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Thread: Isolating pontic steppe admixture in south asians in nmonte

  1. #11
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    Using SIS3 instead of Saidu outlier & adding WSHG back in:

    "sample": "Brahmin_Khas_Nepal:pnb_AGUser",
    "fit": 2.5494,
    "IRN_Shahr_I_Sokhta_BA3": 53.33,
    "UZB_Dzharkutan1_BA": 20.83,
    "RUS_Sintashta_MLBA": 13.33,
    "NPL_Chokhopani_2700BP": 6.67,
    "RUS_West_Siberia_N": 5.83,


    So it looks like I've around 20% or so Steppe (15% or so being Sintashta-like & rest like pre-Neolithic Central Asia WSHG).

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  3. #12
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    Quote Originally Posted by pnb123 View Post
    Using SIS3 instead of Saidu outlier & adding WSHG back in:

    "sample": "Brahmin_Khas_Nepal:pnb_AGUser",
    "fit": 2.5494,
    "IRN_Shahr_I_Sokhta_BA3": 53.33,
    "UZB_Dzharkutan1_BA": 20.83,
    "RUS_Sintashta_MLBA": 13.33,
    "NPL_Chokhopani_2700BP": 6.67,
    "RUS_West_Siberia_N": 5.83,


    So it looks like I've around 20% or so Steppe (15% or so being Sintashta-like & rest like pre-Neolithic Central Asia WSHG).
    Try seeing how Krasnoyarsk affects the fit as well as removing West Siberia. In some cases, removing West Siberian improves the fit for individuals.
    pegasus modeling:

    sample": "Punjabi_Jat:Sapporo_AGUser",
    "fit": 1.1506,
    "IRN_Shahr_I_Sokhta_BA3": 43.33,
    "TKM_Gonur1_BA": 31.67,
    "RUS_Sintashta_MLBA": 25,
    "closestDistances": [

  4. #13
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    Quote Originally Posted by Sapporo View Post
    Try seeing how Krasnoyarsk affects the fit as well as removing West Siberia. In some cases, removing West Siberian improves the fit for individuals.
    Replacing Sintashta with Krasnoyarsk. Best fit I got.

    "sample": "Brahmin_Khas_Nepal:pnb_AGUser",
    "fit": 2.3483,
    "IRN_Shahr_I_Sokhta_BA3": 55,
    "UZB_Dzharkutan1_BA": 18.33,
    "RUS_Krasnoyarsk_MLBA": 16.67,
    "NPL_Chokhopani_2700BP": 5.83,
    "RUS_West_Siberia_N": 4.17,

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  6. #14
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    Quote Originally Posted by Sapporo View Post
    I understand what you're doing. I just don't think it's a more accurate model to isolate Pontic Steppe than the standard SISBA3 + Pontic Steppe pop + BMAC pop. As for SSo being a far better option, that's the case for many Gangetic populations or those with less of an Iran N pull but for others, the difference is negligible or SISBA3 is preferred. Regardless, SSo is not an ideal ancient to isolate Pontic Steppe ancestry since it is part Pontic Steppe itself. Somewhere in the vicinity of 10% I believe.

    Regarding West Siberia N, I sometimes question why it is added to models even when it makes a negligible improvement to the fit or in some cases, it even worsens it since there is enough Siberian admixture accounted for in other ancients (such as Sarazm En). It's the same case for Chokhopani which is needed for some groups such as Khas Brahmins or Pashtuns with minor East Asian admixture but tends to worsen or not improve fits for others.

    The model you are using above is definitely inflating BMAC proportions due to the inclusion of both Irula + SSo. Both are AASI heavy and much lower Iran N than most of the individuals you are modeling. Try running that model without Irula and a variation that uses SISBA3 instead of SSo for a more accurate Pontic Steppe estimate.

    The model I've been trying to use for isolating Pontic Steppe is SISBA3 + Krasnoyarsk + Sarazm En (for overflow of West Siberia) + Gonur1 BA. This works for NWers obviously but something additional is needed for individuals who are Iran N deficient (otherwise using SSo will eat up some Steppe) or individuals who are more AASI than SIS BA3.
    It seems that preference for SIS3 vs. SSo varies even among your area, with some strongly preferring one over the other but I take your point. Steppe percentage does vary depending on which you use, but the one that is more realistic would have to depend on what formal stats say.
    And that partially explains the reason for why I set the model up as I did, there are less of such unknown variables so to speak and again it was just preliminary, it can be worked on/improved etc.

    As for SSo having steppe, I was under the impression that it was WSHG, not pontic. Maybe someone more knowledgeable can chime in.
    Last edited by Censored; 07-27-2019 at 04:03 AM.

  7. #15
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    Quote Originally Posted by Censored View Post
    I created this thread because the topic comes up so frequently that its worth having a discussion in its own right. We often struggle to do this when modeling so it might be worth coming up with ideas as to how to do it.

    One model that seems to work well combines ancients and moderns. It is a compact Paniya, Velamas, Krasnoyarsk(or Sintashta), Dzharkutan1, Naxi model. I wanted to use mostly moderns but there is no steppe deficient modern population that is also high Iran_N, so I added Dzharkutan.
    Krasnoyarsk instead of Sintashta to capture slight excess Siberian. It will work for people who are fully indigenous to the region so if you have a foreign ancestor you may need to add something else in.

    Default penalty:
    Attachment 32014

    The fits are decent but more importantly, we see the steppe values we expect. Midi scores .8-1.67% each run(most likely just noise), Tip and Kush both get zero, and importantly they get little to no BMAC, as expected. Meanwhile my friend aaronbee gets about 26% steppe which is also reasonable.

    p.s. im aware the dzarkutan for balochis is inflated
    These Steppe MLBA numbers look quite accurate to me +/- 2-3% they match more or less with what I have seen with these members , Though I should mention Velamas score 8-10% SIS1 like ancestry (well the average) ,in that case sticking with SIS3 should be used. Even though Paniya and SIS3 fall on the same cline they are significantly different , also it avoids the inflating of one component when simulated AASI is used.

    *edit* I tried SIS3 its bad with that set up. I can see why Velamas work so exceedingly well for so many groups their minor Turan component allows other ancestries to the come through for many other groups as well. Though they are a modern population. Could you add a component for WSHG/MA1 ancestry too.
    Last edited by pegasus; 07-27-2019 at 04:16 AM.

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  9. #16
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    Quote Originally Posted by pegasus View Post
    These Steppe MLBA numbers look quite accurate to me +/- 2-3% they match more or less with what I have seen with these members , Though I should mention Velamas score 8-10% SIS1 like ancestry (well the average) ,in that case sticking with SIS3 should be used. Even though Paniya and SIS3 fall on the same cline they are significantly different , also it avoids the inflating of one component when simulated AASI is used.

    *edit* I tried SIS3 its bad with that set up. I can see why Velamas work so exceedingly well for so many groups their minor Turan component allows other ancestries to the come through for many other groups as well. Though they are a modern population. Could you add a component for WSHG/MA1 ancestry too.
    Yup, I realized just now that Velamas do have some tribal ancestry which would make them overlap slightly with Paniya+that SIS1 like ancestry overlapping with Dzharkutan could be a little problematic.

    I did one with WSHG and one with MA1:
    siberianadded.jpg

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  11. #17
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    edit..
    Last edited by pegasus; 07-27-2019 at 05:29 AM.

  12. #18
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    Quote Originally Posted by Sapporo View Post
    Try seeing how Krasnoyarsk affects the fit as well as removing West Siberia. In some cases, removing West Siberian improves the fit for individuals.
    Yes in some cases all their WSHG is the IVCp and BMAC components so it is sufficient but for other groups like those Rors they need an extra shot of it. Dividing out Sidelkino ratios also works provided your Sidelkino value is accurate but in Steppe MLBA populations its always in the same range 53-55%. They don't have direct MA1 related ancestry , its all via EHG.


    "sample": "RUS_Krasnoyarsk_MLBA:Average",
    "fit": 4.5942,
    "RUS_Sidelkino_HG": 55.83,
    "Anatolia_Barcin_N": 28.33,
    "GEO_CHG": 12.5,
    "IRN_Ganj_Dareh_N": 3.33,
    "NPL_Chokhopani_2700BP": 0,
    "RUS_MA1": 0,
    "Simulated_AASI_NW_by_DMXX": 0
    "Simulated_AASI_South_by_DMXX": 0,


    Problem is with S/SC Asians they have MA1 and ancestry with EHG tendencies so they can bleed into each other so it ends up inflating EHG dramatically in one run and depressing it another.

    The method I found works best for groups with considerable Steppe MLBA ancestry is this but it would represent ceiling values with BMAC pops it goes down slightly.



    "sample": "Tajik_Rushan:tdj521_rushan",
    "fit": 2.2266,
    "RUS_Krasnoyarsk_MLBA": 48.33,
    "IRN_Tepe_Hissar_C": 27.5,
    "TKM_Parkhai_En": 15,
    "RUS_West_Siberia_N": 4.17,
    "IRN_Shahr_I_Sokhta_BA3": 2.5,
    "NPL_Chokhopani_2700BP": 2.5,
    "closestDistances": [


    "sample": "Tajik_Ishkashim:tdj269_ishkashim",
    "fit": 2.3564,
    "RUS_Krasnoyarsk_MLBA": 40,
    "IRN_Tepe_Hissar_C": 23.33,
    "IRN_Shahr_I_Sokhta_BA3": 15.83,
    "TKM_Parkhai_En": 10,
    "RUS_West_Siberia_N": 6.67,
    "NPL_Chokhopani_2700BP": 4.17,




    "sample": "Punjabi_Jat:aaronbee2010_AGUser",
    "fit": 1.7734,
    "IRN_Shahr_I_Sokhta_BA3": 41.67,
    "RUS_Krasnoyarsk_MLBA": 30,
    "IRN_Tepe_Hissar_C": 22.5,
    "RUS_West_Siberia_N": 2.5,
    "TKM_Parkhai_En": 2.5,
    "NPL_Chokhopani_2700BP": 0.83,



    With BMAC pop ( reality)


    "sample": "Tajik_Rushan:tdj521_rushan",
    "fit": 1.8684,
    "UZB_Dzharkutan1_BA": 48.33,
    "RUS_Krasnoyarsk_MLBA": 45,
    "NPL_Chokhopani_2700BP": 4.17,
    "IRN_Shahr_I_Sokhta_BA3": 2.5,
    "closestDistances": [


    "sample": "Punjabi_Jat:aaronbee2010_AGUser",
    "fit": 1.7624,
    "IRN_Shahr_I_Sokhta_BA3": 38.33,
    "UZB_Dzharkutan1_BA": 31.67,
    "RUS_Krasnoyarsk_MLBA": 28.33,
    "NPL_Chokhopani_2700BP": 1.67,
    "closestDistances": [

    "sample": "Tajik_Ishkashim:tdj269_ishkashim",
    "fit": 2.32,
    "RUS_Krasnoyarsk_MLBA": 41.67,
    "UZB_Dzharkutan1_BA": 38.33,
    "IRN_Shahr_I_Sokhta_BA3": 14.17,
    "NPL_Chokhopani_2700BP": 5.83,
    "closestDistances": [

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  14. #19
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    Edit: double post
    Last edited by Sapporo; 07-27-2019 at 12:40 PM.
    pegasus modeling:

    sample": "Punjabi_Jat:Sapporo_AGUser",
    "fit": 1.1506,
    "IRN_Shahr_I_Sokhta_BA3": 43.33,
    "TKM_Gonur1_BA": 31.67,
    "RUS_Sintashta_MLBA": 25,
    "closestDistances": [

  15. #20
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    Quote Originally Posted by Censored View Post
    It seems that preference for SIS3 vs. SSo varies even among your area, with some strongly preferring one over the other but I take your point. Steppe percentage does vary depending on which you use, but the one that is more realistic would have to depend on what formal stats say.
    And that partially explains the reason for why I set the model up as I did, there are less of such unknown variables so to speak and again it was just preliminary, it can be worked on/improved etc.

    As for SSo having steppe, I was under the impression that it was WSHG, not pontic. Maybe someone more knowledgeable can chime in.
    It varies depending on the individual. MDL strongly prefers SSo over SISBA3 but from what I've seen most NW South Asians only "slightly prefer" SISBA3 over SSo and vice versa. I haven't looked into the formal stats of SISBA3 vs. SSo but I'll inquire with DMXX if there is actually any data available to support one over the other.

    As for SSo, see below:
    (default pen) Saidu Sharif Outlier Modeling.png

    Sso Not Averaged.png
    Last edited by Sapporo; 07-27-2019 at 12:55 PM.
    pegasus modeling:

    sample": "Punjabi_Jat:Sapporo_AGUser",
    "fit": 1.1506,
    "IRN_Shahr_I_Sokhta_BA3": 43.33,
    "TKM_Gonur1_BA": 31.67,
    "RUS_Sintashta_MLBA": 25,
    "closestDistances": [

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