Running Gedmatch calculators on the command line with stevenliuyi/admix

Quote:

---

Originally Posted by Leto

Can you run some of those Cardona samples? Dodecad K12b would suffice.

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K12b: https://pastebin.com/raw/PMTtnzZn
K13: https://pastebin.com/raw/Pe0wM1ea

Quote:

---

Originally Posted by Leto

EDIT
That post on Anthrogenica by David Bush links to a different study
https://onlinelibrary.wiley.com/doi/...002/ajhb.23194

---

Yeah sorry, that was the right study. The files at Anthrogenica contain all 651 samples from this dataset: https://www.ncbi.nlm.nih.gov/geo/que...i?acc=GSE73996. Its title is "The genetic basis of adaptation to climatic stress in Siberian indigenous populations", so I confused it with Cardona et al. 2014, "Genome-Wide Analysis of Cold Adaptation in Indigenous Siberian Populations". But I think the dataset is from an unpublished paper whose samples were later used in the paper you linked to.

Thank you for running them, I see a few valuable populations in that spreadsheet! :thumb001: I'm processing them for Dodecad and maybe a couple for K13 too.

Here's how you can use my K13 datasheet for the Reich dataset to make a polygonal diagram where each corner of the polygon represents one component:

Code:

---

library(tidyverse)
library(ggforce)
library(colorspace)

t=read.csv("https://drive.google.com/uc?export=download&id=15Mvba7Bw07VtixiBO_EctOhPPECGzZC9",row.names=1,check.names=F)
t=t/100

fst=as.matrix(as.dist(read.csv(header=F,text=",,,,,,,,,,,,
19,,,,,,,,,,,,
28,36,,,,,,,,,,,
26,32,36,,,,,,,,,,
26,35,28,21,,,,,,,,,
52,62,50,48,39,,,,,,,,
64,65,76,57,60,82,,,,,,,
114,114,122,110,111,127,76,,,,,,
111,111,123,109,112,130,83,56,,,,,
138,137,154,138,144,161,120,113,105,,,,
179,181,187,177,176,191,146,166,177,217,,,
122,127,124,116,108,121,113,145,151,185,203,,
146,150,150,140,135,141,133,164,170,204,220,41,")))

t=t[!grepl("\\.REF|rel\\.|fail\\.|\\.contam|Ignore_|_dup|_contam|_lc|_father|_mother|_son|_daughter|_brother|_sister|_relative|_sibling|_twin|Neanderthal|Denisova|Vindija_light|Gorilla|Macaque|Marmoset|Orangutan|Primate_Chimp|hg19ref|_o",rownames(t)),]

rownames(t)=%sub("\\.(SG|DG|SDG|WGA|WGC)|_published","",rownames(t))
t=data.frame(aggregate(t,list(sub(":.*","",rownames(t))),mean),row.names=1,check.names=F)

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

t=as.data.frame(cbind(t[,2],t[,9],t[,8],rowSums(t[,c(1,3:7,10:13)])))
names(t)=c("Baltic","Siberian","East_Asian","Other")

start=ifelse(ncol(t)==4,.25,0)
corners=sapply(c(sin,cos),function(x)x(head((start+seq(0,2,length.out=ncol(t)+1))*pi,-1)))
corners=corners*min(2/diff(apply(corners,2,range)))
corners[,2]=corners[,2]-mean(range(corners[,2]))

xy=as.data.frame(as.matrix(t)%*%corners)
grid=as.data.frame(rbind(cbind(corners,rbind(corners[-1,],corners[1,])),cbind(corners,matrix(apply(corners,2,mean),ncol=2,nrow=ncol(t),byrow=T))))

seg=lapply(1:3,function(i)as.matrix(dist(mds))%>%apply(1,function(x)unlist(xy[names(sort(x)[i]),],use.names=F))%>%t%>%cbind(xy))%>%do.call(rbind,.)%>%setNames(paste0("V",1:4))

k=as.factor(cutree(hclust(dist(mds)),32))

set.seed(1)
hue=seq(0,360,length.out=nlevels(k)+1)%>%head(-1)%>%sample()
pal1=hex(colorspace::HSV(hue,.5,1))
pal2=hex(colorspace::HSV(hue,.3,1))

angle=head(seq(360,0,length.out=ncol(t)+1),-1)
angle=ifelse(angle>90&angle<=270,angle+180,angle)

ggplot(xy,aes(x=V1,y=V2))+
geom_polygon(data=as.data.frame(corners),fill="gray25")+
# geom_text(data=as.data.frame(corners),aes(x=1.04*V1,y=1.04*V2),label=colnames(t),size=3.2,angle=angle,color="gray85")+
geom_text(data=as.data.frame(corners),aes(x=V1,y=V2+sign(V2)*.02),vjust=(1-corners[,2])/2,hjust=(1+corners[,1])/2,label=colnames(t),size=3.2,color="gray85")+
geom_segment(data=grid,aes(x=V1,y=V2,xend=V3,yend=V4),color="gray30",size=.4)+
ggforce::geom_mark_hull(aes(group=!!k,color=!!k,fill=!!k),concavity=1000,radius=unit(.15,"cm"),expand=unit(.15,"cm"),alpha=.15,size=.1)+
geom_segment(data=seg,aes(x=V1,y=V2,xend=V3,yend=V4),color="gray10",size=.25)+
geom_point(aes(color=k),size=.5)+
geom_text(aes(label=rownames(xy),color=!!k),size=2.2,vjust=-.6)+
coord_fixed(xlim=c(-1,1),ylim=c(-1,1))+
scale_fill_manual(values=pal1)+
scale_color_manual(values=pal2)+
theme(
  axis.text=element_blank(),
  axis.ticks=element_blank(),
  axis.title=element_blank(),
  legend.position="none",
  panel.background=element_rect(fill="gray20"),
  panel.grid=element_blank(),
  plot.background=element_rect(fill="gray20",color=NA,size=0),
  plot.margin=margin(0,0,0,0)
)

ggsave("1.png",width=11,height=11)

---

Here you can see that CentralYakutia_IA (yak030) has even higher Siberian than the Nganasan average. The yak030 sample is missing from G25, but in the Reich dataset, it's the closest ancient sample to Nganasans:

https://i.ibb.co/mFqvXJ8/1.png

Here some populations like Scottish and BedouinB are outliers, because their samples suffer from the calculator effect:

https://i.ibb.co/KWHTxmV/eurogenes-k...gon-modern.png

This shows all ancient and modern populations:

https://i.ibb.co/GQCpCKM/eurogenes-k...ch-polygon.jpg

Quote:

---

Originally Posted by Komintasavalta

Here's how you can use my K13 datasheet for the Reich dataset to make a polygonal diagram where each corner of the polygon represents one component:

---

I appreciate your interest in those graphs, I just find it hard to read some of them.

@Lucas and @michal3141: Where can I get the FST matrix for your K47 and K25 calculators? I'm using Mantel's test to check if high-K calculators produce higher correlation with f2 distance than low-K calculators.

I also want to test some very low-K calculators like Gedrosia K3, but I need its .alleles and .F files and its FST matrix.

Or can some Windows user just post the .F and .alleles files for all calculators from Admixture Studio? I also need MDLP World 22.

Quote:

---

Originally Posted by Komintasavalta

I now finished running K13 for all 14,313 samples in the 1240K+HO version of the Reich dataset: https://drive.google.com/file/d/15Mv...EctOhPPECGzZC9.

---

I now also ran Eurogenes K13 for all samples in the 1240K version of the Reich dataset: https://drive.google.com/file/d/15Mv...EctOhPPECGzZC9.

It should produce more accurate results particularly for ancient samples with low SNP count, because the 1240K version has about two times as many SNPs as the 1240K+HO version, but it has over 3 times as many SNPs that overlap with Eurogenes K13:

Code:

---

$ wget https://reichdata.hms.harvard.edu/pub/datasets/amh_repo/curated_releases/V50/V50.0/SHARE/public.dir/v50.0_{HO,1240K}_public.snp
$ for x in 1240K HO;do cut -d\  -f1 /usr/local/lib/python3.9/site-packages/admix/data/K13.alleles|LC_ALL=C sort|comm -12 <(awk '{print$1}' v50.0_${x}_public.snp|LC_ALL=C sort) -|wc -l|echo "$(cat) out of $(wc -l< v50.0_${x}_public.snp) SNPs overlap with $x";done
177354 out of 1233013 SNPs overlap with 1240K
52770 out of 597573 SNPs overlap with HO

---

For samples that were included in both versions, the average in admixture percentages was 1.0 percentage points. But for samples with less than 200,000 SNPs, it was common for the difference to be over 2 percentage points:

https://i.imgur.com/ud4lw0A.png

Here's a PCA of samples that are at least 8000 years old and that have at least 100,000 SNPs:

https://i.imgur.com/s8fBF5s.png

This makes population averages where samples with less than 100,000 SNPs are excluded:

Code:

---

curl -LsO https://reichdata.hms.harvard.edu/pub/datasets/amh_repo/curated_releases/V50/V50.0/SHARE/public.dir/v50.0_1240K_public.anno
curl -Lso reich.1240k.k13 'https://drive.google.com/uc?export=download&id=15Mvba7Bw07VtixiBO_EctOhPPECGzZC9'
tav()(awk '{n[$1]++;for(i=2;i<=NF;i++){a[$1,i]+=$i}}END{for(i in n){o=i;for(j=2;j<=NF;j++)o=o FS sprintf("%f",a[i,j]/n[i]);print o}}' "FS=${1-$'\t'}")
awk -F\\t '$21>=1e5{print$2}' v50.0_1240K_public.anno|awk -F'[:,]' 'NR==FNR{a[$0];next}$2 in a' - reich.1240k.k13|sed 's/:[^,]*//'|tav ,|sort>reich.1240k.k13.ave

---

I think these calculators would give more accurate results with low-SNP ancient samples if they just included all SNPs from the 1240K dataset with no LD pruning. But out of the calculators that come with the Python admix script, even the calculators with the highest SNP count only include about 200,000 SNPs:

Code:

---

$ cd /usr/local/lib/python3.9/site-packages/admix/data/
$ wc -l *.alleles|sort -n
  27187 Africa9.alleles
  35648 AncientNearEast13.alleles
  38496 puntDNAL.alleles
  76261 MichalK25.alleles
  76267 K47.alleles
  92632 KurdishK10.alleles
  101647 K7AMI.alleles
  101647 K8AMI.alleles
  112117 TurkicK11.alleles
  118536 MDLPK27.alleles
  159620 EUtest13.alleles
  159620 Jtest14.alleles
  164990 Eurasia7.alleles
  165688 K13M2.alleles
  165688 K14M1.alleles
  165688 K18M4.alleles
  165688 K25R1.alleles
  165688 K36.alleles
  165688 K7M1.alleles
  166255 globe10.alleles
  166255 globe13.alleles
  166770 K12b.alleles
  166770 K7b.alleles
  169131 weac2.alleles
  170288 E11.alleles
  170822 world9.alleles
  182705 K13.alleles
  184719 K15.alleles
  188173 HarappaWorld.alleles

---

You can post mumbo gumbo graphs all day but you'll never have the out of the box thinking to create a calculator like this: https://www.theapricity.com/forum/sh...lator-(Scaled)

The east shifted mind can't comprehend my level of humor. Feel free to build off my western ideas.

It's up to you to take this whole game to the next level. Don't get lost in the minutiae. You need more WOG-like big picture thinking.

Quote:

---

Originally Posted by Komintasavalta

@Lucas and @michal3141: Where can I get the FST matrix for your K47 and K25 calculators? I'm using Mantel's test to check if high-K calculators produce higher correlation with f2 distance than low-K calculators.

I also want to test some very low-K calculators like Gedrosia K3, but I need its .alleles and .F files and its FST matrix.

Or can some Windows user just post the .F and .alleles files for all calculators from Admixture Studio? I also need MDLP World 22.

---

could you run this trough k36?

https://easyupload.io/i17wjl

Quote:

---

Originally Posted by vbnetkhio

could you run this trough k36?

https://easyupload.io/i17wjl

---

Code:

---

EG600001,0,0,0,0,0,8.29,0,0,6.46,0,12.16,1.37,13.16,8.97,3.07,10.33,0,8.52,0,2.51,0,6.92,0,10.34,.91,0,0,0,0,0,0,0,3.15,0,3.82,0
EG600002,0,0,0,.71,0,3.64,0,0,10.4,0,19.84,.63,12.07,7.0,4.96,3.47,0,9.02,0,4.06,0,3.62,6.65,9.05,0,0,.03,0,0,0,0,0,2.68,0,0,2.18
EG600003,0,0,1.06,5.43,0,6.28,0,0,5.6,0,22.26,0,14.45,9.11,.38,2.49,0,9.73,0,0,0,3.05,8.77,8.26,0,.05,0,0,0,0,0,0,2.86,0,.22,0
EG600004,0,1.5,.56,1.62,0,4.55,0,0,9.56,0,18.25,6.78,6.77,12.39,.86,9.7,0,5.5,0,0,0,5.9,3.78,6.42,0,.06,0,0,0,0,0,0,2.1,0,0,3.69
EG600005,0,0,0,.34,0,7.82,0,0,8.96,0,14.55,0,17.11,9.36,3.83,5.69,0,13.86,0,1.67,0,3.46,3.14,10.2,0,0,0,0,0,0,0,0,0,0,0,0
EG600006,0,0,.49,1.25,0,8.49,0,0,3.91,0,22.96,.98,16.9,10.69,6.73,5.06,0,5.15,0,0,0,6.08,0,8.97,0,0,0,0,0,0,0,0,.12,0,1.16,1.07
EG600007,0,0,1.85,3.48,0,10.37,0,0,9.23,0,16.16,1.92,13.73,6.82,4.78,5.59,0,5.51,0,3.03,0,3.57,5.14,6.0,0,0,0,0,0,0,0,0,.54,0,.56,1.73
EG600008,0,0,.6,3.1,0,6.59,0,0,4.07,0,17.74,6.13,8.88,13.1,0,9.98,0,10.45,0,0,.75,3.7,4.28,4.82,0,0,0,0,0,.18,.95,0,2.48,0,0,2.18
EG600009,.02,.7,.41,4.02,0,6.38,0,0,5.17,0,19.08,1.29,14.11,6.67,3.47,11.09,0,6.95,0,0,0,6.26,.74,8.25,0,0,0,0,0,0,0,0,1.53,0,3.08,.8
EG600010,0,0,2.92,.26,0,7.38,0,0,8.49,0,19.72,4.7,11.26,9.62,3.8,4.25,0,11.69,0,1.05,.22,4.54,2.46,5.72,0,0,0,0,0,0,0,0,1.21,0,0,.72
EG600011,0,0,0,0,0,8.4,0,.01,7.35,0,19.67,0,9.93,8.62,2.11,7.89,0,14.29,0,0,0,5.66,3.53,3.4,0,0,0,0,.2,0,0,0,0,0,5.29,3.65
EG600012,0,0,1.29,3.78,0,4.48,0,0,6.21,0,21.14,0,13.77,8.39,.84,10.33,0,7.13,0,.94,0,3.78,2.01,11.73,0,0,0,0,0,0,0,0,2.27,0,1.91,0
EG600013,0,0,2.94,.5,0,8.8,0,0,12.44,0,22.06,0,10.45,13.01,4.09,3.87,0,3.37,0,0,0,12.04,.87,3.35,0,0,0,0,0,0,0,0,1.71,0,.51,0
EG600014,0,0,.91,.18,0,6.11,0,0,7.41,0,26.3,0,10.05,7.8,5.19,8.02,0,2.7,0,.21,0,8.22,4.14,9.38,0,0,0,0,0,0,0,0,0,0,2.34,1.05
EG600015,.59,0,1.4,1.81,0,7.42,0,0,6.49,0,26.37,0,16.12,10.71,2.18,4.85,0,2.55,.04,0,0,7.61,1.53,7.82,0,.22,0,0,0,0,0,0,1.89,.01,0,.38
EG600016,0,0,0,4.1,0,8.61,0,0,4.45,0,22.21,0,14.25,14.32,2.1,6.3,0,3.59,0,0,0,2.89,5.02,8.03,0,0,0,0,0,.49,0,0,.05,0,2.93,.65
EG600017,0,0,0,1.32,0,5.34,0,0,5.37,0,25.8,0,17.02,12.76,1.51,2.75,0,8.14,0,0,0,.83,3.56,11.23,0,0,0,0,0,0,0,0,4.36,0,0,0
EG600018,0,0,.33,1.66,0,8.83,0,0,8.26,0,20.28,0,12.11,5.89,3.98,4.53,0,12.44,0,.88,0,5.35,3.85,5.33,0,0,0,0,0,0,1.08,0,2.64,0,0,2.56
EG600019,0,0,6.44,1.26,0,7.77,0,2.74,3.14,1.62,16.69,0,10.57,13.85,7.59,1.37,0,0,0,0,0,3.13,4.79,5.28,0,0,0,.11,4.53,0,2.04,0,3.62,0,3.45,0
EG600020,0,.44,1.4,1.98,0,3.03,0,0,13.16,0,20.95,0,12.25,14.97,.65,9.09,0,8.48,0,0,0,2.58,5.02,4.33,0,0,0,0,0,0,0,0,1.67,0,0,0
EG600021,0,0,0,.45,0,6.3,0,0,6.43,0,29.09,0,20.5,8.12,4.5,3.72,0,4.26,0,0,0,8.15,.8,4.75,0,0,0,0,0,0,0,0,2.94,0,0,0
EG600022,0,0,0,0,0,7.18,0,0,10.02,0,24.61,0,16.59,7.85,2.74,6.84,0,8.36,0,1.09,.38,5.11,0,5.21,0,0,0,0,0,0,.12,0,2.57,0,1.14,.18
EG600023,0,0,0,.24,0,5.06,0,0,7.67,0,25.24,0,14.04,13.24,4.09,5.81,0,6.27,0,0,0,4.06,1.59,9.17,0,0,0,0,0,0,0,0,1.24,0,2.27,0
EG600024,0,0,0,.84,0,7.79,0,0,9.44,0,19.08,0,15.42,7.69,7.78,5.91,0,0,0,0,0,6.23,4.0,10.2,0,0,0,0,0,.47,.74,0,2.37,0,2.04,0
EG600025,0,0,0,.83,0,5.25,0,0,5.19,0,23.27,0,16.29,14.8,0,7.54,0,5.97,0,0,0,7.33,3.74,5.61,0,0,0,0,0,0,0,0,4.02,0,.16,0
EG600026,0,0,0,1.71,.06,6.04,0,0,3.39,0,24.88,1.97,20.42,7.22,3.28,6.43,0,2.68,0,0,0,5.85,.97,10.11,0,0,0,0,0,.11,0,0,2.83,.09,0,1.97
EG600027,.47,0,0,0,0,7.36,0,0,8.3,0,22.74,0,8.08,9.89,6.7,5.15,0,10.41,0,0,0,3.85,5.99,7.41,0,0,0,.12,0,0,0,0,0,0,1.85,1.68
EG600028,0,0,0,1.2,0,7.47,0,0,7.21,0,26.37,.53,21.36,8.91,4.52,2.08,0,5.41,0,0,0,4.08,0,7.41,0,0,0,0,0,0,.86,0,.61,0,1.97,0
EG600029,0,0,0,.62,0,9.07,0,0,8.45,.3,22.62,0,22.53,11.27,.71,0,0,8.62,0,0,0,3.01,3.85,5.01,0,0,0,0,.05,0,0,.92,2.66,0,.3,0
EG600030,0,0,0,0,0,8.51,0,0,5.56,0,17.19,0,15.66,11.54,2.97,7.25,0,13.48,0,0,0,6.02,2.01,7.77,0,0,0,0,0,0,.72,0,1.32,0,0,0
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EG600035,0,.01,.19,2.3,0,8.59,0,0,4.96,0,12.64,2.0,12.55,8.44,4.87,1.91,0,6.97,0,2.25,0,5.54,3.75,11.33,0,0,0,0,0,0,0,0,2.87,0,2.93,5.92
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---

There were some indel alleles like rs3831308 (TCCTGCCCTACCCA), so I got this error: "Error: --recode 23 cannot be used with multi-character allele names." Therefore I excluded all alleles with multi-character names:

Code:

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plink --bfile ukr_filtered --exclude <(awk 'length($5)>1||length($6)>1' ukr_filtered.bim|cut -f2) --make-bed --out ukr

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