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Thread: G25 models on VURers and other Uralics

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    Quote Originally Posted by Joqool View Post
    Although keep in mind that the Mari are extremely genetically drifted in G25 so the results could be a bit inaccurate.
    We can get an extremely good fit for Chuvashes by modeling them as 60% Mari and 40% Mishar (d=.009) or 70% Mari and 30% Mordovian (d=.013). Doesn't it mean that even if Maris are drifted, their drift is shared by Chuvashes?

    Like Maris, Chuvashes are also far from every other population average in G25:

    Distance to: Chuvash
    .048 Besermyan
    .049 Udmurt
    .056 Mari
    .064 Tatar_Kazan
    .069 FIN_Levanluhta_IA
    .071 Saami
    .072 RUS_Chalmny-Varre
    .073 Komi
    .077 Saami_Kola
    .086 VK2020_NOR_North_VA_o2
    .087 Tatar_Mishar
    .090 RUS_Mezhovskaya
    .092 Tatar_Lipka
    .096 MDA_Cimmerian
    .097 RUS_Tagar
    .097 VK2020_NOR_North_VA_o1

    Therefore it is surprising that my two-way models for Chuvashes have such good fit.

    Quote Originally Posted by Joqool View Post
    Actually VK2020_NOR_North_LN_HG inflated Anatolian a bit, if you add RUS_Volga_Kama and Bolshoy_Oleni_Ostrov_o again, it might lower EEF a bit.

    Can you do the same models for the Saami, Mari, Besermyan, Saami_Kola and Chuvash individuals? (try removing the VK2020_NOR_LN_HG and replace it with Baltic_LVA_MN instead, the EEF should go down).
    In the model below, if I changed Baltic_LVA_MN into NOR_N_HG, it reduced the average distance from .0567 to .0561. The combined average proportion of Barcin and CHG stayed as 28.1% (from 21.3 Barcin + 6.8% CHG to 20.9% Barcin + 7.2% CHG).



    Above some Saami individuals randomly have 0% RUS_Bolshoy_Oleni_Ostrov_o but others have 10-20%. Also one Chuvash has 42% RUS_Bolshoy_Oleni_Ostrov_o but another has 13%. The individuals with lower BOO_o have higher EHG (Volga-Kama_N) and higher kra001 (Krasnoyarsk_BA).

    RUS_Bolshoy_Oleni_Ostrov_o is just half EHG and half RUS_Krasnoyarsk_BA, so it's not needed as an additional component in modeling Uralics:

    Target: RUS_Bolshoy_Oleni_Ostrov_o
    d=.018 - 79% RUS_Bolshoy_Oleni_Ostrov + 21% RUS_Krasnoyarsk_BA
    d=.029 - 47% RUS_Samara_HG + 53% RUS_Krasnoyarsk_BA
    d=.034 - 48% RUS_Karelia_HG + 52% RUS_Krasnoyarsk_BA
    d=.034 - 48% RUS_Sidelkino_HG + 52% RUS_Krasnoyarsk_BA
    d=.035 - 47% RUS_Khvalynsk_En + 53% RUS_Krasnoyarsk_BA
    d=.036 - 52% RUS_Sintashta_MLBA_o3 + 48% RUS_Krasnoyarsk_BA
    d=.038 - 47% RUS_Volga-Kama_N + 53% RUS_Krasnoyarsk_BA
    d=.040 - 46% RUS_Veretye_Meso + 54% RUS_Krasnoyarsk_BA

    Quote Originally Posted by Joqool View Post
    It sucks that there are so much wog (CHG+Anatolian) blood among Uralics. It must be nice if they were still purely indigenous HG+Siberian without Anatolian and CHG.
    I think we're still pure enough... I'm terrified to imagine what even purer Uralics would be like.

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    Quote Originally Posted by Komintasavalta View Post
    We can get an extremely good fit for Chuvashes by modeling them as 60% Mari and 40% Mishar (d=.009) or 70% Mari and 30% Mordovian (d=.013). Doesn't it mean that even if Maris are drifted, their drift is shared by Chuvashes?

    Like Maris, Chuvashes are also far from every other population average in G25:

    Distance to: Chuvash
    .048 Besermyan
    .049 Udmurt
    .056 Mari
    .064 Tatar_Kazan
    .069 FIN_Levanluhta_IA
    .071 Saami
    .072 RUS_Chalmny-Varre
    .073 Komi
    .077 Saami_Kola
    .086 VK2020_NOR_North_VA_o2
    .087 Tatar_Mishar
    .090 RUS_Mezhovskaya
    .092 Tatar_Lipka
    .096 MDA_Cimmerian
    .097 RUS_Tagar
    .097 VK2020_NOR_North_VA_o1

    Therefore it is surprising that my two-way models for Chuvashes have such good fit.



    In the model below, if I changed Baltic_LVA_MN into NOR_N_HG, it reduced the average distance from .0567 to .0561. The combined average proportion of Barcin and CHG stayed as 28.1% (from 21.3 Barcin + 6.8% CHG to 20.9% Barcin + 7.2% CHG).



    Above some Saami individuals randomly have 0% RUS_Bolshoy_Oleni_Ostrov_o but others have 10-20%. Also one Chuvash has 42% RUS_Bolshoy_Oleni_Ostrov_o but another has 13%. The individuals with lower BOO_o have higher EHG (Volga-Kama_N) and higher kra001 (Krasnoyarsk_BA).

    RUS_Bolshoy_Oleni_Ostrov_o is just half EHG and half RUS_Krasnoyarsk_BA, so it's not needed as an additional component in modeling Uralics:

    Target: RUS_Bolshoy_Oleni_Ostrov_o
    d=.018 - 79% RUS_Bolshoy_Oleni_Ostrov + 21% RUS_Krasnoyarsk_BA
    d=.029 - 47% RUS_Samara_HG + 53% RUS_Krasnoyarsk_BA
    d=.034 - 48% RUS_Karelia_HG + 52% RUS_Krasnoyarsk_BA
    d=.034 - 48% RUS_Sidelkino_HG + 52% RUS_Krasnoyarsk_BA
    d=.035 - 47% RUS_Khvalynsk_En + 53% RUS_Krasnoyarsk_BA
    d=.036 - 52% RUS_Sintashta_MLBA_o3 + 48% RUS_Krasnoyarsk_BA
    d=.038 - 47% RUS_Volga-Kama_N + 53% RUS_Krasnoyarsk_BA
    d=.040 - 46% RUS_Veretye_Meso + 54% RUS_Krasnoyarsk_BA



    I think we're still pure enough... I'm terrified to imagine what even purer Uralics would be like.
    Yes it looks the Chuvash are genetically drifted in G25 as well, not as much as the Mari though.

    Interesting. I managed to get around 13-14% EEF for the Saami:GS000035025 and Saami:saami2 who are the two most Mongoloid-shifted and least EEF affected modern Saami samples. But then I also include Yamnaya_KAZ_Mereke into the run which seems to hide the CHG wog score as the Yamnaya component absorbs it (Yamnaya are EHG+CHG thus they absorb any other CHG score) and could be absorbing minor EEF making the percentage of Anatolian wog blood goes down. What I have noticed is including the Yamnaya populations including Mereke seem to drastically improve the fits for Saamis and many Euros.

    You are right, it is no longer needed for modeling.

    Purer Uralics like Uyelgi ancient sample in G25 and Khanty, Mansi, Nenets should have less CHG and Anatolian wog contamination as they are also much more Mongoloid, thus inversely correlates to the reduction of the churka/wog affinity.

    Seems like the winner of the lowest wog contamination (Anatolian+CHG) is Saami:GS000035025 individual followed by Saami:saami2. even the Mari individuals still have a bit higher wogs than these Saamis. Btw these Saamis are also around 27% East Eurasian, so maybe the Mongoloid help reduce any wog affinity? Its interesting how Saamis still have the least wog affinity compared to most other Uralics except Mansi, Khanty, Nenets and Selkup who have even much less.

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    Actually you can also model BOO as kra001 + WSHG + Norwegian HG (intermediate between SHG and EHG) + wog:

    Target: RUS_Bolshoy_Oleni_Ostrov (d=.013)
    34.8 RUS_Krasnoyarsk_BA
    33.4 RUS_Sosonivoy_HG
    25.8 VK2020_NOR_North_LN_HG
    6.0 TUR_Barcin_N

    The fit with RUS_Tyumen_HG is worse than with RUS_Sosonivoy_HG, even though Sosonivoy clusters together with Botai and Tyumen_HG:

    Target: RUS_Bolshoy_Oleni_Ostrov (d=.018)
    35.6 RUS_Krasnoyarsk_BA
    29.2 VK2020_NOR_North_LN_HG
    29.0 RUS_Tyumen_HG
    6.2 TUR_Barcin_N

    The fit with Botai is almost as good:

    Target: RUS_Bolshoy_Oleni_Ostrov (d=.015)
    33.4 RUS_Krasnoyarsk_BA
    32.2 KAZ_Botai
    29.4 VK2020_NOR_North_LN_HG
    5.0 TUR_Barcin_N

    Without the wog, the distance more than doubles:

    Target: RUS_Bolshoy_Oleni_Ostrov (d=.028)
    36.4 RUS_Krasnoyarsk_BA
    34.0 VK2020_NOR_North_LN_HG
    29.6 RUS_Sosonivoy_HG

    Quote Originally Posted by Joqool View Post
    Its interesting how Saamis still have the least wog affinity compared to most other Uralics except Mansi, Khanty, Nenets and Selkup who have even much less.
    Yeah and Nganasan. And one Uralic people that everyone has forgotten about are the Enets. I haven't even seen their genetic results, and I don't think even travv or me has posted a classification thread about an Enets girl.

    (I just searched VKontakte for girls from the main Forest Yukaghir village, but I didn't find any good ones. Maybe I'll search for Enets girls next.)

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    Quote Originally Posted by Komintasavalta View Post
    Actually you can also model BOO as kra001 + WSHG + Norwegian HG (intermediate between SHG and EHG) + wog:

    Target: RUS_Bolshoy_Oleni_Ostrov (d=.013)
    34.8 RUS_Krasnoyarsk_BA
    33.4 RUS_Sosonivoy_HG
    25.8 VK2020_NOR_North_LN_HG
    6.0 TUR_Barcin_N

    The fit with RUS_Tyumen_HG is worse than with RUS_Sosonivoy_HG, even though Sosonivoy clusters together with Botai and Tyumen_HG:

    Target: RUS_Bolshoy_Oleni_Ostrov (d=.018)
    35.6 RUS_Krasnoyarsk_BA
    29.2 VK2020_NOR_North_LN_HG
    29.0 RUS_Tyumen_HG
    6.2 TUR_Barcin_N

    The fit with Botai is almost as good:

    Target: RUS_Bolshoy_Oleni_Ostrov (d=.015)
    33.4 RUS_Krasnoyarsk_BA
    32.2 KAZ_Botai
    29.4 VK2020_NOR_North_LN_HG
    5.0 TUR_Barcin_N

    Without the wog, the distance more than doubles:

    Target: RUS_Bolshoy_Oleni_Ostrov (d=.028)
    36.4 RUS_Krasnoyarsk_BA
    34.0 VK2020_NOR_North_LN_HG
    29.6 RUS_Sosonivoy_HG



    Yeah and Nganasan. And one Uralic people that everyone has forgotten about are the Enets. I haven't even seen their genetic results, and I don't think even travv or me has posted a classification thread about an Enets girl.

    (I just searched VKontakte for girls from the main Forest Yukaghir village, but I didn't find any good ones. Maybe I'll search for Enets girls next.)
    Yup there is minor wog blood in BOO. However the BOO outlier sample if I remembered correctly, have almost zero Neolithic contamination.

    There are probably new Enets samples, I will have to find them.

    Ancient Uralics like Uyelgi, I predicted would probably have wog (CHG+EEF) contamination somewhere close to the Khanty's level I believe. Have to try it out.

    Can you try to run your model on these ancient Uralics? I wanted to see if they will have lower wog than Saamis:
    Code:
    Uyelgi1_scaled,0.086506,-0.132019,0.106725,0.086887,-0.068013,-0.027331,0.043712,0.025153,-0.027815,-0.050297,0.068528,-0.023379,0.032111,-0.041012,-0.02158,0.022275,-0.020601,-0.02065,-0.026271,-0.030014,-0.049662,0.010881,-0.009367,-0.01458,0.008622
    Uyelgi2_scaled,0.106994,-0.114755,0.083721,0.088179,-0.082785,-0.063587,0.019506,-0.017076,-0.019021,-0.057222,0.041734,-0.023079,0.007433,-0.054499,-0.044381,-0.011535,0.000782,-0.022297,-0.035698,-0.033766,0.021213,0.026214,0.034879,0.010845,0.019639
    RUS_Krasnoyarsk_MLBA_o,0.102441,-0.076165,0.094657,0.094639,-0.053241,-0.001116,-0.004465,0.000462,-0.006749,-0.036994,0.025495,-0.013038,0.010704,-0.044039,0.011265,-0.000265,-0.013299,0,0.009553,-0.003502,-0.003119,0.006801,0.005423,0.00482,-0.003233
    VK2020_NOR_North_VA_o1,0.106994,-0.079211,0.116153,0.076551,-0.035699,-0.00251,-0.00141,0.01223,0,-0.034443,0.031016,-0.012289,0.01888,-0.031516,-0.011536,0.002121,-0.011083,0.003927,-0.005782,-0.014007,0.013351,-0.001855,0.001109,0.002771,-0.002395
    FIN_Levanluhta_IA:DA238,0.099026,-0.081242,0.117662,0.084626,-0.028313,-0.001394,-0.00094,0.009,0.001432,-0.036994,0.038648,-0.005095,0.032705,-0.016377,-0.004343,0.002784,0.012908,0.005574,-0.010684,0.004127,0.018592,0.005564,-0.003944,-0.002289,-0.001317

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    Quote Originally Posted by Joqool View Post
    Can you try to run your model on these ancient Uralics? I wanted to see if they will have lower wog than Saamis:
    Uyelgi gets a sh*t fit in my model:



    However when I put all lines from the ancient averages file in sources, the model that Vahaduo generated automatically for Uyelgi still had a sh*t fit:

    Target: Uyelgi1_scaled (d=.102)
    27.0 RUS_Krasnoyarsk_BA
    22.6 RUS_Bolshoy_Oleni_Ostrov_o
    15.8 RUS_Samara_HG
    11.0 Baltic_EST_BA
    8.6 KAZ_Mereke_MBA
    8.6 Saka_Tian_Shan_o
    3.0 IRN_HotuIIIb_Meso
    1.2 RUS_Yana_MA
    1.2 VK2020_NOR_North_VA_o1
    0.8 VUT_2300BP_all
    0.2 RUS_Sintashta_MLBA_o3

    Target: Uyelgi2_scaled (d=.099)
    34.0 RUS_Srubnaya_MLBA_o
    30.6 RUS_Krasnoyarsk_BA
    13.4 VK2020_NOR_North_VA_o1
    9.4 DEU_MA_o
    7.0 RUS_AfontovaGora3
    5.6 VUT_2300BP_all

    (DEU_MA_o which is the main source of wog ancestry for Uyelgi2 is similar to modern Italians.)

    Quote Originally Posted by Joqool View Post
    Yup there is minor wog blood in BOO. However the BOO outlier sample if I remembered correctly, have almost zero Neolithic contamination.
    The outlier has a bit of wog too but it's just more kra001:

    Target Distance KAZ_Botai NOR_N_HG RUS_Krasnoyarsk_BA RUS_Samara_HG TUR_Barcin_N
    RUS_Bolshoy_Oleni_Ostrov .016 28.4 19.4 34.4 13.4 4.4
    RUS_Bolshoy_Oleni_Ostrov_o .021 26.2 13.4 47.4 10.0 3.0

    The outlier gets a good fit as 20% kra001 and 80% regular BOO:

    Target: RUS_Bolshoy_Oleni_Ostrov_o
    d=.018 - 79% RUS_Bolshoy_Oleni_Ostrov + 21% RUS_Krasnoyarsk_BA
    d=.029 - 47% RUS_Samara_HG + 53% RUS_Krasnoyarsk_BA
    d=.034 - 48% RUS_Karelia_HG + 52% RUS_Krasnoyarsk_BA
    d=.034 - 48% RUS_Sidelkino_HG + 52% RUS_Krasnoyarsk_BA
    d=.035 - 47% RUS_Khvalynsk_En + 53% RUS_Krasnoyarsk_BA
    d=.036 - 52% RUS_Sintashta_MLBA_o3 + 48% RUS_Krasnoyarsk_BA
    d=.038 - 47% RUS_Volga-Kama_N + 53% RUS_Krasnoyarsk_BA
    d=.040 - 46% RUS_Veretye_Meso + 54% RUS_Krasnoyarsk_BA

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    Quote Originally Posted by Komintasavalta View Post
    We can get an extremely good fit for Chuvashes by modeling them as 60% Mari and 40% Mishar (d=.009) or 70% Mari and 30% Mordovian (d=.013). Doesn't it mean that even if Maris are drifted, their drift is shared by Chuvashes?
    It's the opposite, Chuvashes have Mari drift.
    There are 3 Uralic drifts in G25, a Khanty, Mari and a Nganasan drift. That means if you want low distances, you should use those 3 as sources, even when modelling ancient samples. It's another question if this is historically accurate.

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    Quote Originally Posted by vbnetkhio View Post
    It's the opposite, Chuvashes have Mari drift.
    There are 3 Uralic drifts in G25, a Khanty, Mari and a Nganasan drift. That means if you want low distances, you should use those 3 as sources, even when modelling ancient samples. It's another question if this is historically accurate.
    There's also a cline with Selkups and Kets on PC4 below. If I didn't include Kets, Selkups were at the end of the cline on PC4.



    It's interesting how on PCs 1 and 2, there is a sequence of north-south clusters that progress from west to east. In the first column, there are actually two clusters, but Latvians are at the top of the more northern cluster and Hungarians are at the bottom of the more southern cluster. In the second column, Kola Saami are at the top and Mishars are at the bottom. Next non-Kola Saami are at the top and Bashkirs are at the bottom. Then there's again a column with two or three clusters, where Khanty and Mansi are at the top, Swamp Tatars are in the middle, and other Siberian Tatars and Bashkirs are at the bottom.



    Then on PC5 Maris are again at one extreme and Kets are at the other extreme:



    Code:
    library(tidyverse)
    library(colorspace)
    
    download.file("https://drive.google.com/uc?export=download&id=1HYrDwxEXv82DvDLoq736pS5ZTGJA4dn5","modernind")
    t=read.csv("modernind",header=T,row.names=1)
    pick=c("Bashkir","Besermyan","Chuvash","Estonian","Finnish","Finnish_East","Hungarian","Ingrian","Karelian","Ket","Khanty","Komi","Latvian","Mansi","Mari","Mordovian","Nenets","Nganassan","Norwegian","Saami","Saami_Kola","Selkup","Swedish","Tatar_Kazan","Tatar_Mishar","Tatar_Siberian","Tatar_Siberian_Zabolotniye","Udmurt","Vepsian")
    t=t[sub(":.*","",row.names(t))%in%pick,]
    
    k=cutree(hclust(dist(t)),k=12)
    p=prcomp(t)
    p2=as.data.frame(p$x)
    p2$cluster=as.vector(k)
    write.csv(k,"clusters",quote=F)
    pct=paste0(colnames(p$x)," (",sprintf("%.1f",p$sdev/sum(p$sdev)*100),"%)")
    
    ggplot(p2,aes(x=-PC1,y=-PC2))+
    geom_point(aes(color=as.factor(cluster)),size=.5)+
    geom_polygon(data=p2%>%group_by(cluster)%>%slice(chull(PC1,PC2)),alpha=.2,aes(color=as.factor(cluster),fill=as.factor(cluster)),size=.3)+
    geom_text(label=rownames(p2),aes(color=as.factor(cluster)),size=2.2,vjust=-.7)+
    theme(
      aspect.ratio=3/4,
      axis.text=element_text(color="black"),
      axis.ticks.length=unit(0,"pt"),
      axis.ticks.x=element_blank(),
      axis.ticks.y=element_blank(),
      legend.position="none",
      panel.background=element_rect(fill="white"),
      panel.grid.major=element_line(color="gray75",size=.2),
      panel.grid.minor=element_line(color="white",size=.13),
      plot.background=element_rect(fill="white"),
      text=element_text(color="black")
    
    )+
    scale_x_continuous(breaks=seq(-1,1,.05),expand=expansion(mult=.12))+
    scale_y_continuous(breaks=seq(-1,1,.05),expand=expansion(mult=.08))+
    xlab(pct[1])+ylab(pct[2])+
    scale_color_discrete_qualitative(palette="Set 2",c=80,l=40)+
    ggsave("output.png")
    
    system("/usr/local/bin/mogrify -trim -bordercolor white -border 20x20 output.png")
    Last edited by Komintasavalta; 03-08-2021 at 11:41 PM.

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