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Thread: Global25 automated nMonte for South/Central Asian members

  1. #3811
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    Quote Originally Posted by Censored View Post
    I know I am late but as far as all the talk about clines and who is “distinct” from who else, I would just say that the same rules should apply to those populations within clines as those outside them. Clines are meaningless when they are so massive and I really don’t think that Kalash are much like a person from Bihar even if they’re somehow part of the same Cline. Genetic distances matter, and if there is a large distance(what counts as large itself is subjective to an extent) between two individuals or two pops, then they are indeed distinct from each other regardless of whether they’re on the same cline or not.

    When you’re talking about ancient populations as diverged as AASI which is East Eurasian and Onge like and steppe which is similar to white Europeans, it doesn’t really make sense to just go by ratios of who has what. We probably wouldn’t use the same logic to other parts of our lives, so why here?
    I put some numbers behind this. Didn't find Biharis but used Bangladeshis that should be close enough.


    Model Sample Details Fit Map Irula Naxi Sintashta MLBA Udegram IA
    1 Irula +Naxi +Sintashta_MLBA +Udegram_IA Bengali_Bangladesh:Average 1.6137 Open Map 65.83 5 3.33 25.83
    2 Irula +Naxi +Sintashta_MLBA +Udegram_IA Kalash:Average 1.5431 Open Map 0 0 13.33 86.67



    Model Sample Details Fit Map Irula
    1 Irula Bengali_Bangladesh:Average 6.5211 Open Map 100
    2 Irula Kalash:Average 24.0294 Open Map 100



    Model Sample Details Fit Map Tajik Rushan
    1 Tajik_Rushan Bengali_Bangladesh:Average 22.697 Open Map 100
    2 Tajik_Rushan Kalash:Average 7.3016 Open Map 100


    Model Sample Details Fit Map Pashtun
    1 Pashtun Bengali_Bangladesh:Average 15.4089 Open Map 100
    2 Pashtun Kalash:Average 2.1389 Open Map 100

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    Non Brahmin or Rajput Biharis are going to be more AASI shifted and less SE Asian shifted than Bangladeshis.
    I4285 I4285 1873-1661 calBCE (343025 BP, PSUAMS-2536) BMAC Sappali_Tepe_BA Sappali Tepe Uzbekistan U7a3 L1a
    I5604 I5604 1880-1697 calBCE (346520 BP, PSUAMS-2774) BMAC Bustan_BA Bustan Uzbekistan K1a1 L1a
    I6667 I6667 1497-1413 calBCE (317020 BP, PSUAMS-2998) Parkhai_LBA_o Parkhai_LBA_o Parkhai II Turkmenistan HV2a
    I6669 I6669 3082-2909 calBCE (436525 BP, PSUAMS-2950) Parkhai_EN Parkhai_EN Parkhai II Turkmenistan HV2
    I4899 I4899 1600-1300 BCE BMAC Bustan_BA Bustan Uzbekistan R0 J

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  5. #3813
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    Quote Originally Posted by agent_lime View Post
    I put some numbers behind this. Didn't find Biharis but used Bangladeshis that should be close enough.


    Model Sample Details Fit Map Irula Naxi Sintashta MLBA Udegram IA
    1 Irula +Naxi +Sintashta_MLBA +Udegram_IA Bengali_Bangladesh:Average 1.6137 Open Map 65.83 5 3.33 25.83
    2 Irula +Naxi +Sintashta_MLBA +Udegram_IA Kalash:Average 1.5431 Open Map 0 0 13.33 86.67



    Model Sample Details Fit Map Irula
    1 Irula Bengali_Bangladesh:Average 6.5211 Open Map 100
    2 Irula Kalash:Average 24.0294 Open Map 100



    Model Sample Details Fit Map Tajik Rushan
    1 Tajik_Rushan Bengali_Bangladesh:Average 22.697 Open Map 100
    2 Tajik_Rushan Kalash:Average 7.3016 Open Map 100


    Model Sample Details Fit Map Pashtun
    1 Pashtun Bengali_Bangladesh:Average 15.4089 Open Map 100
    2 Pashtun Kalash:Average 2.1389 Open Map 100
    For Bihar, there is Bihar Muslim sample set in the Narasimhan data set:
    SteppeMLBA/IVP/AASI
    Muslim Bihar 0.133 0.502 0.365

    Others that could be similar:
    Srivastava UP 0.162 0.527 0.311
    Kurmi UP 0.155 0.539 0.307
    Kurmi MP 0.144 0.532 0.324
    Chamar UP 0.182 0.375 0.443
    Punjabi 0.150 0.529 0.322
    Dhobi 0.134 0.499 0.367
    Nai 0.167 0.514 0.319

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  7. #3814
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    Also would like to add one more thing-that although there are the three main components(emba/mlba steppe), Iranian Neolithic, and AASI, we also cannot forget the minor ones such as NE/SE Asian, west Siberian hunter gatherer, and Barcin.


    My feeling is that the so called Cline would be much less smooth when the minor components are taken into account along with the major ones. Already the baloch are no longer part of the cline with G25 PCA. Who knows, when there is a 100 dimensional rather than 25 dimensional PCA than there may be further deterioration of the cline.
    Last edited by Censored; 01-13-2019 at 10:13 AM.

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  9. #3815
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    Quote Originally Posted by Censored View Post
    Also would like to add one more thing-that although there are the three main components(emba/mlba steppe), Iranian Neolithic, and AASI, we also cannot forget the minor ones such as NE/SE Asian, west Siberian hunter gatherer, and Barcin.


    My feeling is that the so called Cline would be much less smooth when the minor components are taken into account along with the major ones. Already the baloch are no longer part of the cline with G25 PCA. Who knows, when there is a 100 dimensional rather than 25 dimensional PCA than there may be further deterioration of the cline.
    I think those minor components like WSHG and Barcin are already included in the major components.
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  11. #3816
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    Quote Originally Posted by agent_lime View Post
    I put some numbers behind this. Didn't find Biharis but used Bangladeshis that should be close enough.


    Model Sample Details Fit Map Irula Naxi Sintashta MLBA Udegram IA
    1 Irula +Naxi +Sintashta_MLBA +Udegram_IA Bengali_Bangladesh:Average 1.6137 Open Map 65.83 5 3.33 25.83
    2 Irula +Naxi +Sintashta_MLBA +Udegram_IA Kalash:Average 1.5431 Open Map 0 0 13.33 86.67



    Model Sample Details Fit Map Irula
    1 Irula Bengali_Bangladesh:Average 6.5211 Open Map 100
    2 Irula Kalash:Average 24.0294 Open Map 100



    Model Sample Details Fit Map Tajik Rushan
    1 Tajik_Rushan Bengali_Bangladesh:Average 22.697 Open Map 100
    2 Tajik_Rushan Kalash:Average 7.3016 Open Map 100


    Model Sample Details Fit Map Pashtun
    1 Pashtun Bengali_Bangladesh:Average 15.4089 Open Map 100
    2 Pashtun Kalash:Average 2.1389 Open Map 100
    You need only compare Bengalis and Kalash directly to see the enormous difference.
    Quote Originally Posted by poi View Post
    I think those minor components like WSHG and Barcin are already included in the major components.
    There’s Barcin and WSHG like ancestry in Sintashta true, but those components also exist outside and independently of the major components. For example in the Narasimhan paper, IVCp was described as a mix of Iranian Neolithic, AASI, AND WSHG. Meanwhile BMAC had a sizable amount of Barcin. SE Asian like admixture also exists independently of pure AASI seeing as how simulated ASI south by DMXX is east shifted relative to the other kind.

  12. #3817
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    Quote Originally Posted by poi View Post
    Alright ladies and gents, I have run the modern-relationship-model for the non-tribal part of the South Asian cline. Note that the S/E end of this graph are at the MIDDLE of the massive South Asian cline, so this is just one-half. I'm sure the graph can be enriched with additional pops (I only ran around 30 pops). I can add more to fill in, but let me know if that's even necessary.

    Frequency data: https://docs.google.com/spreadsheets...#gid=744611124

    PC1 - 87.89% variance
    PC2 - 11.97% variance

    PCA on just the biggest PC1
     




    PCA on both PC1/PC2 - var coverage over 99.8%
     


    Can you please add me to the list, thank you

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  14. #3818
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    Hey poi, could you redo that old model of Ganj-Dareh, Han, Khvylinsk, Dai, Barcin, Levant_N, Yoruba, West Siberia N, and simulated AASI, except the simulated AASI is the two new ones by DMXX. Add in proto AA if you want as well but I’m not interested in that. Do everyone

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    Quote Originally Posted by Censored View Post
    Hey poi, could you redo that old model of Ganj-Dareh, Han, Khvylinsk, Dai, Barcin, Levant_N, Yoruba, West Siberia N, and simulated AASI, except the simulated AASI is the two new ones by DMXX. Add in proto AA if you want as well but I’m not interested in that. Do everyone
    Running all S/SC Asian samples and groups, including users and ancients, using that that 11 component model. I am running the models using 2 separate configurations -- the first one is letting nMonte3 logic pick the right mix of reference groups' individuals while the other one is using averages of those reference groups. It is a massive run where I've kept the nCycles to be 1000 but Batch to be 120, so the scores will be either 0 or a multiple of 0.8333. And shouldn't take too long.

    Quote Originally Posted by jortita View Post
    Can you please add me to the list, thank you
    You were being run as "Assamese", although you're the lone reference in that group. I will make sure to include you in the future runs.
    Last edited by poi; 01-14-2019 at 08:01 PM.
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  18. #3820
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    Data from the runs. https://docs.google.com/spreadsheets...#gid=565748935

    Note that the column "Approach" has 2 rows for each Group/Sample. For Approach=Averages, each components' coords are averaged while Approach=Individuals lets nMonte3 logic pick the right mix of individuals. If you are comparing across test samples/groups, use the Approach=Averages column since the reference pops are not that volatile. But Approach=Individuals should encapsulate the true diversity within each reference component, thus the fit will be tighter. I think it looks okay for the most part. For South Asians, the Simulated references by DMXX appear to playing nicely

    It is a huge 11 component run, so some overlaps are likely happening, so don't take the numbers too literally... individual test sample may be off, but the group averages should be better. I can run another iteration of the components if somebody has suggestions.
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