Uralics

[anglesqueville]

Like many of you I presume, I've played a lot with the "new toy of eurogenes", as Arvorigad called it: 4mix.r. I'm norman, but my topic is not Scandinavia. I got these results, with unexpected populations:

first, for info ( "Pat", that's me): Pat = 55% French + 31% French + 5% French + 9% French @ D = 0.0596
And now (look on the distances D= !) :
Pat = 21% French + 76% French + 2% French + 1% Khakass @ D = 0.0595
Pat = 55% French + 31% French + 13% French + 1% Bashkir @ D = 0.0593
Pat = 10% French + 20% French + 65% French + 5% Mari @ D = 0.0554
Pat = 7% French + 82% French + 5% French + 6% Udmurt @ D = 0.0555
Pat = 8% French + 80% French + 5% French + 7% Chuvash @ D = 0.0554
Pat = 26% French + 63% French + 0% Mari + 11% Komi @ D = 0.0497
Pat = 19% French + 56% French + 3% French + 22% Erzya @ D = 0.0411
Pat = 8% French + 61% French + 7% French + 24% Finnish @ D = 0.0276
Pat = 8% French + 61% French + 5% French + 26% Estonian @ D = 0.0197

I'm curious about your thoughts.

[Kale]

Well all the ones with Uralics in them are terrible fits. The best fit, 74% French + 26% Estonian, makes more sense...mostly French with a bit of something more North. I bet French + Danish or Norwegian (maybe even British) would be a better fit.

[Generalissimo]

Like many of you I presume, I've played a lot with the "new toy of eurogenes", as Arvorigad called it: 4mix.r. I'm norman, but my topic is not Scandinavia. I got these results, with unexpected populations:

first, for info ( "Pat", that's me): Pat = 55% French + 31% French + 5% French + 9% French @ D = 0.0596
And now (look on the distances D= !) :
Pat = 21% French + 76% French + 2% French + 1% Khakass @ D = 0.0595
Pat = 55% French + 31% French + 13% French + 1% Bashkir @ D = 0.0593
Pat = 10% French + 20% French + 65% French + 5% Mari @ D = 0.0554
Pat = 7% French + 82% French + 5% French + 6% Udmurt @ D = 0.0555
Pat = 8% French + 80% French + 5% French + 7% Chuvash @ D = 0.0554
Pat = 26% French + 63% French + 0% Mari + 11% Komi @ D = 0.0497
Pat = 19% French + 56% French + 3% French + 22% Erzya @ D = 0.0411
Pat = 8% French + 61% French + 7% French + 24% Finnish @ D = 0.0276
Pat = 8% French + 61% French + 5% French + 26% Estonian @ D = 0.0197

I'm curious about your thoughts.

The fits aren't very good, so you can safely discount these results.

[anglesqueville]

Do you considere this as a "good fit": 26% French + 23% Basque_French + 2% Norwegian + 49% Norwegian @ D = 0.0072 ? That's the best I get with modern populations. Furthermore, if the "uralic" fits are terrible, they are better anyway than the 100% french. There's something I don't understand in the methodology.

[John Doe]

Like many of you I presume, I've played a lot with the "new toy of eurogenes", as Arvorigad called it: 4mix.r. I'm norman, but my topic is not Scandinavia. I got these results, with unexpected populations:

first, for info ( "Pat", that's me): Pat = 55% French + 31% French + 5% French + 9% French @ D = 0.0596
And now (look on the distances D= !) :
Pat = 21% French + 76% French + 2% French + 1% Khakass @ D = 0.0595
Pat = 55% French + 31% French + 13% French + 1% Bashkir @ D = 0.0593
Pat = 10% French + 20% French + 65% French + 5% Mari @ D = 0.0554
Pat = 7% French + 82% French + 5% French + 6% Udmurt @ D = 0.0555
Pat = 8% French + 80% French + 5% French + 7% Chuvash @ D = 0.0554
Pat = 26% French + 63% French + 0% Mari + 11% Komi @ D = 0.0497
Pat = 19% French + 56% French + 3% French + 22% Erzya @ D = 0.0411
Pat = 8% French + 61% French + 7% French + 24% Finnish @ D = 0.0276
Pat = 8% French + 61% French + 5% French + 26% Estonian @ D = 0.0197

I'm curious about your thoughts.


What is this calculator? May I have a link?

[Helgenes50]

What is this calculator? May I have a link?

http://eurogenes.blogspot.fr/2015/05/4mix-four-way-mixture-modeling-in-r.html

[anglesqueville]

I wrote: " the best I get with modern populations". I mean, if I decide to take no british populations! With british, that's without any interest, as anybody knows that normans and britts are nearly identical: for example Target = 6% French + 66% Orcadian + 23% Basque_French + 4.99999999999999% French @ D = 0.0066. If I take "dutch" I get a little closer, but again, no interest: Target = 6% Basque_French + 34% Orcadian + 19% Basque_French + 41% Dutch @ D = 0.0063. Well, my interest was with far uralics, all these europeans are so boring. I'm disapointed (and I still don't understand how I can get a better fit, even not very good, with 22% Erzya, than with 100% French).

[Kale]

So what exactly was the point of this endeavor then?

[anglesqueville]

Good question Kale. Nothing precise. After an hour playing with "french+anything", avoiding the french+norwegian, french+SE_English, etc (too close), and the French+Japanese, French+Maasai (too far), and after a long listing of 0% with spaniards, italians, greeks, bedouin, lebanese etc, I fell on the uralics: Mari, Udmurt, etc. Of course the distances were great, but less than with 100% French.

[Kale]

Yeah they have enough of the North European French is missing, without toooo much of the East Asian that would destroy the fit. I could've saved you a couple hours and told you that would probably happen :P

[Tolan]

I don't have results for K8, also, I used 4mix with steppeK10.

The map is better for understanding...
I did (for me) with the French population as reference + another country.
Then, i made this map: colors represents the oracle distance and the number represents the percentage of the population with the best results (the difference being for France).

This map gives two informations. For example, if my best results with England are 79% French + 21% English, the distance is best with Ukraine: 85% French + 15% Ukrainians.

http://gen3553.pagesperso-orange.fr/images/steppek10.png

[Helgenes50]

@ Tolan

Thanks for this map, nice work and very good idea.

[Tolan]

@ Tolan

Thanks for this map, nice work and very good idea.

Merci!

For those who would like to try with Steppek10, here is what to put in the .csv file:


,NE,EA,Sib,Oce,WHG,Sub,Hin,Ste,Ame,Sou
Abkhasian,45.58,0.67,0.46,0.16,0.00,0.09,26.37,26. 20,0.07,0.40
Adygei,39.26,2.77,2.00,0.19,3.01,0.00,23.58,28.83, 0.23,0.12
Afghan_Pashtun,14.39,4.65,4.13,1.99,8.80,0.00,48.2 1,13.26,1.28,3.30
Afghan_Tadjik,16.58,11.73,6.97,1.43,6.91,0.22,38.7 2,12.42,1.31,3.71
Afghan_Turkmen,18.25,16.74,14.46,0.99,8.97,0.10,24 .98,13.09,1.90,0.51
Afghan_Uzbeki,17.36,12.83,5.75,1.21,7.88,0.00,40.1 2,11.01,0.87,2.98
Albanian,49.33,0.25,0.42,0.55,23.24,0.27,4.95,20.3 0,0.03,0.66
Alberstedt_LN,23.41,0.60,0.00,0.00,40.46,0.00,3.99 ,31.54,0.00,0.00
Algerian,58.46,0.49,1.44,0.99,13.35,22.93,1.14,0.0 2,0.66,0.52
Altaian,3.09,36.74,34.10,0.20,5.88,0.00,7.88,9.22, 2.15,0.73
Ami,0.00,9.20,0.27,0.00,0.00,0.00,0.00,0.00,0.00,9 0.52
Armenian,55.16,0.29,0.12,0.26,0.32,0.05,26.79,16.7 7,0.10,0.14
Ashkenazi_Jew,54.56,0.66,0.65,0.46,16.83,1.52,10.6 1,13.77,0.19,0.76
Assyrian,57.31,0.30,0.24,0.54,0.58,0.10,28.15,12.3 4,0.27,0.16
Atayal,0.00,0.00,0.20,0.00,0.00,0.00,0.00,0.00,0.0 0,99.79
Australian,0.00,5.37,0.59,82.20,0.00,0.05,5.29,0.0 0,0.13,6.37
Balkar,38.63,3.92,3.13,0.14,2.63,0.00,22.05,28.58, 0.41,0.50
Balochi,14.47,1.03,1.08,1.44,6.62,0.85,68.21,2.86, 0.87,2.58
Basque,42.17,0.19,0.03,0.14,50.23,0.01,2.72,4.33,0 .18,0.01
BedouinA,60.70,0.66,0.69,0.57,2.69,11.41,17.38,4.8 6,0.69,0.35
Belarusian,23.25,0.31,0.88,0.34,39.05,0.00,1.03,34 .11,0.32,0.71
Bell_Beaker_LN,23.51,0.00,0.25,0.14,44.69,0.12,5.8 2,24.83,0.50,0.14
BenzigerodeHeimburg_LN,16.52,0.00,0.00,0.04,37.83, 0.85,4.19,39.80,0.77,0.00
Bergamo,49.06,0.19,0.02,0.23,30.15,0.07,4.77,14.95 ,0.22,0.34
Bolivian,3.56,0.46,0.32,0.09,2.65,0.17,0.41,0.69,9 1.37,0.28
Bosnian,35.70,0.38,0.27,0.34,31.43,0.02,3.57,27.35 ,0.17,0.75
Bougainville,0.00,0.00,0.07,78.98,0.00,0.00,0.00,0 .00,0.00,20.94
Brahui,14.25,0.71,0.61,1.48,6.97,0.94,70.04,1.36,1 .07,2.59
Bulgarian,42.51,0.60,0.72,0.38,26.21,0.03,4.91,23. 91,0.24,0.49
Burmese,0.00,49.52,2.58,3.33,0.66,0.07,11.16,0.00, 0.56,32.12
Burusho,2.02,9.65,2.32,2.39,10.66,0.02,62.38,4.04, 1.89,4.61
Buryat,2.87,45.25,41.62,0.41,1.47,0.00,2.79,4.23,0 .63,0.74
Cambodian,0.00,23.11,0.00,3.43,0.68,0.20,8.77,0.18 ,0.09,63.52
Chechen,35.43,1.39,1.37,0.19,5.04,0.03,24.96,30.67 ,0.73,0.19
Chuvash,14.08,5.18,18.05,0.29,26.77,0.02,3.95,29.8 6,1.50,0.31
Corded_Ware_LN,8.03,0.08,0.00,0.44,25.92,0.31,3.31 ,61.27,0.06,0.59
Croatian,36.87,0.37,0.43,0.45,32.46,0.03,1.35,27.3 9,0.26,0.39
Cyprian,62.86,0.22,0.41,0.52,6.34,0.78,15.65,12.76 ,0.10,0.38
Cypriot,62.97,0.23,0.49,0.39,6.35,0.90,17.00,11.25 ,0.19,0.23
Czech,29.82,0.26,0.26,0.29,37.69,0.01,1.73,29.28,0 .31,0.34
Dai,0.00,36.93,0.00,0.84,0.00,0.00,0.00,0.00,0.04, 62.19
Daur,1.19,60.61,27.13,0.46,0.07,0.00,0.22,0.75,0.6 3,8.95
Dolgan,2.68,24.29,63.24,0.47,4.14,0.00,1.07,3.81,0 .15,0.15
Druze,63.06,0.00,0.28,0.82,2.60,3.21,21.26,8.04,0. 49,0.25
East_Sicilian,56.22,0.10,0.69,0.32,17.85,1.62,10.4 3,12.00,0.29,0.48
Egyptian,61.88,0.31,0.88,0.79,2.47,15.69,13.84,2.8 7,0.59,0.68
English,31.11,0.09,0.06,0.35,40.93,0.00,3.05,24.14 ,0.20,0.07
Erzya,18.75,0.78,6.63,0.20,34.99,0.00,1.57,35.14,0 .69,1.24
Esan,0.00,0.00,0.00,0.00,0.00,99.99,0.00,0.00,0.00 ,0.00
Esperstedt_MN,63.51,0.00,0.00,0.00,36.49,0.00,0.00 ,0.00,0.00,0.00
Estonian,17.69,0.09,1.90,0.28,42.99,0.00,0.00,35.9 4,0.55,0.58
Even,5.43,19.43,54.05,0.29,9.34,0.00,0.36,9.78,0.7 0,0.61
Finnish,17.12,0.22,7.41,0.13,41.08,0.00,0.30,32.71 ,0.64,0.39
French,37.02,0.34,0.05,0.25,38.70,0.12,3.34,19.72, 0.29,0.18
French_South,42.79,0.31,0.24,0.07,45.13,0.05,2.25, 8.49,0.43,0.22
Gambian,1.74,0.00,0.05,0.26,0.07,97.82,0.03,0.00,0 .00,0.03
Georgian,48.42,0.12,0.15,0.09,0.00,0.00,26.27,24.9 4,0.00,0.02
Greek,51.80,0.23,0.41,0.37,20.03,0.14,7.41,19.03,0 .34,0.23
Greenland,0.28,9.90,38.80,0.17,3.35,0.00,1.33,5.50 ,39.58,1.08
GujaratiA,2.12,0.09,2.54,3.72,13.14,0.00,65.13,3.5 4,1.86,7.85
Halberstadt_LBA,22.27,0.00,0.00,0.00,43.54,0.00,4. 86,28.49,0.84,0.00
Han,0.00,59.99,0.72,0.02,0.00,0.00,0.01,0.03,0.02, 39.21
Hungarian,32.96,0.55,0.66,0.16,35.21,0.01,2.32,27. 30,0.43,0.40
HungaryGamba_BA,35.10,0.00,0.00,0.00,42.52,0.00,0. 00,22.38,0.00,0.00
HungaryGamba_EN,71.97,0.00,0.00,0.16,26.30,0.00,0. 00,0.88,0.68,0.00
HungaryGamba_HG,0.00,0.00,0.00,0.00,99.99,0.00,0.0 0,0.00,0.00,0.00
Icelandic,26.15,0.10,0.20,0.05,42.84,0.00,2.98,27. 21,0.42,0.05
Iranian,42.31,1.10,0.83,0.76,2.10,1.86,36.92,12.23 ,0.63,1.26
Iranian_Jew,57.71,0.18,0.48,0.21,0.34,0.25,31.54,8 .64,0.33,0.32
Iranian_Jewish,55.04,0.66,0.34,0.26,0.28,0.32,32.5 0,10.04,0.19,0.36
Iraqi_Jew,60.95,0.12,0.00,0.36,0.37,0.60,27.72,9.2 7,0.22,0.40
Iraqi_Jewish,60.23,0.23,0.19,0.52,0.20,0.49,28.68, 8.53,0.47,0.47
Jordanian,59.23,0.66,0.71,0.52,2.29,7.35,20.47,7.7 1,0.43,0.63
Kalash,0.18,1.75,0.79,1.13,14.04,0.00,74.54,3.29,1 .77,2.50
Karelia_HG,0.00,0.00,1.46,0.00,37.42,0.00,0.00,48. 10,13.01,0.00
Karitiana,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00, 99.99,0.00
Karsdorf_LN,5.09,0.00,0.00,1.13,15.50,0.00,0.00,76 .52,0.00,1.76
Kinh,0.24,40.80,0.00,0.96,0.08,0.00,0.85,0.19,0.16 ,56.72
Korean,0.00,69.52,9.01,0.00,0.00,0.00,0.00,0.00,0. 00,21.47
Kosovar,47.53,0.01,0.42,0.31,24.79,0.00,5.66,20.74 ,0.32,0.22
Kostenki14,9.35,0.00,3.14,7.81,34.54,6.72,28.53,0. 00,2.94,6.96
Kumyk,36.92,3.50,3.01,0.27,4.99,0.04,25.10,25.08,0 .34,0.75
Kurdish,45.76,0.49,1.10,0.42,2.32,0.16,34.67,14.33 ,0.40,0.35
Kyrgyz,7.20,38.95,25.01,0.22,6.08,0.00,8.55,9.66,1 .64,2.69
La_Brana-1,0.00,0.00,0.00,2.21,97.43,0.00,0.00,0.00,0.00,0. 36
Lahu,0.00,47.33,0.00,1.40,0.00,0.00,0.31,0.00,0.13 ,50.82
LBK_EN,74.65,0.00,0.00,0.28,24.71,0.00,0.00,0.35,0 .00,0.00
Lebanese,58.48,0.64,0.76,0.94,3.56,4.76,21.11,8.81 ,0.70,0.26
Lebanese_Christian,65.80,0.16,0.24,0.56,1.63,1.37, 20.22,9.34,0.33,0.36
Lebanese_Muslim,58.76,0.59,0.67,0.64,3.51,3.76,21. 52,9.91,0.45,0.20
Lezgin,33.02,0.46,0.79,0.15,4.73,0.05,28.73,30.97, 0.86,0.24
Libyan_Jew,63.30,0.17,0.55,0.58,10.83,4.92,12.43,6 .54,0.50,0.17
Lithuanian,18.35,0.30,0.36,0.24,43.75,0.00,0.00,36 .70,0.08,0.22
Loschbour,0.00,0.00,0.00,0.44,99.56,0.00,0.00,0.00 ,0.00,0.00
MA1,0.00,0.00,1.61,3.32,16.29,0.46,24.41,33.76,20. 15,0.00
Macedonian,42.79,0.47,0.11,0.17,27.21,0.02,4.41,24 .11,0.30,0.42
Malay,0.24,14.90,0.29,5.35,0.70,0.34,9.21,0.07,0.3 2,68.57
Maltese,55.57,0.09,0.56,0.31,18.01,2.94,10.17,11.5 9,0.38,0.38
Mandenka,1.43,0.01,0.05,0.08,0.00,98.29,0.02,0.00, 0.01,0.11
Mansi,0.75,5.70,43.20,0.45,17.69,0.00,4.26,23.42,3 .86,0.65
Mayan,3.01,0.54,0.73,0.22,1.79,1.12,0.48,0.91,90.9 1,0.30
Mende,0.01,0.01,0.00,0.08,0.02,99.84,0.00,0.00,0.0 0,0.05
Miao,0.00,59.22,0.00,0.16,0.00,0.00,0.00,0.00,0.02 ,40.60
Mixe,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,99.99 ,0.00
Mixtec,0.90,0.99,0.64,0.39,1.10,0.49,0.12,0.23,95. 12,0.01
Moksha,20.17,2.09,4.33,0.48,35.11,0.00,2.21,34.37, 0.80,0.44
Mongola,1.35,63.24,14.68,0.09,0.16,0.04,0.46,1.97, 1.07,16.94
Mongolian,4.03,44.89,30.94,0.49,3.64,0.00,4.65,6.4 3,1.93,3.02
Montenegrin,41.37,0.16,0.41,0.17,28.94,0.01,2.84,2 5.20,0.36,0.54
Moroccan,60.03,0.66,0.86,0.84,14.98,19.42,1.42,0.7 7,0.56,0.46
Moroccan_Jew,61.06,0.19,0.00,0.22,13.12,4.10,12.05 ,7.43,1.06,0.76
Moroccan_Jewish,60.83,0.19,0.26,0.34,14.12,4.08,11 .67,7.38,0.42,0.72
Motala12,0.00,0.00,0.00,0.57,71.41,0.00,0.00,25.92 ,2.10,0.00
Naxi,0.00,74.27,3.05,1.66,0.00,0.00,0.17,0.00,0.02 ,20.83
Nganasan,0.00,0.00,99.99,0.00,0.00,0.00,0.00,0.00, 0.00,0.00
Nogai,28.57,12.78,9.42,0.37,7.57,0.00,16.66,22.76, 0.79,1.09
North_Ossetian,38.73,3.89,2.90,0.13,2.65,0.00,22.3 5,28.48,0.32,0.55
Norwegian,26.08,0.08,0.86,0.12,42.58,0.00,3.37,26. 46,0.46,0.00
Orcadian,27.80,0.00,0.00,0.13,41.76,0.07,3.97,25.4 7,0.78,0.00
Oroqen,0.23,49.49,42.00,0.19,0.23,0.00,0.10,0.13,0 .19,7.45
Palestinian,61.29,0.52,0.62,0.58,2.07,8.31,18.36,7 .23,0.62,0.40
Papuan,0.00,0.00,0.00,99.99,0.00,0.00,0.00,0.00,0. 00,0.00
Pathan,8.90,1.99,1.45,2.31,10.06,0.00,60.53,8.11,1 .95,4.69
Piapoco,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,99 .99,0.00
Pima,0.80,0.50,0.66,0.10,0.29,0.00,0.00,0.34,97.11 ,0.22
Polish,24.41,0.16,0.62,0.18,39.59,0.01,0.50,33.87, 0.34,0.33
Quechua,4.11,0.63,0.72,0.07,3.39,0.25,0.23,1.22,89 .12,0.24
Romanian,41.48,0.83,1.02,0.35,26.93,0.01,4.69,23.9 3,0.39,0.37
Russian,22.09,0.71,2.44,0.54,37.55,0.00,1.39,34.21 ,0.41,0.66
Russian_Kargopol,18.43,0.82,7.00,0.41,36.86,0.00,0 .84,34.32,0.75,0.57
Saami_WGA,8.48,2.45,20.11,0.43,34.26,0.00,0.00,32. 68,1.14,0.45
Samara_HG,0.00,0.00,0.00,0.00,31.59,0.00,0.00,58.8 8,9.52,0.00
Samaritan,72.22,0.00,0.00,0.36,0.00,1.92,19.88,4.9 7,0.64,0.00
Sardinian,63.92,0.18,0.14,0.20,34.84,0.27,0.01,0.0 9,0.15,0.22
Scottish,28.00,0.04,0.42,0.06,41.80,0.00,4.00,25.1 3,0.54,0.00
Sephardic_Jewish,59.62,0.15,0.61,0.53,13.25,2.17,1 3.34,9.86,0.19,0.26
Serbian,39.67,0.31,0.56,0.38,29.84,0.02,3.00,25.41 ,0.34,0.47
Serbian_Bosnia,36.42,0.47,0.15,0.29,32.14,0.00,2.1 2,27.49,0.26,0.66
She,0.00,60.49,0.00,0.00,0.00,0.00,0.00,0.00,0.00, 39.50
Sicilian,54.71,0.29,0.30,0.31,19.21,1.80,9.71,13.0 4,0.11,0.53
South_Italian,57.70,0.18,0.24,0.35,16.62,1.12,10.8 8,12.21,0.44,0.26
Spain_EN,66.72,0.00,0.00,0.71,32.56,0.00,0.00,0.00 ,0.00,0.00
Spain_MN,55.42,0.00,0.00,0.00,44.58,0.00,0.00,0.00 ,0.00,0.00
Spanish,45.28,0.22,0.24,0.23,37.54,1.36,3.23,11.24 ,0.35,0.30
Spanish_North,42.89,0.00,0.07,0.10,46.58,0.00,1.77 ,8.28,0.29,0.00
Stuttgart,73.95,0.00,0.00,0.30,25.75,0.00,0.00,0.0 0,0.00,0.00
Surui,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.00,99.9 9,0.00
Syrian,57.36,0.38,0.59,0.64,2.53,5.33,22.58,9.14,0 .62,0.84
Tadjik,17.85,8.97,4.92,0.67,10.32,0.00,37.52,16.17 ,1.79,1.79
Tajik_Pomiri,12.19,5.23,2.11,0.90,14.27,0.00,43.63 ,18.72,2.39,0.55
Thai,0.66,30.83,0.14,2.85,0.78,0.03,9.50,0.08,0.08 ,55.06
Tu,1.30,67.72,8.20,0.82,1.00,0.00,2.97,1.14,0.80,1 6.05
Tubalar,0.29,29.26,28.23,0.14,10.26,0.00,10.07,18. 28,3.46,0.00
Tujia,0.00,63.81,0.25,0.04,0.00,0.00,0.00,0.00,0.0 0,35.89
Tunisian,59.21,0.48,1.08,0.69,11.76,19.74,5.37,0.5 1,0.57,0.60
Turkish,47.62,3.48,3.17,0.40,6.25,0.31,21.09,16.61 ,0.49,0.56
Turkish_Jew,59.28,0.11,0.70,0.33,13.19,2.34,12.91, 10.64,0.27,0.23
Turkmen,23.68,14.33,11.15,0.77,6.93,0.19,26.75,13. 31,1.09,1.81
Tuscan,51.51,0.24,0.15,0.36,25.50,0.48,5.69,15.58, 0.26,0.23
Tuvinian,1.67,39.13,42.66,0.52,2.79,0.04,3.33,7.12 ,1.33,1.42
Ukrainian_Belgorod,24.78,0.27,1.37,0.47,36.85,0.01 ,1.82,33.85,0.09,0.49
Ukrainian_Kharkov,25.64,0.07,0.45,0.00,37.45,0.00, 0.80,33.47,0.20,1.90
Ukrainian_Lviv,27.47,0.15,1.10,0.59,36.51,0.19,1.3 9,31.75,0.45,0.40
Ukrainian_Poltava,27.41,0.03,1.16,0.05,37.09,0.00, 0.94,32.09,0.65,0.58
Ust_Ishim,4.10,4.90,4.25,15.73,12.85,12.72,25.90,0 .00,1.96,17.59
Utah_British,29.56,0.28,0.08,0.22,41.47,0.38,2.44, 24.88,0.59,0.09
Utah_German,30.28,0.36,0.02,0.10,39.96,0.36,2.03,2 6.15,0.50,0.24
Utah_Scandinavian,26.25,0.14,0.36,0.38,41.96,0.36, 1.83,28.16,0.33,0.21
Utah_USA,30.88,0.18,0.16,0.20,40.46,0.37,2.70,24.5 0,0.34,0.20
Uygur,10.38,30.70,12.97,0.75,6.50,0.00,19.94,10.71 ,1.71,6.34
Uzbek,16.33,21.25,13.63,0.51,8.03,0.04,22.67,13.79 ,0.92,2.83
West_Sicilian,53.40,0.10,0.23,0.18,20.76,1.39,9.07 ,14.41,0.32,0.14
Xibo,0.76,63.86,18.32,0.30,0.13,0.00,0.83,1.13,0.4 2,14.24
Yakut,2.40,31.70,59.48,0.14,1.36,0.00,1.46,2.99,0. 38,0.09
Yamnaya,0.00,0.00,0.00,0.03,0.00,0.00,0.00,99.96,0 .00,0.00
Yemen,55.19,0.69,1.56,0.87,0.75,17.45,19.71,2.57,0 .75,0.47
Yi,0.02,71.94,3.41,1.26,0.00,0.00,0.06,0.00,0.28,2 3.03
Yoruba,0.00,0.00,0.00,0.01,0.00,99.97,0.00,0.00,0. 01,0.00
Yukagir,3.17,14.62,64.51,0.11,5.55,0.00,0.19,5.43, 5.59,0.82
Zapotec,1.06,0.95,1.31,0.10,1.19,0.02,0.44,0.97,93 .95,0.00

[anglesqueville]

Bravo for the map Tolan. Can someone tell me what is considered as a "good fit" with SteppeK10?

[Dr_McNinja]

I don't have results for K8, also, I used 4mix with steppeK10.

The map is better for understanding...
I did (for me) with the French population as reference + another country.
Then, i made this map: colors represents the oracle distance and the number represents the percentage of the population with the best results (the difference being for France).

This map gives two informations. For example, if my best results with England are 79% French + 21% English, the distance is best with Ukraine: 85% French + 15% Ukrainians.

http://gen3553.pagesperso-orange.fr/images/steppek10.pngNice map. Did you draw it by hand in Photoshop?

[randwulf]

This is an interesting tool. I used the SteppeK10 from Tolan to try to analyze my parents. My mother on paper is an English/West-South German mix. In the Eurogenes tools, the West-South German is usually French. She has some Scottish also. The English should be somewhat higher than the French. I added Orcadian as the fourth population to get something somewhat close to the expected mix but not known:

getMix('steppeavg.csv', 'FaySteppe.txt', 'English', 'French', 'Scottish', 'Orcadian')
[1] Target = 58% English + 42% French + 0% Scottish + 0% Orcadian @ D = 3.2389

Not a bad result.

For my father, his ancestry on paper is entirely West-South German (i.e. "French" on this calculator). But, he always shows 2%-6% East Asian mixed components that are out of place (somewhat like anglesqueville has been noticing). Davidski analyzed it specifically and said it looked like a recent ancestor (3-6 generations before my father) because there were fairly large segments of Asian DNA in some of the chromosomes and that it was something like Han. So, I tried getting him to produce a "single" ancestor result with populations somewhat close to the paper ancestry but not known to be there:

getMix('steppeavg.csv', 'BernardSteppe.txt', 'French', 'Basque', 'Spanish', 'Bergamo')
[1] Target = 100% French + 0% Basque + 0% Spanish + 0% Bergamo @ D = 4.6509

Then I tried finding East Asian populations that pulled the usual 3% amount from the French that was higher than the normal amount in this calculator. I started with Han, but it only matched 1%:

getMix('steppeavg.csv', 'BernardSteppe.txt', 'French', 'Han', 'Spanish', 'Bergamo')
[1] Target = 99% French + 1% Han + 0% Spanish + 0% Bergamo @ D = 4.5504

Just about any NE Asian influenced population seemed to work:

getMix('steppeavg.csv', 'BernardSteppe.txt', 'French', 'Mongolian', 'Spanish', 'Bergamo')
[1] Target = 97% French + 3% Mongolian + 0% Spanish + 0% Bergamo @ D = 4.1496

> getMix('steppeavg.csv', 'BernardSteppe.txt', 'French', 'Turkmen', 'Spanish', 'Bergamo')
[1] Target = 96% French + 3.99999999999999% Turkmen + 0% Spanish + 0% Bergamo @ D = 4.2736

> getMix('steppeavg.csv', 'BernardSteppe.txt', 'French', 'Dolgan', 'Spanish', 'Bergamo')
[1] Target = 97% French + 3% Dolgan + 0% Spanish + 0% Bergamo @ D = 3.825

If I tried putting in a more Eastern European (than French, that is) population into the mix, it totally blew up the known paper ancestry:

getMix('steppeavg.csv', 'BernardSteppe.txt', 'French', 'Hungarian', 'Spanish', 'Bergamo')
[1] Target = 0% French + 71% Hungarian + 29% Spanish + 0% Bergamo @ D = 2.0054

At any rate, kind of fun to poke around with this.

[Tolan]

Nice map. Did you draw it by hand in Photoshop?

With Paint of Windows

[Tolan]

Do you considere this as a "good fit": 26% French + 23% Basque_French + 2% Norwegian + 49% Norwegian @ D = 0.0072 ? That's the best I get with modern populations. Furthermore, if the "uralic" fits are terrible, they are better anyway than the 100% french. There's something I don't understand in the methodology.


If you are more north than the French average, then probably you have a little more Indo-Europeans steppes than the French average.
So, the Uralic components can come from there!

I put my results with Eutest, and "Erzya" and "Udmurt" allow me to have good results (with Scotland and Ireland):
http://gen3553.pagesperso-orange.fr/images/Eutest.png

[anglesqueville]

However Tolan I can see the same with K10Steppes, and with seemingly ( if I'm understanding something) better fits than Randulf:

[1] Target = 1% Mongolian + 18% French + 70% French + 11% French @ D = 1.695
[1] Target = 2% Even + 45% French + 45% French + 8% French @ D = 1.455
[1] Target = 3% Saami_WGA + 25% French + 64% French + 8% French @ D = 1.5422

But I agree, the key is perhaps here: [1] Target = 2% MA1 + 5% French + 84% French + 9% French @ D = 1.6258 and here: Target = 1% Karitiana + 16% French + 72% French + 11% French @ D = 1.5597 . Furthermore if someone happens to tell that percentages below than 5% with distances greater than 0.008 are only "noise effects" (as somebody told me yesterday by PM from another forum) I'll reply that such a calculator is just without any interest. If all this is just for knowing I am a mix of northern "french", "british" and "scandinavian", I don't need any calculator.

[Helgenes50]

However Tolan I can see the same with K10Steppes, and with seemingly ( if I'm understanding something) better fits than Randulf:

[1] Target = 1% Mongolian + 18% French + 70% French + 11% French @ D = 1.695
[1] Target = 2% Even + 45% French + 45% French + 8% French @ D = 1.455
[1] Target = 3% Saami_WGA + 25% French + 64% French + 8% French @ D = 1.5422

But I agree, the key is perhaps here: [1] Target = 2% MA1 + 5% French + 84% French + 9% French @ D = 1.6258 and here: Target = 1% Karitiana + 16% French + 72% French + 11% French @ D = 1.5597 . Furthermore if someone happens to tell that percentages below than 5% with distances greater than 0.008 are only "noise effects" (as somebody told me yesterday by PM from another forum) I'll reply that such a calculator is just without any interest. If all this is just for knowing I am a mix of northern "french", "british" and "scandinavian", I don't need any calculator.

Target = 2% MA1 + 5% French + 84% French + 9% French @ D = 1.6258
Which file, please, did you use ? K10 or K8

[anglesqueville]

Salut Helgenes. K10Steppes in my last post.

[Tolan]

However Tolan I can see the same with K10Steppes, and with seemingly ( if I'm understanding something) better fits than Randulf:

[1] Target = 1% Mongolian + 18% French + 70% French + 11% French @ D = 1.695
[1] Target = 2% Even + 45% French + 45% French + 8% French @ D = 1.455
[1] Target = 3% Saami_WGA + 25% French + 64% French + 8% French @ D = 1.5422

But I agree, the key is perhaps here: [1] Target = 2% MA1 + 5% French + 84% French + 9% French @ D = 1.6258 and here: Target = 1% Karitiana + 16% French + 72% French + 11% French @ D = 1.5597 . Furthermore if someone happens to tell that percentages below than 5% with distances greater than 0.008 are only "noise effects" (as somebody told me yesterday by PM from another forum) I'll reply that such a calculator is just without any interest. If all this is just for knowing I am a mix of northern "french", "british" and "scandinavian", I don't need any calculator.

And with French + Yamnaya, what's your result?

[Helgenes50]

However Tolan I can see the same with K10Steppes, and with seemingly ( if I'm understanding something) better fits than Randulf:

[1] Target = 1% Mongolian + 18% French + 70% French + 11% French @ D = 1.695
[1] Target = 2% Even + 45% French + 45% French + 8% French @ D = 1.455
[1] Target = 3% Saami_WGA + 25% French + 64% French + 8% French @ D = 1.5422

But I agree, the key is perhaps here: [1] Target = 2% MA1 + 5% French + 84% French + 9% French @ D = 1.6258 and here: Target = 1% Karitiana + 16% French + 72% French + 11% French @ D = 1.5597 . Furthermore if someone happens to tell that percentages below than 5% with distances greater than 0.008 are only "noise effects" (as somebody told me yesterday by PM from another forum) I'll reply that such a calculator is just without any interest. If all this is just for knowing I am a mix of northern "french", "british" and "scandinavian", I don't need any calculator.

We have the same result with MA1

Target = 8% French + 84% French + 5.99999999999999% French + 2% MA1 @ D = 4.343
Target = 8% French + 84% French + 8% French + 0% Even @ D = 4.4098
Target = 2% French + 21% French + 72% French + 4.99999999999999% Saami_WGA @ D = 4.0637

Once more, the results of my mother are higher
Always closer to those of the English

Target = 30% French + 24% French + 42% French + 3.99999999999999% MA1 @ D = 3.1991

[anglesqueville]

With yamnaya... bof...: Target = 9% French + 83% French + 6.99999999999999% French + 1% Yamnaya @ D = 1.6137
But: [1] Target = 5% French + 63% French + 30% French + 2% Karelia_HG @ D = 1.7287

[Helgenes50]

With yamnaya... bof...: Target = 9% French + 83% French + 6.99999999999999% French + 1% Yamnaya @ D = 1.6137
But: [1] Target = 5% French + 63% French + 30% French + 2% Karelia_HG @ D = 1.7287

Mine

Target = 48% French + 18% French + 30% French + 3.99999999999999% Yamnaya @ D = 2.7782

Target = 2% French + 21% French + 72% French + 4.99999999999999% Karelia_HG @ D = 3.5562

[anglesqueville]

While I'm at it, and as Arvorigad provides a good example, I have a question of methodology or interpretation: if we compare his fit with French/karelia against mine, we see:
Me: 2% Karel / D= 1.72 He: 5% karel / D=3.5 . For him, 2 times more karelian admixture but two times more distant. Can we say that one of these fits is better? Or perhaps my question does'nt make sense?

[Tolan]

Me:
[1] Target = 63% French + 27% French + 6.99999999999999% French + 3% Yamnaya @ D = 0.5567

[1] Target = 74% French + 10% French + 11% French + 4.99999999999999% Corded_Ware_LN @ D = 1.0089
[1] Target = 0% French + 42% French + 50% French + 8% BenzigerodeHeimburg_LN @ D = 1.6549
[1] Target = 42% French + 50% French + 4% French + 3.99999999999999% Karelia_HG @ D = 1.9251
[1] Target = 31% French + 58% French + 8% French + 3% MA1 @ D = 2.1913
[1] Target = 0% French + 42% French + 50% French + 8% Halberstadt_LBA @ D = 2.35
[1] Target = 42% French + 22% French + 30% French + 5.99999999999999% Bell_Beaker_LN @ D = 2.5947
[1] Target = 84% French + 2.99999999999999% French + 5% French + 8% HungaryGamba_BA @ D = 2.7345
[1] Target = 42% French + 50% French + 8% French + 0% Esperstedt_MN @ D = 2.7738



While I'm at it, and as Arvorigad provides a good example, I have a question of methodology or interpretation: if we compare his fit with French/karelia against mine, we see:
Me: 2% Karel / D= 1.72 He: 5% karel / D=3.5 . For him, 2 times more karelian admixture but two times more distant. Can we say that one of these fits is better? Or perhaps my question does'nt make sense?

It depends if we are away or not from the French average..
This is not necessarily a good choice if you are too far away to take as a reference the French population.

For me, Yamnaya is the best of all populations, including modern populations where I associate with the French average.
*
And I think the most important thing is to compare the distance found between several population, rather than comparing with distance with other French since we do not have the same distance to the French average

[anglesqueville]

.....

[anglesqueville]

Yes, I believe I'm beginning to understand ( I'm slow, my wife uses to tell that normans share many genes with their cows....):
[1] Target = 10% French + 20% French + 65% French + 5% Mari @ D = 0.0554
[1] Target = 7% French + 48% Norwegian + 45% French + 0% Mari @ D = 0.0178
The 5% 'Mari' represent alleles (of mine) that are not present in the french admixture, but are in the norwegian one, and as I'm closer to a mix norwegian-french than to the french sample, the second distance is smaller than the first one. Am I right? If my understanding is right, I agree that my uralic-like percentages are likely for ANE extra scores ( 'extra' with respect to the french sample). If true, in one respect, those uralic scores can work as scandinavian markers for northwesterners?

[randwulf]

I guess I can get a closer "distance" for my father's mix by ignoring the paper with the DNA clue of extra East Asian and instead building a "box" around his West German ancestry including Eastern European populations that can absorb the East Asian, but I am not sure I learn much from it since none of it represents the paper trail:

[1] Target = 25% Spanish + 20% Finnish + 22% Russian + 33% Bergamo @ D = 1.0403

instead of:

[1] Target = 3% French + 93% French + 1% French + 3% Mongolian @ D = 4.1496

Trying the French with Yamnaya isn't too bad:

[1] Target = 0% French + 46% French + 50% French + 4% Yamnaya @ D = 2.979

but, I don't get a good explanation for what seems to be some missing information from my paper trail.