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Thread: G25 shows many Uralics to be genetically closer to Turkics than to most Euros?

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    G25 shows many Uralics to be genetically closer to Turkics than to most Euros?

    How accurate are these G25 distance runs for Volga Ural Finno-Ugrics such as Mari, Udmurt, Saami? It shows them to be genetically closer to many Central Asians/Turkics than to most Euros?

    For example:

    Distance to: Udmurt
     

    0.12189238 KAZ_Turk
    0.12750545 Finnish_East
    0.12993164 TUR_Ottoman
    0.13114207 Turkmen
    0.13310796 Tatar_Siberian
    0.13366572 Turkmen_Uzbekistan
    0.13509091 Russian_Kostroma
    0.13773585 Tajik
    0.14179315 Finnish
    0.14393915 Iran_Turkmen
    0.14934155 Uyelgi
    0.15093452 Uzbek
    0.15098617 Tajik_Ishkashim
    0.15121243 Cossack_Kuban
    0.15155844 Tatar_Siberian_Zabolotniye
    0.15325256 KAZ_Kipchak
    0.15961422 Tajik_Yagnobi
    0.15979505 Russian_Tver
    0.16977181 Cossack_Ukrainian
    0.17017327 Estonian
    0.17036028 Kho_Singanali
    0.17152629 Mansi
    0.17241783 Nogai
    0.17265718 Russian_Voronez
    0.17468024 Turkish_South
    0.17552127 Ukrainian
    0.17796271 Russian_Smolensk
    0.18020743 Swedish
    0.18059071 Polish
    0.18066436 KAZ_Karluk
    0.18103482 Hungarian
    0.18154135 Turkish_North
    0.18316431 Czech
    0.18382164 Hazara_Afghanistan
    0.18472297 Dutch
    0.18579149 Khanty
    0.18660326 Croatian
    0.18817467 Scottish
    0.18890984 Uygur
    0.18988173 KAZ_Karakhanid
    0.19010287 German
    0.19164537 English
    0.19445041 Hazara
    0.19510247 Romanian
    0.19868197 Karakalpak
    0.22298694 Italian_Lombardy
    0.22317477 Spanish_Aragon


    Distance to: Mari
     

    0.13837635 Uyelgi
    0.13857526 Tatar_Siberian
    0.14622919 Tatar_Siberian_Zabolotniye
    0.14918141 KAZ_Turk
    0.15559553 TUR_Ottoman
    0.15879922 Mansi
    0.16609517 Turkmen
    0.16666745 Turkmen_Uzbekistan
    0.16914000 KAZ_Kipchak
    0.17229907 Uzbek
    0.17348576 Khanty
    0.17705239 Finnish_East
    0.17783813 Nogai
    0.18422090 Iran_Turkmen
    0.18522712 Russian_Kostroma
    0.18790293 Tajik
    0.19185233 KAZ_Karluk
    0.19255765 Finnish
    0.19571190 KAZ_Karakhanid
    0.19586713 Hazara_Afghanistan
    0.19884563 Cossack_Kuban
    0.19923069 Uygur
    0.19996889 Karakalpak
    0.20260554 Tajik_Ishkashim
    0.20344348 Hazara
    0.21081083 Russian_Tver
    0.21232139 Tajik_Yagnobi
    0.21650129 Kho_Singanali
    0.21763448 Estonian
    0.21768552 Turkish_South
    0.21984970 Cossack_Ukrainian
    0.22059257 Russian_Voronez
    0.22396805 Turkish_North
    0.22486058 Ukrainian
    0.22557314 Russian_Smolensk
    0.22981453 Polish
    0.23081106 Hungarian
    0.23172467 Swedish
    0.23404842 Czech
    0.23549899 Croatian
    0.23635703 Dutch
    0.23980132 Scottish
    0.24026907 German
    0.24241418 English
    0.24252065 Romanian
    0.26721612 Italian_Lombardy
    0.26748702 Spanish_Aragon


    Distance to: Chuvash (they seem to be genetically Finno-Ugric but linguistically Turkicized so I decided to include them):
     

    0.12697437 Finnish_East
    0.13185412 Russian_Kostroma
    0.13547962 KAZ_Turk
    0.14117864 Finnish
    0.14130519 TUR_Ottoman
    0.14159654 Turkmen
    0.14536088 Turkmen_Uzbekistan
    0.14605465 Cossack_Kuban
    0.14825872 Tatar_Siberian
    0.15145396 Tajik
    0.15316901 Iran_Turkmen
    0.15719659 Russian_Tver
    0.16395802 Uzbek
    0.16425498 Uyelgi
    0.16611622 Cossack_Ukrainian
    0.16630835 Estonian
    0.16691995 Tajik_Ishkashim
    0.16711976 KAZ_Kipchak
    0.16722733 Russian_Voronez
    0.16950335 Tajik_Yagnobi
    0.16963873 Tatar_Siberian_Zabolotniye
    0.17097408 Ukrainian
    0.17250406 Russian_Smolensk
    0.17616421 Polish
    0.17674324 Turkish_South
    0.17715033 Hungarian
    0.17969626 Swedish
    0.18044180 Czech
    0.18160432 Turkish_North
    0.18169810 Nogai
    0.18176735 Croatian
    0.18444626 Dutch
    0.18773898 German
    0.18799141 Kho_Singanali
    0.18842893 Scottish
    0.18943428 Mansi
    0.18993492 Romanian
    0.19112287 English
    0.19421863 KAZ_Karluk
    0.19689561 Hazara_Afghanistan
    0.20067179 Uygur
    0.20383598 KAZ_Karakhanid
    0.20461346 Khanty
    0.20627424 Hazara
    0.20868481 Karakalpak
    0.21788876 Italian_Lombardy
    0.21880817 Spanish_Aragon


    Distance to: Besermyan
     

    0.11497378 Finnish_East
    0.11902068 KAZ_Turk
    0.12301626 Turkmen
    0.12590966 Tajik
    0.12619302 TUR_Ottoman
    0.12693284 Turkmen_Uzbekistan
    0.12768960 Finnish
    0.13288259 Iran_Turkmen
    0.13653846 Cossack_Kuban
    0.14012210 Tajik_Ishkashim
    0.14058789 Tatar_Siberian
    0.14485117 Russian_Tver
    0.14576898 Tajik_Yagnobi
    0.14934584 Uzbek
    0.15365141 Cossack_Ukrainian
    0.15491138 KAZ_Kipchak
    0.15644797 Estonian
    0.15693969 Russian_Voronez
    0.15923196 Turkish_South
    0.15935893 Ukrainian
    0.16220037 Russian_Smolensk
    0.16241108 Kho_Singanali
    0.16320339 Hungarian
    0.16378753 Swedish
    0.16424232 Polish
    0.16475083 Tatar_Siberian_Zabolotniye
    0.16496983 Uyelgi
    0.16518581 Turkish_North
    0.16602951 Czech
    0.16752121 Dutch
    0.17100790 Scottish
    0.17249135 German
    0.17440339 English
    0.17480491 Nogai
    0.17646359 Romanian
    0.18258877 KAZ_Karluk
    0.18496177 Hazara_Afghanistan
    0.18749240 Mansi
    0.19027060 Uygur
    0.19410352 KAZ_Karakhanid
    0.19595124 Hazara
    0.20178567 Khanty
    0.20248736 Karakalpak
    0.20400626 Italian_Lombardy
    0.20457841 Spanish_Aragon


    Distance to: Saami
     

    0.12036459 Finnish_East
    0.13609329 Russian_Kostroma
    0.13826217 Finnish
    0.14216068 Tatar_Siberian
    0.14229851 KAZ_Turk
    0.15078272 Cossack_Kuban
    0.15566540 TUR_Ottoman
    0.15607780 Tatar_Siberian_Zabolotniye
    0.15925793 Uyelgi
    0.16075159 Russian_Tver
    0.16336793 Turkmen
    0.16459138 Turkmen_Uzbekistan
    0.16632238 Estonian
    0.16787364 KAZ_Kipchak
    0.17240465 Cossack_Ukrainian
    0.17367742 Uzbek
    0.17400811 Mansi
    0.17518874 Russian_Voronez
    0.17553305 Tajik
    0.17868672 Iran_Turkmen
    0.17927329 Ukrainian
    0.17952044 Russian_Smolensk
    0.18157993 Nogai
    0.18279075 Swedish
    0.18381285 Polish
    0.18837516 Tajik_Ishkashim
    0.18848438 Khanty
    0.18862550 Czech
    0.18949914 Hungarian
    0.19087737 Dutch
    0.19452394 Scottish
    0.19587038 Croatian
    0.19658845 Tajik_Yagnobi
    0.19695641 KAZ_Karluk
    0.19719818 German
    0.19782542 English
    0.20194140 Hazara_Afghanistan
    0.20239142 KAZ_Karakhanid
    0.20432707 Uygur
    0.20636918 Karakalpak
    0.20693335 Turkish_South
    0.20699293 Kho_Singanali
    0.20953171 Romanian
    0.21102027 Hazara
    0.21361686 Turkish_North
    0.23259576 Spanish_Aragon
    0.23812838 Italian_Lombardy


    Most East Asian-shifted Udmurt individual:
    Distance to: Udmurt:udmurd8
     

    0.11310293 KAZ_Turk
    0.11499101 Tatar_Siberian
    0.12093356 TUR_Ottoman
    0.12768661 Turkmen
    0.12839383 Turkmen_Uzbekistan
    0.13030726 Tatar_Siberian_Zabolotniye
    0.13274178 Uyelgi
    0.13967246 KAZ_Kipchak
    0.14008865 Uzbek
    0.14366467 Tajik
    0.14503560 Iran_Turkmen
    0.15002433 Finnish_East
    0.15051991 Mansi
    0.15733351 Nogai
    0.15807654 Russian_Kostroma
    0.15932898 Tajik_Ishkashim
    0.16412459 Finnish
    0.16433979 Khanty
    0.16542659 KAZ_Karluk
    0.16977493 Hazara_Afghanistan
    0.17150233 Tajik_Yagnobi
    0.17278467 KAZ_Karakhanid
    0.17424742 Cossack_Kuban
    0.17470455 Uygur
    0.17473450 Kho_Singanali
    0.17977632 Hazara
    0.18225833 Karakalpak
    0.18257704 Turkish_South
    0.18263746 Russian_Tver
    0.19037902 Turkish_North
    0.19219383 Cossack_Ukrainian
    0.19334510 Estonian
    0.19543075 Russian_Voronez
    0.19809225 Ukrainian
    0.20046634 Russian_Smolensk
    0.20108449 Swedish
    0.20161450 Hungarian
    0.20302204 Polish
    0.20437588 Czech
    0.20477301 Dutch
    0.20718733 Croatian
    0.20785913 Scottish
    0.21031218 German
    0.21127078 English
    0.21365826 Romanian
    0.23881898 Italian_Lombardy
    0.23934645 Spanish_Aragon


    Most East Eurasian admixed Mari:
    Distance to: Mari:mari1
     

    0.13044563 Uyelgi
    0.13218886 Tatar_Siberian
    0.13714602 Tatar_Siberian_Zabolotniye
    0.14773372 KAZ_Turk
    0.14953210 Mansi
    0.15484139 TUR_Ottoman
    0.16383451 Khanty
    0.16611238 KAZ_Kipchak
    0.16693582 Turkmen
    0.16705886 Turkmen_Uzbekistan
    0.16971483 Uzbek
    0.17234959 Nogai
    0.18345203 Finnish_East
    0.18648667 KAZ_Karluk
    0.18677072 Iran_Turkmen
    0.18954934 KAZ_Karakhanid
    0.19130795 Tajik
    0.19187852 Russian_Kostroma
    0.19206068 Hazara_Afghanistan
    0.19396984 Karakalpak
    0.19471678 Uygur
    0.19885824 Hazara
    0.19894694 Finnish
    0.20712006 Cossack_Kuban
    0.20717671 Tajik_Ishkashim
    0.21750578 Tajik_Yagnobi
    0.21774718 Russian_Tver
    0.22037648 Kho_Singanali
    0.22267565 Turkish_South
    0.22484249 Estonian
    0.22698380 Cossack_Ukrainian
    0.22792117 Russian_Voronez
    0.22879830 Turkish_North
    0.23218075 Ukrainian
    0.23248012 Russian_Smolensk
    0.23707678 Polish
    0.23795651 Hungarian
    0.23846213 Swedish
    0.24116640 Czech
    0.24271266 Croatian
    0.24317177 Dutch
    0.24626055 Scottish
    0.24734527 German
    0.24910385 English
    0.24978596 Romanian
    0.27362461 Italian_Lombardy
    0.27402262 Spanish_Aragon


    Most East Asian-shifted Saami individual:
    Distance to: Saami:GS000035025
     

    0.13407839 Tatar_Siberian
    0.14117080 Tatar_Siberian_Zabolotniye
    0.14321710 Finnish_East
    0.14642509 KAZ_Turk
    0.14651089 Uyelgi
    0.15780722 Mansi
    0.15928761 Russian_Kostroma
    0.16073724 TUR_Ottoman
    0.16114704 Finnish
    0.16732838 KAZ_Kipchak
    0.17090740 Khanty
    0.17232113 Turkmen
    0.17241098 Turkmen_Uzbekistan
    0.17544998 Uzbek
    0.17696642 Cossack_Kuban
    0.17726949 Nogai
    0.18446833 Russian_Tver
    0.18893651 Estonian
    0.19022350 Tajik
    0.19088560 Iran_Turkmen
    0.19145738 KAZ_Karluk
    0.19547822 Cossack_Ukrainian
    0.19573315 KAZ_Karakhanid
    0.19761805 Russian_Voronez
    0.19982472 Karakalpak
    0.19993139 Hazara_Afghanistan
    0.20143800 Russian_Smolensk
    0.20256351 Uygur
    0.20258413 Ukrainian
    0.20368092 Tajik_Ishkashim
    0.20707970 Swedish
    0.20715251 Polish
    0.20743505 Hazara
    0.21261985 Czech
    0.21328762 Hungarian
    0.21449101 Tajik_Yagnobi
    0.21520710 Dutch
    0.21845366 Scottish
    0.21924110 Kho_Singanali
    0.21940491 Croatian
    0.22140021 German
    0.22170772 English
    0.22419244 Turkish_South
    0.23101902 Turkish_North
    0.23282629 Romanian
    0.25525533 Spanish_Aragon
    0.25976153 Italian_Lombardy


    Like how accurate are these genetic distance runs?

    P.S. I didn't include the Komi, Mordovian and Saami_Kola as they seem to be closer to Europeans than to Turkics/Central Asians according to G25.
    Last edited by Tsakhur; 07-05-2021 at 02:19 AM.

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    Why is this surprising?

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    Quote Originally Posted by Kulin View Post
    Why is this surprising?
    I don't actually find it surprising but wondering if the G25 distance runs like those posted above are accurate or not. Because I got very different results when I run the genetic distance for these groups when using Gedmatch calculators especially Eurogenes K13, Dodecad K12b where they seem to be closer to Euros overall. I want to know whether G25 or Gedmatch calculators are more reliable for determining the genetic distance of Volga Ural Finno-Ugrics to other populations such as to Turkics and mainstream Europeans.

    Furthermore, I have read that these groups are still mainly Indo-European genetically speaking albeit with very heavy East Asian ancestry. So I would have thought they would still be closer to Eastern or Northern Euros than to Central Asians or Turkics.
    Last edited by Tsakhur; 07-04-2021 at 06:46 PM.

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    Double.

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    Scaled or unscaled coordinates? Because only scaled make sense for distances.

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    Quote Originally Posted by michal3141 View Post
    Scaled or unscaled coordinates? Because only scaled make sense for distances.
    Scaled.

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    Quote Originally Posted by Tsakhur View Post
    Because I got very different results when I run the genetic distance for these groups when using Gedmatch calculators especially Eurogenes K13, Dodecad K12b where they seem to be closer to Euros overall.
    They're not closer if you take the matrix of FST distances between the components into account (http://bga101.blogspot.com/2013/11/u...-gedmatch.html).

    For example in K13, the Baltic component has about 6 times higher FST distance to the Siberian component (.111) than to the North_Atlantic component (.019). But Vahaduo doesn't take it into account.

    I'm not sure if it's the right way to account for the FST distances, but if you use matrix multiplication to multiply the percentages in the K13 updated datasheet with a matrix of FST distances, then the Mari average has the lowest Euclidean distance to these populations:

    0.000 Mari
    0.144 Chuvash
    0.186 Crimean_Tatar
    0.205 Bashkir
    0.232 Turkmen_Uzbekistan
    0.246 Afghan_Turkmen
    0.261 Nogay
    0.284 Tatar
    0.323 Afghan_Tadjik
    0.347 Uzbeki
    0.378 Afghan_Hazara
    0.381 Turkmen
    0.401 Tajik_Mountain
    0.403 Tajik_Lowland
    0.404 Burusho
    0.461 Pamiri_Tajikistan
    0.480 Uygur
    0.501 Hazara
    0.515 Afghan_Pashtun
    0.524 Pathan
    0.527 Brahmin_UP
    0.540 East_Finnish
    0.540 Turk_North_West
    0.541 Russian_Northern_Dvina
    0.545 Kalash
    0.546 Bangladeshi
    0.549 Punjabi_Jat
    0.550 Kshatriya
    0.561 Sindhi
    0.564 Karakalpak
    0.568 Russian_Kargopol
    0.569 Gujarati
    0.578 Mordovian
    0.582 Yagnobi
    0.582 Turk_South
    0.585 Finnish
    0.587 Dharkar
    0.593 Russian_Kostroma
    0.597 Balkan_Gypsy
    0.598 Kanjar
    0.603 Gypsy_Wallachia
    0.607 Uttar_Pradesh
    0.611 Turk_Anatolia
    0.614 Turk_Central_Black_Sea
    0.620 Balkar
    0.621 Kol
    0.624 Iran_Bandari
    0.625 Brahui
    0.631 Turk_Central_West
    0.634 Russian_average
    0.635 North_Kannadi
    0.635 Makrani
    0.635 Kurumba
    0.636 Velamas
    0.638 Dusadh
    0.642 Balochi
    0.645 Turk_Central_East
    0.647 Chenchu
    0.648 Kabardin
    0.656 Turk_Trakya
    0.665 Piramalai
    0.665 North-Swedish
    0.666 Southwest_Finnish
    0.667 Chamar
    0.668 Kumyk
    0.675 Parsi_India
    0.676 Estonian

    You can try it yourself by downloading R here: https://cran.r-project.org. Then paste this code to the R console:

    Code:
    t=read.csv("https://pastebin.com/raw/YeGkn84t",row.names=1,check.names=F) # K13 updated
    t=t/100
    
    fst=as.matrix(as.dist(read.csv(text=",North_Atlantic,Baltic,West_Med,West_Asian,East_Med,Red_Sea,South_Asian,East_Asian,Siberian,Amerindian,Oceanian,Northeast_African,Sub-Saharan
    North_Atlantic,,,,,,,,,,,,,
    Baltic,19,,,,,,,,,,,,
    West_Med,28,36,,,,,,,,,,,
    West_Asian,26,32,36,,,,,,,,,,
    East_Med,26,35,28,21,,,,,,,,,
    Red_Sea,52,62,50,48,39,,,,,,,,
    South_Asian,64,65,76,57,60,82,,,,,,,
    East_Asian,114,114,122,110,111,127,76,,,,,,
    Siberian,111,111,123,109,112,130,83,56,,,,,
    Amerindian,138,137,154,138,144,161,120,113,105,,,,
    Oceanian,179,181,187,177,176,191,146,166,177,217,,,
    Northeast_African,122,127,124,116,108,121,113,145,151,185,203,,
    Sub-Saharan,146,150,150,140,135,141,133,164,170,204,220,41,",row.names=1,check.names=F)))
    fst=fst/mean(fst)
    t=as.matrix(t)%*%fst
    
    d=as.matrix(dist(t))
    s=sort(d["Mari",])
    cat(paste(sprintf("%.3f",s),names(s)),sep="\n")

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    Quote Originally Posted by Nganasankhan View Post
    They're not closer if you take the matrix of FST distances between the components into account (http://bga101.blogspot.com/2013/11/u...-gedmatch.html).

    For example in K13, the Baltic component has about 6 times higher FST distance to the Siberian component (.111) than to the North_Atlantic component (.019). But Vahaduo doesn't take it into account.

    I'm not sure if it's the right way to account for the FST distances, but if you use matrix multiplication to multiply the percentages in the K13 updated datasheet with a matrix of FST distances, then the Mari average has the lowest Euclidean distance to these populations:

    0.000 Mari
    0.144 Chuvash
    0.186 Crimean_Tatar
    0.205 Bashkir
    0.232 Turkmen_Uzbekistan
    0.246 Afghan_Turkmen
    0.261 Nogay
    0.284 Tatar
    0.323 Afghan_Tadjik
    0.347 Uzbeki
    0.378 Afghan_Hazara
    0.381 Turkmen
    0.401 Tajik_Mountain
    0.403 Tajik_Lowland
    0.404 Burusho
    0.461 Pamiri_Tajikistan
    0.480 Uygur
    0.501 Hazara
    0.515 Afghan_Pashtun
    0.524 Pathan
    0.527 Brahmin_UP
    0.540 East_Finnish
    0.540 Turk_North_West
    0.541 Russian_Northern_Dvina
    0.545 Kalash
    0.546 Bangladeshi
    0.549 Punjabi_Jat
    0.550 Kshatriya
    0.561 Sindhi
    0.564 Karakalpak
    0.568 Russian_Kargopol
    0.569 Gujarati
    0.578 Mordovian
    0.582 Yagnobi
    0.582 Turk_South
    0.585 Finnish
    0.587 Dharkar
    0.593 Russian_Kostroma
    0.597 Balkan_Gypsy
    0.598 Kanjar
    0.603 Gypsy_Wallachia
    0.607 Uttar_Pradesh
    0.611 Turk_Anatolia
    0.614 Turk_Central_Black_Sea
    0.620 Balkar
    0.621 Kol
    0.624 Iran_Bandari
    0.625 Brahui
    0.631 Turk_Central_West
    0.634 Russian_average
    0.635 North_Kannadi
    0.635 Makrani
    0.635 Kurumba
    0.636 Velamas
    0.638 Dusadh
    0.642 Balochi
    0.645 Turk_Central_East
    0.647 Chenchu
    0.648 Kabardin
    0.656 Turk_Trakya
    0.665 Piramalai
    0.665 North-Swedish
    0.666 Southwest_Finnish
    0.667 Chamar
    0.668 Kumyk
    0.675 Parsi_India
    0.676 Estonian

    You can try it yourself by downloading R here: https://cran.r-project.org. Then paste this code to the R console:

    Code:
    t=read.csv("https://pastebin.com/raw/YeGkn84t",row.names=1,check.names=F) # K13 updated
    t=t/100
    
    fst=as.matrix(as.dist(read.csv(text=",North_Atlantic,Baltic,West_Med,West_Asian,East_Med,Red_Sea,South_Asian,East_Asian,Siberian,Amerindian,Oceanian,Northeast_African,Sub-Saharan
    North_Atlantic,,,,,,,,,,,,,
    Baltic,19,,,,,,,,,,,,
    West_Med,28,36,,,,,,,,,,,
    West_Asian,26,32,36,,,,,,,,,,
    East_Med,26,35,28,21,,,,,,,,,
    Red_Sea,52,62,50,48,39,,,,,,,,
    South_Asian,64,65,76,57,60,82,,,,,,,
    East_Asian,114,114,122,110,111,127,76,,,,,,
    Siberian,111,111,123,109,112,130,83,56,,,,,
    Amerindian,138,137,154,138,144,161,120,113,105,,,,
    Oceanian,179,181,187,177,176,191,146,166,177,217,,,
    Northeast_African,122,127,124,116,108,121,113,145,151,185,203,,
    Sub-Saharan,146,150,150,140,135,141,133,164,170,204,220,41,",row.names=1,check.names=F)))
    fst=fst/mean(fst)
    t=as.matrix(t)%*%fst
    
    d=as.matrix(dist(t))
    s=sort(d["Mari",])
    cat(paste(sprintf("%.3f",s),names(s)),sep="\n")
    Would you say the Vahaduo G25 distance run is overall more accurate for Volga Ural Finno-Ugrics than many Vahaduo Gedmatch such as Eurogenes K13 or Dodecad K12b?

    Thanks I will try it out.

    It's strange to me that that Mari are genetically closer to several South Asians such as Bangladeshi, Punjabi Jat, Uttar Pradesh, North_Kannadi than to Russian_Kargopol, Russian_average, Estonian, Finnish according to the FST multiplication method you posted.
    Last edited by Tsakhur; 07-05-2021 at 03:09 AM.

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    Quote Originally Posted by Kulin View Post
    Why is this surprising?
    You don't find this surprising?

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    Quote Originally Posted by Nganasankhan View Post
    They're not closer if you take the matrix of FST distances between the components into account (http://bga101.blogspot.com/2013/11/u...-gedmatch.html).

    For example in K13, the Baltic component has about 6 times higher FST distance to the Siberian component (.111) than to the North_Atlantic component (.019). But Vahaduo doesn't take it into account.

    I'm not sure if it's the right way to account for the FST distances, but if you use matrix multiplication to multiply the percentages in the K13 updated datasheet with a matrix of FST distances, then the Mari average has the lowest Euclidean distance to these populations:

    0.000 Mari
    0.144 Chuvash
    0.186 Crimean_Tatar
    0.205 Bashkir
    0.232 Turkmen_Uzbekistan
    0.246 Afghan_Turkmen
    0.261 Nogay
    0.284 Tatar
    0.323 Afghan_Tadjik
    0.347 Uzbeki
    0.378 Afghan_Hazara
    0.381 Turkmen
    0.401 Tajik_Mountain
    0.403 Tajik_Lowland
    0.404 Burusho
    0.461 Pamiri_Tajikistan
    0.480 Uygur
    0.501 Hazara
    0.515 Afghan_Pashtun
    0.524 Pathan
    0.527 Brahmin_UP
    0.540 East_Finnish
    0.540 Turk_North_West
    0.541 Russian_Northern_Dvina
    0.545 Kalash
    0.546 Bangladeshi
    0.549 Punjabi_Jat
    0.550 Kshatriya
    0.561 Sindhi
    0.564 Karakalpak
    0.568 Russian_Kargopol
    0.569 Gujarati
    0.578 Mordovian
    0.582 Yagnobi
    0.582 Turk_South
    0.585 Finnish
    0.587 Dharkar
    0.593 Russian_Kostroma
    0.597 Balkan_Gypsy
    0.598 Kanjar
    0.603 Gypsy_Wallachia
    0.607 Uttar_Pradesh
    0.611 Turk_Anatolia
    0.614 Turk_Central_Black_Sea
    0.620 Balkar
    0.621 Kol
    0.624 Iran_Bandari
    0.625 Brahui
    0.631 Turk_Central_West
    0.634 Russian_average
    0.635 North_Kannadi
    0.635 Makrani
    0.635 Kurumba
    0.636 Velamas
    0.638 Dusadh
    0.642 Balochi
    0.645 Turk_Central_East
    0.647 Chenchu
    0.648 Kabardin
    0.656 Turk_Trakya
    0.665 Piramalai
    0.665 North-Swedish
    0.666 Southwest_Finnish
    0.667 Chamar
    0.668 Kumyk
    0.675 Parsi_India
    0.676 Estonian

    You can try it yourself by downloading R here: https://cran.r-project.org. Then paste this code to the R console:

    Code:
    t=read.csv("https://pastebin.com/raw/YeGkn84t",row.names=1,check.names=F) # K13 updated
    t=t/100
    
    fst=as.matrix(as.dist(read.csv(text=",North_Atlantic,Baltic,West_Med,West_Asian,East_Med,Red_Sea,South_Asian,East_Asian,Siberian,Amerindian,Oceanian,Northeast_African,Sub-Saharan
    North_Atlantic,,,,,,,,,,,,,
    Baltic,19,,,,,,,,,,,,
    West_Med,28,36,,,,,,,,,,,
    West_Asian,26,32,36,,,,,,,,,,
    East_Med,26,35,28,21,,,,,,,,,
    Red_Sea,52,62,50,48,39,,,,,,,,
    South_Asian,64,65,76,57,60,82,,,,,,,
    East_Asian,114,114,122,110,111,127,76,,,,,,
    Siberian,111,111,123,109,112,130,83,56,,,,,
    Amerindian,138,137,154,138,144,161,120,113,105,,,,
    Oceanian,179,181,187,177,176,191,146,166,177,217,,,
    Northeast_African,122,127,124,116,108,121,113,145,151,185,203,,
    Sub-Saharan,146,150,150,140,135,141,133,164,170,204,220,41,",row.names=1,check.names=F)))
    fst=fst/mean(fst)
    t=as.matrix(t)%*%fst
    
    d=as.matrix(dist(t))
    s=sort(d["Mari",])
    cat(paste(sprintf("%.3f",s),names(s)),sep="\n")
    very good..this is more accurate because it takes in account the distances of components. Could you do the same with K15 ? The distances are found here:

    https://docs.google.com/file/d/0B9o3...CJEEBSqPXjkrZw
    Last edited by sweuro; 07-17-2021 at 12:04 PM.

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