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Thread: An nMonte and 4mix Guide for the Participants of the Basal-rich K7 and/or Global 10 T

  1. #461
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    Just take out PC6 from the Global 10 and off you go.

    I have no idea why that works, because PC6 is such an informative dimension, but I think it might be heavily skewed by modern drift in Northern Europe.

    By the way, Iron Age models with England_IA, Nordic_IA, Altai_IA, etc. look awesome for Europeans.

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  3. #462
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    Quote Originally Posted by Generalissimo View Post
    Just take out PC6 from the Global 10 and off you go. ...
    I have no idea why that works, because PC6 is such an informative dimension, but I think it might be heavily skewed by modern drift in Northern Europe.
    Another possible cause might be because on a PC4/PC6 plot the crowded cline from Yamnaya to modern Europeans is not parallel to the PC6 dimension but some 30 degrees tilted. In that direction it projects some 15 percent on the PC4 dimension, which may not be very helpful.

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  5. #463
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    Quote Originally Posted by Huijbregts View Post
    Another possible cause might be because on a PC4/PC6 plot the crowded cline from Yamnaya to modern Europeans is not parallel to the PC6 dimension but some 30 degrees tilted. In that direction it projects some 15 percent on the PC4 dimension, which may not be very helpful.
    I just found that in the dataset 'Central & West Eurasia3' (without East-Eurasians) the corresponding clines are parallel to the corresponding dimensions PC1,PC2

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  7. #464
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    My results from earlier were decent nMonte models as far as distances. I wasn't too happy that PC1/PC2/PC3 weren't some of the closer matches in the nine dimensions (minus PC6, as it was removed). I was interested in the earlier conversation in this thread about the "variations" within the population. So, I decided to allow some controlled overfitting/correlation to let nMonte pick out the Yamnaya Samara, Barcin, and WHG-SHG populations (and Levant, which wasn't selected for any of us) that best worked for each person, with out groups. I used this spreadsheet:

    Code:
    ,PC1,PC2,PC3,PC4,PC5,PC7,PC8,PC9,PC10
    Hungary_HG,0.0185,0.0219,6.00E-04,-0.0922,-0.0196,0.0629,-0.0053,-0.0439,-0.0064
    Iberia_HG:I0585,0.0166,0.016,0,-0.0762,-0.0158,0.0604,-0.006,-0.0439,-0.0031
    Loschbour:Loschbour,0.0189,0.0206,0.0012,-0.0975,-0.0216,0.0706,-0.0036,-0.0535,-0.0045
    Bichon:Bichon,0.0166,0.0188,0.0003,-0.0933,-0.0204,0.0705,-0.0079,-0.0511,-0.0047
    Motala_HG:I0014,0.0189,0.0169,0.005,-0.0971,-0.0199,0.0595,-0.0009,-0.0345,-0.0086
    Villabruna:I9030,0.0147,0.0213,0,-0.0915,-0.0197,0.0688,-0.0071,-0.0501,-0.0057
    Yamnaya_Samara:I0231,0.0181,0.0168,0.0034,-0.0549,-0.0075,0.0349,0.0049,0.0123,-0.0077
    Yamnaya_Samara:I0357,0.018,0.0187,0.0019,-0.0513,-0.0043,0.0322,-0.0049,0.0046,-0.0049
    Yamnaya_Samara:I0370,0.0174,0.018,0.0038,-0.055,-0.0053,0.0327,0.0043,0.0144,-0.0072
    Yamnaya_Samara:I0429,0.0188,0.0178,0.0033,-0.0591,-0.005,0.0396,0.0025,0.0143,-0.0049
    Yamnaya_Samara:I0438,0.0178,0.0171,0.0036,-0.0583,-0.0071,0.0351,0.0047,0.0129,-0.0053
    Yamnaya_Samara:I0439,0.0173,0.0181,-0.0003,-0.0545,-0.0051,0.032,0.0011,0.0127,-0.0035
    Yamnaya_Samara:I0441,0.0178,0.0179,0.0035,-0.053,-0.0021,0.039,0.0037,0.0088,-0.003
    Yamnaya_Samara:I0443,0.0182,0.0196,0.0018,-0.0527,-0.0048,0.0337,0.0041,0.0172,-0.0053
    Yamnaya_Samara:I0444,0.0185,0.0189,0.0031,-0.0555,-0.0072,0.0364,0.0013,0.0074,-0.0085
    Barcin_N:Bar31,0.0139,0.0348,-0.0026,0.0223,0.0036,-0.011,-0.0039,-0.0021,0.0039
    Barcin_N:Bar8,0.0153,0.0338,0.0001,0.02,0.0015,-0.0093,-0.0015,-0.0008,0.0037
    Barcin_N:I0707,0.0172,0.0358,-0.0012,0.0272,0.0059,-0.0151,0.0003,-0.0007,0.0038
    Barcin_N:I0708,0.0168,0.0331,-0.0026,0.0236,0.0054,-0.0126,0.0024,0.007,0.0069
    Barcin_N:I0709,0.0165,0.0365,-0.001,0.0244,0.0043,-0.0125,0.0011,-0.0019,0.005
    Barcin_N:I0736,0.0164,0.0355,-0.0029,0.0236,0.0053,-0.0155,-0.0032,-0.0025,0.0014
    Barcin_N:I0744,0.0164,0.0359,-0.0037,0.0289,0.0049,-0.0145,-0.0023,0.0013,0.0052
    Barcin_N:I0745,0.0159,0.035,-0.0034,0.0241,0.0016,-0.014,0.0017,-0.002,0.0049
    Barcin_N:I0746,0.0165,0.0346,-0.0016,0.0229,0.0022,-0.0161,0.0002,-0.0007,0.0033
    Barcin_N:I1096,0.0167,0.0363,0.0005,0.0223,0.0012,-0.0122,-0.0033,-0.0025,0.0089
    Barcin_N:I1097,0.0169,0.0349,-0.0017,0.0271,0.0035,-0.0111,0.0015,-0.0035,0.0035
    Barcin_N:I1098,0.0159,0.0355,-0.0035,0.0218,0.0056,-0.0157,-0.0017,0.0009,0.0037
    Barcin_N:I1099,0.0159,0.0359,-0.0017,0.0215,0.0037,-0.0117,-0.0058,-0.0013,0.0059
    Barcin_N:I1100,0.0162,0.034,-0.0053,0.0213,0.0032,-0.0141,-0.0018,-0.0068,0.0067
    Barcin_N:I1101,0.0158,0.0352,-0.0027,0.0242,0.0045,-0.0135,0.0048,-0.0044,0.0022
    Barcin_N:I1103,0.0156,0.0336,-0.0034,0.0227,0.0049,-0.016,0.0008,-0.0003,0.0061
    Barcin_N:I1579,0.0168,0.0362,-0.0022,0.0232,0.0029,-0.0123,0.001,-0.0025,0.007
    Barcin_N:I1580,0.0165,0.0357,-0.0029,0.0253,0.0033,-0.014,-0.0003,0.0048,0.0018
    Barcin_N:I1581,0.0156,0.0348,-0.0035,0.0268,0.0044,-0.0179,-0.0038,-0.0039,0.0062
    Barcin_N:I1583,0.0157,0.0354,-0.0036,0.025,0.0047,-0.019,0.0009,-0.0064,0.0066
    Barcin_N:I1585,0.0165,0.035,-0.0018,0.0246,0.0035,-0.0103,-0.0013,-0.0006,0.0059
    Levant_N:I0867,0.0097,0.0339,-0.0046,0.0527,0.0055,-0.0404,0.0009,-0.013,0.0049
    Levant_N:I1699,0.01,0.0327,-0.0048,0.049,0.0059,-0.0421,0.003,-0.0096,0.0052
    Levant_N:I1704,0.0101,0.0313,-0.0064,0.0494,0.0029,-0.037,0.0024,-0.0167,0.0014
    Anatolia_BA:I2495,0.013,0.0309,-0.0024,0.0231,0.0056,-0.0105,0.0025,0.0134,-0.0001
    Anatolia_BA:I2499,0.0138,0.0299,-0.0034,0.0225,0.0086,-0.0062,0.0014,0.0151,0.0012
    Anatolia_BA:I2683,0.0132,0.0314,-0.002,0.0239,0.0055,-0.0101,-0.0006,0.0188,0.0016
    Ami,0.0084,-0.0826,0.0277,0.0438,0.0144,0.0473,0.0008,0.0177,-0.003
    Biaka,-0.1228,0.0048,0.0047,-0.0462,0.1389,-0.0122,-0.0006,0.0032,0.0067
    Bougainville,-0.0011,-0.0615,-0.1605,-0.0041,-0.0024,0.0011,0.1985,-0.0045,0.0064
    Chukchi,0.0116,-0.0636,0.0342,-0.0553,-0.0216,-0.075,0.0072,-0.007,0.0013
    Eskimo_Sireniki,0.0115,-0.0623,0.0325,-0.0579,-0.0217,-0.0751,0.0063,-0.0108,0.0025
    Han,0.009,-0.0807,0.0315,0.0239,0.0048,0.004,-0.0079,0.0092,0.0001
    Karitiana,0.0119,-0.0536,0.0236,-0.0493,-0.0123,-0.0292,0.0025,-0.0046,-0.0001
    Austroasiatic_Kharia,0.0037,-0.0431,-0.0141,0.0203,0.0141,0.0492,-0.0023,-0.0586,0.0056
    Andamanese_Onge,0.0013,-0.0453,-0.0257,0.012,0.0071,0.0272,0.0002,-0.0424,0.0009
    Papuan,-0.0037,-0.0578,-0.2166,-0.0239,-0.0121,-0.0246,-0.0794,0.0107,-0.0034
    She,0.0085,-0.0814,0.0322,0.0259,0.0055,0.0084,-0.0075,0.0094,-0.0013
    Ulchi,0.0098,-0.0761,0.0351,-0.0247,-0.014,-0.0623,-0.0004,-0.0001,0.0006
    Yoruba,-0.1169,0.006,0.0071,0.0004,-0.0461,0.0124,-0.0002,0.0078,-0.0155
    Hadza,-0.0882,0.0041,-0.0003,0.0047,0.0212,-0.0115,0.002,-0.028,-0.0271
    Bantu_SA_Tswana,-0.1167,0.0053,0.005,-0.0093,-0.0002,0.0021,0.0036,0.0007,-0.0238
    Pima,0.0114,-0.0514,0.025,-0.0466,-0.012,-0.0311,0.0023,-0.0057,-0.0005
    Piramalai,0.0072,-0.0156,-0.0152,0.011,0.0133,0.0358,-0.0028,-0.0497,0.0062
    Nganasan,0.01116,-0.06922,0.04114,-0.0641,-0.02772,-0.10372,0.01248,-0.00556,0.00258
    The models weren't too wild given the many options and correlation. I grouped the types for easier interpretation:

    Me:

    [1] "distance%=0.2447 / distance=0.002447"

    Barcin_N:I1096 33.90
    Barcin_N:I0707 12.45
    Yamnaya_Samara:I0444 24.60
    Yamnaya_Samara:I0441 8.65
    Loschbour:Loschbour 10.90
    Motala_HG:I0014 8.35
    Ami 1.15

    Dad:

    [1] "distance%=0.1131 / distance=0.001131"

    Barcin_N:I1096 43.30
    Yamnaya_Samara:I0357 19.15
    Yamnaya_Samara:I0429 10.85
    Yamnaya_Samara:I0444 7.70
    Motala_HG:I0014 7.15
    Hungary_HG 5.95
    Loschbour:Loschbour 4.30
    Ulchi 1.45
    Han 0.15

    Mom:

    [1] "distance%=0.3419 / distance=0.003419"

    Barcin_N:I1099 30.9
    Barcin_N:I1096 8.3
    Barcin_N:I1585 2.8
    Yamnaya_Samara:I0441 39.2
    Loschbour:Loschbour 11.5
    Bichon:Bichon 7.3

    Wife:

    [1] "distance%=0.2326 / distance=0.002326"

    Barcin_N:I1096 43.55
    Yamnaya_Samara:I0444 15.20
    Yamnaya_Samara:I0443 13.85
    Yamnaya_Samara:I0439 6.10
    Yamnaya_Samara:I0357 4.05
    Motala_HG:I0014 9.95
    Hungary_HG 7.30

    Father in law:

    [1] "distance%=0.2746 / distance=0.002746"

    Barcin_N:I1096 47.80
    Yamnaya_Samara:I0429 16.05
    Yamnaya_Samara:I0357 4.45
    Motala_HG:I0014 30.30
    Yoruba 0.85
    She 0.55

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  9. #465
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    Quote Originally Posted by randwulf View Post
    My results from earlier were decent nMonte models as far as distances. I wasn't too happy that PC1/PC2/PC3 weren't some of the closer matches in the nine dimensions (minus PC6, as it was removed). I was interested in the earlier conversation in this thread about the "variations" within the population. So, I decided to allow some controlled overfitting/correlation to let nMonte pick out the Yamnaya Samara, Barcin, and WHG-SHG populations (and Levant, which wasn't selected for any of us) that best worked for each person, with out groups. I used this spreadsheet:
    Me

    [1] "distance%=0.1243 / distance=0.001243"

    Ravai:Son

    Barcin_N:I1096 46.95
    Bichon:Bichon 21.10
    Barcin_N:I1099 19.00
    Yamnaya_Samara:I0357 9.95
    Han 3.00

    Father:

    [1] "distance%=0.2078 / distance=0.002078"

    Ravai_father

    Barcin_N:I1096 62.90
    Hungary_HG 9.55
    Yamnaya_Samara:I0444 9.40
    Yamnaya_Samara:I0357 7.60
    Motala_HG:I0014 6.65
    Han 2.65
    Yoruba 1.25
    Bichon:Bichon 0.00
    Paternal: R1b-U152+ L2+ BY4245+ BY3485+ BY3478+ , Giovanni Domenicus Rabai, b. 1609, Savona, Italy
    Maternal: Haplogroup H65, María García Martínez, b. 1746, Cuenca, Spain

    Manuel David Rabaez 1974, Manuel Rabaez 1948, Manuel Rabaez 1912, Antonio Rabay 1868, Antonio Rabay 1833, Manuel Rabay 1791, Manuel Rabay 1764, Pedro Rabai 1727, Pedro Joseph Rabai 1691, Giovanni Battista Rabai 1647, Jo. Domenicus Rabai 1609, Pietrus Rabai (work in progress...)

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  11. #466
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    Quote Originally Posted by Ravai View Post
    Me

    [1] "distance%=0.1243 / distance=0.001243"

    Ravai:Son

    Barcin_N:I1096 46.95
    Bichon:Bichon 21.10
    Barcin_N:I1099 19.00
    Yamnaya_Samara:I0357 9.95
    Han 3.00

    Father:

    [1] "distance%=0.2078 / distance=0.002078"

    Ravai_father

    Barcin_N:I1096 62.90
    Hungary_HG 9.55
    Yamnaya_Samara:I0444 9.40
    Yamnaya_Samara:I0357 7.60
    Motala_HG:I0014 6.65
    Han 2.65
    Yoruba 1.25
    Bichon:Bichon 0.00
    You'll probably get best results and a best fit by using the BBC or the IA samples
    My results from David Wesolowski's Ancestry Detective Service:

    West British (Britonic?) 42.3%
    Continental Northern and Eastern European 36.6%
    Central French 21.1%

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  13. #467
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    Quote Originally Posted by Ravai View Post
    Ravai

    With these populations you get probably a best result, closer to the truth.

    You get African in the first version, before restricted
    On the advice of David this one is the best if the African is real,
    As Spanish, this African makes sense



    [1] "distance%=0.5015 / distance=0.005015"

    Ravai

    Iberia_BA 63.0
    Iberia_ChL 26.6
    Anatolia_BA 9.8
    Yoruba 0.6



    [1] "distance%=0.5084 / distance=0.005084"

    Ravai

    Iberia_BA 64.8
    Iberia_ChL 25.1
    Anatolia_BA 10.2


    With these populations your steppe is boosted
    My results from David Wesolowski's Ancestry Detective Service:

    West British (Britonic?) 42.3%
    Continental Northern and Eastern European 36.6%
    Central French 21.1%

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