Post LM Genetics K36 Correlation Maps with links to interactive maps

[Daco Celtic]

Post your correlation maps with links to interactive maps

https://fusiontables.googleuserconte...mplt=2&hml=KML



https://fusiontables.googleuserconte...mplt=2&hml=KML



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

[Creoda]

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

[Creoda]

My mother's, from the English Midlands

https://fusiontables.google.com/embe...mplt=2&hml=KML


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.googleuserconte...mplt=2&hml=KML


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]

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

[Daco Celtic]

bump

[farke1]

Sweet thread. Here's mine and my top 30 correlation results:


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]

[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.