Global 25 updated

[Slavic Italian]

Ok. These are new models someone ran for me and they seem very solid. I included all of their remarks. This person is sharp.

With the Gradient Descent modeler and all the moderns, your mix produced a variety of results as it "descends" into a model that is closest to your settings. It was pretty interesting seeing this effect. Here are a couple of examples:

distance: 0.0048187086

Ashkenazi_Jew 0.2754507064819336
Czech 0.2528912425041199
Finnish 0.1299866884946823
Spanish_Cataluna 0.12324933707714081
Icelandic 0.11544333398342133
Orcadian 0.10297867655754091

distance: 0.0035611873

Czech 0.31403765082359314
Portuguese 0.15008947253227234
Sephardic_Jew 0.13388401269978953
Scottish 0.1178065836429596
German 0.0717332367205188
Icelandic 0.06666687875986099
Belgian 0.05457156151533127
Ket 0.045374151319265366
Polish 0.02754770219326136
Italian_Jew 0.01315315067768097
Biaka 0.005125758238136768
English 7.325155637422087e-06
Hungarian 2.271303858354989e-06

nMonte split things up quite a bit to produce its model with the full moderns spreadsheet. I am showing yours down to 0.6% because of how split up things got:

[1] "1. CLOSEST SINGLE ITEM DISTANCE%"
German:Germany27 French:France25 Hungarian:S_Hungarian-2 German:Germany5 French:France39 German:Germany21
1.484082 1.700088 1.789749 1.815847 1.891692 1.896549
Hungarian:HungarianE5 German:Germany4
1.912982 1.917107

[1] "2. FULL TABLE nMONTE"
[1] "penalty= 0.001"
[1] "Ncycles= 1000"
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14 PC15
Mixed 0.0107000 0.013500 0.0113000 0.00720 0.0104000 0.0026000 -0.000500 0.0007000 0.0041000 0.0017000 0.0004000 -0.000500 -0.0007000 0.003600 0.0048000
fitted 0.0105468 0.012487 0.0119128 0.00746 0.0107546 0.0026064 0.000711 0.0022706 0.0033256 0.0019512 -0.0008748 0.000226 -0.0011978 0.002774 0.0051188
dif -0.0001532 -0.001013 0.0006128 0.00026 0.0003546 0.0000064 0.001211 0.0015706 -0.0007744 0.0002512 -0.0012748 0.000726 -0.0004978 -0.000826 0.0003188
PC16 PC17 PC18 PC19 PC20 PC21 PC22 PC23 PC24 PC25
Mixed -0.0049000 -0.014000 -0.0017000 0.0001000 -0.0007000 0.0029000 -0.0001000 0.0032000 0.0018000 -0.0009000
fitted -0.0049268 -0.011213 -0.0000444 0.0007912 -0.0008332 0.0019424 0.0001154 0.0020336 0.0020078 -0.0004106
dif -0.0000268 0.002787 0.0016556 0.0006912 -0.0001332 -0.0009576 0.0002154 -0.0011664 0.0002078 0.0004894
[1] "distance%=0.4797"

Mixed

German,18.2
French,16.2
Croatian,6.2
Hungarian,6
Swedish,4.4
Czech,4.2
Finnish,2.8
English_Cornwall,2.4
Orcadian,2.4
Belgian,2.2
Italian_Abruzzo,1.6
Slovenian,1.6
Belarusian,1.4
Bosnian,1.4
Dutch,1.4
Austrian,1.2
English,1.2
Mozabite,1.2
Spanish_Castilla_La_Mancha,1
Spanish_Valencia,1
Ashkenazi_Jew,0.8
BedouinB,0.8
Norwegian,0.8
Scottish,0.8
Serbian,0.8
French_East,0.6
Italian_Bergamo,0.6
Karelian,0.6
Polish,0.6
Portuguese,0.6
Russian_Smolensk,0.6
Spanish_Cataluna,0.6
Ukrainian,0.6

It took me a bit of experimenting, but I found a rather simple model that worked well with Gradient Descent. I tried adding one at a time other population references that seemed possibly significant, but it stuck to models like this one and did not use the others:

distance: 0.0037246794

French 0.38853468745946884
Ashkenazi_Jew 0.2867520749568939
Finnish 0.2352791130542755
Ukrainian 0.08943414688110352

nMonte does like it, too, though the distance penalty causes it to emphasize the French a little more:

[1] "1. CLOSEST SINGLE ITEM DISTANCE%"
French:France25 French:France39 French:France7 French:France18 French:France8 Finnish:HG00311 French:France40 French:France12
1.700088 1.891692 1.949897 2.199523 2.262145 2.416423 2.490141 2.505454

[1] "2. FULL TABLE nMONTE"
[1] "penalty= 0.001"
[1] "Ncycles= 1000"
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14 PC15
Mixed 0.0107000 0.0135000 0.011300 0.0072000 0.0104000 0.0026000 -0.000500 0.0007000 0.0041000 0.0017000 0.0004000 -0.0005000 -0.0007000 0.003600 0.0048000
fitted 0.0107368 0.0127256 0.012473 0.0081298 0.0115772 0.0023058 0.001891 0.0034646 0.0041238 0.0029416 0.0005688 -0.0008758 -0.0012726 0.001676 0.0049044
dif 0.0000368 -0.0007744 0.001173 0.0009298 0.0011772 -0.0002942 0.002391 0.0027646 0.0000238 0.0012416 0.0001688 -0.0003758 -0.0005726 -0.001924 0.0001044
PC16 PC17 PC18 PC19 PC20 PC21 PC22 PC23 PC24 PC25
Mixed -0.004900 -0.0140000 -0.0017000 0.0001000 -0.0007000 0.002900 -1.0e-04 0.0032000 0.0018000 -0.0009000
fitted -0.004923 -0.0116132 0.0004276 0.0003882 -0.0014642 0.001809 -5.7e-05 0.0029608 0.0018676 0.0002118
dif -0.000023 0.0023868 0.0021276 0.0002882 -0.0007642 -0.001091 4.3e-05 -0.0002392 0.0000676 0.0011118
[1] "distance%=0.6067"

French,53.8
Finnish,18.6
Ashkenazi_Jew,17.6
Ukrainian,10

A lot of the minor stuff like the Siberian, African, SW Asian, etc. that show up in a full spreadsheet run are part of the substructure of these four populations and I even tried putting some of those into the reference spreadsheet for the model, but they really weren't necessary and weren't picked. This makes me suspect that those minor items really are mediated to your admixture through something like these four populations. These may not be exactly your background, but it seems like something like these or close neighbors are ancestral. For example, the French could be standing in for some German and/or English.

[Arborean]

Ok. These are new models someone ran for me and they seem very solid. I included all of their remarks. This person is sharp.

With the Gradient Descent modeler and all the moderns, your mix produced a variety of results as it "descends" into a model that is closest to your settings. It was pretty interesting seeing this effect. Here are a couple of examples:

distance: 0.0048187086

Ashkenazi_Jew 0.2754507064819336
Czech 0.2528912425041199
Finnish 0.1299866884946823
Spanish_Cataluna 0.12324933707714081
Icelandic 0.11544333398342133
Orcadian 0.10297867655754091

distance: 0.0035611873

Czech 0.31403765082359314
Portuguese 0.15008947253227234
Sephardic_Jew 0.13388401269978953
Scottish 0.1178065836429596
German 0.0717332367205188
Icelandic 0.06666687875986099
Belgian 0.05457156151533127
Ket 0.045374151319265366
Polish 0.02754770219326136
Italian_Jew 0.01315315067768097
Biaka 0.005125758238136768
English 7.325155637422087e-06
Hungarian 2.271303858354989e-06

nMonte split things up quite a bit to produce its model with the full moderns spreadsheet. I am showing yours down to 0.6% because of how split up things got:

[1] "1. CLOSEST SINGLE ITEM DISTANCE%"
German:Germany27 French:France25 Hungarian:S_Hungarian-2 German:Germany5 French:France39 German:Germany21
1.484082 1.700088 1.789749 1.815847 1.891692 1.896549
Hungarian:HungarianE5 German:Germany4
1.912982 1.917107

[1] "2. FULL TABLE nMONTE"
[1] "penalty= 0.001"
[1] "Ncycles= 1000"
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14 PC15
Mixed 0.0107000 0.013500 0.0113000 0.00720 0.0104000 0.0026000 -0.000500 0.0007000 0.0041000 0.0017000 0.0004000 -0.000500 -0.0007000 0.003600 0.0048000
fitted 0.0105468 0.012487 0.0119128 0.00746 0.0107546 0.0026064 0.000711 0.0022706 0.0033256 0.0019512 -0.0008748 0.000226 -0.0011978 0.002774 0.0051188
dif -0.0001532 -0.001013 0.0006128 0.00026 0.0003546 0.0000064 0.001211 0.0015706 -0.0007744 0.0002512 -0.0012748 0.000726 -0.0004978 -0.000826 0.0003188
PC16 PC17 PC18 PC19 PC20 PC21 PC22 PC23 PC24 PC25
Mixed -0.0049000 -0.014000 -0.0017000 0.0001000 -0.0007000 0.0029000 -0.0001000 0.0032000 0.0018000 -0.0009000
fitted -0.0049268 -0.011213 -0.0000444 0.0007912 -0.0008332 0.0019424 0.0001154 0.0020336 0.0020078 -0.0004106
dif -0.0000268 0.002787 0.0016556 0.0006912 -0.0001332 -0.0009576 0.0002154 -0.0011664 0.0002078 0.0004894
[1] "distance%=0.4797"

Mixed

German,18.2
French,16.2
Croatian,6.2
Hungarian,6
Swedish,4.4
Czech,4.2
Finnish,2.8
English_Cornwall,2.4
Orcadian,2.4
Belgian,2.2
Italian_Abruzzo,1.6
Slovenian,1.6
Belarusian,1.4
Bosnian,1.4
Dutch,1.4
Austrian,1.2
English,1.2
Mozabite,1.2
Spanish_Castilla_La_Mancha,1
Spanish_Valencia,1
Ashkenazi_Jew,0.8
BedouinB,0.8
Norwegian,0.8
Scottish,0.8
Serbian,0.8
French_East,0.6
Italian_Bergamo,0.6
Karelian,0.6
Polish,0.6
Portuguese,0.6
Russian_Smolensk,0.6
Spanish_Cataluna,0.6
Ukrainian,0.6

It took me a bit of experimenting, but I found a rather simple model that worked well with Gradient Descent. I tried adding one at a time other population references that seemed possibly significant, but it stuck to models like this one and did not use the others:

distance: 0.0037246794

French 0.38853468745946884
Ashkenazi_Jew 0.2867520749568939
Finnish 0.2352791130542755
Ukrainian 0.08943414688110352

nMonte does like it, too, though the distance penalty causes it to emphasize the French a little more:

[1] "1. CLOSEST SINGLE ITEM DISTANCE%"
French:France25 French:France39 French:France7 French:France18 French:France8 Finnish:HG00311 French:France40 French:France12
1.700088 1.891692 1.949897 2.199523 2.262145 2.416423 2.490141 2.505454

[1] "2. FULL TABLE nMONTE"
[1] "penalty= 0.001"
[1] "Ncycles= 1000"
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14 PC15
Mixed 0.0107000 0.0135000 0.011300 0.0072000 0.0104000 0.0026000 -0.000500 0.0007000 0.0041000 0.0017000 0.0004000 -0.0005000 -0.0007000 0.003600 0.0048000
fitted 0.0107368 0.0127256 0.012473 0.0081298 0.0115772 0.0023058 0.001891 0.0034646 0.0041238 0.0029416 0.0005688 -0.0008758 -0.0012726 0.001676 0.0049044
dif 0.0000368 -0.0007744 0.001173 0.0009298 0.0011772 -0.0002942 0.002391 0.0027646 0.0000238 0.0012416 0.0001688 -0.0003758 -0.0005726 -0.001924 0.0001044
PC16 PC17 PC18 PC19 PC20 PC21 PC22 PC23 PC24 PC25
Mixed -0.004900 -0.0140000 -0.0017000 0.0001000 -0.0007000 0.002900 -1.0e-04 0.0032000 0.0018000 -0.0009000
fitted -0.004923 -0.0116132 0.0004276 0.0003882 -0.0014642 0.001809 -5.7e-05 0.0029608 0.0018676 0.0002118
dif -0.000023 0.0023868 0.0021276 0.0002882 -0.0007642 -0.001091 4.3e-05 -0.0002392 0.0000676 0.0011118
[1] "distance%=0.6067"

French,53.8
Finnish,18.6
Ashkenazi_Jew,17.6
Ukrainian,10

A lot of the minor stuff like the Siberian, African, SW Asian, etc. that show up in a full spreadsheet run are part of the substructure of these four populations and I even tried putting some of those into the reference spreadsheet for the model, but they really weren't necessary and weren't picked. This makes me suspect that those minor items really are mediated to your admixture through something like these four populations. These may not be exactly your background, but it seems like something like these or close neighbors are ancestral. For example, the French could be standing in for some German and/or English.

Would you be so kind as to run my coordinates with moderns and then ancients? I never seem to get nmonte to work. Thank you in advance! I included scaled and unscaled. Don’t know which you use.

,PC1,PC2,PC3,PC4,PC5,PC6,PC7,PC8,PC9,PC10,PC11,PC1 2,PC13,PC14,PC15,PC16,PC17,PC18,PC19,PC20,PC21,PC2 2,PC23,PC24,PC25
Arborean_scaled,0.127482,0.150298,0.020365,-0.02584,0.028621,-0.007251,0.00611,0.002769,-0.002863,0.022233,0.001624,0.007343,-0.011596,0.003165,-0.02158,0.006895,0.030379,-0.00266,0.006159,-0.007379,-0.010606,0.000124,0.000986,0.000964,-0.002036

,PC1,PC2,PC3,PC4,PC5,PC6,PC7,PC8,PC9,PC10,PC11,PC1 2,PC13,PC14,PC15,PC16,PC17,PC18,PC19,PC20,PC21,PC2 2,PC23,PC24,PC25
Arborean,0.0112,0.0148,0.0054,-0.008,0.0093,-0.0026,0.0026,0.0012,-0.0014,0.0122,0.001,0.0049,-0.0078,0.0023,-0.0159,0.0052,0.0233,-0.0021,0.0049,-0.0059,-0.0085,0.0001,0.0008,0.0008,-0.0017

[Slavic Italian]

Would you be so kind as to run my coordinates with moderns and then ancients? I never seem to get nmonte to work. Thank you in advance! I included scaled and unscaled. Don’t know which you use.

,PC1,PC2,PC3,PC4,PC5,PC6,PC7,PC8,PC9,PC10,PC11,PC1 2,PC13,PC14,PC15,PC16,PC17,PC18,PC19,PC20,PC21,PC2 2,PC23,PC24,PC25
Arborean_scaled,0.127482,0.150298,0.020365,-0.02584,0.028621,-0.007251,0.00611,0.002769,-0.002863,0.022233,0.001624,0.007343,-0.011596,0.003165,-0.02158,0.006895,0.030379,-0.00266,0.006159,-0.007379,-0.010606,0.000124,0.000986,0.000964,-0.002036

,PC1,PC2,PC3,PC4,PC5,PC6,PC7,PC8,PC9,PC10,PC11,PC1 2,PC13,PC14,PC15,PC16,PC17,PC18,PC19,PC20,PC21,PC2 2,PC23,PC24,PC25
Arborean,0.0112,0.0148,0.0054,-0.008,0.0093,-0.0026,0.0026,0.0012,-0.0014,0.0122,0.001,0.0049,-0.0078,0.0023,-0.0159,0.0052,0.0233,-0.0021,0.0049,-0.0059,-0.0085,0.0001,0.0008,0.0008,-0.0017

I mentioned I had someone run them for me. Go to Anthrogenica and ask randwulf to do it. He did it for me and you can see he even made comments to clarify things.

https://anthrogenica.com/forumdisplay.php?144-Ancient-(aDNA)

[Gründig]

Ok. These are new models someone ran for me and they seem very solid. I included all of their remarks. This person is sharp.

With the Gradient Descent modeler and all the moderns, your mix produced a variety of results as it "descends" into a model that is closest to your settings. It was pretty interesting seeing this effect. Here are a couple of examples:

distance: 0.0048187086

Ashkenazi_Jew 0.2754507064819336
Czech 0.2528912425041199
Finnish 0.1299866884946823
Spanish_Cataluna 0.12324933707714081
Icelandic 0.11544333398342133
Orcadian 0.10297867655754091

distance: 0.0035611873

Czech 0.31403765082359314
Portuguese 0.15008947253227234
Sephardic_Jew 0.13388401269978953
Scottish 0.1178065836429596
German 0.0717332367205188
Icelandic 0.06666687875986099
Belgian 0.05457156151533127
Ket 0.045374151319265366
Polish 0.02754770219326136
Italian_Jew 0.01315315067768097
Biaka 0.005125758238136768
English 7.325155637422087e-06
Hungarian 2.271303858354989e-06

nMonte split things up quite a bit to produce its model with the full moderns spreadsheet. I am showing yours down to 0.6% because of how split up things got:

[1] "1. CLOSEST SINGLE ITEM DISTANCE%"
German:Germany27 French:France25 Hungarian:S_Hungarian-2 German:Germany5 French:France39 German:Germany21
1.484082 1.700088 1.789749 1.815847 1.891692 1.896549
Hungarian:HungarianE5 German:Germany4
1.912982 1.917107

[1] "2. FULL TABLE nMONTE"
[1] "penalty= 0.001"
[1] "Ncycles= 1000"
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14 PC15
Mixed 0.0107000 0.013500 0.0113000 0.00720 0.0104000 0.0026000 -0.000500 0.0007000 0.0041000 0.0017000 0.0004000 -0.000500 -0.0007000 0.003600 0.0048000
fitted 0.0105468 0.012487 0.0119128 0.00746 0.0107546 0.0026064 0.000711 0.0022706 0.0033256 0.0019512 -0.0008748 0.000226 -0.0011978 0.002774 0.0051188
dif -0.0001532 -0.001013 0.0006128 0.00026 0.0003546 0.0000064 0.001211 0.0015706 -0.0007744 0.0002512 -0.0012748 0.000726 -0.0004978 -0.000826 0.0003188
PC16 PC17 PC18 PC19 PC20 PC21 PC22 PC23 PC24 PC25
Mixed -0.0049000 -0.014000 -0.0017000 0.0001000 -0.0007000 0.0029000 -0.0001000 0.0032000 0.0018000 -0.0009000
fitted -0.0049268 -0.011213 -0.0000444 0.0007912 -0.0008332 0.0019424 0.0001154 0.0020336 0.0020078 -0.0004106
dif -0.0000268 0.002787 0.0016556 0.0006912 -0.0001332 -0.0009576 0.0002154 -0.0011664 0.0002078 0.0004894
[1] "distance%=0.4797"

Mixed

German,18.2
French,16.2
Croatian,6.2
Hungarian,6
Swedish,4.4
Czech,4.2
Finnish,2.8
English_Cornwall,2.4
Orcadian,2.4
Belgian,2.2
Italian_Abruzzo,1.6
Slovenian,1.6
Belarusian,1.4
Bosnian,1.4
Dutch,1.4
Austrian,1.2
English,1.2
Mozabite,1.2
Spanish_Castilla_La_Mancha,1
Spanish_Valencia,1
Ashkenazi_Jew,0.8
BedouinB,0.8
Norwegian,0.8
Scottish,0.8
Serbian,0.8
French_East,0.6
Italian_Bergamo,0.6
Karelian,0.6
Polish,0.6
Portuguese,0.6
Russian_Smolensk,0.6
Spanish_Cataluna,0.6
Ukrainian,0.6

It took me a bit of experimenting, but I found a rather simple model that worked well with Gradient Descent. I tried adding one at a time other population references that seemed possibly significant, but it stuck to models like this one and did not use the others:

distance: 0.0037246794

French 0.38853468745946884
Ashkenazi_Jew 0.2867520749568939
Finnish 0.2352791130542755
Ukrainian 0.08943414688110352

nMonte does like it, too, though the distance penalty causes it to emphasize the French a little more:

[1] "1. CLOSEST SINGLE ITEM DISTANCE%"
French:France25 French:France39 French:France7 French:France18 French:France8 Finnish:HG00311 French:France40 French:France12
1.700088 1.891692 1.949897 2.199523 2.262145 2.416423 2.490141 2.505454

[1] "2. FULL TABLE nMONTE"
[1] "penalty= 0.001"
[1] "Ncycles= 1000"
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14 PC15
Mixed 0.0107000 0.0135000 0.011300 0.0072000 0.0104000 0.0026000 -0.000500 0.0007000 0.0041000 0.0017000 0.0004000 -0.0005000 -0.0007000 0.003600 0.0048000
fitted 0.0107368 0.0127256 0.012473 0.0081298 0.0115772 0.0023058 0.001891 0.0034646 0.0041238 0.0029416 0.0005688 -0.0008758 -0.0012726 0.001676 0.0049044
dif 0.0000368 -0.0007744 0.001173 0.0009298 0.0011772 -0.0002942 0.002391 0.0027646 0.0000238 0.0012416 0.0001688 -0.0003758 -0.0005726 -0.001924 0.0001044
PC16 PC17 PC18 PC19 PC20 PC21 PC22 PC23 PC24 PC25
Mixed -0.004900 -0.0140000 -0.0017000 0.0001000 -0.0007000 0.002900 -1.0e-04 0.0032000 0.0018000 -0.0009000
fitted -0.004923 -0.0116132 0.0004276 0.0003882 -0.0014642 0.001809 -5.7e-05 0.0029608 0.0018676 0.0002118
dif -0.000023 0.0023868 0.0021276 0.0002882 -0.0007642 -0.001091 4.3e-05 -0.0002392 0.0000676 0.0011118
[1] "distance%=0.6067"

French,53.8
Finnish,18.6
Ashkenazi_Jew,17.6
Ukrainian,10

A lot of the minor stuff like the Siberian, African, SW Asian, etc. that show up in a full spreadsheet run are part of the substructure of these four populations and I even tried putting some of those into the reference spreadsheet for the model, but they really weren't necessary and weren't picked. This makes me suspect that those minor items really are mediated to your admixture through something like these four populations. These may not be exactly your background, but it seems like something like these or close neighbors are ancestral. For example, the French could be standing in for some German and/or English.

Did you find these accurate?

The same guy ran mine too and I think he did a great job. I'll have to post them.

[Slavic Italian]

Did you find these accurate?

The same guy ran mine too and I think he did a great job. I'll have to post them.

Here's the deal. I match with MANY Finns on every service. I've done them all. Same deal with Slavs and I match here and there with Jews. Now one side of my family says they are Italians. The truth is they might be Israelis who were hiding from something. Many people say I look like a Jew. I'm not sure what to think but yes everything seems to be spot on but I match with Italians as well so...........................I believe I am more than four ethnicities. I also match with Germans. I match with a few French here and there. I do not think there are boundaries or limitations to ethnicities when you consider migrations and other factors. But I feel randwulf is sharp. I feel these models have limitations.

[Slavic Italian]

I'm running my own numbers and getting good results.

Model Sample Sample Age BCE Fit Austrian English Russian Smolensk Sephardic Jew
1 Austrian +English +Russian_Smolensk +Sephardic_Jew Custom_-_AGUser_mixed 1.1089 30.83 35 12.5 21.67

Model Sample Sample Age BCE Fit Austrian English Polish Sephardic Jew
1 Austrian +English +Polish +Sephardic_Jew Custom_-_AGUser_mixed 0.9342 27.5 31.67 19.17 21.67

[Gründig]

I'm running my own numbers and getting good results.

Model Sample Sample Age BCE Fit Austrian English Russian Smolensk Sephardic Jew
1 Austrian +English +Russian_Smolensk +Sephardic_Jew Custom_-_AGUser_mixed 1.1089 30.83 35 12.5 21.67

Model Sample Sample Age BCE Fit Austrian English Polish Sephardic Jew
1 Austrian +English +Polish +Sephardic_Jew Custom_-_AGUser_mixed 0.9342 27.5 31.67 19.17 21.67

nice.

Which data sheets are you using?

and im assuming unscaled?

[Slavic Italian]

nice.

Which data sheets are you using?

and im assuming unscaled?


Mine and yes because I am not a paying member.

[Joso]

ahhaaahahhahahahahahahahhaahhahaahhaaha these italians wooooooooooooah

[Slavic Italian]

ahhaaahahhahahahahahahahhaahhahaahhaaha these italians wooooooooooooah

Your point kid?

[Joso]

Your point kid?

The point is you are a slav wannabe, hahaha OWD!

[Slavic Italian]

The point is you are a slav wannabe, hahaha OWD!

It's pretty much confirmed that I am.

Model Sample Sample Age BCE Fit Ashkenazi Jew Austrian English Polish
1 Ashkenazi_Jew +Austrian +English +Polish Custom_-_AGUser_mixed 1.0185 25 27.5 29.17 18.33

[Joso]

It's pretty much confirmed that I am.

Model Sample Sample Age BCE Fit Ashkenazi Jew Austrian English Polish
1 Ashkenazi_Jew +Austrian +English +Polish Custom_-_AGUser_mixed 1.0185 25 27.5 29.17 18.33

Ok then slavic friend

[Slavic Italian]

I'm even more Germanic than Italian.

Balto - North Slavic 35.2%
Northwest European 25.8%
Italian Greek 16.6%
Iberian 7.5%
Balkan 4.6%
Basque 3.8%
North African 3.2%
Persian Jewish 1.7%
Caucasus 1%
Other 0.6

[Slavic Italian]

Here is another model

Model Sample Sample Age BCE Fit Austrian English Italian South Polish
1 Austrian +English +Italian_South +Polish Custom_-_AGUser_mixed 1.2561 30.83 25.83 25.83 17.5

[Ajeje Brazorf]

Here is another model

Model Sample Sample Age BCE Fit Austrian English Italian South Polish
1 Austrian +English +Italian_South +Polish Custom_-_AGUser_mixed 1.2561 30.83 25.83 25.83 17.5

Could you post your MDLP K23b Oracle-4 results?

[Slavic Italian]

MDLP K23b Oracle Rev 2014 Sep 16

Admix Results (sorted):


# Population Percent
1 European_Hunters_Gatherers 31.10
2 Caucasian 25.87
3 European_Early_Farmers 23.57
4 South_Central_Asian 5.58
5 Near_East 3.86
6 Ancestral_Altaic 3.38
7 North_African 3.34
8 Melano_Polynesian 1.16
9 Amerindian 1.10


Finished reading population data. 620 populations found.
23 components mode.

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

Least-squares method.

Using 1 population approximation:
1 German-Volga_ @ 4.904706
2 Italian_North_ @ 7.876259
3 South_German_ @ 8.031116
4 Serb_Serbia_ @ 9.597931
5 Belgian_ @ 9.842412
6 Dutch_ @ 9.936640
7 English_ @ 10.140695
8 Irish_ @ 10.338227
9 Montenegrian_ @ 10.377346
10 Austrian_ @ 10.468511
11 Frisian_ @ 10.469931
12 German_East_ @ 10.495495
13 Bulgarian_ @ 11.006995
14 North_European_ @ 11.620901
15 Macedonian_ @ 11.792734
16 English_Kent_GBR_ @ 11.982930
17 English_Cornwall_GBR_ @ 12.173752
18 Welsh_ @ 12.694141
19 CEU_ @ 12.700011
20 North_German_ @ 13.020061

Using 2 populations approximation:
1 50% Greek_Thessaloniki_ +50% Icelandic_ @ 3.035263


Using 3 populations approximation:
1 50% Dutch_ +25% Italian_Jew_ +25% North_German_ @ 1.498868


Using 4 populations approximation:

1 Dutch_ + Dutch_ + Italian_Jew_ + North_German_ @ 1.498868
2 Dutch_ + French_Jew_ + Hungarian_Budapest_ + Orcadian_ @ 1.499117
3 French_ + French_Jew_ + Irish_ + Russian_Meshtchyora_ @ 1.501701
4 Dutch_ + French_Jew_ + Hungarian_Budapest_ + Scottish_Argyll_Bute_GBR_ @ 1.513560
5 French_Jew_ + German_ + German-Volga_ + Scottish_Argyll_Bute_GBR_ @ 1.522293
6 German_ + Irish_ + Irish_ + Italian_Jew_ @ 1.526144
7 Dutch_ + Hungarian_ + Italian_Jew_ + Scottish_Argyll_Bute_GBR_ @ 1.532168
8 Hungarian_Budapest_ + Icelandic_ + Irish_ + Italian_Jew_ @ 1.533118
9 Frisian_ + German_ + Irish_ + Moroccan_Jew_ @ 1.556919
10 Dutch_ + Italian_Jew_ + Scottish_Argyll_Bute_GBR_ + Slovenian_ @ 1.571388
11 Irish_ + Italian_Jew_ + Scottish_Argyll_Bute_GBR_ + Slovak_ @ 1.574080
12 Dutch_ + French_Jew_ + Hungarian_ + Scottish_Argyll_Bute_GBR_ @ 1.584949
13 Dutch_ + Hungarian_Budapest_ + Italian_Jew_ + Orcadian_ @ 1.586977
14 Dutch_ + Hungarian_Budapest_ + Italian_Jew_ + Scottish_Argyll_Bute_GBR_ @ 1.591111
15 French_ + Irish_ + Russian-West_ + Sephardic_Jew_ @ 1.595613
16 French_Jew_ + German_ + German-Volga_ + Orcadian_ @ 1.597571
17 Finn_West_ + Irish_ + Italian_North_ + Sicilian_West_ @ 1.599358
18 Finn_West_ + Italian_North_ + North_European_ + Sicilian_West_ @ 1.611504
19 Dane_ + Dutch_ + Dutch_ + Italian_Jew_ @ 1.619680
20 English_ + German_ + Irish_ + Italian_Jew_ @ 1.627658

[Ajeje Brazorf]

MDLP K23b Oracle Rev 2014 Sep 16

Admix Results (sorted):


# Population Percent
1 European_Hunters_Gatherers 31.10
2 Caucasian 25.87
3 European_Early_Farmers 23.57
4 South_Central_Asian 5.58
5 Near_East 3.86
6 Ancestral_Altaic 3.38
7 North_African 3.34
8 Melano_Polynesian 1.16
9 Amerindian 1.10


Finished reading population data. 620 populations found.
23 components mode.

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

Least-squares method.

Using 1 population approximation:
1 German-Volga_ @ 4.904706
2 Italian_North_ @ 7.876259
3 South_German_ @ 8.031116
4 Serb_Serbia_ @ 9.597931
5 Belgian_ @ 9.842412
6 Dutch_ @ 9.936640
7 English_ @ 10.140695
8 Irish_ @ 10.338227
9 Montenegrian_ @ 10.377346
10 Austrian_ @ 10.468511
11 Frisian_ @ 10.469931
12 German_East_ @ 10.495495
13 Bulgarian_ @ 11.006995
14 North_European_ @ 11.620901
15 Macedonian_ @ 11.792734
16 English_Kent_GBR_ @ 11.982930
17 English_Cornwall_GBR_ @ 12.173752
18 Welsh_ @ 12.694141
19 CEU_ @ 12.700011
20 North_German_ @ 13.020061

Using 2 populations approximation:
1 50% Greek_Thessaloniki_ +50% Icelandic_ @ 3.035263


Using 3 populations approximation:
1 50% Dutch_ +25% Italian_Jew_ +25% North_German_ @ 1.498868


Using 4 populations approximation:

1 Dutch_ + Dutch_ + Italian_Jew_ + North_German_ @ 1.498868
2 Dutch_ + French_Jew_ + Hungarian_Budapest_ + Orcadian_ @ 1.499117
3 French_ + French_Jew_ + Irish_ + Russian_Meshtchyora_ @ 1.501701
4 Dutch_ + French_Jew_ + Hungarian_Budapest_ + Scottish_Argyll_Bute_GBR_ @ 1.513560
5 French_Jew_ + German_ + German-Volga_ + Scottish_Argyll_Bute_GBR_ @ 1.522293
6 German_ + Irish_ + Irish_ + Italian_Jew_ @ 1.526144
7 Dutch_ + Hungarian_ + Italian_Jew_ + Scottish_Argyll_Bute_GBR_ @ 1.532168
8 Hungarian_Budapest_ + Icelandic_ + Irish_ + Italian_Jew_ @ 1.533118
9 Frisian_ + German_ + Irish_ + Moroccan_Jew_ @ 1.556919
10 Dutch_ + Italian_Jew_ + Scottish_Argyll_Bute_GBR_ + Slovenian_ @ 1.571388
11 Irish_ + Italian_Jew_ + Scottish_Argyll_Bute_GBR_ + Slovak_ @ 1.574080
12 Dutch_ + French_Jew_ + Hungarian_ + Scottish_Argyll_Bute_GBR_ @ 1.584949
13 Dutch_ + Hungarian_Budapest_ + Italian_Jew_ + Orcadian_ @ 1.586977
14 Dutch_ + Hungarian_Budapest_ + Italian_Jew_ + Scottish_Argyll_Bute_GBR_ @ 1.591111
15 French_ + Irish_ + Russian-West_ + Sephardic_Jew_ @ 1.595613
16 French_Jew_ + German_ + German-Volga_ + Orcadian_ @ 1.597571
17 Finn_West_ + Irish_ + Italian_North_ + Sicilian_West_ @ 1.599358
18 Finn_West_ + Italian_North_ + North_European_ + Sicilian_West_ @ 1.611504
19 Dane_ + Dutch_ + Dutch_ + Italian_Jew_ @ 1.619680
20 English_ + German_ + Irish_ + Italian_Jew_ @ 1.627658

Interesting results, you come out as 3/4 North European and 1/4 Italian. Where in Italy are you from? I suppose the south.

[Slavic Italian]

Interesting results, you come out as 3/4 North European and 1/4 Italian. Where in Italy are you from? I suppose the south.

Calabria.

[Slavic Italian]

I'm still getting nice results with many different combos with Austrian and English as my foundations.

Model Sample Sample Age BCE Fit Austrian English Finnish Sephardic Jew
1 Austrian +English +Finnish +Sephardic_Jew Custom_-_AGUser_mixed 1.2477 39.17 32.5 8.33 20

Model Sample Sample Age BCE Fit Austrian English Finnish Italian South
1 Austrian +English +Finnish +Italian_South Custom_-_AGUser_mixed 1.4234 37.5 28.33 9.17 25

Model Sample Sample Age BCE Fit Austrian English Italian South Russian Smolensk
1 Austrian +English +Italian_South +Russian_Smolensk Custom_-_AGUser_mixed 1.3779 33.33 34.17 24.17 8.33

Model Sample Sample Age BCE Fit Austrian English Iran Recent
1 Austrian +English +Iran_recent Custom_-_AGUser_mixed 1.7122 62.5 28.33 9.17

Model Sample Sample Age BCE Fit Austrian English Iran Recent Polish
1 Austrian +English +Iran_recent +Polish Custom_-_AGUser_mixed 1.6823 58.33 25 9.17 7.5

Model Sample Sample Age BCE Fit Austrian English Iran Recent Polish
1 Austrian +English +Iran_recent +Polish Custom_-_AGUser_mixed 1.642 52.5 27.5 10.83 9.17

Model Sample Sample Age BCE Fit Austrian English Iranian Persian Polish
1 Austrian +English +Iranian_Persian +Polish Custom_-_AGUser_mixed 1.85 55 26.67 9.17 9.17

[Gründig]

I'm still getting nice results with many different combos with Austrian and English as my foundations.

Model Sample Sample Age BCE Fit Austrian English Finnish Sephardic Jew
1 Austrian +English +Finnish +Sephardic_Jew Custom_-_AGUser_mixed 1.2477 39.17 32.5 8.33 20

Model Sample Sample Age BCE Fit Austrian English Finnish Italian South
1 Austrian +English +Finnish +Italian_South Custom_-_AGUser_mixed 1.4234 37.5 28.33 9.17 25

Model Sample Sample Age BCE Fit Austrian English Italian South Russian Smolensk
1 Austrian +English +Italian_South +Russian_Smolensk Custom_-_AGUser_mixed 1.3779 33.33 34.17 24.17 8.33

Model Sample Sample Age BCE Fit Austrian English Iran Recent
1 Austrian +English +Iran_recent Custom_-_AGUser_mixed 1.7122 62.5 28.33 9.17

Model Sample Sample Age BCE Fit Austrian English Iran Recent Polish
1 Austrian +English +Iran_recent +Polish Custom_-_AGUser_mixed 1.6823 58.33 25 9.17 7.5

Model Sample Sample Age BCE Fit Austrian English Iran Recent Polish
1 Austrian +English +Iran_recent +Polish Custom_-_AGUser_mixed 1.642 52.5 27.5 10.83 9.17

Model Sample Sample Age BCE Fit Austrian English Iranian Persian Polish
1 Austrian +English +Iranian_Persian +Polish Custom_-_AGUser_mixed 1.85 55 26.67 9.17 9.17

is this all on Poi's calculator?

[Joso]

is this all on Poi's calculator?

No

[Slavic Italian]

is this all on Poi's calculator?

Yes.

[Slavic Italian]

No

Kid quit responding for me.

[Joso]

Kid quit responding for me.

why

[Slavic Italian]

why

Because you are disrespecting boundaries and you were wrong.

[Joso]

Because you are disrespecting boundaries and you were wrong.

Ok sorry

[Slavic Italian]

Could you post your MDLP K23b Oracle-4 results?

I am American. I have never been to Italy. I wanted to clarify that.

[Slavic Italian]

I am doing really well with this calc.

Austrian +English +Russian_Smolensk +Sephardic_Jew Custom_-_AGUser_mixed 1.1089 30.83 35 12.5 21.67

[Slavic Italian]

I'm still getting a lot of good fits.

Model Sample Sample Age BCE Fit Austrian English Iranian Jew Italian South
1 Austrian +English +Iranian_Jew +Italian_South Custom_-_AGUser_mixed 1.4323 46.67 36.67 6.67 10

[Slavic Italian]

Not a bad fit.

Model Sample Sample Age BCE Fit Austrian English Iran Recent Italian South
1 Austrian +English +Iran_recent +Italian_South Custom_-_AGUser_mixed 1.2968 52.5 29.17 5 13.33

[Ajeje Brazorf]

How do these qpAdm or nMonte work? Do they automatically give you the closest fit?

[Slavic Italian]

How do these qpAdm or nMonte work? Do they automatically give you the closest fit?

You have to plug in the ethnicities you think or know you might be.

[Slavic Italian]

My closest fits.

0.7416
Austrian 28.33
French 17.5
German 43.33
Sephardic_Jew 10.83



0.7849
Ashkenazi_Jew 13.33
Austrian 23.33
French 19.17
German 44.17

[Gründig]

My closest fit.

Model Sample Sample Age BCE Fit Austrian French German Italian South
1 Austrian +French +German +Italian_South Custom_-_AGUser_mixed 1.0522 30 20.83 41.67 7.5

Which percentage goes to what? It's hard to tell.

Is this in line to your known ancestry? I ask cause I can get some super close distances that are blatanty wrong. Obviously if yours aligns with your ancestry, that's cool.

Quote from David:

"From my experience in free hand modeling, distances as high as 4 or 5 can still be acceptable, because I've tested a number of people with complex ancestry, and managed to model their ancestry basically perfectly based on paper trails with distances that high.

So like I said above, a strong model should accurately reflect your ancestry, whether it be self-reported or as suggested by many different analyses, but it doesn't necessarily have to show a super low distance


However, if you're working with the web tool and testing many different combinations of populations, while also limiting the number of populations to around 5, then you should try to find low distances of around 1-2, because this is likely to be informative"

[Slavic Italian]

Which percentage goes to what? It's hard to tell.

Is this in line to your known ancestry? I ask cause I can get some super close distances that are blatanty wrong. Obviously if yours aligns with your ancestry, that's cool.

Quote from David:

"From my experience in free hand modeling, distances as high as 4 or 5 can still be acceptable, because I've tested a number of people with complex ancestry, and managed to model their ancestry basically perfectly based on paper trails with distances that high.

So like I said above, a strong model should accurately reflect your ancestry, whether it be self-reported or as suggested by many different analyses, but it doesn't necessarily have to show a super low distance


However, if you're working with the web tool and testing many different combinations of populations, while also limiting the number of populations to around 5, then you should try to find low distances of around 1-2, because this is likely to be informative"

Those two recent models nailed much of my ancestry minus Slav references and Italian. I get similar results with South Italians reference. This is very close as well.

1.3779
Austrian 33.33
English 34.17
Italian_South 24.17
Russian_Smolensk 8.33

[Slavic Italian]

Which percentage goes to what? It's hard to tell.

Is this in line to your known ancestry? I ask cause I can get some super close distances that are blatanty wrong. Obviously if yours aligns with your ancestry, that's cool.

Quote from David:

"From my experience in free hand modeling, distances as high as 4 or 5 can still be acceptable, because I've tested a number of people with complex ancestry, and managed to model their ancestry basically perfectly based on paper trails with distances that high.

So like I said above, a strong model should accurately reflect your ancestry, whether it be self-reported or as suggested by many different analyses, but it doesn't necessarily have to show a super low distance


However, if you're working with the web tool and testing many different combinations of populations, while also limiting the number of populations to around 5, then you should try to find low distances of around 1-2, because this is likely to be informative"

I wanted to let you know David said those two good fits I posted yesterday were not realistic models for me because I did not include any East Slav or Sephardic Jew.

[Slavic Italian]

I guess this is the most realistic model for me and closest fit based on that model.

Model Sample Sample Age BCE Fit Austrian English Russian Smolensk Sephardic Jew
1 Austrian +English +Russian_Smolensk +Sephardic_Jew Custom 1.1089 30.83 35 12.5 21.67

[Slavic Italian]

1 Austrian +French +Russian_Kursk +Sephardic_Jew 1.0381 37.5 45.83 7.5 9.17

[J. Ketch]

Fit 0.6432

Dutch 27.5
German 6.67
Irish 46.67
Scottish 19.17

[J. Ketch]

From the non-updated datasheet

[1] "distance%=1.5026"

Orcadian,30.4
Wales_CA_EBA,21.8
Basque_French,13.6
English_Cornwall,12.8
Nordic_IA,11.6
Solyanka_MLBA,6.4
Satan_MLBA_Alakul,2.2
Scotland_LBA,1.2

[Gründig]

This isn't the closest I can get but so far its the most realistic. Fits right into what David said it should when using the Poi tool: 5 or less populations, between 1% and 2% and based on my actual paper trail and other calculators/DNA analyses.

"distance%=1.7664"

German,66.67
Irish,15
French,10.87
Austrian,7.5

[Hulu]

This is one of the closest I get



Fit Distance : 1.7665 Albanian Balkans N Greek Italian Abruzzo
23.33 11.67 25.83 39.17

[Hulu]

Formatting is a bitch :D When I try to edit looks right, then the final version is distorted

[Hulu]

Fit 2.2102
Albanian 17.5
Greek 32.5
Italian Abruzzo 50
Italian Tuscan 0


I played around a bit, look what a difference regions have, If I put Tuscan there, it's 0 and all the extra stuff goes mostly to Abruzzo and a bit of Greek

[Slavic Italian]

I'm still doing well.

Austrian +Orcadian +Russian_Smolensk +Sephardic_Jew 1.0783 39.17 34.17 7.5 19.17

[Slavic Italian]

Fit Albanian Greek Italian Abruzzo Italian Tuscan
2.2102 17.5 32.5 50 0

I played around a bit, look what a difference regions have, If I put Tuscan there, it's 0 and all the extra stuff goes mostly to Abruzzo and a bit of Greek

You need to eliminate one of the Italian samples for something else. Then you would probably get a better fit.

[Slavic Italian]

I got a really nice fit here.

Austrian +Belarusian +French +Sephardic_Jew 0.9543 28.33 12.5 46.67 12.5

Same here.

Ashkenazi_Jew +Austrian +Belarusian +French 1.0585 12.5 35 7.5 45

Austrian +Czech +Orcadian +Sephardic_Jew 1.0232 36.67 15.83 29.17 18.33

[Hulu]

You need to eliminate one of the Italian samples for something else. Then you would probably get a better fit.

No, I know, but I thought Tuscan should be close, but it's not.

[Slavic Italian]

No, I know, but I thought Tuscan should be close, but it's not.

Don't give up on matching it with other samples.

[Hulu]

I got a really nice fit here.

Austrian +Belarusian +French +Sephardic_Jew 0.9543 28.33 12.5 46.67 12.5

Same here.

Ashkenazi_Jew +Austrian +Belarusian +French 1.0585 12.5 35 7.5 45

Austrian +Czech +Orcadian +Sephardic_Jew 1.0232 36.67 15.83 29.17 18.33

They don't really correspond with Slavic Italian name

[Hulu]

Don't give up on matching it with other samples.

Still 0

Fit 2.2297
Albanian 19.17
Balkans IA 31.67
Greek 49.17
Italian Tuscan 0

Ok, a bit more here, but less than others

Fit 2.5013
Albanian 22.5
Greek 42.5
Italian Tuscan 13.33
Sicilian East 21.67


Another 0 Tuscan

Fit 2.0968
Albanian 48.33
Balkans BA 21.67
Italian Tuscan 0
Sicilian East 30

[Slavic Italian]

Still 0

Fit 2.2297
Albanian 19.17
Balkans IA 31.67
Greek 49.17
Italian Tuscan 0

Ok, a bit more here, but less than others

Fit 2.5013
Albanian 22.5
Greek 42.5
Italian Tuscan 13.33
Sicilian East 21.67


Another 0 Tuscan

Fit 2.0968
Albanian 48.33
Balkans BA 21.67
Italian Tuscan 0
Sicilian East 30

That middle one is pretty good. Surprised you are not getting closer. Somewhere you are overlooking a critical reference.

[Hulu]

That middle one is pretty good. Surprised you are not getting closer. Somewhere you are overlooking a critical reference.

I do get closer, I was insisting on Tuscan, Check this one.

Fit Distance : 1.7665 Albanian Balkans N Greek Italian Abruzzo
23.33 11.67 25.83 39.17

[Slavic Italian]

I do get closer, I was insisting on Tuscan, Check this one.

Fit Distance : 1.7665 Albanian Balkans N Greek Italian Abruzzo
23.33 11.67 25.83 39.17

That is pretty good.

[Slavic Italian]

Still 0

Fit 2.2297
Albanian 19.17
Balkans IA 31.67
Greek 49.17
Italian Tuscan 0

Ok, a bit more here, but less than others

Fit 2.5013
Albanian 22.5
Greek 42.5
Italian Tuscan 13.33
Sicilian East 21.67


Another 0 Tuscan

Fit 2.0968
Albanian 48.33
Balkans BA 21.67
Italian Tuscan 0
Sicilian East 30

David said try to find a ethnicity that makes up 50% of your ancestry. That's what he told me.

[Slavic Italian]

This is a really good fit.

French +German +Russian_Smolensk +Sephardic_Jew 0.903 22.5 57.5 8.33 11.67


I'm in a zone now.

Belarusian +French +German +Sephardic_Jew 0.8111 8.3 15.83 62.5 13.33

[Slavic Italian]

I'm trying to use English as my foundation. This is one of the models that gets that particular reference over 50%. I got total balance in the two Slavic references.


Belarusian +English +Russian_Smolensk +Sephardic_Jew 1.1816 10 54.17 10 25.83


Belarusian +English +Russian_Orel +Sephardic_Jew 1.1819 13.33 54.17 7.5 25

[Slavic Italian]

I'm getting a lot of good models.


English +Italian_South +Russian_Orel +Sephardic_Jew 1.2853 55 5.83 20 19.17


English +Italian_South +Russian_Smolensk +Sephardic_Jew Custom 1.1732 50.83 10 21.67 17.5

[Slavic Italian]

I think these are my best models.

1 .1732
English 50.83
Italian_South 10
Russian_Smolensk 21.67
Sephardic_Jew 17.5

1.2228
English 54.17
Italian_South 4.17
Russian_Orel 20.83
Sephardic_Jew 20.83

1.265
English 54.17
Italian_South 5
Russian_Voronez 20.83
Sephardic_Jew 20

1.372
English 56.67
Italian_South 10
Russian_Tver 16.67
Sephardic_Jew 16.67

[Slavic Italian]

They don't really correspond with Slavic Italian name

The recent ones I posted do but I am quite mixed. I can plug in a couple Iranian reference and get similar results.


1 Austrian +English +Iran_recent +Italian_South 1.2968 52.5 29.17 5 13.33

[Gründig]

The recent ones I posted do but I am quite mixed. I can plug in a couple Iranian reference and get similar results.


1 Austrian +English +Iran_recent +Italian_South 1.2968 52.5 29.17 5 13.33

Have you tried messing around with ancients?

Or have you been plotted on any of his PCA's?

[Slavic Italian]

Have you tried messing around with ancients?

Or have you been plotted on any of his PCA's?

I plot between everybody. No on the ancients but I will.

[Hulu]

The recent ones I posted do but I am quite mixed. I can plug in a couple Iranian reference and get similar results.


1 Austrian +English +Iran_recent +Italian_South 1.2968 52.5 29.17 5 13.33

I dont see Slavic anywhere. Run one with Slavic and Italian and see what else comes up. You have listed a million places in your ancestry, do you actually know what percentages of what do you have?

[Slavic Italian]

I dont see Slavic anywhere. Run one with Slavic and Italian and see what else comes up. You have listed a million places in your ancestry, do you actually know what percentages of what do you have?

How did you overlook this..............twice?

1 .1732
English 50.83
Italian_South 10
Russian_Smolensk 21.67
Sephardic_Jew 17.5

1.2228
English 54.17
Italian_South 4.17
Russian_Orel 20.83
Sephardic_Jew 20.83

1.265
English 54.17
Italian_South 5
Russian_Voronez 20.83
Sephardic_Jew 20

1.372
English 56.67
Italian_South 10
Russian_Tver 16.67
Sephardic_Jew 16.67

[Hulu]

How did you overlook this..............twice?

1 .1732
English 50.83
Italian_South 10
Russian_Smolensk 21.67
Sephardic_Jew 17.5

1.2228
English 54.17
Italian_South 4.17
Russian_Orel 20.83
Sephardic_Jew 20.83

1.265
English 54.17
Italian_South 5
Russian_Voronez 20.83
Sephardic_Jew 20

1.372
English 56.67
Italian_South 10
Russian_Tver 16.67
Sephardic_Jew 16.67

I meant in the post I quoted. So does this correspond to your ancestry? Cause you are approx quarter slavic + italian.

[Slavic Italian]

I meant in the post I quoted. So does this correspond to your ancestry? Cause you are approx quarter slavic + italian.

I am more mixed than this. I have some Iranian, Finn, German, and even a small amount of Chinese and Vietnamese.

[Hulu]

I am more mixed than this. I have some Iranian, Finn, German, and even a small amount of Chinese and Vietnamese.

But what is the main ancestry? You come out as 55% English

[J. Ketch]

I am more mixed than this. I have some Iranian, Finn, German, and even a small amount of Chinese and Vietnamese.
What is your score for the ancient populations run that David would have given you (WHG, Barcin, Yamnaya etc)?

[Slavic Italian]

But what is the main ancestry? You come out as 55% English

These plus German.

English 50.83
Italian_South 10
Russian_Smolensk 21.67
Sephardic_Jew 17.5

[Hulu]

These plus German.

English 50.83
Italian_South 10
Russian_Smolensk 21.67
Sephardic_Jew 17.5

Do you know what percentages? Your real ancestry I mean. Anyway, your name is confusing, didn't think you'd identify with the minor parts of your ancestry.

[Slavic Italian]

Do you know what percentages? Your real ancestry I mean. Anyway, your name is confusing, didn't think you'd identify with the minor parts of your ancestry.

They are not the minor parts. You want me to change my name to the English Slav? These are my real ancestries. I am mixed. I am not one, two, or three parts. Percentages? All these calculators are wrong.

Balto-NorthSlavic 35.2%
NorthwestEuropean 25.8%
ItalianGreek 16.6%
Iberian 7.5%
Balkan 4.6%
Basque 3.8%
NorthAfrican 3.2%
PersianJewish 1.7%
Caucasus 1%
Other 0.6%

[Gründig]

What is your score for the ancient populations run that David would have given you (WHG, Barcin, Yamnaya etc)?

What did yours come out as?

[J. Ketch]

What did yours come out as?
[1] "distance%=5.8442"

Yamnaya_Samara,48.8
Barcin_N,36.4
WHG,14.8

[J. Ketch]

[1] "distance%=5.8442"

Yamnaya_Samara,48.8
Barcin_N,36.4
WHG,14.8
Changing Yamnaya Samara to Poltavka and Barcin to the Ukraine Neolithic Outlier brings the distance down considerably though.

Fit 3.8853

Poltavka 51.67
Ukraine N O 35.83
WHG 12.5

[Slavic Italian]

My best ancient fit so far.

England_Roman +Germany_Medieval +Sephardic_Jew +Sweden_Viking_Age 1.3609 6.67 42.5 25.83 25

[Slavic Italian]

Another good modern fit.

English +Russian_Smolensk +Sephardic_Jew +Sicilian_West 1.1318 51.67 20.83 20 7.5

[Slavic Italian]

I got a nice fit here as I know I have ancestry from these regions.

English +Russian_Smolensk +Scottish +Sephardic_Jew 1.1135 30 20 25 25

English +Russian_Orel +Scottish +Sephardic_Jew 1.2392 34.17 20 22.5 23.33

This one is interesting.

Czech +English +Scottish +Sephardic_Jew 1.0522 38.33 17.5 22.5 21.67

This is my closest reading yet.

Czech +Irish +Sephardic_Jew +Sicilian_East 0.9277 43.33 32.5 15 9.17

Another close one

Czech +Irish +Italian_South +Sephardic_Jew 0.9855 45.83 30.83 3.33 20

Ashkenazi_Jew +Czech +Irish +Italian_South 0.9954 19.17 39.17 35 6.67

[Slavic Italian]

Do you know what percentages? Your real ancestry I mean. Anyway, your name is confusing, didn't think you'd identify with the minor parts of your ancestry.

You wanted more Slav?

Czech +English +Scottish +Sephardic_Jew 1.0522 38.33 17.5 22.5 21.67

Czech +English +Sephardic_Jew +Sicilian_East 1.1328 42.5 34.17 16.67 6.67

Czech +Irish +Sephardic_Jew +Ukrainian 0.9743 36.67 35.83 22.5 5

Czech +Irish +Polish +Sephardic_Jew 1.0079 36.67 33.33 8.33 21.67

Ashkenazi_Jew +Czech +Irish +Italian_South 0.9954 19.17 39.17 35 6.67

Ashkenazi_Jew +Czech +Scottish +Sicilian_East 1.1569 19.17 35.83 39.17 5.83

[Slavic Italian]

My closest fit.

Ashkenazi_Jew +Czech +Irish +Sicilian_East 0.9218 19.17 40 34.17 6.67

[Slavic Italian]

I get very close fits when I use German but the rest of the model lacks balance.


Ashkenazi_Jew +Czech +German +Italian_South 0.9275 13.33 5.83 78.33 2.5

Czech +German +Sephardic_Jew +Sicilian_East 0.9039 10.83 73.33 12.5 3.33

German +Sephardic_Jew +Sicilian_East +Ukrainian 0.8061 73.33 12.5 2.5 11.67

German +Polish +Sephardic_Jew +Sicilian_East 0.839 74.17 10.83 11.67 3.33

German +Russian_Smolensk +Sephardic_Jew +Sicilian_East 0.8777 80 5 13.33 1.67

Belarusian +German +Sephardic_Jew +Sicilian_East 0.8803 5 79.17 14.17 1.67

German +Polish +Sephardic_Jew +Sicilian_East 0.8388 73.33 11.67 13.33 1.67

[Slavic Italian]

This is my best fit in sponsor mode. Davidski told me not to overload the model with multiple out groups so I am using just five.

French +German +Sephardic_Jew +Sicilian_East +Ukrainian 0.8981 25 52.5 7.5 5 10

[Gründig]

This is my best fit in sponsor mode. Davidski told me not to overload the model with multiple out groups so I am using just five.

French +German +Sephardic_Jew +Sicilian_East +Ukrainian 0.8981 25 52.5 7.5 5 10

You pay the $10?

I did the same, 4-5 populations as he said.

[Slavic Italian]

You pay the $10?

I did the same, 4-5 populations as he said.


I just did $5. Now these are my best fits.

French +German +Irish +Sephardic_Jew +Ukrainian 0.8095 22.5 50 3.33 12.5 11.67

French +German +Lithuanian +Sephardic_Jew +Ukrainian 0.7909 20.83 54.17 1.67 13.33 10

[Gründig]

I just did $5. Now this is my best fit.

French +German +Irish +Sephardic_Jew +Ukrainian 0.8095 22.5 50 3.33 12.5 11.67

French +German +Lithuanian +Sephardic_Jew +Ukrainian 0.7909 20.83 54.17 1.67 13.33 10

Nice, that's close.

Mines not as close but it does fall within the 1%-2% distance David told me to aim for.

[Slavic Italian]

Ashkenazi_Jew +Croatian +German +Ukrainian 0.8814 14.17 14.17 68.33 3.33

[Slavic Italian]

I still think you get more accurate results from running only four out groups.

English +Italian_South +Russian_Smolensk +Sephardic_Jew 1.2234 50.83 12.5 20.83 15.83

English +Russian_Smolensk +Sephardic_Jew +Sicilian_East 1.173 51.67 20.83 16.67 10.83

[Slavic Italian]

Do you know what percentages? Your real ancestry I mean. Anyway, your name is confusing, didn't think you'd identify with the minor parts of your ancestry.

Something I want to point out to you. Every little bit of DNA matters and is significant. For example I have Asian teeth(shovel shaped) and I have little body hair. That is also an Asian trait. I have a little bit of Han and Vietnamese DNA. Read this article. I actually downloaded my 23&me raw data and searched for this Asian marker(rs3827760) and I have it. As small amount as I have it has impacted my physical features.

https://nativeheritageproject.com/2013/02/18/thick-hair-small-boobs-shovel-shaped-teeth-and-more/

[Slavic Italian]

I'm getting a lot of good fits. I'm starting to question this calc.

Croatian +French +German +Italian_South 1.1314 17.5 22.5 51.67 8.33

[Slavic Italian]

I'm even scoring Romanian.

French +German +Italian_South +Romanian 1.1285 23.33 58.33 7.5 10.83

[Slavic Italian]

I get a lot of nice combinations on this calc.


Ashkenazi_Jew +Finnish +French +Russian_Voronez 1.1393 16.67 10 60 13.33

Ashkenazi_Jew +Finnish +French +Russian_Smolensk 1.0364 16.67 7.5 60.83 15

Ashkenazi_Jew +Finnish +French +Polish 1.0047 15 3.33 57.5 24.17

Ashkenazi_Jew +Belarusian +Finnish +French 1.1246 16.67 15 5.83 62.5

[Sp_loa]

Where can I get these tools from and how can I use them?

[Slavic Italian]

Finnish +French +Italian_South +Russian_Smolensk 1.2918 6.67 65.83 13.33 14.17

Finnish +French +Italian_South +Russian_Voronez 1.2678 11.67 60.83 15.83 11.67

Finnish +French +Italian_South +Polish 1.2616 3.33 60.83 12.5 23.33

Belarusian +Finnish +French +Italian_South 1.2672 13.33 7.5 62.5 16.67

Finnish +French +Italian_South +Russian_Kursk 1.2368 12.5 62.5 15.83 9.17

Finnish +French +Italian_South +Russian_Orel 1.2914 10.83 62.5 15 11.67

[Gründig]

Where can I get these tools from and how can I use them?

You have to email Davidski (eurogenes) your raw data file and $12. He will send you back scaled and unscaled coordinates.

[Slavic Italian]

I'm getting good models with Ashkenazi but I do not think I have any Jewish DNA.



Ashkenazi_Jew +Finnish +French +Russian_Smolensk 1.0695 15.83 7.5 61.67 15

Ashkenazi_Jew +Finnish +French +Russian_Voronez 1.092 16.67 10.83 60 12.5

Ashkenazi_Jew +Finnish +French +Russian_Kursk 1.0844 15.83 10 62.5 11.67

[canadienne]

How can i work this? I don't have a computer and i use my mobile phone.

[Gründig]

I'm getting good models with Ashkenazi but I do not think I have any Jewish DNA.



Ashkenazi_Jew +Finnish +French +Russian_Smolensk 1.0695 15.83 7.5 61.67 15

Ashkenazi_Jew +Finnish +French +Russian_Voronez 1.092 16.67 10.83 60 12.5

Ashkenazi_Jew +Finnish +French +Russian_Kursk 1.0844 15.83 10 62.5 11.67

I get some close models with a few very strange populations, that make zero sense.

[Slavic Italian]

I get some close models with a few very strange populations, that make zero sense.

I do not put a lot of stock in these calculators. ANY of them. I know I get a lot of matches from Finland and the Eastern European countries. I also get a lot of matches from Germany. This tells me I have those ethnicities as well as ethnicities from several other nations I have pure matches from such as France and Italy. I consider myself mostly German, Slavic, Finnish, French, and Italian.

[Gründig]

I do not put a lot of stock in these calculators. ANY of them. I know I get a lot of matches from Finland and the Eastern European countries. I also get a lot of matches from Germany. This tells me I have those ethnicities as well as ethnicities from several other nations I have pure matches from such as France and Italy. I consider myself mostly German, Slavic, Finnish, French, and Italian.

I gather information from these tests from looking at them all as one big picture. For the most part, each test or plot puts me in southern/western Germany.

When I use calculators such as nMonte, my break down is typically half or more of German followed by smaller amounts of Dutch, French and Irish. These populations give me a perfect fit for the model and actually come close to being overfit. So i usually take one out and only use 3 pops. They also match my known history.

I can get even closer but the pops don't make any sense, and at that point I'm just adding on random shit to get a closer distance. David said don't do this.

[Slavic Italian]

I gather information from these tests from looking at them all as one big picture. For the most part, each test or plot puts me in southern/western Germany.

When I use calculators such as nMonte, my break down is typically half or more of German followed by smaller amounts of Dutch, French and Irish. These populations give me a perfect fit for the model and actually come close to being overfit. So i usually take one out and only use 3 pops. They also match my known history.

I can get even closer but the pops don't make any sense, and at that point I'm just adding on random shit to get a closer distance. David said don't do this.

I think this is very close to being my ancestry. I think South Italian probably over the Jewish and probably Polish over Ukraine. The French is probably a Germanic/English mix.

1] "distance%=0.6067"

French,53.8
Finnish,18.6
Ashkenazi_Jew,17.6
Ukrainian,10

[Slavic Italian]

Interesting mix.

French +Iranian_Persian +Italian_South +Russian_Smolensk 1.221 72.5 7.5 3.33 16.67
French +Iranian_Zoroastrian +Italian_South +Russian_Smolensk 1.2262 70 7.5 5 17.5

[Slavic Italian]

French +Iranian_Persian +Russian_Smolensk +Sicilian_East 1.1807 70 7.5 18.33 4.17

French +Iranian_Persian +Russian_Smolensk +Sicilian_West 1.1819 70.83 7.5 17.5 4.17

French +Iranian_Zoroastrian +Russian_Smolensk +Sicilian_East 1.1907 70.83 7.5 17.5 4.17

French +Iranian_Zoroastrian +Italian_South +Russian_Smolensk 1.2305 72.5 8.33 2.5 16.67

French +Iranian_Zoroastrian +Italian_South +Russian_Voronez 1.2814 75 6.67 1.67 16.67

[Slavic Italian]

English +Finnish +French +Iranian_Persian +Italian_South +Russian_Smolensk 1.1797 4.17 3.33 66.67 7.5 4.17 14.17

[Gründig]

Have you been plotted on the Celtic VS Germanic plot?

[Slavic Italian]

Have you been plotted on the Celtic VS Germanic plot?

I believe so.

[Slavic Italian]

French +German +Italian_South +Ukrainian 1.069 30.83 45.83 11.67 11.67

[Slavic Italian]

0.9372
French 34.17
German 45.83
Iranian_Persian 5.83
Italian_South 5.83
Ukrainian 8.33

0.9589
Azeri_Iran 7.5
French 40
German 40.83
Italian_South 2.5
Ukrainian 9.17

1.0244
French 35
German 52.5
Iranian_Zoroastrian 7.5
Italian_South 1.67
Russian_Smolensk 3.33

0.9807
Azeri_Iran 7.5
French 37.5
German 45.83
Italian_South 1.67
Ukrainian 7.5

[Gründig]

Heres a simple model that makes sense for me:

1.0583

German,50.83
French,25.83
Dutch,23.33

[Slavic Italian]

0.9884
French 30.83
German 54.17
Iranian_Lor 5.83
Italian_South 4.17
Russian_Smolensk 5

[Slavic Italian]

Heres a simple model that makes sense for me:

1.0583

German,50.83
French,25.83
Dutch,23.33

You know I am pretty mixed so I utilize references that I have matches to on the various DNA services and they ALL work for me on this calc.

[J. Ketch]

My father could be Icelandic, doesn't look like it though.
https://i.postimg.cc/MGs7kGxv/G25-plot-Copy.jpg

[J. Ketch]

Mine and my parents whole sheet comparisons:

Mine

[1] "distance%=1.4993"

Wales_CA_EBA,22.4
Orcadian,19
Basque_French,15.4
Irish,13.6
Nordic_IA,13.6
English_Cornwall,7.2
Solyanka_MLBA,6.2
Satan_MLBA_Alakul,2.6

Mother's

[1] "distance%=1.0065"

Shetlandic,22.6
Wales_CA_EBA,21.2
Karagash_MLBA,12.2
England_IA,11
Alberstedt_LN,9.4
Sweden_N,8.8
Halberstadt_LBA,8.2
CWC_Czech_o,2.4
Solyanka_MLBA,2.2
Beaker_Hungary_o,1.6
Koros_HG,0.4

Father's

[1] "distance%=1.9413"

Orcadian,46
Nordic_IA,27
BenzigerodeHeimburg_LN,10.4
England_Roman,8.6
Spanish_Pais_Vasco,5.8
Ak_Moustafa_MLBA1,2
English_Cornwall,0.2

[J. Ketch]

With pen=0.001

Mine

[1] "distance%=1.5722"

Orcadian,60.8
Scotland_LBA,10
Wales_CA_EBA,6.8
English_Cornwall,5
Nordic_IA,4.8
England_Roman,4
Scottish,2.8
Irish,2.6
Basque_French,0.8
English,0.6
Shetlandic,0.6
Solyanka_MLBA,0.4
Icelandic,0.2
Ireland_MN,0.2
Satan_MLBA_Alakul,0.2
Spanish_Pais_Vasco,0.2

Dad's

[1] "distance%=1.977"

Orcadian,58.4
Nordic_IA,16.2
England_Roman,10.4
BenzigerodeHeimburg_LN,5.4
English_Cornwall,2.4
Icelandic,2
Irish,1.8
Scottish,1.4
Spanish_Pais_Vasco,0.6
Ak_Moustafa_MLBA1,0.4
Scotland_LBA,0.4
Baltic_IA,0.2
Basque_French,0.2
Satan_MLBA_Alakul,0.2

[Slavic Italian]

With pen=0.001

Mine

[1] "distance%=1.5722"

Orcadian,60.8
Scotland_LBA,10
Wales_CA_EBA,6.8
English_Cornwall,5
Nordic_IA,4.8
England_Roman,4
Scottish,2.8
Irish,2.6
Basque_French,0.8
English,0.6
Shetlandic,0.6
Solyanka_MLBA,0.4
Icelandic,0.2
Ireland_MN,0.2
Satan_MLBA_Alakul,0.2
Spanish_Pais_Vasco,0.2

Dad's

[1] "distance%=1.977"

Orcadian,58.4
Nordic_IA,16.2
England_Roman,10.4
BenzigerodeHeimburg_LN,5.4
English_Cornwall,2.4
Icelandic,2
Irish,1.8
Scottish,1.4
Spanish_Pais_Vasco,0.6
Ak_Moustafa_MLBA1,0.4
Scotland_LBA,0.4
Baltic_IA,0.2
Basque_French,0.2
Satan_MLBA_Alakul,0.2

Maybe you are overloading the calculator? Davidski warned me about this. This is about right for me. At least as far as this calc is concerned.

0.9258
Belarusian 2.5
French 35
German 45.83
Iranian_Persian 5.83
Italian_South 5
Ukrainian 5.83

[J. Ketch]

Maybe you are overloading the calculator? Davidski warned me about this. This is about right for me. At least as far as this calc is concerned.

0.9258
Belarusian 2.5
French 35
German 45.83
Iranian_Persian 5.83
Italian_South 5
Ukrainian 5.83
Finding my admixture with modern populations is boring. I already know what it is, and what the calculator gives me.

[Gründig]

With pen=0.001

Mine

[1] "distance%=1.5722"

Orcadian,60.8
Scotland_LBA,10
Wales_CA_EBA,6.8
English_Cornwall,5
Nordic_IA,4.8
England_Roman,4
Scottish,2.8
Irish,2.6
Basque_French,0.8
English,0.6
Shetlandic,0.6
Solyanka_MLBA,0.4
Icelandic,0.2
Ireland_MN,0.2
Satan_MLBA_Alakul,0.2
Spanish_Pais_Vasco,0.2

Dad's

[1] "distance%=1.977"

Orcadian,58.4
Nordic_IA,16.2
England_Roman,10.4
BenzigerodeHeimburg_LN,5.4
English_Cornwall,2.4
Icelandic,2
Irish,1.8
Scottish,1.4
Spanish_Pais_Vasco,0.6
Ak_Moustafa_MLBA1,0.4
Scotland_LBA,0.4
Baltic_IA,0.2
Basque_French,0.2
Satan_MLBA_Alakul,0.2

What exactly is Wales_CA_EBA? What modern day pops do they place by?

[J. Ketch]

What exactly is Wales_CA_EBA? What modern day pops do they place by?
Wales Copper Age/Early Bronze Age. Basically Bell Beaker people. More Steppe shifted than modern Brits. I don't know exactly who they'd place with, possibly Icelanders, or just Northern shifted Brits.

[Gründig]

Heres a very simple model of me with ancients:

"distance%=1.2977"

Italy Medieval Collegno,53.33
Germany Medieval,46.67

[Slavic Italian]

I'm not interested in ancient enough to run any calcs.

[J. Ketch]

I'm not interested in ancient enough to run any calcs.
Where do you place on the Northern European PCA? (the one I posted above). That should be more interesting than running all these modern populations.

[Slavic Italian]

Where do you place on the Northern European PCA? (the one I posted above). That should be more interesting than running all these modern populations.

Mine looks much different than yours and I plot like in the middle of many ethnicities.

[J. Ketch]

Mine looks much different than yours and I plot like in the middle of many ethnicities.
What cluster are you in? Austrian?

Where mixed people land gives a clue to the ancestral mixtures of that population.

[Slavic Italian]

What cluster are you in? Austrian?

Where mixed people land gives a clue to the ancestral mixtures of that population.

Yep. They are generally a Slavic/Germanic mix.

[Slavic Italian]

What cluster are you in? Austrian?

Where mixed people land gives a clue to the ancestral mixtures of that population.

This is the best I can do as far as ancients.

1.7376
Beaker_Northern_Italy 25
English_Cornwall 35
Germany_Medieval 7.5
Iranian_Persian 10.83
Russian_Smolensk 21.67

[Slavic Italian]

I did better here.

1.2411
England_Roman 2.5
Germany_Medieval 16.67
Italy_Medieval_Collegno 61.67
Russian_Smolensk 19.17

1.4625
Basque_French 29.17
Germany_Medieval 35.83
Iranian_Persian 7.5
Italy_Medieval_Collegno_o1 7.5
Russian_Smolensk 20

1.3629
Assyrian 7.5
Basque_French 27.5
Germany_Medieval 34.17
Iranian_Persian 4.17
Italy_Medieval_Collegno_o1 4.17
Russian_Smolensk 22.5

[Slavic Italian]

My best fit.

"sample": "Custom:AGUser_mixed",
"fit": 0.9025,
"German": 73.33,
"Sephardic_Jew": 10.83,
"Russian_Smolensk": 3.33,
"Sicilian_East": 3.33,
"English_Cornwall": 2.5,
"Finnish": 2.5,
"Latvian": 2.5,

[Slavic Italian]

I think this is a better fit.

"sample": "Custom:AGUser_mixed",
"fit": 1.1584,
"German_East": 41.67,
"English_Cornwall": 24.17,
"Sephardic_Jew": 18.33,
"Finnish": 5.83,
"Russian_Smolensk": 5,
"Italian_South": 3.33,
"Iranian_Persian": 1.67,