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Thread: Post your Gedrosia K3 results123 days old

  1. #21
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    Race Scientist kingjohn's Avatar
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    brief description of the components:

    1- E Eurasian - This component peaks in E & SE populations such as Ami, Nivkh, Dai, Han, and Ulchi, at about 100%, followed by Siberian & other Asian populations such as
    Nganasans, Tibetans, Subba, and Mongola.

    2- W Eurasian - This component peaks in Neolithic European farmers such as Stuttgart, and LBK culture, as well as in most modern European populations at over 95%.
    3- SSA (Sub-Saharan African) - This component peaks in Sub-Saharan African populations such as Yoruban, Esan, and Luhiya at over 97%.
    the killer look :)

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  3. #22
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    Quote Originally Posted by kingjohn View Post
    brief description of the components:

    1- E Eurasian - This component peaks in E & SE populations such as Ami, Nivkh, Dai, Han, and Ulchi, at about 100%, followed by Siberian & other Asian populations such as
    Nganasans, Tibetans, Subba, and Mongola.

    2- W Eurasian - This component peaks in Neolithic European farmers such as Stuttgart, and LBK culture, as well as in most modern European populations at over 95%.
    3- SSA (Sub-Saharan African) - This component peaks in Sub-Saharan African populations such as Yoruban, Esan, and Luhiya at over 97%.
    I have no doubt that the SSA picks up other africans, non-bantu.
    Last edited by Kriptc06; 2018-02-04 at 22:16.
    Adieu

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  5. #23
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    it is a smart calculator
    because it used an ancient farmer who lack those sub-sharan alleles
    that are now part of southwest asians genetic substructure ......
    so it is a good way to see the subsharan allells when you use ancient refernce {like stuttgart}
    agree with you more than likely that some of the subsharan take east african allells also.
    the killer look :)

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    # Population Percent
    1 W_Eurasian 97.65
    2 E_Eurasian 1.70

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    Interesting how some report this test underestimated their SSA ancestry, when it seems to be the opposite for my samples, the SSA component seems to be capturing something that is generally captured by a Middle Eastern component, thus inflating the SSA value for my samples.

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  11. #26
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    E_Eurasian 2.31
    W_Eurasian 97.69

    Using 1 population approximation:
    1 Croatian @ 0.475985
    2 French @ 0.520585
    3 Czech @ 0.555164
    4 English @ 0.866721
    5 Albanian @ 0.873654
    6 Norwegian @ 0.904969
    7 Greek @ 1.378345
    8 Bulgarian @ 1.675149
    9 French_South @ 1.736603
    10 Hungarian @ 1.838731
    11 Lithuanian @ 1.965932
    12 Bergamo @ 1.994827
    13 Spanish @ 2.519257
    14 Georgian @ 2.786199
    15 Ukrainian @ 2.793882
    16 LBK_EN @ 2.822281
    17 Sardinian @ 2.827398
    18 Stuttgart @ 3.258173
    19 Armenian @ 3.563203
    20 Belarusian @ 3.716728

    Using 2 populations approximation:
    1 50% Belarusian +50% Stuttgart @ 0.000000
    2 50% Croatian +50% English @ 0.000000
    3 50% Croatian +50% French @ 0.000000
    4 50% Czech +50% English @ 0.000000
    5 50% Czech +50% French @ 0.000000
    6 50% English +50% Norwegian @ 0.000000
    7 50% Stuttgart +50% Ukrainian @ 0.000000
    8 50% French +50% Norwegian @ 0.295731
    9 50% Bergamo +50% Lithuanian @ 0.300550
    10 50% Bergamo +50% Hungarian @ 0.339700
    11 50% French_South +50% Lithuanian @ 0.415309
    12 50% French_South +50% Hungarian @ 0.428146
    13 50% Croatian +50% Croatian @ 0.475985
    14 50% English +50% Hungarian @ 0.494580
    15 50% Albanian +50% Czech @ 0.497104
    16 50% French_South +50% Norwegian @ 0.498756
    17 50% Albanian +50% Croatian @ 0.508769
    18 50% Croatian +50% Czech @ 0.515038
    19 50% French +50% French @ 0.520585
    20 50% English +50% Lithuanian @ 0.554722


    Using 3 populations approximation:
    1 50% Belarusian +25% Stuttgart +25% Stuttgart @ 0.000000

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  13. #27
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    Gives some funny results for multiracial people

    1 W_Eurasian 52.91
    2 E_Eurasian 40.36
    3 SSA 6.73

    Using 1 population approximation:
    1 Malli @ 4.639172
    2 Kurumba @ 5.890129
    3 Velama @ 5.999925
    4 Kanjar @ 6.411045
    5 UP_Kol @ 6.453166
    6 UP_Muslim @ 6.547153
    7 GujaratiD @ 6.659756
    8 UP_Caste @ 7.047938
    9 Piramalai @ 7.684678
    10 Uzbek @ 7.799694
    11 Hazara_Afghan @ 7.905854
    12 Tharu @ 7.951254
    13 Kallar @ 8.409486
    14 Bhil @ 8.450739
    15 Jains @ 9.540061
    16 Meghawal @ 10.132342
    17 Turkmen_Afghan @ 10.133395
    18 Tamil_Brahmin @ 10.169274
    19 GujaratiC @ 10.330241
    20 UP_Chamar @ 10.503424

    Using 2 populations approximation:
    1 50% Kurd_C +50% Onge @ 1.977395
    2 50% Yemenite_Jew +50% Yukagir @ 2.021899
    3 50% Yemenite_Jew +50% Yukagir @ 2.021899
    4 50% Palestinian +50% Yukagir @ 2.081599
    5 50% Palestinian +50% Yukagir @ 2.081599
    6 50% Onge +50% Syrian @ 2.196285
    7 50% Lebanese +50% Onge @ 2.255201
    8 50% Iranian +50% Onge @ 2.303288
    9 50% Saudi +50% Yukagir @ 2.362588
    10 50% Saudi +50% Yukagir @ 2.362588
    11 50% Bhumij +50% Makrani @ 2.531394
    12 50% BedouinA +50% Yukagir @ 2.834320
    13 50% BedouinA +50% Yukagir @ 2.834320
    14 50% Jordanian +50% Yukagir @ 2.853480
    15 50% Jordanian +50% Yukagir @ 2.853480
    16 50% Jordanian +50% Onge @ 2.932180
    17 50% Kurd_N +50% Onge @ 3.052178
    18 50% Birhor +50% Makrani @ 3.084359
    19 50% Makrani +50% Paniyas @ 3.243553
    20 50% Great_Andamanese +50% Makrani @ 3.291610


    Using 3 populations approximation:
    1 50% Bengali +25% Nihali +25% Yemen @ 0.000000
    "Living or dying, it's not a big deal. What we should be concerned about is whether or not we're allowed to crawl to our graves."

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  15. #28
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    Admix Results (sorted):

    # Population Percent
    1 W_Eurasian 86.12
    2 E_Eurasian 12.74
    3 SSA 1.14


    Finished reading population data. 129 populations found.
    3 components mode.

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

    Least-squares method.

    Using 1 population approximation:
    1 Balkar @ 1.360154
    2 Turkish @ 1.733573
    3 Russian @ 3.232862
    4 Iranian @ 3.624996
    5 Finnish @ 3.68315
    6 Mordovian @ 3.906686
    7 Adygei @ 5.617704
    8 Kurd_C @ 6.228177
    9 Chechen @ 7.414957
    10 Kurd_N @ 8.676025
    11 Tajik_Pomiri @ 9.303118
    12 Makrani @ 9.37487
    13 Brahui @ 9.702494
    14 Balochi @ 10.052949
    15 Loschbour @ 10.263605
    16 Abkhasian @ 10.728323
    17 Georgian_Jew @ 11.245158
    18 Estonian @ 11.273224
    19 Belarusian @ 11.933071
    20 Iranian_Jew @ 12.322171

    Using 2 populations approximation:
    1 50% Greek +50% Pashtun_Afghan @ 0.289913
    2 50% Kurd_N +50% Tajik_Pomiri @ 0.320332
    3 50% Sardinian +50% Tajik @ 0.365786
    4 50% Pashtun_Afghan +50% Spanish @ 0.382598
    5 50% LBK_EN +50% Tajik @ 0.42497
    6 50% Armenian +50% Kalash @ 0.442323
    7 50% Albanian +50% Pashtun_Afghan @ 0.51181
    8 50% Balochi +50% Loschbour @ 0.535397
    9 50% French_South +50% Pathan @ 0.547709
    10 50% Pathan +50% Sardinian @ 0.555062
    11 50% Greek +50% Pathan @ 0.567094
    12 50% Pathan +50% Spanish @ 0.579229
    13 50% Bergamo +50% Pathan @ 0.602071
    14 50% LBK_EN +50% Pathan @ 0.615185
    15 50% Brahui +50% Loschbour @ 0.702709
    16 50% Assyrian +50% Kalash @ 0.72481
    17 50% Albanian +50% Pathan @ 0.736652
    18 50% French +50% Pashtun_Afghan @ 0.747395
    19 50% Kalash +50% Sicilian @ 0.760756
    20 50% Georgian +50% Kalash @ 0.780224


    Using 3 populations approximation:
    1 50% Georgian +25% Tajik_Afghan +25% Turkish @ 0.000000

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