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Thread: Global25 automated nMonte for South/Central Asian members

  1. #21
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    Quote Originally Posted by poi View Post
    Narasimhan has an interactive Tableau report on various aDNA public.tableau.com/profile/vagheesh#!/vizhome/TheGenomicFormationofSouthandCentralAsia/Fig_1

    It looks like Ferghana steppe is close to Iron Age. Basically, they're at nightclub after 6am lol.

    The current configuration I have is focused on Bronze Age and prior. I can reconfigure for Iron Age and later pops so get scores using the latter data.
    That tableau is messy as well, Narsimhan uses MLBA Steppe and equates it too ANF and on his PCA plot he has some samples too clustered together and others too far away lol.

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  3. #22
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    Quote Originally Posted by pegasus View Post
    Thats why we count on u lol, doing all this is a very labored and tedious process.
    Haha, it has finished 10,000 combinations for 15 persons' coordinates after running nonstop for almost 24 hrs now. I can iron jeans with it now .

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  5. #23
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    Quote Originally Posted by poi View Post
    Nope, just yours. Send me those and I will run them. I need to setup my old machines to run them in parallel so that there is no wait. My poor macbook will die if I keep running this here... it gets super hot due to 4 Rscript running at once for hours.

    Btw, this Narasimhan paper had aDNA that almost accounted for your entire DNA(assuming Global25 is remotely close lol). I could not believe it got to low 1.5% even with the penalty. No other South Asian so far has it that low. Jortita comes close, but that included Bonda tribals.
    I suspect as we get more ancients this will become the case for most people. We are alike autosomally in the modern sense, however the combinations that make us in the ancient sense are divergent. I have unique yDNA and mtDNA for the region and at some point I suspect this will account for differences when modeling with ancients. If the fit is good for me, it could be totally per chance, as the fit should be good for my "Father", "Mother", "Grandfather" and Grandmother who are less mixed than I. Since I have coordinates for my last 2 generations this is a good way to test if the models are making sense. Best test is that my maternal Aunt should be something between Balq and Sadia. However, I didn't get coordinates for her, so I will do that. I am very curious to see Sadia vs. Jam and Balq vs. the Chitralis. With Balq and her brother the Uzbek ancients may play a role.

    Scaled:



    PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14 PC15 PC16 PC17 PC18 PC19 PC20 PC21 PC22 PC23 PC24 PC25
    Khana 0.07057 -0.00203 -0.10409 0.06783 -0.07571 0.03765 0.002585 0.010615 -0.01084 -0.00492 -0.00016 0.004346 -0.00238 -0.00729 0.005836 0.014452 -0.00169 0.003801 0.004148 -0.01151 0.003743 -0.00717 0.005916 -0.00952 0.002395
    Khana_GF_Sister 0.061464 -0.00914 -0.12181 0.080104 -0.07663 0.041555 0.004465 0.013153 0.011453 -0.00055 -0.00779 0.00015 -0.00535 0.003441 0.016558 -0.00292 -0.02582 0.005194 -0.00113 -0.01538 0.000749 0.000124 -0.00049 -0.01169 0.00467
    Khana_GM 0.080814 0 -0.05506 0.052649 -0.05816 0.030399 0.002115 0.000923 -0.02372 -0.01294 -0.00763 -0.0006 0.002676 -0.00743 0.0095 0.005701 -0.01069 0.001014 0.005279 -0.00913 -0.01061 -0.00099 0.002588 -0.00205 0.008382
    Khana_GM_Brother 0.080814 -0.02133 -0.04601 0.050065 -0.05109 0.026495 0.000705 -0.00323 -0.02004 -0.02041 -0.01315 -0.0012 0.002676 -0.01294 0.005565 0.013259 0.002347 -0.00114 0.002388 -0.00338 -0.00275 -0.00606 0.002711 -0.00361 0.005987
    Khana_Uncle 0.068294 0.008124 -0.10899 0.063954 -0.07171 0.034304 0.00141 0.007615 -0.01554 -0.00966 -0.00536 0.001948 0.001338 -0.00261 0.008279 0.007292 -0.01525 0.006334 0.000126 -0.01426 -0.00449 -0.00284 -0.00062 -0.00916 0.006347


    The Chitralis are in the spreadsheet already. So you should be able to find them.
    Last edited by khanabadoshi; 05-03-2018 at 05:08 AM.
    “Chahar chez est tohfay Multan, Gard-o- Garma, Gada-o- Goristan”.

    Four things are the gift of Multan: Dusty winds, hot seasons, beggars and graveyards.




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  7. #24
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    Quote Originally Posted by pegasus View Post
    Has anyone uploaded the full GED match ids for the paper?? Khana when you are free at some point can you do it or any other brave soul.
    Hook me up with a link to the data and which samples are what (ie. what we need).

    I have no idea what anything is, I'm afraid to admit. You guys are going to have to catch me up on all these new samples, where they are from and what their relevance is. I haven't been able to read ish.
    I intend to upload these samples to Gedmatch, is it like 20-30 samples? If it's that, I can dedicate some time in the week to just get it over with lol. If it's like 40-50 or more, then you guys will have to wait a few more weeks until I'm back to a regular schedule.

    Has my yDNA shown up? R-Z282>R-Y17491>R-YP4858
    Have any of the following mtDNAs? R5a2 (Sadia); U7a3 (Jam); A8a (Myself, Grandmother); U2b1 (2nd Cousin Leghari); M5a2a1a1 (1st Cousin Tareen/Khakwani)
    Last edited by khanabadoshi; 05-03-2018 at 05:04 AM.
    “Chahar chez est tohfay Multan, Gard-o- Garma, Gada-o- Goristan”.

    Four things are the gift of Multan: Dusty winds, hot seasons, beggars and graveyards.




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  9. #25
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    Quote Originally Posted by pegasus View Post
    Thats why we count on u lol, doing all this is a very labored and tedious process.
    Yes, poi has been a godsend! A big boost for the South Asian section of this site. I get to be lazy
    “Chahar chez est tohfay Multan, Gard-o- Garma, Gada-o- Goristan”.

    Four things are the gift of Multan: Dusty winds, hot seasons, beggars and graveyards.




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  11. #26
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    Quote Originally Posted by khanabadoshi View Post
    I suspect as we get more ancients this will become the case for most people. We are alike autosomally in the modern sense, however the combinations that make us in the ancient sense are divergent. I have unique yDNA and mtDNA for the region and at some point I suspect this will account for differences when modeling with ancients. If the fit is good for me, it could be totally per chance, as the fit should be good for my "Father", "Mother", "Grandfather" and Grandmother who are less mixed than I. Since I have coordinates for my last 2 generations this is a good way to test if the models are making sense. Best test is that my maternal Aunt should be something between Balq and Sadia. However, I didn't get coordinates for her, so I will do that. I am very curious to see Sadia vs. Jam and Balq vs. the Chitralis. With Balq and her brother the Uzbek ancients may play a role.

    The Chitralis are in the spreadsheet already. So you should be able to find them.
    Very good point. Mutigenerational modeling can help validate the fitting. I have also purchased kits for my parents and for my wife's parents. My wife hates this stuff, but had her sister already go through this lol


    Quote Originally Posted by khanabadoshi View Post
    Yes, poi has been a godsend! A big boost for the South Asian section of this site. I get to be lazy
    Thanks! I am a software engineer so doing this is my hobby. But I do need to get a couple of computers start crunching if I am to run these coordinates against various configurations. Most development work is done, so it is just a matter of running through the combinations.

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  13. #27
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    Quote Originally Posted by bmoney View Post
    We should aim for 2.5 or under with 3 or 4 pops max using ancients from the immediate surroundings of SA to avoid overfitting
    Everybody's coordinates(using BronzeAge samples) are still running and will run for a while, but yours has finally 2.5% with BronzeAge ancients, non-SouthAsian East Asians, and isolated Onge.

    Code:
    [
      {
        "fit": 2.5133,
        "scores": [
          {
            "group": "Shahr_I_Sokhta_BA3",
            "score": 78
          },
          {
            "group": "Sintashta_MLBA",
            "score": 11.2
          },
          {
            "group": "Hajji_Firuz_ChL",
            "score": 8.6
          },
          {
            "group": "Onge",
            "score": 1.6
          },
          {
            "group": "Dai",
            "score": 0.6
          }
        ]
      },
      {
        "fit": 2.5775,
        "scores": [
          {
            "group": "Shahr_I_Sokhta_BA3",
            "score": 80.6
          },
          {
            "group": "Sintashta_MLBA",
            "score": 14.2
          },
          {
            "group": "Hajji_Firuz_ChL",
            "score": 4.2
          },
          {
            "group": "Dai",
            "score": 1
          }
        ]
      },
      {
        "fit": 2.5811,
        "scores": [
          {
            "group": "Shahr_I_Sokhta_BA3",
            "score": 77
          },
          {
            "group": "Sintashta_MLBA",
            "score": 10.8
          },
          {
            "group": "Hajji_Firuz_ChL",
            "score": 9
          },
          {
            "group": "Onge",
            "score": 2.4
          },
          {
            "group": "Mongola",
            "score": 0.8
          }
        ]
      }
    ]

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  15. #28
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    Sapporo

    Code:
      {
        "fit": 2.308,
        "scores": [
          {
            "group": "Shahr_I_Sokhta_BA2",
            "score": 32
          },
          {
            "group": "Sintashta_MLBA",
            "score": 26.8
          },
          {
            "group": "Shahr_I_Sokhta_BA3",
            "score": 22.4
          },
          {
            "group": "Gonur2_BA",
            "score": 16
          },
          {
            "group": "Mongola",
            "score": 2.8
          }
        ]
      }
    ,
      {
        "fit": 2.3135,
        "scores": [
          {
            "group": "Shahr_I_Sokhta_BA2",
            "score": 43.4
          },
          {
            "group": "Sintashta_MLBA",
            "score": 27
          },
          {
            "group": "Shahr_I_Sokhta_BA3",
            "score": 26.6
          },
          {
            "group": "Mongola",
            "score": 3
          }
        ]
      }
    ,  {
        "fit": 2.5929,
        "scores": [
          {
            "group": "Gonur2_BA",
            "score": 44.6
          },
          {
            "group": "Sintashta_MLBA",
            "score": 26.8
          },
          {
            "group": "Shahr_I_Sokhta_BA3",
            "score": 21.6
          },
          {
            "group": "Ganj_Dareh_N",
            "score": 4.2
          },
          {
            "group": "Mongola",
            "score": 2.8
          }
        ]
      }

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  17. #29
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    Code:
    {
        "fit": 2.8112,
        "scores": [
          {
            "group": "Shahr_I_Sokhta_BA3",
            "score": 74.2
          },
          {
            "group": "Burmese",
            "score": 17.8
          },
          {
            "group": "Sintashta_MLBA",
            "score": 8
          }
        ]
      },
      {
        "fit": 2.8338,
        "scores": [
          {
            "group": "Shahr_I_Sokhta_BA3",
            "score": 70.6
          },
          {
            "group": "Burmese",
            "score": 12.4
          },
          {
            "group": "Onge",
            "score": 9.4
          },
          {
            "group": "Sintashta_MLBA",
            "score": 7.6
          }
        ]
      }

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  19. #30
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    Code:
    [
      {
        "fit": 2.3214,
        "scores": [
          {
            "group": "Shahr_I_Sokhta_BA3",
            "score": 61.4
          },
          {
            "group": "Burmese",
            "score": 24.4
          },
          {
            "group": "Srubnaya_MLBA",
            "score": 14.2
          }
        ]
      }
    ,
      {
        "fit": 2.3441,
        "scores": [
          {
            "group": "Shahr_I_Sokhta_BA3",
            "score": 61.6
          },
          {
            "group": "Burmese",
            "score": 24.4
          },
          {
            "group": "Sintashta_MLBA",
            "score": 14
          }
        ]
      }
    ,
      {
        "fit": 2.2671,
        "scores": [
          {
            "group": "Shahr_I_Sokhta_BA3",
            "score": 60.6
          },
          {
            "group": "Burmese",
            "score": 24.6
          },
          {
            "group": "Sintashta_MLBA",
            "score": 10.2
          },
          {
            "group": "Hajji_Firuz_ChL",
            "score": 4.6
          }
        ]
      },
      {
        "fit": 2.499,
        "scores": [
          {
            "group": "Shahr_I_Sokhta_BA3",
            "score": 55
          },
          {
            "group": "Burmese",
            "score": 17.8
          },
          {
            "group": "Sintashta_MLBA",
            "score": 12.6
          },
          {
            "group": "Onge",
            "score": 10.8
          },
          {
            "group": "Parkhai_LBA",
            "score": 3.8
          }
        ]
      }

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