Gedmatch results of a Saami from Norway

[Zanzibar]

Interesting. I had no idea Salar is an ethnic group in NW China. Even a couple of my Kurdish cousins are named Salar lol.


As for Kurds in Iraq it's certainly higher than their other neighbors in Iraq but E Asian or Siberian ancestry in Kurds is far from uniform just like Kurdish phenotypes are all over the place. I'm limited by the 1240K public samples which only include 2 Kayseri Turk samples and 2 Iranian-Fars samples and some Caucasian samples.

So far I have mostly done one-to-one comparisons using the Kurds with E Asian because that's more interesting to me since I'm more interested in Absolute comparisons over relative comparisons but I would think around 10% post Neolithic accumulated E Asian is probably close. I'll start doing some of those calculations soon.



EDIT: Forgot to mention there's one Central Asian pop that Kurds are very high on the IBS list with. It's Uyghurs. They are number 10 on the Uyghur IBS list. Pashtuns are also high on that list

They are a Turkic ethnic group btw.

What's the average East Eurasian amount that you have observed for Kurds? And do you think its from Steppe or from Turkic invaders into West Asia?

Is it lower than Iraqi Turkmens?

Damn that's remarkable. Maybe some type of connection with Uyghurs through shared potential Turkic affinity?

[Komintasavalta]

Here's the IBS for Finland Saami and Chukchi

Plink calculates IBS as (IBS2 + 0.5*IBS1) / (IBS0 + IBS1 + IBS2) meaning the more positions where the target and the population compared match both alleles at a position (IBS2) the higher the IBS score. What this means is since you get 1 allele from each parent that if BOTH your dad's allele and mom's allele match Saami or Chukchi at every position you get a higher IBS score

The first set of results for CHUKCHI is without removing really ancient common alleles (very ancient ancestry) from the calculation

....

Wouldn't it make sense to calculate the average IBS values of Saami relative to multiple Mongoloid populations, and not just Chukchi? Now on the second list for Chukchi, Saami rank higher than some Southeast Asians, which doesn't make sense as an overall measure of Mongoloidness.

Or could you check how populations of the Volga-Ural compare to Saami, like for example Komis or Kazan Tatars or Maris?

Couldn't we compile a 99x99 (or whatever) table of each population compared against every other population? Then couldn't we use it as a precalculated matrix of genetic distances? Or for example we could take the average values of the columns for multiple Mongoloid populations and sort the rows of the table based on it.

Sorry for being like Joqool and asking too many questions.

I combined the data you posted (without the common old alleles) into a table. I omitted the row for Chukchis because you didn't post the score of Chukchis with themselves.

Population,Chukchi,Saami
Adygei,0.8405,0.8390
Albanian.DG,0.8380,0.8337
Ami.DG,0.8422,0.8381
Armenian,0.8377,0.8349
Balochi,0.8361,0.7755
BantuHerero,0.7795,0.7803
BantuKenya,0.7834,0.7757
BantuTswana,0.7776,0.8405
Basque,0.8383,0.8329
BedouinB,0.8319,0.8256
Bougainville,0.8303,0.8351
Brahui,0.8370,0.8401
Bulgarian,0.8393,0.8354
Burmese,0.8435,0.8378
Burusho,0.8397,0.8392
Chechen,0.8395,0.8339
China_Lahu,0.8425,0.8439
Czech,0.8394,0.8419
Dai,0.8428,0.8341
Daur,0.8466,0.8380
Druze,0.8335,0.8359
Dusun,0.8416,0.8327
English,0.8404,0.8417
Esan,0.7791,0.7762
Eskimo_Naukan.DG,0.8537,0.8383
Eskimo_Sireniki.DG,0.8540,0.8387
Estonian,0.8410,0.8429
Even,0.8482,0.8400
Finnish,0.8422,0.8442
French,0.8388,0.8397
Gambian,0.7817,0.7786
Georgian,0.8364,0.8378
Greek,0.8383,0.8386
Han,0.8460,0.8358
Hazara,0.8433,0.8391
Hezhen,0.8479,0.8381
Hungarian,0.8400,0.8409
Icelandic,0.8403,0.8420
Iranian-Fars,0.8386,0.8372
Irula,0.8367,0.8340
Itelmen,0.8527,0.8377
Japanese,0.8461,0.8359
Jew_Iraqi,0.8354,0.8358
Jordanian,0.8317,0.8321
Ju_hoan_North,0.7687,0.7657
Kalash,0.8378,0.8369
Kapu,0.8376,0.8349
Karitiana,0.8441,0.8348
Khomani_San,0.7690,0.7661
Kinh,0.8436,0.8346
Korean,0.8455,0.8370
Kurds-IRAQ,0.8424,0.8417
Kusunda,0.8388,0.8317
Kyrgyz_Kyrgyzstan,0.8466,0.8405
Lezgin,0.8386,0.8396
Luhya,0.7825,0.7793
Luo,0.7836,0.7799
Makrani,0.8353,0.8341
Mala,0.8380,0.8341
Mandenka,0.7817,0.7780
Mansi,0.8477,0.8436
Masai,0.7974,0.7952
Mayan,0.8464,0.8366
Mbuti,0.7729,0.7702
Mende,0.7801,0.7769
Mixe,0.8458,0.8358
Mixtec,0.8458,0.8373
Mongola,0.8465,0.8373
Mozabite,0.8215,0.8219
Norwegian,0.8405,0.8421
Orcadian,0.8397,0.8414
Papuan,0.8270,0.8222
Pathan,0.8399,0.8375
Piapoco,0.8454,0.8350
Pima,0.8462,0.8361
Polish,0.8408,0.8417
Punjabi,0.8377,0.8362
Russia_Abkhasian,0.8387,0.8391
Russia_NorthOssetian,0.8387,0.8390
Russian,0.8425,0.8436
Saami,0.8439,0.8527
Saharawi,0.8235,0.8226
Samaritan,0.8321,0.8324
Sardinian,0.8373,0.8388
She,0.8448,0.8353
Sindhi,0.8383,0.8367
Somali,0.8065,0.8049
Spanish,0.8384,0.8398
Surui,0.8438,0.8337
Tajik,0.8396,0.8380
Tuscan,0.8389,0.8390
Ulchi,0.8473,0.8372
Uyghur,0.8440,0.8391
Xibo,0.8460,0.8367
Yadava,0.8388,0.8353
Yakut,0.8474,0.8386
Yi,0.8445,0.8349

Here's the data as a plot. I used hierarchical K-means clustering to divide the populations into 16 groups.

library(ggplot2);library(factoextra);library(colorspace)

download.file("https://pastebin.com/raw/FnTJmkAj","chukchisaami")

t<-read.csv("chukchisaami",header=T,row.names=1)
k<-hkmeans(t,16)

ggplot(t,aes(x=Chukchi,y=Saami,label=row.names(t)))+
geom_text(aes(color=as.factor(k$cluster)),size=2.5,show.legend=F)+
theme(
  plot.background=element_rect(fill="gray35"),
  panel.grid.major=element_line(color="gray35"),
  text=element_text(color="gray10"),
  axis.text=element_text(color="gray10"),
  axis.ticks.x=element_blank(),
  axis.ticks.y=element_blank(),
  panel.grid.minor=element_blank()
)+
coord_fixed()+
xlab("430K SNPs - Max-Maf 0.3 - Chukchi")+
ylab("430K SNPs - Max-Maf 0.3 - Saami-Finland")+
scale_color_discrete_qualitative(palette="Set 3")
ggsave("/tmp/a.png")

I couldn't get the version of plink from here to compile with either gcc or clang, and when I tried running the Mac binary, it said `Bad CPU type in executable`: https://zzz.bwh.harvard.edu/plink/download.shtml. But now I realized that there's a version by another developer here, with a working Mac binary: http://www.cog-genomics.org/plink/2.0/. I'll figure it out eventually.

[Ajeje Brazorf]

I checked your G25 Chukchi list. I see a couple of issues with it:

1- It starts out good. The 1st few on the list down to Kirgiz or so look fine . They sort of mirror the 430K IBS list I posted but then things start to go horribly wrong with it. I'll just give one example. Look at Kurds-Iraq on the IBS list. You'll notice that Kurds-Iraq are like number 33 on the Chukchi and they're certainly higher than most Europeans and S Asians which makes sense. Yet on the G25 list they're way down.

The reason for this issue with the G25 is these distances are based on G25 coordinates and of course the coordinates are totally dependent on all the other samples in the run. This of course is problematic because each sample out of the 1000s he uses either pulls or pushes on another sample in some way or form. For example, some W Asian or Europeans samples pull Kurds close to them which in effect distances them from Chukchi.

Whereas with IBS you don't have all this pulling and pushing. Each sample is compared directly with Chukchi allele for allele and checked to see how many alleles it shares with Chukchi. So there's really nothing that can go wrong. You just add up the number of alleles Kurds or some other pop shares with Chukchi. It's that simple. The reason Kurds are so high on the Chukchi list is that there are many positions in their genome where they don't just share 1 allele with Chukchi but rather they share both alleles with Chukchi.

2- The other weird thing I noticed with G25 is once you pass the 1st 10 or so pops on the list the distances start getting huge. This of course also distorts things. If you check the IBS list the distances don't get out of control when you start heading to the bottom of the list.

So for example, would Chukchi really be more genetically akin to Iraqi Kurds than Tajiks? Wow. This contradicts what has been seen in the "vulgar" GEDmatch calculators and on Global25 in general. After years, most of what I consider normal is now based on what those two tools easily accessible to anyone show. As for distances soaring, this happens especially in scaled coordinates but not in unscaled ones. I'll post the unscaled distances and tell me if you find more correlation with your distances. And most importantly, in your opinion, between scaled and unscaled coordinates which one is less worse?

Here's the IBS for Finland Saami and Chukchi

Plink calculates IBS as (IBS2 + 0.5*IBS1) / (IBS0 + IBS1 + IBS2) meaning the more positions where the target and the population compared match both alleles at a position (IBS2) the higher the IBS score. What this means is since you get 1 allele from each parent that if BOTH your dad's allele and mom's allele match Saami or Chukchi at every position you get a higher IBS score

The first set of results for CHUKCHI is without removing really ancient common alleles (very ancient ancestry) from the calculation

615K SNPs - Max-Maf 0.5 - Chukchi
POPULATION	IBS
Eskimo_Sireniki.DG	0.7921
Eskimo_Naukan.DG	0.7914
Itelmen	0.7911
Mansi	0.7873
Kyrgyz_Kyrgyzstan	0.7864
Even	0.7860
Yakut	0.7856
Hezhen	0.7851
Uyghur	0.7848
Mongola	0.7847
Ulchi	0.7847
Xibo	0.7840
Mixtec	0.7835
Daur	0.7831
Japanese	0.7829
Han	0.7829
Saami	0.7825
Korean	0.7825
Hazara	0.7821
Yi	0.7818
Burmese	0.7818
Finnish	0.7817
Mayan	0.7816
Russian	0.7814
She	0.7814
Mixe	0.7808
Norwegian	0.7801
Pima	0.7801
Kinh	0.7801
Estonian	0.7801
Icelandic	0.7800
Polish	0.7799
Kurds-IRAQ	0.7799
Piapoco	0.7798
Burusho	0.7794
English	0.7794
Hungarian	0.7793
Pathan	0.7793
Dai	0.7790
China_Lahu	0.7786
Bulgarian	0.7784
Adygei	0.7784
Czech	0.7784
Orcadian	0.7783
Tajik	0.7779
Chechen	0.7778
French	0.7776
Tuscan	0.7776
Ami.DG	0.7775
Russia_NorthOssetian	0.7775
Iranian-Fars	0.7775
Dusun	0.7774
Spanish	0.7773
Russia_Abkhasian	0.7772
Greek	0.7771
Lezgin	0.7770
Yadava	0.7770
Sindhi	0.7767
Basque	0.7767
Turkish	0.7766
Kapu	0.7765
Mala	0.7764
Karitiana	0.7763
Albanian.DG	0.7761
Punjabi	0.7761
Armenian	0.7759
Sardinian	0.7754
Georgian	0.7753
Surui	0.7753
Brahui	0.7747
Irula	0.7747
Kalash	0.7744
Balochi	0.7737
Jew_Iraqi	0.7736
Kusunda	0.7733
Makrani	0.7730
Druze	0.7717
BedouinB	0.7702
Jordanian	0.7691
Samaritan	0.7670
Saharawi	0.7634
Bougainville	0.7626
Mozabite	0.7623
Papuan	0.7568
Somali	0.7475
Masai	0.7401
Luo	0.7274
BantuKenya	0.7270
Luhya	0.7261
Mandenka	0.7256
Gambian	0.7254
Mende	0.7242
BantuHerero	0.7236
Esan	0.7231
BantuTswana	0.7217
Mbuti	0.7147
Ju_hoan_North	0.7105
Khomani_San	0.7095

This set for CHUKCHI is after removing common old alleles (ancestry). All alleles having a minor allele frequency > 30% have been removed. I like this better because it tends to better reflect more recent ancestry

430K SNPs - Max-Maf 0.3 - Chukchi
POPULATION	IBS
Eskimo_Sireniki.DG	0.8540
Eskimo_Naukan.DG	0.8537
Itelmen	0.8527
Even	0.8482
Hezhen	0.8479
Mansi	0.8477
Yakut	0.8474
Ulchi	0.8473
Daur	0.8466
Kyrgyz_Kyrgyzstan	0.8466
Mongola	0.8465
Mayan	0.8464
Pima	0.8462
Japanese	0.8461
Xibo	0.8460
Han	0.8460
Mixtec	0.8458
Mixe	0.8458
Korean	0.8455
Piapoco	0.8454
She	0.8448
Yi	0.8445
Karitiana	0.8441
Uyghur	0.8440
Saami	0.8439
Surui	0.8438
Kinh	0.8436
Burmese	0.8435
Hazara	0.8433
Dai	0.8428
Russian	0.8425
China_Lahu	0.8425
Kurds-IRAQ	0.8424
Finnish	0.8422
Ami.DG	0.8422
Dusun	0.8416
Estonian	0.8410
Polish	0.8408
Adygei	0.8405
Norwegian	0.8405
English	0.8404
Icelandic	0.8403
Hungarian	0.8400
Pathan	0.8399
Orcadian	0.8397
Burusho	0.8397
Tajik	0.8396
Chechen	0.8395
Czech	0.8394
Bulgarian	0.8393
Tuscan	0.8389
French	0.8388
Yadava	0.8388
Kusunda	0.8388
Russia_NorthOssetian	0.8387
Russia_Abkhasian	0.8387
Lezgin	0.8386
Iranian-Fars	0.8386
Spanish	0.8384
Basque	0.8383
Sindhi	0.8383
Greek	0.8383
Albanian.DG	0.8380
Mala	0.8380
Kalash	0.8378
Armenian	0.8377
Punjabi	0.8377
Kapu	0.8376
Sardinian	0.8373
Brahui	0.8370
Irula	0.8367
Georgian	0.8364
Balochi	0.8361
Jew_Iraqi	0.8354
Makrani	0.8353
Druze	0.8335
Samaritan	0.8321
BedouinB	0.8319
Jordanian	0.8317
Bougainville	0.8303
Papuan	0.8270
Saharawi	0.8235
Mozabite	0.8215
Somali	0.8065
Masai	0.7974
Luo	0.7836
BantuKenya	0.7834
Luhya	0.7825
Gambian	0.7817
Mandenka	0.7817
Mende	0.7801
BantuHerero	0.7795
Esan	0.7791
BantuTswana	0.7776
Mbuti	0.7729
Khomani_San	0.7690
Ju_hoan_North	0.7687

This set for SAAMI is after removing common old alleles (ancestry). All alleles having a minor allele frequency > 30% have been removed. I like this better because it tends to better reflect more recent ancestry

430K SNPs - Max-Maf 0.3 - Saami -Finland
POPULATION	IBS
Saami	0.8527
Finnish	0.8442
Chukchi	0.8439
Mansi	0.8436
Russian	0.8436
Estonian	0.8429
Norwegian	0.8421
Icelandic	0.8420
Czech	0.8419
Polish	0.8417
English	0.8417
Kurds - IRAQ	0.8417
Orcadian	0.8414
Hungarian	0.8409
Adygei	0.8405
Basque	0.8405
Kyrgyz_Kyrgyzstan	0.8405
Bulgarian	0.8401
Even	0.8400
Spanish	0.8398
French	0.8397
Lezgin	0.8396
Chechen	0.8392
Uyghur	0.8391
Hazara	0.8391
Russia_Abkhasian	0.8391
Russia_NorthOssetian	0.8390
Tuscan	0.8390
Albanian.DG	0.8390
Sardinian	0.8388
Eskimo_Sireniki.DG	0.8387
Yakut	0.8386
Greek	0.8386
Eskimo_Naukan.DG	0.8383
Hezhen	0.8381
Armenian	0.8381
Daur	0.8380
Tajik	0.8380
Burusho	0.8378
Georgian	0.8378
Itelmen	0.8377
Pathan	0.8375
Mongola	0.8373
Mixtec	0.8373
Ulchi	0.8372
Iranians - Fars	0.8372
Korean	0.8370
Kalash	0.8369
Sindhi	0.8367
Xibo	0.8367
Mayan	0.8366
Punjabi	0.8362
Pima	0.8361
Japanese	0.8359
Druze	0.8359
Mixe	0.8358
Han	0.8358
Jew_Iraqi	0.8358
Burmese	0.8354
She	0.8353
Yadava	0.8353
Brahui	0.8351
Piapoco	0.8350
Kapu	0.8349
Yi	0.8349
Balochi	0.8349
Karitiana	0.8348
Kinh	0.8346
Mala	0.8341
Makrani	0.8341
Dai	0.8341
Irula	0.8340
China_Lahu	0.8339
Ami.DG	0.8337
Surui	0.8337
BedouinB	0.8329
Dusun	0.8327
Samaritan	0.8324
Jordanian	0.8321
Kusunda	0.8317
Bougainville	0.8256
Saharawi	0.8226
Papuan	0.8222
Mozabite	0.8219
Somali	0.8049
Masai	0.7952
BantuKenya	0.7803
Luo	0.7799
Luhya	0.7793
Gambian	0.7786
Mandenka	0.7780
Mende	0.7769
Esan	0.7762
BantuTswana	0.7757
BantuHerero	0.7755
Mbuti	0.7702
Khomani_San	0.7661
Ju_hoan_North	0.7657

Distance to: Chukchi

Spoiler!

0.02354426	Eskimo_Sireniki
0.02385687	Itelmen
0.02435038	Koryak
0.02594064	Eskimo_Chaplin
0.03086655	Eskimo
0.03359152	Eskimo_Naukan
0.04069513	Greenlander_East
0.04966897	Greenlander_West
0.05955621	Yukagir_Tundra
0.07471429	Tlingit
0.07718228	Even
0.08009604	Negidal
0.08496246	Yukagir_Forest
0.08672651	Ulchi
0.09114151	Nivkh
0.09226023	Nanai
0.09405305	Nganassan
0.09763242	Evenk
0.09785663	Oroqen
0.09960749	Khamnegan
0.10336127	Todzin
0.10403733	Nenets
0.10430008	Cree
0.10430962	Mogush
0.10438220	Tatar_Siberian
0.10542034	Tuvinian
0.10698304	Hezhen
0.10747890	Bashkir
0.10770182	Tatar_Siberian_Zabolotniye
0.10778348	Saami
0.10801949	Chipewyan
0.10903791	Kirghiz
0.10964025	Kazakh
0.10983849	Khakass_Kachins
0.10992862	Altaian
0.11039207	Karakalpak
0.11087479	Udmurt
0.11155696	Kirghiz_China
0.11171845	Selkup
0.11176697	Kalmyk
0.11205860	Khakass
0.11211538	Saami_Kola
0.11211844	Buryat
0.11237019	Besermyan
0.11239256	Tatar_Crimean_steppe
0.11251735	Kazakh_China
0.11257707	Nogai
0.11289023	Hazara
0.11290850	Komi
0.11331383	Turkmen_Uzbekistan
0.11346318	Daur
0.11355366	Hazara_Afghanistan
0.11362959	Mongolian
0.11387833	Uzbek
0.11390229	Tatar_Kazan
0.11416330	Turkmen
0.11428662	Tatar_Lipka
0.11511547	Amerindian_North
0.11527545	Uygur
0.11531743	Tubalar
0.11623223	Khanty
0.11675042	Mansi
0.11676234	Turkish_South
0.11703852	Turkish_Aydin
0.11706098	Shor_Khakassia
0.11717274	Turkish_Rumeli
0.11724582	Turkish_Northwest
0.11726276	Tatar_Mishar
0.11780731	Turkish_Southwest
0.11813502	Turkish_Balikesir
0.11896454	Turkish_North
0.11902240	Turkish_Central
0.11910909	Turkish_Adana
0.11911532	Turkish_Deliorman
0.11914997	Dolgan
0.11920853	Shor
0.11923200	Vepsian
0.11978687	Finnish_East
0.11986855	Tajik
0.12006801	Tajik_Shugnan
0.12014845	Azeri_Turkey
0.12015675	Shor_Mountain
0.12020062	Karelian
0.12024623	Finnish
0.12050528	Italian_Calabria
0.12053540	Russian_Pinega
0.12069967	Roma_Porto
0.12070805	Ashkenazi_Poland
0.12079352	Ashkenazi_Ukraine
0.12080077	Ashkenazi_Russia
0.12081823	Ashkenazi_Lithuania
0.12094511	Roma_Balkans
0.12094942	Sephardic_Jew
0.12103786	Rumelia_East
0.12107358	Turkish_East
0.12107517	Mountain_Jew
0.12108928	Roma_Granada
0.12113365	Ashkenazi_Belarussia
0.12120662	Bosnian
0.12126015	Azeri
0.12134961	Maltese
0.12135867	Cherkes
0.12139484	Lebanese_Muslim
0.12139789	Swiss_Italian
0.12140453	Turkish_Kayseri
0.12143149	Mordovian
0.12158306	Talysh_Azerbaijan
0.12163859	Greek_Macedonia
0.12164504	Syrian
0.12165596	Mongola
0.12168128	Albanian
0.12170438	Slovenian
0.12174062	Bulgarian
0.12176974	Ashkenazi_Germany
0.12178845	Ingrian
0.12180587	Xibo
0.12183413	Greek_Peloponnese
0.12183664	Iraqi
0.12185091	Romanian
0.12188574	Italian_Jew
0.12191847	Kumyk
0.12196573	Austrian
0.12197379	Roma_Bilbao
0.12206559	Romaniote_Jew
0.12207723	Roma_Barcelona
0.12208745	Moldovan
0.12212008	Greek_Central_Macedonia
0.12212811	Montenegrin
0.12216095	Abazin
0.12218176	Italian_Basilicata
0.12220194	Russian_Kostroma
0.12224117	Roma_Madrid
0.12227294	Greek_Dodecanese
0.12229122	Iranian_Fars
0.12229811	Sicilian_East
0.12230970	Iranian_Lor
0.12235552	Ezid
0.12238798	Italian_Abruzzo
0.12240662	Moldovan_o
0.12246975	Iranian_Seyyed
0.12247321	Kabardin
0.12249362	Gagauz
0.12249838	Syrian_Jew
0.12250173	Greek_Crete
0.12253492	Udi
0.12256460	Parsi_Pakistan
0.12257270	Azeri_Dagestan
0.12259073	Italian_Marche
0.12262349	Italian_Northeast
0.12267550	Italian_Molise
0.12276653	Greek_Izmir
0.12282515	Turkish_Trabzon
0.12284943	Hungarian
0.12287783	Libyan_Jew
0.12290040	Lebanese_Christian
0.12292780	Serbian
0.12297603	Greek_Cappadocia
0.12298549	Spanish_Murcia
0.12299543	Croatian
0.12301485	Italian_Tuscany
0.12305433	Italian_Bergamo
0.12306548	Lebanese_Druze
0.12307244	German_East
0.12309356	Parsi_India
0.12310006	Greek_Thessaly
0.12312821	French_Nord
0.12314950	Italian_Apulia
0.12316775	Italian_Umbria
0.12319150	Italian_Campania
0.12323757	French_Alsace
0.12325935	German
0.12327604	Italian_Liguria
0.12328849	Iranian_Zoroastrian
0.12335007	Afrikaner
0.12335086	Czech
0.12338234	Kho_Singanali
0.12340392	Tunisian_Jew
0.12341751	Greek_Central_Anatolia
0.12348593	Chuvash
0.12355747	Italian_Lombardy
0.12358340	Druze
0.12366347	Tajik_Ishkashim
0.12367993	Cypriot
0.12372397	Circassian
0.12374242	Macedonian
0.12374898	Tajik_Yagnobi
0.12376101	Kurdish
0.12376614	Italian_Piedmont
0.12385357	Kaitag
0.12388062	Uthmankhel
0.12392586	Greek_Laconia
0.12395644	Burusho
0.12408085	Karachay
0.12408518	Belgian
0.12410495	Greek_Kos
0.12415247	Slovakian
0.12415490	Georgian_Jew
0.12415579	Pashtun
0.12416487	French_Provence
0.12416533	Italian_Aosta_Valley
0.12420188	Jatt_Pathak
0.12421259	Iranian_Jew
0.12423472	Sicilian_West
0.12429272	Russian_Kursk
0.12429739	Swiss_German
0.12432820	Jordanian
0.12441909	Swedish
0.12452413	Ingushian
0.12453529	French_Corsica
0.12454481	Iranian_Bandari
0.12463384	Italian_Veneto
0.12464378	Palestinian
0.12465318	Tabasaran
0.12465612	Italian_Lazio
0.12466509	Swiss_French
0.12468906	Tajik_Rushan
0.12473518	Cossack_Kuban
0.12473881	Spanish_Cataluna
0.12484674	Moroccan_Jew
0.12484730	Bahun
0.12485052	Yusufzai
0.12491344	Balkar
0.12491523	Armenian
0.12498813	Spanish_Lleida
0.12517568	Russian_Orel
0.12519493	Spanish_Extremadura
0.12519494	Shetlandic
0.12521335	Egyptian
0.12522679	Kubachinian
0.12523232	Dutch
0.12526135	BedouinA
0.12528115	Tarkalani
0.12529671	French_Auvergne
0.12530863	Italian_Trentino-Alto-Adige
0.12534063	Russian_Voronez
0.12534473	Ukrainian
0.12534612	Spanish_Penedes
0.12534831	Spanish_Asturias
0.12535228	Iranian_Mazandarani
0.12536983	Danish
0.12538600	Karaite_Egypt
0.12541074	Spanish_Girona
0.12542330	Spanish_Canarias
0.12542539	Assyrian
0.12549330	Ror
0.12549746	Kamboj
0.12553883	French_Pas-de-Calais
0.12559679	Greek_Trabzon
0.12559773	French_Brittany
0.12560568	English
0.12561489	Iraqi_Jew
0.12563535	French_Occitanie
0.12564728	Avar
0.12575032	Armenian_Hemsheni
0.12576274	Polish
0.12577275	Spanish_Peri-Barcelona
0.12578923	Brahmin_Manipuri
0.12579666	Palestinian_Beit_Sahour
0.12586091	Adygei
0.12589220	Dongxiang
0.12593429	Spanish_Mallorca
0.12601023	Lak
0.12608382	Orcadian
0.12608653	Icelandic
0.12611708	Norwegian
0.12616014	Welsh
0.12617455	Spanish_Valencia
0.12621543	Spanish_Baleares
0.12624898	Spanish_Camp_de_Tarragona
0.12627327	English_Cornwall
0.12632829	Spanish_Galicia
0.12644075	Cossack_Ukrainian
0.12644356	Scottish
0.12651022	Portuguese
0.12660649	French_Paris
0.12663090	Russian_Tver
0.12670022	Chechen
0.12675025	Spanish_Castilla_La_Mancha
0.12675045	Kohistani
0.12678721	Sorb_Niederlausitz
0.12685577	Spanish_Barcelones
0.12686608	Irish
0.12688566	Punjabi_Jatt
0.12699296	Spanish_Castello
0.12704283	Darginian
0.12704904	Estonian
0.12706813	Spanish_Alacant
0.12713545	Khatri
0.12718285	Spanish_Andalucia
0.12720813	Punjabi_Sikh_India
0.12726636	Salar
0.12731238	Spanish_Catalunya_Central
0.12739491	Spanish_Eivissa
0.12741756	Kashmiri_Pandit
0.12742994	Spanish_Terres_de_l'Ebre
0.12753571	Spanish_Pirineu
0.12757183	Spanish_Menorca
0.12772810	Samaritan
0.12778746	Russian_Smolensk
0.12783277	Georgian_Laz
0.12786392	Kalash
0.12787669	Sindhi
0.12795467	Belarusian
0.12800331	Berber_Algeria
0.12818668	Gujar_Pakistan
0.12822204	Spanish_Aragon
0.12823100	French_Seine-Maritime
0.12830021	Spanish_Castilla_Y_Leon
0.12832941	North_Ossetian
0.12844211	Tharu
0.12845537	Newar
0.12850601	Libyan
0.12873995	Ossetian
0.12893764	Gujar_India
0.12900096	Brahmin_Gujarat
0.12901634	Spanish_Cantabria
0.12907200	Burmese
0.12912527	Maori
0.12915484	Brahmin_West_Bengal
0.12925670	Abkhasian
0.12929465	Spanish_Navarra
0.12943315	Ket
0.12945022	Dungan
0.12956750	Lithuanian_PA
0.12972271	Tripuri
0.12973513	French_South
0.13009483	Lithuanian_VA
0.13024980	Kshatriya
0.13026016	Tu
0.13029731	Spanish_La_Rioja
0.13030455	Spanish_Pais_Vasco
0.13052450	Lithuanian_RA
0.13071474	Ronga
0.13087858	Bonan
0.13088234	Jamatia
0.13090653	Kikuyu
0.13091582	Lithuanian_VZ
0.13098812	Makrani
0.13100523	Tunisian_Douz
0.13117148	Ngumba
0.13117653	Thai
0.13131538	Yugur
0.13144649	Balochi
0.13150807	Brahui
0.13152257	Georgian_Imer
0.13164350	Nyaneka
0.13177731	Tibetan_Gangcha
0.13184653	Laka
0.13193155	Algerian
0.13197803	Lithuanian_SZ
0.13198816	Latvian
0.13209154	Mwani
0.13210535	Spanish_Soria
0.13234158	Iyer
0.13235724	Kusunda
0.13244128	Tibetan_Xunhua
0.13244908	Magar
0.13245201	Tibetan_Gannan
0.13268893	Tamang
0.13279111	Lithuanian_PZ
0.13284926	Makhuwa
0.13288420	Basque_French
0.13293164	Basque_Spanish
0.13309819	Berber_Tunisia_Sen
0.13314748	Umbundu
0.13319162	Ganguela
0.13320555	Tunisian
0.13328467	Tswa
0.13334671	Bantu_S.W.
0.13337344	Korean
0.13345663	Tikar_South
0.13347806	Lemande
0.13349433	Onge
0.13350683	Mari
0.13356204	Bengali_Bangladesh
0.13356717	Brahmin_Tamil_Nadu
0.13362438	Kongo
0.13364536	Cameroon_Bafut
0.13371542	Sena
0.13373854	Ndau
0.13374888	Mada
0.13385497	Chopi
0.13387560	Cameroon_Aghem
0.13390890	Changana
0.13393989	Nyanja
0.13395575	Fulani
0.13410615	Tibetan_Xinlong
0.13411220	Jarawa
0.13415501	Tunisian_Berber_Matmata
0.13417232	Bitonga
0.13418660	Punjabi_Lahore
0.13421036	Luo
0.13435382	Lahu
0.13438254	Cameroon_Mbo
0.13441151	Gurung
0.13442176	Yao
0.13446105	Han_Hubei
0.13449082	Yemenite_Amran
0.13457589	Han_Sichuan
0.13477023	Gambian
0.13479618	Mende_Sierra_Leone
0.13490342	Kaba
0.13504933	Han_Chongqing
0.13509350	Sandawe
0.13510331	Moroccan
0.13518553	Mandenka
0.13528979	Manyika
0.13529621	Velamas
0.13533697	Gujarati
0.13546987	Han_Shandong
0.13551711	Kinh_Vietnam
0.13586309	Yi
0.13595173	Luhya_Kenya
0.13602314	Yemenite_Dhamar
0.13603096	Cameroon_Bakoko
0.13608641	Kol
0.13611418	Tujia
0.13615416	Japanese
0.13632360	Bulala
0.13639176	Brahmin_Uttar_Pradesh
0.13639884	Tibetan_Shigatse
0.13642598	Kamboj_o
0.13646228	Cambodian
0.13663606	Tibetan_Shannan
0.13664150	Han_Shanxi
0.13677956	Kanjar
0.13679579	Rai
0.13681556	Tunisian_Berber_Tamezret
0.13685726	Moroccan_North
0.13692183	Moroccan_South
0.13707741	Dong_Guizhou
0.13708048	Tunisian_Berber_Zraoua
0.13714643	Bantu_Kenya
0.13717368	Tibetan_Lhasa
0.13724876	Dharkar
0.13730936	Igbo
0.13735506	Dong_Hunan
0.13749719	Yadava
0.13760505	Zhuang
0.13763415	Gelao
0.13765028	Tibetan_Yunnan
0.13766704	Han_Henan
0.13767242	Han_Jiangsu
0.13777384	Han_Fujian
0.13781969	Jehai
0.13786166	Tunisian_Rbaya
0.13790353	Tibetan_Yajiang
0.13798960	Naxi
0.13806962	Sherpa
0.13809153	Tibetan_Nagqu
0.13813438	Cameroon_Bangwa
0.13825141	Miao
0.13826746	Han_Guangdong
0.13834644	Mulam
0.13848252	Chenchu
0.13866897	Han_Zhejiang
0.13870099	Piramalai
0.13895767	Dai
0.13899533	Tibetan_Chamdo
0.13910975	Maonan
0.13929306	Esan_Nigeria
0.13932423	Yemenite_Ma'rib
0.13936292	Malay
0.13938668	Dusadh
0.13943038	Gupta
0.13943359	Yemenite_Jew
0.13946523	Mozabite
0.13950764	Yoruba
0.13966780	Qiang_Daofu
0.13972034	Qiang_Danba
0.13977621	Relli
0.13994063	Elmolo
0.14003704	Kurumba
0.14021308	Maratha
0.14025725	Uttar_Pradesh
0.14054315	Yemenite_Al_Bayda
0.14057616	She
0.14083223	Ethiopian_Tigray
0.14086792	Berber_MAR_ERR
0.14096617	Ethiopian_Amhara
0.14145210	Eritrean
0.14147559	Berber_Tunisia_Chen
0.14157401	Ethiopian_Afar
0.14166514	Bantu_S.E.
0.14170577	Ethiopian_Agaw
0.14186250	Ogiek
0.14190276	Ethiopian_Oromo
0.14192083	Pallan
0.14216617	Chamar
0.14224111	Han_Shanghai
0.14231370	Sardinian
0.14246623	Yemenite_Al_Jawf
0.14279902	Li
0.14288854	Ethiopian_Wolayta
0.14293443	Bajo
0.14298959	Hakkipikki
0.14338653	Madiga
0.14350672	North_Kannadi
0.14355086	Ethiopian_Jew
0.14357563	Iraqw
0.14360938	Aeta
0.14363769	Sakilli
0.14444001	Hadza
0.14460808	Saudi
0.14465280	BedouinB
0.14486485	Saharawi
0.14494387	Bonda
0.14509721	Asur
0.14515580	Rendille
0.14538998	Agta
0.14544749	Gond
0.14547649	Irula
0.14579323	Gadaba
0.14590516	Indonesian_Bali
0.14594878	Htin_Mal
0.14597497	Mala
0.14599243	Korwa
0.14601512	Batak
0.14610145	Somali_Kenya
0.14623558	Bhumij
0.14656172	Masai
0.14670484	Datog
0.14672734	Somali
0.14673892	Vizayan
0.14704254	Juang
0.14750209	Sengwer
0.14758904	Santhal
0.14767654	Kadar
0.14771804	Yemenite_Mahra
0.14773153	Ho
0.14774460	Indonesian_Java
0.14790026	Birhor
0.14804119	Khonda_Dora
0.14837652	Ethiopian_Ari_blacksmith
0.14838032	Berber_MAR_TIZ
0.14935128	Sudanese
0.14935646	Pulliyar
0.14939213	Ethiopian_Ari_cultivator
0.14960527	Malayan
0.15005299	Mlabri
0.15016904	Paniya
0.15021805	Lebbo
0.15061654	Ethiopian_Ari
0.15067149	Dinka
0.15140928	Luzon
0.15151599	Ethiopian_Anuak
0.15286375	Atayal
0.15320478	Ethiopian_Gumuz
0.15326294	Ami
0.15358694	Hawaiian
0.15474564	Ethiopian_Mursi
0.15728364	Murut
0.15740862	Dusun
0.16119843	Yakut
0.16211852	Sakha
0.16246682	Bedzan
0.16261061	Igorot
0.16829042	Huichol
0.16878742	Bakola
0.16946642	Mixtec
0.16951658	Bolivian_Pando
0.16955864	Zapotec
0.17029353	Pima
0.17092525	Nahua
0.17317379	Bolivian_Cochabamba
0.17330201	Mayan
0.17417610	Quechua
0.17565315	Mixe
0.17738924	Baka
0.17942077	Cachi
0.18185645	Nasoi
0.18392835	Bolivian_LaPaz
0.18410787	Colla
0.18805283	Aymara
0.19209202	Yukpa
0.19342495	Piapoco
0.19372827	Wichi
0.19459449	Biaka
0.19692696	Australian
0.20397956	Karitiana
0.20865096	Surui
0.25045942	Kosipe
0.25748241	Papuan
0.25788324	Koinanbe
0.29120486	Khomani_San
0.29863231	Mbuti
0.40028500	Ju_hoan_North

Distance to: Saami

Spoiler!

0.01578586	Saami_Kola
0.02260558	Komi
0.02551704	Udmurt
0.02716984	Besermyan
0.02980795	Tatar_Kazan
0.02997168	Finnish_East
0.03103574	Karelian
0.03203312	Russian_Pinega
0.03285290	Ingrian
0.03302906	Vepsian
0.03323679	Finnish
0.03496521	Bashkir
0.03526525	Tatar_Mishar
0.03639229	Russian_Kostroma
0.03721062	Tatar_Siberian
0.03938000	Mordovian
0.04043167	Cossack_Kuban
0.04200856	Tatar_Siberian_Zabolotniye
0.04378006	Estonian
0.04461112	Swedish
0.04506173	Yukagir_Forest
0.04544374	Chuvash
0.04597315	Czech
0.04612284	Russian_Kursk
0.04614997	Tatar_Lipka
0.04627660	Russian_Tver
0.04646907	Slovakian
0.04698813	Polish
0.04701985	Hungarian
0.04713931	Moldovan_o
0.04731010	German_East
0.04749170	Russian_Voronez
0.04774898	German
0.04788956	Russian_Orel
0.04802727	Austrian
0.04802792	Ukrainian
0.04858120	Danish
0.04886887	Cossack_Ukrainian
0.04893080	Norwegian
0.04909244	Icelandic
0.04944315	Croatian
0.04970943	Afrikaner
0.04979897	Dutch
0.05034850	Sorb_Niederlausitz
0.05077894	Russian_Smolensk
0.05081844	Tatar_Crimean_steppe
0.05116609	Slovenian
0.05118982	Lithuanian_VA
0.05122421	Belarusian
0.05145291	Lithuanian_PA
0.05162914	Orcadian
0.05177203	Shetlandic
0.05192921	Welsh
0.05201994	Scottish
0.05204844	Moldovan
0.05209198	French_Pas-de-Calais
0.05235815	English
0.05267323	French_Alsace
0.05269180	French_Brittany
0.05284070	Bosnian
0.05295477	French_Nord
0.05300070	Lithuanian_VZ
0.05300762	Lithuanian_SZ
0.05329691	French_Seine-Maritime
0.05334464	Belgian
0.05336308	Latvian
0.05348784	Irish
0.05367781	Turkish_Deliorman
0.05377871	Lithuanian_RA
0.05395478	English_Cornwall
0.05405685	Lithuanian_PZ
0.05415237	Nogai
0.05455616	Swiss_German
0.05488165	Montenegrin
0.05491692	Serbian
0.05551621	Turkish_Rumeli
0.05552509	Uzbek
0.05563769	Turkmen_Uzbekistan
0.05604596	Romanian
0.05640443	Turkmen
0.05697759	Karakalpak
0.05713048	Bulgarian
0.05715389	Tajik_Shugnan
0.05724965	French_Paris
0.05775124	Swiss_French
0.05825879	Italian_Northeast
0.05829453	French_Provence
0.05839335	French_Auvergne
0.05840450	French_Occitanie
0.05857564	Gagauz
0.05864942	Macedonian
0.05878460	Uygur
0.05880991	Tajik_Rushan
0.05881070	Hazara_Afghanistan
0.05891638	Tajik
0.05899030	Tlingit
0.05952454	Italian_Aosta_Valley
0.06003084	Kazakh
0.06026783	Khakass
0.06036840	Khakass_Kachins
0.06062961	Swiss_Italian
0.06064030	Hazara
0.06097041	Spanish_Camp_de_Tarragona
0.06101342	Tajik_Ishkashim
0.06111624	Tubalar
0.06118191	Roma_Barcelona
0.06138222	Italian_Veneto
0.06151121	Turkish_Northwest
0.06152588	Rumelia_East
0.06173887	Tajik_Yagnobi
0.06181061	Spanish_Penedes
0.06212320	Spanish_Girona
0.06214889	Spanish_Mallorca
0.06219787	Italian_Trentino-Alto-Adige
0.06229469	Spanish_Peri-Barcelona
0.06229536	Roma_Madrid
0.06232336	Turkish_Balikesir
0.06237509	Mansi
0.06240232	Spanish_Catalunya_Central
0.06242454	Spanish_Lleida
0.06286326	Spanish_Barcelones
0.06291106	Kumyk
0.06298781	Turkish_Southwest
0.06309969	Spanish_Cataluna
0.06311772	Greek_Central_Macedonia
0.06312310	Spanish_Eivissa
0.06317729	Spanish_Extremadura
0.06329139	Italian_Piedmont
0.06334076	Spanish_Galicia
0.06339657	Maori
0.06346803	Spanish_Baleares
0.06356843	Turkish_South
0.06377589	Turkish_Aydin
0.06384227	Greek_Macedonia
0.06410546	Portuguese
0.06418765	Avar
0.06419586	Spanish_Murcia
0.06421016	Kirghiz_China
0.06431916	Tabasaran
0.06432828	Italian_Bergamo
0.06432959	Spanish_Valencia
0.06438311	Spanish_Pirineu
0.06442066	Spanish_Castilla_Y_Leon
0.06458596	Shor
0.06466029	Spanish_Castello
0.06467127	Spanish_Menorca
0.06469807	Kirghiz
0.06474221	Ror
0.06478133	Spanish_Alacant
0.06478404	Roma_Bilbao
0.06483031	Jatt_Pathak
0.06495809	Spanish_Andalucia
0.06496078	Kaitag
0.06497299	Italian_Tuscany
0.06500171	Ashkenazi_Russia
0.06501007	Spanish_Canarias
0.06508041	Spanish_Terres_de_l'Ebre
0.06510791	Greek_Thessaly
0.06512333	Ashkenazi_Belarussia
0.06517430	Kazakh_China
0.06519655	Roma_Granada
0.06522654	Greek_Peloponnese
0.06529646	Spanish_Castilla_La_Mancha
0.06530030	Kho_Singanali
0.06533892	Albanian
0.06548495	Ashkenazi_Ukraine
0.06561269	Ashkenazi_Lithuania
0.06570042	Italian_Marche
0.06570614	Spanish_Cantabria
0.06578446	Khanty
0.06587552	Roma_Balkans
0.06608362	Italian_Liguria
0.06611451	Ashkenazi_Poland
0.06625800	Lak
0.06628285	Turkish_North
0.06633191	Italian_Molise
0.06638269	Roma_Porto
0.06641397	Darginian
0.06651056	Italian_Lazio
0.06652731	Turkish_Adana
0.06654968	Turkish_Central
0.06655173	Azeri_Dagestan
0.06674650	Cherkes
0.06677869	Sicilian_West
0.06679046	Italian_Umbria
0.06687333	Italian_Lombardy
0.06690053	Italian_Abruzzo
0.06693997	Spanish_Aragon
0.06701431	Greek_Laconia
0.06719558	Kubachinian
0.06724724	Greek_Izmir
0.06735810	Kabardin
0.06740574	Altaian
0.06749224	Mogush
0.06767902	Mari
0.06792585	French_South
0.06796897	Shor_Mountain
0.06804701	Uthmankhel
0.06827280	Maltese
0.06827746	Circassian
0.06830773	Burusho
0.06833185	Pashtun
0.06836773	Shor_Khakassia
0.06837738	Spanish_Navarra
0.06840908	Balkar
0.06844805	Abazin
0.06846624	Spanish_Soria
0.06849638	French_Corsica
0.06853433	Spanish_Asturias
0.06861708	Italian_Basilicata
0.06864945	Chechen
0.06870729	Ashkenazi_Germany
0.06873292	Tarkalani
0.06888391	Turkish_Kayseri
0.06892465	Yusufzai
0.06906530	Azeri
0.06906813	Karachay
0.06919598	Spanish_La_Rioja
0.06922329	Sicilian_East
0.06930588	Greek_Crete
0.06933720	Iranian_Seyyed
0.06944766	Italian_Apulia
0.06951187	Ezid
0.06957562	Ingushian
0.06962029	Turkish_East
0.06964812	Iranian_Fars
0.06967617	Parsi_Pakistan
0.06983952	Italian_Campania
0.06989317	Tuvinian
0.06996376	Iranian_Zoroastrian
0.07008471	Udi
0.07035257	Kamboj
0.07059897	Spanish_Pais_Vasco
0.07061693	Parsi_India
0.07071607	Basque_French
0.07095434	Syrian
0.07100328	Italian_Calabria
0.07101178	Azeri_Turkey
0.07142924	Italian_Jew
0.07147076	Kalash
0.07177052	Talysh_Azerbaijan
0.07181188	Kurdish
0.07215916	Adygei
0.07216960	Basque_Spanish
0.07238217	Punjabi_Jatt
0.07238737	Syrian_Jew
0.07241956	Iraqi
0.07251063	Punjabi_Sikh_India
0.07260557	Kohistani
0.07262497	Greek_Kos
0.07268228	Sephardic_Jew
0.07309332	Khatri
0.07311473	Romaniote_Jew
0.07317926	Lebanese_Muslim
0.07325859	Iranian_Lor
0.07331863	Greek_Dodecanese
0.07335762	Bahun
0.07340815	Mountain_Jew
0.07345386	Moroccan_Jew
0.07365160	Jordanian
0.07376723	Georgian_Jew
0.07395645	Khamnegan
0.07404531	Iranian_Bandari
0.07431281	Kashmiri_Pandit
0.07436652	North_Ossetian
0.07457588	Palestinian
0.07467457	Kalmyk
0.07521717	Todzin
0.07526327	Greek_Central_Anatolia
0.07536391	Lebanese_Druze
0.07538992	Cypriot
0.07555332	Libyan_Jew
0.07567614	Mongolian
0.07571428	Greek_Cappadocia
0.07574176	Assyrian
0.07587216	Druze
0.07598492	Iranian_Jew
0.07601694	Karaite_Egypt
0.07621089	Gujar_Pakistan
0.07621693	Armenian
0.07640400	Nenets
0.07653813	Iranian_Mazandarani
0.07664209	Buryat
0.07672705	BedouinA
0.07684026	Sindhi
0.07689029	Tunisian_Jew
0.07704840	Lebanese_Christian
0.07717929	Brahmin_Gujarat
0.07756808	Ossetian
0.07817291	Gujar_India
0.07831063	Brahmin_Manipuri
0.07835537	Turkish_Trabzon
0.07860061	Oroqen
0.07905378	Selkup
0.07919355	Egyptian
0.07939547	Brahmin_West_Bengal
0.07947198	Palestinian_Beit_Sahour
0.07957183	Armenian_Hemsheni
0.07957503	Greek_Trabzon
0.07992985	Iraqi_Jew
0.08021761	Greenlander_West
0.08091148	Georgian_Laz
0.08107275	Newar
0.08115376	Berber_Algeria
0.08160071	Hezhen
0.08194500	Kshatriya
0.08197935	Makrani
0.08210005	Brahui
0.08212434	Daur
0.08224429	Abkhasian
0.08230950	Libyan
0.08258682	Dongxiang
0.08262644	Balochi
0.08327912	Salar
0.08342940	Kikuyu
0.08343883	Ngumba
0.08389535	Thai
0.08394950	Ket
0.08397075	Samaritan
0.08429159	Kaba
0.08437761	Dungan
0.08448074	Nyaneka
0.08448082	Iyer
0.08479883	Xibo
0.08491570	Brahmin_Uttar_Pradesh
0.08504293	Umbundu
0.08510856	Tunisian_Douz
0.08512021	Tikar_South
0.08517665	Mada
0.08523235	Burmese
0.08524866	Mongola
0.08535594	Brahmin_Tamil_Nadu
0.08546817	Mwani
0.08561846	Laka
0.08579810	Cameroon_Aghem
0.08580119	Makhuwa
0.08584846	Nanai
0.08597207	Bantu_S.W.
0.08610736	Tharu
0.08613591	Ganguela
0.08629600	Cameroon_Mbo
0.08629991	Kongo
0.08634084	Cameroon_Bafut
0.08638412	Georgian_Imer
0.08655686	Cameroon_Bakoko
0.08656451	Ronga
0.08662352	Tswa
0.08668668	Lemande
0.08669485	Tripuri
0.08673156	Dolgan
0.08679304	Jamatia
0.08682201	Ulchi
0.08718358	Nyanja
0.08728352	Sena
0.08735887	Mende_Sierra_Leone
0.08738497	Punjabi_Lahore
0.08744968	Cameroon_Bangwa
0.08745246	Ndau
0.08762670	Luo
0.08773805	Changana
0.08777534	Chopi
0.08785257	Yao
0.08805936	Tunisian
0.08816602	Bitonga
0.08817000	Gambian
0.08818459	Negidal
0.08860250	Kusunda
0.08866963	Sandawe
0.08868311	Algerian
0.08872083	Bonan
0.08894639	Tu
0.08911771	Manyika
0.08915609	Tibetan_Gannan
0.08917762	Mandenka
0.08941822	Berber_Tunisia_Sen
0.08948218	Fulani
0.08954084	Bulala
0.08954586	Kinh_Vietnam
0.08982673	Kol
0.08992528	Yemenite_Amran
0.08996837	Gujarati
0.09034236	Cambodian
0.09037569	Yugur
0.09048074	Bengali_Bangladesh
0.09051213	Kanjar
0.09073184	Magar
0.09092653	Luhya_Kenya
0.09100593	Bantu_Kenya
0.09112520	Onge
0.09119805	Igbo
0.09147701	Lahu
0.09149573	Jarawa
0.09188504	Tunisian_Rbaya
0.09235067	Tamang
0.09248866	Dong_Guizhou
0.09251242	Gelao
0.09265095	Jehai
0.09267050	Zhuang
0.09270295	Dong_Hunan
0.09280921	Mulam
0.09287264	Miao
0.09287721	Tibetan_Xunhua
0.09291078	Yukagir_Tundra
0.09294633	Yemenite_Dhamar
0.09295827	Dai
0.09300540	Moroccan
0.09301426	Tujia
0.09308128	Dharkar
0.09318702	Nivkh
0.09319748	Han_Sichuan
0.09335652	Tunisian_Berber_Matmata
0.09365482	Tibetan_Gangcha
0.09388868	Han_Hubei
0.09391639	Yoruba
0.09395353	Han_Guangdong
0.09406005	Kamboj_o
0.09425621	Velamas
0.09427345	Malay
0.09427407	Han_Chongqing
0.09430832	Maonan
0.09431517	Tibetan_Xinlong
0.09438628	Esan_Nigeria
0.09475240	Elmolo
0.09539554	Han_Shanxi
0.09571862	Yi
0.09574064	Li
0.09582957	Han_Shandong
0.09591501	Han_Fujian
0.09598300	Moroccan_North
0.09605815	Yemenite_Al_Bayda
0.09625863	Uttar_Pradesh
0.09650756	Han_Henan
0.09651204	Eritrean
0.09653414	Ethiopian_Tigray
0.09653831	Korean
0.09659088	Yemenite_Ma'rib
0.09664877	Yadava
0.09669237	Gurung
0.09677967	Han_Zhejiang
0.09697404	Tunisian_Berber_Zraoua
0.09709559	Aeta
0.09713007	Ethiopian_Afar
0.09727150	Dusadh
0.09732115	Evenk
0.09734988	Han_Jiangsu
0.09745746	Ethiopian_Amhara
0.09760825	Moroccan_South
0.09771195	Yemenite_Jew
0.09781921	Naxi
0.09789075	Tunisian_Berber_Tamezret
0.09802946	Sardinian
0.09821219	Ethiopian_Oromo
0.09836081	Chenchu
0.09847120	Ethiopian_Agaw
0.09851749	Bajo
0.09852640	Maratha
0.09860315	Tibetan_Shigatse
0.09865549	Greenlander_East
0.09882330	Ogiek
0.09883309	Ethiopian_Jew
0.09886046	She
0.09888239	Piramalai
0.09900129	Kurumba
0.09948143	Rai
0.09962244	Ethiopian_Wolayta
0.09972537	Bantu_S.E.
0.09977231	Tibetan_Yunnan
0.09978531	Yemenite_Al_Jawf
0.09996400	Mozabite
0.10003603	Tibetan_Shannan
0.10023864	Chamar
0.10060306	Tibetan_Lhasa
0.10077781	Tibetan_Nagqu
0.10083317	Relli
0.10083548	Tibetan_Yajiang
0.10092675	Han_Shanghai
0.10136118	BedouinB
0.10150889	Itelmen
0.10154542	Batak
0.10160748	Sherpa
0.10172143	Qiang_Danba
0.10199566	Iraqw
0.10207170	Saudi
0.10208593	Hadza
0.10226959	Agta
0.10259696	Japanese
0.10310148	Qiang_Daofu
0.10316740	Datog
0.10318405	North_Kannadi
0.10338746	Koryak
0.10343518	Pallan
0.10357925	Berber_MAR_ERR
0.10392051	Somali_Kenya
0.10392375	Rendille
0.10408959	Vizayan
0.10415319	Sakilli
0.10415366	Somali
0.10445951	Indonesian_Bali
0.10451481	Tibetan_Chamdo
0.10505331	Berber_Tunisia_Chen
0.10518700	Htin_Mal
0.10556630	Yemenite_Mahra
0.10597841	Gupta
0.10619718	Madiga
0.10682038	Hakkipikki
0.10715440	Masai
0.10740329	Ethiopian_Ari_blacksmith
0.10758924	Sudanese
0.10767800	Ethiopian_Ari_cultivator
0.10778348	Chukchi
0.10792097	Sengwer
0.10812479	Indonesian_Java
0.10895222	Mala
0.10898593	Gond
0.10913417	Luzon
0.10913866	Even
0.10929467	Saharawi
0.10939467	Ethiopian_Ari
0.10955260	Bonda
0.10960521	Irula
0.10967850	Hawaiian
0.10977476	Atayal
0.11002714	Asur
0.11044979	Gadaba
0.11047650	Lebbo
0.11056432	Mlabri
0.11063629	Korwa
0.11066541	Khonda_Dora
0.11067540	Ethiopian_Anuak
0.11084131	Nganassan
0.11194092	Birhor
0.11195057	Bhumij
0.11217265	Ho
0.11218221	Santhal
0.11284191	Juang
0.11293154	Ethiopian_Gumuz
0.11294242	Kadar
0.11351879	Ami
0.11356703	Dinka
0.11445767	Berber_MAR_TIZ
0.11501138	Yakut
0.11502225	Ethiopian_Mursi
0.11530414	Cree
0.11539958	Pulliyar
0.11542885	Malayan
0.11559135	Murut
0.11577513	Dusun
0.11622853	Paniya
0.11728563	Eskimo_Sireniki
0.11730617	Eskimo_Chaplin
0.11742727	Sakha
0.11957381	Eskimo
0.11979065	Eskimo_Naukan
0.12294331	Igorot
0.12606664	Bedzan
0.13640271	Bakola
0.13781217	Chipewyan
0.14471381	Baka
0.14929018	Amerindian_North
0.15276062	Nasoi
0.16620004	Biaka
0.17162568	Australian
0.19575093	Mixtec
0.19612719	Bolivian_Pando
0.19645905	Zapotec
0.19747616	Nahua
0.19809789	Bolivian_Cochabamba
0.19811467	Huichol
0.19868330	Pima
0.19897830	Quechua
0.20013824	Mayan
0.20302388	Mixe
0.20727431	Cachi
0.21187970	Colla
0.21250426	Bolivian_LaPaz
0.21787237	Aymara
0.22038163	Yukpa
0.22123406	Piapoco
0.22375022	Wichi
0.23075211	Kosipe
0.23170648	Karitiana
0.23633335	Surui
0.23823313	Papuan
0.23844971	Koinanbe
0.27335104	Khomani_San
0.27989849	Mbuti
0.38822779	Ju_hoan_North

[Zoro]

Wouldn't it make sense to calculate the average IBS values of Saami relative to multiple Mongoloid populations, and not just Chukchi? Now on the second list for Chukchi, Saami rank higher than some Southeast Asians, which doesn't make sense as an overall measure of Mongoloidness.

Or could you check how populations of the Volga-Ural compare to Saami, like for example Komis or Kazan Tatars or Maris?

Couldn't we compile a 99x99 (or whatever) table of each population compared against every other population? Then couldn't we use it as a precalculated matrix of genetic distances? Or for example we could take the average values of the columns for multiple Mongoloid populations and sort the rows of the table based on it.

Sorry for being like Joqool and asking too many questions.

I combined the data you posted (without the common old alleles) into a table. I omitted the row for Chukchis because you didn't post the score of Chukchis with themselves.

Population,Chukchi,Saami
Adygei,0.8405,0.8390
Albanian.DG,0.8380,0.8337
Ami.DG,0.8422,0.8381
Armenian,0.8377,0.8349
Balochi,0.8361,0.7755
BantuHerero,0.7795,0.7803
BantuKenya,0.7834,0.7757
BantuTswana,0.7776,0.8405
Basque,0.8383,0.8329
BedouinB,0.8319,0.8256
Bougainville,0.8303,0.8351
Brahui,0.8370,0.8401
Bulgarian,0.8393,0.8354
Burmese,0.8435,0.8378
Burusho,0.8397,0.8392
Chechen,0.8395,0.8339
China_Lahu,0.8425,0.8439
Czech,0.8394,0.8419
Dai,0.8428,0.8341
Daur,0.8466,0.8380
Druze,0.8335,0.8359
Dusun,0.8416,0.8327
English,0.8404,0.8417
Esan,0.7791,0.7762
Eskimo_Naukan.DG,0.8537,0.8383
Eskimo_Sireniki.DG,0.8540,0.8387
Estonian,0.8410,0.8429
Even,0.8482,0.8400
Finnish,0.8422,0.8442
French,0.8388,0.8397
Gambian,0.7817,0.7786
Georgian,0.8364,0.8378
Greek,0.8383,0.8386
Han,0.8460,0.8358
Hazara,0.8433,0.8391
Hezhen,0.8479,0.8381
Hungarian,0.8400,0.8409
Icelandic,0.8403,0.8420
Iranian-Fars,0.8386,0.8372
Irula,0.8367,0.8340
Itelmen,0.8527,0.8377
Japanese,0.8461,0.8359
Jew_Iraqi,0.8354,0.8358
Jordanian,0.8317,0.8321
Ju_hoan_North,0.7687,0.7657
Kalash,0.8378,0.8369
Kapu,0.8376,0.8349
Karitiana,0.8441,0.8348
Khomani_San,0.7690,0.7661
Kinh,0.8436,0.8346
Korean,0.8455,0.8370
Kurds-IRAQ,0.8424,0.8417
Kusunda,0.8388,0.8317
Kyrgyz_Kyrgyzstan,0.8466,0.8405
Lezgin,0.8386,0.8396
Luhya,0.7825,0.7793
Luo,0.7836,0.7799
Makrani,0.8353,0.8341
Mala,0.8380,0.8341
Mandenka,0.7817,0.7780
Mansi,0.8477,0.8436
Masai,0.7974,0.7952
Mayan,0.8464,0.8366
Mbuti,0.7729,0.7702
Mende,0.7801,0.7769
Mixe,0.8458,0.8358
Mixtec,0.8458,0.8373
Mongola,0.8465,0.8373
Mozabite,0.8215,0.8219
Norwegian,0.8405,0.8421
Orcadian,0.8397,0.8414
Papuan,0.8270,0.8222
Pathan,0.8399,0.8375
Piapoco,0.8454,0.8350
Pima,0.8462,0.8361
Polish,0.8408,0.8417
Punjabi,0.8377,0.8362
Russia_Abkhasian,0.8387,0.8391
Russia_NorthOssetian,0.8387,0.8390
Russian,0.8425,0.8436
Saami,0.8439,0.8527
Saharawi,0.8235,0.8226
Samaritan,0.8321,0.8324
Sardinian,0.8373,0.8388
She,0.8448,0.8353
Sindhi,0.8383,0.8367
Somali,0.8065,0.8049
Spanish,0.8384,0.8398
Surui,0.8438,0.8337
Tajik,0.8396,0.8380
Tuscan,0.8389,0.8390
Ulchi,0.8473,0.8372
Uyghur,0.8440,0.8391
Xibo,0.8460,0.8367
Yadava,0.8388,0.8353
Yakut,0.8474,0.8386
Yi,0.8445,0.8349

Here's the data as a plot. I used hierarchical K-means clustering to divide the populations into 16 groups.

library(ggplot2);library(factoextra);library(colorspace)

download.file("https://pastebin.com/raw/FnTJmkAj","chukchisaami")

t<-read.csv("chukchisaami",header=T,row.names=1)
k<-hkmeans(t,16)

ggplot(t,aes(x=Chukchi,y=Saami,label=row.names(t)))+
geom_text(aes(color=as.factor(k$cluster)),size=2.5,show.legend=F)+
theme(
  plot.background=element_rect(fill="gray35"),
  panel.grid.major=element_line(color="gray35"),
  text=element_text(color="gray10"),
  axis.text=element_text(color="gray10"),
  axis.ticks.x=element_blank(),
  axis.ticks.y=element_blank(),
  panel.grid.minor=element_blank()
)+
coord_fixed()+
xlab("430K SNPs - Max-Maf 0.3 - Chukchi")+
ylab("430K SNPs - Max-Maf 0.3 - Saami-Finland")+
scale_color_discrete_qualitative(palette="Set 3")
ggsave("/tmp/a.png")

I couldn't get the version of plink from here to compile with either gcc or clang, and when I tried running the Mac binary, it said `Bad CPU type in executable`: https://zzz.bwh.harvard.edu/plink/download.shtml. But now I realized that there's a version by another developer here, with a working Mac binary: http://www.cog-genomics.org/plink/2.0/. I'll figure it out eventually.

Makes good sense. I just didn’t time. Will try to get to it.

It’s good to have both plink 1.9 and 2.0 since 2 doesn’t have all the programs 1.9 does. Download it from https://www.cog-genomics.org/plink/

[Zoro]

So for example, would Chukchi really be more genetically akin to Iraqi Kurds than Tajiks? Wow. This contradicts what has been seen in the "vulgar" GEDmatch calculators and on Global25 in general. After years, most of what I consider normal is now based on what those two tools easily accessible to anyone show. As for distances soaring, this happens especially in scaled coordinates but not in unscaled ones. I'll post the unscaled distances and tell me if you find more correlation with your distances. And most importantly, in your opinion, between scaled and unscaled coordinates which one is less worse?
]

To be fair we should use a couple more Siberian pops and maybe separate the Tajiks out to see if an outlier is causing issues. I did find some sequencing errors in some of the Simons samples so that should also be looked into.

G25 is a PCA program that’s mostly suitable for PCA work and not for distances or admix proportions. Like I explained in my previous post it seems to work for distances for the 1st few on the list then goes wacko for the rest. Also for some reason distances get huge as you go down the list. Biggest problem is unknown push-pull of other 1000s of samples he used in run. With IBS you just compare 2 samples or pops allele for allele and you don’t have these unknown forces from 1000s of samples affecting coordinates of your target in 25 dimensional space

Scaled of course for PCA work

[Zoro]

They are a Turkic ethnic group btw.

What's the average East Eurasian amount that you have observed for Kurds? And do you think its from Steppe or from Turkic invaders into West Asia?

Is it lower than Iraqi Turkmens?

Damn that's remarkable. Maybe some type of connection with Uyghurs through shared potential Turkic affinity?

Will get back to you later today

[Zanzibar]

Will get back to you later today

How are the results?

[Zoro]

How are the results?

Eurasian DNA has a pretty detailed study where they model whole genome Turks and Kurds as modern Iranian/Armenian/Georgian/Adygei/Abkhasian/Jordanian + E. Asian and Siberian . They said it’ll be out soon but based on https://eurasiandna.com/2659-2/ it looks like around 10% E. Asian

[Flashball]

Its normal for a hybride population to have among them a part of ''european'' face.

[Flashball]

That’s not true otherwise chinese would look African or European . Everything in your body is dictated by genes. They shape your eyes, nose etc.

Now what you maybe referring to is that if you have 2 brothers with 10% E. Asian. It’s possible one brother’s 10% would include genes that affect facial features whereas the other brothers 10% E Asian doesn’t include genes affecting facial features and would thus look more European than his Asian looking brother

This.

Genotype = phénotype.

A sami with 75% euro and 25% ''siberian like'' could look european, his brother more 50-50, other almost fully siberian.

Same thing with askenazi.

Not a surprise...