FilhoV
10-05-2018, 10:36 PM
My LM-Genetics K36
Map is in my signature
nMonte3 oracle (without Jewish references)
nMonte3 provides you with frequencies related to your recent ancestry, and works best for individiuals of a non-mixed background. It is also very adept at detecting
subtle regional differences.
"1. CLOSEST SINGLE ITEM DISTANCE%"
Andalusia Asturia Canarias Portugal Extremadura Castilla Galicia Cataluna
10.80101 11.56410 11.69841 11.70065 12.39954 13.48181 13.83744 14.01530
"2. FULL TABLE nMONTE" (estimated percentages)
distance%=9.6105"
Asturia,41.8
Andalusia,38.6
Sephardi_Portugal_Belmonte,5.6
Canarias,1.6
Portugal,1.4
TUN_Siliana,1
ALG_Kabylie,0.8
Extremadura,0.8
TUN_Sfax,0.8
TUN_Bizerta,0.6
TUN_Sousse,0.6
ALG_North-East,0.4
German_Ashkenazy,0.4
Moroccan_Jew,0.4
Moroccan_North,0.4
Romanian_Jew,0.4
Sephardi_Bulgaria,0.4
ALG_Algier,0.2
ALG_Laghouat,0.2
ALG_North-West,0.2
East_Moroccan_Berbers,0.2
France_Ashkenazy,0.2
GR_Ikaria,0.2
Ireland,0.2
IT_Bolzano,0.2
IT_Campania,0.2
Latvia_Ashkenazy,0.2
nMonte w ith penalization=0 function, rather speculative or showing deeper ancestry
"distance%=9.1217"
Asturia,73.8
Romanian_Jew,7.8
TUN_Gabes,7
ALG_Kabylie,4.2
Sephardi_Portugal_Belmonte,3
Andalusia,2
La_Rioja,1
Hadza,0.8
Pl_Central,0.4
Admix4 oracle (two methods, one of them is more speculative) - all references
The oracle works in a similar way to the Gedmatch Oracles, though the estimates here are far more robust. One shouldn‘t take all of them literaly, but rather as
extreme examples of possible distant admixtures. Admix4 is a different tool which is similar to the Gedmatch oracles. it compares your frequencies to the list of most
similar averages ( The same process as nMonte single item distances) or models you as a combination (two-way, three-way, or four-way) of different populations. In
some cases it will be in line with the actual ethnic combination you inherited from your parents and grandparents ancestries. It may be the case that different
populations show up in each oracle, especially for people of a mixed background.
Least-squares method.
Using 1 population approximation:
1 Andalusia @ 10,975329
2 Asturia @ 11,703176
3 Canarias @ 11,905478
4 Portugal @ 11,927156
5 Extremadura @ 12,583073
6 Castilla @ 13,62269
7 Galicia @ 14,065742
8 Cataluna @ 14,170205
9 Islas_Baleares @ 14,797507
10 Aragón @ 14,977944
500 iterations.
Using 2 populations approximation:
1 Asturia+Canarias @ 10,718277
2 Andalusia+Canarias @ 10,737093
3 Andalusia+Portugal @ 10,938757
4 Andalusia+Andalusia @ 10,975329
5 Andalusia+Asturia @ 11,096333
6 Portugal+Asturia @ 11,27655
7 Extremadura+Asturia @ 11,393305
8 Asturia+Sephardi_Portugal_Belmonte @ 11,525863
9 Extremadura+Portugal @ 11,543144
10 Andalusia+Extremadura @ 11,600963
125250 iterations.
Using 3 populations approximation:
1 50% Asturia +25% Asturia +25% TUIN_Sousse @ 9,53256
2 50% Asturia +25% Andalusia +25% TUIN_Sousse @ 9,60195
3 50% Andalusia +25% Asturia +25% TUIN_Sousse @ 9,814095
4 50% Asturia +25% Cantabria +25% TUIN_Sousse @ 9,894069
5 50% Asturia +25% Asturia +25% TUN_Bizerta @ 9,937851
6 50% Asturia +25% Asturia +25% TUN_Siliana @ 9,966651
7 50% Asturia +25% Extremadura +25% TUIN_Sousse @ 10,01205
8 50% Asturia +25% Castilla +25% TUIN_Sousse @ 10,017112
9 50% Asturia +25% Andalusia +25% TUN_Bizerta @ 10,027525
10 50% Asturia +25% Aragón +25% TUIN_Sousse @ 10,049091
54351107 iterations.
Using 4 populations approximation:
1 Asturia+Asturia+Asturia+TUIN_Sousse @ 9,53256
2 Andalusia+Asturia+Asturia+TUIN_Sousse @ 9,60195
3 Andalusia+Andalusia+Asturia+TUIN_Sousse @ 9,814095
4 Cantabria+Asturia+Asturia+TUIN_Sousse @ 9,894069
5 Asturia+Asturia+Asturia+TUN_Bizerta @ 9,937851
6 Asturia+Asturia+Asturia+TUN_Siliana @ 9,966651
7 Extremadura+Asturia+Asturia+TUIN_Sousse @ 10,01205
8 Castilla+Asturia+Asturia+TUIN_Sousse @ 10,017112
9 Andalusia+Asturia+Asturia+TUN_Bizerta @ 10,027525
10 Andalusia+Cantabria+Asturia+TUIN_Sousse @ 10,038654
11 Aragón+Asturia+Asturia+TUIN_Sousse @ 10,049091
12 Portugal+Asturia+Asturia+TUIN_Sousse @ 10,062857
Gaussian method
Noise dispersion set to 0,130062
Using 1 population approximation:
1 Portugal @ 5,781625
2 Canarias @ 5,858066
3 Cataluna @ 6,574646
4 Andalusia @ 6,802209
5 Galicia @ 6,840331
6 Castilla @ 7,077079
7 Asturia @ 7,212353
8 Extremadura @ 7,418526
9 Islas_Baleares @ 7,592131
10 TUIN_Sousse @ 8,051902
500 iterations.
Using 2 populations approximation:
1 Asturia+Canarias @ 5,478958
2 Castilla+Canarias @ 5,626276
3 Cataluna+Canarias @ 5,662326
4 Andalusia+Canarias @ 5,673488
5 Portugal+Canarias @ 5,698442
6 Islas_Baleares+Canarias @ 5,738619
7 Portugal+Portugal @ 5,781625
8 La_Rioja+Canarias @ 5,852042
9 Canarias+Canarias @ 5,858066
10 Galicia+Canarias @ 5,863261
1 Andalusia 0,94654
2 Principado de Asturias 0,94561
3 Canarias0,93528
4 Portugal 0,93433
5 Spanish_mixed 0,93412
6 Extremadura 0,9268
7 Castilla y León 0,92563
8 Castilla-La Mancha 0,92563
9 Cantabria 0,91284
10 Cataluna 0,91268
11 ES_Galicia 0,90715
12 Islas Baleares 0,89961
13 Aragón 0,89756
14 La Rioja 0,88851
15 Comunidad Valenciana 0,86709
16 FR_South0,85767
17 Sephardi_Portugal(Belmonte) 0,84315
18 FR_mixed 0,81988
19 FR_Central 0,80739
20 IT_Piemonte 0,80678
Map is in my signature
nMonte3 oracle (without Jewish references)
nMonte3 provides you with frequencies related to your recent ancestry, and works best for individiuals of a non-mixed background. It is also very adept at detecting
subtle regional differences.
"1. CLOSEST SINGLE ITEM DISTANCE%"
Andalusia Asturia Canarias Portugal Extremadura Castilla Galicia Cataluna
10.80101 11.56410 11.69841 11.70065 12.39954 13.48181 13.83744 14.01530
"2. FULL TABLE nMONTE" (estimated percentages)
distance%=9.6105"
Asturia,41.8
Andalusia,38.6
Sephardi_Portugal_Belmonte,5.6
Canarias,1.6
Portugal,1.4
TUN_Siliana,1
ALG_Kabylie,0.8
Extremadura,0.8
TUN_Sfax,0.8
TUN_Bizerta,0.6
TUN_Sousse,0.6
ALG_North-East,0.4
German_Ashkenazy,0.4
Moroccan_Jew,0.4
Moroccan_North,0.4
Romanian_Jew,0.4
Sephardi_Bulgaria,0.4
ALG_Algier,0.2
ALG_Laghouat,0.2
ALG_North-West,0.2
East_Moroccan_Berbers,0.2
France_Ashkenazy,0.2
GR_Ikaria,0.2
Ireland,0.2
IT_Bolzano,0.2
IT_Campania,0.2
Latvia_Ashkenazy,0.2
nMonte w ith penalization=0 function, rather speculative or showing deeper ancestry
"distance%=9.1217"
Asturia,73.8
Romanian_Jew,7.8
TUN_Gabes,7
ALG_Kabylie,4.2
Sephardi_Portugal_Belmonte,3
Andalusia,2
La_Rioja,1
Hadza,0.8
Pl_Central,0.4
Admix4 oracle (two methods, one of them is more speculative) - all references
The oracle works in a similar way to the Gedmatch Oracles, though the estimates here are far more robust. One shouldn‘t take all of them literaly, but rather as
extreme examples of possible distant admixtures. Admix4 is a different tool which is similar to the Gedmatch oracles. it compares your frequencies to the list of most
similar averages ( The same process as nMonte single item distances) or models you as a combination (two-way, three-way, or four-way) of different populations. In
some cases it will be in line with the actual ethnic combination you inherited from your parents and grandparents ancestries. It may be the case that different
populations show up in each oracle, especially for people of a mixed background.
Least-squares method.
Using 1 population approximation:
1 Andalusia @ 10,975329
2 Asturia @ 11,703176
3 Canarias @ 11,905478
4 Portugal @ 11,927156
5 Extremadura @ 12,583073
6 Castilla @ 13,62269
7 Galicia @ 14,065742
8 Cataluna @ 14,170205
9 Islas_Baleares @ 14,797507
10 Aragón @ 14,977944
500 iterations.
Using 2 populations approximation:
1 Asturia+Canarias @ 10,718277
2 Andalusia+Canarias @ 10,737093
3 Andalusia+Portugal @ 10,938757
4 Andalusia+Andalusia @ 10,975329
5 Andalusia+Asturia @ 11,096333
6 Portugal+Asturia @ 11,27655
7 Extremadura+Asturia @ 11,393305
8 Asturia+Sephardi_Portugal_Belmonte @ 11,525863
9 Extremadura+Portugal @ 11,543144
10 Andalusia+Extremadura @ 11,600963
125250 iterations.
Using 3 populations approximation:
1 50% Asturia +25% Asturia +25% TUIN_Sousse @ 9,53256
2 50% Asturia +25% Andalusia +25% TUIN_Sousse @ 9,60195
3 50% Andalusia +25% Asturia +25% TUIN_Sousse @ 9,814095
4 50% Asturia +25% Cantabria +25% TUIN_Sousse @ 9,894069
5 50% Asturia +25% Asturia +25% TUN_Bizerta @ 9,937851
6 50% Asturia +25% Asturia +25% TUN_Siliana @ 9,966651
7 50% Asturia +25% Extremadura +25% TUIN_Sousse @ 10,01205
8 50% Asturia +25% Castilla +25% TUIN_Sousse @ 10,017112
9 50% Asturia +25% Andalusia +25% TUN_Bizerta @ 10,027525
10 50% Asturia +25% Aragón +25% TUIN_Sousse @ 10,049091
54351107 iterations.
Using 4 populations approximation:
1 Asturia+Asturia+Asturia+TUIN_Sousse @ 9,53256
2 Andalusia+Asturia+Asturia+TUIN_Sousse @ 9,60195
3 Andalusia+Andalusia+Asturia+TUIN_Sousse @ 9,814095
4 Cantabria+Asturia+Asturia+TUIN_Sousse @ 9,894069
5 Asturia+Asturia+Asturia+TUN_Bizerta @ 9,937851
6 Asturia+Asturia+Asturia+TUN_Siliana @ 9,966651
7 Extremadura+Asturia+Asturia+TUIN_Sousse @ 10,01205
8 Castilla+Asturia+Asturia+TUIN_Sousse @ 10,017112
9 Andalusia+Asturia+Asturia+TUN_Bizerta @ 10,027525
10 Andalusia+Cantabria+Asturia+TUIN_Sousse @ 10,038654
11 Aragón+Asturia+Asturia+TUIN_Sousse @ 10,049091
12 Portugal+Asturia+Asturia+TUIN_Sousse @ 10,062857
Gaussian method
Noise dispersion set to 0,130062
Using 1 population approximation:
1 Portugal @ 5,781625
2 Canarias @ 5,858066
3 Cataluna @ 6,574646
4 Andalusia @ 6,802209
5 Galicia @ 6,840331
6 Castilla @ 7,077079
7 Asturia @ 7,212353
8 Extremadura @ 7,418526
9 Islas_Baleares @ 7,592131
10 TUIN_Sousse @ 8,051902
500 iterations.
Using 2 populations approximation:
1 Asturia+Canarias @ 5,478958
2 Castilla+Canarias @ 5,626276
3 Cataluna+Canarias @ 5,662326
4 Andalusia+Canarias @ 5,673488
5 Portugal+Canarias @ 5,698442
6 Islas_Baleares+Canarias @ 5,738619
7 Portugal+Portugal @ 5,781625
8 La_Rioja+Canarias @ 5,852042
9 Canarias+Canarias @ 5,858066
10 Galicia+Canarias @ 5,863261
1 Andalusia 0,94654
2 Principado de Asturias 0,94561
3 Canarias0,93528
4 Portugal 0,93433
5 Spanish_mixed 0,93412
6 Extremadura 0,9268
7 Castilla y León 0,92563
8 Castilla-La Mancha 0,92563
9 Cantabria 0,91284
10 Cataluna 0,91268
11 ES_Galicia 0,90715
12 Islas Baleares 0,89961
13 Aragón 0,89756
14 La Rioja 0,88851
15 Comunidad Valenciana 0,86709
16 FR_South0,85767
17 Sephardi_Portugal(Belmonte) 0,84315
18 FR_mixed 0,81988
19 FR_Central 0,80739
20 IT_Piemonte 0,80678