and 5th is the CW "German" for comparison.


0.550.150.3
M828815Rise552F999928NE7, I-L1228M325047KO1, I-L68Model for CW GermanyM224345I0103
Ulan iV, Yamnaya4.5 kyaHungary, Apc-Berekalja I6.4kyaHungarian, Tiszaszőlős-Domaháza7.7 kyaCompositionGermany Espersted4.5 kya
Run time9.08Run time6.72Run time9.43Run timeRun time12.04
S-Indian0S-Indian0S-Indian0S-Indian -S-Indian-
Baloch33.24Baloch0Baloch0Baloch 18.28Baloch20.73
Caucasian6.58Caucasian19.04Caucasian0Caucasian 6.48Caucasian5.6
NE-Euro56.02NE-Euro16.69NE-Euro80.37NE-Euro 57.43NE-Euro56.52
SE-Asian0SE-Asian0SE-Asian0SE-Asian -SE-Asian-
Siberian0Siberian0Siberian0Siberian -Siberian-
NE-Asian0NE-Asian0NE-Asian0NE-Asian -NE-Asian-
Papuan0Papuan0Papuan0.53Papuan 0.16Papuan-
American2.46American0American0American 1.35American1.92
Beringian0.75Beringian0Beringian0Beringian 0.41Beringian0.26
Mediterranean0Mediterranean56.18Mediterranean18.59Mediterranean 14.00Mediterranean14.53
SW-Asian0SW-Asian7.96SW-Asian0SW-Asian 1.19SW-Asian-
San0San0San0San -San-
E-African0E-African0E-African0E-African -E-African-
Pygmy0Pygmy0Pygmy0Pygmy -Pygmy-
W-African0.95W-African0.11W-African0.5W-African 0.69W-African0.44

To be perfect, we need to find WHG who has a bit of Baloch (Ukrainian WHG?). And EEF farmer who has much less SW Asian (from Poland?). There is no SW Asian in CW samples, but a lot in Baden samples.
 
I got better numbers when WHG KO1 is substituted with UHG (Unknown Hunter Gatherer or perhaps Ukrainian Hunter Gatherer), which I created myself ;). It is similar to Samara outlier guy, only 3 components, just in different proportions. Again, first 3 guys are the source elements, 4th is Model made from sum of 3 guys, proportions above. 5th is the real ancient CW guy from Germany, our goal.
I wonder how this newly discovered h-g from Ukraine will pan in gedmatch runs?

Perfect with unknown WHG
0.55 0.10 0.35
M828815 Rise552 F999928 NE7, I-L1228 UHG Model for CW Germany M224345 I0103
Ulan iV, Yamnaya 4.5 kya Hungary, Apc-Berekalja I 6.4kya Composition Germany Espersted 4.5 kya
Run time 9.08 Run time 6.72 Run time Run time Run time 12.04
S-Indian - S-Indian - S-Indian - S-Indian - S-Indian -
Baloch 33.24 Baloch - Baloch 6.00 Baloch 20.38 Baloch 20.73
Caucasian 6.58 Caucasian 19.04 Caucasian - Caucasian 5.52 Caucasian 5.60
NE-Euro 56.02 NE-Euro 16.69 NE-Euro 68.50 NE-Euro 56.46 NE-Euro 56.52
SE-Asian - SE-Asian - SE-Asian - SE-Asian - SE-Asian -
Siberian - Siberian - Siberian - Siberian - Siberian -
NE-Asian - NE-Asian - NE-Asian - NE-Asian - NE-Asian -
Papuan - Papuan - Papuan - Papuan - Papuan -
American 2.46 American - American - American 1.35 American 1.92
Beringian 0.75 Beringian - Beringian - Beringian 0.41 Beringian 0.26
Mediterranean - Mediterranean 56.18 Mediterranean 25.00 Mediterranean 14.37 Mediterranean 14.53
SW-Asian - SW-Asian 7.96 SW-Asian - SW-Asian 0.80 SW-Asian -
San - San - San - San - San -
E-African - E-African - E-African - E-African - E-African -
Pygmy - Pygmy - Pygmy - Pygmy - Pygmy -
W-African 0.95 W-African 0.11 W-African 0.50 W-African 0.71 W-African 0.44
 
Here I modified a farmer. Hypothetical EEF farmer who lived in Poland/West Ukraine, who was more heavily mixed with WHG. He is in second column. I used Samara outlier, real sample, as a model for WHG who lived in Ukraine. This modified EEF resembles Hungarian BA.

Perfect with modified EEF and Samara outlier
0.48 0.20 0.33
M828815 Rise552 EEF M630274 I0432 Model for CW Germany M224345 I0103
Ulan iV, Yamnaya 4.5 kya Poland? Samara Poltavka outlier R1a-M417>Z93 2925-2536 BC Composition Germany Espersted 4.5 kya
Run time 9.08 Run time - Run time 7.79 Run time Run time 12.04
S-Indian - S-Indian - S-Indian - S-Indian - S-Indian -
Baloch 33.24 Baloch - Baloch 14.70 Baloch 20.57 Baloch 20.73
Caucasian 6.58 Caucasian 13.00 Caucasian - Caucasian 5.73 Caucasian 5.60
NE-Euro 56.02 NE-Euro 45.00 NE-Euro 63.14 NE-Euro 56.13 NE-Euro 56.52
SE-Asian - SE-Asian - SE-Asian - SE-Asian - SE-Asian -
Siberian - Siberian - Siberian - Siberian - Siberian -
NE-Asian - NE-Asian - NE-Asian - NE-Asian - NE-Asian -
Papuan - Papuan - Papuan - Papuan - Papuan -
American 2.46 American - American - American 1.17 American 1.92
Beringian 0.75 Beringian - Beringian - Beringian 0.36 Beringian 0.26
Mediterranean - Mediterranean 42.00 Mediterranean 22.15 Mediterranean 15.60 Mediterranean 14.53
SW-Asian - SW-Asian - SW-Asian - SW-Asian - SW-Asian -
San - San - San - San - San -
E-African - E-African - E-African - E-African - E-African -
Pygmy - Pygmy - Pygmy - Pygmy - Pygmy -
W-African 0.95 W-African 0.11 W-African - W-African 0.47 W-African 0.44
 
Modeling Baden/Hungarian BA ancestry.

I started by using Hungarian Neolithic Farmer and Hungarian Hunter Gatherer. You know, the guys from the area. It turned to be rather very rough approximation. I could balance Med and NE Euro but Balocha and Caucasian was a miss. Especially Caucasian was too low, and no matter who from Europe I used in the mix the Caucasian admixture was always too low. I tried Yamnaya and Samara guys in the mix, but still I was off. I needed a big help from Near East, from populations high in Caucasian admixture. The best success I had when added Anatolian Bronze Age sample or Kotias the CHG.
First one here is 3 way mix with CHG. There is a possibility that Ukrainian Hunter Gatherer was a mix of WHG 80% and CHG 20% in West Yamnaya, and later it mixed with EEF to become Hungarian BA/Baden type.

0.10.40.5ModelActual Dude
M603839F999928NE7, I-L1228M325047KO1, I-L68Baden/Hungarian BAF999933BR2, J-M67
Kotias CHG8 KYAHungary, Apc-Berekalja I6.4kyaHungarian, Tiszaszőlős-Domaháza7.7 kyaCompositionHungary, Ludas-Varjú-dűlő,3.3kya
Run time13.98Run time6.72Run time9.43Run time Run time15.13
S-Indian0.62S-Indian0S-Indian0S-Indian0.062S-Indian-
Baloch36.63Baloch0Baloch0Baloch3.663Baloch3.15
Caucasian54.15Caucasian19.04Caucasian0Caucasian13.031Caucasian14.73
NE-Euro3.84NE-Euro16.69NE-Euro80.37NE-Euro47.245NE-Euro46.18
SE-Asian0.59SE-Asian0SE-Asian0SE-Asian0.059SE-Asian0.2
Siberian0.77Siberian0Siberian0Siberian0.077Siberian-
NE-Asian0NE-Asian0NE-Asian0NE-Asian0NE-Asian-
Papuan0.15Papuan0Papuan0.53Papuan0.28Papuan0.18
American0American0American0American0American-
Beringian0Beringian0Beringian0Beringian0Beringian-
Mediterranean0Mediterranean56.18Mediterranean18.59Mediterranean31.767Mediterranean31.73
SW-Asian0SW-Asian7.96SW-Asian0SW-Asian3.184SW-Asian3.33
San0San0San0San0San-
E-African0E-African0E-African0E-African0E-African-
Pygmy0.25Pygmy0Pygmy0Pygmy0.025Pygmy-
W-African3.01W-African0.11W-African0.5W-African0.595W-African0.48






Second very good mix is with Anatolia Bronze Age. Denoting a possibility of huge population movement from Anatolia into Balkans during Early Bronze Age, reaching Hungarian Area too. This could account for rather high Caucasian admixture in today's Balkans.


0.10.40.5Model
M536324I1658F999928NE7, I-L1228M325047KO1, I-L68Baden/Hungarian BAF999933BR2, J-M67
Anatolia EBAHungary, Apc-Berekalja I6.4kyaHungarian, Tiszaszőlős-Domaháza7.7 kyaCompositionHungary, Ludas-Varjú-dűlő,3.3kya
Run time8.22Run time6.72Run time9.43Run timeRun time15.13
S-Indian0.27S-Indian0S-Indian0S-Indian0.027S-Indian-
Baloch25.53Baloch0Baloch0Baloch2.553Baloch3.15
Caucasian56.75Caucasian19.04Caucasian0Caucasian13.291Caucasian14.73
NE-Euro4.79NE-Euro16.69NE-Euro80.37NE-Euro47.34NE-Euro46.18
SE-Asian0SE-Asian0SE-Asian0SE-Asian0SE-Asian0.2
Siberian0Siberian0Siberian0Siberian0Siberian-
NE-Asian0NE-Asian0NE-Asian0NE-Asian0NE-Asian-
Papuan0Papuan0Papuan0.53Papuan0.265Papuan0.18
American0American0American0American0American-
Beringian0Beringian0Beringian0Beringian0Beringian-
Mediterranean5.88Mediterranean56.18Mediterranean18.59Mediterranean32.355Mediterranean31.73
SW-Asian6.45SW-Asian7.96SW-Asian0SW-Asian3.829SW-Asian3.33
San0San0San0San0San-
E-African0E-African0E-African0E-African0E-African-
Pygmy0Pygmy0Pygmy0Pygmy0Pygmy-
W-African0.33W-African0.11W-African0.5W-African0.327W-African0.48
 

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