K7b dodecad - maybe the way to go

[vettor]

reading dienekes , eurogenes and other forums it seems K8 is a dead horse for linking with regards to haak paper , some have mentioned to use dodecad k7b.....below are mine and family

Population
South_Asian 0.44%
West_Asian 17.43%
Siberian -
African 0.20%
Southern 26.21%
Atlantic_Baltic 55.72%
East_Asian -


son below
Population
South_Asian 0.06%
West_Asian 16.86%
Siberian -
African 0.10%
Southern 23.42%
Atlantic_Baltic 59.24%
East_Asian 0.32%


and my wife
Population
South_Asian -
West_Asian 16.30%
Siberian -
African 0.26%
Southern 24.04%
Atlantic_Baltic 58.94%
East_Asian 0.47%


note: the small east_asian for wife and son match the other testers for them , that is ftdna and 23andme


and my ftdna and 23andme match in every part except south_asia where I have 0.55% in the other

[Gray Fox]

Here's mine:

Population
South_Asian 1.49%
West_Asian 12.52%
Siberian -
African 0.66%
Southern 13.59%
Atlantic_Baltic 71.69%
East_Asian -

[Wulf Warrior]

Mine

Population
South_Asian 0.67%
West_Asian 11.71%
Siberian 0.93%
African 0.07%
Southern 13.53%
Atlantic_Baltic 73.10%
East_Asian -

[Calamus]

Mine:

Population
South_Asian 0.85%
West_Asian 7.73%
Siberian 1.98%
African -
Southern 7.56%
Atlantic_Baltic 81.88%
East_Asian -

[Jessie]

Mine

# Population Percent
1 Atlantic_Baltic 75.57
2 Southern 11.79
3 West_Asian 11.51
4 Siberian 0.63
5 South_Asian 0.5

Mother

# Population Percent
1 Atlantic_Baltic 74.39
2 Southern 12.17
3 West_Asian 11.53
4 South_Asian 1.42
5 Siberian 0.5

Brother

# Population Percent
1 Atlantic_Baltic 75.65
2 West_Asian 11.8
3 Southern 11.17
4 South_Asian 1.28
5 Siberian 0.11

Daughter

# Population Percent
1 Atlantic_Baltic 75.4
2 Southern 12.97
3 West_Asian 10.24
4 South_Asian 0.8
5 Siberian 0.59

[SwampThing27]

Population
South_Asian 2.23%
West_Asian 12.03%
Siberian 0.68%
African 0.88%
Southern 14.46%
Atlantic_Baltic 69.71%

The oracle-4 for this test actually gives me one of the lowest fst distances for any of the tests.
1 Lithuanian + Portuguese + Romanians + Swedish @ 1.577914

[Alpine Hominin]

Mine:

Admix Results (sorted):

# Population Percent
1 Atlantic_Baltic 73.21
2 Southern 15.33
3 West_Asian 8.41
4 South_Asian 1.69
5 Siberian 1.32
6 African 0.03

[Rick]

Population
South_Asian -
West_Asian 12.40%
Siberian 0.37%
African 0.18%
Southern 14.85%
Atlantic_Baltic 72.20%
East_Asian -

[Jessie]

Very low scores like SwampThing says I get 0 which I've never seen before. The rest of my family get scores of 0.5. Myself and daughter are the only ones who get the 0 result.

Using 2 populations approximation:
1 50% Dutch +50% German @ 0.584702


Using 3 populations approximation:
1 50% Argyll +25% British +25% Hungarians @ 0.000000


Using 4 populations approximation:
1 Argyll + Argyll + British + Hungarians @ 0.000000
2 Argyll + Argyll + British_Isles + Hungarians @ 0.000000
3 Argyll + Argyll + Hungarians + Irish @ 0.000000
4 Argyll + CEU30 + Hungarians + Orkney @ 0.000000
5 Argyll + Dutch + Hungarians + Orkney @ 0.000000
6 Argyll + German + German + German @ 0.000000
7 Argyll + Lithuanians + N_Italian + Norwegian @ 0.000000
8 Belorussian + British_Isles + Lithuanians + North_Italian @ 0.000000
9 Belorussian + Dutch + Dutch + French @ 0.000000
10 Belorussian + Irish + Lithuanians + North_Italian @ 0.000000
11 Belorussian + Lithuanians + North_Italian + Orcadian @ 0.000000
12 British + Cornwall + Mixed_Germanic + Ukranians @ 0.000000
13 British + English + Hungarians + Polish @ 0.000000
14 British + Hungarians + Kent + Polish @ 0.000000
15 British + Mixed_Germanic + Mixed_Germanic + Ukranians @ 0.000000
16 British_Isles + English + Hungarians + Polish @ 0.000000
17 British_Isles + Hungarians + Kent + Polish @ 0.000000
18 British_Isles + Mixed_Germanic + Mixed_Germanic + Ukranians @ 0.000000
19 CEU30 + CEU30 + Hungarians + Swedish @ 0.000000
20 CEU30 + Cornwall + English + Ukranians @ 0.000000

Daughter

Using 2 populations approximation:
1 50% Cornwall +50% Cornwall @ 0.642712


Using 3 populations approximation:
1 50% Argyll +25% Belorussian +25% Cataluna @ 0.000000


Using 4 populations approximation:
1 Argyll + Argyll + Belorussian + Cataluna @ 0.000000
2 Argyll + Baleares + Lithuanian + Orkney @ 0.000000
3 Argyll + Cantabria + Polish + Polish @ 0.000000
4 Argyll + Cataluna + German + Lithuanian @ 0.000000
5 Argyll + Cataluna + Lithuanian + Mixed_Germanic @ 0.000000
6 Argyll + Dutch + Lithuanian + Valencia @ 0.000000
7 Argyll + French + French + Lithuanian @ 0.000000
8 Baleares + CEU30 + Lithuanian + Swedish @ 0.000000
9 Baleares + Dutch + Lithuanians + Orcadian @ 0.000000
10 Belorussian + Cataluna + English + Polish @ 0.000000
11 Belorussian + Cornwall + Cornwall + French @ 0.000000
12 British + CEU30 + French + Polish @ 0.000000
13 British + Cornwall + French + Polish @ 0.000000
14 British + French + Kent + Polish @ 0.000000
15 British_Isles + CEU30 + French + Polish @ 0.000000
16 British_Isles + Cornwall + French + Polish @ 0.000000
17 Castilla_La_Mancha + CEU30 + Lithuanian + Polish @ 0.000000
18 Cataluna + CEU30 + Dutch + Lithuanian @ 0.000000
19 Cataluna + Dutch + Polish + Swedish @ 0.000000
20 Cataluna + Irish + Polish + Polish @ 0.000000

[MfA]

.....

[Gray Fox]

That is odd, Jessie. I too receive rather low gd's for this run..

Using 2 populations approximation:
1 50% Hungarians +50% Mixed_Germanic @ 1.602404


Using 3 populations approximation:
1 50% British_Isles +25% Lithuanian +25% O_Italian @ 1.079036


Using 4 populations approximation:
1 CEU30 + Orcadian + Orcadian + Romanians @ 0.962975
2 CEU30 + Lithuanian + O_Italian + Orcadian @ 1.032406
3 Argyll + British_Isles + Orcadian + Romanians @ 1.045412
4 Argyll + British_Isles + British_Isles + Romanians @ 1.046733
5 Argyll + British + Orcadian + Romanians @ 1.054753
6 CEU30 + Orcadian + Orkney + Romanians @ 1.056519
7 Argyll + British_Isles + Irish + Romanians @ 1.061211
8 British_Isles + CEU30 + Lithuanian + O_Italian @ 1.061382
9 Argyll + British + British_Isles + Romanians @ 1.068559
10 British_Isles + Orcadian + Orcadian + Romanians @ 1.071036
11 British + Orcadian + Orcadian + Romanians @ 1.076196
12 Argyll + British + Irish + Romanians @ 1.077065
13 British_Isles + CEU30 + Orcadian + Romanians @ 1.077388
14 British_Isles + British_Isles + Lithuanian + O_Italian @ 1.079036
15 Dutch + Orcadian + Orcadian + Romanians @ 1.080225
16 Bulgarian + Orcadian + Orcadian + Orcadian @ 1.081673
17 Argyll + CEU30 + Orcadian + Romanians @ 1.082200
18 British_Isles + Dutch + Orcadian + Romanians @ 1.088562
19 British_Isles + Bulgarian + Orcadian + Orcadian @ 1.089565
20 CEU30 + Irish + Orcadian + Romanians @ 1.091962

[Kaido]

Population
South_Asian 31.67%
West_Asian 45.90%
Siberian 3.26%
African 0.28%
Southern 5.33%
Atlantic_Baltic 13.56%
East_Asian -

[BalkanKiwi]

One of the few calculators that picks up my Polish in the Oracle but the Mixed Mode is odd.


Population
South_Asian 1.79%
West_Asian 13.78%
Siberian -
African -
Southern 15.49%
Atlantic_Baltic 68.76%
East_Asian 0.14%

First 5 mixed populations. The secondary population is baffling.

Mixed Mode Population Sharing:

# Primary Population (source) Secondary Population (source) Distance
1 88% British_Isles (Dodecad) + 12% Iranian_Jews (Behar) @ 0.73
2 88% British_Isles (Dodecad) + 12% Iraq_Jews (Behar) @ 0.74
3 88.2% British (Dodecad) + 11.8% Iranian_Jews (Behar) @ 0.8
4 90% Cornwall (1000Genomes) + 10% Uzbekistan_Jews (Behar) @ 0.8
5 88.2% British (Dodecad) + 11.8% Iraq_Jews (Behar) @ 0.82

1 50% Lithuanian +50% N_Italian @ 1.949568


Using 3 populations approximation:
1 50% British_Isles +25% Lithuanian +25% S_Italian_Sicilian @ 1.164295


Using 4 populations approximation:
1 British_Isles + Lithuanian + Orcadian + S_Italian_Sicilian @ 1.098230
2 British + Lithuanian + Orcadian + S_Italian_Sicilian @ 1.142910
3 Lithuanian + Orcadian + Orcadian + Sicilian @ 1.148125
4 Lithuanian + Orcadian + Orcadian + S_Italian_Sicilian @ 1.157268
5 British_Isles + British_Isles + Lithuanian + S_Italian_Sicilian @ 1.164295
6 British_Isles + Greek + Orcadian + Polish @ 1.171025
7 British_Isles + Lithuanian + Orcadian + Sicilian @ 1.184988
8 British_Isles + Irish + Lithuanian + S_Italian_Sicilian @ 1.205353
9 Ashkenazi + Lithuanian + Orcadian + Orcadian @ 1.211768
10 Irish + Lithuanian + Orcadian + S_Italian_Sicilian @ 1.212182
11 British + British_Isles + Lithuanian + S_Italian_Sicilian @ 1.214782
12 Greek + Orcadian + Orcadian + Polish @ 1.216809
13 British + Greek + Orcadian + Polish @ 1.218620
14 British + Lithuanian + Orcadian + Sicilian @ 1.232746
15 Irish + Lithuanian + Orcadian + Sicilian @ 1.253166
16 British + Irish + Lithuanian + S_Italian_Sicilian @ 1.254188
17 British_Isles + Lithuanian + Orkney + S_Italian_Sicilian @ 1.259488
18 British_Isles + British_Isles + Greek + Polish @ 1.259511
19 British_Isles + British_Isles + Lithuanian + Sicilian @ 1.260756
20 British_Isles + Lithuanian + Orkney + Sicilian @ 1.265599

[MitchellSince1893]

Father ancestry.com
Admix Results (sorted):

# Population Percent
1 Atlantic_Baltic 71.77
2 Southern 13.19
3 West_Asian 12.72
4 South_Asian 1.39
5 Siberian 0.8
6 African 0.13

Single Population Sharing:

# Population (source) Distance
1 Mixed_Germanic (Dodecad) 3.14
2 German (Dodecad) 3.41
3 Hungarians (Behar) 3.51
4 Dutch (Dodecad) 4.2
5 Cornwall (1000Genomes) 4.2
6 CEU30 (1000Genomes) 4.44
7 Kent (1000Genomes) 4.8
8 English (Dodecad) 5.27
9 British (Dodecad) 6.32
10 Ukranians (Yunusbayev) 6.4
11 Argyll (1000Genomes) 6.45
12 Irish (Dodecad) 6.46
13 British_Isles (Dodecad) 6.51
14 Orcadian (HGDP) 6.87
15 French (HGDP) 7.27
16 Mixed_Slav (Dodecad) 7.44
17 Orkney (1000Genomes) 7.56
18 French (Dodecad) 7.67
19 Polish (Dodecad) 8.92
20 Norwegian (Dodecad) 10.16

Mixed Mode Population Sharing:

# Primary Population (source) Secondary Population (source) Distance
1 91.5% British (Dodecad) + 8.5% Iranian (Dodecad) @ 0.46
2 91.7% British (Dodecad) + 8.3% Iranians (Behar) @ 0.49
3 91.6% British (Dodecad) + 8.4% Kurd (Dodecad) @ 0.52
4 93.1% Cornwall (1000Genomes) + 6.9% Tajiks (Yunusbayev) @ 0.56
5 92.8% Kent (1000Genomes) + 7.2% Turkmens (Yunusbayev) @ 0.59
6 91.5% British (Dodecad) + 8.5% Kurds (Yunusbayev) @ 0.63
7 94.8% Mixed_Germanic (Dodecad) + 5.2% Tajiks (Yunusbayev) @ 0.64
8 90% British_Isles (Dodecad) + 10% Turks (Behar) @ 0.66
9 89.5% Orcadian (HGDP) + 10.5% Turks (Behar) @ 0.69
10 92.2% English (Dodecad) + 7.8% Turkmens (Yunusbayev) @ 0.69
11 91.3% British_Isles (Dodecad) + 8.7% Iranian (Dodecad) @ 0.69
12 90.3% British (Dodecad) + 9.7% Turks (Behar) @ 0.69
13 91.5% British_Isles (Dodecad) + 8.5% Iranians (Behar) @ 0.71
14 90.1% Irish (Dodecad) + 9.9% Turks (Behar) @ 0.71
15 89.5% Orcadian (HGDP) + 10.5% Turkish (Dodecad) @ 0.71
16 91.4% British_Isles (Dodecad) + 8.6% Kurd (Dodecad) @ 0.72
17 93.7% Cornwall (1000Genomes) + 6.3% Turkmens (Yunusbayev) @ 0.74
18 95.3% Mixed_Germanic (Dodecad) + 4.7% Turkmens (Yunusbayev) @ 0.75
19 93.4% CEU30 (1000Genomes) + 6.6% Turkmens (Yunusbayev) @ 0.76
20 90% Irish (Dodecad) + 10% Turkish (Dodecad) @ 0.78

Father FTDNA

Admix Results (sorted):

# Population Percent
1 Atlantic_Baltic 71.72
2 Southern 13.18
3 West_Asian 12.79
4 South_Asian 1.38
5 Siberian 0.82
6 African 0.12

Single Population Sharing:

# Population (source) Distance
1 Mixed_Germanic (Dodecad) 3.2
2 Hungarians (Behar) 3.44
3 German (Dodecad) 3.46
4 Dutch (Dodecad) 4.25
5 Cornwall (1000Genomes) 4.27
6 CEU30 (1000Genomes) 4.5
7 Kent (1000Genomes) 4.86
8 English (Dodecad) 5.34
9 British (Dodecad) 6.39
10 Ukranians (Yunusbayev) 6.4
11 Argyll (1000Genomes) 6.5
12 Irish (Dodecad) 6.52
13 British_Isles (Dodecad) 6.57
14 Orcadian (HGDP) 6.93
15 French (HGDP) 7.28
16 Mixed_Slav (Dodecad) 7.45
17 Orkney (1000Genomes) 7.61
18 French (Dodecad) 7.69
19 Polish (Dodecad) 8.95
20 Norwegian (Dodecad) 10.21

Mixed Mode Population Sharing:

# Primary Population (source) Secondary Population (source) Distance
1 91.4% British (Dodecad) + 8.6% Iranian (Dodecad) @ 0.46
2 91.6% British (Dodecad) + 8.4% Iranians (Behar) @ 0.51
3 91.5% British (Dodecad) + 8.5% Kurd (Dodecad) @ 0.53
4 92.9% Cornwall (1000Genomes) + 7.1% Tajiks (Yunusbayev) @ 0.58
5 92.7% Kent (1000Genomes) + 7.3% Turkmens (Yunusbayev) @ 0.6
6 94.7% Mixed_Germanic (Dodecad) + 5.3% Tajiks (Yunusbayev) @ 0.62
7 91.4% British (Dodecad) + 8.6% Kurds (Yunusbayev) @ 0.63
8 89.4% Orcadian (HGDP) + 10.6% Turks (Behar) @ 0.65
9 89.9% British_Isles (Dodecad) + 10.1% Turks (Behar) @ 0.68
10 90% Irish (Dodecad) + 10% Turks (Behar) @ 0.68
11 89.4% Orcadian (HGDP) + 10.6% Turkish (Dodecad) @ 0.69
12 91.2% British_Isles (Dodecad) + 8.8% Iranian (Dodecad) @ 0.69
13 92.1% English (Dodecad) + 7.9% Turkmens (Yunusbayev) @ 0.71
14 90.2% British (Dodecad) + 9.8% Turks (Behar) @ 0.71
15 91.3% British_Isles (Dodecad) + 8.7% Kurd (Dodecad) @ 0.72
16 91.4% British_Isles (Dodecad) + 8.6% Iranians (Behar) @ 0.73
17 95.2% Mixed_Germanic (Dodecad) + 4.8% Turkmens (Yunusbayev) @ 0.74
18 93.3% CEU30 (1000Genomes) + 6.7% Turkmens (Yunusbayev) @ 0.76
19 89.9% Irish (Dodecad) + 10.1% Turkish (Dodecad) @ 0.76
20 93.6% Cornwall (1000Genomes) + 6.4% Turkmens (Yunusbayev) @ 0.77

Father 23andme
Admix Results (sorted):

# Population Percent
1 Atlantic_Baltic 71.88
2 Southern 13.1
3 West_Asian 12.77
4 South_Asian 1.32
5 Siberian 0.82
6 African 0.12

Single Population Sharing:

# Population (source) Distance
1 Mixed_Germanic (Dodecad) 3.06
2 German (Dodecad) 3.3
3 Hungarians (Behar) 3.56
4 Dutch (Dodecad) 4.08
5 Cornwall (1000Genomes) 4.15
6 CEU30 (1000Genomes) 4.35
7 Kent (1000Genomes) 4.72
8 English (Dodecad) 5.21
9 British (Dodecad) 6.24
10 Ukranians (Yunusbayev) 6.27
11 Argyll (1000Genomes) 6.34
12 Irish (Dodecad) 6.37
13 British_Isles (Dodecad) 6.43
14 Orcadian (HGDP) 6.77
15 Mixed_Slav (Dodecad) 7.31
16 French (HGDP) 7.37
17 Orkney (1000Genomes) 7.45
18 French (Dodecad) 7.78
19 Polish (Dodecad) 8.79
20 Norwegian (Dodecad) 10.05

Mixed Mode Population Sharing:

# Primary Population (source) Secondary Population (source) Distance
1 91.6% British (Dodecad) + 8.4% Iranian (Dodecad) @ 0.45
2 91.7% British (Dodecad) + 8.3% Kurd (Dodecad) @ 0.51
3 91.8% British (Dodecad) + 8.2% Iranians (Behar) @ 0.53
4 94.9% Mixed_Germanic (Dodecad) + 5.1% Tajiks (Yunusbayev) @ 0.57
5 93.1% Cornwall (1000Genomes) + 6.9% Tajiks (Yunusbayev) @ 0.59
6 89.6% Orcadian (HGDP) + 10.4% Turks (Behar) @ 0.59
7 92.9% Kent (1000Genomes) + 7.1% Turkmens (Yunusbayev) @ 0.61
8 91.6% British (Dodecad) + 8.4% Kurds (Yunusbayev) @ 0.61
9 90.2% Irish (Dodecad) + 9.8% Turks (Behar) @ 0.62
10 89.6% Orcadian (HGDP) + 10.4% Turkish (Dodecad) @ 0.63
11 91.4% British_Isles (Dodecad) + 8.6% Iranian (Dodecad) @ 0.67
12 93.5% CEU30 (1000Genomes) + 6.5% Turkmens (Yunusbayev) @ 0.7
13 91.5% British_Isles (Dodecad) + 8.5% Kurd (Dodecad) @ 0.7
14 90.1% British_Isles (Dodecad) + 9.9% Turks (Behar) @ 0.7
15 90.2% Irish (Dodecad) + 9.8% Turkish (Dodecad) @ 0.71
16 92.3% Kent (1000Genomes) + 7.7% Tajiks (Yunusbayev) @ 0.73
17 91.6% British_Isles (Dodecad) + 8.4% Iranians (Behar) @ 0.73
18 90.4% British (Dodecad) + 9.6% Turks (Behar) @ 0.74
19 92.3% English (Dodecad) + 7.7% Turkmens (Yunusbayev) @ 0.75
20 95.4% Mixed_Germanic (Dodecad) + 4.6% Turkmens (Yunusbayev) @ 0.75

Me 23andme

Kit M165555

Admix Results (sorted):

# Population Percent
1 Atlantic_Baltic 74.78
2 Southern 12.3
3 West_Asian 11.22
4 South_Asian 1.18
5 Siberian 0.4
6 African 0.12

Single Population Sharing:

# Population (source) Distance
1 Mixed_Germanic (Dodecad) 1.19
2 German (Dodecad) 1.22
3 CEU30 (1000Genomes) 1.32
4 Dutch (Dodecad) 1.56
5 Cornwall (1000Genomes) 1.64
6 Kent (1000Genomes) 1.91
7 English (Dodecad) 2.38
8 British (Dodecad) 3.17
9 Irish (Dodecad) 3.27
10 British_Isles (Dodecad) 3.34
11 Argyll (1000Genomes) 3.45
12 Orcadian (HGDP) 3.68
13 Orkney (1000Genomes) 4.4
14 Ukranians (Yunusbayev) 5.69
15 Mixed_Slav (Dodecad) 6.32
16 Hungarians (Behar) 6.52
17 Polish (Dodecad) 6.61
18 Norwegian (Dodecad) 7.16
19 Swedish (Dodecad) 8.22
20 French (HGDP) 8.8

Mixed Mode Population Sharing:

# Primary Population (source) Secondary Population (source) Distance
1 95.7% British (Dodecad) + 4.3% Iranian (Dodecad) @ 0.28
2 94.9% British (Dodecad) + 5.1% Kumyks (Yunusbayev) @ 0.31
3 95% British (Dodecad) + 5% Adygei (HGDP) @ 0.32
4 85.7% Orcadian (HGDP) + 14.3% Romanians (Behar) @ 0.32
5 94.7% British_Isles (Dodecad) + 5.3% Kumyks (Yunusbayev) @ 0.33
6 97.9% CEU30 (1000Genomes) + 2.1% Tajiks (Yunusbayev) @ 0.33
7 95.5% British_Isles (Dodecad) + 4.5% Iranian (Dodecad) @ 0.33
8 95.9% British (Dodecad) + 4.1% Abhkasians (Yunusbayev) @ 0.34
9 95.8% British (Dodecad) + 4.2% Kurd (Dodecad) @ 0.35
10 95.1% British (Dodecad) + 4.9% Balkars (Yunusbayev) @ 0.35
11 94.8% British_Isles (Dodecad) + 5.2% Balkars (Yunusbayev) @ 0.36
12 95.8% British (Dodecad) + 4.2% Iranians (Behar) @ 0.36
13 95.2% British (Dodecad) + 4.8% North_Ossetians (Yunusbayev) @ 0.36
14 95.8% British (Dodecad) + 4.2% Kurds (Yunusbayev) @ 0.37
15 96% British (Dodecad) + 4% Georgians (Behar) @ 0.37
16 95.6% British_Isles (Dodecad) + 4.4% Kurd (Dodecad) @ 0.37
17 95.1% Orcadian (HGDP) + 4.9% Syrians (Behar) @ 0.38
18 96.2% Argyll (1000Genomes) + 3.8% Saudis (Behar) @ 0.39
19 94.8% British_Isles (Dodecad) + 5.2% Adygei (HGDP) @ 0.39
20 94.9% British_Isles (Dodecad) + 5.1% North_Ossetians (Yunusbayev) @ 0.39

Mom 23andme
Admix Results (sorted):

# Population Percent
1 Atlantic_Baltic 74.35
2 Southern 12.69
3 West_Asian 9.89
4 South_Asian 3.03
5 African 0.04

Single Population Sharing:

# Population (source) Distance
1 Cornwall (1000Genomes) 2.41
2 CEU30 (1000Genomes) 2.58
3 Mixed_Germanic (Dodecad) 2.68
4 Kent (1000Genomes) 2.88
5 English (Dodecad) 3.03
6 German (Dodecad) 3.33
7 Dutch (Dodecad) 3.38
8 British (Dodecad) 3.62
9 British_Isles (Dodecad) 3.73
10 Irish (Dodecad) 4.03
11 Orcadian (HGDP) 4.29
12 Argyll (1000Genomes) 4.53
13 Orkney (1000Genomes) 5.09
14 Ukranians (Yunusbayev) 7.17
15 Hungarians (Behar) 7.28
16 Mixed_Slav (Dodecad) 7.67
17 Polish (Dodecad) 7.72
18 Norwegian (Dodecad) 7.8
19 French (HGDP) 8.64
20 French (Dodecad) 8.74

Mixed Mode Population Sharing:

# Primary Population (source) Secondary Population (source) Distance
1 97% English (Dodecad) + 3% North_Kannadi (Behar) @ 0.14
2 97% English (Dodecad) + 3% Hakkipikki (Metspalu) @ 0.17
3 96.9% English (Dodecad) + 3.1% Chamar (Metspalu) @ 0.19
4 96.9% English (Dodecad) + 3.1% SAKILLI (Behar) @ 0.24
5 96.9% English (Dodecad) + 3.1% Chenchus (Metspalu) @ 0.32
6 97% English (Dodecad) + 3% MALAYAN (Behar) @ 0.32
7 96.8% English (Dodecad) + 3.2% Kol (Metspalu) @ 0.33
8 97.1% English (Dodecad) + 2.9% Pulliyar (Metspalu) @ 0.33
9 96.9% English (Dodecad) + 3.1% Gond (Metspalu) @ 0.39
10 96.8% English (Dodecad) + 3.2% Uttar_Pradesh_Scheduled_Caste (Metspalu) @ 0.4
11 96.8% English (Dodecad) + 3.2% Dusadh (Metspalu) @ 0.4
12 96.9% English (Dodecad) + 3.1% Piramalai_Kallars (Metspalu) @ 0.41
13 97.2% Kent (1000Genomes) + 2.8% Pulliyar (Metspalu) @ 0.42
14 97% English (Dodecad) + 3% Nihali (Metspalu) @ 0.44
15 96.9% English (Dodecad) + 3.1% Kurumba (Metspalu) @ 0.47
16 97.2% Kent (1000Genomes) + 2.8% MALAYAN (Behar) @ 0.47
17 96.8% English (Dodecad) + 3.2% Tamil_Nadu_Scheduled_Caste (Metspalu) @ 0.49
18 96.6% English (Dodecad) + 3.4% Kanjars (Metspalu) @ 0.52
19 96.6% English (Dodecad) + 3.4% Dharkars (Metspalu) @ 0.56
20 96.6% English (Dodecad) + 3.4% Muslim (Metspalu) @ 0.58

[sweuro]

South_Asian 0.6%
West_Asian 8.0%
Siberian -
African -
Southern 29.0%
Atlantic_Baltic 61.9%
East_Asian 0.4%

[Anglecynn]

South_Asian 0.47%
West_Asian 11.34%
Siberian -
African 0.20%
Southern 14.52%
Atlantic_Baltic 72.82%
East_Asian 0.65%

[Anglecynn]

Paternal Grandmother:
South_Asian 0.52%
West_Asian 10.72%
Siberian -
African -
Southern 14.01%
Atlantic_Baltic 73.77%
East_Asian 0.98%

Mother:
South_Asian 1.17%
West_Asian 14.58%
Siberian -
African -
Southern 14.51%
Atlantic_Baltic 69.62%
East_Asian 0.12%

Aunt:
South_Asian 1.60%
West_Asian 11.82%
Siberian 0.31%
African -
Southern 13.68%
Atlantic_Baltic 72.54%
East_Asian -

Father:
South_Asian 0.96%
West_Asian 10.17%
Siberian -
African -
Southern 13.77%
Atlantic_Baltic 74.63%
East_Asian 0.47%

Paternal Grandfather:
South_Asian 0.26%
West_Asian 11.08%
Siberian 0.99%
African -
Southern 12.61%
Atlantic_Baltic 75.07%
East_Asian -

[Kurd]

My data:

http://i1377.photobucket.com/albums/ah42/dil88888/Dodecad%207b%20Admix_zpswj2orr0v.jpg (http://s1377.photobucket.com/user/dil88888/media/Dodecad%207b%20Admix_zpswj2orr0v.jpg.html)

Admix Results (sorted):
# Population Percent
1 West_Asian 49.15
2 Southern 25.09
3 South_Asian 11.49
4 Atlantic_Baltic 10.54
5 Siberian 1.63
6 East_Asian 1.48


Finished reading population data. 223 populations found.
7 components mode.

--------------------------------

Least-squares method.

Using 1 population approximation:
1 Iranians @ 6.108822
2 Iranian @ 6.141660
3 Kurd @ 8.752208
4 Kurds @ 9.464022
5 Turkmens @ 12.945082
6 Uzbekistan_Jews @ 13.435940
7 Turks @ 15.396648
8 Armenians_15 @ 15.404054
9 Armenians @ 16.099955
10 Turkish @ 16.278605
11 Azerbaijan_Jews @ 16.479912
12 Armenian @ 16.701637
13 Abhkasians @ 16.887365
14 Georgians @ 17.605608
15 Kumyks @ 17.816484
16 Assyrian @ 17.895311
17 Georgia_Jews @ 17.982361
18 Balkars @ 18.160213
19 North_Ossetians @ 18.202219
20 Adygei @ 18.616194

Using 2 populations approximation:
1 50% Brahui +50% Cypriots @ 2.690752


Using 3 populations approximation:
1 50% Assyrian +25% Pathan +25% Turkmens @ 1.534793


Using 4 populations approximation:
1 Abhkasians + Bnei_Menashe_Jews + Iranian_Jews + Kumyks @ 0.797772
2 Azerbaijan_Jews + Kurd + Sindhi + Turkish @ 0.919808
3 Georgia_Jews + Kurd + Sindhi + Turks @ 0.923322
4 Azerbaijan_Jews + Kurd + Sindhi + Turks @ 0.934143
5 Burusho + Iranians + Uzbekistan_Jews + Uzbekistan_Jews @ 0.936596
6 Burusho + Kurds + Uzbekistan_Jews + Uzbekistan_Jews @ 0.945803
7 Armenian + Burusho + Iranians + Uzbekistan_Jews @ 0.952330
8 Georgia_Jews + Kurd + Sindhi + Turkish @ 1.001530
9 Assyrian + Kurd + Sindhi + Turks @ 1.002892
10 Burusho + Kurd + Uzbekistan_Jews + Uzbekistan_Jews @ 1.015708

[Varun R]

I also get zero-distance options with this calculator, but not as many as Jessie.

South_Asian 51.74%
West_Asian 36.11%
Siberian 1.32%
African -
Southern 1.05%
Atlantic_Baltic 8.91%
East_Asian 0.87%

Least-squares method.

Using 1 population approximation:
1 Kshatriya @ 3.084333
2 Meena @ 3.126503
3 Brahmins_from_Uttar_Pradesh @ 5.237469
4 Brahmins_from_Tamil_Nadu @ 5.776241
5 Indian @ 6.577243
6 Iyengar @ 6.649311
7 Iyer @ 6.956335
8 Cochin_Jews @ 9.018291
9 Lambadi @ 11.701207
10 Muslim @ 11.837384
11 GIH30 @ 12.382917
12 Bengali @ 12.469771
13 INS30 @ 12.595812
14 Meghawal @ 12.680249
15 Tharus @ 12.821611
16 Dharkars @ 13.102820
17 Kurmi @ 13.182428
18 Kanjars @ 14.215796
19 Velamas @ 16.888435
20 Sindhi @ 17.450455

Using 2 populations approximation:
1 50% Jatt +50% Tamil_Nadu_Scheduled_Caste @ 1.514837


Using 3 populations approximation:
1 50% Dusadh +25% Jatt +25% Pathan @ 0.746161


Using 4 populations approximation:
1 Chamar + Jatt + Kurmi + Pathan @ 0.000000
2 Dharkars + Jatt + Pathan + SAKILLI @ 0.000000
3 Jatt + Pathan + SAKILLI + Tharus @ 0.000000
4 Brahmins_from_Uttar_Pradesh + Dharkars + Kurmi + Pathan @ 0.254174
5 Dusadh + Jatt + Pathan + Uttar_Pradesh_Scheduled_Caste @ 0.282951
6 Jatt + Muslim + Pathan + SAKILLI @ 0.300659
7 Brahmins_from_Uttar_Pradesh + Kanjars + Kurmi + Pathan @ 0.400268
8 Iyer + Jatt + North_Kannadi + Pathan @ 0.406372
9 Brahmins_from_Uttar_Pradesh + Kurmi + Pathan + Tharus @ 0.485447
10 Brahmins_from_Uttar_Pradesh + Dusadh + Iyengar + Pathan @ 0.494046
11 Brahmins_from_Uttar_Pradesh + Hakkipikki + Jatt + Sindhi @ 0.497657
12 Brahmins_from_Uttar_Pradesh + Pathan + Tharus + Tharus @ 0.501334
13 Jatt + Pathan + Uttar_Pradesh_Scheduled_Caste + Uttar_Pradesh_Scheduled_Caste @ 0.505324
14 Brahmins_from_Uttar_Pradesh + Kurmi + Muslim + Pathan @ 0.514703
15 Brahmins_from_Uttar_Pradesh + INS30 + Kanjars + Pathan @ 0.528799
16 Jatt + Kanjars + Pathan + SAKILLI @ 0.595227
17 Brahmins_from_Uttar_Pradesh + Kanjars + Lambadi + Pathan @ 0.596898
18 Chamar + INS30 + Jatt + Pathan @ 0.600917
19 Brahmins_from_Tamil_Nadu + Brahmins_from_Uttar_Pradesh + Dusadh + Pathan @ 0.613913
20 Brahmins_from_Uttar_Pradesh + Kshatriya + Pathan + Piramalai_Kallars @ 0.627862

[Silesian]

That is odd, Jessie. I too receive rather low gd's for this run............

What the frig I'm also getting low gd's in this run Dodecad K7b!!. I'm also related to Yamnaya 5000 year old R1b-Z2103+ they be my R1b hunter gatherer kin from 7500+/- years ago, my mtdna is U5b2a2 Poland/Lithuania also 10K+/- :)
Very low gd @ 4 ancestors!
# Population Percent
1 Atlantic_Baltic 75.63
2 West_Asian 13.32
3 Southern 8.23
4 Siberian 1.68
5 South_Asian 1.14

Using 1 population approximation:
1 Ukranians @ 1.721381
2 Mixed_Slav @ 2.030648
3 Polish @ 3.661185
4 Dutch @ 3.806851
5 Argyll @ 4.087516



Using 4 populations approximation:
1 Lithuanian + Lithuanian + Romanians + Swedish @ 0.450739
2 Bulgarians + Lithuanian + Lithuanian + Swedish @ 0.488208
3 Lithuanian + Lithuanian + Norwegian + Romanians @ 0.555385
4 Mixed_Slav + Orcadian + Ukranians + Ukranians @ 0.574491
5 Irish + Mixed_Slav + Ukranians + Ukranians @ 0.587858

My 1 population approximation looks like R1b expansion from Yamnaya to Western Europe
from Yamnaya >1 Ukranians @ 1.721381>2 Mixed_Slav @ 2.030648>3 Polish @ 3.661185>4 Dutch @ 3.806851>5 Argyl @ 4.087516

[randwulf]

# Population Percent
1 Atlantic_Baltic 68.86
2 Southern 18.86
3 West_Asian 10.34
4 Siberian 1.16
5 South_Asian 0.66
6 African 0.13

Using 1 population approximation:
1 French @ 1.889869
2 French @ 2.135813
3 Hungarians @ 5.927735
4 Mixed_Germanic @ 8.240623
5 Cornwall @ 8.688669
6 German @ 9.095123
7 Kent @ 9.467692
8 Cataluna @ 9.634157
9 CEU30 @ 9.730251
10 English @ 9.738262
11 Dutch @ 10.377683
12 British @ 11.268208
13 Valencia @ 11.325397
14 Spaniards @ 11.469537
15 British_Isles @ 11.505244
16 Cantabria @ 11.830611
17 Irish @ 11.835626
18 Orcadian @ 12.365472
19 Argyll @ 12.478243
20 Orkney @ 13.090164

Using 2 populations approximation:
1 50% Castilla_La_Mancha +50% Ukranians @ 0.448853


Using 3 populations approximation:
1 50% Cataluna +25% Hungarians +25% Mixed_Slav @ 0.000000


Using 4 populations approximation:
1 Aragon + British + North_Italian + Russian @ 0.000000
2 Aragon + British_Isles + North_Italian + Russian @ 0.000000
3 Aragon + Irish + North_Italian + Russian @ 0.000000
4 Aragon + North_Italian + Orcadian + Russian @ 0.000000
5 Cataluna + Cataluna + Hungarians + Mixed_Slav @ 0.000000
6 Cantabria + Cornwall + North_Italian + Russian @ 0.251801
7 English + Pais_Vasco + Russian + TSI30 @ 0.263652
8 Cornwall + French_Basque + Russian + TSI30 @ 0.263802
9 Baleares + Baleares + CEU30 + Russian @ 0.270971
10 Aragon + British + North_Italian + Russian_B @ 0.274725
11 Aragon + British_Isles + North_Italian + Russian_B @ 0.274725
12 Aragon + Irish + North_Italian + Russian_B @ 0.274725
13 Aragon + North_Italian + Orcadian + Russian_B @ 0.274725
14 Cantabria + English + North_Italian + Russian @ 0.288602
15 Lithuanians + Romanians + Sardinian + Swedish @ 0.292084
16 Cantabria + CEU30 + North_Italian + Russian @ 0.311279
17 Cornwall + Pais_Vasco + Russian + TSI30 @ 0.311305
18 English + French_Basque + Russian + TSI30 @ 0.313602
19 Cantabria + Kent + North_Italian + Russian @ 0.313727
20 CEU30 + French_Basque + Russian + TSI30 @ 0.336280

[vettor]

reading dienekes , eurogenes and other forums it seems K8 is a dead horse for linking with regards to haak paper , some have mentioned to use dodecad k7b.....below are mine and family

Population
South_Asian 0.44%
West_Asian 17.43%
Siberian -
African 0.20%
Southern 26.21%
Atlantic_Baltic 55.72%
East_Asian -


son below
Population
South_Asian 0.06%
West_Asian 16.86%
Siberian -
African 0.10%
Southern 23.42%
Atlantic_Baltic 59.24%
East_Asian 0.32%


and my wife
Population
South_Asian -
West_Asian 16.30%
Siberian -
African 0.26%
Southern 24.04%
Atlantic_Baltic 58.94%
East_Asian 0.47%


note: the small east_asian for wife and son match the other testers for them , that is ftdna and 23andme


and my ftdna and 23andme match in every part except south_asia where I have 0.55% in the other


my split runs like this below, with the first 9 all being possible. The O-Italians ( other italians ) which represent italians in france, switzerland, austria, slovenia, croatia is also a possiblitiy. TS130 = tuscan also

as for the 4 pop it was all zero....but I never worried about the 4 pop when I can have a choice "which number is my best fit system"


maybe a gifted person can plot these!


Using 1 population approximation:
1 N_Italian @ 2.622177
2 North_Italian @ 5.212535
3 Bulgarians @ 5.698929
4 Romanians @ 5.854271
5 Bulgarian @ 6.665785
6 O_Italian @ 7.458990
7 TSI30 @ 9.477503
8 Tuscan @ 9.569585
9 Baleares @ 10.537197
10 Extremadura @ 11.658178
11 Andalucia @ 11.959792
12 Murcia @ 12.010961
13 Galicia @ 12.170531
14 Spaniards @ 12.471816
15 Portuguese @ 12.475193
16 Castilla_La_Mancha @ 13.961695
17 Castilla_Y_Leon @ 13.994128
18 Cataluna @ 14.196594
19 Canarias @ 14.244229
20 Spanish @ 14.249528

Using 2 populations approximation:
1 50% North_Italian +50% Romanians @ 0.696227


Using 3 populations approximation:
1 50% French +25% Orkney +25% Samaritians @ 0.253235

Orkney matches my 50% of british matches I get..........and I have no clue what Samaritians means here in this program

.....................
my son came out with all numbers in pop 4 ...............
Using 2 populations approximation:
1 50% C_Italian +50% German @ 0.550077


Using 3 populations approximation:
1 50% C_Italian +25% CEU30 +25% German @ 0.321740


Using 4 populations approximation:
1 Bulgarian + N_Italian + Orcadian + TSI30 @ 0.274975
2 Bulgarians + English + French + S_Italian_Sicilian @ 0.275000
3 Bulgarians + English + N_Italian + O_Italian @ 0.275000
4 British + German + S_Italian_Sicilian + Tuscan @ 0.296765
5 British_Isles + German + S_Italian_Sicilian + Tuscan @ 0.296765
6 Dutch + Kent + S_Italian_Sicilian + Tuscan @ 0.296765
7 German + Irish + S_Italian_Sicilian + Tuscan @ 0.296765
8 German + Orcadian + S_Italian_Sicilian + Tuscan @ 0.296765
9 Bulgarians + N_Italian + Orcadian + Tuscan @ 0.299975
10 British_Isles + Bulgarians + N_Italian + Tuscan @ 0.300000
11 Argyll + Cypriots + French + French @ 0.321740
12 Argyll + Cypriots + French + French @ 0.321740
13 Argyll + German + S_Italian_Sicilian + TSI30 @ 0.321740
14 British + C_Italian + German + Greek @ 0.321740
15 British_Isles + C_Italian + German + Greek @ 0.321740
16 C_Italian + C_Italian + CEU30 + German @ 0.321740
17 C_Italian + C_Italian + Dutch + Kent @ 0.321740
18 C_Italian + Greek + Kent + Kent @ 0.321740
19 Cypriots + French + French + Orcadian @ 0.321740
20 Cypriots + French + French + Orkney @ 0.321740

[Wulf Warrior]

My Oracle results,

Using 1 population approximation:
1 Mixed_Germanic @ 1.898567
2 German @ 2.377824
3 Cornwall @ 2.979753
4 CEU30 @ 3.428417
5 Dutch @ 3.547239
6 Kent @ 3.634569
7 English @ 4.164146
8 Hungarians @ 5.043178
9 British @ 5.451066
10 Irish @ 5.673334
11 British_Isles @ 5.679526
12 Argyll @ 5.854284
13 Orcadian @ 6.176802
14 Ukranians @ 6.753726
15 Orkney @ 6.901247
16 French @ 7.429059
17 Mixed_Slav @ 7.593461
18 French @ 7.775058
19 Polish @ 8.845141
20 Norwegian @ 9.735869

Using 2 populations approximation:
1 50% British +50% Hungarians @ 0.265358


Using 3 populations approximation:
1 50% French +25% Polish +25% Ukranians @ 0.000000


Using 4 populations approximation:
1 Argyll + Baleares + Polish + Ukranians @ 0.000000
2 Argyll + Belorussian + N_Italian + Orkney @ 0.000000
3 Argyll + N_Italian + Norwegian + Polish @ 0.000000
4 Baleares + Hungarians + Lithuanian + Polish @ 0.000000
5 Belorussian + British + North_Italian + Polish @ 0.000000
6 Belorussian + British_Isles + North_Italian + Polish @ 0.000000
7 Belorussian + Cornwall + N_Italian + Swedish @ 0.000000
8 British + French + Hungarians + Mixed_Slav @ 0.000000
9 British_Isles + French + Hungarians + Mixed_Slav @ 0.000000
10 Cornwall + French + Hungarians + Polish @ 0.000000
11 Cornwall + French + Hungarians + Polish @ 0.000000
12 Dutch + Polish + Spaniards + Ukranians @ 0.000000
13 English + N_Italian + Polish + Swedish @ 0.000000
14 French + Lithuanian + Lithuanian + North_Italian @ 0.000000
15 French + French + Polish + Ukranians @ 0.000000
16 French + Lithuanian + Lithuanian + North_Italian @ 0.000000
17 Kent + N_Italian + Polish + Swedish @ 0.000000
18 British + English + Hungarians + Hungarians @ 0.255508
19 British_Isles + English + Hungarians + Hungarians @ 0.255508
20 French + Hungarians + Mixed_Germanic + Polish @ 0.256283

[BalkanKiwi]

My Oracle results,

Using 1 population approximation:
1 Mixed_Germanic @ 1.898567
2 German @ 2.377824
3 Cornwall @ 2.979753
4 CEU30 @ 3.428417
5 Dutch @ 3.547239
6 Kent @ 3.634569
7 English @ 4.164146
8 Hungarians @ 5.043178
9 British @ 5.451066
10 Irish @ 5.673334
11 British_Isles @ 5.679526
12 Argyll @ 5.854284
13 Orcadian @ 6.176802
14 Ukranians @ 6.753726
15 Orkney @ 6.901247
16 French @ 7.429059
17 Mixed_Slav @ 7.593461
18 French @ 7.775058
19 Polish @ 8.845141
20 Norwegian @ 9.735869

Using 2 populations approximation:
1 50% British +50% Hungarians @ 0.265358


Using 3 populations approximation:
1 50% French +25% Polish +25% Ukranians @ 0.000000


Using 4 populations approximation:
1 Argyll + Baleares + Polish + Ukranians @ 0.000000
2 Argyll + Belorussian + N_Italian + Orkney @ 0.000000
3 Argyll + N_Italian + Norwegian + Polish @ 0.000000
4 Baleares + Hungarians + Lithuanian + Polish @ 0.000000
5 Belorussian + British + North_Italian + Polish @ 0.000000
6 Belorussian + British_Isles + North_Italian + Polish @ 0.000000
7 Belorussian + Cornwall + N_Italian + Swedish @ 0.000000
8 British + French + Hungarians + Mixed_Slav @ 0.000000
9 British_Isles + French + Hungarians + Mixed_Slav @ 0.000000
10 Cornwall + French + Hungarians + Polish @ 0.000000
11 Cornwall + French + Hungarians + Polish @ 0.000000
12 Dutch + Polish + Spaniards + Ukranians @ 0.000000
13 English + N_Italian + Polish + Swedish @ 0.000000
14 French + Lithuanian + Lithuanian + North_Italian @ 0.000000
15 French + French + Polish + Ukranians @ 0.000000
16 French + Lithuanian + Lithuanian + North_Italian @ 0.000000
17 Kent + N_Italian + Polish + Swedish @ 0.000000
18 British + English + Hungarians + Hungarians @ 0.255508
19 British_Isles + English + Hungarians + Hungarians @ 0.255508
20 French + Hungarians + Mixed_Germanic + Polish @ 0.256283

How do you find its ability to match English/Western European with Polish/Eastern Euro compared to other calculators?

[vettor]

What the frig I'm also getting low gd's in this run Dodecad K7b!!. I'm also related to Yamnaya 5000 year old R1b-Z2103+ they be my R1b hunter gatherer kin from 7500+/- years ago, my mtdna is U5b2a2 Poland/Lithuania also 10K+/- :)
Very low gd @ 4 ancestors!
# Population Percent
1 Atlantic_Baltic 75.63
2 West_Asian 13.32
3 Southern 8.23
4 Siberian 1.68
5 South_Asian 1.14

Using 1 population approximation:
1 Ukranians @ 1.721381
2 Mixed_Slav @ 2.030648
3 Polish @ 3.661185
4 Dutch @ 3.806851
5 Argyll @ 4.087516



Using 4 populations approximation:
1 Lithuanian + Lithuanian + Romanians + Swedish @ 0.450739
2 Bulgarians + Lithuanian + Lithuanian + Swedish @ 0.488208
3 Lithuanian + Lithuanian + Norwegian + Romanians @ 0.555385
4 Mixed_Slav + Orcadian + Ukranians + Ukranians @ 0.574491
5 Irish + Mixed_Slav + Ukranians + Ukranians @ 0.587858

My 1 population approximation looks like R1b expansion from Yamnaya to Western Europe
from Yamnaya >1 Ukranians @ 1.721381>2 Mixed_Slav @ 2.030648>3 Polish @ 3.661185>4 Dutch @ 3.806851>5 Argyl @ 4.087516

quick, call wolgang Haak to test your isotopic structure since you look like R1b Yamnya...........your the proof B)

[Ignis90]

I don't see any reason to prefer an old (3 years +) ADMIXTURE run over a 1 month old run (post Lazaridis et al. and ANE discovery). Still interesting nonetheless.

[DMXX]

I don't understand the premise of this thread; who has been claiming K8 is any more informative than other runs? How does all of this tie in with Haak et al.?

IIRC, the Reich et al. (2009) paper on the Indian Subcontinent found increased K=n values were more informative but began tailing off after around 16. Zack at the Harappa Project (again IIRC) found something similar. Don't have institutional access on this laptop and can't remember which month Zack posted the comparisons.

Given this, I fail to see why a calculator with a sub-par number of K's is being used for anything.

[sweuro]

I don't understand the premise of this thread; who has been claiming K8 is any more informative than other runs? How does all of this tie in with Haak et al.?

I can see something related to Haak et al. For example in this run, Lithuanians do have 11% West_Asian, but only 2% Southern. This is in agreement with the "other source of near-east being caucasus-like" , aka the Yamnaya ancestry, instead of the usual European-Farmers who score 40-50% of the Southern component. For instance, other northern-euros like the British, do have a bit more of the Southern (11%) than West-Asian (9%) indicating they recevied both ways, Yamnaya and EEF ancestry.

[J Man]

I don't understand the premise of this thread; who has been claiming K8 is any more informative than other runs? How does all of this tie in with Haak et al.?

IIRC, the Reich et al. (2009) paper on the Indian Subcontinent found increased K=n values were more informative but began tailing off after around 16. Zack at the Harappa Project (again IIRC) found something similar. Don't have institutional access on this laptop and can't remember which month Zack posted the comparisons.

Given this, I fail to see why a calculator with a sub-par number of K's is being used for anything.

The Eurogenes K8 test probably is the best one out there so far because it is based on actual real ancient components such as WHG and ANE. Of course it will be improved though once we get ancient DNA from Anatolia and the Near East.

[vettor]

I don't understand the premise of this thread; who has been claiming K8 is any more informative than other runs? How does all of this tie in with Haak et al.?

IIRC, the Reich et al. (2009) paper on the Indian Subcontinent found increased K=n values were more informative but began tailing off after around 16. Zack at the Harappa Project (again IIRC) found something similar. Don't have institutional access on this laptop and can't remember which month Zack posted the comparisons.

Given this, I fail to see why a calculator with a sub-par number of K's is being used for anything.

unsure if this is relevant, butI I think as per davidski comment in eurogenes


All Southern Europeans carry WHG, and even many Near Easterners and Central Asians do.

But the WHG that you see in the K8 results is soaked up, in some cases completely, by the EEF component in Haak et al.

The reason this happens is because the K8 doesn't have an EEF component, but rather an ENF component, which lacks WHG. However, this is very difficult to model, and we probably won't see it in scientific literature until someone sequences the genome of an early Neolithic farmer from the Near East who has 0% WHG.

February 14, 2015 at 5:45 PM




he was afraid/concerned with the haak paper page 3 sentence which was

the Yamnaya steppe herders of this time were descended not only from the preceding eastern European hunter-gatherers, but from a
population of Near Eastern ancestry.

with the key word "descended"


IMO, there was something missing from k8 between the yamnya mix of people/halpogroups and its movement into central Europe

[Sapporo]

Admix Results (sorted):

# Population Percent
1 West_Asian 43.26
2 South_Asian 38.28
3 Atlantic_Baltic 14.09
4 Siberian 2.34
5 Southern 1.97
6 African 0.06

Single Population Sharing:

# Population (source) Distance
1 Jatt (Dodecad) 3.58
2 Pathan (HGDP) 7.96
3 Sindhi (HGDP) 8.51
4 Burusho (HGDP) 9.4
5 Brahmins_from_Uttar_Pradesh (Metspalu) 14.01
6 Kshatriya (Metspalu) 17
7 Bnei_Menashe_Jews (Behar) 18.54
8 Cochin_Jews (Behar) 18.94
9 Brahmins_from_Tamil_Nadu (Metspalu) 19.25
10 Indian (Dodecad) 19.8
11 Iyengar (Dodecad) 20.39
12 Iyer (Dodecad) 20.91
13 Balochi (HGDP) 23.42
14 Brahui (HGDP) 24.46
15 GIH30 (Dodecad) 25.19
16 Muslim (Metspalu) 25.4
17 INS30 (SGVP) 25.98
18 Tajiks (Yunusbayev) 26.13
19 Tharus (Metspalu) 26.4
20 Dharkars (Metspalu) 26.61

Mixed Mode Population Sharing:

# Primary Population (source) Secondary Population (source) Distance
1 63.9% Pathan (HGDP) + 36.1% Brahmins_from_Uttar_Pradesh (Metspalu) @ 0.91
2 85.8% Jatt (Dodecad) + 14.2% Bnei_Menashe_Jews (Behar) @ 1.86
3 68.8% Pathan (HGDP) + 31.2% Kshatriya (Metspalu) @ 2.19
4 74.6% Jatt (Dodecad) + 25.4% Sindhi (HGDP) @ 2.25
5 90.5% Sindhi (HGDP) + 9.5% FIN30 (1000Genomes) @ 2.37
6 90.6% Sindhi (HGDP) + 9.4% Finnish (Dodecad) @ 2.38
7 88.3% Jatt (Dodecad) + 11.7% Cochin_Jews (Behar) @ 2.58
8 78.5% Pathan (HGDP) + 21.5% Kanjars (Metspalu) @ 2.65
9 77.1% Pathan (HGDP) + 22.9% Muslim (Metspalu) @ 2.65
10 89.8% Sindhi (HGDP) + 10.2% Russian (HGDP) @ 2.69
11 69.5% Kshatriya (Metspalu) + 30.5% Lezgins (Behar) @ 2.79
12 78% Pathan (HGDP) + 22% Dharkars (Metspalu) @ 2.82
13 87.7% Pathan (HGDP) + 12.3% MALAYAN (Behar) @ 2.86
14 97.1% Jatt (Dodecad) + 2.9% Saudis (Behar) @ 2.88
15 84.9% Pathan (HGDP) + 15.1% Chamar (Metspalu) @ 2.88
16 97.1% Jatt (Dodecad) + 2.9% Yemen_Jews (Behar) @ 2.88
17 82.8% Pathan (HGDP) + 17.2% Kol (Metspalu) @ 2.9
18 91% Sindhi (HGDP) + 9% Lithuanians (Behar) @ 2.92
19 85.9% Pathan (HGDP) + 14.1% North_Kannadi (Behar) @ 2.94
20 90.6% Sindhi (HGDP) + 9.4% Lithuanian (Dodecad) @ 2.95

[Salkin]

Mine:

Admix Results (sorted):

# Population Percent
1 Atlantic_Baltic 78.24
2 Southern 10.21
3 West_Asian 8.87
4 South_Asian 1.93


Finished reading population data. 223 populations found.
7 components mode.

--------------------------------

Least-squares method.

Using 1 population approximation:
1 Orcadian @ 1.503206
2 Orkney @ 1.594583
3 Irish @ 1.938345
4 British_Isles @ 1.988384
5 British @ 2.109282
6 Argyll @ 2.307661
7 Norwegian @ 3.606255
8 English @ 3.751871
9 CEU30 @ 3.821961
10 Kent @ 3.952892
11 Dutch @ 4.372849
12 Cornwall @ 4.574340
13 Swedish @ 4.878574
14 Polish @ 5.027469
15 German @ 5.269440
16 Mixed_Germanic @ 5.524284
17 Belorussian @ 6.500319
18 Mixed_Slav @ 6.893234
19 Ukranians @ 7.306576
20 Russian_B @ 9.047541

Using 2 populations approximation:
1 50% Orcadian +50% Orkney @ 1.478899


Using 3 populations approximation:
1 50% British +25% British_Isles +25% Swedish @ 1.213076


Using 4 populations approximation:
1 British + British_Isles + British_Isles + Swedish @ 1.200214
2 British + British + British_Isles + Swedish @ 1.213076
3 British_Isles + British_Isles + British_Isles + Swedish @ 1.223879
4 British + British_Isles + Orcadian + Swedish @ 1.224925
5 British + British + Orcadian + Swedish @ 1.225049
6 British + British + British + Swedish @ 1.228861
7 British_Isles + British_Isles + Orcadian + Swedish @ 1.259884
8 British_Isles + Norwegian + Orcadian + Orcadian @ 1.265241
9 British + Norwegian + Orcadian + Orcadian @ 1.273005
10 British_Isles + British_Isles + Norwegian + Orcadian @ 1.273802
11 British + British_Isles + Norwegian + Orcadian @ 1.290037
12 British_Isles + British_Isles + Irish + Swedish @ 1.290981
13 British + British_Isles + Irish + Swedish @ 1.294696
14 British + British + Irish + Swedish @ 1.304714
15 British + British + Norwegian + Orcadian @ 1.308938
16 Norwegian + Orcadian + Orcadian + Orcadian @ 1.312757
17 British + Orcadian + Orcadian + Swedish @ 1.328913
18 Argyll + British_Isles + British_Isles + Norwegian @ 1.331457
19 British_Isles + British_Isles + British_Isles + Norwegian @ 1.332441
20 British_Isles + Irish + Norwegian + Orcadian @ 1.335969

My mother's - she gets some zeroes:

Admix Results (sorted):

# Population Percent
1 Atlantic_Baltic 77.35
2 Southern 10.75
3 West_Asian 9.51
4 Siberian 1.59


Finished reading population data. 223 populations found.
7 components mode.

--------------------------------

Least-squares method.

Using 1 population approximation:
1 Irish @ 1.677277
2 British @ 1.856568
3 Orcadian @ 1.858666
4 Argyll @ 1.942245
5 Orkney @ 1.960497
6 British_Isles @ 2.024271
7 CEU30 @ 2.936200
8 Kent @ 2.938064
9 English @ 2.950818
10 Dutch @ 3.365587
11 Cornwall @ 3.627491
12 German @ 3.934596
13 Norwegian @ 4.199999
14 Mixed_Germanic @ 4.412161
15 Polish @ 5.074643
16 Swedish @ 5.595928
17 Mixed_Slav @ 6.065968
18 Ukranians @ 6.356528
19 Belorussian @ 6.749728
20 Russian_B @ 8.716027

Using 2 populations approximation:
1 50% Mixed_Germanic +50% Norwegian @ 0.962122


Using 3 populations approximation:
1 50% Belorussian +25% Mixed_Slav +25% Pais_Vasco @ 0.484370


Using 4 populations approximation:
1 Argyll + Cantabria + Lithuanians + Russian @ 0.000000
2 Cantabria + Irish + Lithuanians + Russian @ 0.000000
3 Cantabria + Lithuanians + Mixed_Slav + Swedish @ 0.000000
4 Cantabria + Lithuanians + Orcadian + Russian @ 0.000000
5 Cantabria + Lithuanians + Orkney + Russian @ 0.000000
6 Belorussian + Cataluna + Lithuanian + Norwegian @ 0.256850
7 Lithuanians + Orkney + Russian + Valencia @ 0.262994
8 Belorussian + Lithuanian + Spanish + Swedish @ 0.267611
9 Belorussian + Lithuanian + Swedish + Valencia @ 0.267611
10 Belorussian + Castilla_La_Mancha + Lithuanian + Swedish @ 0.270586
11 Cantabria + Lithuanians + Orcadian + Russian_B @ 0.274725
12 Cantabria + Lithuanians + Orkney + Russian_B @ 0.274725
13 Andalucia + Lithuanian + Lithuanian + Norwegian @ 0.291613
14 Extremadura + Lithuanian + Lithuanian + Norwegian @ 0.291613
15 Galicia + Lithuanian + Lithuanian + Norwegian @ 0.291613
16 Aragon + Lithuanians + Mixed_Slav + Swedish @ 0.299908
17 Belorussian + Extremadura + Lithuanians + Norwegian @ 0.317611
18 Belorussian + Lithuanian + Norwegian + Valencia @ 0.320585
19 Lithuanians + Mixed_Slav + Norwegian + Valencia @ 0.324907
20 British_Isles + Cantabria + Lithuanians + Russian @ 0.348181

[Kurd]

In reading some of the comments, I noticed that many are in favor of this calculator, and a few don’t find it that useful.

I feel that a calculator’s usefulness is very subjective. It really depends on what is expected is from it. A calculator’s accuracy is determined by a few parameters, which include among other things, the numbers and sizes of the reference populations that are relevant to the end user’s ancestry, the assignment algorithims (the manner and order SNPs are assigned to geographic regions).

If for example, the expectation is to have admixture narrowed down to a sub-regional population, and have ancient genomes define admixture, then as DMXX has suggested, a higher k, say around 16, and one that is rich in ancient genome references is of course more appropriate.

Also, the admixture program, and Oracle program are sort of independent. From what I have seen so far under this thread, it seems that the European members are happy with the Oracle program. For them it appears to be performing at expectation, as it seems that the European references are good. This is reflected in the small distances in the single and mixed mode population comparisons with the various calculator references.

As for myself, I have gotten better Oracle results from some other calculators. It seems that this calculator did not have adequate SE Kurd references. The Iranian references used were substantially “closer” to me than the Kurd references.

If the expectation of the end user is to be compared with ancient populations, or to have ancient genomes define admixture, then obviously this calculator is not appropriate. A calculator that uses nothing but genomes from ancient references would be the right choice. As of now, I do not know of any such calculator, but I hoping someone would put one together that incorporates all the ancient genomes that have been discovered, that have sufficient overlapping SNPs with our raw data.

Finally, I tested this calculator with 2 ancient genomes, one from pre-Yamanya admixed Europeans, LBK Stuttgart, and one from post-Yamnaya admixed Europeans, BR2 Hungary. Here are the results:

LBK Stuttgart, 7ky
Population
South_Asian -
West_Asian 4.54%
Siberian -
African 0.17%
Southern 51.27%
Atlantic_Baltic 44.03%
East_Asian -

# Population (source) Distance
1 Sardinian (HGDP) 9.65
2 Canarias (1000Genomes) 20.78
3 C_Italian (Dodecad) 22.89
4 TSI30 (Metspalu) 22.9
5 Murcia (1000Genomes) 24.01
6 Tuscan (HGDP) 24.1
7 Ashkenazi (Dodecad) 24.15
8 S_Italian_Sicilian (Dodecad) 24.22
9 Sicilian (Dodecad) 24.24
10 North_Italian (HGDP) 24.61
11 Ashkenazy_Jews (Behar) 24.69
12 Morocco_Jews (Behar) 25.04
13 Andalucia (1000Genomes) 25.1
14 Greek (Dodecad) 25.11
15 Portuguese (Dodecad) 25.52
16 Moroccan (Dodecad) 25.72
17 O_Italian (Dodecad) 25.78
18 Galicia (1000Genomes) 25.87
19 Baleares (1000Genomes) 25.97
20 Castilla_Y_Leon (1000Genomes) 26.01
Mixed Mode Population Sharing:
# Primary Population (source) Secondary Population (source) Distance
1 83.2% Sardinian (HGDP) + 16.8% Saudis (Behar) @ 1.32
2 83.4% Sardinian (HGDP) + 16.6% Yemen_Jews (Behar) @ 1.56
3 83.8% Sardinian (HGDP) + 16.2% Samaritians (Behar) @ 3.1
4 82.4% Sardinian (HGDP) + 17.6% Bedouin (HGDP) @ 3.13
5 83.2% Sardinian (HGDP) + 16.8% Palestinian (HGDP) @ 4.19
6 83.6% Sardinian (HGDP) + 16.4% Jordanians (Behar) @ 4.43
7 83.8% Sardinian (HGDP) + 16.2% Egyptans (Behar) @ 4.64
8 84.3% Sardinian (HGDP) + 15.7% Druze (HGDP) @ 4.8
9 74.8% Sardinian (HGDP) + 25.2% Morocco_Jews (Behar) @ 5.02
10 83.9% Sardinian (HGDP) + 16.1% Lebanese (Behar) @ 5.05

BR2 Hungary 3.2ky
Population
South_Asian -
West_Asian 10.10%
Siberian -
African 0.48%
Southern 19.50%
Atlantic_Baltic 69.91%
East_Asian -

Single Population Sharing:
# Population (source) Distance
1 French (HGDP) 0.78
2 French (Dodecad) 1.02
3 Hungarians (Behar) 6.41
4 Mixed_Germanic (Dodecad) 7.58
5 Cornwall (1000Genomes) 7.87
6 German (Dodecad) 8.45
7 Kent (1000Genomes) 8.55
8 English (Dodecad) 8.71
9 CEU30 (1000Genomes) 8.92
10 Cataluna (1000Genomes) 8.96
11 Dutch (Dodecad) 9.7
12 British (Dodecad) 10.23
13 British_Isles (Dodecad) 10.43
14 Valencia (1000Genomes) 10.5
15 Spaniards (Behar) 10.77
16 Irish (Dodecad) 10.8
17 Cantabria (1000Genomes) 10.86
18 Orcadian (HGDP) 11.31
19 Pais_Vasco (1000Genomes) 11.47
20 Argyll (1000Genomes) 11.57

Mixed Mode Population Sharing:
# Primary Population (source) Secondary Population (source) Distance
1 56% German (Dodecad) + 44% Spaniards (Behar) @ 0.28
2 65% Mixed_Germanic (Dodecad) + 35% Andalucia (1000Genomes) @ 0.41
3 58.7% Mixed_Germanic (Dodecad) + 41.3% Spaniards (Behar) @ 0.41
4 52.6% Dutch (Dodecad) + 47.4% Spaniards (Behar) @ 0.42
5 62.4% German (Dodecad) + 37.6% Andalucia (1000Genomes) @ 0.43
6 66.8% Mixed_Germanic (Dodecad) + 33.2% Murcia (1000Genomes) @ 0.52
7 59.2% Dutch (Dodecad) + 40.8% Andalucia (1000Genomes) @ 0.55
8 61.3% German (Dodecad) + 38.7% Galicia (1000Genomes) @ 0.58
9 99.6% French (HGDP) + 0.4% ASW30 (HapMap3) @ 0.59
10 53.2% Argyll (1000Genomes) + 46.8% Baleares (1000Genomes) @ 0.59

There does not appear to be any inconsistencies between these results and the few excerpts that I have seen posted referencing the Haak paper. I do encourage members who are more familiar with the paper to comment on these results.

[vettor]

In reading some of the comments, I noticed that many are in favor of this calculator, and a few don’t find it that useful.

I feel that a calculator’s usefulness is very subjective. It really depends on what is expected is from it. A calculator’s accuracy is determined by a few parameters, which include among other things, the numbers and sizes of the reference populations that are relevant to the end user’s ancestry, the assignment algorithims (the manner and order SNPs are assigned to geographic regions).

If for example, the expectation is to have admixture narrowed down to a sub-regional population, and have ancient genomes define admixture, then as DMXX has suggested, a higher k, say around 16, and one that is rich in ancient genome references is of course more appropriate.

Also, the admixture program, and Oracle program are sort of independent. From what I have seen so far under this thread, it seems that the European members are happy with the Oracle program. For them it appears to be performing at expectation, as it seems that the European references are good. This is reflected in the small distances in the single and mixed mode population comparisons with the various calculator references.

As for myself, I have gotten better Oracle results from some other calculators. It seems that this calculator did not have adequate SE Kurd references. The Iranian references used were substantially “closer” to me than the Kurd references.

If the expectation of the end user is to be compared with ancient populations, or to have ancient genomes define admixture, then obviously this calculator is not appropriate. A calculator that uses nothing but genomes from ancient references would be the right choice. As of now, I do not know of any such calculator, but I hoping someone would put one together that incorporates all the ancient genomes that have been discovered, that have sufficient overlapping SNPs with our raw data.

Finally, I tested this calculator with 2 ancient genomes, one from pre-Yamanya admixed Europeans, LBK Stuttgart, and one from post-Yamnaya admixed Europeans, BR2 Hungary. Here are the results:

LBK Stuttgart, 7ky
Population
South_Asian -
West_Asian 4.54%
Siberian -
African 0.17%
Southern 51.27%
Atlantic_Baltic 44.03%
East_Asian -

# Population (source) Distance
1 Sardinian (HGDP) 9.65
2 Canarias (1000Genomes) 20.78
3 C_Italian (Dodecad) 22.89
4 TSI30 (Metspalu) 22.9
5 Murcia (1000Genomes) 24.01
6 Tuscan (HGDP) 24.1
7 Ashkenazi (Dodecad) 24.15
8 S_Italian_Sicilian (Dodecad) 24.22
9 Sicilian (Dodecad) 24.24
10 North_Italian (HGDP) 24.61
11 Ashkenazy_Jews (Behar) 24.69
12 Morocco_Jews (Behar) 25.04
13 Andalucia (1000Genomes) 25.1
14 Greek (Dodecad) 25.11
15 Portuguese (Dodecad) 25.52
16 Moroccan (Dodecad) 25.72
17 O_Italian (Dodecad) 25.78
18 Galicia (1000Genomes) 25.87
19 Baleares (1000Genomes) 25.97
20 Castilla_Y_Leon (1000Genomes) 26.01
Mixed Mode Population Sharing:
# Primary Population (source) Secondary Population (source) Distance
1 83.2% Sardinian (HGDP) + 16.8% Saudis (Behar) @ 1.32
2 83.4% Sardinian (HGDP) + 16.6% Yemen_Jews (Behar) @ 1.56
3 83.8% Sardinian (HGDP) + 16.2% Samaritians (Behar) @ 3.1
4 82.4% Sardinian (HGDP) + 17.6% Bedouin (HGDP) @ 3.13
5 83.2% Sardinian (HGDP) + 16.8% Palestinian (HGDP) @ 4.19
6 83.6% Sardinian (HGDP) + 16.4% Jordanians (Behar) @ 4.43
7 83.8% Sardinian (HGDP) + 16.2% Egyptans (Behar) @ 4.64
8 84.3% Sardinian (HGDP) + 15.7% Druze (HGDP) @ 4.8
9 74.8% Sardinian (HGDP) + 25.2% Morocco_Jews (Behar) @ 5.02
10 83.9% Sardinian (HGDP) + 16.1% Lebanese (Behar) @ 5.05

BR2 Hungary 3.2ky
Population
South_Asian -
West_Asian 10.10%
Siberian -
African 0.48%
Southern 19.50%
Atlantic_Baltic 69.91%
East_Asian -

Single Population Sharing:
# Population (source) Distance
1 French (HGDP) 0.78
2 French (Dodecad) 1.02
3 Hungarians (Behar) 6.41
4 Mixed_Germanic (Dodecad) 7.58
5 Cornwall (1000Genomes) 7.87
6 German (Dodecad) 8.45
7 Kent (1000Genomes) 8.55
8 English (Dodecad) 8.71
9 CEU30 (1000Genomes) 8.92
10 Cataluna (1000Genomes) 8.96
11 Dutch (Dodecad) 9.7
12 British (Dodecad) 10.23
13 British_Isles (Dodecad) 10.43
14 Valencia (1000Genomes) 10.5
15 Spaniards (Behar) 10.77
16 Irish (Dodecad) 10.8
17 Cantabria (1000Genomes) 10.86
18 Orcadian (HGDP) 11.31
19 Pais_Vasco (1000Genomes) 11.47
20 Argyll (1000Genomes) 11.57

Mixed Mode Population Sharing:
# Primary Population (source) Secondary Population (source) Distance
1 56% German (Dodecad) + 44% Spaniards (Behar) @ 0.28
2 65% Mixed_Germanic (Dodecad) + 35% Andalucia (1000Genomes) @ 0.41
3 58.7% Mixed_Germanic (Dodecad) + 41.3% Spaniards (Behar) @ 0.41
4 52.6% Dutch (Dodecad) + 47.4% Spaniards (Behar) @ 0.42
5 62.4% German (Dodecad) + 37.6% Andalucia (1000Genomes) @ 0.43
6 66.8% Mixed_Germanic (Dodecad) + 33.2% Murcia (1000Genomes) @ 0.52
7 59.2% Dutch (Dodecad) + 40.8% Andalucia (1000Genomes) @ 0.55
8 61.3% German (Dodecad) + 38.7% Galicia (1000Genomes) @ 0.58
9 99.6% French (HGDP) + 0.4% ASW30 (HapMap3) @ 0.59
10 53.2% Argyll (1000Genomes) + 46.8% Baleares (1000Genomes) @ 0.59

There does not appear to be any inconsistencies between these results and the few excerpts that I have seen posted referencing the Haak paper. I do encourage members who are more familiar with the paper to comment on these results.

point taken

but if you want this BR Hungarian to be a hungarian, then maybe this is the way

Kit Num: F999933
Threshold of components set to 1.000
Threshold of method set to 0.25%
Personal data has been read. 20 approximations mode.
Gedmatch.Com
MDLP K=8 4-Ancestors Oracle
This program is based on 4-Ancestors Oracle Version 0.96 by Alexandr Burnashev.
Questions about results should be sent to him at: [email protected]
Original concept proposed by Sergey Kozlov.
Many thanks to Alexandr for helping us get this web version developed.

Admix Results (sorted):

# Population Percent
1 East_European 37.11
2 Paleo_Mediterranean 24.87
3 West_European 24.47
4 Caucasian 10.73
5 Paleo_Scandinavian 1.59
6 South_Central_Asian 1.22


Finished reading population data. 73 populations found.
8 components mode.

--------------------------------

Least-squares method.

Using 1 population approximation:
1 HNG @ 13.096346
2 BLG @ 14.194614
3 SLV @ 14.723685
4 GER @ 15.502356
5 CRT @ 17.827982
6 BSN @ 18.872555
7 SRB @ 19.082020
8 RMN @ 20.711857
9 SLK @ 21.622833
10 MCD @ 21.867672
11 MNT @ 22.924067
12 GGZ @ 26.392282
13 FRN @ 26.640478
14 WUKR @ 28.088476
15 CEU @ 29.054943
16 FIN @ 30.243456
17 SWD @ 32.063717
18 UKR @ 32.449532
19 CUKR @ 32.873066
20 NITAL @ 33.069458


and the LBK below, matches your sardinian
Kit Num: F999916
Threshold of components set to 1.000
Threshold of method set to 0.25%
Personal data has been read. 20 approximations mode.
Gedmatch.Com
MDLP K=8 4-Ancestors Oracle
This program is based on 4-Ancestors Oracle Version 0.96 by Alexandr Burnashev.
Questions about results should be sent to him at: [email protected]
Original concept proposed by Sergey Kozlov.
Many thanks to Alexandr for helping us get this web version developed.

Admix Results (sorted):

# Population Percent
1 Paleo_Mediterranean 63.53
2 West_European 19.53
3 Caucasian 16.94


Finished reading population data. 73 populations found.
8 components mode.

--------------------------------

Least-squares method.

Using 1 population approximation:
1 SRD @ 20.630960
2 CRS @ 22.827448
3 SIC @ 23.885744
4 CITAL @ 26.915306
5 NITAL @ 27.455641
6 ASHK @ 29.542480
7 GRK @ 33.299362
8 KSV @ 34.534298
9 PRT @ 37.158596
10 CPR @ 38.265881
11 IBR @ 40.302605
12 SPN @ 40.438301
13 GGZ @ 46.568909
14 BLG @ 46.676292
15 RMN @ 46.947178
16 MCD @ 48.226307
17 MNT @ 49.707516
18 FRN @ 51.335663
19 SRB @ 54.388084
20 TRK @ 56.747826



how do we judge what is correct?


the interesting thing is mdlp k=8 has all GD numbers very high

[Anglecynn]

I think there was a 4 ancestors oracle download using ancient samples for either Eurogenes K13 or K15, might find it in Eurogenes blog past posts.

[bored]

Admix Results (sorted):

# Population Percent
1 South_Asian 41.52
2 West_Asian 39.52
3 Atlantic_Baltic 13.25
4 East_Asian 2.68
5 Siberian 2.59
6 Southern 0.44

Single Population Sharing:

# Population (source) Distance
1 Jatt (Dodecad) 6.69
2 Brahmins_from_Uttar_Pradesh (Metspalu) 9.85
3 Burusho (HGDP) 10.91
4 Sindhi (HGDP) 11
5 Pathan (HGDP) 12.87
6 Kshatriya (Metspalu) 13.05
7 Brahmins_from_Tamil_Nadu (Metspalu) 15.6
8 Cochin_Jews (Behar) 15.79
9 Indian (Dodecad) 16.17
10 Iyengar (Dodecad) 16.72
11 Iyer (Dodecad) 17.1
12 Bnei_Menashe_Jews (Behar) 18.49
13 Muslim (Metspalu) 21.04
14 GIH30 (Dodecad) 21.68
15 INS30 (SGVP) 22.07
16 Tharus (Metspalu) 22.15
17 Dharkars (Metspalu) 22.4
18 Kanjars (Metspalu) 23.42
19 Velamas (Metspalu) 25.92
20 Balochi (HGDP) 27.79

[tamilgangster]

Im surprised that u show so much more similarity to pathans than to chamars. Im guessing the chamar sample are from uttar pradesh or bihar not punjab

[Kurd]

Neither one is incorrect. They both kind of convey the same information in slightly different format. The k8 renames W. Asian Caucasian, Southern -> Paleo Mediterranean, Atlantic Baltic is broken up into E & W European. A better question may be which one is more informative. I like the Dodecad K7 much better than the MDLP K8, because it appears to have a larger reference population, which translates into a better Oracle, which returns references with smaller distances. In fact, the MDLP K8 appears to be very deficient in Middle Eastern and S Asian references, as my best surrogate is Turkic with a distance of 8, and my next surrogate is at a distance of 20!

Generally, I prefer higher k calculators for ancestry. The main reason I look at low k calculators is for plotting purposes. I can visualize genetic distance between 2 populations or individuals with just 2 plots. With higher k, I would need to look at multiple plots to visualize distance.

[QUOTE]if you want this BR Hungarian to be a hungarian, then maybe this is the way


I really do not attach much importance to this. Realistically, a good calculator would not show Stuttgart to have a German closest surrogate.

[Sapporo]

Im surprised that u show so much more similarity to pathans than to chamars. Im guessing the chamar sample are from uttar pradesh or bihar not punjab

I believe the Chamar samples are from Uttar Pradesh. We don't have any Punjabi Chamar samples. My bet is that most are much more southern shifted vs. their Jatt, Khatri, Rajput, etc. counterparts. They are probably much less homogeneous than other groups in Punjab.

Also, the HGDP Pathan are actually Pakistani Pashtun from Kurram Valley in FATA essentially on the Eastern Afghan border. Not North Indian Delhi or Uttar Pradesh "Pathans." Just wanted to clarify as many assume they are samples from India.

[Sapporo]

Population
South_Asian 31.67%
West_Asian 45.90%
Siberian 3.26%
African 0.28%
Southern 5.33%
Atlantic_Baltic 13.56%
East_Asian -

Can you post your oracle?

[vettor]

Admix Results (sorted):


# Population Percent
1 West_Asian 48.54
2 Southern 30.01
3 Atlantic_Baltic 9.86
4 South_Asian 6.23
5 African 2.23
6 East_Asian 1.66
7 Siberian 1.46

Single Population Sharing:


# Population (source) Distance
1 Iranians (Behar) 4.62
2 Kurd (Dodecad) 5.61
3 Iranian (Dodecad) 6.14
4 Kurds (Yunusbayev) 6.24
5 Uzbekistan_Jews (Behar) 6.78
6 Armenians_15 (Yunusbayev) 8.93
7 Azerbaijan_Jews (Behar) 9.56
8 Armenians (Behar) 9.61
9 Armenian (Dodecad) 9.86
10 Georgia_Jews (Behar) 10.9
11 Turks (Behar) 11.01
12 Assyrian (Dodecad) 11.02
13 Turkish (Dodecad) 11.44
14 Iranian_Jews (Behar) 13.45
15 Georgians (Behar) 14.41
16 Abhkasians (Yunusbayev) 14.44
17 Iraq_Jews (Behar) 15.27
18 Turkmens (Yunusbayev) 16.37
19 Syrians (Behar) 16.54
20 Lebanese (Behar) 17.22

wow, iranians match iranian

[vettor]

[QUOTE
how do we judge what is correct?


the interesting thing is mdlp k=8 has all GD numbers very high


Neither one is incorrect. They both kind of convey the same information in slightly different format. The k8 renames W. Asian Caucasian, Southern -> Paleo Mediterranean, Atlantic Baltic is broken up into E & W European. A better question may be which one is more informative. I like the Dodecad K7 much better than the MDLP K8, because it appears to have a larger reference population, which translates into a better Oracle, which returns references with smaller distances. In fact, the MDLP K8 appears to be very deficient in Middle Eastern and S Asian references, as my best surrogate is Turkic with a distance of 8, and my next surrogate is at a distance of 20!

Generally, I prefer higher k calculators for ancestry. The main reason I look at low k calculators is for plotting purposes. I can visualize genetic distance between 2 populations or individuals with just 2 plots. With higher k, I would need to look at multiple plots to visualize distance.


I really do not attach much importance to this. Realistically, a good calculator would not show Stuttgart to have a German closest surrogate.
[/QUOTE]
maybe the distortion of results is because dodecad k7b pulls information from too many genetic companies

[kenji.aryan]

# Population Percent
1 South_Asian 43.56
2 West_Asian 37.23
3 Atlantic_Baltic 10.09
4 Siberian 4.58
5 Southern 2.33
6 East_Asian 2.2

[Rukha]

Admix Results (sorted):

# Population Percent
1 West_Asian 47.46
2 South_Asian 22.36
3 Atlantic_Baltic 18.21
4 Southern 6.32
5 Siberian 3.61
6 East_Asian 1.56
7 African 0.48

Single Population Sharing:

# Population (source) Distance
1 Pathan (HGDP) 10.08
2 Tajiks (Yunusbayev) 11.98
3 Burusho (HGDP) 13.99
4 Jatt (Dodecad) 16.27
5 Turkmens (Yunusbayev) 18.12
6 Balochi (HGDP) 18.13
7 Brahui (HGDP) 19.62
8 Sindhi (HGDP) 19.71
9 Makrani (HGDP) 20.62
10 Lezgins (Behar) 22.51
11 Kumyks (Yunusbayev) 22.66
12 Chechens (Yunusbayev) 23.36
13 North_Ossetians (Yunusbayev) 23.99
14 Balkars (Yunusbayev) 24.12
15 Adygei (HGDP) 24.36
16 Iranian (Dodecad) 25.35
17 Bnei_Menashe_Jews (Behar) 26.13
18 Iranians (Behar) 26.87
19 Nogais (Yunusbayev) 27.66
20 Kurd (Dodecad) 28.42

[Kaido]

Can you post your oracle?


Here ya go

[Stephen1986]

My results -

Atlantic_Baltic 76.87%
Southern 12.55%
West_Asian 9.31%
South_Asian 1.27%

Using 1 population approximation:
1 English @ 0.992084
2 British @ 1.056827
3 British_Isles @ 1.193225
4 Kent @ 1.349600
5 CEU30 @ 1.663391
6 Irish @ 1.756328
7 Cornwall @ 1.875658
8 Orcadian @ 2.173900
9 Orkney @ 2.945168
10 Argyll @ 2.998575
11 Mixed_Germanic @ 3.151777
12 Dutch @ 3.169403
13 German @ 3.375461
14 Norwegian @ 5.989038
15 Polish @ 6.902108
16 Swedish @ 7.445379
17 Ukranians @ 7.711852
18 Mixed_Slav @ 7.820509
19 Belorussian @ 8.808717
20 Hungarians @ 9.742098

Using 2 populations approximation:
1 50% British +50% English @ 0.472903


Using 3 populations approximation:
1 50% British +25% British +25% Cornwall @ 0.425664


Using 4 populations approximation:
1 British + British + British + Cornwall @ 0.425664
2 British_Isles + British_Isles + English + Kent @ 0.448028
3 British_Isles + British_Isles + Cornwall + English @ 0.457686
4 British + British + English + English @ 0.472903
5 British + British_Isles + English + Kent @ 0.473028
6 British + British_Isles + Cornwall + English @ 0.478285
7 British + British + British_Isles + Cornwall @ 0.497820
8 British + British + English + Kent @ 0.498028
9 British + British + Cornwall + English @ 0.499286
10 British_Isles + British_Isles + British_Isles + Cornwall @ 0.503695
11 British_Isles + British_Isles + Cornwall + Cornwall @ 0.504689
12 British + British_Isles + British_Isles + Cornwall @ 0.527062
13 British + British_Isles + English + English @ 0.527715
14 British_Isles + British_Isles + English + English @ 0.535831
15 British_Isles + British_Isles + Cornwall + Kent @ 0.538552
16 British + Cornwall + English + Orcadian @ 0.555867
17 British + British_Isles + Cornwall + Kent @ 0.557292
18 British + British + CEU30 + English @ 0.557293
19 British + British + Cornwall + Kent @ 0.576507
20 British_Isles + British_Isles + Kent + Kent @ 0.581240

My brother's results -

Atlantic_Baltic 73.12%
Southern 13.59%
West_Asian 11.12%
South_Asian 2.12%
African 0.06%

Using 1 population approximation:

1 Mixed_Germanic @ 2.252037
2 Cornwall @ 2.899225
3 German @ 3.159497
4 CEU30 @ 3.402004
5 Kent @ 3.701636
6 Dutch @ 3.846507
7 English @ 4.085913
8 British @ 5.288985
9 British_Isles @ 5.463542
10 Irish @ 5.621370
11 Hungarians @ 5.897004
12 Argyll @ 5.952035
13 Orcadian @ 6.035850
14 Orkney @ 6.887809
15 Ukranians @ 7.482753
16 French @ 7.517809
17 French @ 7.747902
18 Mixed_Slav @ 8.260936
19 Polish @ 9.123048
20 Norwegian @ 9.865435

Using 2 populations approximation:
1 50% British_Isles +50% Hungarians @ 1.929914


Using 3 populations approximation:
1 50% British_Isles +25% Lithuanian +25% North_Italian @ 1.598594


Using 4 populations approximation:
1 British_Isles + CEU30 + Lithuanian + North_Italian @ 1.539111
2 Dutch + Polish + Polish + Portuguese @ 1.544326
3 CEU30 + Lithuanian + North_Italian + Orcadian @ 1.551295
4 Cornwall + Lithuanian + North_Italian + Orcadian @ 1.576653
5 British + CEU30 + Lithuanian + North_Italian @ 1.585203
6 British_Isles + Cornwall + Lithuanian + N_Italian @ 1.593606
7 British_Isles + British_Isles + Lithuanian + North_Italian @ 1.598594
8 British_Isles + Lithuanian + North_Italian + Orcadian @ 1.599064
9 British_Isles + British_Isles + Lithuanian + N_Italian @ 1.609816
10 British_Isles + CEU30 + Lithuanian + N_Italian @ 1.612319
11 British + Lithuanian + North_Italian + Orcadian @ 1.612392
12 British + British_Isles + Lithuanian + North_Italian @ 1.621351
13 CEU30 + Irish + Lithuanian + North_Italian @ 1.622135
14 British + CEU30 + Lithuanian + N_Italian @ 1.622459
15 British + British_Isles + Lithuanian + N_Italian @ 1.623989
16 British_Isles + Dutch + Lithuanian + North_Italian @ 1.625257
17 British_Isles + English + Lithuanian + N_Italian @ 1.628155
18 British + Cornwall + Lithuanian + N_Italian @ 1.635487
19 Cornwall + Lithuanian + N_Italian + Orcadian @ 1.636665
20 British + British + Lithuanian + N_Italian @ 1.641471

[kenji.aryan]

Its interesting to see that usually siblings have ~3 % difference in their admixture.

[Shaikorth]

I don't see any reason to prefer an old (3 years +) ADMIXTURE run over a 1 month old run (post Lazaridis et al. and ANE discovery). Still interesting nonetheless.

In general ADMIXTURE results should be interpreted more carefully and with awareness of software's limitations.

I just noticed that Maciamo at Eupedia suggested Yamnaya being an older population than EHG's because the Upper Paleolithic MA-1 shares its Armenian-like component in ADMIXTURE while EHG does not. This is not the case at all, as formal testing shows MA-1 has no Near Eastern and only shows "West Asian" in ADMIXTURE because it has contributed to Armenians and other West Asians, just as it has to Native Americans. If it is mixed in any way, there is no formal signal that has been found for such mixture. Yamnaya on the other hand is clearly mixed and has Near Eastern on top of EHG according to formal tests.

Scientists from Harvard lab used much more sophisticated methods than ADMIXTURE to arrive into their conclusions and population models. ADMIXTURE gives results that may be easier to read, but these results are relative depending on selection of populations in the run, and do not truly prove or disprove actual admixture events. Carefully done ADMIXTURE runs can approximate results from things like the Lazaridis model, but something like the Dodecad K12b Eupedia uses or the ones in Gedmatch aren't designed for that.

[fil]

Here are mine;

Admix Results (sorted):

# Population Percent
1 South_Asian 51.99
2 West_Asian 38.43
3 Southern 5.16
4 East_Asian 2.83
5 Siberian 0.84
6 Atlantic_Baltic 0.75
7 African 0.01

Single Population Sharing:

# Population (source) Distance
1 Cochin_Jews (Behar) 5.26
2 Indian (Dodecad) 7.58
3 Brahmins_from_Tamil_Nadu (Metspalu) 7.65
4 Iyengar (Dodecad) 8.88
5 Iyer (Dodecad) 9.15
6 Kshatriya (Metspalu) 10.47
7 GIH30 (Dodecad) 10.91
8 INS30 (SGVP) 11.8
9 Tharus (Metspalu) 13.31
10 Brahmins_from_Uttar_Pradesh (Metspalu) 13.43
11 Muslim (Metspalu) 13.56
12 Dharkars (Metspalu) 14.11
13 Velamas (Metspalu) 14.44
14 Kanjars (Metspalu) 15.38
15 Bnei_Menashe_Jews (Behar) 15.65
16 Sindhi (HGDP) 15.83
17 Tamil_Nadu_Scheduled_Caste (Metspalu) 17.47
18 Kurumba (Metspalu) 18.79
19 Uttar_Pradesh_Scheduled_Caste (Metspalu) 19.74
20 Dusadh (Metspalu) 19.87

Mixed Mode Population Sharing:

# Primary Population (source) Secondary Population (source) Distance
1 58% Chenchus (Metspalu) + 42% Brahui (HGDP) @ 1.44
2 52.8% Hakkipikki (Metspalu) + 47.2% Brahui (HGDP) @ 1.87
3 57.8% Chenchus (Metspalu) + 42.2% Balochi (HGDP) @ 1.9
4 52.6% Hakkipikki (Metspalu) + 47.4% Balochi (HGDP) @ 2.16
5 54.1% Brahui (HGDP) + 45.9% MALAYAN (Behar) @ 2.18
6 51.8% North_Kannadi (Behar) + 48.2% Brahui (HGDP) @ 2.35
7 55.3% Brahui (HGDP) + 44.7% Pulliyar (Metspalu) @ 2.41
8 55.8% SAKILLI (Behar) + 44.2% Brahui (HGDP) @ 2.56
9 79.8% Velamas (Metspalu) + 20.2% Abhkasians (Yunusbayev) @ 2.56
10 80% Velamas (Metspalu) + 20% Georgians (Behar) @ 2.58
11 51.6% North_Kannadi (Behar) + 48.4% Balochi (HGDP) @ 2.63
12 55.6% SAKILLI (Behar) + 44.4% Balochi (HGDP) @ 2.66
13 59.6% Chenchus (Metspalu) + 40.4% Makrani (HGDP) @ 2.66
14 77.4% Velamas (Metspalu) + 22.6% Iranian (Dodecad) @ 2.68
15 78.3% Velamas (Metspalu) + 21.7% Kurd (Dodecad) @ 2.69
16 77.3% Velamas (Metspalu) + 22.7% Iranians (Behar) @ 2.8
17 62.9% Dusadh (Metspalu) + 37.1% Brahui (HGDP) @ 2.81
18 64.3% Dusadh (Metspalu) + 35.7% Makrani (HGDP) @ 2.83
19 67.3% Tamil_Nadu_Scheduled_Caste (Metspalu) + 32.7% Makrani (HGDP) @ 2.87
20 78.6% Velamas (Metspalu) + 21.4% Kurds (Yunusbayev) @ 2.88

What does "southern" mean? Is it common in South India?

[Sapporo]

What does "southern" mean? Is it common in South India?

I believe that component is relatively similar to SW Asian. It peaks in SW Asia (Arab Gulf countries) and is found in appreciable amounts in the rest of West Asia.

[Leeroy Jenkins]

Admix Results (sorted):

# Population Percent
1 Atlantic_Baltic 73.64
2 Southern 12.65
3 West_Asian 11.88
4 South_Asian 1.12


Using 1 population approximation:
1 Mixed_Germanic @ 1.571730
2 German @ 1.779034
3 Dutch @ 2.578653
4 Cornwall @ 2.704949
5 CEU30 @ 2.745188
6 Kent @ 3.247236
7 English @ 3.789115
8 British @ 4.848737
9 Argyll @ 4.954529
10 Irish @ 4.954607

Using 2 populations approximation:
1 50% Hungarians +50% Orcadian @ 0.520260


Using 3 populations approximation:
1 50% Hungarians +25% Orcadian +25% Orcadian @ 0.520260


Using 4 populations approximation:
1 CEU30 + Lithuanian + North_Italian + Polish @ 0.444886
2 Argyll + Lithuanian + N_Italian + Orcadian @ 0.504096
3 Hungarians + Hungarians + Orcadian + Orcadian @ 0.520260
4 British + Lithuanian + North_Italian + Polish @ 0.535619
5 British_Isles + Lithuanian + North_Italian + Polish @ 0.549831
6 Argyll + Irish + Lithuanian + N_Italian @ 0.558904
7 Argyll + Argyll + Lithuanian + N_Italian @ 0.567460
8 Argyll + British_Isles + Lithuanian + N_Italian @ 0.582166
9 Hungarians + Hungarians + Irish + Orcadian @ 0.594986
10 Argyll + British + Lithuanian + N_Italian @ 0.617494

[Kurd]

What does "southern" mean? Is it common in South India?


Southern is Mediterranean or EEF. It is present in relatively high quantities in Greek, Italians, and the Middle East.

[Leeroy Jenkins]

My wife's results:


Admix Results (sorted):

# Population Percent
1 Atlantic_Baltic 72.68
2 Southern 14.74
3 West_Asian 10.27
4 Siberian 1.11


Finished reading population data. 223 populations found.
7 components mode.

--------------------------------

Least-squares method.

Using 1 population approximation:
1 Mixed_Germanic @ 3.141739
2 Cornwall @ 3.497559
3 German @ 3.870420
4 Kent @ 4.182544
5 CEU30 @ 4.420527
6 English @ 4.551703
7 Dutch @ 5.107220
8 Hungarians @ 5.606057
9 British @ 5.970550
10 French @ 6.137460

Using 2 populations approximation:
1 50% Lithuanian +50% Spaniards @ 1.288256


Using 3 populations approximation:
1 50% Belorussian +25% Extremadura +25% French @ 0.495224


Using 4 populations approximation:
1 Belorussian + Canarias + German + Polish @ 0.000000
2 Belorussian + Canarias + Kent + Polish @ 0.280280
3 Canarias + Hungarians + Lithuanians + Orkney @ 0.288803
4 Argyll + Canarias + Hungarians + Lithuanians @ 0.288903
5 Canarias + Hungarians + Irish + Lithuanians @ 0.338903
6 Canarias + Hungarians + Lithuanian + Norwegian @ 0.358269
7 Canarias + Hungarians + Lithuanians + Orcadian @ 0.363928
8 Belorussian + Canarias + Cornwall + Polish @ 0.378225
9 Argyll + Canarias + Swedish + Ukranians @ 0.390218
10 Belorussian + Canarias + Mixed_Germanic + Polish @ 0.399885

Never seen someone modeled perfectly (0.000000) using the 4 pop.

[Sapporo]

Southern is Mediterranean or EEF. It is present in relatively high quantities in Greek, Italians, and the Middle East.

https://docs.google.com/spreadsheet/ccc?key=0ArAJcY18g2GadHZ6SHpiLTNTa3lsUmZJY2pQblVRR 2c#gid=0

It seems highest in Saudis and Yemeni Jews followed by Levantines, North Africans and Southern Euros + West Asians? Definitely Mediterranean or EEF like but in the context of other calculators, some type of component with affinities to SW Asian and Mediterranean.