K13 European countries only (no small regions)

[Scandal]

You can find it here: https://universe-apricity.github.io/Calculators/k13-euro-countries-only.html

English, Bulgarian, Italian, Lithuanian averages are missing, others may be missing too. If you have those averages please post it and then I will add it to the spreadsheet.

[Demirkazık]

Target: Demir
Distance: 608.5167% / 6.08516686
82.4 Turkish
17.0 Finnish
0.6 Maltese


Bulgarian,21.75,24.06,17.91,11.91,20.12,1.95,0.11, 0.52,0.43,0.57,0.41,0.11,0.16
Lithuanian,28.87,53.90,7.17,3.91,1.48,0.76,1.34,0. 06,0.82,0.77,0.41,0.29,0.22

[Far_away]

4.03841553 Romanian
4.21166238 Bulgarian
5.68805766 Serb
7.62694565 North_Macedonian
11.68666762 Albanian
11.88607168 Croat
14.22569506 Hungarian
16.83371617 Slovene
18.28896389 Austrian
18.77029568 Slovak
19.93099847 Czech
24.51040187 German
24.77184087 Ukrainian
25.94996724 Maltese
26.22240836 Portuguese
27.15294459 Polish
29.05208943 Russian
29.44500637 Belarusian
32.06279152 Dutch
33.16729865 Swedish
33.77687819 Welsh
34.47846719 Danish
34.55653050 Scottish
34.87794289 Finnish
35.00474682 Icelandic


Distance: 265.9590% / 2.65958958
66.6 Bulgarian
21.8 Albanian
11.6 Latvian

[J. Ketch]

English,49.61,23.53,13.65,5.57,3.99,0.71,1.07,0.15 ,0.29,0.59,0.42,0.20,0.15

Belgian,45.28,22.19,15.55,5.67,7.39,1.12,0.94,0.26 ,0.10,0.53,0.44,0.33,0.20

[Dušan]

Target: Dušan(K13)
Distance: 3.9923% / 3.99227837 | R2P
66.7 North_Macedonian
33.3 Latvian



Target: Dušan(K13)
Distance: 4.4009% / 4.40086061 | ADC: 0.25x RC
84.0 Serb
8.7 Latvian
7.3 Russian



Distance to: Dušan(K13)
6.56443448 Serb
6.87965842 Croat
9.42169836 Romanian
9.52364426 Hungarian
11.24661727 Slovene
11.95482329 Slovak
12.96903620 Bulgarian
13.84989170 Czech
15.88680899 North_Macedonian
16.40985375 Ukrainian

[Scandal]

Distance to: Hungarian
3.11151089 Croat
3.17638159 Slovene
5.62067612 Slovak
6.61521731 Czech
8.72684364 Austrian
9.04645787 Serb
12.11293523 Romanian
13.41598301 German
14.78197889 Ukrainian
15.30438499 Polish
18.65991693 Russian
18.69345072 North_Macedonian
19.22912895 Belarusian
20.76183277 Swedish
21.70251138 Dutch
22.22744925 Finnish
22.78873406 Estonian
22.82570700 Danish
23.44459853 Albanian
23.46451576 Norwegian
23.84174281 Icelandic
23.95216274 Welsh
24.56511347 Scottish
25.88063369 Irish
25.99356266 Portuguese



Target: Hungarian
Distance: 39.5138% / 0.39513777 | R3P
61.2 Serb
22.6 Swedish
16.2 Russian

[Scandal]

Distance to: Universe
6.73538418 Czech
6.97090382 Slovak
8.39338430 Slovene
8.71251973 Croat
9.02685438 Hungarian
9.83136817 Ukrainian
11.48936465 Polish
13.67288192 Belarusian
13.85171830 Serb
14.40038888 Russian
16.36288789 Austrian
17.26718564 Romanian
18.32727203 Estonian
18.51441600 Finnish
19.28505898 German
23.69669597 Latvian
24.07346257 North_Macedonian
24.14022784 Swedish
26.92092123 Danish
26.99119671 Dutch
27.60258321 Norwegian
28.50506095 Albanian
28.52890815 Icelandic
29.69013641 Welsh
30.12236213 Scottish


Target: Universe
Distance: 430.1741% / 4.30174117 | R3P
50.6 Czech
31.4 Latvian
18.0 Maltese

[Kiel]

Distance to: Kiel
4.85070098 Austrian
4.86303403 German
10.91387649 Dutch
12.50244376 Hungarian
12.61757901 Slovene
12.76315008 Welsh
13.42812347 Scottish
13.45643341 Swedish
13.85108660 Danish
13.96164747 Icelandic

Target: Kiel
Distance: 119.8763% / 1.19876343 | R3P
60.6 Icelandic
23.6 Romanian
15.8 Albanian

[Scandal]

Target: Demir
Distance: 608.5167% / 6.08516686
82.4 Turkish
17.0 Finnish
0.6 Maltese


Bulgarian,21.75,24.06,17.91,11.91,20.12,1.95,0.11, 0.52,0.43,0.57,0.41,0.11,0.16
Lithuanian,28.87,53.90,7.17,3.91,1.48,0.76,1.34,0. 06,0.82,0.77,0.41,0.29,0.22



English,49.61,23.53,13.65,5.57,3.99,0.71,1.07,0.15 ,0.29,0.59,0.42,0.20,0.15

Belgian,45.28,22.19,15.55,5.67,7.39,1.12,0.94,0.26 ,0.10,0.53,0.44,0.33,0.20
Added

[majevica]

Target: K13
Distance: 233.2111% / 2.33211081 | ADC: 0.25x RC
72.4 Croat
22.6 Serb
5.0 Turkish

[Gergő Marosvári]

Distance to: Gergő
6.04878500 Slovak
6.24205896 Croat
6.78558767 Hungarian
6.95900855 Slovene
7.03305766 Czech
10.93225045 Ukrainian
11.31377921 Serb
12.69915745 Polish
14.64097674 Romanian
14.71090072 Austrian
15.25688042 Belarusian
15.34612981 Russian
18.45112463 German
18.72797106 Bulgarian
20.28965254 Estonian
20.59056337 Finnish
20.88514304 Lithuanian
21.62681900 North_Macedonian
24.05878634 Belgian
24.31552796 Swedish
25.64039586 Latvian
26.21029187 Albanian
26.48967346 Dutch
26.83824324 Danish
27.34045903 English

Target: Gergő
Distance: 417.4400% / 4.17439990 | ADC: 0.25x RC
62.4 Croat
28.0 Ukrainian
9.6 Czech

Target: Gergő
Distance: 419.2893% / 4.19289305 | ADC: 0.5x RC
68.8 Croat
31.2 Ukrainian

[celticdragongod]

Distance to: CDG
1.92647346 Dutch
2.85324377 English
2.93586444 Scottish
3.16815404 Welsh
3.24530430 Icelandic
4.18041864 Irish
4.21359704 Danish
4.47904008 Norwegian
6.78703175 Swedish
8.01976932 Belgian
9.10460872 German
15.11110519 Austrian
21.17588015 Slovene
22.04982540 Czech
22.33437037 Hungarian
22.99296197 Slovak
25.13234569 Croat
25.40812468 Portuguese
28.08485179 Polish
29.05848413 Serb
29.31733276 Finnish
30.60547827 Romanian
31.09395761 Ukrainian
31.28376096 Estonian
32.99409947 Russian

Target: CDG
Distance: 91.2835% / 0.91283504
61.6 Scottish
30.6 Danish
3.2 Albanian
2.4 Finnish
2.2 Welsh

[Thracian]

Target: Thracian
Distance: 533.4573% / 5.33457290 | ADC: 0.5x RC
42.8 Bulgarian
30.2 North_Macedonian
27.0 Turkish

[gixajo]

Target: gixajo
Distance: 8.6407% / 8.64072670
91.5 Portuguese
8.5 Irish

Target: gixajo_dad
Distance: 6.7206% / 6.72057289
100.0 Portuguese

Target: gixajo_mom
Distance: 9.6715% / 9.67153404
84.8 Portuguese
15.2 Irish

Target: gixajo_enana
Distance: 15.1984% / 15.19837718
86.6 Portuguese
13.4 Irish

[gixajo]

You can find it here: https://universe-apricity.github.io/Calculators/k13-euro-countries-only.html

English, Bulgarian, Italian, Lithuanian averages are missing, others may be missing too. If you have those averages please post it and then I will add it to the spreadsheet.


Spanish,40.54,10.23,27.54,2.40,12.98,2.76,0.59,0.3 4,0.25,0.33,0.28,1.07,0.70
Italian,23.54,8.83,22.95,12.32,26.28,4.27,0.47,0.2 2,0.18,0.24,0.28,0.19,0.21

[Beowulf]

Target: Beowulf
Distance: 1.4356% / 1.43557775
63.7 Portuguese
13.8 Spain
13.0 English
9.5 Serb

[gixajo]

Target: Beowulf
Distance: 1.4356% / 1.43557775
rb

Que buenas distancias consigues, nosotros con el K13 no bajamos de 4.

[Beowulf]

Que buenas distancias consigues, nosotros con el K13 no bajamos de 4.

Yo uso esto, que te muestra las distancias de manera distinta https://promotopic.com/calculator/vahd.html

[Scandal]

Spanish,40.54,10.23,27.54,2.40,12.98,2.76,0.59,0.3 4,0.25,0.33,0.28,1.07,0.70
Italian,23.54,8.83,22.95,12.32,26.28,4.27,0.47,0.2 2,0.18,0.24,0.28,0.19,0.21

Updated.

[gixajo]

Yo uso esto, que te muestra las distancias de manera distinta https://promotopic.com/calculator/vahd.html

Ese es el que he usado yo.

[Vitez]

Target: Vitez
Distance: 1.7683% / 1.76826003 | R2P
63.6 Polish
36.4 Italian

Target: Vitez
Distance: 2.0073% / 2.00727944 | ADC: 0.25x
67.4 Croat
29.8 Hungarian
1.3 Turkish
1.0 Portuguese
0.5 Russian

Distance to: Vitez
2.49943994 Croat
3.46581881 Hungarian
6.00067496 Slovene
6.75402843 Serb
7.74925158 Slovak
9.14110497 Czech
9.80085200 Romanian
10.52239992 Austrian

[Beowulf]

Ese es el que he usado yo.

Pues entonces no se, alomejor le caigo mejor al sitio ese.

[Nurzat]

Distance to: Nurzat
5.90683502 Croat
6.08685469 Serb
7.56280371 Hungarian
8.15082818 Romanian
10.01991018 Slovene

Distance to: Nurzat_dad_Suceava_county
6.97065994 Croat
8.20788645 Serb
8.92429269 Hungarian
10.60121691 Romanian
10.90351320 Slovak

Distance to: Nurzat_mom_Vaslui_county
5.80762430 Serb
6.90662001 Croat
7.48483133 Romanian
8.45790163 Hungarian
10.77598719 Slovene

Distance to: Nurzat_Csango_friend_Neamț_county
5.55208970 Hungarian
6.19155877 Croat
7.73084083 Serb
8.16033700 Slovene
8.51110451 Austrian
9.46415871 Romanian
10.61519194 Slovak
11.66119205 Czech
13.67365350 Bulgarian
14.28053570 German


Distance to: Nurzat_Botoșani_county_friend - Ukrainian origin last name
6.09602329 Serb
6.30339591 Croat
8.70777239 Romanian
8.92842091 Hungarian
11.11324885 Slovene

Distance to: Nurzat_Botoșani_county_friend - Hungarian origin last name
5.34647547 Croat
6.56894969 Hungarian
7.76548131 Slovene
7.87810256 Slovak
8.82683409 Czech
9.21472192 Serb
12.49689962 Romanian
12.93067670 Ukrainian
14.71294328 Austrian
15.20037171 Polish

Distance to: Nurzat_Neamț_county_friend - Romanian last name
5.30716497 Romanian
6.46225193 Bulgarian
7.08187828 Serb
10.41594451 North_Macedonian
11.86320783 Croat

[djipon]

Target: Djipon
Distance: 1.3357% / 1.33566131
40.4 Maltese ?????
39.0 Bulgarian
10.0 Estonian
5.1 Latvian
4.9 Russian
0.6 Turkish

Target: Mom
Distance: 3.0481% / 3.04814999
52.3 Maltese ???
37.0 Latvian
10.7 Armenian

Target: Dad
Distance: 1.6108% / 1.61081896
54.7 Albanian
16.0 Bulgarian
15.7 Maltese ???
9.5 Latvian
4.1 Turkish

Why is whole family getting massive amounts of Maltese? :D It should be Greek, if nothing else...

[PaulieVanZant]

Target Stahl
Distance 104.4734% 1.04473428
21.2 Dutch
14.4 German
13.0 Danish
13.0 Norwegian
11.6 Czech
6.4 Latvian
5.8 Icelandic
5.4 Maltese
4.2 Swedish
3.2 Lithuanian
1.8 Finnish

[vader]

Target: Vader
Distance: 314.6231% / 3.14623147
91.6 Portuguese
6.2 Spanish
2.2 Danish

[gixajo]

With Italian and Spanish added (I haven´t removed Portuguese) distances are still quite bad:

Target: gixajo
Distance: 6.8519% / 6.85185537
92.9 Spanish
7.1 Irish

Target: gixajo_dad
Distance: 5.7436% / 5.74357798
98.7 Spanish
1.3 Albanian

Target: gixajo_mom
Distance: 7.0781% / 7.07807476
87.8 Spanish
12.2 Irish

Target: gixajo_enana
Distance: 12.3746% / 12.37463323
92.2 Spanish
7.8 Irish

[Luke35]

Putting my sister thru the paces:

Target: Mr.G_sister
Distance: 1.1460% / 1.14598768
23.3 Irish
22.0 Polish
17.0 Lithuanian
12.5 Portuguese
8.9 Maltese
6.0 Spanish
3.9 Czech
3.8 Swedish
2.6 Italian

Target: Mr.G_sister
Distance: 2.0226% / 2.02262522 | R2P
65.6 Slovene
34.4 Belgian

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

Target: Mr.G_sister
Distance: 2.8093% / 2.80930869
61.0 Hungarian
39.0 Mr.G_mother

Target: Mr.G_sister
Distance: 2.6431% / 2.64311128
55.0 Slovak
45.0 Mr.G_mother

[ScandinavianCelt]

converted to G25:

Target: K13.MergedRSID.Brad
Distance: 0.7199% / 0.00719853
52.8 Swedish
35.4 Irish
8.4 Turkish
1.8 Finnish
1.6 Portuguese

raw:

Target: K13.MergedRSID.Brad
Distance: 261.5567% / 2.61556659 | ADC: 0.25x RC
42.2 Dutch
28.6 Danish
23.0 Swedish
6.2 Armenian

[nittionia]

Target: nittionia
Distance: 466.1463% / 4.66146289
40.6 Danish
29.8 Spanish
29.6 Swedish

Target: nittioniasdad
Distance: 483.4581% / 4.83458147
59.4 Swedish
25.2 Czech
13.0 Portuguese
2.4 Albanian

[rothaer]

I never got why some people at all deal with K13 data when there is also K36 data available. K13 can nothing that K36 can not. Why would anyone voluntarily use something worse without any corresponding benefit? :icon_ask:

[oszkar07]

Target: Oszkar
Distance: 226.5212% / 2.26521222 | R3P
60.2 Czech
29.4 Dutch
10.4 Portuguese

Target: Oszkar
Distance: 241.3569% / 2.41356880 | ADC: 0.25x RC
53.0 German
41.8 Czech
5.2 Portuguese


Distance to: Oszkar
5.96673277 German
5.98472222 Austrian
7.78249960 Slovene
9.00404909 Hungarian
9.03074748 Czech
9.87401134 Slovak
11.79202273 Croat
12.06940346 Belgian
12.55726085 Swedish
13.67268445 Dutch
14.57431302 Danish
14.64322369 English
15.24642253 Norwegian
15.67877546 Icelandic
16.22210221 Welsh
16.71436209 Serb
16.73810921 Scottish
17.19915986 Polish
18.07818852 Irish
18.92773626 Ukrainian
19.11602731 Romanian
21.44843118 Finnish
21.84309044 Russian
22.24666492 Belarusian
22.84712017 Estonian

[oszkar07]

Target: OszkarArmenianWife
Distance: 240.4959% / 2.40495917
99.2 Armenian
0.8 Ukrainian

[Nurzat]

I never got why some people at all deal with K13 data when there is also K36 data available. K13 can nothing that K36 can not. Why would anyone voluntarily use something worse without any corresponding benefit? :icon_ask:

too many components in K36, many of them not very informative, data seems spread a bit randomly, you don't know what to make out of many components (Easterners who score Basque, Westerners who score a bit of Armenian etc) and not the same availability of various regional and national scores as for K13.

I myself favour K13 even above G25 - plotting PC1 and PC2 in G25 leaves aside some % of data variation, while a t-SNE plot of K13 components in theory doesn't leave anything out and 13 components are few enough to show some clear geographical consistency

[paradox]

Target: paradox
Distance: 4.4186% / 4.41856076 | ADC: 0.25x RC
73.0 Bulgarian
14.8 Maltese
12.2 Armenian

Target: Father
Distance: 2.2538% / 2.25381481 | ADC: 0.25x RC
72.8 Romanian
15.7 Maltese
11.4 Dutch
0.1 Bulgarian

[rothaer]

too many components in K36,

Too many for what exactly? Is there a problem for Vahaduo to deal with 36 components compared to 13?


many of them not very informative,

Every information is an advantage and even a component with - theoretically - zero information is no disadvantage.


data seems spread a bit randomly, you don't know what to make out of many components (Easterners who score Basque, Westerners who score a bit of Armenian etc)

The only wrong is to have labeled the components anything else than component 1, component 2 etc. because are otherwise caused a lot of confusion. However, in a comparion, the labelling of the K36 components is completely irrelevant.

Of course, someone scoring "Italian" or "Basque" is not indicative of having any Italian or Basque ancestry.

K36 is - not surprisingly when having more components - more capable in distinguishing. F. i. it can distinguish a Germanic-Finnic mixture from a Germanic-Slavic mixture, which K13 cannot (unfortunately also G25 not, but the latter has other advantages). It's not by chance that LM Genetics is using K36.


and not the same availability of various regional and national scores as for K13.

That would to my perception be the only legit reason, if it's applicable. It's a pity that people at all collected K13 data instead of K36 which could as for today be done without any additions effort compatred to K13. I guess, the reason for going on K13 was the once lower effort and the opinion that it's sufficient.


I myself favour K13 even above G25 - plotting PC1 and PC2 in G25 leaves aside some % of data variation, while a t-SNE plot of K13 components in theory doesn't leave anything out and 13 components are few enough to show some clear geographical consistency

Plotting in a 2-dimensional PCA is topic of its own, because you the have to reduce 13 or 36 components to two which alsways is a huge loss of information. on the other hand you can construct a PCA in 1000 different ways in how to define PC1 and PC2 out of the other components.

When it comes to a Vahaduo calculation all dimensions are used.

What is a "t-SNE plot"?

[Scandal]

Too many for what exactly? Is there a problem for Vahaduo to deal with 36 components compared to 13?



Every information is an advantage and even a component with - theoretically - zero information is no disadvantage.



The only wrong is to have labeled the components anything else than component 1, component 2 etc. because are otherwise caused a lot of confusion. However, in a comparion, the labelling of the K36 components is completely irrelevant.

Of course, someone scoring "Italian" or "Basque" is not indicative of having any Italian or Basque ancestry.

K36 is - not surprisingly when having more components - more capable in distinguishing. F. i. it can distinguish a Germanic-Finnic mixture from a Germanic-Slavic mixture, which K13 cannot (unfortunately also G25 not, but the latter has other advantages). It's not by chance that LM Genetics is using K36.



That would to my perception be the only legit reason, if it's applicable. It's a pity that people at all collected K13 data instead of K36 which could as for today be done without any additions effort compatred to K13. I guess, the reason for going on K13 was the once lower effort and the opinion that it's sufficient.



Plotting in a 2-dimensional PCA is topic of its own, because you the have to reduce 13 or 36 components to two which alsways is a huge loss of information. on the other hand you can construct a PCA in 1000 different ways in how to define PC1 and PC2 out of the other components.

When it comes to a Vahaduo calculation all dimensions are used.

What is a "t-SNE plot"?
There's that k36 converter somewhere, I can't find it. It transforms that gedmatch format to vahaduo format. That makes k36 mroe simple to use.

[majevica]

There's that k36 converter somewhere, I can't find it. It transforms that gedmatch format to vahaduo format. That makes k36 mroe simple to use.

https://allelocator.ovh/horizontaltovertical.html

[Scandal]

https://allelocator.ovh/horizontaltovertical.html
Oh wow. Thanks a lot.

[Voskos]

Too many for what exactly? Is there a problem for Vahaduo to deal with 36

yes, it leads to overfit apparently. some guys with background in math could explain this better than me. not just for vahaduo, but too many components will give overfit in any model, like using sets of G25 coords to model a population for example, G25 has too many components as well.
https://en.wikipedia.org/wiki/Overfitting

[Scandal]

I never got why some people at all deal with K13 data when there is also K36 data available. K13 can nothing that K36 can not. Why would anyone voluntarily use something worse without any corresponding benefit? :icon_ask:
K36 European countries only calculator (no small regions)
https://www.theapricity.com/forum/showthread.php?384536-K36-European-countries-only-calculator-(no-small-regions)

[Dušan]

You can find it here: https://universe-apricity.github.io/Calculators/k13-euro-countries-only.html

English, Bulgarian, Italian, Lithuanian averages are missing, others may be missing too. If you have those averages please post it and then I will add it to the spreadsheet.

PCA plot:

https://i.imgur.com/n300b0y.png

Greek is also missing.

[Nurzat]

What is a "t-SNE plot"?

for multi-dimensional data, using PCA you can reduce it to 2 principal components that capture most of the data’s variance, as we all know. this helps in understanding the overall structure but might miss some local details. using t-SNE instead, you map the data directly to 2 dimensions. this might not capture all the variance but will show clusters and local patterns clearer. so PCA is good for simplifying data and retaining most of the overall variance and t-SNE is better for revealing clusters and local structures, as it prioritizes neighbour-joining and keeping the relative distances between x number of neighbours, as you set it.

I'd be curious to plot K36 data as t-SNE (I use Python with Plotly for it), I will look for some such "coordinates".

and thanks for the detailed reply, you have your points and made me curious over K36

[cass]

Target: cass
Distance: 234.8790% / 2.34878968
62.0 Polish
10.2 Irish
9.6 Russian
9.2 Spanish
7.6 Austrian
1.4 Latvian

https://i.ibb.co/zfJs0K6/k13.jpg (https://ibb.co/Lp5S24N)

[Cybele]

Target: Cybele
Distance: 292.7378% / 2.92737805 | ADC: 0.25x RC
62.6 Romanian
17.8 Spanish
13.0 Austrian
6.6 Turkish


Distance to: Cybele
6.42607968 Romanian
7.71716269 North_Macedonian
8.11704996 Serb
8.29156198 Bulgarian
11.96634029 Albanian
12.54709528 Croat
13.40014552 Hungarian
13.86609895 Austrian
15.94370409 Slovene
18.71415240 Italian
18.71677590 Slovak
19.17450912 Portuguese
19.86594070 Czech
20.09515613 German
21.28835597 Belgian
21.86713058 Spanish
25.92037615 Maltese
26.79346935 English
26.79910446 Ukrainian
26.97061549 Dutch
28.03419162 Polish
28.21129561 Welsh
29.12794363 Scottish
29.25787074 Swedish
30.13163786 Danish

[Grace O'Malley]

Target: Grace
Distance: 209.3615% / 2.09361461
76.4 Icelandic
23.6 Irish

With Icelandic removed

Target: Grace
Distance: 242.3435% / 2.42343462
51.2 Norwegian
48.8 Irish

[noricum]

Target: K13Noricum(austrian)
Distance: 274.9699% / 2.74969884
53.0 Czech
14.6 Swedish
11.2 Portuguese
10.8 Latvian
10.4 Albanian

Distance to: K13Noricum(austrian)
4.03892312 Slovene
5.10388088 Czech
5.82825017 Hungarian
5.94019360 Slovak
7.96898990 Croat
9.02480471 Austrian
11.33349461 German
13.68353390 Serb
14.12883222 Polish
14.80293214 Ukrainian
16.79250130 Romanian
17.05883056 Belgian
17.05883056 Belgian
17.12187490 Swedish
18.39554566 Belarusian
18.60263422 Russian
19.12713517 Dutch
19.60079590 Danish
19.98308285 Finnish
20.06096708 English
20.06096708 English
20.36542167 Norwegian
20.59849266 Estonian
21.06173782 Bulgarian
21.06173782 Bulgarian


Target: K13_noricum_mom
Distance: 139.9187% / 1.39918719
31.4 Lithuanian
19.4 Maltese
11.4 Norwegian
10.6 Polish
9.2 Portuguese
7.8 Finnish
5.4 Latvian
2.0 Czech
1.6 Albanian
0.6 Croat
0.4 Austrian
0.2 Slovene

Distance to: K13_noricum_mom
3.07668653 Slovak
3.48644518 Czech
3.74320718 Slovene
4.61956708 Hungarian
5.95710500 Croat
11.35882476 Ukrainian
11.44803040 Austrian
11.58959879 Polish
12.33730927 Serb
14.53148994 German
15.30624382 Russian
15.59133734 Belarusian
15.59387700 Romanian
18.61034927 Finnish
19.05709842 Estonian
19.91613165 Bulgarian
19.91613165 Bulgarian
20.30922204 Swedish
20.57537849 Belgian
20.57537849 Belgian
20.74851320 Lithuanian
20.74851320 Lithuanian
22.41104415 North_Macedonian
22.54716390 Dutch
22.82064197 Danish



Target: noricum_paternal_halfbro(austrian)
Distance: 257.1523% / 2.57152300
62.2 Latvian
18.6 Portuguese
14.2 Maltese
3.4 Albanian
1.6 Czech

Distance to: noricum_paternal_halfbro(austrian)
6.42766676 Slovak
6.81549705 Ukrainian
7.00281372 Czech
8.61199745 Polish
9.03035437 Slovene
10.11569078 Croat
10.23070379 Hungarian
10.65592324 Belarusian
11.36080983 Russian
15.68553793 Serb
15.88778462 Estonian
16.21798693 Lithuanian
16.21798693 Lithuanian
16.78793019 Finnish
17.65098014 Austrian
19.19971094 Romanian
20.27287103 German
20.84914387 Latvian
23.29967382 Bulgarian
23.29967382 Bulgarian
24.61484918 Swedish
26.18795907 North_Macedonian
26.44251312 Belgian
26.44251312 Belgian
27.56239467 Danish



Target: Lavanttal_Valley(Austria)
Distance: 88.4506% / 0.88450569
57.8 Slovene
16.2 Albanian
13.0 Latvian
9.6 Norwegian
3.4 Spanish

Distance to: Lavanttal_Valley(Austria)
1.44865455 Slovene
2.67349958 Hungarian
4.65006452 Croat
4.83718927 Slovak
5.62319304 Czech
8.31997596 Austrian
10.78036178 Serb
12.39595095 German
14.01183072 Romanian
14.33405037 Polish
14.38468978 Ukrainian
17.91359260 Belgian
17.91359260 Belgian
18.35091551 Bulgarian
18.35091551 Bulgarian
18.38188510 Russian
18.56079201 Belarusian
19.39574438 Swedish
20.28904384 North_Macedonian
20.67803182 Dutch
21.37376663 Finnish
21.41082203 English
21.41082203 English
21.56435253 Danish
21.73127700 Estonian


Target: noricum_inlaw(lower austrian)
Distance: 350.8914% / 3.50891441
40.0 Austrian
28.6 Spanish
21.0 Polish
5.8 Armenian
4.6 Irish

Distance to: noricum_inlaw(lower austrian)
5.18828488 Austrian
10.43552107 German
10.97683470 Hungarian
11.62871016 Slovene
11.87319249 Belgian
11.87319249 Belgian
12.26063620 Croat
13.55854343 Serb
14.70411847 Romanian
14.96587786 Slovak
15.72452861 Czech
16.83450029 English
16.83450029 English
17.10107891 Dutch
17.50531348 Portuguese
17.68256769 North_Macedonian
17.93083378 Bulgarian
17.93083378 Bulgarian
18.15910791 Welsh
19.13003136 Scottish
19.35281375 Spanish
19.57225587 Swedish
20.14292432 Danish
20.28033037 Icelandic
20.33558212 Irish

[leachim_x]

Micha_K13,45.46,26.21,13.48,5.69,4.30,2.50,1.94,0. 00,0.00,0.19,0.24,0.00,0.00


Distance to: Micha_K13
4.68490128 German
4.69149230 Dutch
5.37278326 English
5.77171552 Belgian
6.78857864 Welsh
7.34872098 Scottish
7.34942175 Danish
7.51088543 Icelandic
7.53774502 Swedish
8.06544481 Norwegian
8.62218650 Irish
10.51824605 Austrian
16.71518770 Slovene
17.79436147 Hungarian
17.95321698 Czech
18.76524713 Slovak
20.54195220 Croat
22.69154909 Portuguese
23.81472654 Spanish
24.43320896 Serb
24.71375528 Polish
26.08983519 Romanian
27.32330507 Finnish
27.41601357 Ukrainian
29.00120170 Estonian

[leachim_x]

Distance to: Micha_K13
3.17157689 Dandelion(Flemish)
3.24254530 Aynora(german)
3.30857976 frankhammer(British_NZer)
3.66558317 Caismeachd(Scot)
3.67726801 Jackson(english)
3.86190368 Inf99Mom(usa)
3.93124662 Christofer(German_USA)
4.02980148 **JC**(British-German)
4.04102710 Solothurn(English)
4.12337241 Token(German)
4.15239690 Morticia(American)
4.23687385 InfamousAngel99(amerimutt)
4.27101861 Mr.G_mother(german)
4.30305705 Gooding(Anglo_American)
4.46462764 CordedWhelp_father(American)
4.65348257 Calpalsister(British+French+Ukrainian)
4.73635936 GötterfunkeDad(German+Dutch)
4.90559884 Jackson_father(english)
5.01245449 Teutone(german)
5.02688771 CreodaMum(English)
5.28245208 MotherKyp(SouthGerman)
5.29342044 Oneeye(Anglo_American)
5.36525861 Dreger22310(french_breton)
5.47586523 Thorns(white_american)
5.54530432 wisteria(usa)

I really like this project :):thumb001: