Why is the amount of East Eurasian ancestry of Saamis and other Uralics underestimate by some here?

**Zanzibar** · 03-09-2021, 06:05 AM

I have seen some users here underestimate or downplay the East Eurasian ancestry of some Finno-Ugrics such as Saami, saying that they have only minor 5-10% Mongoloid or maybe 15%, acting as if they are not that different from the average Euros, when that's not true at all.

When they along with VURers such as Udmurt, Mari have around 25-35% Mongoloid on average, some are even approaching 45-50% Mongoloid especially when counting groups like Khanty, Mansi and some Turkics like Bashkirs who are literally "balanced" Eurasians.

In Saamis in G25 are around 27% Mongoloid, Saami_Kola are close to 20% Mongoloid, while Mari are closer to 32% East Eurasian, Chuvash and Udmurts are both around 25% East Eurasian. The Mongoloid component is Krasnoyarsk_BA/kra001 which is an ancient Siberian population most closely related to the Nganasan, Yukaghir and Evenk. The amount of East Eurasian that Saamis, these VURers is the literal opposition version of Altaians, Kyrgyzs, Khakass, some Kazakhs who have around 25-32% West Eurasian.

Target: Saami
Distance: 2.8661% / 0.02866055
34.4 Baltic_EST_BA
26.4 RUS_Krasnoyarsk_BA
24.6 FIN_Levanluhta_IA_o
14.6 Yamnaya_KAZ_Mereke

Target: Saami_Kola
Distance: 1.8163% / 0.01816303
44.2 Baltic_EST_BA
21.4 FIN_Levanluhta_IA_o
19.4 RUS_Krasnoyarsk_BA
6.6 Yamnaya_KAZ_Mereke
4.8 SWE_BA
3.6 UKR_Sredny_Stog_II_En

Target: Mari
Distance: 8.7117% / 0.08711679
46.0 UKR_Sredny_Stog_II_En
31.8 RUS_Krasnoyarsk_BA
22.2 Baltic_EST_BA

Target: Chuvash
Distance: 5.7643% / 0.05764286
50.8 UKR_Sredny_Stog_II_En
24.6 Baltic_EST_BA
24.6 RUS_Krasnoyarsk_BA

Target: Udmurt
Distance: 2.2666% / 0.02266610
47.8 UKR_Sredny_Stog_II_En
24.6 RUS_Krasnoyarsk_BA
13.8 Baltic_EST_BA
13.8 Yamnaya_KAZ_Mereke

The distance fits for Mari and Chuvash are horrible though but they both are genetically very drifted in G25.

From the amount of their Mongoloid ancestry, Saami, Chuvash, Mari, Udmurt are literally the reverse/opposition version of Altaian, Kyrgyz and Khakass who are around 25-32% Caucasoid.

Target: Altaian
Distance: 2.1939% / 0.02193893
49.2 MNG_North_N
16.2 RUS_Shamanka_N
10.0 RUS_Afanasievo
8.2 Oroqen
7.6 RUS_Sintashta_MLBA
4.6 TUR_Barcin_N
4.2 TJK_Sarazm_En

Target: Althai_Kizhi
Distance: 2.4724% / 0.02472399
44.6 MNG_North_N
24.4 RUS_Shamanka_N
10.6 RUS_Afanasievo
8.8 RUS_Sintashta_MLBA
5.8 Oroqen
3.2 TUR_Barcin_N
2.6 TJK_Sarazm_En

Target: Kirghiz
Distance: 2.0017% / 0.02001725
23.0 Oroqen
22.4 MNG_North_N
21.4 RUS_Devils_Gate_Cave_N
15.4 RUS_Sintashta_MLBA
8.8 TJK_Sarazm_En
4.6 TUR_Barcin_N
4.4 RUS_Afanasievo

Target: Kirghiz_China
Distance: 2.3150% / 0.02314983
29.2 Oroqen
20.2 RUS_Devils_Gate_Cave_N
19.8 RUS_Sintashta_MLBA
17.4 MNG_North_N
11.2 TJK_Sarazm_En
2.2 TUR_Barcin_N

Target: Khakass
Distance: 2.8290% / 0.02828997
67.6 RUS_Shamanka_N
15.2 RUS_Afanasievo
15.2 RUS_Sintashta_MLBA
1.2 TJK_Sarazm_En
0.8 TUR_Barcin_N

Target: Khakass_Kachins
Distance: 2.3621% / 0.02362103
56.2 RUS_Shamanka_N
16.8 MNG_North_N
12.6 RUS_Afanasievo
8.6 RUS_Sintashta_MLBA
3.6 TUR_Barcin_N
1.8 Oroqen
0.4 TJK_Sarazm_En

Target: Kazakh_China
Distance: 2.3730% / 0.02372980
32.0 Oroqen
20.8 RUS_Devils_Gate_Cave_N
19.2 RUS_Sintashta_MLBA
18.2 MNG_North_N
8.8 TJK_Sarazm_En
1.0 TUR_Barcin_N

Only the Kazakh are more Caucasoid than the Mari, Udmurt, Saami, Chuvash are Mongoloid:

Target: Kazakh
Distance: 1.9419% / 0.01941909
24.2 Oroqen
21.2 MNG_North_N
14.8 RUS_Sintashta_MLBA
14.4 RUS_Devils_Gate_Cave_N
9.0 RUS_Afanasievo
8.6 TJK_Sarazm_En
7.8 TUR_Barcin_N

Therefore, Mari, Chuvash, Udmurt, Saami are literally the opposite version of Altaians, Kyrgyz and Khakass. In my opinion, these Uralics have enough Mongoloid to be seen more of a Hapa or transitional race between Europeans and Asians than only European.

Now if we included the Bashkir, Mansi and Khanty, they are literally Eurasians/Hapas as they are around 47-50% Mongoloid.

Target: Bashkir
Distance: 2.2061% / 0.02206102
52.2 UKR_Sredny_Stog_II_En
30.0 RUS_Shamanka_N
10.6 RUS_Krasnoyarsk_BA
4.0 Baltic_EST_BA
3.2 Yamnaya_KAZ_Mereke

Target: Mansi
Distance: 4.7446% / 0.04744556
48.4 RUS_Krasnoyarsk_BA
32.0 UKR_Sredny_Stog_II_En
16.8 RUS_AfontovaGora3
2.8 Baltic_EST_BA

Target: Khanty
Distance: 4.7254% / 0.04725353
50.0 RUS_Krasnoyarsk_BA
30.6 UKR_Sredny_Stog_II_En
19.4 RUS_AfontovaGora3

Target: Khants
Distance: 4.6029% / 0.04602942
49.6 RUS_Krasnoyarsk_BA
31.0 UKR_Sredny_Stog_II_En
16.8 RUS_AfontovaGora3
2.6 Baltic_EST_BA

P.S.-The Bashkir need Shamanka_N to improve their fits as they have significant Turkic ancestry while surprisingly the Chuvash don't need any Shamanka_N but maybe its because they are genetic drifted, that's why they don't need the input.

**Lemminkäinen** · 03-09-2021, 08:10 AM

Because the Admixture program usually calculates non-European admixtures using European references (assumed to be a zero level) and Eurasian admixtures are present almost everywhere in Europe. But I don't trust in G25 either, because (being Davidski's test?) it is based on PCA components and PCA results depend on the used sample set. If some population or group is underrepredented it gets too little weight and conversely. If it is extremely inbred and even overrepresented it gets too much weight.

**~~Komintasavalta~~** · 03-09-2021, 08:39 AM

I tried doing qpAdm models of the population named Saami.DG in the v44.3_HO dataset. I excluded models with one or more negative weight (where feasible is false) and I sorted the models by their p score.

I'm probably doing something wrong, and I still don't know how to pick the outgroups. I mostly just picked outgroups that resulted in little decrease in the number of SNPs that remained after filtering. I also tried to pick left populations that resulted in little decrease in the SNP count.

I got 374794 out of 597573 SNPs after filtering, out of which 349558 were polymorphic.

In the image above, the models whose p score is above .05 have a constant of about 30-35% Nganasan ancestry. However EHG and CHG and SHG are also part Mongoloid. So if we consider Nganasan to be fully Mongoloid, Saami might also be closer to 40% than 30% Mongoloid.

Both individuals in the population Saami.DG were from Utsjoki, which is part of the Northern Saami region within Finland:

Code:

$ awk 'NR==1||/Saami...DG/' g/v44.3_HO_public/v44.3_HO_public.anno|cut -f2,4,9,10|tr \\t \;
Version ID;Publication (or OK to use in a paper);Locality;Country
S_Saami-1.DG;MallickNature2016;Utsjoki;Finland
S_Saami-2.DG;MallickNature2016;Utsjoki;Finland

Among Finnish Saami, there are an estimated 2,000 speakers of Northern Saami, 300 speakers of Inari Saami, and 300 speakers of Skolt Saami (https://fi.wikipedia.org/wiki/Saamelaiskielet). Out of four groups of Saami measured by Karin Mark, Skolt Saami had the lightest pigmentation, followed by Inari Saami, Finnish Northern Saami, and Kola Saami (https://www.etis.ee/Portal/Publicati...8-b9b3010eabad).

Scandinavian Northern Saami might be even more Mongoloid than Finnish Northern Saami, or at least Coon wrote that the Saami of the Scandinavian inland were the darkest and most brachycephalic (https://www.theapricity.com/snpa/chapter-IX2.htm):

The selected "pure" groups, Bryn's Reindeer Lapps, and some of Geyer's mountain and forest Lapps from Sweden, have seventy per cent or over of this dark hair, while the fairest Lapps, with a majority of brown and blond shades, are found in Finland and in the Kola Peninsula.

Pure dark eyes are found among one-third of Reindeer Lapps, and among as few as eight per cent in the total of Lapps from Norway.[14] Pure light and light-mixed eyes are commonest among the Lapps of Finland, where they total between thirty and forty per cent, and least common among the Reindeer Lapps of interior Norway and Sweden. Even among the purest selected sub-groups, such as that of Geyer, who isolated from a larger Swedish Lapp sample a few individuals of most pronounced Lappish type, at least a third are light or light-mixed in iris color. [...]

There are, however, regional differences; the center of extreme round headedness lies among the inland groups in northern Norway, while the Swedish, Finnish, and Kola Peninsula Lapps become progressively narrower headed. The mean for the purest Reindeer Lapps of Norway is 87; for the easternmost Lapps, 80 to 83.

Code for ADMIXTOOLS 2:

Code:

target="Saami.DG"
left=c("Turkey_Boncuklu_N.SG","Armenia_Caucasus_KuraAraxes","Latvia_HG","Sweden_Motala_HG","Russia_HG_Karelia","Russia_HG_Tyumen","Nganasan")
right=c("Mbuti.DG","Mixe.DG","Ami.DG","Papuan.DG","Chimp.REF","Ju_hoan_North","Biaka.DG","Yoruba.DG","Altai_Neanderthal.DG")

pops=c(left,right,target)

unlink("/tmp/f2",recursive=T)
extract_f2(pref="g/v44.3_HO_public/v44.3_HO_public",pops=pops,outdir="/tmp/f2")
f2=f2_from_precomp("/tmp/f2")
qp=qpadm(f2,left=left,right=right,target=target)

qp2=qp$popdrop%>%dplyr::filter(feasible==T&f4rank!=0)%>%arrange(desc(p))%>%dplyr::select(!c(wt,dof,chisq,f4rank,dofdiff,chisqdiff,p_nested,feasible,best,dofdiff,chisqdiff,p_nested))
write_csv(qp2,"/tmp/qp")

Code to generate the bar chart:

Code:

library(tidyverse)
library(reshape2)
library(colorspace)

t=read_csv("/tmp/qp")

# t=t[t$p>.05,]

pvalue=sub("^0","",sprintf("%.3f",t$p))
t=t[-2]
t2=melt(t,id.var="pat")

ggplot(t2,aes(x=fct_rev(factor(pat,level=t$pat)),y=value,fill=variable))+
geom_bar(stat="identity",width=1,position=position_fill(reverse=T))+
geom_text(aes(label=round(100*value)),position=position_stack(vjust=.5,reverse=T),size=3.5)+
coord_flip()+
theme(
  axis.text.x=element_blank(),
  axis.text=element_text(color="black"),
  axis.ticks=element_blank(),
  axis.title.x=element_blank(),
  legend.box.just="center",
  legend.box.margin=margin(0),
  legend.box.spacing=unit(.05,"in"),
  legend.direction="vertical",
  legend.justification="center",
  legend.margin=margin(0),
  legend.text=element_text(size=12),
  legend.title=element_blank(),
  panel.border=element_blank(),
  text=element_text(size=16)
)+
xlab("")+
scale_x_discrete(labels=rev(pvalue),expand=c(0,0))+
scale_y_discrete(expand=c(0,0))+
scale_fill_manual("legend",values=hex(HSV(c(45,45,210,210,120,120,300),c(.6,.6,.6,.6,.6,.6,.6),c(1,.6,1,.6,1,.6,1))))
ggsave("/tmp/a.png",width=7,height=7)

**Zoro** · 03-09-2021, 10:31 AM

.........

**Zoro** · 03-09-2021, 10:32 AM

Originally Posted by Komintasavalta

I tried doing qpAdm models of the population named Saami.DG in the v44.3_HO dataset. I excluded models with one or more negative weight (where feasible is false) and I sorted the models by their p score.

I'm probably doing something wrong, and I still don't know how to pick the outgroups. I mostly just picked outgroups that resulted in little decrease in the number of SNPs that remained after filtering. I also tried to pick left populations that resulted in little decrease in the SNP count.

I got 374794 out of 597573 SNPs after filtering, out of which 349558 were polymorphic.

In the image above, the models whose p score is above .05 have a constant of about 30-35% Nganasan ancestry. However EHG and CHG and SHG are also part Mongoloid. So if we consider Nganasan to be fully Mongoloid, Saami might also be closer to 40% than 30% Mongoloid.

Both individuals in the population Saami.DG were from Utsjoki, which is part of the Northern Saami region within Finland:

Code:

$ awk 'NR==1||/Saami...DG/' g/v44.3_HO_public/v44.3_HO_public.anno|cut -f2,4,9,10|tr \\t \;
Version ID;Publication (or OK to use in a paper);Locality;Country
S_Saami-1.DG;MallickNature2016;Utsjoki;Finland
S_Saami-2.DG;MallickNature2016;Utsjoki;Finland

Among Finnish Saami, there are an estimated 2,000 speakers of Northern Saami, 300 speakers of Inari Saami, and 300 speakers of Skolt Saami (https://fi.wikipedia.org/wiki/Saamelaiskielet). Out of four groups of Saami measured by Karin Mark, Skolt Saami had the lightest pigmentation, followed by Inari Saami, Finnish Northern Saami, and Kola Saami (https://www.etis.ee/Portal/Publicati...8-b9b3010eabad).

Scandinavian Northern Saami might be even more Mongoloid than Finnish Northern Saami, or at least Coon wrote that the Saami of the Scandinavian inland were the darkest and most brachycephalic (https://www.theapricity.com/snpa/chapter-IX2.htm):

The selected "pure" groups, Bryn's Reindeer Lapps, and some of Geyer's mountain and forest Lapps from Sweden, have seventy per cent or over of this dark hair, while the fairest Lapps, with a majority of brown and blond shades, are found in Finland and in the Kola Peninsula.

Pure dark eyes are found among one-third of Reindeer Lapps, and among as few as eight per cent in the total of Lapps from Norway.[14] Pure light and light-mixed eyes are commonest among the Lapps of Finland, where they total between thirty and forty per cent, and least common among the Reindeer Lapps of interior Norway and Sweden. Even among the purest selected sub-groups, such as that of Geyer, who isolated from a larger Swedish Lapp sample a few individuals of most pronounced Lappish type, at least a third are light or light-mixed in iris color. [...]

There are, however, regional differences; the center of extreme round headedness lies among the inland groups in northern Norway, while the Swedish, Finnish, and Kola Peninsula Lapps become progressively narrower headed. The mean for the purest Reindeer Lapps of Norway is 87; for the easternmost Lapps, 80 to 83.

Code for ADMIXTOOLS 2:

Code:

target="Saami.DG"
left=c("Turkey_Boncuklu_N.SG","Armenia_Caucasus_KuraAraxes","Latvia_HG","Sweden_Motala_HG","Russia_HG_Karelia","Russia_HG_Tyumen","Nganasan")
right=c("Mbuti.DG","Mixe.DG","Ami.DG","Papuan.DG","Chimp.REF","Ju_hoan_North","Biaka.DG","Yoruba.DG","Altai_Neanderthal.DG")

pops=c(left,right,target)

unlink("/tmp/f2",recursive=T)
extract_f2(pref="g/v44.3_HO_public/v44.3_HO_public",pops=pops,outdir="/tmp/f2")
f2=f2_from_precomp("/tmp/f2")
qp=qpadm(f2,left=left,right=right,target=target)

qp2=qp$popdrop%>%dplyr::filter(feasible==T&f4rank!=0)%>%arrange(desc(p))%>%dplyr::select(!c(wt,dof,chisq,f4rank,dofdiff,chisqdiff,p_nested,feasible,best,dofdiff,chisqdiff,p_nested))
write_csv(qp2,"/tmp/qp")

Code to generate the bar chart:

Code:

library(tidyverse)
library(reshape2)
library(colorspace)

t=read_csv("/tmp/qp")

# t=t[t$p>.05,]

pvalue=sub("^0","",sprintf("%.3f",t$p))
t=t[-2]
t2=melt(t,id.var="pat")

ggplot(t2,aes(x=fct_rev(factor(pat,level=t$pat)),y=value,fill=variable))+
geom_bar(stat="identity",width=1,position=position_fill(reverse=T))+
geom_text(aes(label=round(100*value)),position=position_stack(vjust=.5,reverse=T),size=3.5)+
coord_flip()+
theme(
  axis.text.x=element_blank(),
  axis.text=element_text(color="black"),
  axis.ticks=element_blank(),
  axis.title.x=element_blank(),
  legend.box.just="center",
  legend.box.margin=margin(0),
  legend.box.spacing=unit(.05,"in"),
  legend.direction="vertical",
  legend.justification="center",
  legend.margin=margin(0),
  legend.text=element_text(size=12),
  legend.title=element_blank(),
  panel.border=element_blank(),
  text=element_text(size=16)
)+
xlab("")+
scale_x_discrete(labels=rev(pvalue),expand=c(0,0))+
scale_y_discrete(expand=c(0,0))+
scale_fill_manual("legend",values=hex(HSV(c(45,45,210,210,120,120,300),c(.6,.6,.6,.6,.6,.6,.6),c(1,.6,1,.6,1,.6,1))))
ggsave("/tmp/a.png",width=7,height=7)

I like how you used R to visualize the results and sort by p-value and that you posted your details on how you ran (although I can't see your SE). So looking at the details here's how you can improve the accuracy of your models:

1- Pright are used as references to distinguish between various sources used to model. Therefore, they should be pretty diverse in their ancestry. I noticed that Africans are over represented in pright. You really only need one group of Africans unless you are trying to model a target using multiple African pops. I would just keep Mbuti and drop the rest of the Africans from pright

2- Neanderthal and Chimp are pretty useless in differentiating between different Eurasians because all Eurasians are pretty much similarly related to them (Even with Neanderthal the difference between various Eurasians is just a couple of percent). Drop them

3- I would add Mesolithic Ancient Siberian Kolyma-Diploid to pright because some of your sources are quite differentially related to it because of its mix of ancient E Asian and Siberian (Yana type)

4- I would also for sure add CHG ( the sample labeled Kotias KK1 has the highest number of SNPs in the dataset you are using) because some of your sources are quite differentially related to it

5- I would also for sure add WHG ( the sample labeled Bichon Bichon has the highest number of SNPs in the dataset you are using) because some of your sources are quite differentially related to it

6- I would also add Tyumen to pright and use Devils-Gate in sources for Neolithic E Asian

7- I would add Iran-N to pright

8- I would add Iberouma . These have decent SNPs
Morocco_Iberomaurusian TAF010
Morocco_Iberomaurusian TAF011
Morocco_Iberomaurusian TAF013
Morocco_Iberomaurusian TAF014

9- I would add Kostenki14 I0876 to pright. It has many SNPs

10- I would add GoyetQ116-1 Q116-1 to pright

11- Drop Nganasan from sources and add Shamanka-EN instead. It's always better to keep it cosistently Ancients. Shamanka would be more ancestral to Uralics than Nganasan.

These are decent ones

Russia_Shamanka_Eneolithic DA245 3772618 4676043 0.81
Russia_Shamanka_Eneolithic DA246 3680753 4668444 0.79
Russia_Shamanka_Eneolithic DA247 3733439 4676043 0.80
Russia_Shamanka_Eneolithic DA248 3744767 4676043 0.80
Russia_Shamanka_Eneolithic DA249 3590256 4668444 0.77
Russia_Shamanka_Eneolithic DA252 3732230 4668444 0.80
Russia_Shamanka_Eneolithic DA253 3703323 4668444 0.79

12- You really need Anatolia-N in pright also if you're not going to use it as a source.

Your SNPs will drop and p-values but your models will be significantly more accurate and your SE should be better too.

Don't forget to download the latest Admixtools 2. It has some significant fixes.

**Lemminkäinen** · 03-09-2021, 10:46 AM

I have always used the right group used previously by studies or Davidski to have something to compare with. In principle I undersand that the right group should be build of ancestral populations of common ancestry for all left population, being enough archaic to cover all left populations, not being remarkable dominant for any of them, but not too distant to be at equal distant for all.

P-values lower than 0.05 are significat compared to the null hypothesis.

**Zoro** · 03-09-2021, 11:33 AM

Originally Posted by Joqool

I have seen some users here underestimate or downplay the East Eurasian ancestry of some Finno-Ugrics such as Saami, saying that they have only minor 5-10% Mongoloid or maybe 15%, acting as if they are not that different from the average Euros, when that's not true at all.

E Asians have higher genetic similarity with Saamis than other mainland Europeans. I wouldn't rely on G25 for that though because the results can be misleading. In general with any calculator the amount of E Asian will change depending on what other components the calculator uses.

You have to do a gene to gene comparison between E Asian and each European population one at a time to get an accurate picture. Here is IBS similarity with Mongola sample based on Plink --genome flag using 400,000 SNPs.

As you can see Mongola has about the same amount of IBS with Saamis as with some S Asians and not that much more than Iraqi Kurd or some Finns which would be a shocker to you if you just went by calculator results.

NO	FID1	FID2	IID2	PI_HAT	IBS
1	S_Mongola-1	Korean	S_Korean-1	0.157	0.81068
2	S_Mongola-1	Han	S_Han-1	0.1538	0.81020
3	S_Mongola-1	Japanese	S_Japanese-1	0.1603	0.80999
4	S_Mongola-1	Xibo	S_Xibo-2	0.1463	0.80968
5	S_Mongola-1	Korean	S_Korean-2	0.1546	0.80955
6	S_Mongola-1	Han	S_Han-2	0.1562	0.80910
7	S_Mongola-1	Tujia	S_Tujia-2	0.1522	0.80896
8	S_Mongola-1	Japanese	S_Japanese-2	0.148	0.80880
9	S_Mongola-1	She	S_She-1	0.1542	0.80875
10	S_Mongola-1	She	S_She-2	0.1535	0.80870
11	S_Mongola-1	Naxi	S_Naxi-1	0.1527	0.80869
12	S_Mongola-1	Japanese	S_Japanese-3	0.1426	0.80865
13	S_Mongola-1	Hezhen	S_Hezhen-2	0.1438	0.80863
14	S_Mongola-1	Yi	S_Yi-1	0.1494	0.80853
15	S_Mongola-1	Xibo	S_Xibo-1	0.1408	0.80837
16	S_Mongola-1	Miao	S_Miao-2	0.1534	0.80827
17	S_Mongola-1	Kinh	S_Kinh-1	0.1488	0.80800
18	S_Mongola-1	Naxi	S_Naxi-3	0.1516	0.80795
19	S_Mongola-1	Hezhen	S_Hezhen-1	0.1514	0.80782
20	S_Mongola-1	Tujia	S_Tujia-1	0.1519	0.80772
21	S_Mongola-1	Mongola	S_Mongola-2	0.1456	0.80755
22	S_Mongola-1	Miao	S_Miao-1	0.1518	0.80748
23	S_Mongola-1	Ulchi	S_Ulchi-1	0.1642	0.80746
24	S_Mongola-1	Oroqen	S_Oroqen-1	0.1575	0.80745
25	S_Mongola-1	Yi	S_Yi-2	0.1529	0.80724
26	S_Mongola-1	Daur	S_Daur-2	0.1422	0.80716
27	S_Mongola-1	Ulchi	S_Ulchi-2	0.1566	0.80713
28	S_Mongola-1	Oroqen	S_Oroqen-2	0.1588	0.80693
29	S_Mongola-1	Dai	S_Dai-1	0.1463	0.80672
30	S_Mongola-1	Even	S_Even-3	0.1583	0.80661
31	S_Mongola-1	Dai	S_Dai-2	0.1519	0.80603
32	S_Mongola-1	Tu	S_Tu-2	0.1387	0.80580
33	S_Mongola-1	Kinh	S_Kinh-2	0.1415	0.80574
34	S_Mongola-1	Thai	S_Thai-2	0.1401	0.80573
35	S_Mongola-1	China_Lahu	S_Lahu-1	0.1524	0.80558
36	S_Mongola-1	Burmese	S_Burmese-1	0.1385	0.80540
37	S_Mongola-1	Tu	S_Tu-1	0.1354	0.80530
38	S_Mongola-1	Ami.DG	S_Ami1	0.1575	0.80503
39	S_Mongola-1	Ami.DG	S_Ami2	0.1595	0.80502
40	S_Mongola-1	Even	S_Even-2	0.1555	0.80488
41	S_Mongola-1	Yakut	S_Yakut-1	0.1485	0.80419
42	S_Mongola-1	China_Lahu	S_Lahu-2	0.1523	0.80397
43	S_Mongola-1	Igorot	S_Igorot-2	0	0.80313
44	S_Mongola-1	Dusun	S_Dusun-2	0	0.80309
45	S_Mongola-1	Dusun	S_Dusun-1	0	0.80308
46	S_Mongola-1	Thai	S_Thai-1	0.1275	0.80306
47	S_Mongola-1	Igorot	S_Igorot-1	0	0.80301
48	S_Mongola-1	Cambodian	S_Cambodian-1	0.1407	0.80241
49	S_Mongola-1	Even	S_Even-1	0.1214	0.80214
50	S_Mongola-1	Burmese	S_Burmese-2	0.1169	0.80213
51	S_Mongola-1	Yakut	S_Yakut-2	0.1438	0.80209
52	S_Mongola-1	Cambodian	S_Cambodian-2	0.134	0.80188
53	S_Mongola-1	Eskimo_Sireniki.DG	S_Sireniki1	0	0.80124
54	S_Mongola-1	Kyrgyz_Kyrgyzstan	S_Kyrgyz-1	0.1127	0.79908
55	S_Mongola-1	Kyrgyz_Kyrgyzstan	S_Kyrgyz-2	0.1005	0.79815
56	S_Mongola-1	Itelmen	S_Itelman-1	0	0.79809
57	S_Mongola-1	Eskimo_Naukan.DG	S_Naukan2	0	0.79789
58	S_Mongola-1	Eskimo_Chaplin.DG	S_Chaplin1	0	0.79770
59	S_Mongola-1	Eskimo_Naukan.DG	S_Naukan1	0	0.79751
60	S_Mongola-1	Eskimo_Sireniki.DG	S_Sireniki2	0	0.79749
61	S_Mongola-1	Kusunda	S_Kusunda-1	0.1132	0.79740
62	S_Mongola-1	Tubalar	S_Tubalar-2	0	0.79509
63	S_Mongola-1	Tubalar	S_Tubalar-1	0.1107	0.79490
64	S_Mongola-1	Chukchi	S_Chukchi-1	0.0841	0.79357
65	S_Mongola-1	Uyghur	S_Uygur-1	0.0898	0.79336
66	S_Mongola-1	Mexico_Zapotec.DG	S_Zapotec1	0	0.79282
67	S_Mongola-1	Mansi	S_Mansi-1	0	0.79238
68	S_Mongola-1	Hazara	S_Hazara-1	0	0.79204
69	S_Mongola-1	Pima	S_Pima-1	0	0.79198
70	S_Mongola-1	Uyghur	S_Uygur-2	0	0.79197
71	S_Mongola-1	Hazara	S_Hazara-2	0	0.79170
72	S_Mongola-1	Mayan	S_Mayan-2	0	0.79120
73	S_Mongola-1	Mixtec	S_Mixtec-1	0	0.79120
74	S_Mongola-1	Mixe	S_Mixe-2	0	0.79115
75	S_Mongola-1	Mexico_Zapotec.DG	S_Zapotec2	0	0.79101
76	S_Mongola-1	Mayan	S_Mayan-1	0	0.79087
77	S_Mongola-1	Quechua	S_Quechua-3	0	0.79075
78	S_Mongola-1	Mixe	S_Mixe-3	0	0.79044
79	S_Mongola-1	Piapoco	S_Piapoco-2	0	0.79029
80	S_Mongola-1	Quechua	S_Quechua-1	0	0.79023
81	S_Mongola-1	Quechua	S_Quechua-2	0	0.78995
82	S_Mongola-1	Pima	S_Pima-2	0	0.78978
83	S_Mongola-1	Mansi	S_Mansi-2	0	0.78962
84	S_Mongola-1	Khonda_Dora	S_Khonda_Dora-1	0	0.78847
85	S_Mongola-1	Tlingit	S_Tlingit-2	0	0.78816
86	S_Mongola-1	Mixtec	S_Mixtec-2	0	0.78811
87	S_Mongola-1	Maori	S_Maori-1	0.0542	0.78805
88	S_Mongola-1	Piapoco	S_Piapoco-1	0	0.78747
89	S_Mongola-1	Karitiana	S_Karitiana-2	0	0.78742
90	S_Mongola-1	Surui	S_Surui-1	0	0.78727
91	S_Mongola-1	Surui	S_Surui-2	0	0.78565
92	S_Mongola-1	Karitiana	S_Karitiana-1	0	0.78561
93	S_Mongola-1	Bengali	S_Bengali-1	0	0.78436
94	S_Mongola-1	Kusunda	S_Kusunda-2	0	0.78408
95	S_Mongola-1	Tlingit	S_Tlingit-1	0	0.78388
96	S_Mongola-1	Relli	S_Relli-1	0	0.78344
97	S_Mongola-1	Kapu	S_Kapu-2	0	0.78280
98	S_Mongola-1	Madiga	S_Madiga-1	0	0.78227
99	S_Mongola-1	Madiga	S_Madiga-2	0	0.78175
100	S_Mongola-1	Mala	S_Mala-3	0	0.78161
101	S_Mongola-1	Yadava	S_Yadava-1	0	0.78157
102	S_Mongola-1	Bengali	S_Bengali-2	0	0.78140
103	S_Mongola-1	Kapu	S_Kapu-1	0	0.78130
104	S_Mongola-1	Irula	S_Irula-2	0	0.78128
105	S_Mongola-1	Mala	S_Mala-2	0	0.78128
106	S_Mongola-1	Punjabi	S_Punjabi-1	0	0.78107
107	S_Mongola-1	Irula	S_Irula-1	0	0.78107
108	S_Mongola-1	Burusho	S_Burusho-2	0	0.78081
109	S_Mongola-1	Yadava	S_Yadava-2	0	0.78078
110	S_Mongola-1	Saami	S_Saami-1	0	0.78063
111	S_Mongola-1	Brahmin	S_Brahmin-2	0	0.78031
112	S_Mongola-1	Saami	S_Saami-2	0	0.78012
113	S_Mongola-1	Relli	S_Relli-2	0	0.77974
114	S_Mongola-1	Punjabi	S_Punjabi-3	0	0.77920
115	S_Mongola-1	Bougainville	S_Bougainville-1	0	0.77900
116	S_Mongola-1	Burusho	S_Burusho-1	0	0.77885
117	S_Mongola-1	Punjabi	S_Punjabi-2	0	0.77885
118	S_Mongola-1	Brahmin	S_Brahmin-1	0	0.77874
119	S_Mongola-1	Bougainville	S_Bougainville-2	0	0.77866
120	S_Mongola-1	Sindhi	S_Sindhi-2	0	0.77851
121	S_Mongola-1	Pathan	S_Pathan-1	0	0.77838
122	S_Mongola-1	Punjabi	S_Punjabi-4	0	0.77776
123	S_Mongola-1	Kurd-Iraq	WGS	0	0.77625
124	S_Mongola-1	Pathan	S_Pathan-2	0	0.77597
125	S_Mongola-1	Ossetian-North	S_Ossetian-1	0	0.77575
126	S_Mongola-1	Russian	S_Russian-1	0	0.77570
127	S_Mongola-1	Finnish	S_Finnish-1	0	0.77476
128	S_Mongola-1	Sindhi	S_Sindhi-1	0	0.77473
129	S_Mongola-1	Turkish-Kayseri	S_Turkish-Kayseri-1	0	0.77463
130	S_Mongola-1	Tajik	S_Tajik-2	0	0.77448
131	S_Mongola-1	YANA_UP_WGS	Yana1	0	0.77422
132	S_Mongola-1	Ossetian-North	S_Ossetian-2	0	0.77413
133	S_Mongola-1	Papuan	S_Papuan-10	0	0.77381
134	S_Mongola-1	Balochi	S_Balochi-2	0	0.77365
135	S_Mongola-1	Brahui	S_Brahui-1	0	0.77363
136	S_Mongola-1	Adygei	S_Adygei-1	0	0.77334
137	S_Mongola-1	Makrani	S_Makrani-1	0	0.77334
138	S_Mongola-1	Finnish	S_Finnish-3	0	0.77319
139	S_Mongola-1	Adygei	S_Adygei-2	0	0.77319
140	S_Mongola-1	Kalash	S_Kalash-2	0	0.77319
141	S_Mongola-1	Turkish-Kayseri	S_Turkish-Kayseri-2	0	0.77319
142	S_Mongola-1	Chechen	S_Chechen-1	0	0.77312
143	S_Mongola-1	Papuan	S_Papuan-9	0	0.77307
144	S_Mongola-1	Russian	S_Russian-2	0	0.77288
145	S_Mongola-1	Icelandic	S_Icelandic-1	0	0.77260
146	S_Mongola-1	Finnish	S_Finnish-2	0	0.77258
147	S_Mongola-1	Papuan	S_Papuan-12	0	0.77257
148	S_Mongola-1	Kalash	S_Kalash-1	0	0.77247
149	S_Mongola-1	Lezgin	S_Lezgin-1	0	0.77245
150	S_Mongola-1	Papuan	S_Papuan-8	0	0.77232
151	S_Mongola-1	Russia_Abkhasian	S_Abkhasian-1	0	0.77197
152	S_Mongola-1	Iranian-Fars	S_Iranian-Fars-1	0	0.77194
153	S_Mongola-1	Brahui	S_Brahui-2	0	0.77178
154	S_Mongola-1	Russia_Abkhasian	S_Abkhasian-2	0	0.77170
155	S_Mongola-1	Papuan	S_Papuan-1	0	0.77164
156	S_Mongola-1	Norwegian	S_Norwegian-1	0	0.77159
157	S_Mongola-1	Orcadian	S_Orcadian-2	0	0.77158
158	S_Mongola-1	Estonian	S_Estonian-1	0	0.77155
159	S_Mongola-1	Papuan	S_Papuan-7	0	0.77150
160	S_Mongola-1	Papuan	S_Papuan-11	0	0.77146
161	S_Mongola-1	Estonian	S_Estonian-2	0	0.77144
162	S_Mongola-1	Papuan	S_Papuan-13	0	0.77131
163	S_Mongola-1	Tajik	S_Tajik-1	0	0.77131
164	S_Mongola-1	Papuan	S_Papuan-14	0	0.77129
165	S_Mongola-1	Hungarian	S_Hungarian-2	0	0.77120
166	S_Mongola-1	Czech	S_Czech-2	0	0.77120
167	S_Mongola-1	Papuan	S_Papuan-3	0	0.77119
168	S_Mongola-1	Icelandic	S_Icelandic-2	0	0.77119
169	S_Mongola-1	Hungarian	S_Hungarian-1	0	0.77111
170	S_Mongola-1	Polish	S_Polish-1	0	0.77110
171	S_Mongola-1	Bulgarian	S_Bulgarian-1	0	0.77106
172	S_Mongola-1	Greek	S_Greek-1	0	0.77103
173	S_Mongola-1	Iranian-Fars	S_Iranian-Fars-2	0	0.77103
174	S_Mongola-1	Papuan	S_Papuan-5	0	0.77101
175	S_Mongola-1	French	S_French-2	0	0.77082
176	S_Mongola-1	Georgian	S_Georgian-1	0	0.77071
177	S_Mongola-1	Balochi	S_Balochi-1	0	0.77062
178	S_Mongola-1	Spanish	S_Spanish-1	0	0.77061
179	S_Mongola-1	Armenian	S_Armenian-1	0	0.77054
180	S_Mongola-1	Papuan	S_Papuan-6	0	0.77049
181	S_Mongola-1	Bergamo	S_Bergamo-2	0	0.77017
182	S_Mongola-1	Papuan	S_Papuan-2	0	0.77008
183	S_Mongola-1	Bulgarian	S_Bulgarian-2	0	0.77007
184	S_Mongola-1	Papuan	S_Papuan-4	0	0.77005
185	S_Mongola-1	Spanish	S_Spanish-2	0	0.76981
186	S_Mongola-1	Greek	S_Greek-2	0	0.76981
187	S_Mongola-1	Basque	S_Basque-1	0	0.76979
188	S_Mongola-1	English	S_English-1	0	0.76977
189	S_Mongola-1	Lezgin	S_Lezgin-2	0	0.76975
190	S_Mongola-1	Tuscan	S_Tuscan-2	0	0.76960
191	S_Mongola-1	Albanian.DG	S_Albanian1	0	0.76953
192	S_Mongola-1	English	S_English-2	0	0.76951
193	S_Mongola-1	Armenian	S_Armenian-2	0	0.76950
194	S_Mongola-1	Sardinian	S_Sardinian-2	0	0.76946
195	S_Mongola-1	Orcadian	S_Orcadian-1	0	0.76909
196	S_Mongola-1	Tuscan	S_Tuscan-1	0	0.76906
197	S_Mongola-1	Jew_Iraqi	S_Iraqi_Jew-1	0	0.76901
198	S_Mongola-1	Basque	S_Basque-2	0	0.76888
199	S_Mongola-1	Georgian	S_Georgian-2	0	0.76886
200	S_Mongola-1	Jew_Iraqi	S_Iraqi_Jew-2	0	0.76865
201	S_Mongola-1	Jordanian	S_Jordanian-3	0	0.76809
202	S_Mongola-1	French	S_French-1	0	0.76796
203	S_Mongola-1	BedouinB	S_BedouinB-2	0	0.76779
204	S_Mongola-1	Druze	S_Druze-1	0	0.76757
205	S_Mongola-1	Druze	S_Druze-2	0	0.76754
206	S_Mongola-1	Makrani	S_Makrani-2	0	0.76747
207	S_Mongola-1	Jew_Yemenite	S_Yemenite_Jew-2	0	0.76622
208	S_Mongola-1	Jew_Yemenite	S_Yemenite_Jew-1	0	0.76575
209	S_Mongola-1	Sardinian	S_Sardinian-1	0	0.76564
210	S_Mongola-1	BedouinB	S_BedouinB-1	0	0.76460
211	S_Mongola-1	Jordanian	S_Jordanian-2	0	0.76413
212	S_Mongola-1	Samaritan	S_Samaritan-1	0	0.76396
213	S_Mongola-1	Jordanian	S_Jordanian-1	0	0.76261
214	S_Mongola-1	Saharawi	S_Saharawi-2	0	0.75981
215	S_Mongola-1	Saharawi	S_Saharawi-1	0	0.75964
216	S_Mongola-1	Mozabite	S_Mozabite-1	0	0.75937
217	S_Mongola-1	Mozabite	S_Mozabite-2	0	0.75824
222	S_Mongola-1	Somali	S_Somali-1	0	0.74788
224	S_Mongola-1	Masai	S_Masai-2	0	0.74381
226	S_Mongola-1	Masai	S_Masai-1	0	0.74274
232	S_Mongola-1	Gambian	S_Gambian-2	0	0.73200
233	S_Mongola-1	BantuKenya	S_BantuKenya-1	0	0.73139
234	S_Mongola-1	Luo	S_Luo-2	0	0.73107
235	S_Mongola-1	BantuKenya	S_BantuKenya-2	0	0.73020
236	S_Mongola-1	Luhya	S_Luhya-1	0	0.73005
237	S_Mongola-1	Luhya	S_Luhya-2	0	0.73002
238	S_Mongola-1	Mandenka	S_Mandenka-2	0	0.72934
239	S_Mongola-1	Gambian	S_Gambian-1	0	0.72933
240	S_Mongola-1	Esan	S_Esan-2	0	0.72920
241	S_Mongola-1	Yoruba	S_Yoruba-2	0	0.72879
242	S_Mongola-1	Mandenka	S_Mandenka-1	0	0.72872
243	S_Mongola-1	Yoruba	S_Yoruba-1	0	0.72816
244	S_Mongola-1	Esan	S_Esan-1	0	0.72810
245	S_Mongola-1	Mende	S_Mende-1	0	0.72793
246	S_Mongola-1	Mende	S_Mende-2	0	0.72788
247	S_Mongola-1	Biaka	S_Biaka-1	0	0.72484
248	S_Mongola-1	Biaka	S_Biaka-2	0	0.72347
249	S_Mongola-1	Mbuti	S_Mbuti-3	0	0.72046
250	S_Mongola-1	Mbuti	S_Mbuti-1	0	0.72010
251	S_Mongola-1	Mbuti	S_Mbuti-2	0	0.72005
252	S_Mongola-1	Khomani_San	S_Khomani_San-2	0	0.71521
253	S_Mongola-1	Ju_hoan_North	S_Ju_hoan_North-2	0	0.71514
254	S_Mongola-1	Ju_hoan_North	S_Ju_hoan_North-3	0	0.71460
255	S_Mongola-1	Khomani_San	S_Khomani_San-1	0	0.71302

**Zoro** · 03-09-2021, 01:41 PM

It's pretty amazing that the above IBS list was able to properly order Mbuti, Khomani, and Ju-Hoan in terms of IBS with Mongola.

Does anyone know why Mongola is slightly closer to Mbuti than Khomani and Ju-Hoan ?

Hint: The late paleolithic African paper

**Zanzibar** · 03-09-2021, 02:00 PM

Originally Posted by Zoro

E Asians have higher genetic similarity with Saamis than other mainland Europeans. I wouldn't rely on G25 for that though because the results can be misleading. In general with any calculator the amount of E Asian will change depending on what other components the calculator uses.

You have to do a gene to gene comparison between E Asian and each European population one at a time to get an accurate picture. Here is IBS similarity with Mongola sample based on Plink --genome flag using 400,000 SNPs.

As you can see Mongola has about the same amount of IBS with Saamis as with some S Asians and not that much more than Iraqi Kurd or some Finns which would be a shocker to you if you just went by calculator results.

NO	FID1	FID2	IID2	PI_HAT	IBS
1	S_Mongola-1	Korean	S_Korean-1	0.157	0.81068
2	S_Mongola-1	Han	S_Han-1	0.1538	0.81020
3	S_Mongola-1	Japanese	S_Japanese-1	0.1603	0.80999
4	S_Mongola-1	Xibo	S_Xibo-2	0.1463	0.80968
5	S_Mongola-1	Korean	S_Korean-2	0.1546	0.80955
6	S_Mongola-1	Han	S_Han-2	0.1562	0.80910
7	S_Mongola-1	Tujia	S_Tujia-2	0.1522	0.80896
8	S_Mongola-1	Japanese	S_Japanese-2	0.148	0.80880
9	S_Mongola-1	She	S_She-1	0.1542	0.80875
10	S_Mongola-1	She	S_She-2	0.1535	0.80870
11	S_Mongola-1	Naxi	S_Naxi-1	0.1527	0.80869
12	S_Mongola-1	Japanese	S_Japanese-3	0.1426	0.80865
13	S_Mongola-1	Hezhen	S_Hezhen-2	0.1438	0.80863
14	S_Mongola-1	Yi	S_Yi-1	0.1494	0.80853
15	S_Mongola-1	Xibo	S_Xibo-1	0.1408	0.80837
16	S_Mongola-1	Miao	S_Miao-2	0.1534	0.80827
17	S_Mongola-1	Kinh	S_Kinh-1	0.1488	0.80800
18	S_Mongola-1	Naxi	S_Naxi-3	0.1516	0.80795
19	S_Mongola-1	Hezhen	S_Hezhen-1	0.1514	0.80782
20	S_Mongola-1	Tujia	S_Tujia-1	0.1519	0.80772
21	S_Mongola-1	Mongola	S_Mongola-2	0.1456	0.80755
22	S_Mongola-1	Miao	S_Miao-1	0.1518	0.80748
23	S_Mongola-1	Ulchi	S_Ulchi-1	0.1642	0.80746
24	S_Mongola-1	Oroqen	S_Oroqen-1	0.1575	0.80745
25	S_Mongola-1	Yi	S_Yi-2	0.1529	0.80724
26	S_Mongola-1	Daur	S_Daur-2	0.1422	0.80716
27	S_Mongola-1	Ulchi	S_Ulchi-2	0.1566	0.80713
28	S_Mongola-1	Oroqen	S_Oroqen-2	0.1588	0.80693
29	S_Mongola-1	Dai	S_Dai-1	0.1463	0.80672
30	S_Mongola-1	Even	S_Even-3	0.1583	0.80661
31	S_Mongola-1	Dai	S_Dai-2	0.1519	0.80603
32	S_Mongola-1	Tu	S_Tu-2	0.1387	0.80580
33	S_Mongola-1	Kinh	S_Kinh-2	0.1415	0.80574
34	S_Mongola-1	Thai	S_Thai-2	0.1401	0.80573
35	S_Mongola-1	China_Lahu	S_Lahu-1	0.1524	0.80558
36	S_Mongola-1	Burmese	S_Burmese-1	0.1385	0.80540
37	S_Mongola-1	Tu	S_Tu-1	0.1354	0.80530
38	S_Mongola-1	Ami.DG	S_Ami1	0.1575	0.80503
39	S_Mongola-1	Ami.DG	S_Ami2	0.1595	0.80502
40	S_Mongola-1	Even	S_Even-2	0.1555	0.80488
41	S_Mongola-1	Yakut	S_Yakut-1	0.1485	0.80419
42	S_Mongola-1	China_Lahu	S_Lahu-2	0.1523	0.80397
43	S_Mongola-1	Igorot	S_Igorot-2	0	0.80313
44	S_Mongola-1	Dusun	S_Dusun-2	0	0.80309
45	S_Mongola-1	Dusun	S_Dusun-1	0	0.80308
46	S_Mongola-1	Thai	S_Thai-1	0.1275	0.80306
47	S_Mongola-1	Igorot	S_Igorot-1	0	0.80301
48	S_Mongola-1	Cambodian	S_Cambodian-1	0.1407	0.80241
49	S_Mongola-1	Even	S_Even-1	0.1214	0.80214
50	S_Mongola-1	Burmese	S_Burmese-2	0.1169	0.80213
51	S_Mongola-1	Yakut	S_Yakut-2	0.1438	0.80209
52	S_Mongola-1	Cambodian	S_Cambodian-2	0.134	0.80188
53	S_Mongola-1	Eskimo_Sireniki.DG	S_Sireniki1	0	0.80124
54	S_Mongola-1	Kyrgyz_Kyrgyzstan	S_Kyrgyz-1	0.1127	0.79908
55	S_Mongola-1	Kyrgyz_Kyrgyzstan	S_Kyrgyz-2	0.1005	0.79815
56	S_Mongola-1	Itelmen	S_Itelman-1	0	0.79809
57	S_Mongola-1	Eskimo_Naukan.DG	S_Naukan2	0	0.79789
58	S_Mongola-1	Eskimo_Chaplin.DG	S_Chaplin1	0	0.79770
59	S_Mongola-1	Eskimo_Naukan.DG	S_Naukan1	0	0.79751
60	S_Mongola-1	Eskimo_Sireniki.DG	S_Sireniki2	0	0.79749
61	S_Mongola-1	Kusunda	S_Kusunda-1	0.1132	0.79740
62	S_Mongola-1	Tubalar	S_Tubalar-2	0	0.79509
63	S_Mongola-1	Tubalar	S_Tubalar-1	0.1107	0.79490
64	S_Mongola-1	Chukchi	S_Chukchi-1	0.0841	0.79357
65	S_Mongola-1	Uyghur	S_Uygur-1	0.0898	0.79336
66	S_Mongola-1	Mexico_Zapotec.DG	S_Zapotec1	0	0.79282
67	S_Mongola-1	Mansi	S_Mansi-1	0	0.79238
68	S_Mongola-1	Hazara	S_Hazara-1	0	0.79204
69	S_Mongola-1	Pima	S_Pima-1	0	0.79198
70	S_Mongola-1	Uyghur	S_Uygur-2	0	0.79197
71	S_Mongola-1	Hazara	S_Hazara-2	0	0.79170
72	S_Mongola-1	Mayan	S_Mayan-2	0	0.79120
73	S_Mongola-1	Mixtec	S_Mixtec-1	0	0.79120
74	S_Mongola-1	Mixe	S_Mixe-2	0	0.79115
75	S_Mongola-1	Mexico_Zapotec.DG	S_Zapotec2	0	0.79101
76	S_Mongola-1	Mayan	S_Mayan-1	0	0.79087
77	S_Mongola-1	Quechua	S_Quechua-3	0	0.79075
78	S_Mongola-1	Mixe	S_Mixe-3	0	0.79044
79	S_Mongola-1	Piapoco	S_Piapoco-2	0	0.79029
80	S_Mongola-1	Quechua	S_Quechua-1	0	0.79023
81	S_Mongola-1	Quechua	S_Quechua-2	0	0.78995
82	S_Mongola-1	Pima	S_Pima-2	0	0.78978
83	S_Mongola-1	Mansi	S_Mansi-2	0	0.78962
84	S_Mongola-1	Khonda_Dora	S_Khonda_Dora-1	0	0.78847
85	S_Mongola-1	Tlingit	S_Tlingit-2	0	0.78816
86	S_Mongola-1	Mixtec	S_Mixtec-2	0	0.78811
87	S_Mongola-1	Maori	S_Maori-1	0.0542	0.78805
88	S_Mongola-1	Piapoco	S_Piapoco-1	0	0.78747
89	S_Mongola-1	Karitiana	S_Karitiana-2	0	0.78742
90	S_Mongola-1	Surui	S_Surui-1	0	0.78727
91	S_Mongola-1	Surui	S_Surui-2	0	0.78565
92	S_Mongola-1	Karitiana	S_Karitiana-1	0	0.78561
93	S_Mongola-1	Bengali	S_Bengali-1	0	0.78436
94	S_Mongola-1	Kusunda	S_Kusunda-2	0	0.78408
95	S_Mongola-1	Tlingit	S_Tlingit-1	0	0.78388
96	S_Mongola-1	Relli	S_Relli-1	0	0.78344
97	S_Mongola-1	Kapu	S_Kapu-2	0	0.78280
98	S_Mongola-1	Madiga	S_Madiga-1	0	0.78227
99	S_Mongola-1	Madiga	S_Madiga-2	0	0.78175
100	S_Mongola-1	Mala	S_Mala-3	0	0.78161
101	S_Mongola-1	Yadava	S_Yadava-1	0	0.78157
102	S_Mongola-1	Bengali	S_Bengali-2	0	0.78140
103	S_Mongola-1	Kapu	S_Kapu-1	0	0.78130
104	S_Mongola-1	Irula	S_Irula-2	0	0.78128
105	S_Mongola-1	Mala	S_Mala-2	0	0.78128
106	S_Mongola-1	Punjabi	S_Punjabi-1	0	0.78107
107	S_Mongola-1	Irula	S_Irula-1	0	0.78107
108	S_Mongola-1	Burusho	S_Burusho-2	0	0.78081
109	S_Mongola-1	Yadava	S_Yadava-2	0	0.78078
110	S_Mongola-1	Saami	S_Saami-1	0	0.78063
111	S_Mongola-1	Brahmin	S_Brahmin-2	0	0.78031
112	S_Mongola-1	Saami	S_Saami-2	0	0.78012
113	S_Mongola-1	Relli	S_Relli-2	0	0.77974
114	S_Mongola-1	Punjabi	S_Punjabi-3	0	0.77920
115	S_Mongola-1	Bougainville	S_Bougainville-1	0	0.77900
116	S_Mongola-1	Burusho	S_Burusho-1	0	0.77885
117	S_Mongola-1	Punjabi	S_Punjabi-2	0	0.77885
118	S_Mongola-1	Brahmin	S_Brahmin-1	0	0.77874
119	S_Mongola-1	Bougainville	S_Bougainville-2	0	0.77866
120	S_Mongola-1	Sindhi	S_Sindhi-2	0	0.77851
121	S_Mongola-1	Pathan	S_Pathan-1	0	0.77838
122	S_Mongola-1	Punjabi	S_Punjabi-4	0	0.77776
123	S_Mongola-1	Kurd-Iraq	WGS	0	0.77625
124	S_Mongola-1	Pathan	S_Pathan-2	0	0.77597
125	S_Mongola-1	Ossetian-North	S_Ossetian-1	0	0.77575
126	S_Mongola-1	Russian	S_Russian-1	0	0.77570
127	S_Mongola-1	Finnish	S_Finnish-1	0	0.77476
128	S_Mongola-1	Sindhi	S_Sindhi-1	0	0.77473
129	S_Mongola-1	Turkish-Kayseri	S_Turkish-Kayseri-1	0	0.77463
130	S_Mongola-1	Tajik	S_Tajik-2	0	0.77448
131	S_Mongola-1	YANA_UP_WGS	Yana1	0	0.77422
132	S_Mongola-1	Ossetian-North	S_Ossetian-2	0	0.77413
133	S_Mongola-1	Papuan	S_Papuan-10	0	0.77381
134	S_Mongola-1	Balochi	S_Balochi-2	0	0.77365
135	S_Mongola-1	Brahui	S_Brahui-1	0	0.77363
136	S_Mongola-1	Adygei	S_Adygei-1	0	0.77334
137	S_Mongola-1	Makrani	S_Makrani-1	0	0.77334
138	S_Mongola-1	Finnish	S_Finnish-3	0	0.77319
139	S_Mongola-1	Adygei	S_Adygei-2	0	0.77319
140	S_Mongola-1	Kalash	S_Kalash-2	0	0.77319
141	S_Mongola-1	Turkish-Kayseri	S_Turkish-Kayseri-2	0	0.77319
142	S_Mongola-1	Chechen	S_Chechen-1	0	0.77312
143	S_Mongola-1	Papuan	S_Papuan-9	0	0.77307
144	S_Mongola-1	Russian	S_Russian-2	0	0.77288
145	S_Mongola-1	Icelandic	S_Icelandic-1	0	0.77260
146	S_Mongola-1	Finnish	S_Finnish-2	0	0.77258
147	S_Mongola-1	Papuan	S_Papuan-12	0	0.77257
148	S_Mongola-1	Kalash	S_Kalash-1	0	0.77247
149	S_Mongola-1	Lezgin	S_Lezgin-1	0	0.77245
150	S_Mongola-1	Papuan	S_Papuan-8	0	0.77232
151	S_Mongola-1	Russia_Abkhasian	S_Abkhasian-1	0	0.77197
152	S_Mongola-1	Iranian-Fars	S_Iranian-Fars-1	0	0.77194
153	S_Mongola-1	Brahui	S_Brahui-2	0	0.77178
154	S_Mongola-1	Russia_Abkhasian	S_Abkhasian-2	0	0.77170
155	S_Mongola-1	Papuan	S_Papuan-1	0	0.77164
156	S_Mongola-1	Norwegian	S_Norwegian-1	0	0.77159
157	S_Mongola-1	Orcadian	S_Orcadian-2	0	0.77158
158	S_Mongola-1	Estonian	S_Estonian-1	0	0.77155
159	S_Mongola-1	Papuan	S_Papuan-7	0	0.77150
160	S_Mongola-1	Papuan	S_Papuan-11	0	0.77146
161	S_Mongola-1	Estonian	S_Estonian-2	0	0.77144
162	S_Mongola-1	Papuan	S_Papuan-13	0	0.77131
163	S_Mongola-1	Tajik	S_Tajik-1	0	0.77131
164	S_Mongola-1	Papuan	S_Papuan-14	0	0.77129
165	S_Mongola-1	Hungarian	S_Hungarian-2	0	0.77120
166	S_Mongola-1	Czech	S_Czech-2	0	0.77120
167	S_Mongola-1	Papuan	S_Papuan-3	0	0.77119
168	S_Mongola-1	Icelandic	S_Icelandic-2	0	0.77119
169	S_Mongola-1	Hungarian	S_Hungarian-1	0	0.77111
170	S_Mongola-1	Polish	S_Polish-1	0	0.77110
171	S_Mongola-1	Bulgarian	S_Bulgarian-1	0	0.77106
172	S_Mongola-1	Greek	S_Greek-1	0	0.77103
173	S_Mongola-1	Iranian-Fars	S_Iranian-Fars-2	0	0.77103
174	S_Mongola-1	Papuan	S_Papuan-5	0	0.77101
175	S_Mongola-1	French	S_French-2	0	0.77082
176	S_Mongola-1	Georgian	S_Georgian-1	0	0.77071
177	S_Mongola-1	Balochi	S_Balochi-1	0	0.77062
178	S_Mongola-1	Spanish	S_Spanish-1	0	0.77061
179	S_Mongola-1	Armenian	S_Armenian-1	0	0.77054
180	S_Mongola-1	Papuan	S_Papuan-6	0	0.77049
181	S_Mongola-1	Bergamo	S_Bergamo-2	0	0.77017
182	S_Mongola-1	Papuan	S_Papuan-2	0	0.77008
183	S_Mongola-1	Bulgarian	S_Bulgarian-2	0	0.77007
184	S_Mongola-1	Papuan	S_Papuan-4	0	0.77005
185	S_Mongola-1	Spanish	S_Spanish-2	0	0.76981
186	S_Mongola-1	Greek	S_Greek-2	0	0.76981
187	S_Mongola-1	Basque	S_Basque-1	0	0.76979
188	S_Mongola-1	English	S_English-1	0	0.76977
189	S_Mongola-1	Lezgin	S_Lezgin-2	0	0.76975
190	S_Mongola-1	Tuscan	S_Tuscan-2	0	0.76960
191	S_Mongola-1	Albanian.DG	S_Albanian1	0	0.76953
192	S_Mongola-1	English	S_English-2	0	0.76951
193	S_Mongola-1	Armenian	S_Armenian-2	0	0.76950
194	S_Mongola-1	Sardinian	S_Sardinian-2	0	0.76946
195	S_Mongola-1	Orcadian	S_Orcadian-1	0	0.76909
196	S_Mongola-1	Tuscan	S_Tuscan-1	0	0.76906
197	S_Mongola-1	Jew_Iraqi	S_Iraqi_Jew-1	0	0.76901
198	S_Mongola-1	Basque	S_Basque-2	0	0.76888
199	S_Mongola-1	Georgian	S_Georgian-2	0	0.76886
200	S_Mongola-1	Jew_Iraqi	S_Iraqi_Jew-2	0	0.76865
201	S_Mongola-1	Jordanian	S_Jordanian-3	0	0.76809
202	S_Mongola-1	French	S_French-1	0	0.76796
203	S_Mongola-1	BedouinB	S_BedouinB-2	0	0.76779
204	S_Mongola-1	Druze	S_Druze-1	0	0.76757
205	S_Mongola-1	Druze	S_Druze-2	0	0.76754
206	S_Mongola-1	Makrani	S_Makrani-2	0	0.76747
207	S_Mongola-1	Jew_Yemenite	S_Yemenite_Jew-2	0	0.76622
208	S_Mongola-1	Jew_Yemenite	S_Yemenite_Jew-1	0	0.76575
209	S_Mongola-1	Sardinian	S_Sardinian-1	0	0.76564
210	S_Mongola-1	BedouinB	S_BedouinB-1	0	0.76460
211	S_Mongola-1	Jordanian	S_Jordanian-2	0	0.76413
212	S_Mongola-1	Samaritan	S_Samaritan-1	0	0.76396
213	S_Mongola-1	Jordanian	S_Jordanian-1	0	0.76261
214	S_Mongola-1	Saharawi	S_Saharawi-2	0	0.75981
215	S_Mongola-1	Saharawi	S_Saharawi-1	0	0.75964
216	S_Mongola-1	Mozabite	S_Mozabite-1	0	0.75937
217	S_Mongola-1	Mozabite	S_Mozabite-2	0	0.75824
222	S_Mongola-1	Somali	S_Somali-1	0	0.74788
224	S_Mongola-1	Masai	S_Masai-2	0	0.74381
226	S_Mongola-1	Masai	S_Masai-1	0	0.74274
232	S_Mongola-1	Gambian	S_Gambian-2	0	0.73200
233	S_Mongola-1	BantuKenya	S_BantuKenya-1	0	0.73139
234	S_Mongola-1	Luo	S_Luo-2	0	0.73107
235	S_Mongola-1	BantuKenya	S_BantuKenya-2	0	0.73020
236	S_Mongola-1	Luhya	S_Luhya-1	0	0.73005
237	S_Mongola-1	Luhya	S_Luhya-2	0	0.73002
238	S_Mongola-1	Mandenka	S_Mandenka-2	0	0.72934
239	S_Mongola-1	Gambian	S_Gambian-1	0	0.72933
240	S_Mongola-1	Esan	S_Esan-2	0	0.72920
241	S_Mongola-1	Yoruba	S_Yoruba-2	0	0.72879
242	S_Mongola-1	Mandenka	S_Mandenka-1	0	0.72872
243	S_Mongola-1	Yoruba	S_Yoruba-1	0	0.72816
244	S_Mongola-1	Esan	S_Esan-1	0	0.72810
245	S_Mongola-1	Mende	S_Mende-1	0	0.72793
246	S_Mongola-1	Mende	S_Mende-2	0	0.72788
247	S_Mongola-1	Biaka	S_Biaka-1	0	0.72484
248	S_Mongola-1	Biaka	S_Biaka-2	0	0.72347
249	S_Mongola-1	Mbuti	S_Mbuti-3	0	0.72046
250	S_Mongola-1	Mbuti	S_Mbuti-1	0	0.72010
251	S_Mongola-1	Mbuti	S_Mbuti-2	0	0.72005
252	S_Mongola-1	Khomani_San	S_Khomani_San-2	0	0.71521
253	S_Mongola-1	Ju_hoan_North	S_Ju_hoan_North-2	0	0.71514
254	S_Mongola-1	Ju_hoan_North	S_Ju_hoan_North-3	0	0.71460
255	S_Mongola-1	Khomani_San	S_Khomani_San-1	0	0.71302

Very interesting. How much East Eurasian ancestry for the Saamis can we infer from this IBS comparison?

**Zanzibar** · 03-09-2021, 02:11 PM

Originally Posted by Komintasavalta

I tried doing qpAdm models of the population named Saami.DG in the v44.3_HO dataset. I excluded models with one or more negative weight (where feasible is false) and I sorted the models by their p score.

I'm probably doing something wrong, and I still don't know how to pick the outgroups. I mostly just picked outgroups that resulted in little decrease in the number of SNPs that remained after filtering. I also tried to pick left populations that resulted in little decrease in the SNP count.

I got 374794 out of 597573 SNPs after filtering, out of which 349558 were polymorphic.

In the image above, the models whose p score is above .05 have a constant of about 30-35% Nganasan ancestry. However EHG and CHG and SHG are also part Mongoloid. So if we consider Nganasan to be fully Mongoloid, Saami might also be closer to 40% than 30% Mongoloid.

Both individuals in the population Saami.DG were from Utsjoki, which is part of the Northern Saami region within Finland:

Code:

$ awk 'NR==1||/Saami...DG/' g/v44.3_HO_public/v44.3_HO_public.anno|cut -f2,4,9,10|tr \\t \;
Version ID;Publication (or OK to use in a paper);Locality;Country
S_Saami-1.DG;MallickNature2016;Utsjoki;Finland
S_Saami-2.DG;MallickNature2016;Utsjoki;Finland

Among Finnish Saami, there are an estimated 2,000 speakers of Northern Saami, 300 speakers of Inari Saami, and 300 speakers of Skolt Saami (https://fi.wikipedia.org/wiki/Saamelaiskielet). Out of four groups of Saami measured by Karin Mark, Skolt Saami had the lightest pigmentation, followed by Inari Saami, Finnish Northern Saami, and Kola Saami (https://www.etis.ee/Portal/Publicati...8-b9b3010eabad).

Scandinavian Northern Saami might be even more Mongoloid than Finnish Northern Saami, or at least Coon wrote that the Saami of the Scandinavian inland were the darkest and most brachycephalic (https://www.theapricity.com/snpa/chapter-IX2.htm):

The selected "pure" groups, Bryn's Reindeer Lapps, and some of Geyer's mountain and forest Lapps from Sweden, have seventy per cent or over of this dark hair, while the fairest Lapps, with a majority of brown and blond shades, are found in Finland and in the Kola Peninsula.

Pure dark eyes are found among one-third of Reindeer Lapps, and among as few as eight per cent in the total of Lapps from Norway.[14] Pure light and light-mixed eyes are commonest among the Lapps of Finland, where they total between thirty and forty per cent, and least common among the Reindeer Lapps of interior Norway and Sweden. Even among the purest selected sub-groups, such as that of Geyer, who isolated from a larger Swedish Lapp sample a few individuals of most pronounced Lappish type, at least a third are light or light-mixed in iris color. [...]

There are, however, regional differences; the center of extreme round headedness lies among the inland groups in northern Norway, while the Swedish, Finnish, and Kola Peninsula Lapps become progressively narrower headed. The mean for the purest Reindeer Lapps of Norway is 87; for the easternmost Lapps, 80 to 83.

Code for ADMIXTOOLS 2:

Code:

target="Saami.DG"
left=c("Turkey_Boncuklu_N.SG","Armenia_Caucasus_KuraAraxes","Latvia_HG","Sweden_Motala_HG","Russia_HG_Karelia","Russia_HG_Tyumen","Nganasan")
right=c("Mbuti.DG","Mixe.DG","Ami.DG","Papuan.DG","Chimp.REF","Ju_hoan_North","Biaka.DG","Yoruba.DG","Altai_Neanderthal.DG")

pops=c(left,right,target)

unlink("/tmp/f2",recursive=T)
extract_f2(pref="g/v44.3_HO_public/v44.3_HO_public",pops=pops,outdir="/tmp/f2")
f2=f2_from_precomp("/tmp/f2")
qp=qpadm(f2,left=left,right=right,target=target)

qp2=qp$popdrop%>%dplyr::filter(feasible==T&f4rank!=0)%>%arrange(desc(p))%>%dplyr::select(!c(wt,dof,chisq,f4rank,dofdiff,chisqdiff,p_nested,feasible,best,dofdiff,chisqdiff,p_nested))
write_csv(qp2,"/tmp/qp")

Code to generate the bar chart:

Code:

library(tidyverse)
library(reshape2)
library(colorspace)

t=read_csv("/tmp/qp")

# t=t[t$p>.05,]

pvalue=sub("^0","",sprintf("%.3f",t$p))
t=t[-2]
t2=melt(t,id.var="pat")

ggplot(t2,aes(x=fct_rev(factor(pat,level=t$pat)),y=value,fill=variable))+
geom_bar(stat="identity",width=1,position=position_fill(reverse=T))+
geom_text(aes(label=round(100*value)),position=position_stack(vjust=.5,reverse=T),size=3.5)+
coord_flip()+
theme(
  axis.text.x=element_blank(),
  axis.text=element_text(color="black"),
  axis.ticks=element_blank(),
  axis.title.x=element_blank(),
  legend.box.just="center",
  legend.box.margin=margin(0),
  legend.box.spacing=unit(.05,"in"),
  legend.direction="vertical",
  legend.justification="center",
  legend.margin=margin(0),
  legend.text=element_text(size=12),
  legend.title=element_blank(),
  panel.border=element_blank(),
  text=element_text(size=16)
)+
xlab("")+
scale_x_discrete(labels=rev(pvalue),expand=c(0,0))+
scale_y_discrete(expand=c(0,0))+
scale_fill_manual("legend",values=hex(HSV(c(45,45,210,210,120,120,300),c(.6,.6,.6,.6,.6,.6,.6),c(1,.6,1,.6,1,.6,1))))
ggsave("/tmp/a.png",width=7,height=7)

Very interesting is Saami.DG the same as the Saami samples in G25? Can you try to model the Saami and Mari and see how much EEF they have now using qpAdm if you can?