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Quote:
Originally Posted by Kaspias
Basically these mean that you're significantly E Asian shifted compared to Bulgarians (keep in mind Bulgarians themselves have Siberian and E Asian). Your best model based on p-value seems to be Uzbeks and Turkmen but if you want to fine tune this even more add Mongolians or Han to pright. Your p-values may drop but that's fine. I also don't get how you have Tatar-Tomsk in both pright and pleft at the same time
@Kaspias
What command did you use to extract and to do FST?
I replaced Tatar_Tomsk with Bashkir for the right while running Tomsk on the left, and the Tomsk was on the right while running other populations(Uzbek, Turkmen...)Quote:
Originally Posted by Zoro
Thank you for your input. Now I have a question, I realized while using ancient populations for the right the SNP number reduces to around ~100k and I now have ~600k with moderns. I constantly hear ancient for the right is a better idea, but considering the SNP amount which one would you say preferably for my case?
I'd like to model Balkan populations with Medieval samples for example, but believe so the SNP amount will be around 80K. Is that enough for a decent run or too low?
Quote:
Originally Posted by Zoro
Code:fst_blocks = fst("fstdir")
print((fst_blocks), n=2000)
I think it should be enough. Use the highest quality samples and stay away from 2 similar sources. Give it a tryQuote:
Originally Posted by Kaspias
Do you remember that you asked me the population to use while representing Anatolian Turk's Anatolian ancestry? I have found the Roopkund outlier in the spreadsheet who is a Central Anatolian Greek from the classical Ottoman Era, and simulated possible scenarios using 3 different Turkic populations.Quote:
Originally Posted by Zoro
Simon's Turkish samples are from Hodoğlugil's study and collected in Kayseri: http://simonsfoundation.s3.amazonaws...ion_update.txt
These Kayseri samples have ~6% East Eurasian on average -referencing Gedmatch- so we can draw further conclusions based on it.
Code:# right
"Mbuti.DG",
"Han.DG",
"Saami.DG",
"Icelandic.DG",
"Sardinian.DG",
"Punjabi.DG",
"Eskimo_Chaplin.DG",
"BedouinB.DG",
"Basque.DG"
! 90852 SNPs remain after filtering. 78431 are polymorphic.
Code:target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Anatolian_Turkish.DG Anatolian_Greek_Medieval 0.863 0.0363 23.8
2 Anatolian_Turkish.DG Kimak.SG 0.137 0.0363 3.76
f4rank dof chisq p dofdiff chisqdiff p_nested
<int> <int> <dbl> <dbl> <int> <dbl> <dbl>
1 1 7 8.43 2.96e- 1 9 419. 1.00e-84
2 0 16 428. 5.31e-81 NA NA NA
! 99251 SNPs remain after filtering. 85286 are polymorphic.
Code:target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Anatolian_Turkish.DG Anatolian_Greek_Medieval 0.816 0.0472 17.3
2 Anatolian_Turkish.DG Gokturk.SG 0.184 0.0472 3.90
f4rank dof chisq p dofdiff chisqdiff p_nested
<int> <int> <dbl> <dbl> <int> <dbl> <dbl>
1 1 7 15.8 2.74e- 2 9 303. 6.10e-60
2 0 16 319. 3.31e-58 NA NA NA
! 247268 SNPs remain after filtering. 213710 are polymorphic.
Code:target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Anatolian_Turkish.DG Anatolian_Greek_Medieval 0.851 0.0283 30.1
2 Anatolian_Turkish.DG Ottoman_MA2195.SG 0.149 0.0283 5.27
f4rank dof chisq p dofdiff chisqdiff p_nested
<int> <int> <dbl> <dbl> <int> <dbl> <dbl>
1 1 7 17.2 1.64e- 2 9 728. 6.69e-151
2 0 16 745. 3.16e-148 NA NA NA
Looks like the 1st model is best with a p-value of 0.30. The other ones can sort of be rejected.Quote:
Originally Posted by Kaspias
As far as the Kayseri samples I kind of guessed that they would score that much on Gedmatch. Interestingly I didn't get nearly as many passing W Asian + E Asian models for them as I did for Iraqi Kurds. Probably has to do with a good modern W Asian source. Another reason I like using Ancients.
I also got this sort of passing model for the Kayseri Turks p-value 0.05
Admix SE
Turkish Armenian 57% 7%
Turkish Bulgarian 35% 7%
Turkish Yakut 8% 1%
Again those Turks would have more than 8% NE Asian because Armenians and Bulgarians also have some
I think you should try modeling yourself with these also plus Siberian and E Asian. You can use the ancient pright list I use which I posted earlier. I'm posting their missingness rate. They're not that bad.
Anatolia_EBA I2495 3682752 4676043 0.79
Anatolia_EBA I2683 3761883 4668444 0.81
Anatolia_EBA.SG MA2210_final 4094550 4668444 0.88
Anatolia_EBA.SG MA2212_final 4120486 4676043 0.88
Anatolia_EBA.SG MA2213_final 3984232 4668444 0.85
Anatolia_Epipaleolithic ZBC_IPB001 3830320 4676043 0.82
Anatolia_IA.SG MA2198_final 4157111 4668444 0.89
Anatolia_MLBA.SG MA2200_final 3750514 4676043 0.80
Anatolia_MLBA.SG MA2203_final 4091029 4668444 0.88
Anatolia_MLBA.SG MA2205_final 4152321 4676043 0.89
As far as your modern pright list I would add maybe Armenians or Iranians
I did this to get FST distances:
There's probably an easier way to do this in R, but this converts the FST pairs into a table:Code:$ printf %s\\n Mansi Finnish Nganasan Selkup Karelian Udmurt Mordovian>pops
$ R -e 'library(admixtools);fst=fst("g/v44.3_HO_public/v44.3_HO_public",pop1=readLines("pops"));write.csv(fst,"fst",quote=F)'
ℹ Reading allele frequencies from packedancestrymap files...
ℹ v44.3_HO_public.geno has 13197 samples and 597573 SNPs
ℹ Calculating allele frequencies from 19 samples in 4 populations
ℹ Expected size of allele frequency data: 86 MB
597k SNPs read...
✔ 597573 SNPs read in total
! 593124 SNPs remain after filtering. 414780 are polymorphic.
ℹ Allele frequency matrix for 593124 SNPs and 4 populations is 62 MB
ℹ Computing pairwise f2 for all SNPs and population pairs requires 493 MB RAM without splitting
ℹ Computing without splitting since 493 < 8000 (maxmem)...
ℹ Data written to f2/
ℹ Reading precomputed data for 4 populations...
ℹ Reading f2 data for pair 10 out of 10...
Warning message:
In read_f2(dir, pops, pops2, afprod = afprod, fst = fst, remove_na = remove_na, :
Discarding 1 block(s) due to missing values!
Discarded block(s): 535
>
>
$ cat fst
,pop1,pop2,est,se,z,p
1,Finnish,Karelian,0.00129340940098996,0.000385024618276676,3.35929013261311,0.000781429796323533
2,Finnish,Mordovian,0.00543917401762932,0.0003253493038913,16.7179519137578,9.6995724807741e-63
3,Finnish,Nganasan,0.119054350470445,0.00113575044283692,104.824392736371,0
4,Finnish,Selkup,0.0601437871347565,0.000773515188052884,77.753854175963,0
5,Finnish,Udmurt,0.0187032075983067,0.000585527652038594,31.9424839001009,6.87035668325847e-224
6,Karelian,Mordovian,0.00590771927078587,0.000239357005605168,24.6816225656289,1.68484078541802e-134
7,Karelian,Udmurt,0.019523384287915,0.000473936956593016,41.1940533784545,0
8,Mansi,Finnish,0.0402190424166203,0.000841540850181047,47.7921450966614,0
9,Mansi,Karelian,0.0399509729801598,0.000728931940431647,54.8075489139662,0
10,Mansi,Mordovian,0.0383778238793512,0.000668216221602333,57.4332418739013,0
11,Mansi,Nganasan,0.0602924170396429,0.000867560662779261,69.4964855212476,0
12,Mansi,Selkup,0.0223050689999093,0.000513096315174833,43.4715049401769,0
13,Mansi,Udmurt,0.0240652073778455,0.000663882926915772,36.2491734644333,1.02394799408991e-287
14,Nganasan,Karelian,0.118602793551424,0.00108015804818372,109.801333009418,0
15,Nganasan,Mordovian,0.11745770899229,0.00099405063691636,118.160689838352,0
16,Nganasan,Selkup,0.0504386703379596,0.000674035384985417,74.8308938395731,0
17,Nganasan,Udmurt,0.0911528579608077,0.000973329536820952,93.650561821567,0
18,Selkup,Karelian,0.0595410382563537,0.00071504905760348,83.268466160781,0
19,Selkup,Mordovian,0.0579452329275561,0.000631133692337951,91.8113446184526,0
20,Selkup,Udmurt,0.0409818006630253,0.000612173976633259,66.9446958337084,0
21,Udmurt,Mordovian,0.0170968626475659,0.000406949757311595,42.0122197897642,0
And this creates a heatmap of the table:Code:$ awk -F, 'NR>1{print$3","$2","$4;print$2","$3","$4}' fst|awk -F, '{print$1","$1","}1'|sort -u>/tmp/a
$ cut -d, -f3 /tmp/a|awk '{printf"%.6f"(NR%n?",":"\n"),$0}' n=$(awk 'END{print NR^.5}' /tmp/a) -|paste -d, <(cut -d, -f1 /tmp/a|sort -u) -|cat <(cut -d, -f1 /tmp/a|sort -u|paste -sd, -|sed s/^/,/) ->/tmp/b
$ cat /tmp/b
,Finnish,Karelian,Mansi,Mordovian,Nganasan,Selkup,Udmurt
Finnish,0.000000,0.001293,0.040219,0.005439,0.119054,0.060144,0.018703
Karelian,0.001293,0.000000,0.039951,0.005908,0.118603,0.059541,0.019523
Mansi,0.040219,0.039951,0.000000,0.038378,0.060292,0.022305,0.024065
Mordovian,0.005439,0.005908,0.038378,0.000000,0.117458,0.057945,0.017097
Nganasan,0.119054,0.118603,0.060292,0.117458,0.000000,0.050439,0.091153
Selkup,0.060144,0.059541,0.022305,0.057945,0.050439,0.000000,0.040982
Udmurt,0.018703,0.019523,0.024065,0.017097,0.091153,0.040982,0.000000
https://i.ibb.co/GRjLJm3/fst.pngCode:R -e 'install.packages(c("pheatmap","colorspace"),repos="https://cloud.r-project.org")'
R -e 'library(pheatmap)
library(colorspace)
t<-read.csv("/tmp/b",header=T,row.names=1,check.names=F)
t[t==0]=NA
pheatmap(
1e4*t,
filename="/tmp/a.png",
legend=F,
clustering_callback=function(...){hclust(as.dist(t))},
cellwidth=18,
cellheight=12,
border_color=NA,
display_numbers=T,
number_format="%.0f",
number_color="black",
fontsize_number=6,
colorRampPalette(hex(HSV(c(210,180,150,120,90,60,30,0),.5,1)))(256)
)'
At first I got an error that there were too many missing blocks, so I tried adding a `maxmiss=Inf` parameter:
However it gave me nonsensical results where the distance between Finns and Maris was an order of magnitude bigger than the distance between Finns and Mansi:Code:R -e 'library("admixtools");extract_f2(pref="g/v44.3_HO_public/v44.3_HO_public",pops=c("Finnish","Mansi","Mari.SG","Estonian.DG"),outdir="f2",maxmiss=Inf);f2=f2_from_precomp("f2");fst=fst(f2);write.csv(fst,"fst",quote=F)'
Code:,pop1,pop2,est,se,z,p
1,Estonian.DG,Finnish,0.000904946578981571,0.000400460532337746,2.25976471064156,0.0238358576870844
2,Estonian.DG,Mansi,0.015818211648642,0.00055465784565136,28.5188639675077,6.83699132061968e-179
3,Estonian.DG,Mari.SG,0.174033745411937,0.00101794728786578,170.96538051279,0
4,Finnish,Mansi,0.0139136259691746,0.000307938735432869,45.1830977016134,0
5,Finnish,Mari.SG,0.17331351350787,0.000760056331041605,228.027195392683,0
6,Mansi,Mari.SG,0.17490109671494,0.000788482478735399,221.819890018931,0
Thanks again. I have had Iranians on the pright but it somehow reduced the p-value, so I removed it.Quote:
Originally Posted by Zoro
I think these 3 models are really crucial while answering "how the genome of Oghuz was?" We had been using DA89 for a long time, but I recently started to question the accuracy of our method(I think DA89 is 3/4 Gokturk and 1/4 Sogdian, this turns it into a false-candidate for Oghuz) and came to the conclusion that Oghuz should be in between Kipchak and Kimak after searching on historical perspective(The one that helped most was: İlk Oğuzlar, Osman Karatay). Both of the Kipchak samples we have is not representative so went with the Kimak-like option. Apparently I was right, because this is the only passing model when using Medieval samples. In addition, the region where these samples are collected(Kayseri) were housing Cappadocian Greeks that is what I used for the native admixture of them, so the result is pretty solid and one can come with guesses on Western Anatolia(10-40% Kimak?), too.
This is a very weak pright list.Quote:
Originally Posted by Kaspias
Any suggestions?Quote:
Originally Posted by Token
I have tried to use the ancient ones before it, like Devils Gate, Ust Ishim, and so on but the SNP number was only 9k, so went with the modern option.
The crucial thing is to avoid using low coverage samples because the SNP overlap is always defined by the weakest link of the chain, and always use allsnps=YES (maxmiss=1 in Admixtools2, i believe). There is not much secret in choosing the pright, choose populations that don't violate the qpAdm assumption of no geneflow from pleft into pright (in practice, select prehistoric samples - the older the better), and make sure the populations in pright are all asymmetrically related to the populations in pleft.Quote:
Originally Posted by Kaspias
You get "A" for effort. Nice heatmap.Quote:
Originally Posted by Komintasavalta
They had some errors in the code. There's a new version dated yesterday of Admixtools. Remove it and re-install it. Also :
indicates you have too many uninformative non-polymorphic SNPs. To remove them set maxmaf=0.45Quote:
593124 SNPs remain after filtering. 414780 are polymorphic
For FST it's crucial to have maxmiss at default which is 0 because you want all your samples to overlap each other exactly for unbiased results
The good thing about having SE and p-values is you get an idea which models are feasible unlike with Admixture or G25 where you get a bunch of feasible models and you have no idea which ones are no good (distance in G25 is NOT a substitute for p-values and SE by any means). Of course there are other issues besides model viability in G25.Quote:
Originally Posted by Kaspias
For FST it's crucial to have maxmiss at default which is 0 because you want all your samples to overlap each other exactly for unbiased results
They had some errors in the code. There's a new version dated yesterday of Admixtools. Remove it and re-install it and re-run extract at default (maxmiss=0). When you run extract add the option maxmaf=0.45 to get rid of minor allele which have a frequency > 45% across all pops (very common old alleles shared by most global pops). Now I'm getting 940,000 polymorphic SNPs on the Simons samples at maxmiss=0 with the latest download of Admixtools.
Also Dilawer informed me that Plink doesn't maintain allele order. This can screw calculations up in Admixtools. Here is a couple of rows from the .snp file
rs199706086 1 0 10250 A C
rs112750067 1 0 10327 T C
rs201725126 1 0 13116 G T
rs200579949 1 0 13118 G A
rs180734498 1 0 13302 T C
rs79585140 1 0 14907 G A
rs75454623 1 0 14930 A G
Column 5 is supposed to be for the Reference or Ancestral allele and col 6 for the Derived or Minor allele. If you check dbSNP website you'll notice that the ones I bolded are wrong order. In other words one should be T G and the other A G. Plink screws up allele order.
I fixed the allele order in my .snp file using Dilawer's script so now it agrees with dbSNP. There were thousands of such mistakes in my .snp file
This prints all columns and all rows of tables, prints only 3 significant digits, and doesn't display negative numbers in red (https://tibble.tidyverse.org/reference/formatting.html): `options(tibble.width=Inf,tibble.print_max=Inf, pillar.sigfig=3,pillar.neg=F)`.
`options(width=Sys.getenv("COLUMNS"))` or `options(width=180)` increases the width of the terminal.
`print(tbl,width=Inf,n=Inf)` or `as.data.frame(tbl)` displays a whole tibble. This displays an HTML table in a browser: `install.packages("formattable");library(formattab le);formattable(tbl)`.
This removes columns from a tibble and formats the table as CSV where doubles have 3 digits after the decimal point:
This omits models with only one population (where f4rank is 0) and models that are not feasible (with one or more negative weight). It then sorts the remaining models by their p value:Code:> qp$popdrop%>select(!c(pat,wt,dof,dofdiff,chisqdiff,p_nested))%>%mutate(across(where(is.double),round,3))%>%format_csv%>%cat
chisq,p,f4rank,Nganasan,Norway_N_HG.SG,Russia_AfontovaGora3,Turkey_Epipaleolithic,feasible,best
3.251,0.354,3,0.128,0.132,0.116,0.625,TRUE,NA
15.538,0.004,2,0.151,1.265,-0.416,NA,FALSE,TRUE
5.152,0.272,2,0.144,0.302,NA,0.554,TRUE,TRUE
3.827,0.43,2,0.123,NA,0.176,0.701,TRUE,TRUE
11.855,0.018,2,NA,-0.814,0.64,1.173,FALSE,TRUE
28.572,0,1,0.072,0.928,NA,NA,TRUE,NA
116.62,0,1,-0.108,NA,1.108,NA,FALSE,NA
11.9,0.036,1,0.179,NA,NA,0.821,TRUE,NA
22.973,0,1,NA,1.388,-0.388,NA,FALSE,NA
28.13,0,1,NA,0.622,NA,0.378,TRUE,NA
16.715,0.005,1,NA,NA,0.272,0.728,TRUE,NA
1386.434,0,0,1,NA,NA,NA,TRUE,NA
32.704,0,0,NA,1,NA,NA,TRUE,NA
125.18,0,0,NA,NA,1,NA,TRUE,NA
36.405,0,0,NA,NA,NA,1,TRUE,NA
This saves the popdrop table to a CSV file (I know my pright sucks or whatever):Code:> qp$popdrop%>%dplyr::filter(feasible==T&f4rank!=0)%>%arrange(desc(p))%>%dplyr::select(!c(pat,wt,dof,chisq,f4rank,feasible,best,dofdiff,chisqdiff,p_nested))%>%mutate(across(where(is.double),round,3))%>%as.data.frame
p Nganasan Norway_N_HG.SG Russia_AfontovaGora3 Turkey_Epipaleolithic
1 0.430 0.123 NA 0.176 0.701
2 0.354 0.128 0.132 0.116 0.625
3 0.272 0.144 0.302 NA 0.554
4 0.036 0.179 NA NA 0.821
5 0.005 NA NA 0.272 0.728
6 0.000 NA 0.622 NA 0.378
7 0.000 0.072 0.928 NA NA
This generates a stacked bar chart of the models sorted by their p value:Code:target="Finnish"
left=c("Turkey_Boncuklu_N.SG","Latvia_HG","Norway_N_HG.SG","Russia_HG_Karelia","Russia_HG_Tyumen","Russia_AfontovaGora3","Nganasan")
right=c("Mbuti.DG","Mixe.DG","Ami.DG","Czech_Vestonice16","Papuan.DG","Ethiopia_4500BP_published.SG","Russia_Kostenki14","Ju_hoan_North.SDG","Morocco_Iberomaurusian")
pops=c(left,right,target)
unlink("f2",recursive=T)
extract_f2(pref="g/v44.3_HO_public/v44.3_HO_public",pops=pops,outdir="f2")
f2=f2_from_precomp("f2")
qp=qpadm(f2,left=left,right=right,target=target)
qp2=qp$popdrop%>%dplyr::filter(feasible==T&f4rank!=0)%>%arrange(desc(p))%>%dplyr::select(!c(wt,dof,chisq,f4rank,feasible,best,dofdiff,chisqdiff,p_nested))
write_csv(qp2,"/tmp/a")
https://i.ibb.co/vV9f0g5/qp.pngCode:library(tidyverse)
library(cowplot)
library(reshape2)
t=read_csv("/tmp/a")
abbr=c("Turk","Latv","Norw","Kare","Tyum","AG3","Ngan")
l=lapply(t$pat,function(x)abbr[unlist(gregexpr("0",x))]%>%paste(collapse=" "))
t$lab=paste0(l," (",sub("^0","",sprintf("%.3f",t$p)),")")
t=t[-c(1,2)]
t2=melt(t,id.var="lab")
p=ggplot(t2,aes(x=fct_rev(factor(lab,level=t$lab)),y=value,fill=variable),label=pvalue)+
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=4)+
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,unit="cm"),
legend.box.spacing=unit(.05,"in"),
legend.direction="horizontal",
legend.justification="center",
legend.margin=margin(0,unit="cm"),
legend.text=element_text(size=12),
legend.title=element_blank(),
panel.border=element_blank(),
text=element_text(size=18)
)+
guides(fill=guide_legend(ncol=3,label.position="right",byrow=T))+
scale_x_discrete(expand=c(0,0))+
scale_y_discrete(expand=c(0,0))+
xlab("")+
scale_fill_manual("legend",values=c("#be661f","#66f6ff","#3397f5","#22419c","#39de39","#157f0a","#ef50ed"))
ggdraw(p)
leg=get_legend(p)
p=p+theme(legend.position="none")
ggdraw(plot_grid(p,leg,ncol=1,rel_heights=c(1,.2)))
ggsave("output.png",width=7,height=7)
Why do Finns get such a high percentage of Turkey_Boncuklu? Is it because of bad right populations or something?
Hi everyone, could someone run with qpAdm North_Italians, Tuscans and Sicilians with this model?
WHG
Steppe_EMBA (Samara i think it's ok)
Barcin_N
Iran_N
Levant_PPNB
I want to see if Levant_PPNB it's necessary to run italians, in particulary southerns.
Thank you.
which of these should be used as outgroups when modelling modern pops wiith mesolithic/neolithic samples?
Austria_Krems1_1 I2483 Austria_Krems1_2_twin.I2483 I2484 Austria_Krems1_2_twin.I2483_all I2484_all Austria_KremsWA3 I1577 Belgium_UP_GoyetQ116_1_published GoyetQ116-1_udg_published Belgium_UP_GoyetQ116_1_published_all GoyetQ116-1_published Belgium_UP_GoyetQ376-19_published GoyetQ376-19_published_d Belgium_UP_GoyetQ53_1_published_lc GoyetQ53-1_published_d Belgium_UP_GoyetQ56_16_published_lc GoyetQ56-16_published_d Belgium_UP_Magdalenian GoyetQ-2 Belgium_UP_Magdalenian_udg GoyetQ-2_udg China_Tianyuan Tianyuan Czech_Pavlov1 Pavlov1_d Czech_Vestonice13 Vestonice13_d Czech_Vestonice14_lc Vestonice14_d Czech_Vestonice15 Vestonice15_d Czech_Vestonice16 Vestonice16 Czech_Vestonice43 Vestonice43_d France_Rigney1_published Rigney1_published_d Germany_Brillenhohle_published_lc Brillenhohle_published_d Germany_Burkhardtshohle_published Burkhardtshohle_published_d Germany_HohleFels49_published HohleFels49_published_d Germany_HohleFels79_published_lc HohleFels79_published_d Italy_South_HG_Ostuni1 Ostuni1_d Italy_South_HG_Ostuni2 Ostuni2_d Italy_South_HG_Paglicci108_published_lc Paglicci108_published_d Italy_South_HG_Paglicci133_published Paglicci133_published Romania_Cioclovina_published_lc Cioclovina1_published_d Romania_Muierii Muierii2_d Romania_Oase Oase1_d Russia_Kostenki12 Kostenki12 Russia_Kostenki14 Kostenki14 Russia_Kostenki14.SG Kostenki14.SG Russia_Sunghir1.SG Sunghir1.SG Russia_Sunghir2.SG Sunghir2.SG Russia_Sunghir3.SG Sunghir3.SG Russia_Sunghir4.SG Sunghir4.SG Russia_Ust_Ishim_HG_published.DG Ust_Ishim_published.DG Russia_Ust_Ishim.DG UstIshim_snpAD.DG Russia_Yana_UP.SG Yana_old.SG Russia_Yana_UP.SG Yana_old2.SG Spain_ElMiron ElMiron_d
Kostenki14 and Ust-Ishim were used here, but this was a couple of years ago and new samples have been published since, so should something be added?
https://eurogenes.blogspot.com/2017/...lithic-to.html
most of these Paleolithic samples were analyzed here:
https://www.ncbi.nlm.nih.gov/pmc/art...ort=objectonly
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4943878/
I ran a PCA on all Reich samples older than 8000 ybp, to see into which clusters the more recently published samples fall. I removed Neanderthals and Denisovans, and African, Middle Eastern and Amerindian samples because they were outliers and skewed the European dimensions. I didn't do ld and maf pruning because they PCA. I guess Paleolithic samples probably require completely different ld and maf settings from modern pops.
The result is very similar to the MDS plot from the study:
https://i.imgur.com/KwBcpN2.png
The conclusions would be Sunghir samples fall into the Vestonice cluster, Yana clusters with Ust-Ishim, and Geometric and Azilian samples plot with El Miron.
I was trying to extract new Turkish samples but received a few errors during the process. Does the files needs to be imputed in Linux by referencing to their genetic linkage?(by using SHAPEIT perhaps) At least that's what I understood, although have no idea how to do it.
Any tips on what is going on here?
Files can be found here in case someone would like to try it.
Code:+ extract_f2("originknownturkish",outdir = "balkanturks", pops = pops)
i Reading allele frequencies from PLINK files...
Warning: 1 parsing failure.
row col expected actual file
1460 X6 embedded null 'originknownturkish.fam'
i originknownturkish.geno has 1460 samples and 423261 SNPs
i Calculating allele frequencies from 1 samples in 1 populations
i Expected size of allele frequency data: 102 MB
423k SNPs read...
√ 423261 SNPs read in total
! 421395 SNPs remain after filtering. 0 are polymorphic.
i Allele frequency matrix for 421395 SNPs and 1 populations is 34 MB
i Computing pairwise f2 for all SNPs and population pairs requires 67 MB RAM without splitting
i Computing without splitting since 67 < 8000 (maxmem)...
Error in cpp_get_block_lengths(numchr, dat[[distcol]], blgsize) :
upper value must be greater than lower value
Ek olarak: Warning messages:
1: In get_block_lengths(afdat$snpfile, blgsize = blgsize) :
No genetic linkage map or base positions found!Each chromosome will be its own block, which can make standard error estimates inaccurate.
2: In get_block_lengths(afdat$snpfile[poly, ], blgsize = blgsize) :
No genetic linkage map found! Defining blocks by base pair distance of 2e+06
Hi brothers, could you help me convert a file from 23andme to qpadm ?