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Thread: Global25 automated nMonte for South/Central Asian members

  1. #7841
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    Quote Originally Posted by ThaYamamoto View Post
    I figured it out, using DMXX's NW SPGT derived sim is causing this massive inflation of Barcin. Sticking to the SiSBA3 derived sim and the moderns derived south sim fix this issue. Those EEF amounts were way too high for SA, something about the SGPT sim that messes things up.

    edit: the gonur derived and sisBA2 sims work really well as well, particularly for me.
    Interesting. I moved away from AASI DMXX for the opposite reason, because I thought it interacts with ANF/Barcin way too much, plus fits were much worse for NW pops than with AASI NW DMXX. My reasoning is that a truer AASI component would tend to isolate and exclude West Eurasian admixture to a much larger extent. A component that is consuming ANF shows to me that it is more ASI (essentially AASI+West, Eurasian) shifted, rather than simulating actual, fundamentally South Eurasian AASI.

    Also, I think you are underestimating the ANF shift in certain NW SA pops. There are two clines for elevated ANF, one is the Vedic cline which goes up proportionally with Steppe MLBA peaking in Haryana Jatts/Rors. The other cline is the more recent East Iranic/Pamirid mediated ANF cline (seemingly less related to Steppe), where there is a pretty smooth transition between Pashtuns who score v decent Barcin, and West origin Punjabi pops such as Kamboj/Khatri/Arain etc (with a relative dip occurring in East Punjab, Kashmiris and Gujjars). Also, based on my runs Dards such as Kalash are on average not as Barcin shifted as western origin punjabi biraderi's even, indicating that they are largely of pre-East Iranic/Pamirid stock.

    I'm basing this off of my experimentations with basal models that IMO work well for South/Central Asian pops. It's still very much in progress, but I have a spreadsheet open for anyone to reference, if interested:

    https://docs.google.com/spreadsheets...326ck/htmlview
    Last edited by kamil154; 12-04-2020 at 11:38 PM.
    G25 Neolithic model

    "sample": "kamil154",
    "distance": 2.2284,
    "Ganj_Dareh_N": 41,
    "Barcin_N": 18,
    "Simulated_AASI_NW_by_DMXX": 17,
    "Karelia_HG": 10.5,
    "GEO_CHG": 3.5,
    "Tyumen_HG": 3,
    "LAO_Hoabinhian": 2.5,
    "LapaDoSanto_9600BP": 2.5,
    "Boshan_N": 2

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  3. #7842
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    Settings: Scaled/Penalties: ON, Grouping: OFF


    Using AASI:

    sample: kamil154
    distance: 2.7205
    Ganj_Dareh_N: 43
    Simulated_AASI_by_DMXX: 18.5
    Barcin_N: 14
    Karelia_HG: 11.5
    GEO_CHG: 6
    Tyumen_HG: 3.5
    LapaDoSanto_9600BP: 2
    Boshan_N: 1.5

    sample: kamil154
    distance: 2.8092
    Ganj_Dareh_N: 43
    Simulated_AASI_by_DMXX: 18.5
    Barcin_N: 15
    Karelia_HG: 8
    GEO_CHG: 6.5
    Tyumen_HG: 5.5
    LapaDoSanto_9600BP: 3
    Boshan_N: 0.5


    Using AASI + Hoabinhian:

    sample: kamil154
    distance: 2.8607
    Ganj_Dareh_N: 42
    Simulated_AASI_by_DMXX: 18
    Barcin_N: 14.5
    Karelia_HG: 9.5
    GEO_CHG: 6.5
    Tyumen_HG: 5
    LAO_Hoabinhian: 2.5
    LapaDoSanto_9600BP: 2

    sample: kamil154
    distance: 3.0051
    Ganj_Dareh_N: 42
    Simulated_AASI_by_DMXX: 18
    Barcin_N: 15
    Karelia_HG: 8.5
    GEO_CHG: 6.5
    Tyumen_HG: 4.5
    LapaDoSanto_9600BP: 2.5
    LAO_Hoabinhian: 2
    Boshan_N: 1


    Using AASI NW + Hoabinhian:

    sample: kamil154
    distance: 2.2284
    Ganj_Dareh_N: 41
    Barcin_N: 18
    Simulated_AASI_NW_by_DMXX: 17
    Karelia_HG: 10.5
    GEO_CHG: 3.5
    Tyumen_HG: 3
    LAO_Hoabinhian: 2.5
    LapaDoSanto_9600BP: 2.5
    Boshan_N: 2

    sample: kamil154
    distance: 2.3254
    Ganj_Dareh_N: 40.5
    Barcin_N: 17.5
    Simulated_AASI_NW_by_DMXX: 17
    Karelia_HG: 9.5
    GEO_CHG: 4.5
    Tyumen_HG: 5.0
    LAO_Hoabinhian: 3.0
    LapaDoSanto_9600BP: 2.5
    Boshan_N: 0.5
    Last edited by kamil154; 12-04-2020 at 11:25 PM.
    G25 Neolithic model

    "sample": "kamil154",
    "distance": 2.2284,
    "Ganj_Dareh_N": 41,
    "Barcin_N": 18,
    "Simulated_AASI_NW_by_DMXX": 17,
    "Karelia_HG": 10.5,
    "GEO_CHG": 3.5,
    "Tyumen_HG": 3,
    "LAO_Hoabinhian": 2.5,
    "LapaDoSanto_9600BP": 2.5,
    "Boshan_N": 2

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  5. #7843
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    Quote Originally Posted by kamil154 View Post
    Interesting. I moved away from AASI DMXX for the opposite reason, because I thought it interacts with ANF/Barcin way too much, plus fits were much worse for NW pops than with AASI NW DMXX. My reasoning is that a truer AASI component would tend to isolate and exclude West Eurasian admixture to a much larger extent. A component that is consuming ANF shows to me that it is more ASI (essentially AASI+West, Eurasian) shifted, rather than simulating actual, fundamentally South Eurasian AASI.

    Also, I think you are underestimating the ANF shift in certain NW SA pops. There are two clines for elevated ANF, one is the Vedic cline which goes up proportionally with Steppe MLBA peaking in Haryana Jatts/Rors. The other cline is the more recent East Iranic/Pamirid mediated ANF cline (seemingly less related to Steppe), where there is a pretty smooth transition between Pashtuns who score v decent Barcin, and West origin Punjabi pops such as Kamboj/Khatri/Arain etc (with a relative dip occurring in East Punjab, Kashmiris and Gujjars). Also, based on my runs Dards such as Kalash are on average not as Barcin shifted as western origin punjabi biraderi's even, indicating that they are largely of pre-East Iranic/Pamirid stock.

    I'm basing this off of my experimentations with basal models that IMO work well for South/Central Asian pops. It's still very much in progress, but I have a spreadsheet open for anyone to reference, if interested:

    https://docs.google.com/spreadsheets...326ck/htmlview
    The NW sims are great (SiSBA2,SiSBA3,Gonur2) but one of them is SPGT which is causing the issue - maybe this one has been averaged as part of the Genoplot version or is in fact the one available on Genoplot? It seems to cause an issue imo, but really I don't know much about South or Central Asian populations i normally turn to pegasus or agentlime for that...however i think you might be overestimating barcin in SA but honestly i'm not too sure thats just where i'm leaning atm as steppe mlba are what, 30% barcin and a far more diluted version of steppe mlba arrived and mixed in the subcontinent? i can't see NW SAs having particularly high EEF but hopefully another user can enlighten us.

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  7. #7844
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    Quote Originally Posted by kamil154 View Post
    Settings: Scaled/Penalties: ON, Grouping: OFF


    Using AASI:

    sample: kamil154
    distance: 2.7205
    Ganj_Dareh_N: 43
    Simulated_AASI_by_DMXX: 18.5
    Barcin_N: 14
    Karelia_HG: 11.5
    GEO_CHG: 6
    Tyumen_HG: 3.5
    LapaDoSanto_9600BP: 2
    Boshan_N: 1.5

    sample: kamil154
    distance: 2.8092
    Ganj_Dareh_N: 43
    Simulated_AASI_by_DMXX: 18.5
    Barcin_N: 15
    Karelia_HG: 8
    GEO_CHG: 6.5
    Tyumen_HG: 5.5
    LapaDoSanto_9600BP: 3
    Boshan_N: 0.5


    Using AASI + Hoabinhian:

    sample: kamil154
    distance: 2.8607
    Ganj_Dareh_N: 42
    Simulated_AASI_by_DMXX: 18
    Barcin_N: 14.5
    Karelia_HG: 9.5
    GEO_CHG: 6.5
    Tyumen_HG: 5
    LAO_Hoabinhian: 2.5
    LapaDoSanto_9600BP: 2

    sample: kamil154
    distance: 3.0051
    Ganj_Dareh_N: 42
    Simulated_AASI_by_DMXX: 18
    Barcin_N: 15
    Karelia_HG: 8.5
    GEO_CHG: 6.5
    Tyumen_HG: 4.5
    LapaDoSanto_9600BP: 2.5
    LAO_Hoabinhian: 2
    Boshan_N: 1


    Using AASI NW + Hoabinhian:

    sample: kamil154
    distance: 2.2284
    Ganj_Dareh_N: 41
    Barcin_N: 18
    Simulated_AASI_NW_by_DMXX: 17
    Karelia_HG: 10.5
    GEO_CHG: 3.5
    Tyumen_HG: 3
    LAO_Hoabinhian: 2.5
    LapaDoSanto_9600BP: 2.5
    Boshan_N: 2

    sample: kamil154
    distance: 2.3254
    Ganj_Dareh_N: 40.5
    Barcin_N: 17.5
    Simulated_AASI_NW_by_DMXX: 17
    Karelia_HG: 9.5
    GEO_CHG: 4.5
    Tyumen_HG: 5.0
    LAO_Hoabinhian: 3.0
    LapaDoSanto_9600BP: 2.5
    Boshan_N: 0.5
    Your Chokopani is being split into other components . In your case yes, it does suffice, most others esp interior subcontinental populations they need AASI S or the regular AASI DMXX , even with some of the IVC samples the AASI NW is not sufficient at all otherwise it compensates by looking to E/SE Asian sources.


    sample: Shahr I Sokhta BA2:I11456
    distance: 2.7732
    Ganj_Dareh_N: 60
    Simulated_AASI_NW_by_DMXX: 0
    RUS_AfontovaGora3: 8
    Barcin_N: 0
    Simulated_AASI_by_DMXX: 32



    sample: Rakhigarhi
    distance: 2.1369
    Ganj_Dareh_N: 53
    Simulated_AASI_NW_by_DMXX: 0
    RUS_AfontovaGora3: 8.5
    Barcin_N: 0
    Simulated_AASI_by_DMXX: 38.5

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  9. #7845
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    Quote Originally Posted by ThaYamamoto View Post
    The NW sims are great (SiSBA2,SiSBA3,Gonur2) but one of them is SPGT which is causing the issue - maybe this one has been averaged as part of the Genoplot version or is in fact the one available on Genoplot? It seems to cause an issue imo, but really I don't know much about South or Central Asian populations i normally turn to pegasus or agentlime for that...however i think you might be overestimating barcin in SA but honestly i'm not too sure thats just where i'm leaning atm as steppe mlba are what, 30% barcin and a far more diluted version of steppe mlba arrived and mixed in the subcontinent? i can't see NW SAs having particularly high EEF but hopefully another user can enlighten us.
    Wait someone had 30%? Was it with Penalties off? With penalities engaged, only Pashtuns/Tajiks go into the early-mid 20's.
    G25 Neolithic model

    "sample": "kamil154",
    "distance": 2.2284,
    "Ganj_Dareh_N": 41,
    "Barcin_N": 18,
    "Simulated_AASI_NW_by_DMXX": 17,
    "Karelia_HG": 10.5,
    "GEO_CHG": 3.5,
    "Tyumen_HG": 3,
    "LAO_Hoabinhian": 2.5,
    "LapaDoSanto_9600BP": 2.5,
    "Boshan_N": 2

  10. #7846
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    Quote Originally Posted by pegasus View Post
    Your Chokopani is being split into other components . In your case yes, it does suffice, most others esp interior subcontinental populations they need AASI S or the regular AASI DMXX , even with some of the IVC samples the AASI NW is not sufficient at all otherwise it compensates by looking to E/SE Asian sources.


    sample: Shahr I Sokhta BA2:I11456
    distance: 2.7732
    Ganj_Dareh_N: 60
    Simulated_AASI_NW_by_DMXX: 0
    RUS_AfontovaGora3: 8
    Barcin_N: 0
    Simulated_AASI_by_DMXX: 32



    sample: Rakhigarhi
    distance: 2.1369
    Ganj_Dareh_N: 53
    Simulated_AASI_NW_by_DMXX: 0
    RUS_AfontovaGora3: 8.5
    Barcin_N: 0
    Simulated_AASI_by_DMXX: 38.5
    Well. Yeah, you're right. The Hoabinhian is overcompensating for the AASI, and in Gangetic pops especially its going up into 5+% which isn't real in anyway.
    G25 Neolithic model

    "sample": "kamil154",
    "distance": 2.2284,
    "Ganj_Dareh_N": 41,
    "Barcin_N": 18,
    "Simulated_AASI_NW_by_DMXX": 17,
    "Karelia_HG": 10.5,
    "GEO_CHG": 3.5,
    "Tyumen_HG": 3,
    "LAO_Hoabinhian": 2.5,
    "LapaDoSanto_9600BP": 2.5,
    "Boshan_N": 2

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  12. #7847
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    Quote Originally Posted by ThaYamamoto View Post
    The NW sims are great (SiSBA2,SiSBA3,Gonur2) but one of them is SPGT which is causing the issue - maybe this one has been averaged as part of the Genoplot version or is in fact the one available on Genoplot? It seems to cause an issue imo, but really I don't know much about South or Central Asian populations i normally turn to pegasus or agentlime for that...however i think you might be overestimating barcin in SA but honestly i'm not too sure thats just where i'm leaning atm as steppe mlba are what, 30% barcin and a far more diluted version of steppe mlba arrived and mixed in the subcontinent? i can't see NW SAs having particularly high EEF but hopefully another user can enlighten us.
    Nvm. I realized you wrote Steppe themselves were 30% Barcin.

    Did you go over my spreadsheet? To me it is clear that there is an additional ANF cline extending into NW South from the west that is unrelated to Steppe. The ratio of Barcin to Steppe in populations influenced by East Iranic influx is quite indicative that it is not coming from SteppeMLBA/BMAC alone. It could be that whatever East Iranic/Pamirid vector population that admixed into SPGT (to bring forth the Pashtun ethnogenesis), would have been additionally ANF shifted.
    Last edited by kamil154; 12-05-2020 at 06:53 AM.
    G25 Neolithic model

    "sample": "kamil154",
    "distance": 2.2284,
    "Ganj_Dareh_N": 41,
    "Barcin_N": 18,
    "Simulated_AASI_NW_by_DMXX": 17,
    "Karelia_HG": 10.5,
    "GEO_CHG": 3.5,
    "Tyumen_HG": 3,
    "LAO_Hoabinhian": 2.5,
    "LapaDoSanto_9600BP": 2.5,
    "Boshan_N": 2

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  14. #7848
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    While I figure out a more parsimonious model for those Ghaznavids, esp 2959. Extending the model I had with Bustan_o2 to Loebanr and Udegram since its more Steppe shifted and thus closer with modern populations. Though I wanted to first look at Narasimhan's model ( SIS2 + Central Steppe MLBA) on qpAdm and its a barely passing, poor working model (p=0.06, ) and based on the high chisq values (21.78) you know immediately there is something wrong. Though a bigger mistake was making a universal model for potentially billions of people. I think in the rush to publish, a bare-bones approach is sufficient but is often not realistic as is the case here.


    best coefficients: 0.767 0.233

     
    Code:
                 
    
    details: YES
    allsnps: YES
    ## qpAdm version: 1201
    seed: 1312951279
    packed geno read OK
    end of inpack
    
    left pops:
    Pakistan_IA_Udegram
    ShahrISokhtaSubset
    Kazakhstan_MLBA_OyDzhaylau
    
    right pops:
    Cameroon_SMA.DG
    Russia_Steppe_Eneolithic
    ONG.SG
    PPNB
    Iran_GanjDareh_N
    DevilsCave_N.SG
    Russia_UstBelaya_Angara
    EHG
    Anatolia_N
    WSHG
    Russia_MA1_HG.SG
    CHG
    Russia_MLBA_Sintashta
    Sweden_HG_Motala
    Kazakhstan_MLBA_Dali
    
      0  Pakistan_IA_Udegram   12
      1   ShahrISokhtaSubset    6
      2 Kazakhstan_MLBA_OyDzhaylau    6
      3      Cameroon_SMA.DG    1
      4 Russia_Steppe_Eneolithic    3
      5               ONG.SG    6
      6                 PPNB   11
      7     Iran_GanjDareh_N    8
      8      DevilsCave_N.SG    4
      9 Russia_UstBelaya_Angara   11
     10                  EHG    3
     11           Anatolia_N   30
     12                 WSHG    3
     13     Russia_MA1_HG.SG    1
     14                  CHG    2
     15 Russia_MLBA_Sintashta   19
     16     Sweden_HG_Motala    6
     17 Kazakhstan_MLBA_Dali    3
    jackknife block size:     0.050
    snps: 1150513  indivs: 135
    number of blocks for block jackknife: 714
    ## ncols: 1150513
    coverage:  Pakistan_IA_Udegram 1086384
    coverage:   ShahrISokhtaSubset 900907
    coverage: Kazakhstan_MLBA_OyDzhaylau 999558
    coverage:      Cameroon_SMA.DG 1140138
    coverage: Russia_Steppe_Eneolithic 1008226
    coverage:               ONG.SG 1143397
    coverage:                 PPNB 871625
    coverage:     Iran_GanjDareh_N 1057358
    coverage:      DevilsCave_N.SG 1149702
    coverage: Russia_UstBelaya_Angara 1092594
    coverage:                  EHG 1027057
    coverage:           Anatolia_N 1141220
    coverage:                 WSHG 858019
    coverage:     Russia_MA1_HG.SG 805960
    coverage:                  CHG 1149558
    coverage: Russia_MLBA_Sintashta 1115156
    coverage:     Sweden_HG_Motala 1038843
    coverage: Kazakhstan_MLBA_Dali 931588
    Effective number of blocks:   619.299
    numsnps used: 1150513
    codimension 1
    f4info: 
    f4rank: 1 dof:     13 chisq:    21.780 tail:         0.0588669885 dofdiff:     15 chisqdiff:   -21.780 taildiff:                    1
    B:
              scale     1.000 
    Russia_Steppe_Eneolithic     0.987 
             ONG.SG    -0.346 
               PPNB     0.913 
    Iran_GanjDareh_N    -0.201 
    DevilsCave_N.SG    -0.163 
    Russia_UstBelaya_Angara     0.099 
                EHG     1.494 
         Anatolia_N     1.114 
               WSHG     1.022 
    Russia_MA1_HG.SG     0.673 
                CHG     0.453 
    Russia_MLBA_Sintashta     1.516 
    Sweden_HG_Motala     1.722 
    Kazakhstan_MLBA_Dali     1.248 
    A:
              scale   373.235 
    ShahrISokhtaSubset    -0.412 
    Kazakhstan_MLBA_OyDzhaylau     1.353 
    
    
    full rank
    f4info: 
    f4rank: 2 dof:      0 chisq:     0.000 tail:                    1 dofdiff:     13 chisqdiff:    21.780 taildiff:         0.0588669885
    B:
              scale   817.782   273.400 
    Russia_Steppe_Eneolithic    -1.021     0.977 
             ONG.SG     0.513    -0.283 
               PPNB    -0.887     0.919 
    Iran_GanjDareh_N    -0.057    -0.214 
    DevilsCave_N.SG     0.272    -0.112 
    Russia_UstBelaya_Angara    -0.118     0.130 
                EHG    -1.351     1.513 
         Anatolia_N    -1.324     1.077 
               WSHG    -0.938     1.042 
    Russia_MA1_HG.SG    -0.900     0.656 
                CHG    -0.678     0.417 
    Russia_MLBA_Sintashta    -1.436     1.525 
    Sweden_HG_Motala    -1.611     1.740 
    Kazakhstan_MLBA_Dali    -1.196     1.245 
    A:
              scale     1.414     1.414 
    ShahrISokhtaSubset     1.414     0.000 
    Kazakhstan_MLBA_OyDzhaylau     0.000     1.414 
    
    
    best coefficients:     0.767     0.233 
    Jackknife mean:      0.766493794     0.233506206 
          std. errors:     0.012     0.012 
    
    error covariance (* 1,000,000)
           153       -153 
          -153        153 
    
    
    summ: Pakistan_IA_Udegram    2      0.058867     0.766     0.234        153       -153        153 
    
        fixed pat  wt  dof     chisq       tail prob
               00  0    13    21.780        0.058867     0.767     0.233 
               01  1    14   289.206               0     1.000     0.000 
               10  1    14  1885.006               0     0.000     1.000 
    best pat:           00         0.058867              -  -
    best pat:           01      2.09717e-53  chi(nested):   267.426 p-value for nested model:     4.12863e-60
    
    coeffs:     0.767     0.233 
    
    ## dscore:: f_4(Base, Fit, Rbase, right2)
    ## genstat:: f_4(Base, Fit, right1, right2)
    
    details:   ShahrISokhtaSubset Russia_Steppe_Eneolithic    -0.001249   -5.328429
    details: Kazakhstan_MLBA_OyDzhaylau Russia_Steppe_Eneolithic     0.003574   14.597964
    dscore: Russia_Steppe_Eneolithic f4:    -0.000123 Z:    -0.618517
    
    details:   ShahrISokhtaSubset               ONG.SG     0.000628    2.795940
    details: Kazakhstan_MLBA_OyDzhaylau               ONG.SG    -0.001034   -4.219314
    dscore:               ONG.SG f4:     0.000240 Z:     1.235293
    
    details:   ShahrISokhtaSubset                 PPNB    -0.001084   -4.824008
    details: Kazakhstan_MLBA_OyDzhaylau                 PPNB     0.003362   14.507829
    dscore:                 PPNB f4:    -0.000047 Z:    -0.246264
    
    details:   ShahrISokhtaSubset     Iran_GanjDareh_N    -0.000069   -0.320004
    details: Kazakhstan_MLBA_OyDzhaylau     Iran_GanjDareh_N    -0.000783   -3.419016
    dscore:     Iran_GanjDareh_N f4:    -0.000236 Z:    -1.274021
    
    details:   ShahrISokhtaSubset      DevilsCave_N.SG     0.000332    1.432064
    details: Kazakhstan_MLBA_OyDzhaylau      DevilsCave_N.SG    -0.000410   -1.686730
    dscore:      DevilsCave_N.SG f4:     0.000159 Z:     0.800311
    
    details:   ShahrISokhtaSubset Russia_UstBelaya_Angara    -0.000144   -0.683950
    details: Kazakhstan_MLBA_OyDzhaylau Russia_UstBelaya_Angara     0.000475    2.035058
    dscore: Russia_UstBelaya_Angara f4:     0.000001 Z:     0.003983
    
    details:   ShahrISokhtaSubset                  EHG    -0.001652   -6.576246
    details: Kazakhstan_MLBA_OyDzhaylau                  EHG     0.005535   19.792167
    dscore:                  EHG f4:     0.000025 Z:     0.114544
    
    details:   ShahrISokhtaSubset           Anatolia_N    -0.001619   -7.860523
    details: Kazakhstan_MLBA_OyDzhaylau           Anatolia_N     0.003939   17.875377
    dscore:           Anatolia_N f4:    -0.000322 Z:    -1.853681
    
    details:   ShahrISokhtaSubset                 WSHG    -0.001148   -4.530268
    details: Kazakhstan_MLBA_OyDzhaylau                 WSHG     0.003812   14.667595
    dscore:                 WSHG f4:     0.000010 Z:     0.044785
    
    details:   ShahrISokhtaSubset     Russia_MA1_HG.SG    -0.001101   -4.294186
    details: Kazakhstan_MLBA_OyDzhaylau     Russia_MA1_HG.SG     0.002398    8.399950
    dscore:     Russia_MA1_HG.SG f4:    -0.000284 Z:    -1.306961
    
    details:   ShahrISokhtaSubset                  CHG    -0.000829   -3.542116
    details: Kazakhstan_MLBA_OyDzhaylau                  CHG     0.001524    6.001496
    dscore:                  CHG f4:    -0.000280 Z:    -1.370382
    
    details:   ShahrISokhtaSubset Russia_MLBA_Sintashta    -0.001756   -8.671091
    details: Kazakhstan_MLBA_OyDzhaylau Russia_MLBA_Sintashta     0.005579   25.654787
    dscore: Russia_MLBA_Sintashta f4:    -0.000045 Z:    -0.262654
    
    details:   ShahrISokhtaSubset     Sweden_HG_Motala    -0.001970   -8.743864
    details: Kazakhstan_MLBA_OyDzhaylau     Sweden_HG_Motala     0.006366   24.744212
    dscore:     Sweden_HG_Motala f4:    -0.000025 Z:    -0.129741
    
    details:   ShahrISokhtaSubset Kazakhstan_MLBA_Dali    -0.001463   -6.014131
    details: Kazakhstan_MLBA_OyDzhaylau Kazakhstan_MLBA_Dali     0.004554   17.088642
    dscore: Kazakhstan_MLBA_Dali f4:    -0.000059 Z:    -0.285732



    G25 model
    sample: Loebanr IA:Average
    distance: 1.1416
    Shahr_I_Sokhta_BA2: 54
    Alalakh_MLBA_o: 30
    CG_CentralSteppeMLBA: 13
    Devils_Gate_Cave_N: 3


    Nmonte is extremely useful and models near 1 tend to do exceedingly well on qpAdm.

    The model above gets a tail probability of 0.94 and low chisq (5.42) which is a resounding success.

    coeffs: 0.578 0.142 0.280

     
    Code:
    details: YES
    allsnps: YES
    ## qpAdm version: 1201
    seed: 1791496889
    packed geno read OK
    end of inpack
    
    left pops:
    Pakistan_IA_Loebanr
    ShahrISokhtaSubset
    Kazakhstan_MLBA_OyDzhaylau
    Alalakh_MLBA_outlier
    
    right pops:
    Cameroon_SMA.DG
    Russia_Steppe_Eneolithic
    ONG.SG
    PPNB
    Iran_GanjDareh_N
    DevilsCave_N.SG
    Russia_UstBelaya_Angara
    EHG
    Anatolia_N
    WSHG
    Russia_MA1_HG.SG
    CHG
    Russia_MLBA_Sintashta
    Sweden_HG_Motala
    Kazakhstan_MLBA_Dali
    
      0  Pakistan_IA_Loebanr   29
      1   ShahrISokhtaSubset    4
      2 Kazakhstan_MLBA_OyDzhaylau    6
      3 Alalakh_MLBA_outlier    1
      4      Cameroon_SMA.DG    1
      5 Russia_Steppe_Eneolithic    3
      6               ONG.SG    6
      7                 PPNB   11
      8     Iran_GanjDareh_N    8
      9      DevilsCave_N.SG    4
     10 Russia_UstBelaya_Angara   11
     11                  EHG    3
     12           Anatolia_N   30
     13                 WSHG    3
     14     Russia_MA1_HG.SG    1
     15                  CHG    2
     16 Russia_MLBA_Sintashta   19
     17     Sweden_HG_Motala    6
     18 Kazakhstan_MLBA_Dali    3
    jackknife block size:     0.050
    snps: 1150513  indivs: 151
    number of blocks for block jackknife: 714
    ## ncols: 1150513
    coverage:  Pakistan_IA_Loebanr 1112533
    coverage:   ShahrISokhtaSubset 841356
    coverage: Kazakhstan_MLBA_OyDzhaylau 999558
    coverage: Alalakh_MLBA_outlier 733730
    coverage:      Cameroon_SMA.DG 1140138
    coverage: Russia_Steppe_Eneolithic 1008226
    coverage:               ONG.SG 1143397
    coverage:                 PPNB 871625
    coverage:     Iran_GanjDareh_N 1057358
    coverage:      DevilsCave_N.SG 1149702
    coverage: Russia_UstBelaya_Angara 1092594
    coverage:                  EHG 1027057
    coverage:           Anatolia_N 1141220
    coverage:                 WSHG 858019
    coverage:     Russia_MA1_HG.SG 805960
    coverage:                  CHG 1149558
    coverage: Russia_MLBA_Sintashta 1115156
    coverage:     Sweden_HG_Motala 1038843
    coverage: Kazakhstan_MLBA_Dali 931588
    Effective number of blocks:   620.147
    numsnps used: 1150513
    codimension 1
    f4info: 
    f4rank: 2 dof:     12 chisq:     5.422 tail:          0.942388735 dofdiff:     14 chisqdiff:    -5.422 taildiff:                    1
    B:
              scale     1.000     1.000 
    Russia_Steppe_Eneolithic     0.998    -0.473 
             ONG.SG    -0.360     0.962 
               PPNB     0.963    -0.460 
    Iran_GanjDareh_N    -0.036    -2.449 
    DevilsCave_N.SG    -0.192     0.606 
    Russia_UstBelaya_Angara     0.071     0.679 
                EHG     1.445     1.186 
         Anatolia_N     1.147    -0.520 
               WSHG     1.047     0.517 
    Russia_MA1_HG.SG     0.745     0.310 
                CHG     0.530    -1.449 
    Russia_MLBA_Sintashta     1.490     0.385 
    Sweden_HG_Motala     1.694     1.179 
    Kazakhstan_MLBA_Dali     1.216     0.370 
    A:
              scale   421.848  1853.017 
    ShahrISokhtaSubset    -0.593     0.638 
    Kazakhstan_MLBA_OyDzhaylau     1.571     0.447 
    Alalakh_MLBA_outlier     0.426    -1.547 
    
    
    full rank
    f4info: 
    f4rank: 3 dof:      0 chisq:     0.000 tail:                    1 dofdiff:     12 chisqdiff:     5.422 taildiff:          0.942388735
    B:
              scale   767.219   257.893   820.497 
    Russia_Steppe_Eneolithic    -1.180     0.955     1.146 
             ONG.SG     0.769    -0.247    -0.828 
               PPNB    -1.034     0.925     1.248 
    Iran_GanjDareh_N    -0.485    -0.149     1.774 
    DevilsCave_N.SG     0.454    -0.113    -0.477 
    Russia_UstBelaya_Angara     0.179     0.145    -0.347 
                EHG    -1.266     1.488     0.313 
         Anatolia_N    -1.305     1.095     1.381 
               WSHG    -0.924     1.079     0.512 
    Russia_MA1_HG.SG    -0.593     0.775     0.445 
                CHG    -0.947     0.436     1.448 
    Russia_MLBA_Sintashta    -1.413     1.484     1.046 
    Sweden_HG_Motala    -1.376     1.740     0.713 
    Kazakhstan_MLBA_Dali    -1.085     1.232     0.887 
    A:
              scale     1.732     1.732     1.732 
    ShahrISokhtaSubset     1.732     0.000     0.000 
    Kazakhstan_MLBA_OyDzhaylau     0.000     1.732     0.000 
    Alalakh_MLBA_outlier     0.000     0.000     1.732 
    
    
    best coefficients:     0.578     0.142     0.280 
    Jackknife mean:      0.578193161     0.142688156     0.279118682 
          std. errors:     0.026     0.014     0.033 
    
    error covariance (* 1,000,000)
           651        124       -775 
           124        209       -333 
          -775       -333       1108 
    
    
    summ: Pakistan_IA_Loebanr    3      0.942389     0.578     0.143     0.279        651        124       -775        209       -333   ...
          1108 
    
        fixed pat  wt  dof     chisq       tail prob
              000  0    12     5.422        0.942389     0.578     0.142     0.280 
              001  1    13    67.774     2.05727e-09     0.779     0.221     0.000 
              010  1    13    70.603     6.21401e-10     0.470     0.000     0.530 
              100  1    13   172.782     5.01717e-30     0.000    -0.093     1.093  infeasible
              011  2    14   305.560               0     1.000     0.000     0.000 
              101  2    14  2443.138               0     0.000     1.000     0.000 
              110  2    14   182.387               0     0.000     0.000     1.000 
    best pat:          000         0.942389              -  -
    best pat:          001      2.05727e-09  chi(nested):    62.352 p-value for nested model:      2.8728e-15
    best pat:          110      2.12279e-31 not nested
    
    coeffs:     0.578     0.142     0.280 
    
    ## dscore:: f_4(Base, Fit, Rbase, right2)
    ## genstat:: f_4(Base, Fit, right1, right2)
    
    details:   ShahrISokhtaSubset Russia_Steppe_Eneolithic    -0.001538   -6.774035
    details: Kazakhstan_MLBA_OyDzhaylau Russia_Steppe_Eneolithic     0.003703   16.556287
    details: Alalakh_MLBA_outlier Russia_Steppe_Eneolithic     0.001396    4.858396
    dscore: Russia_Steppe_Eneolithic f4:     0.000029 Z:     0.183390
    
    details:   ShahrISokhtaSubset               ONG.SG     0.001002    4.371614
    details: Kazakhstan_MLBA_OyDzhaylau               ONG.SG    -0.000959   -4.384131
    details: Alalakh_MLBA_outlier               ONG.SG    -0.001009   -3.232779
    dscore:               ONG.SG f4:     0.000161 Z:     0.939506
    
    details:   ShahrISokhtaSubset                 PPNB    -0.001348   -5.948918
    details: Kazakhstan_MLBA_OyDzhaylau                 PPNB     0.003588   16.428496
    details: Alalakh_MLBA_outlier                 PPNB     0.001521    5.009939
    dscore:                 PPNB f4:     0.000157 Z:     0.953099
    
    details:   ShahrISokhtaSubset     Iran_GanjDareh_N    -0.000633   -3.028302
    details: Kazakhstan_MLBA_OyDzhaylau     Iran_GanjDareh_N    -0.000577   -2.719729
    details: Alalakh_MLBA_outlier     Iran_GanjDareh_N     0.002162    7.543206
    dscore:     Iran_GanjDareh_N f4:     0.000156 Z:     1.030928
    
    details:   ShahrISokhtaSubset      DevilsCave_N.SG     0.000592    2.619381
    details: Kazakhstan_MLBA_OyDzhaylau      DevilsCave_N.SG    -0.000439   -2.018981
    details: Alalakh_MLBA_outlier      DevilsCave_N.SG    -0.000582   -1.881875
    dscore:      DevilsCave_N.SG f4:     0.000117 Z:     0.699839
    
    details:   ShahrISokhtaSubset Russia_UstBelaya_Angara     0.000233    1.090067
    details: Kazakhstan_MLBA_OyDzhaylau Russia_UstBelaya_Angara     0.000561    2.661066
    details: Alalakh_MLBA_outlier Russia_UstBelaya_Angara    -0.000423   -1.425976
    dscore: Russia_UstBelaya_Angara f4:     0.000096 Z:     0.617991
    
    details:   ShahrISokhtaSubset                  EHG    -0.001650   -6.658884
    details: Kazakhstan_MLBA_OyDzhaylau                  EHG     0.005771   23.157414
    details: Alalakh_MLBA_outlier                  EHG     0.000381    1.139039
    dscore:                  EHG f4:    -0.000025 Z:    -0.142706
    
    details:   ShahrISokhtaSubset           Anatolia_N    -0.001701   -8.221310
    details: Kazakhstan_MLBA_OyDzhaylau           Anatolia_N     0.004246   20.923528
    details: Alalakh_MLBA_outlier           Anatolia_N     0.001683    6.079075
    dscore:           Anatolia_N f4:     0.000092 Z:     0.614684
    
    details:   ShahrISokhtaSubset                 WSHG    -0.001204   -4.770469
    details: Kazakhstan_MLBA_OyDzhaylau                 WSHG     0.004183   17.587067
    details: Alalakh_MLBA_outlier                 WSHG     0.000624    1.804747
    dscore:                 WSHG f4:     0.000074 Z:     0.417226
    
    details:   ShahrISokhtaSubset     Russia_MA1_HG.SG    -0.000773   -2.877014
    details: Kazakhstan_MLBA_OyDzhaylau     Russia_MA1_HG.SG     0.003003   11.290830
    details: Alalakh_MLBA_outlier     Russia_MA1_HG.SG     0.000543    1.541732
    dscore:     Russia_MA1_HG.SG f4:     0.000133 Z:     0.683320
    
    details:   ShahrISokhtaSubset                  CHG    -0.001235   -5.495189
    details: Kazakhstan_MLBA_OyDzhaylau                  CHG     0.001691    7.338363
    details: Alalakh_MLBA_outlier                  CHG     0.001764    5.544847
    dscore:                  CHG f4:     0.000020 Z:     0.122007
    
    details:   ShahrISokhtaSubset Russia_MLBA_Sintashta    -0.001842   -9.258065
    details: Kazakhstan_MLBA_OyDzhaylau Russia_MLBA_Sintashta     0.005754   29.992091
    details: Alalakh_MLBA_outlier Russia_MLBA_Sintashta     0.001274    4.775041
    dscore: Russia_MLBA_Sintashta f4:     0.000111 Z:     0.778148
    
    details:   ShahrISokhtaSubset     Sweden_HG_Motala    -0.001793   -7.884868
    details: Kazakhstan_MLBA_OyDzhaylau     Sweden_HG_Motala     0.006745   28.664516
    details: Alalakh_MLBA_outlier     Sweden_HG_Motala     0.000869    2.736303
    dscore:     Sweden_HG_Motala f4:     0.000167 Z:     0.991463
    
    details:   ShahrISokhtaSubset Kazakhstan_MLBA_Dali    -0.001414   -6.007695
    details: Kazakhstan_MLBA_OyDzhaylau Kazakhstan_MLBA_Dali     0.004778   19.945755
    details: Alalakh_MLBA_outlier Kazakhstan_MLBA_Dali     0.001081    3.555779
    dscore: Kazakhstan_MLBA_Dali f4:     0.000165 Z:     0.978178



    With Udegram

    sample: Udegram IA:Average
    distance: 1.2253
    Shahr_I_Sokhta_BA2: 51.5
    Alalakh_MLBA_o: 30.5
    CG_CentralSteppeMLBA: 15.5
    Devils_Gate_Cave_N: 2.5

    coeffs: 0.545 0.157 0.298

    The p value is 0.65, chisq 9.5 , still a very good model but not as sterling as the one for Loebanr.

     
    Code:
    left pops:
    Pakistan_IA_Udegram
    ShahrISokhtaSubset
    Kazakhstan_MLBA_OyDzhaylau
    Alalakh_MLBA_outlier
    
    right pops:
    Cameroon_SMA.DG
    Russia_Steppe_Eneolithic
    ONG.SG
    PPNB
    Iran_GanjDareh_N
    DevilsCave_N.SG
    Russia_UstBelaya_Angara
    EHG
    Anatolia_N
    WSHG
    Russia_MA1_HG.SG
    CHG
    Russia_MLBA_Sintashta
    Sweden_HG_Motala
    Kazakhstan_MLBA_Dali
    
      0  Pakistan_IA_Udegram   12
      1   ShahrISokhtaSubset    4
      2 Kazakhstan_MLBA_OyDzhaylau    6
      3 Alalakh_MLBA_outlier    1
      4      Cameroon_SMA.DG    1
      5 Russia_Steppe_Eneolithic    3
      6               ONG.SG    6
      7                 PPNB   11
      8     Iran_GanjDareh_N    8
      9      DevilsCave_N.SG    4
     10 Russia_UstBelaya_Angara   11
     11                  EHG    3
     12           Anatolia_N   30
     13                 WSHG    3
     14     Russia_MA1_HG.SG    1
     15                  CHG    2
     16 Russia_MLBA_Sintashta   19
     17     Sweden_HG_Motala    6
     18 Kazakhstan_MLBA_Dali    3
    jackknife block size:     0.050
    snps: 1150513  indivs: 134
    number of blocks for block jackknife: 714
    ## ncols: 1150513
    coverage:  Pakistan_IA_Udegram 1086384
    coverage:   ShahrISokhtaSubset 841356
    coverage: Kazakhstan_MLBA_OyDzhaylau 999558
    coverage: Alalakh_MLBA_outlier 733730
    coverage:      Cameroon_SMA.DG 1140138
    coverage: Russia_Steppe_Eneolithic 1008226
    coverage:               ONG.SG 1143397
    coverage:                 PPNB 871625
    coverage:     Iran_GanjDareh_N 1057358
    coverage:      DevilsCave_N.SG 1149702
    coverage: Russia_UstBelaya_Angara 1092594
    coverage:                  EHG 1027057
    coverage:           Anatolia_N 1141220
    coverage:                 WSHG 858019
    coverage:     Russia_MA1_HG.SG 805960
    coverage:                  CHG 1149558
    coverage: Russia_MLBA_Sintashta 1115156
    coverage:     Sweden_HG_Motala 1038843
    coverage: Kazakhstan_MLBA_Dali 931588
    Effective number of blocks:   619.984
    numsnps used: 1150513
    codimension 1
    f4info: 
    f4rank: 2 dof:     12 chisq:     9.505 tail:          0.659283387 dofdiff:     14 chisqdiff:    -9.505 taildiff:                    1
    B:
              scale     1.000     1.000 
    Russia_Steppe_Eneolithic     1.030    -0.513 
             ONG.SG    -0.398     0.884 
               PPNB     0.952    -0.486 
    Iran_GanjDareh_N    -0.064    -2.483 
    DevilsCave_N.SG    -0.209     0.618 
    Russia_UstBelaya_Angara     0.061     0.613 
                EHG     1.460     1.186 
         Anatolia_N     1.140    -0.521 
               WSHG     1.030     0.562 
    Russia_MA1_HG.SG     0.669     0.188 
                CHG     0.516    -1.458 
    Russia_MLBA_Sintashta     1.511     0.465 
    Sweden_HG_Motala     1.688     1.136 
    Kazakhstan_MLBA_Dali     1.218     0.372 
    A:
              scale   439.172  1865.252 
    ShahrISokhtaSubset    -0.668     0.682 
    Kazakhstan_MLBA_OyDzhaylau     1.546     0.502 
    Alalakh_MLBA_outlier     0.405    -1.511 
    
    
    full rank
    f4info: 
    f4rank: 3 dof:      0 chisq:     0.000 tail:                    1 dofdiff:     12 chisqdiff:     9.505 taildiff:          0.659283387
    B:
              scale   673.087   273.400   909.261 
    Russia_Steppe_Eneolithic    -1.136     0.977     1.244 
             ONG.SG     0.674    -0.283    -0.911 
               PPNB    -1.023     0.919     1.258 
    Iran_GanjDareh_N    -0.508    -0.214     1.820 
    DevilsCave_N.SG     0.453    -0.112    -0.511 
    Russia_UstBelaya_Angara     0.053     0.130    -0.470 
                EHG    -1.228     1.513     0.263 
         Anatolia_N    -1.313     1.077     1.316 
               WSHG    -1.008     1.042     0.223 
    Russia_MA1_HG.SG    -0.794     0.656     0.095 
                CHG    -0.843     0.417     1.545 
    Russia_MLBA_Sintashta    -1.378     1.525     1.009 
    Sweden_HG_Motala    -1.455     1.740     0.549 
    Kazakhstan_MLBA_Dali    -1.067     1.245     0.824 
    A:
              scale     1.732     1.732     1.732 
    ShahrISokhtaSubset     1.732     0.000     0.000 
    Kazakhstan_MLBA_OyDzhaylau     0.000     1.732     0.000 
    Alalakh_MLBA_outlier     0.000     0.000     1.732 
    
    
    best coefficients:     0.545     0.157     0.298 
    Jackknife mean:      0.544640504     0.157889939     0.297469558 
          std. errors:     0.029     0.017     0.038 
    
    error covariance (* 1,000,000)
           865        163      -1028 
           163        277       -440 
         -1028       -440       1468 
    
    
    summ: Pakistan_IA_Udegram    3      0.659283     0.545     0.158     0.297        865        163      -1028        277       -440   ...
          1468 
    
        fixed pat  wt  dof     chisq       tail prob
              000  0    12     9.505        0.659283     0.545     0.157     0.298 
              001  1    13    65.846      4.6268e-09     0.762     0.238     0.000 
              010  1    13    71.241     4.74058e-10     0.419     0.000     0.581 
              100  1    13   153.386     4.28029e-26     0.000    -0.062     1.062  infeasible
              011  2    14   297.055               0     1.000     0.000     0.000 
              101  2    14  1857.229               0     0.000     1.000     0.000 
              110  2    14   157.138     2.66737e-26     0.000     0.000     1.000 
    best pat:          000         0.659283              -  -
    best pat:          001       4.6268e-09  chi(nested):    56.340 p-value for nested model:     6.09483e-14
    best pat:          110      2.66737e-26 not nested
    
    coeffs:     0.545     0.157     0.298 
    
    ## dscore:: f_4(Base, Fit, Rbase, right2)
    ## genstat:: f_4(Base, Fit, right1, right2)
    
    details:   ShahrISokhtaSubset Russia_Steppe_Eneolithic    -0.001687   -6.969215
    details: Kazakhstan_MLBA_OyDzhaylau Russia_Steppe_Eneolithic     0.003574   14.567897
    details: Alalakh_MLBA_outlier Russia_Steppe_Eneolithic     0.001368    4.481113
    dscore: Russia_Steppe_Eneolithic f4:     0.000052 Z:     0.287129
    
    details:   ShahrISokhtaSubset               ONG.SG     0.001002    4.183735
    details: Kazakhstan_MLBA_OyDzhaylau               ONG.SG    -0.001034   -4.208131
    details: Alalakh_MLBA_outlier               ONG.SG    -0.001002   -3.165803
    dscore:               ONG.SG f4:     0.000084 Z:     0.456758
    
    details:   ShahrISokhtaSubset                 PPNB    -0.001520   -6.324735
    details: Kazakhstan_MLBA_OyDzhaylau                 PPNB     0.003362   14.475753
    details: Alalakh_MLBA_outlier                 PPNB     0.001384    4.502844
    dscore:                 PPNB f4:     0.000114 Z:     0.636879
    
    details:   ShahrISokhtaSubset     Iran_GanjDareh_N    -0.000755   -3.379922
    details: Kazakhstan_MLBA_OyDzhaylau     Iran_GanjDareh_N    -0.000783   -3.408573
    details: Alalakh_MLBA_outlier     Iran_GanjDareh_N     0.002001    6.755425
    dscore:     Iran_GanjDareh_N f4:     0.000062 Z:     0.363909
    
    details:   ShahrISokhtaSubset      DevilsCave_N.SG     0.000672    2.768372
    details: Kazakhstan_MLBA_OyDzhaylau      DevilsCave_N.SG    -0.000410   -1.682684
    details: Alalakh_MLBA_outlier      DevilsCave_N.SG    -0.000563   -1.753600
    dscore:      DevilsCave_N.SG f4:     0.000134 Z:     0.716437
    
    details:   ShahrISokhtaSubset Russia_UstBelaya_Angara     0.000078    0.345909
    details: Kazakhstan_MLBA_OyDzhaylau Russia_UstBelaya_Angara     0.000475    2.030439
    details: Alalakh_MLBA_outlier Russia_UstBelaya_Angara    -0.000517   -1.688198
    dscore: Russia_UstBelaya_Angara f4:    -0.000037 Z:    -0.210992
    
    details:   ShahrISokhtaSubset                  EHG    -0.001824   -6.781799
    details: Kazakhstan_MLBA_OyDzhaylau                  EHG     0.005535   19.756738
    details: Alalakh_MLBA_outlier                  EHG     0.000289    0.820181
    dscore:                  EHG f4:    -0.000035 Z:    -0.168833
    
    details:   ShahrISokhtaSubset           Anatolia_N    -0.001950   -8.870829
    details: Kazakhstan_MLBA_OyDzhaylau           Anatolia_N     0.003939   17.820709
    details: Alalakh_MLBA_outlier           Anatolia_N     0.001447    5.070931
    dscore:           Anatolia_N f4:    -0.000010 Z:    -0.062552
    
    details:   ShahrISokhtaSubset                 WSHG    -0.001498   -5.518420
    details: Kazakhstan_MLBA_OyDzhaylau                 WSHG     0.003812   14.645076
    details: Alalakh_MLBA_outlier                 WSHG     0.000246    0.690626
    dscore:                 WSHG f4:    -0.000142 Z:    -0.700558
    
    details:   ShahrISokhtaSubset     Russia_MA1_HG.SG    -0.001180   -4.295217
    details: Kazakhstan_MLBA_OyDzhaylau     Russia_MA1_HG.SG     0.002398    8.385422
    details: Alalakh_MLBA_outlier     Russia_MA1_HG.SG     0.000105    0.293092
    dscore:     Russia_MA1_HG.SG f4:    -0.000234 Z:    -1.112995
    
    details:   ShahrISokhtaSubset                  CHG    -0.001252   -5.032251
    details: Kazakhstan_MLBA_OyDzhaylau                  CHG     0.001524    5.987119
    details: Alalakh_MLBA_outlier                  CHG     0.001700    5.149322
    dscore:                  CHG f4:     0.000065 Z:     0.334287
    
    details:   ShahrISokhtaSubset Russia_MLBA_Sintashta    -0.002048   -9.508064
    details: Kazakhstan_MLBA_OyDzhaylau Russia_MLBA_Sintashta     0.005579   25.578391
    details: Alalakh_MLBA_outlier Russia_MLBA_Sintashta     0.001110    4.003680
    dscore: Russia_MLBA_Sintashta f4:     0.000094 Z:     0.582594
    
    details:   ShahrISokhtaSubset     Sweden_HG_Motala    -0.002161   -8.944971
    details: Kazakhstan_MLBA_OyDzhaylau     Sweden_HG_Motala     0.006366   24.693970
    details: Alalakh_MLBA_outlier     Sweden_HG_Motala     0.000603    1.853985
    dscore:     Sweden_HG_Motala f4:     0.000005 Z:     0.029574
    
    details:   ShahrISokhtaSubset Kazakhstan_MLBA_Dali    -0.001586   -6.183141
    details: Kazakhstan_MLBA_OyDzhaylau Kazakhstan_MLBA_Dali     0.004554   17.061868
    details: Alalakh_MLBA_outlier Kazakhstan_MLBA_Dali     0.000906    2.960893
    dscore: Kazakhstan_MLBA_Dali f4:     0.000124 Z:     0.645057


    As to why Well Lady all the way in Hatay, Turkey works so well, she is definitely part of the population that moved with Mittani Indo Aryans, and clearly other Indo Aryan groups who moved eastwards. Her timing is perfect and as is her ancestry, she has excess WSHG and CHG which these populations need and what is missing in IVC populations. The first Narasimhan model is quite deficient for ANF and CHG .
    Last edited by pegasus; 12-05-2020 at 08:15 AM.

  15. The Following 6 Users Say Thank You to pegasus For This Useful Post:

     Arlus (12-06-2020),  Jatt1 (12-05-2020),  Johnny ola (12-05-2020),  Sapporo (12-05-2020),  ThaYamamoto (12-05-2020),  vishankar (12-05-2020)

  16. #7849
    Registered Users
    Posts
    1,053
    Ethnicity
    Brahmin (mixed)
    Nationality
    Indian
    Y-DNA (P)
    R-1A (Z-93)
    mtDNA (M)
    M-30

    It has been discussed at some point and I apologize, but can someone enlighten me on the contribution of "central asian kingdoms/civilzations", like Kushans, xiognu, etc. to south asian genetics? Also, iff anyone also has historical articles explaining the migration patterns of these central asian nomads/civilizations, I'm very interested to learn more about it.

  17. The Following User Says Thank You to 26284729292 For This Useful Post:

     Sapporo (12-06-2020)

  18. #7850
    Gold Class Member
    Posts
    2,290
    Sex
    Location
    US/ India
    Ethnicity
    Punjabi Khatri/J&K/Multan
    Nationality
    India
    Y-DNA (P)
    J2b2a
    mtDNA (M)
    U2b

    India United States of America India Punjab Jammu and Kashmir
    Quote Originally Posted by Kirtan24 View Post
    How can one run these? Do I need a specific programme for this?
    Shifting to G25 thread. Here you go-

    Code:
     [1,] "41.3% Kanjar + 58.7% Punjabi_Jatt"                  "0.0136"
      [2,] "37.3% Dharkar + 62.7% Punjabi_Jatt"                 "0.0141"
      [3,] "36.8% Kamboj_o + 63.2% Punjabi_Jatt"                "0.0144"
      [4,] "31.5% Piramalai + 68.5% Punjabi_Jatt"               "0.0146"
      [5,] "44.7% Gujarati + 55.3% Punjabi_Jatt"                "0.0146"
      [6,] "39.6% Kanjar + 60.4% Khatri"                        "0.0148"
      [7,] "32.4% Dharkar + 67.6% Gujar_Pakistan"               "0.0149"
      [8,] "68% Gujar_Pakistan + 32% Kamboj_o"                  "0.0151"
      [9,] "25.3% Hakkipikki + 74.7% Punjabi_Jatt"              "0.0151"
     [10,] "67.8% Punjabi_Jatt + 32.2% Yadava"                  "0.0151"
     [11,] "63.9% Gujar_Pakistan + 36.1% Kanjar"                "0.0151"
     [12,] "63.3% Punjabi_Jatt + 36.7% Velamas"                 "0.0152"
     [13,] "40.2% Kol + 59.8% Punjabi_Jatt"                     "0.0152"
     [14,] "71.4% Gujar_Pakistan + 28.6% Uttar_Pradesh"         "0.0153"
     [15,] "54.4% Iyer + 45.6% Punjabi_Jatt"                    "0.0153"
     [16,] "24.1% Mala + 75.9% Punjabi_Jatt"                    "0.0154"
     [17,] "26.3% Pallan + 73.7% Punjabi_Jatt"                  "0.0154"
     [18,] "95.7% Brahmin_Gujarat + 4.3% Dharkar"               "0.0154"
     [19,] "99.3% Brahmin_Gujarat + 0.7% Koryak"                "0.0155"
     [20,] "99.3% Brahmin_Gujarat + 0.7% Itelmen"               "0.0155"
     [21,] "95.3% Brahmin_Gujarat + 4.7% Kanjar"                "0.0155"
     [22,] "35.6% Dharkar + 64.4% Khatri"                       "0.0155"
     [23,] "91.3% Brahmin_Gujarat + 8.7% Brahmin_Uttar_Pradesh" "0.0155"
     [24,] "99.4% Brahmin_Gujarat + 0.6% Even"                  "0.0155"
     [25,] "30.9% Brahmin_West_Bengal + 69.1% Gujar_India"      "0.0155"
     [26,] "73% Punjabi_Jatt + 27% Sakilli"                     "0.0155"
     [27,] "99.3% Brahmin_Gujarat + 0.7% Chukchi"               "0.0156"
     [28,] "99.4% Brahmin_Gujarat + 0.6% Nivh"                  "0.0156"
     [29,] "99.4% Brahmin_Gujarat + 0.6% Negidal"               "0.0156"
     [30,] "99.4% Brahmin_Gujarat + 0.6% Ulchi"                 "0.0156"
     [31,] "99.4% Brahmin_Gujarat + 0.6% Eskimo_Sireniki"       "0.0156"
     [32,] "99.4% Brahmin_Gujarat + 0.6% Nanai"                 "0.0156"
     [33,] "29.3% Chamar + 70.7% Punjabi_Jatt"                  "0.0156"
     [34,] "99.4% Brahmin_Gujarat + 0.6% Eskimo_Chaplin"        "0.0156"
     [35,] "99.4% Brahmin_Gujarat + 0.6% Yukagir_Tundra"        "0.0156"
     [36,] "99.4% Brahmin_Gujarat + 0.6% Igorot"                "0.0156"
     [37,] "99.4% Brahmin_Gujarat + 0.6% Eskimo"                "0.0156"
     [38,] "99.4% Brahmin_Gujarat + 0.6% Dusun"                 "0.0156"
     [39,] "97.5% Brahmin_Gujarat + 2.5% Gupta"                 "0.0156"
     [40,] "99.4% Brahmin_Gujarat + 0.6% Oroqen"                "0.0156"
     [41,] "0.5% Ami + 99.5% Brahmin_Gujarat"                   "0.0156"
     [42,] "99.4% Brahmin_Gujarat + 0.6% Luzon"                 "0.0156"
     [43,] "99.4% Brahmin_Gujarat + 0.6% Eskimo_Naukan"         "0.0156"
     [44,] "99.5% Brahmin_Gujarat + 0.5% Nganassan"             "0.0156"
     [45,] "0.5% Atayal + 99.5% Brahmin_Gujarat"                "0.0156"
     [46,] "96.8% Brahmin_Gujarat + 3.2% Kamboj_o"              "0.0156"
     [47,] "99.4% Brahmin_Gujarat + 0.6% Khamnegan"             "0.0156"
     [48,] "99.5% Brahmin_Gujarat + 0.5% Murut"                 "0.0156"
     [49,] "99.4% Brahmin_Gujarat + 0.6% Daur"                  "0.0157"
     [50,] "99.5% Brahmin_Gujarat + 0.5% Hezhen"                "0.0157"
     [51,] "98% Brahmin_Gujarat + 2% Relli"                     "0.0157"
     [52,] "97.4% Brahmin_Gujarat + 2.6% Uttar_Pradesh"         "0.0157"
     [53,] "99.4% Brahmin_Gujarat + 0.6% Buryat"                "0.0157"
     [54,] "2.4% Bengali_Bangladesh + 97.6% Brahmin_Gujarat"    "0.0157"
     [55,] "99.5% Brahmin_Gujarat + 0.5% Evenk"                 "0.0157"
     [56,] "99.4% Brahmin_Gujarat + 0.6% Todzin"                "0.0157"
     [57,] "98.3% Brahmin_Gujarat + 1.7% Hakkipikki"            "0.0157"
     [58,] "99.4% Brahmin_Gujarat + 0.6% Tuvinian"              "0.0157"
     [59,] "99.4% Brahmin_Gujarat + 0.6% Kalmyk"                "0.0157"
     [60,] "99.4% Brahmin_Gujarat + 0.6% Malay"                 "0.0157"
     [61,] "99.5% Brahmin_Gujarat + 0.5% Lebbo"                 "0.0157"
     [62,] "99.4% Brahmin_Gujarat + 0.6% Mogush"                "0.0157"
     [63,] "99.5% Brahmin_Gujarat + 0.5% Vizayan"               "0.0157"
     [64,] "99.5% Brahmin_Gujarat + 0.5% Indonesian_Java"       "0.0157"
     [65,] "94.2% Brahmin_Gujarat + 5.8% Brahmin_West_Bengal"   "0.0157"
     [66,] "46.2% Gujar_Pakistan + 53.8% Kshatriya"             "0.0157"
     [67,] "99.4% Brahmin_Gujarat + 0.6% Mongolian"             "0.0157"
     [68,] "99.5% Brahmin_Gujarat + 0.5% Cambodian"             "0.0157"
     [69,] "99.5% Brahmin_Gujarat + 0.5% Mongola"               "0.0157"
     [70,] "99.5% Brahmin_Gujarat + 0.5% Indonesian_Bali"       "0.0157"
     [71,] "99.4% Brahmin_Gujarat + 0.6% Greenlander_East"      "0.0157"
     [72,] "31.4% Dusadh + 68.6% Punjabi_Jatt"                  "0.0157"
     [73,] "97.9% Brahmin_Gujarat + 2.1% Piramalai"             "0.0157"
     [74,] "99.3% Brahmin_Gujarat + 0.7% Kirghiz"               "0.0157"
     [75,] "99.5% Brahmin_Gujarat + 0.5% Kinh_Vietnam"          "0.0157"
     [76,] "99.5% Brahmin_Gujarat + 0.5% Htin_Mal"              "0.0157"
     [77,] "99.5% Brahmin_Gujarat + 0.5% Thai"                  "0.0157"
     [78,] "99.5% Brahmin_Gujarat + 0.5% Japanese"              "0.0157"
     [79,] "98.2% Brahmin_Gujarat + 1.8% Chenchu"               "0.0157"
     [80,] "99.5% Brahmin_Gujarat + 0.5% Xibo"                  "0.0157"
     [81,] "99.6% Brahmin_Gujarat + 0.4% Korean"                "0.0157"
     [82,] "0.6% Altaian + 99.4% Brahmin_Gujarat"               "0.0157"
     [83,] "99.5% Brahmin_Gujarat + 0.5% Mlabri"                "0.0157"
     [84,] "99.3% Brahmin_Gujarat + 0.7% Greenlander_West"      "0.0157"
     [85,] "99.6% Brahmin_Gujarat + 0.4% Han"                   "0.0157"
     [86,] "99.6% Brahmin_Gujarat + 0.4% Dai"                   "0.0157"
     [87,] "99.5% Brahmin_Gujarat + 0.5% Lahu"                  "0.0157"
     [88,] "99.6% Brahmin_Gujarat + 0.4% She"                   "0.0157"
     [89,] "99.6% Brahmin_Gujarat + 0.4% Tujia"                 "0.0157"
     [90,] "99.6% Brahmin_Gujarat + 0.4% Hawaiian"              "0.0157"
     [91,] "99.6% Brahmin_Gujarat + 0.4% Miao"                  "0.0157"
     [92,] "99.4% Brahmin_Gujarat + 0.6% Khakass"               "0.0157"
     [93,] "99.5% Brahmin_Gujarat + 0.5% Dungan"                "0.0157"
     [94,] "99.3% Brahmin_Gujarat + 0.7% Yukagir_Forest"        "0.0157"
     [95,] "99.5% Brahmin_Gujarat + 0.5% Dolgan"                "0.0157"
     [96,] "99.3% Brahmin_Gujarat + 0.7% Juang"                 "0.0157"
     [97,] "99.3% Brahmin_Gujarat + 0.7% Tharu"                 "0.0157"
     [98,] "99.3% Brahmin_Gujarat + 0.7% Shor_Khakassia"        "0.0157"
     [99,] "0.7% Bonda + 99.3% Brahmin_Gujarat"                 "0.0157"
    [100,] "99.6% Brahmin_Gujarat + 0.4% Han_NChina"            "0.0157"
    Ancient-
    Code:
    [1,] "55.3% IND_Roopkund_A + 44.7% PAK_Barikot_IA"                            "0.0191"
      [2,] "34.2% IND_Roopkund_A + 65.8% PAK_Saidu_Sharif_H"                        "0.0198"
      [3,] "46.4% IND_Roopkund_A + 53.6% PAK_Loebanr_IA"                            "0.0212"
      [4,] "46.5% IND_Roopkund_A + 53.5% PAK_Barikot_H"                             "0.0212"
      [5,] "49.5% IND_Roopkund_A + 50.5% PAK_Udegram_IA"                            "0.0213"
      [6,] "78.3% PAK_Saidu_Sharif_H + 21.7% PAK_Saidu_Sharif_H_o"                  "0.0215"
      [7,] "69.3% IND_Roopkund_A + 30.7% UZB_Bustan_BA_o1"                          "0.0215"
      [8,] "44.7% IND_Roopkund_A + 55.3% PAK_Katelai_IA"                            "0.0217"
      [9,] "44.8% IND_Roopkund_A + 55.2% PAK_Gogdara_IA"                            "0.0221"
     [10,] "59.5% PAK_Barikot_IA + 40.5% PAK_Saidu_Sharif_H_o"                      "0.0225"
     [11,] "72.7% IND_Roopkund_A + 27.3% TKM_Namazga_Tepe_En_o"                     "0.0226"
     [12,] "74.9% IND_Roopkund_A + 25.1% TKM_Parkhai_LBA_o"                         "0.0226"
     [13,] "62.8% IND_Roopkund_A + 37.2% PAK_Udegram_MA_Ghaznavid"                  "0.0227"
     [14,] "67.5% IND_Roopkund_A + 32.5% PAK_Loebanr_IA_o"                          "0.0229"
     [15,] "29.6% IND_Roopkund_A + 70.4% PAK_Aligrama_H"                            "0.0231"
     [16,] "62.9% IND_Roopkund_A + 37.1% PAK_Singoor_MA"                            "0.0241"
     [17,] "68.1% PAK_Barikot_H + 31.9% PAK_Saidu_Sharif_H_o"                       "0.0245"
     [18,] "51.1% IND_Roopkund_A + 48.9% PAK_Butkara_IA"                            "0.0245"
     [19,] "47.9% PAK_Saidu_Sharif_H_o + 52.1% PAK_Udegram_MA_Ghaznavid"            "0.0246"
     [20,] "76.4% IND_Roopkund_A + 23.6% TKM_IA"                                    "0.0249"
     [21,] "68.3% PAK_Loebanr_IA + 31.7% PAK_Saidu_Sharif_H_o"                      "0.025" 
     [22,] "82.5% PAK_Aligrama_H + 17.5% PAK_Saidu_Sharif_H_o"                      "0.0251"
     [23,] "34.6% PAK_Saidu_Sharif_H_o + 65.4% PAK_Udegram_IA"                      "0.0252"
     [24,] "69.9% PAK_Katelai_IA + 30.1% PAK_Saidu_Sharif_H_o"                      "0.0254"
     [25,] "69.8% PAK_Gogdara_IA + 30.2% PAK_Saidu_Sharif_H_o"                      "0.0255"
     [26,] "29% IND_Roopkund_A + 71% PAK_Barikot_MA"                                "0.026" 
     [27,] "46.9% PAK_Loebanr_IA_o + 53.1% PAK_Saidu_Sharif_H_o"                    "0.0261"
     [28,] "73.7% IND_Roopkund_A + 26.3% TJK_Ksirov_H_Kushan"                       "0.027" 
     [29,] "74% IND_Roopkund_A + 26% UZB_Dzharkutan1_BA"                            "0.0271"
     [30,] "75% IND_Roopkund_A + 25% TKM_Geoksyur_En"                               "0.0271"
     [31,] "71.6% IND_Roopkund_A + 28.4% KGZ_Aigyrzhal_BA"                          "0.0272"
     [32,] "76% IND_Roopkund_A + 24% Saka_Tian_Shan_o"                              "0.0273"
     [33,] "75.1% IND_Roopkund_A + 24.9% TKM_Namazga_Tepe_En"                       "0.0273"
     [34,] "74.4% IND_Roopkund_A + 25.6% TKM_Gonur1_BA"                             "0.0274"
     [35,] "76.3% IND_Roopkund_A + 23.7% RUS_Steppe_Maykop_o"                       "0.0275"
     [36,] "80.9% IRN_Shahr_I_Sokhta_BA2 + 19.1% RUS_Chalmny-Varre"                 "0.0276"
     [37,] "74.1% IND_Roopkund_A + 25.9% TJK_Sarazm_En"                             "0.0276"
     [38,] "83.2% PAK_Barikot_MA + 16.8% PAK_Saidu_Sharif_H_o"                      "0.0277"
     [39,] "79.2% IND_Roopkund_A + 20.8% IRN_Hajji_Firuz_BA"                        "0.0279"
     [40,] "78.8% IRN_Shahr_I_Sokhta_BA2 + 21.2% RUS_Mezhovskaya"                   "0.0279"
     [41,] "37.7% IND_Roopkund_A + 62.3% UZB_Bustan_BA_o2"                          "0.028" 
     [42,] "17% Baltic_EST_IA + 83% IRN_Shahr_I_Sokhta_BA2"                         "0.0283"
     [43,] "78.9% IND_Roopkund_A + 21.1% RUS_Kubano-Tersk_Late"                     "0.0285"
     [44,] "47.9% PAK_Saidu_Sharif_H_o + 52.1% PAK_Singoor_MA"                      "0.0285"
     [45,] "74.2% IND_Roopkund_A + 25.8% UZB_Bustan_BA"                             "0.0285"
     [46,] "79% IND_Roopkund_A + 21% RUS_Vonyuchka_En"                              "0.0286"
     [47,] "59.5% PAK_Saidu_Sharif_H_o + 40.5% TKM_Namazga_Tepe_En_o"               "0.0287"
     [48,] "78.6% IND_Roopkund_A + 21.4% Yamnaya_UKR_Ozera_o"                       "0.0288"
     [49,] "28.2% IND_Roopkund_A + 71.8% PAK_Butkara_H"                             "0.0289"
     [50,] "16.8% Baltic_EST_MA + 83.2% IRN_Shahr_I_Sokhta_BA2"                     "0.0289"
     [51,] "64.4% PAK_Butkara_IA + 35.6% PAK_Saidu_Sharif_H_o"                      "0.029" 
     [52,] "83.4% IRN_Shahr_I_Sokhta_BA2 + 16.6% RUS_Ingria_IA"                     "0.029" 
     [53,] "77.8% IRN_Shahr_I_Sokhta_BA2 + 22.2% RUS_Tagar"                         "0.029" 
     [54,] "16.9% FIN_Levanluhta_IA_o + 83.1% IRN_Shahr_I_Sokhta_BA2"               "0.0292"
     [55,] "76.3% IND_Roopkund_A + 23.7% UZB_Sappali_Tepe_BA_o"                     "0.0292"
     [56,] "82.7% IRN_Shahr_I_Sokhta_BA2 + 17.3% UKR_Dereivka_I_En2"                "0.0293"
     [57,] "76.5% IND_Roopkund_A + 23.5% TKM_Parkhai_EBA"                           "0.0294"
     [58,] "75.5% IND_Roopkund_A + 24.5% UZB_Sappali_Tepe_BA"                       "0.0295"
     [59,] "77.6% IND_Roopkund_A + 22.4% TKM_Parkhai_LBA"                           "0.0295"
     [60,] "73.1% IND_Roopkund_A + 26.9% TKM_Gonur1_BA_o"                           "0.0295"
     [61,] "82.6% IRN_Shahr_I_Sokhta_BA2 + 17.4% RUS_Sunghir_MA"                    "0.0295"
     [62,] "55.5% PAK_Saidu_Sharif_H_o + 44.5% UZB_Bustan_BA_o1"                    "0.0297"
     [63,] "20.3% ARM_LBA + 79.7% IND_Roopkund_A"                                   "0.0298"
     [64,] "80.1% IRN_Shahr_I_Sokhta_BA2 + 19.9% KAZ_Zevakinskiy_BA"                "0.0298"
     [65,] "80.3% IND_Roopkund_A + 19.7% Kura-Araxes_RUS_Velikent"                  "0.0298"
     [66,] "81.1% IRN_Shahr_I_Sokhta_BA2 + 18.9% KAZ_Satan_MLBA_Alakul"             "0.03"  
     [67,] "17.1% HUN_Avar_Szolad + 82.9% IRN_Shahr_I_Sokhta_BA2"                   "0.03"  
     [68,] "56.5% IND_Roopkund_A + 43.5% PAK_Arkotkila_IA"                          "0.0301"
     [69,] "82.5% IRN_Shahr_I_Sokhta_BA2 + 17.5% SWE_IA"                            "0.0301"
     [70,] "20% ARM_MBA + 80% IND_Roopkund_A"                                       "0.0302"
     [71,] "82.8% IRN_Shahr_I_Sokhta_BA2 + 17.2% SWE_Viking_Age_Sigtuna"            "0.0302"
     [72,] "75.6% IND_Roopkund_A + 24.4% Saka_Kazakh_steppe_o1"                     "0.0302"
     [73,] "15.8% Baltic_LTU_Late_Antiquity_low_res + 84.2% IRN_Shahr_I_Sokhta_BA2" "0.0303"
     [74,] "78.5% IRN_Shahr_I_Sokhta_BA2 + 21.5% RUS_Priobrazhenka_LBA"             "0.0303"
     [75,] "83.3% IRN_Shahr_I_Sokhta_BA2 + 16.7% KAZ_Golden_Horde_Euro"             "0.0304"
     [76,] "18.1% Corded_Ware_Baltic + 81.9% IRN_Shahr_I_Sokhta_BA2"                "0.0304"
     [77,] "76.2% IRN_Shahr_I_Sokhta_BA2 + 23.8% KAZ_Chanchar2_LBA"                 "0.0304"
     [78,] "18.9% FIN_Levanluhta_IA + 81.1% IRN_Shahr_I_Sokhta_BA2"                 "0.0305"
     [79,] "81.5% IRN_Shahr_I_Sokhta_BA2 + 18.5% KAZ_Lisakovskiy_MLBA_Alakul"       "0.0305"
     [80,] "77.6% IND_Roopkund_A + 22.4% TKM_Parkhai_MBA"                           "0.0306"
     [81,] "76.4% IND_Roopkund_A + 23.6% TKM_Tepe_Anau_En"                          "0.0307"
     [82,] "26.8% IRN_Shahr_I_Sokhta_BA2 + 73.2% PAK_Saidu_Sharif_H"                "0.0308"
     [83,] "16.3% Baltic_LTU_BA + 83.7% IRN_Shahr_I_Sokhta_BA2"                     "0.0309"
     [84,] "79.8% IRN_Shahr_I_Sokhta_BA2 + 20.2% Scythian_UKR"                      "0.0309"
     [85,] "78% IND_Roopkund_A + 22% TKM_Sumbar_LBA"                                "0.0309"
     [86,] "73.1% IND_Roopkund_A + 26.9% IRN_Shahr_I_Sokhta_BA1"                    "0.031" 
     [87,] "82.1% IRN_Shahr_I_Sokhta_BA2 + 17.9% POL_Unetice_EBA"                   "0.031" 
     [88,] "80.8% IRN_Shahr_I_Sokhta_BA2 + 19.2% RUS_Krasnoyarsk_MLBA"              "0.031" 
     [89,] "17.2% Bell_Beaker_CZE_o + 82.8% IRN_Shahr_I_Sokhta_BA2"                 "0.031" 
     [90,] "77.6% IND_Roopkund_A + 22.4% IRN_Tepe_Hissar_C"                         "0.031" 
     [91,] "17.7% Corded_Ware_POL + 82.3% IRN_Shahr_I_Sokhta_BA2"                   "0.0311"
     [92,] "78.9% IND_Roopkund_A + 21.1% KAZ_Katon_Karagay_LBA"                     "0.0311"
     [93,] "80.6% IRN_Shahr_I_Sokhta_BA2 + 19.4% KAZ_Oy_Dzhaylau_MLBA"              "0.0311"
     [94,] "79.5% IRN_Shahr_I_Sokhta_BA2 + 20.5% KAZ_Dali_MLBA"                     "0.0311"
     [95,] "21.4% Gepidian_SRB_ACD + 78.6% IRN_Shahr_I_Sokhta_BA2"                  "0.0312"
     [96,] "15.7% Baltic_EST_BA + 84.3% IRN_Shahr_I_Sokhta_BA2"                     "0.0312"
     [97,] "83.1% IRN_Shahr_I_Sokhta_BA2 + 16.9% USA_colonial_period"               "0.0313"
     [98,] "76.9% IND_Roopkund_A + 23.1% TKM_Gonur3_BA"                             "0.0313"
     [99,] "17.8% Bell_Beaker_Scotland + 82.2% IRN_Shahr_I_Sokhta_BA2"              "0.0313"
    [100,] "15.6% Baltic_LVA_BA + 84.4% IRN_Shahr_I_Sokhta_BA2"                     "0.0314"

  19. The Following 4 Users Say Thank You to agent_lime For This Useful Post:

     Jatt1 (12-07-2020),  Kirtan24 (12-07-2020),  pegasus (12-07-2020),  ThaYamamoto (12-07-2020)

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