New Czech samples from Prague

[Peterski]

These are new previously unpublished academic samples, not the same ones as the older group of Prague Czechs.

Dodecad K12b:


CzechPrague1,4.30,1.50,0.63,0,30.03,48.02,0,0,2.37 ,0,13.16,0
CzechPrague2,4.10,0.54,0.86,0.39,30.55,48.26,0.14, 0,2.44,0,12.73,0
CzechPrague3,6.27,0.49,2.17,0,27.48,41.99,1.00,0.1 3,3.96,0,16.52,0
CzechPrague4,5.75,0.01,0,0.31,34.73,45.25,0,0.30,1 .42,0,12.22,0
CzechPrague5,6.37,1.17,0,0.58,26.63,49.94,0,0,2.89 ,0.34,12.10,0
CzechPrague6,4.74,0.60,0,0,28.21,52.99,0.99,0,1.00 ,0.46,11.01,0
CzechPrague7,5.26,0.53,0,0.21,33.32,48.26,0.35,0,1 .91,0.19,9.98,0

Eurogenes K13:


CzechPrague1,33.37,33.72,15.44,4.92,7.56,1.11,0.31 ,0,1.34,1.56,0.09,0.57,0
CzechPrague2,30.28,36.76,16.69,5.86,6.96,1.53,1.34 ,0,0.41,0.17,0,0,0
CzechPrague3,29.50,29.61,14.32,7.35,15.61,0,0.64,0 ,0.90,0,1.14,0.94,0
CzechPrague4,36.94,31.58,15.93,5.03,8.13,0.02,0,0, 0,0.60,1.70,0,0.08
CzechPrague5,31.86,38.30,11.52,5.66,7.41,1.94,0,0. 18,1.46,1.18,0.50,0,0
CzechPrague6,34.41,38.68,11.93,6.68,4.64,0.26,0.34 ,0.36,1.02,1.13,0,0.05,0.51
CzechPrague7,37.34,33.10,15.99,4.03,7.36,0.41,0.32 ,0,0.46,0,1.00,0,0

Eurogenes K15:


CzechPrague1,26.40,16.63,19.85,14.64,10.96,3.97,4. 46,1.02,0,0,0.41,1.29,0,0.36,0
CzechPrague2,19.74,19.72,21.52,17.99,11.54,5.13,2. 83,1.27,0.26,0,0,0,0,0,0
CzechPrague3,20.19,18.34,15.37,15.63,10.54,6.23,11 .89,0,0,0,0,0,0.90,0.90,0
CzechPrague4,26.61,23.34,18.87,10.43,10.96,3.72,4. 62,0,0,0,0,0.08,1.37,0,0
CzechPrague5,23.67,16.67,22.89,17.16,8.23,4.35,3.8 6,1.56,0,0,0.63,0.78,0.20,0,0
CzechPrague6,21.18,23.89,24.35,16.36,6.22,5.35,0.9 7,0.24,0,0.05,0.08,0.78,0,0.17,0.34
CzechPrague7,26.45,22.01,22.33,9.92,11.43,3.50,3.6 0,0.15,0,0,0,0,0.61,0,0

[Peterski]

Using Eurogenes K15:


<tbody>
Distance to:
CzechPrague1


4.78668988
Hungarian


5.15846670
Slovak


5.58972271
Czech


6.10271251
East_German


6.68234816
Slovenian


6.89436727
Austria-Burgenland


7.10265443
North_Croat


8.68007488
Sorb_Lusatia


9.27800086
Croatian


9.99710958
Austrian

</tbody>


<tbody>
Distance to:
CzechPrague6


6.66613831
Sorb_Lusatia


7.03600028
Czech


7.25953167
North_Croat


7.88209997
Polish


7.89550505
South_Polish


9.52401176
Polish_Kielce


9.67715638
Slovak


9.79075584
Austrian


9.84867504
Russian_Smolensk


9.96729884
Slovenian

</tbody>


<tbody>
Distance to:
CzechPrague2


5.70717093
Croatian


5.94588934
North_Croat


6.15326742
Czech


6.19650025
Slovenian


7.12026589
Slovak


8.06136465
South_Polish


8.14130211
Sorb_Lusatia


8.55500438
Austrian


8.82959229
Hungarian


9.21055916
Moldavian

</tbody>


<tbody>
Distance to:
CzechPrague3


5.07122273
Serbian


6.40765168
Romania_Nw


6.80106380
Serbian_Bosnia


7.55782211
Szekely


7.69517381
South_Serbian


7.71000649
Romania_Ne


7.79167607
Bosnian


8.11820177
Austrian


8.16652313
Bosniak


8.34774685
Montenegrin

</tbody>


<tbody>
Distance to:
CzechPrague4


4.51349089
East_German


5.69185383
Austria-Burgenland


7.20217328
Czech


8.07553713
Austrian


8.15870701
Hungarian


8.55445498
French_Alsace


8.64608582
North_Croat


8.72536091
Slovenian


8.81738623
German_Bavarian


9.83061036
Swiss_German

</tbody>


<tbody>
Distance to:
CzechPrague5


2.41188441
Slovak


4.41243697
Sorb_Lusatia


4.88441399
Czech


5.23291506
South_Polish


5.68785548
Ukrainian_Lviv


5.78964593
North_Croat


6.56407648
Hungarian


6.79046390
Slovenian


6.86541332
Polish_Masuria


6.96104877
Croatian

</tbody>


<tbody>
Distance to:
CzechPrague7


5.96395842
East_German


6.07786969
Czech


8.05510397
Hungarian


8.16706048
Slovenian


8.20752094
Austria-Burgenland


8.63627234
North_Croat


9.35001202
Slovak


9.56997910
Austrian


10.28426954
Sorb_Lusatia


11.55504219
Croatian

</tbody>

Obviously CzechPrague3 is an outlier.

[vbnetkhio]

These are new previously unpublished academic samples, not the same ones as the older group of Prague Czechs.

Dodecad K12b:


CzechPrague1,4.30,1.50,0.63,0,30.03,48.02,0,0,2.37 ,0,13.16,0CzechPrague2,4.10,0.54,0.86,0.39,30.55,4 8.26,0.14,0,2.44,0,12.73,0
CzechPrague3,6.27,0.49,2.17,0,27.48,41.99,1.00,0.1 3,3.96,0,16.52,0
CzechPrague4,5.75,0.01,0,0.31,34.73,45.25,0,0.30,1 .42,0,12.22,0
CzechPrague5,6.37,1.17,0,0.58,26.63,49.94,0,0,2.89 ,0.34,12.10,0
CzechPrague6,4.74,0.60,0,0,28.21,52.99,0.99,0,1.00 ,0.46,11.01,0
CzechPrague7,5.26,0.53,0,0.21,33.32,48.26,0.35,0,1 .91,0.19,9.98,0

Eurogenes K13:


CzechPrague1,33.37,33.72,15.44,4.92,7.56,1.11,0.31 ,0,1.34,1.56,0.09,0.57,0
CzechPrague2,30.28,36.76,16.69,5.86,6.96,1.53,1.34 ,0,0.41,0.17,0,0,0
CzechPrague3,29.50,29.61,14.32,7.35,15.61,0,0.64,0 ,0.90,0,1.14,0.94,0
CzechPrague4,36.94,31.58,15.93,5.03,8.13,0.02,0,0, 0,0.60,1.70,0,0.08
CzechPrague5,31.86,38.30,11.52,5.66,7.41,1.94,0,0. 18,1.46,1.18,0.50,0,0
CzechPrague6,34.41,38.68,11.93,6.68,4.64,0.26,0.34 ,0.36,1.02,1.13,0,0.05,0.51
CzechPrague7,37.34,33.10,15.99,4.03,7.36,0.41,0.32 ,0,0.46,0,1.00,0,0

Eurogenes K15:


CzechPrague1,26.40,16.63,19.85,14.64,10.96,3.97,4. 46,1.02,0,0,0.41,1.29,0,0.36,0
CzechPrague2,19.74,19.72,21.52,17.99,11.54,5.13,2. 83,1.27,0.26,0,0,0,0,0,0
CzechPrague3,20.19,18.34,15.37,15.63,10.54,6.23,11 .89,0,0,0,0,0,0.90,0.90,0
CzechPrague4,26.61,23.34,18.87,10.43,10.96,3.72,4. 62,0,0,0,0,0.08,1.37,0,0
CzechPrague5,23.67,16.67,22.89,17.16,8.23,4.35,3.8 6,1.56,0,0,0.63,0.78,0.20,0,0
CzechPrague6,21.18,23.89,24.35,16.36,6.22,5.35,0.9 7,0.24,0,0.05,0.08,0.78,0,0.17,0.34
CzechPrague7,26.45,22.01,22.33,9.92,11.43,3.50,3.6 0,0.15,0,0,0,0,0.61,0,0

old set for comparison:

Czech1,37.71,34.65,9.43,11.73,3.48,0,1.17,1.1,0,0, 0,0.26,0.45
Czech2,33.23,36.57,10.23,5.07,8.32,3.94,0.92,0,0,1 .48,0.24,0,0
Czech3,35.23,33.06,13.54,8.11,7.56,0,2.01,0,0.25,0 ,0.22,0,0.02
Czech4,32.91,41.27,8.38,5.6,5.51,4.32,0,0.31,0.68, 0.9,0.04,0.05,0.02
Czech5,34.62,34.25,12.19,7.54,7.59,1.01,1.79,0,0.7 7,0,0.16,0.09,0
Czech6,35,34.46,11.7,5.37,9.29,1.29,0.31,0.18,0,0. 3,0.63,0.88,0.6
Czech7,35.19,31.22,15.73,3.38,8.36,2.65,0,0.1,0.71 ,1.08,0,1.43,0.16
Czech8,39.62,33.29,8.84,5.79,8.85,0,0.84,0,0.07,0. 87,1.68,0,0.14
Czech9,31.71,39.83,11.22,9.12,5.03,0,0.67,0,0,1.81 ,0.41,0,0.2
Czech10,27.7,39.98,15.78,3.97,6.85,0,2.55,0.14,2.4 8,0.44,0,0.1,0

[Leto]

They are quite different from each other.

By the way, fix Czech2 in the first set.

[vbnetkhio]

Obviously CzechPrague3 is an outlier.

does it have enough SNPs for gedmatch?

[Ion Basescul]

Using Eurogenes K15:


<tbody>
Distance to:
CzechPrague1


4.78668988
Hungarian


5.15846670
Slovak


5.58972271
Czech


6.10271251
East_German


6.68234816
Slovenian


6.89436727
Austria-Burgenland


7.10265443
North_Croat


8.68007488
Sorb_Lusatia


9.27800086
Croatian


9.99710958
Austrian

</tbody>


<tbody>
Distance to:
CzechPrague6


6.66613831
Sorb_Lusatia


7.03600028
Czech


7.25953167
North_Croat


7.88209997
Polish


7.89550505
South_Polish


9.52401176
Polish_Kielce


9.67715638
Slovak


9.79075584
Austrian


9.84867504
Russian_Smolensk


9.96729884
Slovenian

</tbody>


<tbody>
Distance to:
CzechPrague2


5.70717093
Croatian


5.94588934
North_Croat


6.15326742
Czech


6.19650025
Slovenian


7.12026589
Slovak


8.06136465
South_Polish


8.14130211
Sorb_Lusatia


8.55500438
Austrian


8.82959229
Hungarian


9.21055916
Moldavian

</tbody>


<tbody>
Distance to:
CzechPrague3


5.07122273
Serbian


6.40765168
Romania_Nw


6.80106380
Serbian_Bosnia


7.55782211
Szekely


7.69517381
South_Serbian


7.71000649
Romania_Ne


7.79167607
Bosnian


8.11820177
Austrian


8.16652313
Bosniak


8.34774685
Montenegrin

</tbody>


<tbody>
Distance to:
CzechPrague4


4.51349089
East_German


5.69185383
Austria-Burgenland


7.20217328
Czech


8.07553713
Austrian


8.15870701
Hungarian


8.55445498
French_Alsace


8.64608582
North_Croat


8.72536091
Slovenian


8.81738623
German_Bavarian


9.83061036
Swiss_German

</tbody>


<tbody>
Distance to:
CzechPrague5


2.41188441
Slovak


4.41243697
Sorb_Lusatia


4.88441399
Czech


5.23291506
South_Polish


5.68785548
Ukrainian_Lviv


5.78964593
North_Croat


6.56407648
Hungarian


6.79046390
Slovenian


6.86541332
Polish_Masuria


6.96104877
Croatian

</tbody>


<tbody>
Distance to:
CzechPrague7


5.96395842
East_German


6.07786969
Czech


8.05510397
Hungarian


8.16706048
Slovenian


8.20752094
Austria-Burgenland


8.63627234
North_Croat


9.35001202
Slovak


9.56997910
Austrian


10.28426954
Sorb_Lusatia


11.55504219
Croatian

</tbody>

Obviously CzechPrague3 is an outlier.

So are 4, 2 and 7

[Peterski]

So are 4, 2 and 7

No, I think they're within the range.

CzechPrague3 scores like a Csango.


does it have enough SNPs for gedmatch?

They have enough for GEDmatch.

[vbnetkhio]

So are 4, 2 and 7

4 and 7 are close to the edge, but not outliers. 2 is an average Czech in K13.

none of them is a clear outlier:
https://i.imgur.com/rXkeMQo.png

[Ion Basescul]

2 and 7 are close to the edge, but not outliers. 4 is an average Czech in K13.

none of them is a clear outlier:
https://i.imgur.com/rXkeMQo.png

But 4 and 7 clearly have recent non-Czech ancestry. I don't like it when the people on the ground from academia just grab samples randomly, without knowing if people are mixed or not. Do this on a large enough scale and every country will have huge ranges in inter-individual results like in Latin America.

[gixajo]

Averages with and without n.3:
Dodecad 12

CzechPrague:D12_n=7,5.26,0.69,0.52,0.21,30.14,47.8 2,0.35,0.06,2.28,0.14,12.53,0.00
CzechPrague:D12_n=6,5.09,0.73,0.25,0.25,30.58,48.7 9,0.25,0.05,2.01,0.17,11.87,0.00
Eurogenes K13

CzechPrague:K13_n=7,33.39,34.54,14.55,5.65,8.24,0. 75,0.42,0.08,0.80,0.66,0.63,0.22,0.08
CzechPrague:K13_n=6,34.03,35.36,14.58,5.36,7.01,0. 88,0.39,0.09,0.78,0.77,0.55,0.10,0.10
Eurogenes K15

CzechPrague:K15_n=7,23.46,20.09,20.74,14.59,9.98,4 .61,4.60,0.61,0.04,0.01,0.16,0.42,0.44,0.20,0.05
CzechPrague:K15_n=6,24.01,20.38,21.64,14.42,9.89,4 .34,3.39,0.71,0.04,0.01,0.19,0.49,0.36,0.09,0.06

[gixajo]

All of them from updated datasheets:

Dodecad 12


Distance to: CzechPrague:D12_n=6
1.68875694 Czech
3.91809903 German
4.58427748 Slovak
5.01484795 Hungarian_North
5.08179102 Hungarian_Alföld

Distance to: CzechPrague:D12_n=7
1.84469510 Czech
3.84536084 Hungarian_Alföld
3.93228941 Hungarian_North
4.19104999 Slovenian
4.47050333 Slovak

K13

Distance to: CzechPrague:K13_n=6
3.02395436 Czech
3.84256425 German_East
4.01537047 Slovenian
4.29890684 Slovak
5.14325772 Hungarian_North

Distance to: CzechPrague:K13_n=7
2.61026819 Slovenian
3.69651187 Czech
3.92548086 Hungarian_Transdanubia+Budapest
4.01738721 Hungarian_North
4.12973365 Slovak

K15

Distance to: CzechPrague:K15_n6
1.45268717 Czech
4.13862296 North_Croat
4.95983871 East_German
5.04712561 Slovenian
5.20070574 Slovak

Distance to: CzechPrague:K15_n7
2.04985365 Czech
3.30003030 North_Croat
3.97100840 Slovenian
4.58936815 Hungarian
5.09817614 East_German

[gixajo]

And since you are here gathered, who should we talk to to update the Spanish references of the Vahaduo gedmatch calculators?

And what origin or origin must the Gedmatch kits have to be accepted in Vahaduo as references?

I have the kit numbers of the individuals used in the Catalan county references that are in the G25.(Pirineu, Barcelonés, pero-barcelona, Lleida, camp de Tarragona etc...)

And apart from those, hundreds of numbers of Spanish kits (and 80 Portuguese) some with self-proclaimed origins, others of unknown or partial origins, and I do not know what requirements are valid to be accepted as valid.

[gixajo]

And do not feel sight by my presence here, you can continue talking.;)

[cakmir7y]

old set for comparison:

Czech1,37.71,34.65,9.43,11.73,3.48,0,1.17,1.1,0,0, 0,0.26,0.45
Czech2,33.23,36.57,10.23,5.07,8.32,3.94,0.92,0,0,1 .48,0.24,0,0
Czech3,35.23,33.06,13.54,8.11,7.56,0,2.01,0,0.25,0 ,0.22,0,0.02
Czech4,32.91,41.27,8.38,5.6,5.51,4.32,0,0.31,0.68, 0.9,0.04,0.05,0.02
Czech5,34.62,34.25,12.19,7.54,7.59,1.01,1.79,0,0.7 7,0,0.16,0.09,0
Czech6,35,34.46,11.7,5.37,9.29,1.29,0.31,0.18,0,0. 3,0.63,0.88,0.6
Czech7,35.19,31.22,15.73,3.38,8.36,2.65,0,0.1,0.71 ,1.08,0,1.43,0.16
Czech8,39.62,33.29,8.84,5.79,8.85,0,0.84,0,0.07,0. 87,1.68,0,0.14
Czech9,31.71,39.83,11.22,9.12,5.03,0,0.67,0,0,1.81 ,0.41,0,0.2
Czech10,27.7,39.98,15.78,3.97,6.85,0,2.55,0.14,2.4 8,0.44,0,0.1,0

Strange, I dont see my K13 data in here. Do you have the datasaet for czech republic divided into bohemia adn moravia?

[Jana]

And since you are here gathered, who should we talk to to update the Spanish references of the Vahaduo gedmatch calculators?

And what origin or origin must the Gedmatch kits have to be accepted in Vahaduo as references?

I have the kit numbers of the individuals used in the Catalan county references that are in the G25.(Pirineu, Barcelonés, pero-barcelona, Lleida, camp de Tarragona etc...)

And apart from those, hundreds of numbers of Spanish kits (and 80 Portuguese) some with self-proclaimed origins, others of unknown or partial origins, and I do not know what requirements are valid to be accepted as valid.

Well it's up to you or whoever is making Spanish averages to decide, but my personal criteria for sample to be included in regional average is following:
4 grandparents born in same region and at least 7/8 Croatian ancestry (other 1/8 can be foreign from minorities who lived there in history)

This is just how I do it, you are free to decide for yourself as everyone is.

[Jana]

It would be wonderful if we can get regional averages for 3 historical regions of Czech Republic: Bohemia, Moravia & Silesia.

[Dunai]

And since you are here gathered, who should we talk to to update the Spanish references of the Vahaduo gedmatch calculators?

And what origin or origin must the Gedmatch kits have to be accepted in Vahaduo as references?

I have the kit numbers of the individuals used in the Catalan county references that are in the G25.(Pirineu, Barcelonés, pero-barcelona, Lleida, camp de Tarragona etc...)

And apart from those, hundreds of numbers of Spanish kits (and 80 Portuguese) some with self-proclaimed origins, others of unknown or partial origins, and I do not know what requirements are valid to be accepted as valid.

I know it requires great amount of work, but the most accurate would be if you verify if the grandparents of a kit holder are largely from the same region, and yes, 10-15% other ethnic ancestry can still be accepted, like for example a Catalan kit holder has confirmed 3 Catalan grandparents and 1 Spanish, they still fit the Catalan average, since Spanish and Catalans are so close genetically, but for example if that person instead of 1 Spanish grandparent has let's say a Greek or German, than me personally wouldn't include him or her in the Catalan average anymore, since those are quite far away ethnic groups compared to Catalans. And you could use this standard maybe to all regional averages of Iberia or ethnic averages of Iberia. Even if most likely you have to exchange plenty of emails with the kit owners but at least you will be sure the end result will be a high quality research. This is the laborious method I used for my Hungarian averages and even if nowhere perfect, but my regional averages are still more closer to reality than the random academic samples that were used for Hungary until now, which often had heavily mixed Hungarians in them.

[gixajo]

I know it requires great amount of work, but the most accurate would be if you verify if the grandparents of a kit holder are largely from the same region, and yes, 10-15% other ethnic ancestry can still be accepted, like for example a Catalan kit holder has confirmed 3 Catalan grandparents and 1 Spanish, they still fit the Catalan average, since Spanish and Catalans are so close genetically, but for example if that person instead of 1 Spanish grandparent has let's say a Greek or German, than me personally wouldn't include him or her in the Catalan average anymore, since those are quite far away ethnic groups compared to Catalans. And you could use this standard maybe to all regional averages of Iberia or ethnic averages of Iberia. Even if most likely you have to exchange plenty of emails with the kit owners but at least you will be sure the end result will be a high quality research. This is the laborious method I used for my Hungarian averages and even if nowhere perfect, but my regional averages are still more closer to reality than the random academic samples that were used for Hungary until now, which often had heavily mixed Hungarians in them.

Well, it´s practically impossible discriminate Catalan ancestry from Spanish one throught Gedmatch or G25 references, you can do this with Basques, at least with "purest" Basques referents, but not with Catalans(I mean, in a effective way, through finding a "uniqueness"). Any Spanish even from more distant origins from catalonia, in G25 can score quite high in those Catalan references.

I mean, Pirineu is close to North Aragonese and quite close to Basques, or Camp of Tarragona close to Castilla la Mancha, or Valencia, etc...

You can see even some catalan individuals whose closest distance is Galicia or Portugal, and I am speaking about individuals used in Academic papers subsidized by the regional government of Catalonia through Catalan universities ... :picard1:

My criterion for using individuals in regional averages was precisely that, having 4 grandparents born in the same region, or at most 3 in the same province, and a grandparent in a neighboring province or region (and that is similar).

The problem is that it is difficult to trust the origins declared by each one, and that although some individuals have 4 grandparents (even 8 great-grandparents) from the same province, genetically they are very different from the official references.

I have come across several individuals from provinces far to the south, which are genetically indistinguishable from individuals from far to the north of the peninsula, and vice versa.

I try to solve the problem of "outliers" (they are not really) with PCAs and cluster dendrograms, but it often happens to me that those with their ancestors more located in one place, are often more outliers in the PCAs than those with diverse origins.

In a province with few inhabitants, there were 6 kits of confirmed people with at least 3 generations of ancestors in the same province, and the% in a component as important in the Iberian Peninsula as the Atlantic / NorthAtlantic, there were variations of up to 6% .

Discriminating individuals is more complicated than it seemed at first.

Luckily I have a way of contacting someone professional who can help and advise me do this effectively.

[gixajo]

It would be wonderful if we can get regional averages for 3 historical regions of Czech Republic: Bohemia, Moravia & Silesia.

The big cities, or capital cities of the countries, are not usually very homogeneous genetically, because they are a historical focus of attraction of immigrants, from all areas of the country itself.(or even foreigners):icon_yes:

Edit:better than "homogeneous genetically" would be better, because of what I mentioned, they can work better as a general diffuse and unspecified reference of the country, than as a reference to that specific area.:confused:

[Dunai]

Well, it´s practically impossible discriminate Catalan ancestry from Spanish one throught Gedmatch or G25 references, you can do this with Basques, at least with "purest" Basques referents, but not with Catalans(I mean, in a effective way, through finding a "uniqueness"). Any Spanish even from more distant origins from catalonia, in G25 can score quite high in those Catalan references.

I mean, Pirineu is close to North Aragonese and quite close to Basques, or Camp of Tarragona close to Castilla la Mancha, or Valencia, etc...

You can see even some catalan individuals whose closest distance is Galicia or Portugal, and I am speaking about individuals used in Academic papers subsidized by the regional government of Catalonia through Catalan universities ... :picard1:

My criterion for using individuals in regional averages was precisely that, having 4 grandparents born in the same region, or at most 3 in the same province, and a grandparent in a neighboring province or region (and that is similar).

The problem is that it is difficult to trust the origins declared by each one, and that although some individuals have 4 grandparents (even 8 great-grandparents) from the same province, genetically they are very different from the official references.

I have come across several individuals from provinces far to the south, which are genetically indistinguishable from individuals from far to the north of the peninsula, and vice versa.

I try to solve the problem of "outliers" (they are not really) with PCAs and cluster dendrograms, but it often happens to me that those with their ancestors more located in one place, are often more outliers in the PCAs than those with diverse origins.

In a province with few inhabitants, there were 6 kits of confirmed people with at least 3 generations of ancestors in the same province, and the% in a component as important in the Iberian Peninsula as the Atlantic / NorthAtlantic, there were variations of up to 6% .

Discriminating individuals is more complicated than it seemed at first.

Luckily I have a way of contacting someone professional who can help and advise me do this effectively.

I see what you mean. In Spain it is rather hard to distinguish between various regions since you do not get the same large level of genetic distances that happen in Italy or in Greece, from what I seen so far Spain with all the regions seems surprisingly homogenous. So probably regional averages are the only way to go rather than ethnic ones, maybe with the exception of Basque Country, which has a mostly Basque population. Me personally I also used large outliers for my Hungarian averages because based on the email exchanges the person seemed very genuine when they said their ancestry traces back to only that region since generations, even if Atlantic and Baltic levels (these are most indicative for Hungarian case) were very far away from the regional or national average. It could be that it's better to omit the large outliers from both ends when doing averages (there may be a statistics recommendation for this), I don't know yet, if that's the case I should recalculate the current averages, but I am pretty satisfied with the current averages for now, since I believe the larger outliers from both ends even out eventually when combined with dozens of results.

[Jana]

Outliers should absolutely not be excluded if one's ancestry is native to the region, because outliers naturally happen in every population and each region has genetic range. I would consider excluding outliers as manipulation of the results, except if it's obvious they can't be native (Gypsy admixed people for example)

2 villages in same region can be very different genetically as result of different people settling them in let's say medieval or result of inbreeding which creates genetic drift and this was common in European history (eg. Irish travelers don't cluster wuth Irish due to genetic isolation and they are Irish in origins)

So yeah, outliers are as valuable samples for given region as much as are those scoring more typical results for the place.

[Ion Basescul]

North_Atlantic 31.8 Pct
Baltic 38.43 Pct
West_Med 14.17 Pct
West_Asian 3.82 Pct
East_Med 8.79 Pct
Red_Sea -
South_Asian 0.81 Pct
East_Asian -
Siberian -
Amerindian 0.28 Pct
Oceanian 1.19 Pct
Northeast_African 0.68 Pct
Sub-Saharan -

[Ion Basescul]

North_Atlantic 24.36 Pct
Baltic 38.4 Pct
West_Med 12.87 Pct
West_Asian 9.15 Pct
East_Med 11.54 Pct
Red_Sea -
South_Asian -
East_Asian -
Siberian 1.73 Pct
Amerindian 1.38 Pct
Oceanian 0.57 Pct
Northeast_African -
Sub-Saharan -

[Ion Basescul]

North_Atlantic 28.01 Pct
Baltic 38.91 Pct
West_Med 11.52 Pct
West_Asian 9.55 Pct
East_Med 9.93 Pct
Red_Sea -
South_Asian 0.26 Pct
East_Asian -
Siberian 0.61 Pct
Amerindian 1.11 Pct
Oceanian -
Northeast_African -
Sub-Saharan 0.11 Pct

[Ion Basescul]

First one has all known ancestors from the region of Plzen (all Czechs)...the rest don't have family trees, so it's just a guess going from surnames

Population
North_Atlantic 34.78 Pct
Baltic 33.69 Pct
West_Med 10.75 Pct
West_Asian 8.75 Pct
East_Med 10.46 Pct
Red_Sea 0.21 Pct
South_Asian -
East_Asian -
Siberian 0.41 Pct
Amerindian -
Oceanian 0.93 Pct
Northeast_African -
Sub-Saharan -


Surname non-existent in Eastern and barely existent in Central parts. Overwhelmingly popular in Plzen, South Bohemia and Usti nad Labem

North_Atlantic 33.47 Pct
Baltic 37.94 Pct
West_Med 9.93 Pct
West_Asian 5.61 Pct
East_Med 9.61 Pct
Red_Sea 0.43 Pct
South_Asian 1.5 Pct
East_Asian -
Siberian -
Amerindian 1.22 Pct
Oceanian -
Northeast_African 0.3 Pct
Sub-Saharan -


Non-existent in Central and Eastern parts. Overwhelmingly popular in Central Bohemia, and to a lesser degree in Plzen

North_Atlantic 34.08 Pct
Baltic 38.55 Pct
West_Med 11.84 Pct
West_Asian 2.59 Pct
East_Med 9.79 Pct
Red_Sea 0.11 Pct
South_Asian 0.61 Pct
East_Asian -
Siberian 1.04 Pct
Amerindian 0.05 Pct
Oceanian 0.36 Pct
Northeast_African 0.95 Pct
Sub-Saharan -


Non-existent in Central and Eastern parts. Overwhelmingly popular in Central Bohemia, and to a lesser degree in Plzen

North_Atlantic 28.41 Pct
Baltic 39.27 Pct
West_Med 12.11 Pct
West_Asian 8.25 Pct
East_Med 8.52 Pct
Red_Sea -
South_Asian -
East_Asian 1.54 Pct
Siberian 0.62 Pct
Amerindian 0.74 Pct
Oceanian 0.26 Pct
Northeast_African 0.27 Pct
Sub-Saharan -

[Ion Basescul]

Western Czechs from posts 23, 24 and 25

<google-sheets-html-origin style="color: rgb(0, 0, 0); font-size: medium;">
<colgroup><col style="width: 100px;"><col style="width: 100px"><col width="100"><col width="100"><col width="100"><col width="100"><col width="100"><col width="100"><col width="100"><col width="100"><col width="100"><col width="100"><col width="100"><col width="100"></colgroup><tbody>
ID
N_Atlantic
Baltic
West_Med
West_Asian
East_Med
Red_Sea
South_Asian
East_Asian
Siberian
Amerindian
Oceanian
NE_African
Sub-Saharan


W1
34.78
33.69
10.75
8.75
10.46
0.21
0
0
0.41
0
0.93
0
0


W2
33.47
37.94
9.93
5.61
9.61
0.43
1.5
0
0
1.22
0
0.3
0


W3
34.08
38.55
11.84
2.59
9.79
0.11
0.61
0
1.04
0.05
0.36
0.95
0


W4
28.41
39.27
12.11
8.25
8.52
0
0
1.54
0.62
0.74
0.26
0.27
0


W5
24.36
38.4
12.87
9.15
11.54
0
0
0
1.73
1.38
0.57
0
0


W6
28.01
38.91
11.52
9.55
9.93
0
0.26
0
0.61
1.11
0
0
0.11


Average
30.52
37.79
11.50
7.32
9.98
0.13
0.40
0.26
0.74
0.75
0.35
0.25
0.02

</tbody>
</google-sheets-html-origin><google-sheets-html-origin style="color: rgb(0, 0, 0); font-size: medium;">
</google-sheets-html-origin>


Distance to: W1
5.62898748 Hungarian_Transdanubia+Budapest
5.75466767 Slovenian
6.18903062 Hungarian_North
6.19369841 Hungarian
6.24145015 Czech
6.44630902 Croat_North
6.75570130 Austrian
6.89573781 Slovak
6.94994245 German_East
7.12809933 Hungarian_Alföld
8.30906734 Croat
8.68031105 Croat_West
9.04534687 Croat_East
9.63244517 Sorb_Lusatia
9.98718679 Hungarian_Transylvania+Székely
10.01762447 Csángó-Ceangău
10.07833320 Bosniak_Bosnia
10.09013379 Ukrainian_Carpathian
10.25947367 Ukrainian_Galicia
10.56024621 South_Polish
10.65743403 Croat_South
11.70164946 Bosniak
11.91444082 Moldova_Ukrainian
12.05058920 German
12.08460591 Moldova_North


Distance to: W2
3.88414727 Czech
4.65391233 Slovak
4.73728825 Sorb_Lusatia
5.55778733 Hungarian_North
6.41773324 South_Polish
6.42735560 Slovenian
7.04694260 Croat_North
7.23600028 Ukrainian_Galicia
7.52418766 Greater_Poland
7.53207807 German_East
7.66905470 Hungarian_Transdanubia+Budapest
7.93251536 Hungarian
8.07118950 Ukrainian_Carpathian
8.41090364 Hungarian_Alföld
8.43572166 Moldova_Ukrainian
9.28694783 Polish_Masuria
9.42147016 Polish_Kielce
9.55520277 Croat
9.55771939 Croat_East
9.79808655 Silesian
10.11691158 Polish
10.18693771 Bosniak_Bosnia
10.40983189 Austrian
10.49252591 Podlaskie
10.55341177 Lower_Silesia


Distance to: W3
4.86357893 Czech
5.33341354 Sorb_Lusatia
5.77417527 Slovak
6.40944615 Hungarian_North
7.03189164 Slovenian
7.35344137 South_Polish
7.78409918 German_East
7.95381669 Croat_North
7.97450939 Greater_Poland
8.36455020 Ukrainian_Galicia
8.68188920 Hungarian_Transdanubia+Budapest
8.86399458 Hungarian
9.08453081 Ukrainian_Carpathian
9.10784277 Polish_Masuria
9.25207544 Hungarian_Alföld
9.35443745 Silesian
9.45931287 Moldova_Ukrainian
9.92980866 Polish_Kielce
10.47702725 Croat
10.53805959 Polish
10.65679595 Podlaskie
10.72185618 Croat_East
10.97098902 Austrian
11.44219822 Lower_Silesia
11.45701968 Croat_West


Distance to: W4
2.77627448 Ukrainian_Galicia
3.73166183 Ukrainian_Carpathian
3.95540137 Moldova_Ukrainian
4.33876711 Slovak
4.98803569 South_Polish
5.50957349 Hungarian_North
6.31292325 Croat_North
6.53528882 Czech
6.85862231 Bosniak_Bosnia
6.86386189 Croat_East
6.87587813 Sorb_Lusatia
6.89242338 Slovenian
7.15075520 Lower_Silesia
7.35866836 Greater_Poland
7.41679850 Hungarian_Alföld
7.71703311 Ukrainian
7.76385858 Polish_Kielce
7.87108633 Hungarian
7.87528412 Silesian
7.91170652 Croat
8.05499845 Hungarian_Transdanubia+Budapest
8.58170146 Moldova_North
8.90298826 Mazovia
9.01025527 Polish
9.58874340 Polish_Masuria


Distance to: W5
3.93469186 Ukrainian_Carpathian
4.50028888 Bosniak_Bosnia
4.98390409 Moldova_North
5.16297395 Croat_East
5.43782125 Moldova_Ukrainian
6.23183761 Ukrainian_Galicia
7.26009642 Croat
7.32851963 Bosniak
7.68773699 Hungarian_North
7.87821680 Croat_North
8.04155458 Hungarian_Alföld
8.12752730 Croat_South
8.26810740 Slovak
8.43692480 Moldova_Centre
8.61846274 Moldova_average
8.72959335 Serb_Bosnia
8.94979329 Croat_West
9.03458355 Hungarian
9.25826118 Slovenian
9.67005171 Romania_Moldavia_North
9.74196079 South_Polish
9.75012308 Hungarian_Transdanubia+Budapest
10.09576644 Ukrainian
10.11037091 Csángó-Ceangău
10.20232817 Serb_Croatia






Distance to: Average
3.01584151 Slovak
3.39761681 Hungarian_North
4.58387391 Ukrainian_Galicia
4.63479234 Ukrainian_Carpathian
4.71317303 Croat_North
4.73326526 Czech
4.92924944 Slovenian
5.82579608 South_Polish
5.83170644 Moldova_Ukrainian
6.00760352 Hungarian_Alföld
6.06158395 Hungarian
6.16103887 Hungarian_Transdanubia+Budapest
6.23462910 Croat_East
6.23603239 Sorb_Lusatia
6.77036927 Bosniak_Bosnia
6.77990413 Croat
7.94051636 Greater_Poland
8.32817507 Croat_West
9.05930461 Moldova_North
9.08674859 German_East
9.11878830 Polish_Kielce
9.16653151 Silesian
9.20309187 Lower_Silesia
9.40924014 Croat_South
9.68370797 Ukrainian

[cakmir7y]

Those distances are from K13 on vahaduo?

[Ion Basescul]

Those distances are from K13 on vahaduo?

K13 updated on vahaduo

[cakmir7y]

Here are numbers for me and my wife.

wife,30.88,39.43,12.38,5.35,9.65,0,0,0.63,0,1.21,0 ,0,0.42
me,31.56,37.06,13.66,4.66,8.02,2.54,0.51,0,0.66,1. 33,0,0,0


<tbody>
Distance to:
my wife



3.50458271
Slovak


4.70037233
Ukrainian_Galicia


4.72908025
Hungarian_North


4.80520551
Czech


4.92122952
Sorb_Lusatia


5.02793198
South_Polish


5.45989927
Ukrainian_Carpathian


5.73416079
Moldova_Ukrainian


5.90808768
Slovenian


6.06549256
Croat_North


6.53178383
Greater_Poland


7.15411071
Silesian


7.56632672
Hungarian_Alföld


7.67843734
Hungarian


7.75533365
Hungarian_Transdanubia+Budapest


7.77917091
Polish_Kielce


7.89402306
Croat_East


8.31175673
Croat


8.33765555
Bosniak_Bosnia


8.64180537
Polish_Masuria


8.69520558
Ukrainian


8.72237926
Lower_Silesia


8.88820004
Polish


9.26797713
German_East



9.30381642
Mazovia


</tbody>



<tbody>
Distance to:
me



2.62880201
Slovak


3.45040577
Czech


4.01702626
Hungarian_North


4.23135912
Slovenian


4.74494468
Croat_North


5.72630771
Ukrainian_Galicia


5.87253778
Hungarian_Transdanubia+Budapest


6.06619320
Hungarian


6.15758069
Sorb_Lusatia


6.22605814
Hungarian_Alföld


6.27643211
Ukrainian_Carpathian


6.78859337
South_Polish


7.05134739
German_East


7.55886235
Croat


7.78089326
Moldova_Ukrainian


7.86986023
Croat_East


8.25024242
Greater_Poland


8.58388024
Bosniak_Bosnia


8.59225232
Silesian


8.99165168
Croat_West


9.35283380
Polish_Masuria


9.62790735
Polish_Kielce


9.68933950
Austrian


9.71037589
Lower_Silesia


10.21951075
Polish

</tbody>

[Ion Basescul]

From Liberec region

North_Atlantic 34.95 Pct
Baltic 35.37 Pct
West_Med 12.81 Pct
West_Asian 7.66 Pct
East_Med 4.49 Pct
Red_Sea 2.06 Pct
South_Asian 0.97 Pct
East_Asian 0.63 Pct
Siberian -
Amerindian 0.17 Pct
Oceanian -
Northeast_African 0.23 Pct
Sub-Saharan 0.65 Pc

[CommonSense]

The third sample from the original post could have recent Jewish admix. Otherwise, I can't see any other explanation for that result.