Gedmatch calculators on Vahaduo

[Zanzibar]

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

library(admixtools)

t=read.table(text="R Yoruba.DG
R A
A O
A AA
O E
O CH
E EU
E L1
EU WH
EU EH
AA EH
EH EH2
EH EH1
EH AAA
AA AAA
WH CCE
WH Italy_North_Villabruna_HG
CH YM
CH CH1
CH AAN
EH1 YM
EH1 EH0
AAA AAN
CH1 EH0
CH1 Georgia_Kotias.SG
L1 Turkey_N_published
L1 LB
L1 YM1
L1 CW
EH2 LB
EH2 CWC
YM YM1
YM CW
EH0 Russia_HG_Karelia
AAN Nganasan
AAN CCA
LB Germany_EN_LBK
LB CCW
YM1 Russia_Samara_EBA_Yamnaya
CW CCW
CW Estonia_CordedWare
CCW CWC
CWC CCE
CCE CCA
CCA Udmurt")

pop=c("Udmurt","Germany_CordedWare","Estonia_CordedWare","Germany_EN_LBK","Italy_North_Villabruna_HG","Nganasan","Russia_HG_Karelia","Russia_Samara_EBA_Yamnaya","Turkey_N_published","Yoruba.DG","Georgia_Kotias.SG","Chimp.REF")

f2=f2_from_geno("v50.0_HO_public",pops=pop)
gr=qpgraph(f2,t)

plot_graph(gr$edges)
ggsave("1.png",width=9,height=9)
system("mogrify -trim -border 64 -bordercolor white t/1.png")

# plotly_graph(gr$edges)

write_dot(gr$edges) # generate black-and-white graph at graphviz.it

Edit: But when I used `maxmiss=1`, Udmurts got 32% Nganasan. (By default, ADMIXTOOLS 2 discards each SNP which is missing from every sample in a population, but `maxmiss=1` discards no SNPs.)

Well here is qpAdm model of Udmurts by David showing them close to 30% Mongoloid: https://eurogenes.blogspot.com/2017/...lithic-to.html

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

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

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

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

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

[Komintasavalta]

Are you even tried qpGraph only with modern samples? I want to see such model, for some VURs could be.

Not before now, but it seems to work pretty nicely at least for estimating the amount of Mongoloid ancestry:

library(admixtools)
pop=c("Norwegian","Nganasan","Finnish","Yoruba")
b=f2_from_geno("v50.0_HO_public",pops=pop,maxmiss=1)
find=find_graphs(b,pops=pop,outpop=c("Yoruba"),numrep=1,stop_gen=10,numadmix=1)
top=find%>%top_n(1,-score)
plot_graph(top$edges[[1]])
ggsave("1.png",width=4,height=4)

You can also use multiple Mongoloid references and multiple Caucasoid references:

[Lucas]

Not before now, but it seems to work pretty nicely at least for estimating the amount of Mongoloid ancestry:

library(admixtools)
pop=c("Norwegian","Nganasan","Finnish","Yoruba")
b=f2_from_geno("v50.0_HO_public",pops=pop,maxmiss=1)
find=find_graphs(b,pops=pop,outpop=c("Yoruba"),numrep=1,stop_gen=10,numadmix=1)
top=find%>%top_n(1,-score)
plot_graph(top$edges[[1]])
ggsave("1.png",width=4,height=4)

You can also use multiple Mongoloid references and multiple Caucasoid references:

Thanks! This is what I always wanted to see. I hate those boring ancient models with always the same refs on AG.
I must try now as it works.

[Leto]

Luke, please add those new pops, I don't want them to get lost in this influx of unrelated posts.

[Leto]

New Russian averages for Euro K13. The old Russian_Southwest is to be deleted.

Russian_Pskov,27.00,53.07,7.78,4.50,1.25,1.69,0.54,0.03,1.87,0.73,0.58,0.45,0.50
Russian_Ryazan,24.89,47.15,8.72,7.10,4.48,0.69,1.67,0.40,2.97,0.99,0.32,0.39,0.23
Russian_Southwest,25.94,46.84,8.96,5.59,6.50,0.98,0.99,0.72,1.85,0.81,0.27,0.37,0.19
Russian_Tver-Yaroslavl,25.04,49.54,9.31,5.95,1.48,1.02,1.12,0.10,3.82,1.42,0.39,0.66,0.16

[Dušan]

New Russian averages for Euro K13. The old Russian_Southwest is to be deleted.

Russian_Pskov,27.00,53.07,7.78,4.50,1.25,1.69,0.54,0.03,1.87,0.73,0.58,0.45,0.50
Russian_Ryazan,24.89,47.15,8.72,7.10,4.48,0.69,1.67,0.40,2.97,0.99,0.32,0.39,0.23
Russian_Southwest,25.94,46.84,8.96,5.59,6.50,0.98,0.99,0.72,1.85,0.81,0.27,0.37,0.19
Russian_Tver-Yaroslavl,25.04,49.54,9.31,5.95,1.48,1.02,1.12,0.10,3.82,1.42,0.39,0.66,0.16

Cool, I got 39% of this Yaroslavl average in K13 breakdown:

[CommonSense]

New Russian averages for Euro K13. The old Russian_Southwest is to be deleted.

Russian_Pskov,27.00,53.07,7.78,4.50,1.25,1.69,0.54,0.03,1.87,0.73,0.58,0.45,0.50
Russian_Ryazan,24.89,47.15,8.72,7.10,4.48,0.69,1.67,0.40,2.97,0.99,0.32,0.39,0.23
Russian_Southwest,25.94,46.84,8.96,5.59,6.50,0.98,0.99,0.72,1.85,0.81,0.27,0.37,0.19
Russian_Tver-Yaroslavl,25.04,49.54,9.31,5.95,1.48,1.02,1.12,0.10,3.82,1.42,0.39,0.66,0.16

Why are Russians from Pskov such an outlier? That average seems to predominantly Baltic.

[Komintasavalta]

Why are Russians from Pskov such an outlier? That average seems to predominantly Baltic.

In a PCA of European samples from the Reich dataset, Russian_Pskov is also close to Lithuanians:

Distance to Russian_Pskov:
.019 Russian_Smolensk
.021 Lithuanian
.021 Belarusian
.023 Russian_Ryazan
.023 Russian_Orel
.023 Russian_Tver
.024 Russian_Kaluga
.024 Estonian
.025 Russian_Yaroslavl
.026 Russian_Kursk
.026 Russian_Belgorod
.028 Ukrainian_North
.034 Ukrainian
.051 Polish.DG
.051 Moksha

[Leto]

In a PCA of European samples from the Reich dataset, Russian_Pskov is also close to Lithuanians:

Hey I asked you to run the Estonian samples in the other thread. Also another user wanted to see Erzya/Moksha samples. You know the sources

[DontFxxkWithWendy]

Target: DFWW
Distance: 1.6970% / 1.69697324 | ADC: 0.25x RC
53.0 West_Scottish
31.3 Pennsylvania_Dutch
13.3 Moroccan_Jew
1.8 Yemenite_Jewish
0.6 Evens

Seems pretty accurate, but not as good as G25. The Yemenite Jewish is not accurate though.