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Thread: Davidski's Global 25 nMonte results

  1. #1811
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    Quote Originally Posted by Huijbregts View Post
    I wondered whether in Global25 Germany_Medieval is different from Longobard_North.
    The killer app to answer this kind of questions is Random Forest.
    I prepared a dataset of the submodern Europeans in Global25.
    Only 4 of them had 10 or more samples: Longobard_North(N=29), Longobard_South(16), Sarmatian(16) and Germany_Medieval(10).
    Random Forest returned the following confusion matrix:
    Germany_Medieval Longobard_North Longobard_South Sarmatian class.error
    Germany_Medieval 0 9 1 0 1.0000
    Longobard_North 0 29 0 0 0.0000
    Longobard_South 0 1 15 0 0.0625
    Sarmatian 0 0 0 16 0.0000
    In the second row we see that all 29 Longobard_North samples were classified as Longobard_North.
    In the fourth row we see that all 16 Sarmatian samples were classified as Sarmatian.
    In the third row we see that 15/16 of the Longobard_South samples were classified as Longobard_South. The last one was classified as Longobard_North; maybe we should have a second look at the classification of CL47 as Longobard_South.
    In the first row we see that 9/10 of the Germany_Medieval samples were classified as Longobard_North, and the last one as Longobard_South. None was classified as Germany_Medieval.

    So the answer to my question is unambiguous: within the accuracy of Global25, Germany_Medieval cannot be discriminated from Longobard_North.
    What is your opinon on CL47?

  2. #1812
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    Netherlands Belgium
    Quote Originally Posted by Alfred89 View Post
    What is your opinon on CL47?
    Fig. 3 of the paper presents a display. For some samples the percentage of southern admixture is virtually 100%.
    CL47 has 40% southern admixture. It would be odd to call it an admixed Longbard.

    EDIT: I meant un UNadmixed Longobard.
    Last edited by Huijbregts; 11-27-2018 at 07:23 PM.

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  4. #1813
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    R1B-DF99(FGC16982)

    Normandie Germany Imperial
    CL47 on Gedmatch
    Kit Num: Z610815
    Threshold of components set to 1.000
    Threshold of method set to 0.25%
    Personal data has been read. 20 approximations mode.
    Gedmatch.Com
    Eurogenes K13 4-Ancestors Oracle
    This program is based on 4-Ancestors Oracle Version 0.96 by Alexandr Burnashev.
    Questions about results should be sent to him at: Alexandr.Burnashev@gmail.com
    Original concept proposed by Sergey Kozlov.
    Many thanks to Alexandr for helping us get this web version developed.

    K13 Oracle ref data revised 21 Nov 2013

    Admix Results (sorted):

    #
    Population
    Percent
    1
    North_Atlantic
    39.81
    2
    West_Med
    17.78
    3
    Baltic
    17.36
    4
    East_Med
    11.89
    5
    West_Asian
    8.24
    6
    Red_Sea
    4.05


    Finished reading population data. 204 populations found.
    13 components mode.

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

    Least-squares method.

    Using 1 population approximation:
    1 French @ 5.205673
    2 West_German @ 8.448330
    3 South_Dutch @ 9.159266
    4 Spanish_Cataluna @ 11.432345
    5 Portuguese @ 11.646420
    6 Spanish_Galicia @ 12.044484
    7 Spanish_Castilla_Y_Leon @ 12.493620
    8 Spanish_Extremadura @ 13.251985
    9 Spanish_Murcia @ 13.520875
    10 Austrian @ 13.976312
    11 Spanish_Valencia @ 14.277418
    12 Spanish_Castilla_La_Mancha @ 14.682674
    13 Spanish_Cantabria @ 14.845889
    14 North_Italian @ 14.919276
    15 Southeast_English @ 15.582659
    16 Spanish_Andalucia @ 15.600118
    17 Southwest_French @ 15.700663
    18 Serbian @ 16.811514
    19 East_German @ 16.815432
    20 North_German @ 16.817646

    Using 2 populations approximation:
    1 50% Irish +50% Tuscan @ 2.322785


    Using 3 populations approximation:
    1 50% French +25% Irish +25% Italian_Abruzzo @ 1.870208


    Using 4 populations approximation:
    ++++++++++++++++++++++++++++++++++++++++++++++++++ +++++++++++++++++++++++++++++++++++++++
    1 French + French + Irish + Italian_Abruzzo @ 1.870208
    2 Central_Greek + North_Dutch + Spanish_Extremadura + West_Scottish @ 1.927383
    3 French + French + Italian_Abruzzo + North_Dutch @ 1.948720
    4 French + Italian_Abruzzo + South_Dutch + Southwest_English @ 1.961179
    5 French + French + Italian_Abruzzo + West_Scottish @ 1.971860
    6 Central_Greek + Irish + North_Dutch + Spanish_Extremadura @ 1.983070
    7 Irish + Italian_Abruzzo + North_German + Spanish_Extremadura @ 1.984123
    8 French + French + Italian_Abruzzo + Norwegian @ 2.002615
    9 Italian_Abruzzo + North_German + Spanish_Extremadura + West_Scottish @ 2.005415
    10 Italian_Abruzzo + North_German + Orcadian + Spanish_Extremadura @ 2.028505
    11 French + French + Italian_Abruzzo + Orcadian @ 2.036980
    12 Italian_Abruzzo + North_German + Southwest_English + Spanish_Extremadura @ 2.044366
    13 East_Sicilian + North_Dutch + Spanish_Extremadura + West_Scottish @ 2.072854
    14 Italian_Abruzzo + North_Dutch + North_Dutch + Spanish_Extremadura @ 2.080592
    15 East_Sicilian + Irish + North_Dutch + Spanish_Extremadura @ 2.092473
    16 Italian_Abruzzo + Norwegian + South_Dutch + Spanish_Extremadura @ 2.099024
    17 Central_Greek + Norwegian + Spanish_Extremadura + West_Scottish @ 2.108282
    18 French + Italian_Abruzzo + Southwest_English + West_German @ 2.108524
    19 Irish + Norwegian + South_Italian + Spanish_Extremadura @ 2.116427
    20 Italian_Abruzzo + North_German + Southwest_English + Spanish_Galicia @ 2.119279
    FTDNA: West & Central Europe 96%
    K8: French East/German South/Austrian 26%, French North East/Belgian/German West 25%, French North 25%, Irish/Scottish/Welsh 10%, French South/French Basque 9%, German East/Czech/Austrian 5%
    K36: German Rheinland-Pfalz 35%, French North West 27%, Spain 27%, Ireland 11%
    G25: German 40%, French 28%, Spanish 23%, Irish 9%
    K16: German 50%, French 19%, French East 9%, Spanish 8%

  5. #1814
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    Norman/German
    Nationality
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    Y-DNA
    R1B-DF99(FGC16982)

    Normandie Germany Imperial
    CL47 on Gedmatch
    Kit Num: Z610815
    Threshold of components set to 1.000
    Threshold of method set to 0.25%
    Personal data has been read. 20 approximations mode.
    Gedmatch.Com
    Eurogenes K13 4-Ancestors Oracle
    This program is based on 4-Ancestors Oracle Version 0.96 by Alexandr Burnashev.
    Questions about results should be sent to him at: Alexandr.Burnashev@gmail.com
    Original concept proposed by Sergey Kozlov.
    Many thanks to Alexandr for helping us get this web version developed.

    K13 Oracle ref data revised 21 Nov 2013

    Admix Results (sorted):

    #
    Population
    Percent
    1
    North_Atlantic
    39.81
    2
    West_Med
    17.78
    3
    Baltic
    17.36
    4
    East_Med
    11.89
    5
    West_Asian
    8.24
    6
    Red_Sea
    4.05


    Finished reading population data. 204 populations found.
    13 components mode.

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

    Least-squares method.

    Using 1 population approximation:
    1 French @ 5.205673
    2 West_German @ 8.448330
    3 South_Dutch @ 9.159266
    4 Spanish_Cataluna @ 11.432345
    5 Portuguese @ 11.646420
    6 Spanish_Galicia @ 12.044484
    7 Spanish_Castilla_Y_Leon @ 12.493620
    8 Spanish_Extremadura @ 13.251985
    9 Spanish_Murcia @ 13.520875
    10 Austrian @ 13.976312
    11 Spanish_Valencia @ 14.277418
    12 Spanish_Castilla_La_Mancha @ 14.682674
    13 Spanish_Cantabria @ 14.845889
    14 North_Italian @ 14.919276
    15 Southeast_English @ 15.582659
    16 Spanish_Andalucia @ 15.600118
    17 Southwest_French @ 15.700663
    18 Serbian @ 16.811514
    19 East_German @ 16.815432
    20 North_German @ 16.817646

    Using 2 populations approximation:
    1 50% Irish +50% Tuscan @ 2.322785


    Using 3 populations approximation:
    1 50% French +25% Irish +25% Italian_Abruzzo @ 1.870208


    Using 4 populations approximation:
    ++++++++++++++++++++++++++++++++++++++++++++++++++ +++++++++++++++++++++++++++++++++++++++
    1 French + French + Irish + Italian_Abruzzo @ 1.870208
    2 Central_Greek + North_Dutch + Spanish_Extremadura + West_Scottish @ 1.927383
    3 French + French + Italian_Abruzzo + North_Dutch @ 1.948720
    4 French + Italian_Abruzzo + South_Dutch + Southwest_English @ 1.961179
    5 French + French + Italian_Abruzzo + West_Scottish @ 1.971860
    6 Central_Greek + Irish + North_Dutch + Spanish_Extremadura @ 1.983070
    7 Irish + Italian_Abruzzo + North_German + Spanish_Extremadura @ 1.984123
    8 French + French + Italian_Abruzzo + Norwegian @ 2.002615
    9 Italian_Abruzzo + North_German + Spanish_Extremadura + West_Scottish @ 2.005415
    10 Italian_Abruzzo + North_German + Orcadian + Spanish_Extremadura @ 2.028505
    11 French + French + Italian_Abruzzo + Orcadian @ 2.036980
    12 Italian_Abruzzo + North_German + Southwest_English + Spanish_Extremadura @ 2.044366
    13 East_Sicilian + North_Dutch + Spanish_Extremadura + West_Scottish @ 2.072854
    14 Italian_Abruzzo + North_Dutch + North_Dutch + Spanish_Extremadura @ 2.080592
    15 East_Sicilian + Irish + North_Dutch + Spanish_Extremadura @ 2.092473
    16 Italian_Abruzzo + Norwegian + South_Dutch + Spanish_Extremadura @ 2.099024
    17 Central_Greek + Norwegian + Spanish_Extremadura + West_Scottish @ 2.108282
    18 French + Italian_Abruzzo + Southwest_English + West_German @ 2.108524
    19 Irish + Norwegian + South_Italian + Spanish_Extremadura @ 2.116427
    20 Italian_Abruzzo + North_German + Southwest_English + Spanish_Galicia @ 2.119279
    FTDNA: West & Central Europe 96%
    K8: French East/German South/Austrian 26%, French North East/Belgian/German West 25%, French North 25%, Irish/Scottish/Welsh 10%, French South/French Basque 9%, German East/Czech/Austrian 5%
    K36: German Rheinland-Pfalz 35%, French North West 27%, Spain 27%, Ireland 11%
    G25: German 40%, French 28%, Spanish 23%, Irish 9%
    K16: German 50%, French 19%, French East 9%, Spanish 8%

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  7. #1815
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    In #1809 of this thread I demonstrated that Germany_Medieval and Longobard samples can be partially separated on PC1 of Global 25.
    Using the same methods I had a fresh look at the difference between between modern Germans and Dutch. Here is the scatterplot:
    plot_DutchGerm.jpg
    The difference along PC15 is surprisingly big. In the datasheet we find Iberomausian, Loschbour, Villabruna, WHG on the positive end of PC15 and the negative end is all Neolithic.
    This suggests that the Neolithic farmer immigration did not leave a conspicuous mark in the genetics of the Netherlands. Indeed much of the region may have been more useful for fishing and hunting than for agriculture.
    I am less sure of the robustness of the difference on PC24.

  8. The Following 7 Users Say Thank You to Huijbregts For This Useful Post:

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  9. #1816
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    Quote Originally Posted by Huijbregts View Post
    In #1809 of this thread I demonstrated that Germany_Medieval and Longobard samples can be partially separated on PC1 of Global 25.
    Using the same methods I had a fresh look at the difference between between modern Germans and Dutch. Here is the scatterplot:
    plot_DutchGerm.jpg
    The difference along PC15 is surprisingly big. In the datasheet we find Iberomausian, Loschbour, Villabruna, WHG on the positive end of PC15 and the negative end is all Neolithic.
    This suggests that the Neolithic farmer immigration did not leave a conspicuous mark in the genetics of the Netherlands. Indeed much of the region may have been more useful for fishing and hunting than for agriculture.
    I am less sure of the robustness of the difference on PC24.
    See Vlaardingen culture, which lasted well into the age of Corded Ware.
    Last edited by epoch; 11-28-2018 at 07:23 PM.

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