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Thread: Global25 automated nMonte for South/Central Asian members

  1. #8841
    Gold Class Member
    Posts
    4,512
    Location
    Shangri La

    Afghanistan Jammu and Kashmir United States of America Canada
    Here are more updated models with Tarkhan (8 samples) and Punjabi Jat with near perfect tail models with low standard errors. Interestingly for both, 8728 is more than sufficient for their IVC/InPe ancestry because with the Tarkhan, sources which included 1459,1456 cause the model to have very low p values. The InPe source for both populations is very similar. It seems there was considerable homogenization of local populations due to depopulation prior to the arrival of Vedic Indo Aryans from southern Central Asia. Also , even though Kangju have the wrong uniparental markers and are of another time , they are very similar to IA populations like TKM IA. So they are excellent proxies till actual L657/Z2123 Indo Aryan samples from Painted Ware sites are found.






    Tarkhans are more noteworthy since they can be applied to a number of related populations, in particular Brahmins.

    sample: :Tarkhan_Avg
    distance: 1.2816
    CG_IVCp: 59
    KAZ_Kangju: 41

    Tarkhan
    SIS2_I8728
    KAZ_Kangju

    tail: 0.948897137

    coeffs: 0.588 0.412

    ( SIS2_8728 58.8% , Kangju 41.2%)

     
    Code:
    left pops:
    Tarkhan
    SIS2_I8728
    KAZ_Kangju
     
    right pops:
    Cameroon_SMA.DG
    ARM_EBA
    CHG
    Corded_Ware_Baltic_early
    KAZ_Botai
    KAZ_MLBA_OyDzhaylau
    Onge
    PAK_H_SaiduSharif_o
    SRB_Iron_Gates_HG
    TUR_Barcin_N
    Yamnaya_RUS_Kalmykia
     
    codimension 1
    f4info: 
    f4rank: 1 dof:      9 chisq:     3.348 tail:          0.948897137 dofdiff:     11 chisqdiff:    -3.348 taildiff:                    1
    B:
              scale     1.000 
            ARM_EBA     0.744 
                CHG     0.520 
    Corded_Ware_Baltic_early     1.000 
          KAZ_Botai     0.790 
    KAZ_MLBA_OyDzhaylau     1.023 
               Onge    -0.969 
    PAK_H_SaiduSharif_o    -1.145 
    SRB_Iron_Gates_HG     1.250 
       TUR_Barcin_N     1.253 
    Yamnaya_RUS_Kalmykia     1.059 
    A:
              scale   138.365 
         SIS2_I8728    -0.812 
         KAZ_Kangju     1.158 
     
     
    full rank
    f4info: 
    f4rank: 2 dof:      0 chisq:     0.000 tail:                    1 dofdiff:      9 chisqdiff:     3.348 taildiff:          0.948897137
    B:
              scale   166.130   119.899 
            ARM_EBA    -0.660     0.772 
                CHG    -0.583     0.505 
    Corded_Ware_Baltic_early    -0.974     1.009 
          KAZ_Botai    -0.787     0.790 
    KAZ_MLBA_OyDzhaylau    -0.966     1.050 
               Onge     0.789    -1.018 
    PAK_H_SaiduSharif_o     1.450    -1.027 
    SRB_Iron_Gates_HG    -1.240     1.250 
       TUR_Barcin_N    -1.138     1.289 
    Yamnaya_RUS_Kalmykia    -1.081     1.044 
    A:
              scale     1.414     1.414 
         SIS2_I8728     1.414     0.000 
         KAZ_Kangju     0.000     1.414 
     
     
    best coefficients:     0.587917863     0.412082137 
    totmean:      0.588     0.412 
    boot mean:     0.588     0.412 
          std. errors:     0.029317675     0.029317675 
     
    error covariance (* 1,000,000)
           860       -860 
          -860        860 
    hires: 588346 411654        85953     -85953      85953 
    [mean *1.0e6, var *1.0e8]
     
     
    summ: Tarkhan    2      0.948897     0.588345511     0.411654489        860       -860        860 
     
        fixed pat  wt  dof     chisq       tail prob
               00  0     9     3.348        0.948897     0.588     0.412 
               01  1    10   112.626     1.57449e-19     1.000     0.000 
               10  1    10   370.376               0     0.000     1.000 
    best pat:           00         0.948897              -  -
    best pat:           01      1.57449e-19  chi(nested):   109.278 p-value for nested model:     1.41018e-25
     
    coeffs:     0.588     0.412



    Punjabi_Jat
    SIS2_I8728
    KAZ_Kangju

    tail: 0.984954153

    coeffs: 0.445 0.555

    (SIS2_8728 44.5%, Kangju 55.5%)

     
    Code:
    left pops:
    Punjabi_Jat
    SIS2_I8728
    KAZ_Kangju
     
    right pops:
    Cameroon_SMA.DG
    Corded_Ware_Baltic_early
    RUS_Srubnaya_MLBA_Alakul
    TKM_IA
    IRN_ShahrISokhta_BA2
    Alalakh_MLBA_o
    ARM_EBA
    Onge
    PAK_H_SaiduSharif_o
    Yamnaya_RUS_Kalmykia
    CHG
    KAZ_Botai
     
    codimension 1
    f4info: 
    f4rank: 1 dof:     10 chisq:     2.839 tail:          0.984954153 dofdiff:     12 chisqdiff:    -2.839 taildiff:                    1
    B:
              scale     1.000 
    Corded_Ware_Baltic_early     1.132 
    RUS_Srubnaya_MLBA_Alakul     1.436 
             TKM_IA     1.053 
    IRN_ShahrISokhta_BA2    -0.924 
     Alalakh_MLBA_o     0.290 
            ARM_EBA     0.822 
               Onge    -0.934 
    PAK_H_SaiduSharif_o    -1.228 
    Yamnaya_RUS_Kalmykia     1.156 
                CHG     0.653 
          KAZ_Botai     0.888 
    A:
              scale   141.235 
         SIS2_I8728    -1.103 
         KAZ_Kangju     0.885 
     
     
    full rank
    f4info: 
    f4rank: 2 dof:      0 chisq:     0.000 tail:                    1 dofdiff:     10 chisqdiff:     2.839 taildiff:          0.984954153
    B:
              scale   135.017   148.226 
    Corded_Ware_Baltic_early    -1.038     1.185 
    RUS_Srubnaya_MLBA_Alakul    -1.380     1.458 
             TKM_IA    -0.933     1.135 
    IRN_ShahrISokhta_BA2     1.239    -0.629 
     Alalakh_MLBA_o    -0.203     0.358 
            ARM_EBA    -0.682     0.929 
               Onge     1.035    -0.809 
    PAK_H_SaiduSharif_o     1.447    -0.988 
    Yamnaya_RUS_Kalmykia    -0.984     1.308 
                CHG    -0.506     0.805 
          KAZ_Botai    -0.848     0.897 
    A:
              scale     1.414     1.414 
         SIS2_I8728     1.414     0.000 
         KAZ_Kangju     0.000     1.414 
     
     
    best coefficients:     0.444948372     0.555051628 
    totmean:      0.445     0.555 
    boot mean:     0.445     0.555 
          std. errors:     0.030064385     0.030064385 
     
    error covariance (* 1,000,000)
           904       -904 
          -904        904 
    hires: 445170 554830        90387     -90387      90387 
    [mean *1.0e6, var *1.0e8]
     
     
    summ: Punjabi_Jat    2      0.984954     0.445170446     0.554829554        904       -904        904 
     
        fixed pat  wt  dof     chisq       tail prob
               00  0    10     2.839        0.984954     0.445     0.555 
               01  1    11   199.087               0     1.000     0.000 
               10  1    11   203.535               0     0.000     1.000 
    best pat:           00         0.984954              -  -
    best pat:           01      1.15027e-36  chi(nested):   196.248 p-value for nested model:     1.37608e-44
     
    coeffs:     0.445     0.555

    A very important thing to note both populations prefer Kangju over Steppe MLBA in the right pops.


    To play devils advocate here is a Punjab Jat model with SIS2 (8728,1456,1466,8726) and Srubnaya Alakul, it fails catastrophically.

    Punjabi_Jat
    IRN_ShahrISokhta_BA2
    RUS_Srubnaya_MLBA_Alakul

    tail: 0.000141973447
    coeffs: 0.669 0.331

     
    Code:
    left pops:
    Punjabi_Jat
    IRN_ShahrISokhta_BA2
    RUS_Srubnaya_MLBA_Alakul
     
    right pops:
    Cameroon_SMA.DG
    Corded_Ware_Baltic_early
    KAZ_Botai
    KGZ_Aygirdjal_BA
    Nepal_Chokhopani
    RUS_Karelia_HG
    PAK_H_SaiduSharif_o
     
    codimension 1
    f4info: 
    f4rank: 1 dof:      5 chisq:    24.958 tail:       0.000141973447 dofdiff:      7 chisqdiff:   -24.958 taildiff:                    1
    B:
              scale     1.000 
    Corded_Ware_Baltic_early     1.378 
          KAZ_Botai     1.082 
    KGZ_Aygirdjal_BA     0.429 
    Nepal_Chokhopani    -0.241 
     RUS_Karelia_HG     1.531 
    PAK_H_SaiduSharif_o    -0.586 
    A:
              scale   101.356 
    IRN_ShahrISokhta_BA2    -0.627 
    RUS_Srubnaya_MLBA_Alakul     1.268 
     
     
    full rank
    f4info: 
    f4rank: 2 dof:      0 chisq:     0.000 tail:                    1 dofdiff:      5 chisqdiff:    24.958 taildiff:       0.000141973447
    B:
              scale   179.459    73.544 
    Corded_Ware_Baltic_early    -1.205     1.358 
          KAZ_Botai    -1.122     1.016 
    KGZ_Aygirdjal_BA     0.318     0.648 
    Nepal_Chokhopani     0.120    -0.264 
     RUS_Karelia_HG    -1.064     1.595 
    PAK_H_SaiduSharif_o     1.428    -0.300 
    A:
              scale     1.414     1.414 
    IRN_ShahrISokhta_BA2     1.414     0.000 
    RUS_Srubnaya_MLBA_Alakul     0.000     1.414 
     
     
    best coefficients:     0.669260607     0.330739393 
    totmean:      0.669     0.331 
    boot mean:     0.669     0.331 
          std. errors:     0.032968497     0.032968497 
     
    error covariance (* 1,000,000)
          1087      -1087 
         -1087       1087 
    hires: 669461 330539       108692    -108692     108692 
    [mean *1.0e6, var *1.0e8]
    Last edited by pegasus; 07-31-2021 at 09:45 PM.

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  3. #8842
    Registered Users
    Posts
    248
    Location
    NYC
    Ethnicity
    Sindhi
    Nationality
    American

    Quote Originally Posted by karnalIroh View Post
    thankyou ! definitely variations within siblings is to be expected. mine was done on ancestry while my sibling is on 23 and me
    That would explain it. 23&me v5 doesn't have very good coverage. The Steppe/BMAC differences can probably be attributed to that



    You are both essentially slightly North-East shifted Gujars

    Karnal Sibling

    Mixed Oracles Distance
    96.2% Gujar India + 3.8% Chokhopani 2700BP 2.31
    79.6% Gujar India + 20.4% Burusho 2.62
    72.2% Gujar India + 27.8% Potohar Brahmin 2.83
    88.2% Gujar India + 11.8% Kashmiri Pandit 2.87
    98.4% Gujar India + 1.6% Kohistani 2.89
    50% Gujar India + 50% Gujar India 2.89
    98.2% Potohar Brahmin + 1.8% Chokhopani 2700BP 3.24
    94.4% Potohar Rajput + 5.6% Chokhopani 2700BP 3.31
    97.8% Potohar Brahmin + 2.2% Potohar Rajput 3.33
    99.8% Potohar Brahmin + 0.2% Gujar Pakistan 3.34


    Karnal

    Mixed Oracles Distance
    96.8% Gujar India + 3.2% Chokhopani 2700BP 1.85
    79% Gujar India + 21% Burusho 2.01
    74.4% Gujar India + 25.6% Kashmiri Pandit 2.22
    72.2% Gujar India + 27.8% Potohar Brahmin 2.29
    88.4% Gujar India + 11.6% Kohistani 2.34
    50% Gujar India + 50% Gujar India 2.38
    94.8% Potohar Rajput + 5.2% Chokhopani 2700BP 2.58
    75.4% Potohar Brahmin + 24.6% Kashmiri Pandit 2.77
    80% Potohar Brahmin + 20% Potohar Rajput 2.78
    98.8% Potohar Brahmin + 1.2% Chokhopani 2700BP 2.78

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  5. #8843
    Registered Users
    Posts
    242
    Sex
    Location
    Pind
    Y-DNA (P)
    R-Y6
    mtDNA (M)
    W

    Quote Originally Posted by pegasus View Post
    Here are more updated models with Tarkhan (8 samples) and Punjabi Jat with near perfect tail models with low standard errors. Interestingly for both, 8728 is more than sufficient for their IVC/InPe ancestry because with the Tarkhan, sources which included 1459,1456 cause the model to have very low p values. The InPe source for both populations is very similar. It seems there was considerable homogenization of local populations due to depopulation prior to the arrival of Vedic Indo Aryans from southern Central Asia. Also , even though Kangju have the wrong uniparental markers and are of another time , they are very similar to IA populations like TKM IA. So they are excellent proxies till actual L657/Z2123 Indo Aryan samples from Painted Ware sites are found.






    Tarkhans are more noteworthy since they can be applied to a number of related populations, in particular Brahmins.

    sample: :Tarkhan_Avg
    distance: 1.2816
    CG_IVCp: 59
    KAZ_Kangju: 41

    Tarkhan
    SIS2_I8728
    KAZ_Kangju

    tail: 0.948897137

    coeffs: 0.588 0.412

    ( SIS2_8728 58.8% , Kangju 41.2%)

     
    Code:
    left pops:
    Tarkhan
    SIS2_I8728
    KAZ_Kangju
     
    right pops:
    Cameroon_SMA.DG
    ARM_EBA
    CHG
    Corded_Ware_Baltic_early
    KAZ_Botai
    KAZ_MLBA_OyDzhaylau
    Onge
    PAK_H_SaiduSharif_o
    SRB_Iron_Gates_HG
    TUR_Barcin_N
    Yamnaya_RUS_Kalmykia
     
    codimension 1
    f4info: 
    f4rank: 1 dof:      9 chisq:     3.348 tail:          0.948897137 dofdiff:     11 chisqdiff:    -3.348 taildiff:                    1
    B:
              scale     1.000 
            ARM_EBA     0.744 
                CHG     0.520 
    Corded_Ware_Baltic_early     1.000 
          KAZ_Botai     0.790 
    KAZ_MLBA_OyDzhaylau     1.023 
               Onge    -0.969 
    PAK_H_SaiduSharif_o    -1.145 
    SRB_Iron_Gates_HG     1.250 
       TUR_Barcin_N     1.253 
    Yamnaya_RUS_Kalmykia     1.059 
    A:
              scale   138.365 
         SIS2_I8728    -0.812 
         KAZ_Kangju     1.158 
     
     
    full rank
    f4info: 
    f4rank: 2 dof:      0 chisq:     0.000 tail:                    1 dofdiff:      9 chisqdiff:     3.348 taildiff:          0.948897137
    B:
              scale   166.130   119.899 
            ARM_EBA    -0.660     0.772 
                CHG    -0.583     0.505 
    Corded_Ware_Baltic_early    -0.974     1.009 
          KAZ_Botai    -0.787     0.790 
    KAZ_MLBA_OyDzhaylau    -0.966     1.050 
               Onge     0.789    -1.018 
    PAK_H_SaiduSharif_o     1.450    -1.027 
    SRB_Iron_Gates_HG    -1.240     1.250 
       TUR_Barcin_N    -1.138     1.289 
    Yamnaya_RUS_Kalmykia    -1.081     1.044 
    A:
              scale     1.414     1.414 
         SIS2_I8728     1.414     0.000 
         KAZ_Kangju     0.000     1.414 
     
     
    best coefficients:     0.587917863     0.412082137 
    totmean:      0.588     0.412 
    boot mean:     0.588     0.412 
          std. errors:     0.029317675     0.029317675 
     
    error covariance (* 1,000,000)
           860       -860 
          -860        860 
    hires: 588346 411654        85953     -85953      85953 
    [mean *1.0e6, var *1.0e8]
     
     
    summ: Tarkhan    2      0.948897     0.588345511     0.411654489        860       -860        860 
     
        fixed pat  wt  dof     chisq       tail prob
               00  0     9     3.348        0.948897     0.588     0.412 
               01  1    10   112.626     1.57449e-19     1.000     0.000 
               10  1    10   370.376               0     0.000     1.000 
    best pat:           00         0.948897              -  -
    best pat:           01      1.57449e-19  chi(nested):   109.278 p-value for nested model:     1.41018e-25
     
    coeffs:     0.588     0.412



    Punjabi_Jat
    SIS2_I8728
    KAZ_Kangju

    tail: 0.984954153

    coeffs: 0.445 0.555

    (SIS2_8728 44.5%, Kangju 55.5%)

     
    Code:
    left pops:
    Punjabi_Jat
    SIS2_I8728
    KAZ_Kangju
     
    right pops:
    Cameroon_SMA.DG
    Corded_Ware_Baltic_early
    RUS_Srubnaya_MLBA_Alakul
    TKM_IA
    IRN_ShahrISokhta_BA2
    Alalakh_MLBA_o
    ARM_EBA
    Onge
    PAK_H_SaiduSharif_o
    Yamnaya_RUS_Kalmykia
    CHG
    KAZ_Botai
     
    codimension 1
    f4info: 
    f4rank: 1 dof:     10 chisq:     2.839 tail:          0.984954153 dofdiff:     12 chisqdiff:    -2.839 taildiff:                    1
    B:
              scale     1.000 
    Corded_Ware_Baltic_early     1.132 
    RUS_Srubnaya_MLBA_Alakul     1.436 
             TKM_IA     1.053 
    IRN_ShahrISokhta_BA2    -0.924 
     Alalakh_MLBA_o     0.290 
            ARM_EBA     0.822 
               Onge    -0.934 
    PAK_H_SaiduSharif_o    -1.228 
    Yamnaya_RUS_Kalmykia     1.156 
                CHG     0.653 
          KAZ_Botai     0.888 
    A:
              scale   141.235 
         SIS2_I8728    -1.103 
         KAZ_Kangju     0.885 
     
     
    full rank
    f4info: 
    f4rank: 2 dof:      0 chisq:     0.000 tail:                    1 dofdiff:     10 chisqdiff:     2.839 taildiff:          0.984954153
    B:
              scale   135.017   148.226 
    Corded_Ware_Baltic_early    -1.038     1.185 
    RUS_Srubnaya_MLBA_Alakul    -1.380     1.458 
             TKM_IA    -0.933     1.135 
    IRN_ShahrISokhta_BA2     1.239    -0.629 
     Alalakh_MLBA_o    -0.203     0.358 
            ARM_EBA    -0.682     0.929 
               Onge     1.035    -0.809 
    PAK_H_SaiduSharif_o     1.447    -0.988 
    Yamnaya_RUS_Kalmykia    -0.984     1.308 
                CHG    -0.506     0.805 
          KAZ_Botai    -0.848     0.897 
    A:
              scale     1.414     1.414 
         SIS2_I8728     1.414     0.000 
         KAZ_Kangju     0.000     1.414 
     
     
    best coefficients:     0.444948372     0.555051628 
    totmean:      0.445     0.555 
    boot mean:     0.445     0.555 
          std. errors:     0.030064385     0.030064385 
     
    error covariance (* 1,000,000)
           904       -904 
          -904        904 
    hires: 445170 554830        90387     -90387      90387 
    [mean *1.0e6, var *1.0e8]
     
     
    summ: Punjabi_Jat    2      0.984954     0.445170446     0.554829554        904       -904        904 
     
        fixed pat  wt  dof     chisq       tail prob
               00  0    10     2.839        0.984954     0.445     0.555 
               01  1    11   199.087               0     1.000     0.000 
               10  1    11   203.535               0     0.000     1.000 
    best pat:           00         0.984954              -  -
    best pat:           01      1.15027e-36  chi(nested):   196.248 p-value for nested model:     1.37608e-44
     
    coeffs:     0.445     0.555

    A very important thing to note both populations prefer Kangju over Steppe MLBA in the right pops.


    To play devils advocate here is a Punjab Jat model with SIS2 (8728,1456,1466,8726) and Srubnaya Alakul, it fails catastrophically.

    Punjabi_Jat
    IRN_ShahrISokhta_BA2
    RUS_Srubnaya_MLBA_Alakul

    tail: 0.000141973447
    coeffs: 0.669 0.331

     
    Code:
    left pops:
    Punjabi_Jat
    IRN_ShahrISokhta_BA2
    RUS_Srubnaya_MLBA_Alakul
     
    right pops:
    Cameroon_SMA.DG
    Corded_Ware_Baltic_early
    KAZ_Botai
    KGZ_Aygirdjal_BA
    Nepal_Chokhopani
    RUS_Karelia_HG
    PAK_H_SaiduSharif_o
     
    codimension 1
    f4info: 
    f4rank: 1 dof:      5 chisq:    24.958 tail:       0.000141973447 dofdiff:      7 chisqdiff:   -24.958 taildiff:                    1
    B:
              scale     1.000 
    Corded_Ware_Baltic_early     1.378 
          KAZ_Botai     1.082 
    KGZ_Aygirdjal_BA     0.429 
    Nepal_Chokhopani    -0.241 
     RUS_Karelia_HG     1.531 
    PAK_H_SaiduSharif_o    -0.586 
    A:
              scale   101.356 
    IRN_ShahrISokhta_BA2    -0.627 
    RUS_Srubnaya_MLBA_Alakul     1.268 
     
     
    full rank
    f4info: 
    f4rank: 2 dof:      0 chisq:     0.000 tail:                    1 dofdiff:      5 chisqdiff:    24.958 taildiff:       0.000141973447
    B:
              scale   179.459    73.544 
    Corded_Ware_Baltic_early    -1.205     1.358 
          KAZ_Botai    -1.122     1.016 
    KGZ_Aygirdjal_BA     0.318     0.648 
    Nepal_Chokhopani     0.120    -0.264 
     RUS_Karelia_HG    -1.064     1.595 
    PAK_H_SaiduSharif_o     1.428    -0.300 
    A:
              scale     1.414     1.414 
    IRN_ShahrISokhta_BA2     1.414     0.000 
    RUS_Srubnaya_MLBA_Alakul     0.000     1.414 
     
     
    best coefficients:     0.669260607     0.330739393 
    totmean:      0.669     0.331 
    boot mean:     0.669     0.331 
          std. errors:     0.032968497     0.032968497 
     
    error covariance (* 1,000,000)
          1087      -1087 
         -1087       1087 
    hires: 669461 330539       108692    -108692     108692 
    [mean *1.0e6, var *1.0e8]
    How do the Kangju differ from the Steppe MLBA? I'm guessing they were more admixed with WSHG?

    And do other NW Indian populations prefer Kanju as well?

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  7. #8844
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    Quote Originally Posted by desi View Post
    How do the Kangju differ from the Steppe MLBA? I'm guessing they were more admixed with WSHG?

    And do other NW Indian populations prefer Kanju as well?
    The Kangju samples aren't a single homogenous population; there's too much variability between the individuals - specifically regarding East-Eurasian/Siberian admixture, which some of them have a lot.


    Sample Fit Srubnaya MLBA Sappali Tepe BA Okunevo BA Slab Grave EIA 1
    DA226 (KAZ Kangju) 2.20 66 18 8 8
    DA229 (KAZ Kangju) 2.49 60 19 13 8
    KNT002 2.00 59 28 8 5
    DA125 (KAZ Kangju) 2.75 59 25 13 3
    DA121 (KAZ Kangju) 1.57 56 34 8 2
    Average (KAZ Kangju) 0.85 55 28 11 6
    DA206 (KAZ Kangju) 2.11 51 40 3 6
    KNT001 2.42 49 40 6 5
    KNT003 1.59 46 39 13 2
    DA123 (KAZ Kangju) 2.17 46 28 15 11

    The samples used in these qpAdm models are DA121, DA125, and DA206 - which have the least Siberian admixture

    A similar model worked great for the Kalash, only change was Ivc source was swapped from I8728 to I11456 and I11459.

    https://pastebin.com/P5Y1PKPL

    Code:
    left pops:
    Kalash.SDG
    IRN_ShahrISokhta_BA2
    KAZ_Kangju
     
    right pops:
    Cameroon_SMA.DG
    KAZ_MLBA_OyDzhaylau
    KAZ_Otyrar
    PAK_H_SaiduSharif_o
    RUS_Srubnaya_MLBA_Alakul
    SIS2_I8728
    TJK_BA_DashtiKozy
    TKM_IA
    UZB_Bustan_BA
    
    ----------------------------
    
    best coefficients:     0.397324973     0.602675027 
    totmean:      0.397     0.603 
    boot mean:     0.397     0.603 
          std. errors:     0.021988019     0.021988019 
     
    error covariance (* 1,000,000)
           483       -483 
          -483        483 
    hires: 397126 602874        48347     -48347      48347 
    [mean *1.0e6, var *1.0e8]
     
     
    summ: Kalash.SDG    2      0.844727     0.397125812     0.602874188        483       -483        483 
     
        fixed pat  wt  dof     chisq       tail prob
               00  0     7     3.409        0.844727     0.397     0.603 
               01  1     8   392.937               0     1.000     0.000 
               10  1     8   214.595               0     0.000     1.000 
    best pat:           00         0.844727              -  -
    best pat:           10      5.33368e-42  chi(nested):   211.186 p-value for nested model:      7.5705e-48
     
    coeffs:     0.397     0.603
    YDNA (P): R-Y33
    YDNA (P, maternal line): R-Y20756
    YDNA(M): E-Y6938

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  9. #8845
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    Quote Originally Posted by desi View Post
    How do the Kangju differ from the Steppe MLBA? I'm guessing they were more admixed with WSHG?

    And do other NW Indian populations prefer Kanju as well?
    They are primarily Steppe MLBA but with significant BMAC ancestry and some Baikal related ancestry.

    I think there will be variation but in the case of populations above , it looks very similar to some Kangju/TKM IA like population,but there is another group which would be more KGZ BA shifted.

    Sample Fit Srubnaya Alakul MLBA Gonur1 BA Baikal BA
    DA121 (KAZ Kangju) 1.96 61 34 5
    Average (TKM IA) 1.76 54 46 0
    DA206 (KAZ Kangju) 1.94 50 44.5 5.5


    Even for their time frame, these samples have a more LBA/early IA profile esp in context of the new Kushan samples from Uzbekistan , which look heavily admixed with Achaemenids or Parthians as well as what look like Buddhist migrants from Northern Pakistan.

    Rabat 0.650 Loebanr_IA 0.21 Hajji_Firuz_C 0.37 Georgievsky2_LBA 0.42

    https://academic.oup.com/mbe/advance...sab216/6329832
    Last edited by pegasus; 07-31-2021 at 11:12 PM.

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  11. #8846
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    The steppe ancestry for Hindu and Sikh Indians like Brahmins and Jats comes from Indo-Aryans not nomadic central Asian types like Sakhas or Kangjus who are heavily WSHG and East Asian admixed. They can't be the vectors for steppe ancestry for them. Some Muslim groups might have Turkic ancestry which can artificially and vaguely resemble these Iron Age central Asian groups.

  12. #8847
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    Quote Originally Posted by Cynic View Post
    The steppe ancestry for Hindu and Sikh Indians like Brahmins and Jats comes from Indo-Aryans not nomadic central Asian types like Sakhas or Kangjus who are heavily WSHG and East Asian admixed. They can't be the vectors for steppe ancestry for them. Some Muslim groups might have Turkic ancestry which can artificially and vaguely resemble these Iron Age central Asian groups.
    Kangjus are not Sakas ,they are Sogdians lol , and they work as proxies but if one has a basic understanding of statistics, and qpAdm, its easy to see why their profile type would be pretty close to the real McCoy. To the contrary, some other Indo Aryan variants would be significantly WSHG/KGZ BA enriched. If you use Sakas, Kushans or any other Nomadic CA group, the model fails really badly. If you bother to read my earlier post, I stated till L657/Z2123 Painted Ware samples are found these work as great proxies and given the high tail values of those modern populations, they will be very similar.
    Last edited by pegasus; 07-31-2021 at 11:58 PM.

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  14. #8848
    Quote Originally Posted by pegasus View Post
    They are primarily Steppe MLBA but with significant BMAC ancestry and some Baikal related ancestry.

    I think there will be variation but in the case of populations above , it looks very similar to some Kangju/TKM IA like population,but there is another group which would be more KGZ BA shifted.

    Sample Fit Srubnaya Alakul MLBA Gonur1 BA Baikal BA
    DA121 (KAZ Kangju) 1.96 61 34 5
    Average (TKM IA) 1.76 54 46 0
    DA206 (KAZ Kangju) 1.94 50 44.5 5.5


    Even for their time frame, these samples have a more LBA/early IA profile esp in context of the new Kushan samples from Uzbekistan , which look heavily admixed with Achaemenids or Parthians as well as what look like Buddhist migrants from Northern Pakistan.

    Rabat 0.650 Loebanr_IA 0.21 Hajji_Firuz_C 0.37 Georgievsky2_LBA 0.42

    https://academic.oup.com/mbe/advance...sab216/6329832
    Can we look forward to these Uzbek Iron Age samples being uploaded to the global 25 database?

  15. #8849
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    Quote Originally Posted by J-Live View Post
    Can we look forward to these Uzbek Iron Age samples being uploaded to the global 25 database?
    Hopefully someone messages David to upload them soon.

  16. #8850
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    Quote Originally Posted by pegasus View Post
    Kangjus are not Sakas ,they are Sogdians lol , and they work as proxies but if one has a basic understanding of statistics, and qpAdm, its easy to see why their profile type would be pretty close to the real McCoy. To the contrary, some other Indo Aryan variants would be significantly WSHG/KGZ BA enriched. If you use Sakas, Kushans or any other Nomadic CA group, the model fails really badly. If you bother to read my earlier post, I stated till L657/Z2123 Painted Ware samples are found these work as great proxies and given the high tail values of those modern populations, they will be very similar.
    I know this…I am not responding to your comment but the other guy’s. I wish I quoted him. Yes there is some diversity in those groups they’re not heterogenous so yes a few can be utilized for what your trying to do.

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