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Thread: Global25 automated nMonte for South/Central Asian members

  1. #3911
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    Quote Originally Posted by traject View Post
    Wel, partially the reason why I am skeptical is because the Khvalnysk sample is not particularly high-res (I would wait for G25 samples from Wang et al.) and secondly I am skeptical I have such a low percentage of Steppe associated EHG when historically it makes sense to have more of that than WSHG (the inconsistency of the results do not seem encouraging in that aspect). If the same patterns noticed now hold with the higher quality Wang samples, then I may have to reconsider my skepticism.
    Well, the easiest way to currently address your skepticism is to model yourself with higher res Steppe samples such as Sintashta, Srubnaya, Afanasievo, etc. alongside WSHG and see if your Khvalnysk + Barcin N score is roughly comparable. It's also possible that if your Barcin N score is high and you score low Khvalnysk but high WSHG, then your EHG like (WHG + WSHG) Steppe just might be quite low.
    Last edited by Sapporo; 01-17-2019 at 07:44 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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  3. #3912
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    Quote Originally Posted by Sapporo View Post
    Do you score non negligible East Eurasian? WSHG is certainly going to be more common throughout South Asia than Khvalynsk, which we're using as a proxy for Pontic Steppe (+ some of Barcin N). Though, Khvalynsk itself is certainly part WSHG like with the rest being more WHG like.
    You've suddenly reminded me of the old discussions that were had many months ago about a pre-MBA steppe, WSHG-like ancestral layer in the Subcontinent. Well, this was before I created the phantom ASI coordinates.

    Let's see... (cyc=1k bat=0.5k pen=def):

    Code:
    	Model	Sample	Details	Fit	Map	Barcin N	CHG	Ganj Dareh N	Khvalynsk Eneolithic	Simulated AA Basal By DMXX	Simulated ASI South By DMXX	West Siberia N
    1	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Bonda:Average		3.6124	Open Map	0	0	0	0	58.6	41.4	0
    2	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:Average		3.3032	Open Map	1.6	0.8	8.6	2	0	82.8	4.2
    3	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Paniya:Average		2.6309	Open Map	1.2	0.4	4.2	2	0	90.2	2
    4	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Pulliyar:Average		3.5421	Open Map	3.6	0.6	6.4	2.6	0	84.4	2.4
    5	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Santhal:Average		3.011	Open Map	0	0	2.4	0.6	21.8	73.6	1.6
    Interestingly, the peripheral Dravidian tribal average score for WSHG (~3%) is more than Khvalynsk (~2.3%) - WSHG is double Khvalynsk in the Irula. That does seem to confirm the "early WSHG" idea for the Subcontinent.

    Checking the individual Irula samples:

    Code:
    	Model	Sample	Details	Fit	Map	Barcin N	CHG	Ganj Dareh N	Khvalynsk Eneolithic	Simulated AA Basal By DMXX	Simulated ASI South By DMXX	West Siberia N
    1	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-1		3.3661	Open Map	2.4	1.2	7.6	1.8	0	83.4	3.6
    2	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-10		3.3369	Open Map	2.4	1.4	8.6	2	0	82	3.6
    3	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-12		3.3205	Open Map	2	1.2	8.8	2.4	0	81.8	3.8
    4	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-16		3.6854	Open Map	1.4	1.4	6.6	1.4	0	84.8	4.4
    5	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-18		4.7266	Open Map	4.2	2	9.4	3	0	77.4	4
    6	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-22		3.606	Open Map	1.8	0.8	6.6	1.6	0	86.8	2.4
    7	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-24		3.9081	Open Map	1.8	0.4	8.6	2.4	0	83.6	3.2
    8	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-25		3.3501	Open Map	2	1.2	7.2	2	0	84.8	2.8
    9	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-26		3.4611	Open Map	2.8	0.8	7	2.2	0	82.8	4.4
    10	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-27		3.8878	Open Map	1.4	1.2	7.6	1.6	0	85.4	2.8
    11	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-29		3.9534	Open Map	1.4	0.8	8.8	2.2	0	83.6	3.2
    12	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-3		3.4047	Open Map	2.2	0.8	9.6	1.8	0	82.6	3
    13	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-30		5.0169	Open Map	3	1.8	8.4	2	0	79.2	5.6
    14	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-31		3.9712	Open Map	3.4	1	11.4	1.6	0	78.4	4.2
    15	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-33		4.7136	Open Map	1.8	0.8	6.2	1.8	0	85.6	3.8
    16	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-34		3.9023	Open Map	1.4	2	7.6	1.8	0	84	3.2
    17	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-35		3.6811	Open Map	2.8	1.2	7.8	2.2	0	82	4
    18	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-7		3.7554	Open Map	1.4	1.6	6.6	1.2	0	84.6	4.6
    19	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-8		3.7349	Open Map	1.2	1	8.6	2	0	83.4	3.8
    20	Barcin_N +CHG +Ganj_Dareh_N +Khvalynsk_Eneolithic +Simulated_AA_Basal_by_DMXX +Simulated_ASI_South_by_DMXX +West_Siberia_N	Irula:IL-9		3.8867	Open Map	2.6	0.6	7	1.2	0	85.4	3.2
    IL-30 is 6% WSHG and only 2% Khvalynsk. Cool stuff.

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  5. #3913
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    Quote Originally Posted by Sapporo View Post
    Well, the easiest way to currently address your skepticism is to model yourself with higher res Steppe samples such as Sintashta, Srubnaya, Afanasievo, etc. alongside WSHG and see if your Khvalnysk + Barcin N score is roughly comparable. It's also possible that if your Barcin N score is high and you score low Khvalnysk but high WSHG, then your EHG like (WHG + WSHG) Steppe just might be quite low.
    At 500 batches/100 cycles/pen=0,
    Without Barcin:
    WithoutBarcin.png
    With Barcin (which may be warranted given extra West Asian affinities of my ethnic group even given the cost of perhaps overfitting with Sintashta):
    WithBarcin.png

    Adding or removing Barcin causes either WSHG or Sintashta to dominate but the results with Barcin added most closely echo the earlier Elemental SC/S Asia run percentage wise (Ganj Dareh scores and Naxi appear to increase substantially without Barcin). So there may be good merit to the explanation of a pre-existing WSHG layer in the subcontinent perhaps.

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  7. #3914
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    @traject

    Can you do the first run again with default penalty? Anyways, I have a good feeling part of the the West Asian (Barcin N like) ancestry your group has an affinity to might be inflating your Sintashta score since Sintashta has both Barcin N and EHG or Khvalynsk like affinities.
    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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  9. #3915
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    @jortita bro

    I ran these 3 models to find your East Asian and try to distinguish the Bronze-Age derived AA from more recent admixtures. Note that I'm trying to copy DMXX's style, but substance might be lacking...

    Let's start with the basics

    BronzeAge References Only

     

    The fit distance is bad, so looks like we might need moderns to account for the East Asian.

    Added a bunch of modern East Asians (Naxi for the West, Han for the North, Dai for the East, and Vietnam for the Southeast) -- not to see which type of modern East Asian you prefer, but allowing nMonte to try out all modern East Asians realistic to your region.

    With BronzeAge AA and Modern East Asian

     

    It looks like your fit distance goes down considerably with BOTH AA and the moderns, but note the BronzeAge AA still has a significant presence. In this lower distance model, you prefer ~12% Modern East Asian + 12% Ancient AA.

    Just to check how it looks when the Bronze Age AA is removed, I have removed the Bronze Age AA and just modern East Asians to account for your East Asian.

    Only Modern East Asians
     


    The fit suffers indeed.

    My Conclusion -- your modern East Asian appears to be ~12% and ancestral AA around 12%. So, roughly 25% East Asian.
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  11. #3916
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    Regarding the discussion over Cycle/Batch, meaningful(imo) simulations are lowered considerably once you increase the batch (assuming cycle remains constant). So, when we're doing cycle=1000, batch=500 will have much lower meaningful(imo) simulations than when using batch=120. Lowering the batch to crazy low number(like batch=10) is bad, I agree, but batch=120 offers much more meaningful marginal benefit w.r.t. more meaningful simulations than when batch=500.

    If you care about scores like 0.2 versus 0.8333 when both are below 1 and are willing to sacrifice precious simulations that could go over much more references (and potentially lower the distance), go ahead and use higher batch. Otherwise, I recommend you use lower batch=120 and keep cycle=1000, so that nmonte will go through much more references' combinations than when you use batch=500 with cycle=1000.

    Reading the code, that is my takeaway. I am running some tests to validate that.

    tl;dr - lower the batch from 500 to 120 so that the same cycle setting can go through much higher number of potentially distance reducing simulations.
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