MDLP K16 map, made by Ph2ter. MY MOST ACCURATE MAP EVER!!!

[Gergő Marosvári]

I've asked Ph2ter, if he can make a map for my MDLP K16 results. Thankfully, he did that and i got back basically the best map i've ever received!
Thank you so much, Hvala lijepa! :)

https://i.postimg.cc/wM8rgdSW/Gergo-15-K16.png

[chrisbab]

What other maps did you do and what other maps do he offers? I did for me and my parents g25, k13, k15 and mdlp k23b.

[Komintasavalta]

You're closer to Moksha and Mordvins and Russian_Arkhangelsk than to some Lithuanian subpopulations. But it's probably because ph2ter calculated the distances between the populations in the usual way where he doesn't take the FST matrix of the calculator into account.

I didn't find the FST matrix of MLDP K16 anywhere. But in the Eurogenes K13 original datasheet, if you don't account for FST, then Hungarians are closer to Eastern Finns and Erzya than to Lithuanians. And they're even closer to Tatars than to the Irish. But if you multiply the matrix of admixture percentages with an MDS matrix of the FST matrix, then it gives more weight to differences in East Eurasian ancestry, and it moves Uralics and Tatars further from Hungarians. But it also moves Hungarians closer to Northwestern Europeans, because the smallest FST distance in K13 is between the North_Atlantic and Baltic components:

https://i.ibb.co/zHZKvGs/1.png

You can recreate the plot by downloading R from here: https://cran.r-project.org. Then open the R console application, run `install.packages(c("tidyverse","ggforce"))`, and run this:


library(tidyverse)
library(ggforce)

t=read.csv("https://pastebin.com/raw/efn67tkr",r=1) # K13 original

fst=as.matrix(as.dist(read.csv(r=1,text=",North_Atlantic,Baltic,West_Med,West_Asian,East_Me d,Red_Sea,South_Asian,East_Asian,Siberian,Amerindi an,Oceanian,Northeast_African,Sub-Saharan
North_Atlantic,,,,,,,,,,,,,
Baltic,19,,,,,,,,,,,,
West_Med,28,36,,,,,,,,,,,
West_Asian,26,32,36,,,,,,,,,,
East_Med,26,35,28,21,,,,,,,,,
Red_Sea,52,62,50,48,39,,,,,,,,
South_Asian,64,65,76,57,60,82,,,,,,,
East_Asian,114,114,122,110,111,127,76,,,,,,
Siberian,111,111,123,109,112,130,83,56,,,,,
Amerindian,138,137,154,138,144,161,120,113,105,,,,
Oceanian,179,181,187,177,176,191,146,166,177,217,, ,
Northeast_African,122,127,124,116,108,121,113,145, 151,185,203,,
Sub-Saharan,146,150,150,140,135,141,133,164,170,204,22 0,41,")))

t2=as.matrix(t)%*%cmdscale(fst,ncol(fst)-1)

xy=data.frame(x=rank(as.matrix(dist(t))["Hungarian",]),y=rank(as.matrix(dist(t2))["Hungarian",]))
pick=apply(xy<100,1,all)
xy=xy[pick,]
t2=t2[pick,]

xy$k=as.factor(cutree(hclust(dist(t2)),24))

ggplot(xy,aes(x,y))+
geom_abline(linetype="dashed",color="gray80",size=.3)+
ggforce::geom_mark_hull(aes(color=k,fill=k),concav ity=1000,radius=unit(.15,"cm"),expand=unit(.15,"cm"),alpha=.2,size=.15)+
geom_point(aes(color=k),size=.5)+
geom_text(aes(color=k),label=rownames(xy),size=2,v just=-.7)+
scale_x_continuous(breaks=seq(1,1000,10),expand=ex pansion(mult=c(.05,.02)))+
scale_y_continuous(breaks=seq(1,1000,10),expand=ex pansion(mult=c(.05,.02)))+
scale_fill_manual(values=rainbow_hcl(nlevels(xy$k) ,90,60))+
scale_color_manual(values=rainbow_hcl(nlevels(xy$k ),90,60))+
labs(x="Rank of K13 distance to Hungarian (FST not accounted for)",y="Rank of K13 distance to Hungarian (multiplied by MDS matrix of FST matrix)")+
theme(
axis.text=element_text(color="black",size=6),
axis.ticks=element_line(size=.3,color="gray80"),
axis.ticks.length=unit(-.13,"cm"),
axis.text.x=element_text(margin=margin(.2,0,0,0,"cm")),
axis.text.y=element_text(angle=90,vjust=1,hjust=.5 ,margin=margin(0,.2,0,0,"cm")),
legend.position="none",
panel.background=element_rect(fill="white"),
panel.border=element_rect(color="gray85",fill=NA,size=.6),
panel.grid.major=element_line(color="gray85",size=.2),
plot.background=element_rect(fill="white"),
plot.subtitle=element_text(size=7),
plot.title=element_text(size=11)
)

ggsave("1.png",w=6,h=6)

[Sandis]

You're closer to Moksha and Mordvins and Russian_Arkhangelsk than to some Lithuanian subpopulations.

Many Hungarians are closer to Moksha than to Lithuanians in G25, some have similar distances to Moksha and Eastern Lithuanians, but further from Erzya and Western Lithuanians.
Western Lithuanians and Erzya in general are more similar to Paleolithic and Mesolithic Western European and Baltic populations than Eastern Lithuanians and Moksha respectively.
I am currently studying Mordovians.

[Gergő Marosvári]

You're closer to Moksha and Mordvins and Russian_Arkhangelsk than to some Lithuanian subpopulations. But it's probably because ph2ter calculated the distances between the populations in the usual way where he doesn't take the FST matrix of the calculator into account.

I didn't find the FST matrix of MLDP K16 anywhere. But in the Eurogenes K13 original datasheet, if you don't account for FST, then Hungarians are closer to Eastern Finns and Erzya than to Lithuanians. And they're even closer to Tatars than to the Irish. But if you multiply the matrix of admixture percentages with an MDS matrix of the FST matrix, then it gives more weight to differences in East Eurasian ancestry, and it moves Uralics and Tatars further from Hungarians. But it also moves Hungarians closer to Northwestern Europeans, because the smallest FST distance in K13 is between the North_Atlantic and Baltic components:

https://i.ibb.co/zHZKvGs/1.png

You can recreate the plot by downloading R from here: https://cran.r-project.org. Then open the R console application, run `install.packages(c("tidyverse","ggforce"))`, and run this:


library(tidyverse)
library(ggforce)

t=read.csv("https://pastebin.com/raw/efn67tkr",r=1) # K13 original

fst=as.matrix(as.dist(read.csv(r=1,text=",North_Atlantic,Baltic,West_Med,West_Asian,East_Me d,Red_Sea,South_Asian,East_Asian,Siberian,Amerindi an,Oceanian,Northeast_African,Sub-Saharan
North_Atlantic,,,,,,,,,,,,,
Baltic,19,,,,,,,,,,,,
West_Med,28,36,,,,,,,,,,,
West_Asian,26,32,36,,,,,,,,,,
East_Med,26,35,28,21,,,,,,,,,
Red_Sea,52,62,50,48,39,,,,,,,,
South_Asian,64,65,76,57,60,82,,,,,,,
East_Asian,114,114,122,110,111,127,76,,,,,,
Siberian,111,111,123,109,112,130,83,56,,,,,
Amerindian,138,137,154,138,144,161,120,113,105,,,,
Oceanian,179,181,187,177,176,191,146,166,177,217,, ,
Northeast_African,122,127,124,116,108,121,113,145, 151,185,203,,
Sub-Saharan,146,150,150,140,135,141,133,164,170,204,22 0,41,")))

t2=as.matrix(t)%*%cmdscale(fst,ncol(fst)-1)

xy=data.frame(x=rank(as.matrix(dist(t))["Hungarian",]),y=rank(as.matrix(dist(t2))["Hungarian",]))
pick=apply(xy<100,1,all)
xy=xy[pick,]
t2=t2[pick,]

xy$k=as.factor(cutree(hclust(dist(t2)),24))

ggplot(xy,aes(x,y))+
geom_abline(linetype="dashed",color="gray80",size=.3)+
ggforce::geom_mark_hull(aes(color=k,fill=k),concav ity=1000,radius=unit(.15,"cm"),expand=unit(.15,"cm"),alpha=.2,size=.15)+
geom_point(aes(color=k),size=.5)+
geom_text(aes(color=k),label=rownames(xy),size=2,v just=-.7)+
scale_x_continuous(breaks=seq(1,1000,10),expand=ex pansion(mult=c(.05,.02)))+
scale_y_continuous(breaks=seq(1,1000,10),expand=ex pansion(mult=c(.05,.02)))+
scale_fill_manual(values=rainbow_hcl(nlevels(xy$k) ,90,60))+
scale_color_manual(values=rainbow_hcl(nlevels(xy$k ),90,60))+
labs(x="Rank of K13 distance to Hungarian (FST not accounted for)",y="Rank of K13 distance to Hungarian (multiplied by MDS matrix of FST matrix)")+
theme(
axis.text=element_text(color="black",size=6),
axis.ticks=element_line(size=.3,color="gray80"),
axis.ticks.length=unit(-.13,"cm"),
axis.text.x=element_text(margin=margin(.2,0,0,0,"cm")),
axis.text.y=element_text(angle=90,vjust=1,hjust=.5 ,margin=margin(0,.2,0,0,"cm")),
legend.position="none",
panel.background=element_rect(fill="white"),
panel.border=element_rect(color="gray85",fill=NA,size=.6),
panel.grid.major=element_line(color="gray85",size=.2),
plot.background=element_rect(fill="white"),
plot.subtitle=element_text(size=7),
plot.title=element_text(size=11)
)

ggsave("1.png",w=6,h=6)

Oh now i fully understand why my K13 results have Croats and Moldovans on the first two places!

[Gergő Marosvári]

You're closer to Moksha and Mordvins and Russian_Arkhangelsk than to some Lithuanian subpopulations. But it's probably because ph2ter calculated the distances between the populations in the usual way where he doesn't take the FST matrix of the calculator into account.

I didn't find the FST matrix of MLDP K16 anywhere. But in the Eurogenes K13 original datasheet, if you don't account for FST, then Hungarians are closer to Eastern Finns and Erzya than to Lithuanians. And they're even closer to Tatars than to the Irish. But if you multiply the matrix of admixture percentages with an MDS matrix of the FST matrix, then it gives more weight to differences in East Eurasian ancestry, and it moves Uralics and Tatars further from Hungarians. But it also moves Hungarians closer to Northwestern Europeans, because the smallest FST distance in K13 is between the North_Atlantic and Baltic components:

https://i.ibb.co/zHZKvGs/1.png

You can recreate the plot by downloading R from here: https://cran.r-project.org. Then open the R console application, run `install.packages(c("tidyverse","ggforce"))`, and run this:


library(tidyverse)
library(ggforce)

t=read.csv("https://pastebin.com/raw/efn67tkr",r=1) # K13 original

fst=as.matrix(as.dist(read.csv(r=1,text=",North_Atlantic,Baltic,West_Med,West_Asian,East_Me d,Red_Sea,South_Asian,East_Asian,Siberian,Amerindi an,Oceanian,Northeast_African,Sub-Saharan
North_Atlantic,,,,,,,,,,,,,
Baltic,19,,,,,,,,,,,,
West_Med,28,36,,,,,,,,,,,
West_Asian,26,32,36,,,,,,,,,,
East_Med,26,35,28,21,,,,,,,,,
Red_Sea,52,62,50,48,39,,,,,,,,
South_Asian,64,65,76,57,60,82,,,,,,,
East_Asian,114,114,122,110,111,127,76,,,,,,
Siberian,111,111,123,109,112,130,83,56,,,,,
Amerindian,138,137,154,138,144,161,120,113,105,,,,
Oceanian,179,181,187,177,176,191,146,166,177,217,, ,
Northeast_African,122,127,124,116,108,121,113,145, 151,185,203,,
Sub-Saharan,146,150,150,140,135,141,133,164,170,204,22 0,41,")))

t2=as.matrix(t)%*%cmdscale(fst,ncol(fst)-1)

xy=data.frame(x=rank(as.matrix(dist(t))["Hungarian",]),y=rank(as.matrix(dist(t2))["Hungarian",]))
pick=apply(xy<100,1,all)
xy=xy[pick,]
t2=t2[pick,]

xy$k=as.factor(cutree(hclust(dist(t2)),24))

ggplot(xy,aes(x,y))+
geom_abline(linetype="dashed",color="gray80",size=.3)+
ggforce::geom_mark_hull(aes(color=k,fill=k),concav ity=1000,radius=unit(.15,"cm"),expand=unit(.15,"cm"),alpha=.2,size=.15)+
geom_point(aes(color=k),size=.5)+
geom_text(aes(color=k),label=rownames(xy),size=2,v just=-.7)+
scale_x_continuous(breaks=seq(1,1000,10),expand=ex pansion(mult=c(.05,.02)))+
scale_y_continuous(breaks=seq(1,1000,10),expand=ex pansion(mult=c(.05,.02)))+
scale_fill_manual(values=rainbow_hcl(nlevels(xy$k) ,90,60))+
scale_color_manual(values=rainbow_hcl(nlevels(xy$k ),90,60))+
labs(x="Rank of K13 distance to Hungarian (FST not accounted for)",y="Rank of K13 distance to Hungarian (multiplied by MDS matrix of FST matrix)")+
theme(
axis.text=element_text(color="black",size=6),
axis.ticks=element_line(size=.3,color="gray80"),
axis.ticks.length=unit(-.13,"cm"),
axis.text.x=element_text(margin=margin(.2,0,0,0,"cm")),
axis.text.y=element_text(angle=90,vjust=1,hjust=.5 ,margin=margin(0,.2,0,0,"cm")),
legend.position="none",
panel.background=element_rect(fill="white"),
panel.border=element_rect(color="gray85",fill=NA,size=.6),
panel.grid.major=element_line(color="gray85",size=.2),
plot.background=element_rect(fill="white"),
plot.subtitle=element_text(size=7),
plot.title=element_text(size=11)
)

ggsave("1.png",w=6,h=6)

A good friend of mine, made my FST coordinates!
Gergo,10.02575,20.06076,-31.40372,1.94005,9.45841,3.60208,-2.23486,3.52392,-0.34184,-1.08729,0.18582

Distance to: Gergo
1.40070806 Ukrainian_Carpathian
1.41480091 Hungarian_North
1.46686263 Moldova_Ukrainian
1.72532142 Hungarian_Alföld
1.72944030 Hungarian
1.73329606 Slovak
1.77458293 Hungarian_Transdanubia+Budapest
1.85282205 Ukrainian_Galicia
1.87605548 Bosniak_Bosnia
1.95844677 Moldova_North
2.08836163 Croat_North
2.18861691 Croat_East
2.28950325 Czech
2.34751514 Croat
2.35354677 Slovenian
2.48687524 South_Polish
2.68314586 Moldova_Centre
2.68734703 Bosniak
2.74460398 Croat_West
2.75569901 Moldova_average
2.80499105 Croat_South
2.80898804 Ukrainian
2.84490670 German_East
2.86981581 Romania_Moldavia_North
2.88162361 Hungarian_Transylvania+Székely