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Thread: Global25 automated nMonte for South/Central Asian members

  1. #3751
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    Quote Originally Posted by Rahuls77 View Post
    Don't score very far from the averages, the Udegram ones for instance, and its too close to the Saidu Sharif Samples from 2400 years ago.
    That's license for me to claim the throne from the Mianguls(the current titual Valis/Kings of Swat).
    Anyhow, Swat is now dominated by the Pashtuns, who have been migrating in many formats and waves for the past 1000 years.
    I would definitely avoid using Saudi Sharifs as the average simply because they're way too diverse. Other G25 groups like Udegram and Loebanr are very much less so.
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  3. #3752
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    Quote Originally Posted by poi View Post
    I tried to use just 1 SouthAsian source and all peripheries like Bandari/Tajik/etc to avoid overlapping. So, Gujarati would overlap hard with Velamas. Also, I wouldn't use ancient with the modern(unless you're thinking of some distinct proxy). Also, for reference pops, use the averaged and don't let nMonte3 logic pick different references for different test pops(if your intention is to comapre across test pops)
    Gujarati does better than Velamas for me. Also the Iranian Bandari doesn't work for me that well; Tepe Hissar ChL is better source for the IA skeletons and I.

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    Quote Originally Posted by agent_lime View Post
    Gujarati does better than Velamas for me. Also the Iranian Bandari doesn't work for me that well; Tepe Hissar ChL is better source for the IA skeletons and I.
    Gujarati(IA pop) instead of Velamas(Dravidian pop) will certainly lower the fit for almost all IA groups, but I can't use Gujarati to compare across pops in this context I was operating under. Note my test pops involved Dravidian test pops as well for comparative purposes. If you remove the Dravidian pops, sure, use the Gujarati Average (or Midi).
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  6. #3754
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    Quote Originally Posted by poi View Post
    I would definitely avoid using Saudi Sharifs as the average simply because they're way too diverse. Other G25 groups like Udegram and Loebanr are very much less so.
    Udegram Avg is 2.5 something
    Loebanr Avg is 3.3 something

    I am still going to march onto Swat and claim it.

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    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%
     


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  10. #3756
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    Is that Khatri me?

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    Quote Originally Posted by agent_lime View Post
    Is that Khatri me?
    No, all pops are averaged. Your score is part of the Khatri average(you're grouped as such). Some "groups" like Ramgharia and Bhumihar are just the group of one.
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  13. #3758
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    Quote Originally Posted by poi View Post
    I tried to use just 1 SouthAsian source and all peripheries like Bandari/Tajik/etc to avoid overlapping. So, Gujarati would overlap hard with Velamas. Also, I wouldn't use ancient with the modern(unless you're thinking of some distinct proxy). Also, for reference pops, use the averaged and don't let nMonte3 logic pick different references for different test pops(if your intention is to comapre across test pops)
    I don't think we should be using Iranian Bandari. They're relatively Iran N and South Asian shifted versus other NW Iranian populations but are irrelevant for most South Asians. Our Iran N source for BMAC/IVC is going to be closer to the Baloch/Brahui than to Iranian Bandaris. Although, Pamiri Tajiks would definitely take a good chunk of the BMAC like ancestry in South Asia too so I see why you included them. Perhaps, swap them for a more NW Iranian population to account for any non BMAC like West Asian ancestry.

    As for the modern-relationship-model for the non-tribal part of the South Asian cline, wouldn't it be more accurate to just use the actual coordinates for each population and individual rather than the modern population proxies? This way the fit and lack of missing/relevant populations wouldn't matter much. Apples to apples.
    Last edited by Sapporo; 01-11-2019 at 07:45 PM.
    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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  15. #3759
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    Quote Originally Posted by Sapporo View Post
    I don't think we should be using Iranian Bandari. They're relatively Iran N and South Asian shifted versus other NW Iranian populations but are irrelevant for most South Asians. Our Iran N source for BMAC/IVC is going to be closer to the Baloch/Brahui than to Iranian Bandaris. Although, Pamiri Tajiks would definitely take a good chunk of the BMAC like ancestry in South Asia too so I see why you included them. Perhaps, swap them for a more NW Iranian population to account for any non BMAC like West Asian ancestry.

    As for the modern-relationship-model for the non-tribal part of the South Asian cline, wouldn't it be more accurate to just use the actual coordinates for each population and individual rather than the modern population proxies? This way the fit and lack of missing/relevant populations wouldn't matter much. Apples to apples.
    This model based PCA, I have full control of the components and that model has almost 100% variance coverage across the axes(the only loss being the Naxi levels -- which only affects Nepali Brahmins' position just a bit). You could question the model itself, sure, but I'm assuming this model reasonably represents the best modern references that can be used to check interrelations among various South Asian non-tribal groups. I will let Khana do the core PCAs using all 25 coords and very curious to see it.

    Basically, this is a purpose built PCA showing relationships with modern groups. For me, this simplifies quite a bit and is a pretty good representative of what I'm seeing with all these months of checking. I certainly welcome new data and/or interpretation techniques, but -- for me -- this is it.

    How I interpret the whole thing.



    My further interpretation - to address Narasimhan's mislabeling
     

    Rather than Narasimhan using imposter Punjab Jatts(or being being completely incompetent in labeling), considering G25 also has this outlier scoring similarly as Gangetic Brahmins, perhaps there are legitimate differences within the supposedly monolith "Punjab Jatt" ethnic group. Has anyone asked Davidski about the outlier Punjab Jatt sample? Surely, they must exist to have both independent parties having these samples. Btw, I am not denying that Narasimhan did not bother to use other types of Punjabi Jatts. I am just saying that Punjab Jatts might be diverse enough and one end approaching Gangetic Brahmins, while another end going much further NW.

    Last edited by poi; 01-11-2019 at 08:23 PM.
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  17. #3760
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    Quote Originally Posted by poi View Post
    This model based PCA, I have full control of the components and that model has almost 100% variance coverage across the axes(the only loss being the Naxi levels -- which only affects Nepali Brahmins' position just a bit). You could question the model itself, sure, but I'm assuming this model reasonably represents the best modern references that can be used to check interrelations among various South Asian non-tribal groups. I will let Khana do the core PCAs using all 25 coords and very curious to see it.
    Well, I'm not questioning the model itself as it has some validity when looking at models. It's just the fits aren't the greatest for some of the populations (3+) because certain individuals or populations prefer different moderns to one another. I just prefer the code PCA using all 25 coordinates because then you don't have to consider fit and whether certain individuals need populations not represented in the modern proxies selection.

    Basically, this is a purpose built PCA showing relationships with modern groups. For me, this simplifies quite a bit and is a pretty good representative of what I'm seeing with all these months of checking. I certainly welcome new data and/or interpretation techniques, but -- for me -- this is it.

    How I interpret the whole thing.
    Nothing significantly wrong with the model or your interpretation. I just find the global 25 coordinates based PCA more accurate.
    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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