Post LM Genetics K36 Correlation Maps with links to interactive maps

[Coastal Elite]

Post your correlation maps with links to interactive maps

https://fusiontables.googleusercontent.com/embedviz?q=select+col39%3E%3E1+from+1yuwMZL4IAuAVW-BDfVhsmmYHnCLbB4vet1w02SeU&viz=MAP&h=false&lat=54.47181020484116&lng=25.952161566603536&t=1&z=4&l=col39%3E%3E1&y=2&tmplt=2&hml=KML

https://i.imgur.com/W6uE1hI.jpg

https://fusiontables.googleusercontent.com/embedviz?q=select+col39%3E%3E1+from+1RlroR-rSIhWbaAhFdj-3APkB_y2a7tdMWjYwRpeq&viz=MAP&h=false&lat=49.762264324344166&lng=35.58978926052714&t=1&z=4&l=col39%3E%3E1&y=2&tmplt=2&hml=KML

https://i.imgur.com/Y3iAvfy.jpg

Correlation values (top 30)

1 DE Bayern 0,93785
2 Swiss_German 0,93242
3 DE Baden-Württemberg 0,92638
4 DE Rheinland-Pfalz 0,92372
5 Walloons 0,92123
6 Tirol (AUT) 0,91489
7 Flemish 0,90776
8 FR_North-East 0,9064
9 Afrikaner 0,89918
10 Donau_Schwaben 0,89888
11 DE Saarland0,8984
12 FR_Swiss 0,89353
13 German_NW-Romania 0,89072
14 England_South-East 0,88097
15 DE Hessen 0,87863
16 NL_Limburg 0,87854
17 DE Nordrhein-Westfalen 0,87602
18 NL_Zuid_Holland 0,87387
19 FR_North-West 0,872
20 NL_Noord-Holland 0,86168
21 DE Thüringen 0,86102
22 Austria 0,85761
23 NL_Noord_Brabant 0,85751
24 England_North-West 0,85341
25 Sudetenland 0,85148
26 NL_Gelderland 0,85114
27 Cumbria 0,8469
28 England_North-East 0,84656
29 FR_mixed 0,84387
30 FR_West 0,84055

[J. Ketch]

https://i.postimg.cc/g2Z7DSXB/k36-capture.jpg

Correlation values (top 100)

1 Niedersachsen 0.95671
2 England_South-East 0.95594
3 England_North-West 0.95577
4 Northern Ireland 0.95453
5 Dutch_Overijssel 0.95349
6 Dutch_Noord_Brabant 0.95321
7 Dutch_Gelderland 0.95308
8 Scotland0.94984
9 Schleswig-Holstein0.94912
10 Cumbria 0.94902
11 Wales 0.94785
12 Orkney 0.94756
13 Iceland 0.94697
14 Dutch_Limburg 0.94675
15 Dutch_Zuid_Holland 0.94648
16 Mecklenburg-Vorpommern0.9432
17 Dutch_Drenthe 0.94266
18 Ireland 0.94004
19 England_North-East 0.93895
20 England_South-West 0.93885
21 Nordrhein-Westfalen 0.93619
22 Denmark0.93544
23 Hessen 0.93347
24 Dutch_Utrecht 0.93167
25 Flemish 0.9261
26 Dutch_Friesland 0.92508
27 FR_West 0.92474
28 SV_Skane 0.92436
29 FR_Brittany 0.92067
30 Norwegians 0.92039
31 SV_Gotaland 0.91722
32 Dutch_Groningen 0.91657
33 Dutch_Noord_Holland 0.91448
34 FR_North-West 0.90837
35 Walloons0.90681
36 Bayern 0.8881
37 Pommern 0.88559
38 Rheinland-Pfalz 0.88219
39 Sachsen-Anhalt 0.87959
40 Baden-Württemberg 0.87351
41 FR_North-East 0.86685
42 Oberland_Upper_Prussia 0.86236
43 Thüringen 0.8537
44 SV_Svealand 0.84937
45 Saxony 0.84393
46 Swiss_German 0.83741
47 Sudetenland 0.8276
48 Saarland0.81445
49 Austria 0.81419
50 Brandenburg 0.8094
51 Central_Prussia 0.80257
52 Schlesien 0.79662
53 FR_mixed 0.78952
54 IT_Bolzano 0.78507
55 Tirol 0.78507
56 FR_Swiss 0.76618
57 Neumark0.75818
58 Grenzmark 0.75603
59 FR_Central 0.74135
60 Czechs 0.73501
61 North_Norway 0.71706
62 Hungary 0.71351
63 North Sweden 0.70386
64 Lusatian_Sorbs 0.69355
65 FR_South0.67801
66 IT_Aosta 0.67793
67 Slovenia 0.67448
68 PL_Upper_Silesia 0.65414
69 PL_North 0.63634
70 PL_Subcarpathia 0.63626
71 Kashubians 0.63279
72 Greater_Poland (Wielkopolska) 0.63176
73 ES_Galicia 0.61657
74 NE-Prussia 0.61217
75 IT_Friuli 0.61167
76 Croatia 0.61139
77 IT_Piemonte 0.60988
78 Swiss_Italian 0.6034
79 Islas Baleares 0.59788
80 IT_Trentino 0.59659
81 Central Romania 0.59383
82 Slovakia 0.59321
83 IT_Veneto 0.58886
84 Pl_South (Małopolska) 0.58698
85 Ukraine_Western 0.58572
86 Croats_BIH 0.5852
87 Portugal 0.58118
88 Ukraine Kiev region 0.57599
89 Comunidad Valenciana 0.56913
90 Carpathian Rusyns 0.56236
91 Ukraine_Volhyn 0.55924
92 PL_Central 0.55778
93 Dutch_Ashkenazy 0.55478
94 North-East_Romania 0.55259
95 Bosniaks 1 0.55138
96 Bosniaks 2 0.55138
97 Cataluna 0.54768
98 Principado de Asturias 0.54379
99 Cossacks_Kuban 0.53712
100 Spanish_mixed 0.53548

[J. Ketch]

My mother's, from the English Midlands

https://fusiontables.google.com/embedviz?q=select+col39%3E%3E1+from+1BY3PEJaeujorV hglkPtv5OdIIG8iPAMgX9R6U-YK&viz=MAP&h=false&lat=54.92056884806707&lng=18.943170624999993&t=1&z=4&l=col39%3E%3E1&y=2&tmplt=2&hml=KML
https://screenshotscdn.firefoxusercontent.com/images/6a15153e-c538-4651-84e5-72272a4e600e.png

Highest correlation

1 England_South-East 0,97544
2 NL_Noord_Brabant 0,96769
3 Cumbria 0,96458
4 Afrikaner 0,96223
5 England_North-West 0,9621
6 Northern Ireland 0,96074
7 DE Hessen 0,95977
8 DE Niedersachsen 0,95819
9 DE Mecklenburg-Vorpommern 0,95798
10 Shetlands 0,95764
11 NL_Zuid_Holland 0,95692
12 NL_Gelderland 0,95675
13 England_North-East 0,95653
14 DE Nordrhein-Westfalen 0,9542
15 Orkney 0,95382
16 Flemish 0,9537
17 NL_Limburg 0,95357
18 Wales 0,95253
19 DE Schleswig-Holstein 0,95138
20 NL_Overijssel 0,94601

Euclidean distances

https://fusiontables.googleusercontent.com/embedviz?q=select+col39%3E%3E1+from+1IqCkBM3fAgthf qiLUH-n38PHSb8qvRkl-oTO-igh&viz=MAP&h=false&lat=51.44687884437605&lng=24.040826874999993&t=1&z=4&l=col39%3E%3E1&y=2&tmplt=2&hml=KML
https://i.postimg.cc/wBRMCLN7/CaptureA.jpg

1 England_South-East 1,3842243
2 Cumbria 1,6622171
3 England_North-West 1,7025059
4 NL_Noord_Brabant 1,7550127
5 Northern Ireland 1,774319
6 DE Mecklenburg-Vorpommern 1,7880842
7 DE Niedersachsen 1,8093214
8 DE Hessen 1,8124669
9 Afrikaner 1,8294298
10 NL_Gelderland 1,8323301
11 England_North-East 1,8753814
12 DE Nordrhein-Westfalen 1,8838148
13 Orkney 1,8862903
14 Flemish 1,8877463
15 NL_Zuid_Holland 1,8884135
16 Wales 1,9168893
17 Shetlands 1,9362514
18 DE Schleswig-Holstein 1,943611
19 NL_Limburg 2,0090637
20 NL_Overijssel 2,0250324

Furthest Distances

593 Bolivian_Pando 39,806717
594 San 39,841157
595 Vanuatu 40,334467
596 Brong Ghana 40,512245
597 Chane_ARG 40,910107
598 Esan (Nigeria) 41,009719
599 Quechua 41,16069
600 Yoruba (Nigeria) 41,699072
601 Zapotec 42,254257
602 Mixtec 42,289429
603 Mandinka 43,470359
604 Cachi_ARG 43,569137
605 Bolivian_Quechua 43,861554
606 Mixe 44,196634
607 Colla_ARG 44,260501
608 Bolivian_Aymara 46,454631
609 Wichi_ARG 46,801773
610 Nasioi (Melanesians) 47,262824
611 Baining (Melanesians) 48,238248
612 Papuan_Highland 51,594477

[Rædwald]

https://i.postimg.cc/4dzRQ9qX/Screenshot-2018-11-19-at-00-00-29.png

Top 30 Correlation Values

1 Cumbria 0,97522
2 Northern Ireland 0,97063
3 FR_West 0,96393
4 Orkney 0,96361
5 Wales 0,96231
6 England_North-East 0,961
7 England_North-West 0,96026
8 NL_Noord_Brabant 0,95851
9 Ireland 0,95822
10 England_South-West 0,95721
11 England_South-East 0,95707
12 Shetlands 0,95596
13 Scotland 0,95272
14 FR_North-West 0,95137
15 FR_Brittany 0,947
16 NL_Zuid_Holland 0,94522
17 Flemish 0,94037
18 NL_Overijssel 0,93878
19 DE Niedersachsen 0,93803
20 NL_Limburg 0,93725
21 DE Hessen 0,93708
22 NL_Utrecht 0,93642
23 NL_Gelderland 0,9352
24 DE Nordrhein-Westfalen 0,93056
25 Norway_west 0,92933
26 Walloons 0,9256
27 DE Schleswig-Holstein 0,92489
28 DE Mecklenburg-Vorpommern 0,92285
29 Iceland 0,92233
30 Denmark 0,9151

[Coastal Elite]

bump

[farke1]

Sweet thread. Here's mine and my top 30 correlation results:
https://i.imgur.com/wxUZIe8.png

Correlation values (values below 0.2 are insignificant, below 0 completely not important so I don't provide them)
1 NL_Overijssel 0,98242
2 Iceland 0,98107
3 Scotland 0,97579
4 Norway_west 0,97235
5 Orkney 0,96893
6 DE Niedersachsen 0,96766
7 England_North-West 0,96761
8 West_Scottish 0,96647
9 NL_Groningen 0,96399
10 Denmark 0,96192
11 Wales 0,9618
12 Ireland 0,96102
13 NL_Utrecht 0,95968
14 NL_Zuid_Holland 0,95928
15 SV_Skane 0,95753
16 Northern Ireland 0,95529
17 SV_Gotaland 0,95498
18 Norway_south-east 0,95441
19 Cumbria 0,95354
20 NL_Friesland 0,95318
21 NL_Gelderland 0,95126
22 DE Schleswig-Holstein 0,9488
23 Norway_central 0,94763
24 England_South-East 0,94401
25 England_North-East 0,94382
26 DE Mecklenburg-Vorpommern 0,94195
27 England_South-West 0,93889
28 Shetlands 0,93571
29 NL_Noord_Brabant 0,92944
30 DE Nordrhein-Westfalen 0,92887

[frankhammer]

https://i.imgur.com/T9Obhl1.png

[Matxe92]

Isn't this guy just re-using Eurogenes K36 data and setting it up more fancy, and then charge 10 dollars for it?

[farke1]

Isn't this guy just re-using Eurogenes K36 data and setting it up more fancy, and then charge 10 dollars for it?
Lukasz does use the K36 data but he gives you much more information than you can get via something like Gedmatch. You also get nMonte population breakdowns and oracles, Dodecad oracles, your own PCA and MDS plots, this correlation map and also a euclidean map (which is the one in my sig). He also compares your DNA to 276 reference populations, which is way more than any other service I have used at least. Overall it's up to you to decide if it's worth the money or not, but I definitely don't regret spending the money personally.

[Lucas]

Lukasz does use the K36 data but he gives you much more information than you can get via something like Gedmatch. You also get nMonte population breakdowns and oracles, Dodecad oracles, your own PCA and MDS plots, this correlation map and also a euclidean map (which is the one in my sig). He also compares your DNA to 276 reference populations, which is way more than any other service I have used at least. Overall it's up to you to decide if it's worth the money or not, but I definitely don't regret spending the money personally.

I can add that now there are more than 600 references.

Also I use K36 components only for comparing someone genome with my references. I would use mine K47 calculator ( it would be even better as K47 has few Amerindian components) in the same way but in such case I can't use samples collected on Gedmatch (with GEDCOM pointing to one region only and checked in K36 there) which is great addition to rest of the samples from academic datasets (practically all publicly avaialble are used by me).

BTW I just found new academic datasaet for north-eastern Spain (Catalonia, Valencia, Balearic Islands divided into smaller regions) which would be added soon to my spreadsheet.

[Bellbeaking]

Furthest Distances

593 Bolivian_Pando 39,806717
594 San 39,841157
595 Vanuatu 40,334467
596 Brong Ghana 40,512245
597 Chane_ARG 40,910107
598 Esan (Nigeria) 41,009719
599 Quechua 41,16069
600 Yoruba (Nigeria) 41,699072
601 Zapotec 42,254257
602 Mixtec 42,289429
603 Mandinka 43,470359
604 Cachi_ARG 43,569137
605 Bolivian_Quechua 43,861554
606 Mixe 44,196634
607 Colla_ARG 44,260501
608 Bolivian_Aymara 46,454631
609 Wichi_ARG 46,801773
610 Nasioi (Melanesians) 47,262824
611 Baining (Melanesians) 48,238248
612 Papuan_Highland 51,594477

Interesting that Nigerian populations and even San are higher than some OOA populations on this. We are surely more closely related to melanesians even if we share recent ancestry with various SSA population

[Albannach]

It's interesting that I am very close to the Celtic populations of Ireland/Scotland/Wales but not particularly close to Brittany, I wonder why that is? could it be because I have less med component than Bretons, whilst possibly having a bit more Germanic? from the looks of my result and my closeness to the Celtic nations as well as the Netherlands, I am guessing that I am basically Rhenish Bell Beaker with some Scandanavian input.


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

1 Scotland 0.96854
2 Ireland 0.95653
3 Dutch_Overijssel 0.95141
4 Orkney 0.95102
5 Wales 0.94833
6 Dutch_Utrecht 0.94658
7 England_North-West 0.94619
8 Dutch_Groningen 0.94202
9 Niedersachsen 0.93791
10 Iceland 0.93737
11 Northern Ireland 0.9357
12 Dutch_Gelderland 0.93493
13 Dutch_Zuid_Holland 0.92932
14 Cumbria 0.92712
15 Dutch_Friesland 0.92572
16 England_South-East 0.92207
17 Denmark0.9216
18 Mecklenburg-Vorpommern0.91928
19 England_North-East 0.91769
20 SV_Skane 0.9162
21 England_South-West 0.91607
22 SV_Gotaland 0.90689
23 SV_Svealand 0.90689
24 Dutch_Noord_Brabant 0.906
25 Schleswig-Holstein0.90412
26 Norwegians 0.90248
27 Dutch_Noord_Holland 0.90146
28 Nordrhein-Westfalen 0.89218
29 Oberland_Upper_Prussia 0.87621
30 Flemish 0.87598
31 Pommern 0.87591
32 Dutch_Drenthe 0.86902
33 Saxony 0.86882
34 FR_North-West 0.86808
35 Dutch_Limburg 0.86614
36 FR_West 0.86509
37 Sachsen-Anhalt 0.86304
38 Thüringen 0.85254
39 FR_Brittany 0.8513
40 Bayern 0.84743
41 Neumark0.84579
42 Walloons0.83971
43 Hessen 0.83168
44 Rheinland-Pfalz 0.82807
45 Central_Prussia 0.82492
46 FR_North-East 0.81717
47 North Sweden 0.81192
48 Baden-Württemberg 0.80865
49 Brandenburg 0.80323
50 Sudetenland 0.79648
51 Donau_Schwabem 0.79605
52 Saarland0.77971
53 Grenzmark 0.77179
54 Austria 0.77082
55 Swiss_German 0.7692
56 Schlesien 0.75271
57 IT_Bolzano 0.74073
58 Tirol 0.74073
59 Czechs 0.72913
60 FR_mixed 0.7203
61 North_Norway 0.71677
62 FR_Swiss 0.71599
63 Hungary 0.70909
64 Lusatian_Sorbs 0.70794
65 PL_North 0.68553
66 Greater_Poland (Wielkopolska) 0.66819
67 Kashubians 0.66318
68 Sappada 0.66205
69 FR_Central 0.66121
70 PL_Upper_Silesia 0.65561
71 Slovenia 0.65558
72 PL_Subcarpathia 0.6288
73 Cimbri 0.62176
74 Slovakia 0.61092
75 Ukraine_Western 0.60936
76 Ukraine Kiev region 0.6066
77 Pl_South (Małopolska) 0.59904
78 FR_South0.58779
79 Croatia 0.58495
80 IT_Aosta 0.58053
81 Croats_BIH 0.58036
82 Carpathian Rusyns 0.56421
83 Sauris 0.5632
84 Ukraine_Volhyn 0.55942
85 PL_Masovia 0.55886
86 Timau 0.55561
87 Pl_Lublin 0.55171
88 Ukraine_Poltava 0.54816
89 IT_Piemonte 0.54777
90 IT_Friuli 0.54725
91 Swede_Finland 0.54037
92 Volgograd 0.53592
93 Ukraine_Vinnytsya 0.53531
94 IT_Trentino 0.53485
95 Central Romania 0.5336
96 Tver 0.53279
97 Belarus Polesye 0.52767
98 IT_Veneto 0.52258
99 Ukraine_NE 0.52238
100 Belarus West 0.52194

[Rocinante]

I can add that now there are more than 600 references.

Also I use K36 components only for comparing someone genome with my references. I would use mine K47 calculator ( it would be even better as K47 has few Amerindian components) in the same way but in such case I can't use samples collected on Gedmatch (with GEDCOM pointing to one region only and checked in K36 there) which is great addition to rest of the samples from academic datasets (practically all publicly avaialble are used by me).

BTW I just found new academic datasaet for north-eastern Spain (Catalonia, Valencia, Balearic Islands divided into smaller regions) which would be added soon to my spreadsheet.

I did it like half a year ago. Do you recommend re-doing this test for possible updates in the results?

[Thracian]

https://fusiontables.googleusercontent.com/embedviz?q=select+col39%3E%3E1+from+14SPanb9CI7Ibb bvRHtTcH06yCPxhQcY18WYFVVHC&viz=MAP&h=false&lat=42.39878861063711&lng=27.336725312499993&t=1&z=5&l=col39%3E%3E1&y=2&tmplt=2&hml=KML

GR_Thessaly 0,87388
GR_Macedonia1 0,87117
Thrace 0,86796
Albanians_FYROM 0,8635
GR_Peloponese 0,85701
Kosovo 0,85101
GR_Thessaloniki 0,84873
North_Albania 0,84813
GR_Eubea 0,84663
GR_Central 0,84124
GR_Cyclades 0,84109
South_Albania 0,84092
South_Romania 0,83962
Albanians Montenegro 0,83858
Greeks_Azov 0,838
Ipeiros 0,83461
Macedonia_FYROM 0,8331
Bulgaria 0,8311
Crimean Tatars Coast 0,80256
Montenegro 0,80224
IT_Apulia 0,79553
IT_Abruzzo 0,79086
GR_Kalymnos 0,79078
GR_Kythira 0,78319
TR_Istanbul 0,78053
GR_Chios 0,77797
South-East_Romania 0,7754
Romanian_Jew 0,77038
GR_Crete 0,76366
Serbia 0,76336
IT_Lazio 0,76327
IT_Tuscany 0,76322
IT_Friuli 0,76238
IT_Veneto 0,76023
GR_Andros 0,75916
South-West_Romania 0,75814
IT_Marche 0,75797
IT_Campania 0,74877
IT_Calabria 0,74695
Poland_Ashkenazy 0,74352
Sicily_Ragusa 0,74043
IT_North 0,73677
Sicily_Trapani 0,73671
GR_Ikaria 0,73532

[Lucas]

I did it like half a year ago. Do you recommend re-doing this test for possible updates in the results?

Yes, becasue I added many regional Iberian references, and recently updated using academic kits Catalan, Valencia and Baleares.

[Coastal Elite]

Bump

[JamesBond007]

bump

I don't get it why should the maps be interactive ? For idiotic Americans who don't know geography. Farke and LePrieur have the right idea. I'll do mine like theirs in a minute.

[JamesBond007]

It's interesting that I am very close to the Celtic populations of Ireland/Scotland/Wales but not particularly close to Brittany, I wonder why that is? could it be because I have less med component than Bretons, whilst possibly having a bit more Germanic? from the looks of my result and my closeness to the Celtic nations as well as the Netherlands, I am guessing that I am basically Rhenish Bell Beaker with some Scandanavian input.


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

1 Scotland 0.96854
2 Ireland 0.95653
3 Dutch_Overijssel 0.95141
4 Orkney 0.95102
5 Wales 0.94833
6 Dutch_Utrecht 0.94658
7 England_North-West 0.94619
8 Dutch_Groningen 0.94202
9 Niedersachsen 0.93791
10 Iceland 0.93737
11 Northern Ireland 0.9357
12 Dutch_Gelderland 0.93493
13 Dutch_Zuid_Holland 0.92932
14 Cumbria 0.92712
15 Dutch_Friesland 0.92572
16 England_South-East 0.92207
17 Denmark0.9216
18 Mecklenburg-Vorpommern0.91928
19 England_North-East 0.91769
20 SV_Skane 0.9162
21 England_South-West 0.91607
22 SV_Gotaland 0.90689
23 SV_Svealand 0.90689
24 Dutch_Noord_Brabant 0.906
25 Schleswig-Holstein0.90412
26 Norwegians 0.90248
27 Dutch_Noord_Holland 0.90146
28 Nordrhein-Westfalen 0.89218
29 Oberland_Upper_Prussia 0.87621
30 Flemish 0.87598
31 Pommern 0.87591
32 Dutch_Drenthe 0.86902
33 Saxony 0.86882
34 FR_North-West 0.86808
35 Dutch_Limburg 0.86614
36 FR_West 0.86509
37 Sachsen-Anhalt 0.86304
38 Thüringen 0.85254
39 FR_Brittany 0.8513
40 Bayern 0.84743
41 Neumark0.84579
42 Walloons0.83971
43 Hessen 0.83168
44 Rheinland-Pfalz 0.82807
45 Central_Prussia 0.82492
46 FR_North-East 0.81717
47 North Sweden 0.81192
48 Baden-Württemberg 0.80865
49 Brandenburg 0.80323
50 Sudetenland 0.79648
51 Donau_Schwabem 0.79605
52 Saarland0.77971
53 Grenzmark 0.77179
54 Austria 0.77082
55 Swiss_German 0.7692
56 Schlesien 0.75271
57 IT_Bolzano 0.74073
58 Tirol 0.74073
59 Czechs 0.72913
60 FR_mixed 0.7203
61 North_Norway 0.71677
62 FR_Swiss 0.71599
63 Hungary 0.70909
64 Lusatian_Sorbs 0.70794
65 PL_North 0.68553
66 Greater_Poland (Wielkopolska) 0.66819
67 Kashubians 0.66318
68 Sappada 0.66205
69 FR_Central 0.66121
70 PL_Upper_Silesia 0.65561
71 Slovenia 0.65558
72 PL_Subcarpathia 0.6288
73 Cimbri 0.62176
74 Slovakia 0.61092
75 Ukraine_Western 0.60936
76 Ukraine Kiev region 0.6066
77 Pl_South (Małopolska) 0.59904
78 FR_South0.58779
79 Croatia 0.58495
80 IT_Aosta 0.58053
81 Croats_BIH 0.58036
82 Carpathian Rusyns 0.56421
83 Sauris 0.5632
84 Ukraine_Volhyn 0.55942
85 PL_Masovia 0.55886
86 Timau 0.55561
87 Pl_Lublin 0.55171
88 Ukraine_Poltava 0.54816
89 IT_Piemonte 0.54777
90 IT_Friuli 0.54725
91 Swede_Finland 0.54037
92 Volgograd 0.53592
93 Ukraine_Vinnytsya 0.53531
94 IT_Trentino 0.53485
95 Central Romania 0.5336
96 Tver 0.53279
97 Belarus Polesye 0.52767
98 IT_Veneto 0.52258
99 Ukraine_NE 0.52238
100 Belarus West 0.52194

This user does not seem to post here anymore but :

Mainland Scots are intermediate between the native Irish and Welsh and the English/Germanics but the idea that all celtic nations are genetically similar is a myth. Cornwall and Britanny are not related to you. For instance, the Cornish are more related other southern English groups than they are to the Scots or Irish.

[Coastal Elite]

I don't get it why should the maps be interactive ? For idiotic Americans who don't know geography. Farke and LePrieur have the right idea. I'll do mine like theirs in a minute.

Do whatever you want. Just don’t whine like a girl from Vassar College.

[Adamm]

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


1 ALG_Kabylie 1,7336837
2 Moroccan North 1,9246896
3 East Moroccan Berbers 2,8224559
4 TUN_Sfax 2,8333143
5 TUN_Siliana 0,94175
6 ALG_Algier 0,92693
7 ALG_North-West 0,92355
8 Saharawi 0,92311
9 ALG_North-East 0,91694
10 TUN_Tunis 0,91689
11 South Morocco Berbers 0,89625
12 ALG_Laghouat 0,88939
13 TUN_Gabes 0,86456
14 Moroccan Casablanca 0,86426
15 TUN_Sousse 0,86009
16 TUN_Bizerta 0,80961
17 Cirenaica_Libyans 0,70231
18 South Moroccans 0,57116
19 Libyan_Jew 0,54579
20 Moroccan_Jew 0,53822
21 Egyptian Copts 0,52645
22 Copts Sudan 0,526
23 Tunisian_Jew 0,52162
24 Algerian_Jew 0,51455
25 Egyptians 0,51349
26 Sephardi_Portugal(Belmonte) 0,49995
27 ES_Canarias0,46567
28 Jordan 0,42207
29 Sephardi_Bulgaria 0,40478
30 Beja Beni Amer (Sudan) 0,40318
31 Beja Hadendowa (Sudan) 0,39741
32 Malta 0,38991
33 Sephardi_Turkey 0,38713
34 Amhara 0,38611
35 German_Ashkenazy 0,38407
36 Arab Bataheen Sudan 0,38172
37 France_Ashkenazy 0,38162
38 Tigre 0,38037
39 Nubian Halfawieen (Sudan) 0,38034
40 Italian_Jew 0,37922
41 Bedouin 0,371
42 Nubian Mahas (Sudan) 0,35849
43 Syrian_Jew 0,35672
44 Sicily_Messina 0,354
45 Ethiopian_Jew 0,35109
46 Sicily_Palermo 0,3506
47 Romaniote 0,35037
48 PT_Faro 0,3491
49 Nubian Danagla (Sudan) 0,34909
50 PT_Madeira 0,34569
51 Palestina 0,34324
52 Arab Gaalien Sudan 0,34108
53 Poland_Ashkenazy 0,33691
54 Sicily_Caltanisetta 0,33528
55 Sicily_Agrigento 0,33099
56 Portugal 0,32965
57 Sicily_Trapani 0,32591
58 Sicily_Katania 0,32576
59 White_Cubans 0,32468
60 ES_Granada 0,32328
61 Oromo (Ethiopia) 0,3195
62 Somali 0,31888
63 Latvia_Ashkenazy 0,31486
64 Yemen Hadramaut 0,31387
65 Sicily_Ragusa 0,30888
66 Arab Shaigia Sudan 0,3046
67 ES Región de Murcia 0,29671
68 Yemen_North 0,29576
69 ES Castilla y León 0,29515
70 Syria 0,29462
71 IT_Calabria 0,29413
72 ES Extremadura 0,29234
73 ES_Galicia 0,28701
74 ES_Almeria 0,28455
75 ES_Andalusia 0,28327
76 Samaritans 0,27778
77 IT_Sardinia 0,27757
78 IQ_Thi-Qar 0,27707
79 Lebanon_Muslim 0,27642
80 ES Castilla-La Mancha 0,27014
81 ES_Huelva 0,26807
82 PT_Azores 0,26208
83 ES Spanish_mixed 0,25927
84 IT_Campania 0,2592
85 ES Principado de Asturias 0,25726
86 GR_Andros 0,25628
87 FR_Corsica 0,25371
88 IQ_Qadisiyah 0,25251
89 IT_Lazio 0,25076
90 Romanian_Jew 0,2469
91 Belarus_Ashkenazy 0,24446
92 Dutch_Ashkenazy 0,2339
93 ES Comunidad Valenciana 0,23357

[JamesBond007]

https://i.postimg.cc/rFt2yqrv/me.png

https://i.postimg.cc/QMKf1MX3/me2.png

Noise dispersion set to 0,130062
Using 1 population approximation:
1 Scotland
2 SW_England
3 Orkney
5 NL_Friesland
6 Iceland
7 SE_England
8 NL_Groningen
9 Northern_Ireland
10 NW_England

250 iterations.

Using 2 populations approximation:
1 SW_England+Norway_Hedmark
2 Scotland+Scotland
3 NL_Groningen+SW_England
4 NL_Groningen+Scotland
5 Norway_Hedmark+FR_Brittany
6 Scotland+SW_England
7 West_Prussia+Scotland

8 NL_Groningen+NW_England
9 NL_Groningen+FR_Brittany
10 Scotland+Norway_Hedmark
31375 iterations.

Using 3 populations approximation:
1 50% Scotland +25% West_Prussia +25% Scotland
2 50% SW_England +25% SW_England +25% Norway_Hedmark
3 50% SW_England +25% Scotland +25% Norway_Hedmark
4 50% Scotland +25% West_Prussia +25% SW_England
5 50% Scotland +25% Scotland +25% Norway_Hedmark
6 50% Scotland +25% SW_England +25% Norway_Hedmark
7 50% SW_England +25% West_Prussia +25% Norway_Hedmark
8 50% SW_England +25% West_Prussia +25% Scotland
9 50% SW_England +25% West_Prussia +25% NL_Groningen
10 50% SW_England +25% NL_Groningen +25% Denmark

Using 4 populations approximation:
1 West_Prussia+Scotland+Scotland+Scotland
2 SW_England+SW_England+SW_England+Norway_Hedmark
3 Scotland+SW_England+SW_England+Norway_Hedmark
4 West_Prussia+Scotland+Scotland+SW_England
5 Scotland+Scotland+Scotland+Norway_Hedmark
6 Scotland+Scotland+SW_England+Norway_Hedmark
7 West_Prussia+SW_England+SW_England+Norway_Hedmark
8 West_Prussia+Scotland+SW_England+SW_England
9 West_Prussia+NL_Groningen+Scotland+SW_England
10 West_Prussia+NL_Groningen+SW_England+SW_England
11 NL_Groningen+SW_England+SW_England+Denmark
12 NL_Groningen+Scotland+Scotland+Scotland
13 West_Prussia+NL_Groningen+Scotland+Scotland
14 NL_Groningen+Scotland+Scotland+SW_England
15 Scotland+Scotland+Norway_Hedmark+FR_Brittany
16 West_Prussia+Scotland+SW_England+Norway_Hedmark
17 NL_Groningen+Scotland+SW_England+Denmark
18 West_Prussia+SW_England+SW_England+Denmark
19 NL_Groningen+Scotland+SW_England+SW_England
20 West_Prussia+SW_England+SW_England+SW_England
111382856 iterations.

Least-squares method.
Using 1 population approximation:
1 NL_Gelderland
2 NL_Overijssel
3 Niedersachsen
4 NL_Groningen
5 Scotland
6 NL_Zuid_Holland
7 NW_England
8 Schleswig-Holstein
9 Iceland
10 Denmark
250 iterations.

Using 2 populations approximation:
1 NL_Groningen+NL_Gelderland
2 NL_Gelderland+Scotland
3 NL_Gelderland+NL_Overijssel
4 NL_Groningen+Scotland
5 Niedersachsen+Scotland
6 NL_Groningen+NL_Overijssel
7 Schleswig-Holstein+Scotland
8 Niedersachsen+NL_Groningen
9 Niedersachsen+NL_Gelderland
10 NL_Overijssel+Scotland
31375 iterations.

Using 3 populations approximation:
1 50% NL_Gelderland +25% NL_Groningen +25% Scotland
2 50% NL_Groningen +25% NL_Gelderland +25% Scotland
3 50% NL_Gelderland +25% NL_Overijssel +25% Scotland
4 50% NL_Gelderland +25% NL_Groningen +25% NL_Overijssel
5 50% Scotland +25% NL_Groningen +25% NL_Gelderland
6 50% NL_Groningen +25% Tirol +25% Scotland
7 50% NL_Gelderland +25% Niedersachsen +25% Scotland
8 50% NL_Groningen +25% NL_Gelderland +25% NL_Gelderland
9 50% NL_Gelderland +25% NL_Gelderland +25% Scotland
10 50% NL_Gelderland +25% NL_Groningen +25% NW_England

Using 4 populations approximation:
1 NL_Groningen+NL_Gelderland+NL_Gelderland+Scotland
2 NL_Groningen+NL_Gelderland+NL_Overijssel+Scotland
3 Niedersachsen+NL_Groningen+NL_Gelderland+Scotland
4 NL_Groningen+NL_Groningen+NL_Gelderland+Scotland
5 Tirol+NL_Groningen+NL_Friesland+Scotland

6 NL_Gelderland+NL_Gelderland+NL_Overijssel+Scotland
7 NL_Groningen+NL_Gelderland+NL_Gelderland+NL_Overij ssel
8 Niedersachsen+Tirol+NL_Groningen+Scotland
9 NL_Groningen+NL_Gelderland+Scotland+Scotland
10 NL_Zuid_Holland+NL_Groningen+NL_Gelderland+Scotlan d
11 Schleswig-Holstein+NL_Groningen+NL_Gelderland+Scotland
12 Niedersachsen+NL_Groningen+NL_Overijssel+Scotland
13 Tirol+NL_Groningen+NL_Groningen+Scotland
14 Niedersachsen+NL_Gelderland+NL_Overijssel+Scotland
15 Niedersachsen+NL_Gelderland+NL_Gelderland+Scotland
16 NL_Groningen+NL_Groningen+NL_Gelderland+NL_Gelderl and
17 Niedersachsen+Tirol+NL_Friesland+Scotland
18 NL_Gelderland+NL_Gelderland+NL_Gelderland+Scotland
19 NL_Groningen+NL_Gelderland+NL_Gelderland+NW_Englan d
20 Tirol+NL_Friesland+NL_Friesland+Scotland
6 NL_Gelderland+NL_Gelderland+NL_Overijssel+Scotland
7 NL_Groningen+NL_Gelderland+NL_Gelderland+NL_Overij ssel
8 Niedersachsen+Tirol+NL_Groningen+Scotland
9 NL_Groningen+NL_Gelderland+Scotland+Scotland
10 NL_Zuid_Holland+NL_Groningen+NL_Gelderland+Scotlan d
11 Schleswig-Holstein+NL_Groningen+NL_Gelderland+Scotland
12 Niedersachsen+NL_Groningen+NL_Overijssel+Scotland
13 Tirol+NL_Groningen+NL_Groningen+Scotland
14 Niedersachsen+NL_Gelderland+NL_Overijssel+Scotland
15 Niedersachsen+NL_Gelderland+NL_Gelderland+Scotland
16 NL_Groningen+NL_Groningen+NL_Gelderland+NL_Gelderl and
17 Niedersachsen+Tirol+NL_Friesland+Scotland
18 NL_Gelderland+NL_Gelderland+NL_Gelderland+Scotland
19 NL_Groningen+NL_Gelderland+NL_Gelderland+NW_Englan d
20 Tirol+NL_Friesland+NL_Friesland+Scotland

[Kamal900]

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


1 ALG_Kabylie 1,7336837
2 Moroccan North 1,9246896
3 East Moroccan Berbers 2,8224559
4 TUN_Sfax 2,8333143
5 TUN_Siliana 0,94175
6 ALG_Algier 0,92693
7 ALG_North-West 0,92355
8 Saharawi 0,92311
9 ALG_North-East 0,91694
10 TUN_Tunis 0,91689
11 South Morocco Berbers 0,89625
12 ALG_Laghouat 0,88939
13 TUN_Gabes 0,86456
14 Moroccan Casablanca 0,86426
15 TUN_Sousse 0,86009
16 TUN_Bizerta 0,80961
17 Cirenaica_Libyans 0,70231
18 South Moroccans 0,57116
19 Libyan_Jew 0,54579
20 Moroccan_Jew 0,53822
21 Egyptian Copts 0,52645
22 Copts Sudan 0,526
23 Tunisian_Jew 0,52162
24 Algerian_Jew 0,51455
25 Egyptians 0,51349
26 Sephardi_Portugal(Belmonte) 0,49995
27 ES_Canarias0,46567
28 Jordan 0,42207
29 Sephardi_Bulgaria 0,40478
30 Beja Beni Amer (Sudan) 0,40318
31 Beja Hadendowa (Sudan) 0,39741
32 Malta 0,38991
33 Sephardi_Turkey 0,38713
34 Amhara 0,38611
35 German_Ashkenazy 0,38407
36 Arab Bataheen Sudan 0,38172
37 France_Ashkenazy 0,38162
38 Tigre 0,38037
39 Nubian Halfawieen (Sudan) 0,38034
40 Italian_Jew 0,37922
41 Bedouin 0,371
42 Nubian Mahas (Sudan) 0,35849
43 Syrian_Jew 0,35672
44 Sicily_Messina 0,354
45 Ethiopian_Jew 0,35109
46 Sicily_Palermo 0,3506
47 Romaniote 0,35037
48 PT_Faro 0,3491
49 Nubian Danagla (Sudan) 0,34909
50 PT_Madeira 0,34569
51 Palestina 0,34324
52 Arab Gaalien Sudan 0,34108
53 Poland_Ashkenazy 0,33691
54 Sicily_Caltanisetta 0,33528
55 Sicily_Agrigento 0,33099
56 Portugal 0,32965
57 Sicily_Trapani 0,32591
58 Sicily_Katania 0,32576
59 White_Cubans 0,32468
60 ES_Granada 0,32328
61 Oromo (Ethiopia) 0,3195
62 Somali 0,31888
63 Latvia_Ashkenazy 0,31486
64 Yemen Hadramaut 0,31387
65 Sicily_Ragusa 0,30888
66 Arab Shaigia Sudan 0,3046
67 ES Región de Murcia 0,29671
68 Yemen_North 0,29576
69 ES Castilla y León 0,29515
70 Syria 0,29462
71 IT_Calabria 0,29413
72 ES Extremadura 0,29234
73 ES_Galicia 0,28701
74 ES_Almeria 0,28455
75 ES_Andalusia 0,28327
76 Samaritans 0,27778
77 IT_Sardinia 0,27757
78 IQ_Thi-Qar 0,27707
79 Lebanon_Muslim 0,27642
80 ES Castilla-La Mancha 0,27014
81 ES_Huelva 0,26807
82 PT_Azores 0,26208
83 ES Spanish_mixed 0,25927
84 IT_Campania 0,2592
85 ES Principado de Asturias 0,25726
86 GR_Andros 0,25628
87 FR_Corsica 0,25371
88 IQ_Qadisiyah 0,25251
89 IT_Lazio 0,25076
90 Romanian_Jew 0,2469
91 Belarus_Ashkenazy 0,24446
92 Dutch_Ashkenazy 0,2339
93 ES Comunidad Valenciana 0,23357

Can you show me how you did it?

[PT Tagus]

Correlation map

https://i.imgur.com/0Hnbf8r.png

Top 30

1 Portugal_South 0,91165
2 Portugal_Lisboa 0,90039
3 Portugal_Norte 0,89609
4 ES_Galicia 0,8876
5 PT_Azores 0,87909
6 ES Principado de Asturias 0,87842
7 ES_Canarias0,87577
8 ES_Cataluna 0,87064
9 FR_South 0,86499
10 White_Cubans 0,85924
11 Portugal_Centro 0,85831
12 FR_Central 0,8554
13 ES Islas Baleares 0,85453
14 ES_Andalusia_South 0,85208
15 ES Spanish_mixed 0,84699
16 ES_Huelva 0,84638
17 ES Comunidad Valenciana 0,84576
18 Swiss_Italian 0,84418
19 IT_Piemonte0,84341
20 ES Región de Murcia 0,84209
21 ES_Andalusia_North 0,84157
22 FR_Provence 0,84071
23 FR_mixed 0,83775
24 ES Castilla y León 0,83457
25 IT_Liguria 0,83291
26 FR_Franche_Comte 0,8314
27 ES Castilla-La Mancha 0,82927
28 ES Navarra 0,82698
29 IT_Lombardia 0,82023
30 IT_Aosta 0,80893


Euclidean distances map

https://i.imgur.com/TB60hqk.png

Top 30


1 Portugal_South 2,3448414
2 Portugal_Lisboa 2,6319946
3 Portugal_Norte 2,6537092
4 ES_Galicia 2,7744092
5 ES_Canarias2,8045421
6 PT_Azores 2,8226849
7 IT_Piemonte3,0431437
8 ES_Cataluna 3,0626508
9 FR_Central 3,077721
10 FR_South 3,0811071
11 White_Cubans 3,0922576
12 FR_Provence 3,1239191
13 FR_Franche_Comte 3,1284062
14 ES Principado de Asturias 3,196645
15 ES Islas Baleares 3,227397
16 FR_mixed 3,2417637
17 Portugal_Centro 3,2459064
18 Swiss_Italian 3,2548073
19 IT_Liguria 3,2731156
20 ES_Huelva 3,2841004
21 FR_Swiss 3,336928
22 IT_Ladinia 3,3617627
23 IT_Friuli 3,3707947
24 Dutch_Ashkenazy 3,3721499
25 IT_Aosta 3,3724684
26 IT_Veneto 3,3821418
27 ES_Andalusia_South 3,3845548
28 ES Comunidad Valenciana 3,4538872
29 ES Spanish_mixed 3,460046
30 IT_Lombardia 3,528403

[Adamm]

Can you show me how you did it?

This is the K36 report from LM-Genetics, it costs around 10 dollars.

[Kamal900]

This is the K36 report from LM-Genetics, it costs around 10 dollars.

How long does it take for the report to come?

[Adamm]

How long does it take for the report to come?

I guess it depends, it didn't took too long for me. You should DM Lukasz from this forum because he knows more about that as he's involved with that company.