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Thread: Gedmatch calculators on Vahaduo

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    Quote Originally Posted by Komintasavalta View Post
    I don't know if Udmurts have that much steppe in absolute terms, but for example in the qpGraph model in Tambets et al. 2018, Udmurts had 30% Nganasan, 63% Corded Ware, 1% LBK, and 6% EHG, so the proportion of Corded Ware in their non-Nganasan ancestry was around 90%. The proportion of Corded Ware was 62% in Central Russians and 61% in Mordovians, but both obviously had lower Nganasan.

    Also in the G25 model below, out of the ancestry of Udmurts that is not from Krasnoyarsk_BA, about 82% is from Yamnaya (but the model doesn't have an EHG source, and maybe Udmurts have extra EHG that is not from Yamnaya):

    Target: Udmurt
    Sources: GEO_CHG IRN_Ganj_Dareh_N RUS_Krasnoyarsk_BA TUR_Barcin_N Yamnaya_RUS_Samara
    Distance: 0.045
    62% Yamnaya_RUS_Samara
    24% RUS_Krasnoyarsk_BA
    14% TUR_Barcin_N
    Other models for Udmurts in G25:

    Target: Udmurt
    Distance: 3.9071% / 0.03907110
    57.6 Yamnaya_RUS_Samara
    25.6 RUS_Krasnoyarsk_BA
    11.8 TUR_Barcin_N
    5.0 WHG


    Target: Udmurt
    Distance: 3.3185% / 0.03318480
    45.6 Yamnaya_RUS_Samara
    26.0 RUS_Krasnoyarsk_BA
    19.4 Baltic_EST_BA
    9.0 TUR_Barcin_N


    Target: Udmurt
    Distance: 3.3774% / 0.03377407
    37.2 Yamnaya_RUS_Samara
    23.6 RUS_Krasnoyarsk_BA
    21.6 RUS_Volga-Kama_N
    9.2 TUR_Barcin_N
    7.6 TUR_Tepecik_Ciftlik_N
    0.8 Han

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    Quote Originally Posted by Joqool View Post
    Other models for Udmurt:

    Target: Udmurt
    Distance: 3.9071% / 0.03907110
    57.6 Yamnaya_RUS_Samara
    25.6 RUS_Krasnoyarsk_BA
    11.8 TUR_Barcin_N
    5.0 WHG


    Target: Udmurt
    Distance: 3.3185% / 0.03318480
    45.6 Yamnaya_RUS_Samara
    26.0 RUS_Krasnoyarsk_BA
    19.4 Baltic_EST_BA
    9.0 TUR_Barcin_N
    How much Mongoloid is that Krasnoyarsk sample? It sure isn't Andoronvo since those were pretty much whites. Why not use Ymiyakhtakh Yakutia instead?

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    Quote Originally Posted by Joqool View Post
    Other models for Udmurts in G25:

    Target: Udmurt
    Distance: 3.9071% / 0.03907110
    57.6 Yamnaya_RUS_Samara
    25.6 RUS_Krasnoyarsk_BA
    11.8 TUR_Barcin_N
    5.0 WHG


    Target: Udmurt
    Distance: 3.3185% / 0.03318480
    45.6 Yamnaya_RUS_Samara
    26.0 RUS_Krasnoyarsk_BA
    19.4 Baltic_EST_BA
    9.0 TUR_Barcin_N


    Target: Udmurt
    Distance: 3.3774% / 0.03377407
    37.2 Yamnaya_RUS_Samara
    23.6 RUS_Krasnoyarsk_BA
    21.6 RUS_Volga-Kama_N
    9.2 TUR_Barcin_N
    7.6 TUR_Tepecik_Ciftlik_N
    0.8 Han
    Oh sorry, I had WHG in my sources at first, but I forgot to add it back. Here's the models I ran:



    (I use a modified version of Michał's CVXPY script, and I have shell functions for quickly selecting lines from G25 datasheets.)

    Quote Originally Posted by Leto View Post
    Why not use Ymiyakhtakh Yakutia instead?
    Krasnoyarsk_BA is closer to Nganasans. This is a PLINK PCA based on the Reich dataset, where some of the samples from KolymaRiver_LN and CentralYakutia_LN are called RUS_Yakutia_Ymyiakhtakh_LN in G25:


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    Quote Originally Posted by Leto View Post
    How much Mongoloid is that Krasnoyarsk sample? It sure isn't Andoronvo since those were pretty much whites. Why not use Ymiyakhtakh Yakutia instead?
    Like 100% Mongoloid it seems:


    Target: RUS_Krasnoyarsk_BA
    Distance: 6.5864% / 0.06586387
    90.6 Nganassan
    8.0 Han
    1.2 BRA_LapaDoSanto_9600BP
    0.2 Yoruba

    Target: RUS_Krasnoyarsk_BA
    Distance: 6.2975% / 0.06297547
    81.2 Nganassan
    18.4 MNG_North_N
    0.4 BRA_LapaDoSanto_9600BP

    Here are the models with Ymiyakhtakh, fit worsens it seems.

    Target: Udmurt
    Distance: 4.8925% / 0.04892541
    57.4 Yamnaya_RUS_Samara
    25.6 RUS_Yakutia_Ymyiakhtakh_LN
    11.8 TUR_Barcin_N
    5.2 WHG


    Target: Udmurt
    Distance: 4.3376% / 0.04337573
    44.6 Yamnaya_RUS_Samara
    26.0 RUS_Yakutia_Ymyiakhtakh_LN
    20.6 Baltic_EST_BA
    8.8 TUR_Barcin_N

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    Quote Originally Posted by Joqool View Post
    Like 100% Mongoloid it seems:


    Target: RUS_Krasnoyarsk_BA
    Distance: 6.5864% / 0.06586387
    90.6 Nganassan
    8.0 Han
    1.2 BRA_LapaDoSanto_9600BP
    0.2 Yoruba

    Target: RUS_Krasnoyarsk_BA
    Distance: 6.2975% / 0.06297547
    81.2 Nganassan
    18.4 MNG_North_N
    0.4 BRA_LapaDoSanto_9600BP

    Here are the models with Ymiyakhtakh, fit worsens it seems.

    Target: Udmurt
    Distance: 4.8925% / 0.04892541
    57.4 Yamnaya_RUS_Samara
    25.6 RUS_Yakutia_Ymyiakhtakh_LN
    11.8 TUR_Barcin_N
    5.2 WHG


    Target: Udmurt
    Distance: 4.3376% / 0.04337573
    44.6 Yamnaya_RUS_Samara
    26.0 RUS_Yakutia_Ymyiakhtakh_LN
    20.6 Baltic_EST_BA
    8.8 TUR_Barcin_N
    Interesting. Looks like it was some kind of indigenous inhabitant of Siberia, unlike the Andronovans. I wonder if modern Siberian Turkics/Altaians can be understood as a mix of the two. They are heavily R1a-Z93 but autosomally 60-75% East Eurasian depending on the group.

    - - -

    By the way, why can't anyone convert the Yunusbayev data to G25 format? I emailed David weeks ago - no response. On Anthrogenica no one has replied to my thread either. Is it that difficult? I need the Tajiks and Kumyks from that old-ass study.

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    Quote Originally Posted by Komintasavalta View Post
    I don't know if Udmurts have that much steppe in absolute terms, but for example in the qpGraph model in Tambets et al. 2018, Udmurts had 30% Nganasan, 63% Corded Ware, 1% LBK, and 6% EHG, so the proportion of Corded Ware in their non-Nganasan ancestry was around 90%. The proportion of Corded Ware was 62% in Central Russians and 61% in Mordovians, but both obviously had lower Nganasan.

    Also in the G25 model below, out of the ancestry of Udmurts that is not from Krasnoyarsk_BA, about 82% is from Yamnaya (but the model doesn't have an EHG source, and maybe Udmurts have extra EHG that is not from Yamnaya):

    Target: Udmurt
    Sources: GEO_CHG IRN_Ganj_Dareh_N RUS_Krasnoyarsk_BA TUR_Barcin_N Yamnaya_RUS_Samara
    Distance: 0.045
    62% Yamnaya_RUS_Samara
    24% RUS_Krasnoyarsk_BA
    14% TUR_Barcin_N
    Do you know why Udmurts on Tambets using qpAdm are 30% Mongoloid whereas Udmurts on G25 are like 25-26% Mong on average?

    Interesting, I heard a rather expert user on Anthrogenica mention that Udmurts also have some Iran_N or extra CHG so I'm surprised they don't score any in G25 or that Tambets qpAdm model.

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    Quote Originally Posted by Joqool View Post
    Do you know why Udmurts on Tambets using qpAdm are 30% Mongoloid whereas Udmurts on G25 are like 25-26% Mong on average?
    Prolly their Siberian reference is not 100% East Eurasian.
    Quote Originally Posted by Joqool View Post
    Interesting, I heard a rather expert user on Anthrogenica mention that Udmurts also have some Iran_N or extra CHG so I'm surprised they don't score any in G25 or that Tambets qpAdm model.
    They score that even on Gedmatch. Their West Asian average is double that of ethnic Russians.

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    Quote Originally Posted by Joqool View Post
    Do you know why Udmurts on Tambets using qpAdm are 30% Mongoloid whereas Udmurts on G25 are like 25-26% Mong on average?

    Interesting, I heard a rather expert user on Anthrogenica mention that Udmurts also have some Iran_N or extra CHG so I'm surprised they don't score any in G25 or that Tambets qpAdm model.
    It's not qpAdm but qpGraph. And there was no Iran_N or CHG source in their qpGraph model.When I recreated the qpGraph model of Tambets 2018 in ADMIXTOOLS 2, Udmurts only got 26% Nganasan, but it could be because I used different samples:



    Code:
    library(admixtools)
    
    t=read.table(text="R Yoruba.DG
    R A
    A O
    A AA
    O E
    O CH
    E EU
    E L1
    EU WH
    EU EH
    AA EH
    EH EH2
    EH EH1
    EH AAA
    AA AAA
    WH CCE
    WH Italy_North_Villabruna_HG
    CH YM
    CH CH1
    CH AAN
    EH1 YM
    EH1 EH0
    AAA AAN
    CH1 EH0
    CH1 Georgia_Kotias.SG
    L1 Turkey_N_published
    L1 LB
    L1 YM1
    L1 CW
    EH2 LB
    EH2 CWC
    YM YM1
    YM CW
    EH0 Russia_HG_Karelia
    AAN Nganasan
    AAN CCA
    LB Germany_EN_LBK
    LB CCW
    YM1 Russia_Samara_EBA_Yamnaya
    CW CCW
    CW Estonia_CordedWare
    CCW CWC
    CWC CCE
    CCE CCA
    CCA Udmurt")
    
    pop=c("Udmurt","Germany_CordedWare","Estonia_CordedWare","Germany_EN_LBK","Italy_North_Villabruna_HG","Nganasan","Russia_HG_Karelia","Russia_Samara_EBA_Yamnaya","Turkey_N_published","Yoruba.DG","Georgia_Kotias.SG","Chimp.REF")
    
    f2=f2_from_geno("v50.0_HO_public",pops=pop)
    gr=qpgraph(f2,t)
    
    plot_graph(gr$edges)
    ggsave("1.png",width=9,height=9)
    system("mogrify -trim -border 64 -bordercolor white t/1.png")
    
    # plotly_graph(gr$edges)
    
    write_dot(gr$edges) # generate black-and-white graph at graphviz.it
    Edit: But when I used `maxmiss=1`, Udmurts got 32% Nganasan. (By default, ADMIXTOOLS 2 discards each SNP which is missing from every sample in a population, but `maxmiss=1` discards no SNPs.)

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    Quote Originally Posted by Komintasavalta View Post
    It's not qpAdm but qpGraph. The results of qpGraph depend a lot on what outgroups you include, just like with qpAdm, but it also depends on the order of the nodes and what intermediate nodes you include. And there was no Iran_N or CHG source in their qpGraph model.

    When I recreated the qpGraph model of Tambets 2018 in ADMIXTOOLS 2, Udmurts only got 26% Nganasan, but it could be because I used different samples:
    Are you even tried qpGraph only with modern samples? I want to see such model, for some VURs could be.

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    Quote Originally Posted by Komintasavalta View Post
    It's not qpAdm but qpGraph. And there was no Iran_N or CHG source in their qpGraph model.When I recreated the qpGraph model of Tambets 2018 in ADMIXTOOLS 2, Udmurts only got 26% Nganasan, but it could be because I used different samples:



    Code:
    library(admixtools)
    
    t=read.table(text="R Yoruba.DG
    R A
    A O
    A AA
    O E
    O CH
    E EU
    E L1
    EU WH
    EU EH
    AA EH
    EH EH2
    EH EH1
    EH AAA
    AA AAA
    WH CCE
    WH Italy_North_Villabruna_HG
    CH YM
    CH CH1
    CH AAN
    EH1 YM
    EH1 EH0
    AAA AAN
    CH1 EH0
    CH1 Georgia_Kotias.SG
    L1 Turkey_N_published
    L1 LB
    L1 YM1
    L1 CW
    EH2 LB
    EH2 CWC
    YM YM1
    YM CW
    EH0 Russia_HG_Karelia
    AAN Nganasan
    AAN CCA
    LB Germany_EN_LBK
    LB CCW
    YM1 Russia_Samara_EBA_Yamnaya
    CW CCW
    CW Estonia_CordedWare
    CCW CWC
    CWC CCE
    CCE CCA
    CCA Udmurt")
    
    pop=c("Udmurt","Germany_CordedWare","Estonia_CordedWare","Germany_EN_LBK","Italy_North_Villabruna_HG","Nganasan","Russia_HG_Karelia","Russia_Samara_EBA_Yamnaya","Turkey_N_published","Yoruba.DG","Georgia_Kotias.SG","Chimp.REF")
    
    f2=f2_from_geno("v50.0_HO_public",pops=pop)
    gr=qpgraph(f2,t)
    
    plot_graph(gr$edges)
    ggsave("1.png",width=9,height=9)
    system("mogrify -trim -border 64 -bordercolor white t/1.png")
    
    # plotly_graph(gr$edges)
    
    write_dot(gr$edges) # generate black-and-white graph at graphviz.it
    Edit: But when I used `maxmiss=1`, Udmurts got 32% Nganasan. (By default, ADMIXTOOLS 2 discards each SNP which is missing from every sample in a population, but `maxmiss=1` discards no SNPs.)
    Well here is qpAdm model of Udmurts by David showing them close to 30% Mongoloid: https://eurogenes.blogspot.com/2017/...lithic-to.html

    Udmurt
    chisq 7.095
    tail prob 0.419052
    Barcin_Neolithic 0.234
    Caucasus_HG 0.133
    Eastern_HG 0.298
    Nganasan 0.295
    Onge 0.01
    Western_HG 0.028
    Yoruba 0

    Or this data, showing Udmurts around 30% Eastern Eurasian: https://genomebiology.biomedcentral....22-1/figures/6

    These two models from this Anthrogenica thread: https://anthrogenica.com/showthread....lic-qpAdm-runs

    Udmurt
    Nganasan: 0.307±0.007
    Estonia_MN_CCC: 0.150±0.033
    Latvia_LN_CordedWare: 0.387±0.045
    Ukraine_Globular_Amphora: 0.155±0.029
    tail: 0.099657
    chisq: 15.999

    Udmurt
    Nganasan: 0.287±0.009
    Estonia_MN_CCC: 0.176±0.045
    Germany_CordedWare: 0.450±0.068
    Hungary_LBK_MN.SG: 0.088±0.027
    tail: 0.659253
    chisq: 7.688

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