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Thread: Global25 automated nMonte for South/Central Asian members

  1. #5101
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    Quote Originally Posted by Censored View Post
    Neolithic run:

    "sample": "Brahmin_Tamil_Nadu_Mixed:26284729292_AGUser",
    "fit": 2.679,
    "Simulated_AASI": 39.17,
    "IRN_Ganj_Dareh_N": 34.17,
    "RUS_Progress_Eneolithic": 18.33,
    "Anatolia_Barcin_N": 5.83,
    "RUS_West_Siberia_N": 1.67,
    "Han": 0.83,

    "sample": "Brahmin_Tamil_Nadu_Mixed:26284729292_v3_AGUse r",
    "fit": 3.5972,
    "Simulated_AASI": 39.17,
    "IRN_Ganj_Dareh_N": 34.17,
    "RUS_Progress_Eneolithic": 18.33,
    "Anatolia_Barcin_N": 5,
    "RUS_West_Siberia_N": 2.5,
    "Han": 0.83,

    Slightly higher Han, less AASI, same steppe. It's reflected in this model as well:

    "sample": "Brahmin_Tamil_Nadu_Mixed:26284729292_AGUser",
    "fit": 1.897,
    "IRN_Shahr_I_Sokhta_BA3": 37.5,
    "Irula": 24.17,
    "TKM_Gonur1_BA": 15,
    "PAK_Swat_Saidu_Sharif_IA_o": 12.5,
    "RUS_Sintashta_MLBA": 10.83,
    "Han": 0,

    "sample": "Brahmin_Tamil_Nadu_Mixed:26284729292_v3_AGUse r",
    "fit": 1.7989,
    "IRN_Shahr_I_Sokhta_BA3": 31.67,
    "PAK_Swat_Saidu_Sharif_IA_o": 27.5,
    "TKM_Gonur1_BA": 15,
    "Irula": 13.33,
    "RUS_Sintashta_MLBA": 10.83,
    "Han": 1.67,
    "RUS_West_Siberia_N": 0,

    Again higher east Asian, higher AASI, but steppe is constant

    Edit: meant to write same steppe
    This seems to be the same difference. But when I run iron age models, the steppe is elevated. In the base model david sent me, my yamna+barcin was 4% higher. For some reason, maybe the AASI, my modeling prefers gujarati brahmin significantly more than before.

  2. #5102
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    Quote Originally Posted by 26284729292 View Post
    This seems to be the same difference. But when I run iron age models, the steppe is elevated. In the base model david sent me, my yamna+barcin was 4% higher. For some reason, maybe the AASI, my modeling prefers gujarati brahmin significantly more than before.
    Slight differences in AASI can have drastic effects when it comes to models like these. It’s a distinct component, and largely responsible for the long distances between various groups

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     26284729292 (06-22-2019)

  4. #5103
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    Reposting the piramalai model from earlier (somehow it got invalidated)...

    Attachment 31121

  5. #5104
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    Quote Originally Posted by 26284729292 View Post
    Reposting the piramalai model from earlier (somehow it got invalidated)...

    Attachment 31121
    Note how soulblighter who scored lower Gujarati Brahmin than the Iyer average on the other run is now getting higher amounts than everyone else. Granted he has excess Simulated_AASI.

    The nMonte tool now uses individuals and not averages for reference populations. The individuals in the dataset can vary substantially. There is a fair amount of intra-GujBrahmin variation.

  6. The Following 5 Users Say Thank You to client For This Useful Post:

     26284729292 (06-24-2019),  bmoney (06-24-2019),  Kulin (06-24-2019),  parasar (06-24-2019),  soulblighter (06-27-2019)

  7. #5105
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    Quote Originally Posted by 26284729292 View Post
    Reposting the piramalai model from earlier (somehow it got invalidated)...

    Attachment 31121
    I'm really curious about that east Asian shift. It seems to be showing up constantly, same as my minor Caucasian/CHG shift. Makes me wonder what kind of mix could be responsible for it.

    Quote Originally Posted by client View Post
    Note how soulblighter who scored lower Gujarati Brahmin than the Iyer average on the other run is now getting higher amounts than everyone else. Granted he has excess Simulated_AASI.

    The nMonte tool now uses individuals and not averages for reference populations. The individuals in the dataset can vary substantially. There is a fair amount of intra-GujBrahmin variation.
    In this case, with large intergroup variation and few components, using the averages of the group may be beneficial. The difference seems to be reduced when this is done.

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  9. #5106
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    ^except, the nMonte runner does not have the feature to use group averages as reference anymore.
    You may use the freeform runner though, and use Averaged coordinates as inputs-
    http://185.144.156.77:3000/freeform_nmonte.html#

    Get your reference coordinates from here-
    http://eurogenes.blogspot.com/2018/0...drive.html?m=1

  10. #5107
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    Quote Originally Posted by Censored View Post
    I'm really curious about that east Asian shift. It seems to be showing up constantly, same as my minor Caucasian/CHG shift. Makes me wonder what kind of mix could be responsible for it.



    In this case, with large intergroup variation and few components, using the averages of the group may be beneficial. The difference seems to be reduced when this is done.
    No idea where the han shift is coming from? Maybe from my grandma. I scored 88% iyengar and 12% nepaliA on Harappa so that may be a clue. Though unsure.

  11. #5108
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    Quote Originally Posted by client View Post
    ^except, the nMonte runner does not have the feature to use group averages as reference anymore.
    You may use the freeform runner though, and use Averaged coordinates as inputs-
    http://185.144.156.77:3000/freeform_nmonte.html#

    Get your reference coordinates from here-
    http://eurogenes.blogspot.com/2018/0...drive.html?m=1
    ex.jpg
    Quote Originally Posted by 26284729292 View Post
    No idea where the han shift is coming from? Maybe from my grandma. I scored 88% iyengar and 12% nepaliA on Harappa so that may be a clue. Though unsure.
    Your maternal or paternal grandmother? I'd suggest if possible to get your parent on that side tested to see if there's something to this after all.

    "sample": "Brahmin_Tamil_Nadu_Mixed:26284729292_AGUser",
    "fit": 1.2492,
    "Iyer": 72.5,
    "Velamas": 20,
    "Brahmin_Gujarat": 6.67,
    "Brahmin_Khas_Nepal": 0.83,
    "Brahmin_Uttar_Pradesh": 0,

    "sample": "Brahmin_Tamil_Nadu_Mixed:26284729292_v3_AGUse r",
    "fit": 1.4131,
    "Iyer": 59.17,
    "Velamas": 19.17,
    "Brahmin_Khas_Nepal": 17.5,
    "Brahmin_Uttar_Pradesh": 2.5,
    "Brahmin_Gujarat": 1.67,

    Brahmin Khas results in a slight drop in your fit as well and other users from the area don't get as much as you do. But it's mainly on the V3 chip.

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  13. #5109
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    Quote Originally Posted by Censored View Post
    ex.jpg


    Your maternal or paternal grandmother? I'd suggest if possible to get your parent on that side tested to see if there's something to this after all.

    "sample": "Brahmin_Tamil_Nadu_Mixed:26284729292_AGUser",
    "fit": 1.2492,
    "Iyer": 72.5,
    "Velamas": 20,
    "Brahmin_Gujarat": 6.67,
    "Brahmin_Khas_Nepal": 0.83,
    "Brahmin_Uttar_Pradesh": 0,

    "sample": "Brahmin_Tamil_Nadu_Mixed:26284729292_v3_AGUse r",
    "fit": 1.4131,
    "Iyer": 59.17,
    "Velamas": 19.17,
    "Brahmin_Khas_Nepal": 17.5,
    "Brahmin_Uttar_Pradesh": 2.5,
    "Brahmin_Gujarat": 1.67,

    Brahmin Khas results in a slight drop in your fit as well and other users from the area don't get as much as you do. But it's mainly on the V3 chip.
    Maternal. She lives pretty far away but I’ll see if she’s game for it. I know she’s autosomallly mixed, which is why I score elevated Velama here too.
    Last edited by 26284729292; 06-24-2019 at 06:36 AM.

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  15. #5110
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    @Censored
    thanks but are you a sponsor? I'm not one and I don't see that field anymore. Not sure whether 26284729292 is one but looking at his models it's apparent that individual references has been used.

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