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Thread: admixtools2 TUTORIAL for WINDOWS.

  1. #51
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    1 members found this post helpful.
    I browsed through my admixtools1 pastebin and saw that I had an IA Albanian model:

    A:
    scale 1.414 1.414
    HRV_IA 1.414 0.000
    Turkey_OldHittitePeriod.SG 0.000 1.414


    best coefficients: 0.657 0.343
    totmean: 0.657 0.343
    boot mean: 0.660 0.340
    std. errors: 0.087 0.087

    error covariance (* 1,000,000)
    7504 -7504
    -7504 7504


    summ: Albanian.DG 2 0.329386 0.660 0.340 7504 -7504 7504

    fixed pat wt dof chisq tail prob
    00 0 13 14.655 0.329386 0.657 0.343
    01 1 14 28.519 0.0121328 1.000 0.000
    10 1 14 88.451 7.4372e-13 0.000 1.000
    best pat: 00 0.329386 - -
    best pat: 01 0.0121328 chi(nested): 13.864 p-value for nested model: 0.000196562

    coeffs: 0.657 0.343
    Right:

    Mbuti.DG
    Russia_Ust_Ishim.DG
    China_Tianyuan
    Goyet_Neanderthal.SG
    Russia_Sunghir3.SG
    Russia_Kostenki14.SG
    Russia_MA1_HG.SG
    SATP
    Russia_DevilsCave_N.SG
    Papuan.DG
    Switzerland_Bichon.SG
    Greece_Peloponnese_N
    Balkan_HG
    Tarim_EMBA1
    ONG.SG
    edit: p-value being 0.329386 if you missed it (32.9%).

  2. #52
    Regular Member Archetype0ne's Avatar
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    Quote Originally Posted by eupator View Post
    I browsed through my admixtools1 pastebin and saw that I had an IA Albanian model:



    Right:



    edit: p-value being 0.329386 if you missed it (32.9%).
    I was trying to somehow have a 3 way model with both a Slavic as well as Imperial Roman eastern shift.
    For Slavic I tried everything I could think of, Migration Period CZE Early Slav, modern Polish, Avar Slavic Outlier, Sarmatian, Early Sarmatian Russia etc... nothing works. For the eastern shift it was easier but I had to dig back in time, as the Roman Era C4 cluster is too similar to the other proxies and causes the model to fail. So I settled with Israel IA. But for Slavic nothing works but the Ingria IA. Problem is Ingria IA... is Finno Urgic... not Slavic.
    As for the base tried Mycenaean, HRV EBA-MBA, POP_CA, and found that the Maros L283 was good enough(not rly, .1se but as a 3 way model does the job well enough).
    “Man cannot live without a permanent trust in something indestructible in himself, and at the same time that indestructible something as well as his trust in it may remain permanently concealed from him.”

    Franz Kafka

  3. #53
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    Quote Originally Posted by Archetype0ne View Post
    I was trying to somehow have a 3 way model with both a Slavic as well as Imperial Roman eastern shift.
    For Slavic I tried everything I could think of, Migration Period CZE Early Slav, modern Polish, Avar Slavic Outlier, Sarmatian, Early Sarmatian Russia etc... nothing works. For the eastern shift it was easier but I had to dig back in time, as the Roman Era C4 cluster is too similar to the other proxies and causes the model to fail. So I settled with Israel IA. But for Slavic nothing works but the Ingria IA. Problem is Ingria IA... is Finno Urgic... not Slavic.
    As for the base tried Mycenaean, HRV EBA-MBA, POP_CA, and found that the Maros L283 was good enough(not rly, .1se but as a 3 way model does the job well enough).

    It's hard to find a medieval Slavic incursion sample to include in these Balkan models, you see Polish.DG works with my initial GREEKALPOP sample run, but that's a modern and it's different kettle of fish so to speak.

    For my IA Albanian model, you can see there is room for improvement, especially with the s.e. that are high, those published papers aim for 5% and below, so it's up to you to refine it.

    I wrote up this tutorial for admixtools2 for windows, so more people pick it up and work with it, because I don't have the time or patience to bang my head against the wall (hehe), I did some runs for myself and my folks, and some requests for friends (Armenians and Georgians) and called it mostly quits.

    I am very interested to see what people come up with, the more the better, I don't have any predispositions.

  4. #54
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    1 members found this post helpful.
    I am out of upvotes, and found myself many times trying to upvote you mate. Cheers.

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    Can you estimate the Ancient Greek ancestry of Sicilians using LBA Sicilian samples? Because I have failed doing it with G25. Try throwing Etruscans and Daunians because Iron Age Campanians seem intermediate between Daunians and Campanians.

  6. #56
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    Quote Originally Posted by ihype02 View Post
    Can you estimate the Ancient Greek ancestry of Sicilians using LBA Sicilian samples? Because I have failed doing it with G25. Try throwing Etruscans and Daunians because Iron Age Campanians seem intermediate between Daunians and Campanians.
    ihype02: Here are some models for me using Dodecad12b. the first model is Jovialis K8 model which uses Minoan as the major source of ancestry to capture EEF related ancestry in modern Southern Italians (which is the major ancient ancestral source of modern Italians across the board, not only Southern mainland Italians/Sicilians). Obviously, the Yamnaya captures Steppe ancestry (Fernandes et al 2020 documented Steppe ancestry entering Sicily in the Bronze age), I get another Neolithic source from Central Italy (Antonio et al 2019 paper). The Iberomaurasian captures North African/Levant related ancestry which given the Pheonician/Carthagian period, Imperial Rome and in Sicily the Saracen period is part of modern Sicilian genetics at about 4.6% on average (Sazzini et al 2016).

    So as I have said, I think Jovialis K8 model has support in the genetics literature on Southern Italy (Aneli et al 2022 paper published 17 January 2022 "The Genetic Origin of Daunians and the Pan-Mediterranean Southern Italian Iron Age Context") and Raveane et al 2022 "Assessing temporal and geographic contacts across the Adriatic Sea through the analysis of genome-wide data from Southern Italy." So as you can see, Jovialis K8 model fits me quite well. Now, the 2nd two models I did the following. I replaced the Minoan source populations that Jovialis used for his K8 model with 1) All the Bronze age Sicilian samples from Fernandes et al 2020. The distance is 0.7474, which is very good but not as good as with Minoans as source population. 2) Only the LBA Sicilians from Fernandes et al 2020, the distance is now 1.3. So I would start with Jovialis K8 model and then replace the Minoan with the Bronze Age Siclian samples to see how your G25 model works regarding ancient Greek DNA in modern Sicily. I would also use other Southern Italian regions as well since all them in Jovilias's K8 model can be modeled with excellent fits using Minoans as a source population.

    Cheers, hope this makes sense. PT

    Target: PalermoTrapani_ANCESTRY
    Distance: 0.4939% / 0.49385911
    71.8 Minoan
    20.7 Yamnaya
    3.8 C_Italian_N
    2.5 Iberomaurusian
    0.7 C_Italian_ChL
    0.5 Remedello


    Target: PalermoTrapani_ANCESTRY
    Distance: 0.7474% / 0.74740674
    38.5 Anatolian_BA
    19.0 Sicily_LBA_Marcita
    15.2 Yamnaya
    15.2 Sicily_MBA_Buffa_Cave_II
    11.7 Sicily_EBA_lowcov_Vallone_Inferno
    0.4 Sicily_EBA_Contrada_Paolina_Castellucciana


    Target: PalermoTrapani_ANCESTRY
    Distance: 1.3025% / 1.30245948
    45.9 Anatolian_BA
    37.2 Sicily_LBA_Marcita
    16.5 Yamnaya
    0.4 Iberomaurusian
    Last edited by Palermo Trapani; 23-07-22 at 21:51. Reason: formatting

  7. #57
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    1 members found this post helpful.
    Many thanks to Eupator for this straightforward and very useful demonstration on how to use this professional tool on Windows.
    I tried models with some modern Balkan pops(Albanians, Bulgarians, Greeks_1, Greeks_2) and these are the best models I could obtain:

    Code:
    target      left                                  weight     se     z
      <chr>       <chr>                                  <dbl>  <dbl> <dbl>
    1 Albanian.DG Bulgaria_IA                            0.793 0.0817  9.71
    2 Albanian.DG Hungary_Avar_5_daughter.or.mother.AV1  0.207 0.0817  2.53
    > results$popdrop
    # A tibble: 3 x 13
      pat      wt   dof  chisq        p f4rank Bulgari~1 Hunga~2 feasi~3 best  dofdiff
      <chr> <dbl> <dbl>  <dbl>    <dbl>  <dbl>     <dbl>   <dbl> <lgl>   <lgl>   <dbl>
    1 00        0     2  0.458 7.95e- 1      1     0.793   0.207 TRUE    NA         NA
    2 01        1     3  7.18  6.65e- 2      0     1      NA     TRUE    TRUE        0
    3 10        1     3 97.8   4.60e-21      0    NA       1     TRUE    TRUE       NA
    Code:
    target       left                                  weight     se     z
      <chr>        <chr>                                  <dbl>  <dbl> <dbl>
    1 Bulgarian.DG Bulgaria_IA                            0.479 0.0727  6.58
    2 Bulgarian.DG Hungary_Avar_5_daughter.or.mother.AV1  0.341 0.0672  5.07
    3 Bulgarian.DG Armenia_LBA.SG                         0.181 0.0725  2.49
    > results$popdrop
    # A tibble: 7 x 14
      pat      wt   dof    chisq        p f4rank Bulga~1 Hunga~2 Armen~3 feasi~4 best 
      <chr> <dbl> <dbl>    <dbl>    <dbl>  <dbl>   <dbl>   <dbl>   <dbl> <lgl>   <lgl>
    1 000       0     1   0.0349 8.52e- 1      2   0.479   0.341   0.181 TRUE    NA   
    2 001       1     2   6.13   4.68e- 2      1   0.591   0.409  NA     TRUE    TRUE 
    3 010       1     2  26.2    2.02e- 6      1   0.764  NA       0.236 TRUE    TRUE 
    4 100       1     2  50.9    8.74e-12      1  NA       0.640   0.360 TRUE    TRUE 
    5 011       2     3  34.7    1.38e- 7      0   1      NA      NA     TRUE    NA   
    6 101       2     3  67.1    1.80e-14      0  NA       1      NA     TRUE    NA   
    7 110       2     3 121.     5.08e-26      0  NA      NA       1     TRUE    NA
    Code:
    target     left           weight     se     z
      <chr>      <chr>           <dbl>  <dbl> <dbl>
    1 Greek_2.DG Greek_Emporion  0.605 0.0897  6.75
    2 Greek_2.DG Armenia_LBA.SG  0.395 0.0897  4.40
    > results$popdrop
    # A tibble: 3 x 13
      pat      wt   dof chisq        p f4rank Greek_Em~1 Armen~2 feasi~3 best  dofdiff
      <chr> <dbl> <dbl> <dbl>    <dbl>  <dbl>      <dbl>   <dbl> <lgl>   <lgl>   <dbl>
    1 00        0     2  10.2 6.06e- 3      1      0.605   0.395 TRUE    NA         NA
    2 01        1     3  40.8 7.14e- 9      0      1      NA     TRUE    TRUE        0
    3 10        1     3  72.6 1.18e-15      0     NA       1     TRUE    TRUE       NA
    Code:
    target     left                                  weight     se     z
      <chr>      <chr>                                  <dbl>  <dbl> <dbl>
    1 Greek_1.DG Bulgaria_IA                            0.501 0.0939  5.34
    2 Greek_1.DG Hungary_Avar_5_daughter.or.mother.AV1  0.267 0.0788  3.38
    3 Greek_1.DG Armenia_LBA.SG                         0.233 0.0986  2.36
    > results$popdrop
    # A tibble: 7 x 14
      pat      wt   dof  chisq        p f4rank Bulgari~1 Hunga~2 Armen~3 feasi~4 best 
      <chr> <dbl> <dbl>  <dbl>    <dbl>  <dbl>     <dbl>   <dbl>   <dbl> <lgl>   <lgl>
    1 000       0     1  0.849 3.57e- 1      2     0.501   0.267   0.233 TRUE    NA   
    2 001       1     2  9.27  9.70e- 3      1     0.675   0.325  NA     TRUE    TRUE 
    3 010       1     2 16.3   2.89e- 4      1     0.679  NA       0.321 TRUE    TRUE 
    4 100       1     2 44.4   2.27e-10      1    NA       0.473   0.527 TRUE    TRUE 
    5 011       2     3 27.6   4.48e- 6      0     1      NA      NA     TRUE    NA   
    6 101       2     3 84.9   2.67e-18      0    NA       1      NA     TRUE    NA   
    7 110       2     3 83.8   4.59e-18      0    NA      NA       1     TRUE    NA
    I know 'se' are a bit higher than 5% but it seems to me that 'p-value' is fine. Eupator what do you think?

  8. #58
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    You can probably trim the std. errors a bit more just by fine tuning the right list, but the p-values look good.

    Keep in mind that the Greek reference samples are quite diverse in both 1240 and HO (the Greek_2.DG might be a Pontic from Thessaloniki) and sometimes cannot used be as averages very well. This might apply to others in the modern lists.

  9. #59
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    1 members found this post helpful.
    Quote Originally Posted by Aspar View Post
    what do you think?

    The moderns seem to work quite easily,

    this is a run of mine using proximal neighbors as sources:

    Code:
    > results$weights
    # A tibble: 2 × 5
      target left              weight     se     z
      <chr>  <chr>              <dbl>  <dbl> <dbl>
    1 eptr    Albanian           0.510 0.0742  6.87
    2 eptr    Armenian_Hemsheni  0.490 0.0742  6.60
    > results$popdrop
    # A tibble: 3 × 13
      pat      wt   dof chisq        p f4rank Albanian Armenian_Hemsheni
      <chr> <dbl> <dbl> <dbl>    <dbl>  <dbl>    <dbl>             <dbl>
    1 00        0     9  10.1 3.40e- 1      1    0.510             0.490
    2 01        1    10 185.  2.38e-34      0    1                NA    
    
    results$weights
    # A tibble: 2 × 5
      target left              weight     se     z
      <chr>  <chr>              <dbl>  <dbl> <dbl>
    1 eptr  Bulgarian          0.423 0.0662  6.39
    2 eptr  Armenian_Hemsheni  0.577 0.0662  8.71
    > results$popdrop
    # A tibble: 3 × 13
      pat      wt   dof chisq        p f4rank Bulgarian Armenian_Hemsheni
      <chr> <dbl> <dbl> <dbl>    <dbl>  <dbl>     <dbl>             <dbl>
    1 00        0     9  16.0 6.64e- 2      1     0.423             0.577
    2 01        1    10 397.  3.67e-79      0     1                NA    
    3 10        1    10 257.  1.87e-49      0    NA                 1    
    
    results$weights
    # A tibble: 2 × 5
      target left      weight     se     z
      <chr>  <chr>      <dbl>  <dbl> <dbl>
    1 eptr  Bulgarian  0.448 0.0600  7.47
    2 eptr  Armenian   0.552 0.0600  9.20
    > results$popdrop
    # A tibble: 3 × 13
      pat      wt   dof chisq        p f4rank Bulgarian Armenian feasible
      <chr> <dbl> <dbl> <dbl>    <dbl>  <dbl>     <dbl>    <dbl> <lgl>   
    1 00        0     9  10.1 3.46e- 1      1     0.448    0.552 TRUE    
    2 01        1    10 406.  3.92e-81      0     1       NA     TRUE    
    3 10        1    10 318.  2.65e-62      0    NA        1     TRUE  
    
    results$weights
    # A tibble: 2 × 5
      target left     weight     se     z
      <chr>  <chr>     <dbl>  <dbl> <dbl>
    1 eptr  Albanian  0.590 0.0679  8.69
    2 eptr  Georgian  0.410 0.0679  6.04
    > results$popdrop
    # A tibble: 3 × 13
      pat      wt   dof chisq        p f4rank Albanian Georgian feasible
      <chr> <dbl> <dbl> <dbl>    <dbl>  <dbl>    <dbl>    <dbl> <lgl>   
    1 00        0     9  16.2 6.30e- 2      1    0.590    0.410 TRUE    
    2 01        1    10 230.  8.89e-44      0    1       NA     TRUE    
    3 10        1    10 468.  3.12e-94      0   NA        1     TRUE

    Right:
    Code:
    right = c('Mbuti.DG', 'ONG.SG', 'EHG', 'CHG', 'Balkan_HG', 'Iberomaurusian', 'WSHG', 'Mongolia_North_N', 'Turkey_Epipaleolithic', 'Iran_N', 'PPN')

  10. #60
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    Quote Originally Posted by eupator View Post
    You can probably trim the std. errors a bit more just by fine tuning the right list, but the p-values look good.

    Keep in mind that the Greek reference samples are quite diverse in both 1240 and HO (the Greek_2.DG might be a Pontic from Thessaloniki) and sometimes cannot used be as averages very well. This might apply to others in the modern lists.
    You certainly seem on point about Greek_2:

    Code:
    pop1       pop2              est      se
      <chr>      <chr>           <dbl>   <dbl>
    1 Greek_2.DG Armenian.DG -0.00374  0.00283
    2 Greek_2.DG Cretan.DG    0.00119  0.00285
    3 Greek_2.DG Georgian.DG  0.000170 0.00284
    4 Greek_2.DG Greek_1.DG  -0.00652  0.00388
    
    pop1        pop2             est      se
      <chr>       <chr>          <dbl>   <dbl>
    1 Georgian.DG Armenian.DG 0.00358  0.00174
    2 Georgian.DG Cretan.DG   0.00652  0.00175
    3 Georgian.DG Greek_1.DG  0.00608  0.00276
    4 Georgian.DG Greek_2.DG  0.000170 0.00284
    I'm not sure how to interpret the negative values but genetic distance wise Greek_2.DG is closer to Georgian.DG than to Cretan.DG. Also Greek_2.DG is closer to Georgian.DG than Armenian.DG is. Therefore I also think Greek_2 is actually of Pontic origin.
    What's interesting is that as per my model above he can be modeled as 60.5% Greek_Emporium and 39.5% Armenia_LBA. I didn't expect so much Ancient Greek ancestry compared to Armenia_LBA TBH.

    Other than that, Greek_1 seems some Northern Greek folk, perhaps from Macedonia?? Similar to Greek_2 of course which again should be a Macedonian sample but unlike it, Greek_1 seems to pack significant Slavic ancestry, again as per the model above, 67.5% Bulgaria_IA and 32.5% AV2. The model with Greek_Emporium is rejected. Greek_1 is also some 12.5% more 'Slavic' than the Albanian.DG, which can be modeled as 79.3% Bulgaria_IA and 20.7% AV2, and closer to the 'Slavic' percentages of the Bulgarian.DG, around 34.1%. It's further away from Cretan.DG than Bulgarian.DG is:
    Code:
    pop1      pop2             est      se
      <chr>     <chr>          <dbl>   <dbl>
    1 Cretan.DG Armenian.DG  0.00369 0.00168
    2 Cretan.DG Bulgarian.DG 0.00126 0.00173
    3 Cretan.DG Greek_1.DG   0.00175 0.00299
    4 Cretan.DG Greek_2.DG   0.00118 0.00285
    Code:
    pop1       pop2               est      se
      <chr>      <chr>            <dbl>   <dbl>
    1 Greek_1.DG Albanian.DG  -0.00111  0.00429
    2 Greek_1.DG Bulgarian.DG -0.000884 0.00292
    3 Greek_1.DG Cretan.DG     0.00176  0.00300
    4 Greek_1.DG Greek_2.DG   -0.00653  0.00389
    Again, I don't know how to interpret the negative values but if that means closer related than the positive ones then Greek_2.DG is closest to Greek_1.DG. I'm not sure I'm reading this correctly but why would Greek_2.DG be closer to Greek_1.DG than Albanians.DG or Bulgarian.DG are? Perhaps some elevated West Asian ancestry in Greek_1.DG which qpAdm failed to capture or perhaps Greek_2.DG isn't fully Pontic Greek but has some minor mainland Greek Macedonian ancestry?
    When I take into account Armenian.DG fst distances then there isn't anything irregular and Greek_1.DG is even further away from Armenian.DG than Bulgarian.DG is:
    Code:
    pop1        pop2              est      se
      <chr>       <chr>           <dbl>   <dbl>
    1 Armenian.DG Bulgarian.DG  0.00522 0.00168
    2 Armenian.DG Cretan.DG     0.00369 0.00168
    3 Armenian.DG Greek_1.DG    0.00697 0.00295
    4 Armenian.DG Greek_2.DG   -0.00371 0.00283
    What's interesting about Bulgarian.DG is it requires some 18.1% of Armenia_LBA on top of 47.9% Bulgaria_IA and 34.1% AV2 unlike Albanian.DG and Greek_1.DG. His closest is Greek_1.DG which is nothing surprising but I'm not sure how to explain the fact that Cretan.DG and Greek_2.DG are closer to Bulgarian.DG than Albanian.DG is?
    Code:
    pop1         pop2              est      se
      <chr>        <chr>           <dbl>   <dbl>
    1 Bulgarian.DG Albanian.DG  0.00954  0.00338
    2 Bulgarian.DG Cretan.DG    0.00125  0.00173
    3 Bulgarian.DG Greek_1.DG  -0.000884 0.00292
    4 Bulgarian.DG Greek_2.DG   0.00197  0.00282
    As for the Albanians, the model above seems realistic when taking into account their yDna. Anything above 25% Slavic admixture doesn't seem realistic to me.
    Code:
    pop1        pop2              est      se
      <chr>       <chr>           <dbl>   <dbl>
    1 Albanian.DG Bulgarian.DG  0.00954 0.00338
    2 Albanian.DG Cretan.DG     0.0105  0.00342
    3 Albanian.DG Greek_1.DG   -0.00111 0.00429
    4 Albanian.DG Greek_2.DG    0.00744 0.00433
    Again, the fact that Greek_2.DG is closer to Albanian.DG than Bulgarian.DG is, seems to tell about some other Greek ancestry in Greek_2.DG on top of the Pontic one.

  11. #61
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    1 members found this post helpful.
    AFAIK, negative FST values should be essentially considered as 0 for most intents and purposes, meaning there is no notable genetic subdivision between the populations considered.

    Edit:

    Greek_2.DG is probably mixed. Don't forget to take into account the s.e. also.

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    FST run comparison (HO dataset) of neighboring moderns to Greece_Minoan_Lassithi, and Greece_BA_Mycenaean.

    Code:
    pop1                   pop2                 est       se
       <chr>                  <chr>              <dbl>    <dbl>
     1 Greece_Minoan_Lassithi Albanian          0.0168 0.00102 
     2 Greece_Minoan_Lassithi Armenian          0.0189 0.000951
     3 Greece_Minoan_Lassithi Armenian_Hemsheni 0.0233 0.00100 
     4 Greece_Minoan_Lassithi Egyptian          0.0239 0.000836
     5 Greece_Minoan_Lassithi Greek             0.0174 0.000853
     6 Greece_Minoan_Lassithi Italian_Sardinian 0.0206 0.00165 
     7 Greece_Minoan_Lassithi Italian_South     0.0138 0.00111 
     8 Greece_Minoan_Lassithi Lebanese          0.0203 0.000945
     9 Greece_Minoan_Lassithi Syrian            0.0224 0.000937
    10 Greece_Minoan_Lassithi Turkish           0.0193 0.000833
    Code:
    pop1                pop2                  est      se
       <chr>               <chr>               <dbl>   <dbl>
     1 Greece_BA_Mycenaean Albanian          0.00675 0.00141
     2 Greece_BA_Mycenaean Armenian          0.00810 0.00132
     3 Greece_BA_Mycenaean Armenian_Hemsheni 0.0118  0.00137
     4 Greece_BA_Mycenaean Egyptian          0.0131  0.00119
     5 Greece_BA_Mycenaean Greek             0.00701 0.00123
     6 Greece_BA_Mycenaean Italian_Sardinian 0.0105  0.00199
     7 Greece_BA_Mycenaean Italian_South     0.00702 0.00148
     8 Greece_BA_Mycenaean Lebanese          0.00970 0.00130
     9 Greece_BA_Mycenaean Syrian            0.0126  0.00134
    10 Greece_BA_Mycenaean Turkish           0.00861 0.00120

  13. #63
    Regular Member Aspar's Avatar
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    Quote Originally Posted by eupator View Post
    FST run comparison (HO dataset) of neighboring moderns to Greece_Minoan_Lassithi, and Greece_BA_Mycenaean.

    Code:
    pop1                   pop2                 est       se
    <chr>                  <chr>              <dbl>    <dbl>
    1 Greece_Minoan_Lassithi Albanian          0.0168 0.00102 
    2 Greece_Minoan_Lassithi Armenian          0.0189 0.000951
    3 Greece_Minoan_Lassithi Armenian_Hemsheni 0.0233 0.00100 
    4 Greece_Minoan_Lassithi Egyptian          0.0239 0.000836
    5 Greece_Minoan_Lassithi Greek             0.0174 0.000853
    6 Greece_Minoan_Lassithi Italian_Sardinian 0.0206 0.00165 
    7 Greece_Minoan_Lassithi Italian_South     0.0138 0.00111 
    8 Greece_Minoan_Lassithi Lebanese          0.0203 0.000945
    9 Greece_Minoan_Lassithi Syrian            0.0224 0.000937
    10 Greece_Minoan_Lassithi Turkish           0.0193 0.000833
    Code:
    pop1                pop2                  est      se
    <chr>               <chr>               <dbl>   <dbl>
    1 Greece_BA_Mycenaean Albanian          0.00675 0.00141
    2 Greece_BA_Mycenaean Armenian          0.00810 0.00132
    3 Greece_BA_Mycenaean Armenian_Hemsheni 0.0118  0.00137
    4 Greece_BA_Mycenaean Egyptian          0.0131  0.00119
    5 Greece_BA_Mycenaean Greek             0.00701 0.00123
    6 Greece_BA_Mycenaean Italian_Sardinian 0.0105  0.00199
    7 Greece_BA_Mycenaean Italian_South     0.00702 0.00148
    8 Greece_BA_Mycenaean Lebanese          0.00970 0.00130
    9 Greece_BA_Mycenaean Syrian            0.0126  0.00134
    10 Greece_BA_Mycenaean Turkish           0.00861 0.00120
    So genetic distance wise the Albanians and the Greeks are closer to Myceaneans than South Italians are.

    Hmm, this is new to me because on various Eurogenes or Dodecad calculators the opposite was true.

  14. #64
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    Quote Originally Posted by Aspar View Post
    So genetic distance wise the Albanians and the Greeks are closer to Myceaneans than South Italians are.
    Hmm, this is new to me because on various Eurogenes or Dodecad calculators the opposite was true.

    If I remember correctly, a lot of the Greek samples (like a number of the GREEKGRALPOP/Greek that were used in the Lazaridis et al. (2014) nature paper) have West Asian admixture, from what source I don't know (maybe Anatolian/peninsular profile mixes like Greek-2.DG).

    I am not commenting on the validity of stuff like g25, because I am tired of this conversation.

  15. #65
    Regular Member Er Monnezza's Avatar
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    Quote Originally Posted by eupator View Post
    If I remember correctly, a lot of the Greek samples (like a number of the GREEKGRALPOP/Greek that were used in the Lazaridis et al. (2014) nature paper) have West Asian admixture, from what source I don't know (maybe Anatolian/peninsular profile mixes like Greek-2.DG).

    I am not commenting on the validity of stuff like g25, because I am tired of this conversation.
    What about the Albanians, though? Isn't it more likely that a Southern Italian is more akin to the Mycenaeans than an Albanian?

  16. #66
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    Quote Originally Posted by Er Monnezza View Post
    What about the Albanians, though? Isn't it more likely that a Southern Italian is more akin to the Mycenaeans than an Albanian?
    If you believe that the Epirotes where part mycenean ......then ok

    There were 14 epirote tribes noted by historians
    Fathers mtdna ...... T2b17
    Grandfather paternal mtdna ... T1a1e
    Sons mtdna ...... K1a4p
    Mothers line ..... R1b-S8172
    Grandmother paternal side ... I1-CTS6397
    Wife paternal line ..... R1a-PF6155

    "Fear profits man, nothing"

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    Quote Originally Posted by Er Monnezza View Post
    What about the Albanians, though? Isn't it more likely that a Southern Italian is more akin to the Mycenaeans than an Albanian?
    This thread is (mostly) a tutorial on the use of admixtools2.

    I cannot help you with your question, I am afraid, plenty of other threads online discussing g25 functionality and validity.

    It's up to you to figure out the nuances and, if kind enough, to inform us on what is being done wrong.

    Sorry, I can't be more of help.

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    FST run comparison (HO dataset) of neighboring moderns to ponticgreek (Gumusxane).

    Code:
    A tibble: 8 × 4
      pop1    pop2                      est      se
      <chr>   <chr>                   <dbl>   <dbl>
    1 ponticgreek Armenian          -0.000694   0.00286
    2 ponticgreek Armenian_Hemsheni  0.00282    0.00293
    3 ponticgreek Azeri              0.00353    0.00280
    4 ponticgreek Georgian          -0.00000605 0.00276
    5 ponticgreek Greek              0.00338    0.00281
    6 ponticgreek Kurd               0.0100     0.00288
    7 ponticgreek Ossetian           0.00428    0.00283
    8 ponticgreek Turkish            0.00102    0.00274

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    Quote Originally Posted by eupator View Post
    It's up to you to figure out the nuances and, if kind enough, to inform us on what is being done wrong.
    Here's my 2 cents: Fst is sensitive to different sample sizes and data types. Try out f3 with Mbuti or Neanderthal as an outgroup instead, it should be immune to this.

  20. #70
    Regular Member Er Monnezza's Avatar
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    1 members found this post helpful.
    Quote Originally Posted by eupator View Post
    This thread is (mostly) a tutorial on the use of admixtools2.

    I cannot help you with your question, I am afraid, plenty of other threads online discussing g25 functionality and validity.

    It's up to you to figure out the nuances and, if kind enough, to inform us on what is being done wrong.

    Sorry, I can't be more of help.
    Ok, thank you anyway.

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    Quote Originally Posted by voloh View Post
    Here's my 2 cents: Fst is sensitive to different sample sizes and data types. Try out f3 with Mbuti or Neanderthal as an outgroup instead, it should be immune to this.
    Yes, but there's no such mention of switching to f3 in the tutorial:

    ADMIXTOOLS 2 Tutorial • admixtools (uqrmaie1.github.io)

    FST

    FSTFST is closely related to f2f2, but unlike f2f2, it doesn’t function as a building block for other tools in ADMIXTOOLS 2. However, it is the most widely used metric to estimate the genetic distance between populations. Running extract_f2() will create files which don’t only contain f2f2 estimates for each population pair, but also separate FSTFST estimates. The function fst() can either read these pre-computed estimates, or compute them directly from genotype files:
    fst(my_f2_dir)
    fst(prefix, pop1 = "Altai_Neanderthal.DG", pop2 = c("Denisova.DG", "Vindija.DG"))
    To estimate FSTFST without bias, we need at least two independent observations in each population. With pseudohaploid data, we only get one independent observation per sample, and so for populations consisting of only one pseudohaploid sample, FSTFST cannot be estimated without bias. If we want to ignore that bias and get estimates anyway, we can pretend the pseudohaploid samples are actually diploid using the option adjust_pseudohaploid = FALSE.
    fst(prefix, pop1 = "Altai_Neanderthal.DG", pop2 = c("Denisova.DG", "Vindija.DG"),
    adjust_pseudohaploid = FALSE)


  22. #72
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    Quote Originally Posted by Aspar View Post
    So genetic distance wise the Albanians and the Greeks are closer to Myceaneans than South Italians are.
    Hmm, this is new to me because on various Eurogenes or Dodecad calculators the opposite was true.
    Maybe it's because Albanians and Mycenaeans both have a lot of Balkan EEF (GRC_N, ROU_N). In South Italians it's predominantly western EEF (Iberia_N). It can be seen in G25.

  23. #73
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    I think the sampling in the Reich dataset for moderns is not as super strict as in the PCA calculators where only clearly defined clusters of sub-groups exist as references with nothing in between.

    You can see that in the case of the Greek samples, there's only a couple (?) labelled as "outlier" but I haven't inquired too much into it.

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    Quote Originally Posted by eupator View Post
    Yes, but there's no such mention of switching to f3 in the tutorial:

    ADMIXTOOLS 2 Tutorial • admixtools (uqrmaie1.github.io)
    It's mentioned that f3 is used for measuring shared drift vs. an outgrouop.
    In practice this is sometimes used for generating similarity lists, similarly as f2 and fst are used. For example here the image B:
    https://www.researchgate.net/figure/...fig3_303355340 shows the similarity of various Asian populations to Ma'anyan.

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    Quote Originally Posted by voloh View Post
    It's mentioned that f3 is used for measuring shared drift vs. an outgrouop.
    In practice this is sometimes used for generating similarity lists, similarly as f2 and fst are used. For example here the image B:
    https://www.researchgate.net/figure/...fig3_303355340 shows the similarity of various Asian populations to Ma'anyan.

    OK, but this admixtools2 from October 2021, mate (your linked paper is from 2016 referring to the previous iteration).

    This app uses f2, you can read further as to why/how in the github tutorial I keep linking.


    ...

    All of this is based on f-statistics (f2f2, f3f3, and f4f4), and all f-statistics can be derived from f2f2 statistics.
    Because of this, ADMIXTOOLS 2 divides the computations into two steps:

    1. Computing f2f2-statistics and storing them on disk. This can be slow since it accesses the genotype data.
    2. Using f2f2-statistics to fit models. This is fast because f2f2-statistics are very compact compared to genotype data.

    This page shows how standard ADMIXTOOLS analyses can be conducted in ADMIXTOOLS 2. In addition to that, ADMIXTOOLS 2 introduces a range of new methods, mostly focused on admixture graphs, which are intended to make analyses simpler, faster, and most importantly, more robust. These methods focus on quantifying variability by resampling SNPs, automated exploration of graph topologies, and simulating data under admixture graphs

    ...





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