K13 calculator, with 5 classical European ancestry components (GBR+CEU+FIN+IBS+TSI)

[gixajo]

Sorry, I didn´t check those samples before posting and many of them seems to be affected by the calculator effect and are wrong.

I was so excited to have so many samples in my possession that I trusted the source they came from and didn't even bother to check them.

I've been a bit naive making such a silly mistake, so I apologize for this mistake to all of you.

Next calculator will be better!!! (or maybe worse...)

Thank you for your indulgence and understanding.:rolleyes:

I have deleted all.

[gixajo]

Target: gixajo
Distance: 0.3793% / 0.37932168
84.3 IBS.SG
13.1 CEU.SG
1.9 FIN.SG
0.7 TSI.SG

Target: gixajo_dad
Distance: 0.5081% / 0.50813546
95.0 IBS.SG
3.1 TSI.SG
1.9 CEU.SG

Target: gixajo_mom
Distance: 0.2707% / 0.27074566
91.0 IBS.SG
5.6 CEU.SG
3.4 FIN.SG

[gixajo]

I want to see some results of people from British isles, because I think the GBR component should be completed with a component made with individuals from Orkney islands to be more accurate and to get better distances, but I am not sure about if they need that Orcadian component or not considering that CEU would cover probably the lack of those Orcadian individuals.

[17571imre]

Target: Imre
Distance: 0.0514% / 0.05141892
52.1 TSI.SG
24.9 FIN.SG
12.9 GBR.SG
9.8 CEU.SG
0.3 IBS.SG

Target: cb
Distance: 0.1405% / 0.14054086
48.3 FIN.SG
47.7 TSI.SG
4.0 CEU.SG

[gixajo]

,,,,,,,,,,,

So basically 50/50 Southern/Northern European admixture, and quite more Eastern than Western shifted.

[Rædwald]

Target: Rædwald
Distance: 186.8663% / 1.86866289 | ADC: 0.5x RC
68.0 CEU.SG
32.0 GBR.SG


Target: Rædwald
Distance: 131.3211% / 1.31321121 | ADC: 0.25x RC
60.2 CEU.SG
23.4 GBR.SG
10.2 FIN.SG
6.2 IBS.SG

[JamesBond007]

Target: Kevin
Distance: 0.3701% / 0.37005166 | ADC: 0.25x RC
53.5 CEU.SG
46.5 GBR.SG

[Komintasavalta]

Here's averages for all samples from 1000 Genomes. The samples from Spain and UK are divided into different regions, but the samples from Finland are not.


$ curl -Lso reich.1240k.k13 'https://drive.google.com/uc?export=download&id=15Mvba7Bw07VtixiBO_EctOhPPECGzZC9'
$ curl -LsO https://reichdata.hms.harvard.edu/pub/datasets/amh_repo/curated_releases/V50/V50.0/SHARE/public.dir/v50.0_1240K_public.anno
$ grep 1KGPhase3 v50.0_1240K_public.anno |awk -F\\t '{print$2"\t"$15" "$14}'|sed 's/ \.\.//;s/[, ]\{1,\}/_/g'|tr \\t ,|awk 'NR==FNR{a[$1]=$2;next}$1 in a{$1=a[$1];print}' {,O}FS=, - <(cut -d: -f2 reich.1240k.k13)|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("%.2f",a[i,j]/n[i]);print o}}' FS=,|sort|tee 1kg
Bangladesh_Bengalis,1.62,2.65,1.20,14.21,0.53,0.47 ,64.11,9.69,2.22,1.00,1.40,0.46,0.44
Barbados_Afro-Caribbeans,5.46,2.07,1.74,0.89,0.95,0.81,0.67,0.42 ,0.32,0.40,0.31,4.24,81.72
China_Dai_Xishuangbanna,0.26,0.89,0.24,0.13,0.16,0 .18,3.05,89.42,3.57,0.56,1.12,0.18,0.22
China_Han_Beijing,0.19,0.15,0.18,0.23,0.15,0.12,0. 16,76.46,21.60,0.38,0.19,0.11,0.09
China_Southern_Han,0.18,0.34,0.18,0.14,0.10,0.11,0 .34,83.55,14.03,0.39,0.32,0.14,0.17
Colombia_Medellin,25.11,5.57,19.30,2.25,8.75,2.46, 0.73,0.70,0.67,25.91,0.45,1.57,6.55
Finland,33.04,50.09,3.67,1.23,1.17,0.67,1.21,0.68, 6.25,1.04,0.52,0.26,0.19
Gambia_Western_Divisions,0.25,0.24,0.62,0.11,0.42, 1.35,0.39,0.18,0.27,0.21,0.40,8.25,87.32
India_Telugu_sampled_from_United_Kingdom,0.45,0.69 ,0.40,20.08,1.20,0.63,71.24,2.02,0.70,0.64,1.22,0. 38,0.35
Italy_Tuscany,27.74,8.89,26.90,9.17,23.95,1.60,0.3 1,0.30,0.23,0.30,0.30,0.15,0.15
Japan_Tokyo,0.05,0.02,0.16,0.04,0.10,0.13,0.50,65. 83,31.66,0.64,0.58,0.11,0.18
Kenya_Webuye,0.15,0.36,0.19,0.49,0.11,0.30,0.30,0. 14,0.12,0.26,0.26,24.72,72.59
Nigeria_Esan,0.10,0.16,0.13,0.13,0.17,0.45,0.30,0. 06,0.22,0.20,0.22,2.95,94.90
Nigeria_Yoruba_Ibadan,0.10,0.13,0.19,0.11,0.27,0.7 7,0.25,0.22,0.17,0.22,0.21,1.05,96.30
Pakistan,3.04,5.21,0.71,26.38,1.91,0.45,56.94,1.36 ,1.35,1.06,0.80,0.32,0.47
Peru_Lima,6.06,1.00,9.18,1.26,1.78,0.76,0.34,1.05, 0.64,74.77,0.25,0.49,2.41
Puerto_Rico,26.64,7.05,20.85,2.66,9.79,3.56,0.58,0 .53,0.53,13.73,0.53,2.09,11.46
Sierra_Leone_Mende,0.18,0.25,0.28,0.09,0.18,0.53,0 .30,0.17,0.12,0.20,0.31,5.60,91.80
Spain,41.99,10.59,24.47,2.90,13.73,2.79,0.69,0.33, 0.68,0.25,0.44,0.72,0.42
Spain_Andalucia,38.12,6.88,29.76,3.70,15.12,1.33,0 .38,0.77,0.68,0.49,0.76,1.95,0.07
Spain_Aragon,43.80,7.41,32.30,1.38,10.65,1.16,1.43 ,0.05,0.25,0.30,0.49,0.71,0.08
Spain_Baleares,38.47,9.13,30.79,2.66,13.94,2.36,0. 32,0.39,0.39,0.29,0.16,0.65,0.44
Spain_Canarias,40.70,1.13,34.48,3.68,7.96,3.61,2.4 8,0.49,0.00,0.35,1.15,3.92,0.06
Spain_Cantabria,46.24,8.31,31.81,1.14,8.16,1.14,0. 52,0.16,0.19,0.52,0.63,0.62,0.56
Spain_Castilla_la_Mancha,42.50,9.34,27.14,1.44,11. 30,4.49,0.94,0.40,0.13,0.62,0.16,0.92,0.61
Spain_Castilla_y_Leon,44.27,5.58,31.80,1.12,9.08,3 .60,0.50,0.52,0.36,0.21,0.39,1.56,1.00
Spain_Cataluna,42.89,11.22,27.77,1.95,10.90,1.75,1 .09,0.16,0.20,0.63,0.17,0.71,0.56
Spain_Extremadura,38.47,10.08,30.96,2.05,10.05,3.5 8,0.95,0.07,0.40,0.44,0.34,1.10,1.52
Spain_Galicia,39.74,11.25,30.61,0.92,9.94,3.40,0.8 0,0.23,0.36,0.21,0.16,1.24,1.16
Spain_Murcia,37.61,7.80,30.05,1.74,13.42,4.01,0.44 ,1.17,0.34,0.57,0.17,1.56,1.10
Spain_Pais_Vasco,57.08,1.11,39.13,0.04,0.49,0.51,0 .16,0.32,0.29,0.29,0.24,0.16,0.18
Spain_Valencia,43.01,7.53,32.56,2.33,8.91,2.17,0.7 6,0.43,0.07,0.39,0.43,0.75,0.66
Sri_Lanka_Tamils_sampled_from_United_Kingdom,0.47, 0.48,0.84,17.56,1.59,0.88,71.26,2.41,1.10,0.61,1.6 4,0.55,0.60
USA_African-Americans,10.70,4.56,1.96,1.18,0.88,1.07,0.87,0.65 ,0.68,3.13,0.35,3.31,70.67
USA_Gujaratis_Houston_Texas,1.90,3.48,0.34,24.88,0 .99,0.46,64.85,0.54,0.80,0.86,0.51,0.21,0.18
USA_Mexican-American_Los_Angeles,15.75,4.02,16.99,2.23,4.92,1. 71,1.18,1.17,1.75,45.87,0.42,0.82,3.19
USA_Utah,59.83,20.33,7.83,4.11,3.81,1.06,0.90,0.49 ,0.30,0.43,0.36,0.29,0.26
United_Kingdom_England_Cornwall,67.04,16.20,7.10,3 .05,3.25,0.80,0.77,0.36,0.16,0.27,0.43,0.25,0.31
United_Kingdom_England_Cornwall_and_Devon,85.26,5. 57,0.67,0.71,4.08,0.00,1.61,0.41,0.00,1.22,0.32,0. 00,0.15
United_Kingdom_England_Kent,64.35,17.51,8.51,3.46, 2.67,0.97,0.52,0.28,0.27,0.52,0.52,0.29,0.14
United_Kingdom_Scotland_Argyll_and_Bute,84.13,5.23 ,0.25,5.89,0.76,0.70,0.24,0.14,1.01,0.36,0.50,0.79 ,0.00
United_Kingdom_Scotland_Orkney,51.63,24.04,11.56,6 .62,2.54,0.53,0.95,0.32,0.36,0.84,0.34,0.05,0.21
Vietnam_Kinh_Ho_Chi_Minh_City,0.23,1.22,0.30,0.15, 0.20,0.29,4.01,88.55,3.06,0.61,0.98,0.18,0.23

There are no Siberian populations, so even Maris get Japanese ancestry:


$ curl https://pastebin.com/raw/afaMiFSa|tr -d \\r>mix;chmod +x mix;pip3 install cvxpy
[...]
$ ./mix 1kg <(grep Mari.SG reich.1240k.k13)
Mari.SG:Mari.SG (23.078):
73% Finland
13% Japan_Tokyo
10% Pakistan
2% Peru_Lima
1% Nigeria_Esan
0% Kenya_Webuye

The results become more accurate after the matrix of admixture percentages is multiplied by an MDS matrix of the FST matrix for K13:


$ printf %s\\n ,,,,,,,,,,,, 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,>k13fst
$ Rscript -e 'for(f in c("reich.1240k.k13","1kg")){t=read.csv(f,h=T,r=1);fst=as.matrix(as.dist(rea d.csv("k13fst",h=F)));fst=fst/mean(fst);t2=as.matrix(t)%*%cmdscale(fst,ncol(fst)-1);write.table(round(t2,6),paste0(f,".mds"),sep=",",quote=F,col.names=F)}'
$ 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'}")
$ sed 's/:[^,]*//' reich.1240k.k13.mds|tav ,|sort>reich.1240k.k13.mds.ave
$ ./mix 1kg.mds <(grep Mari.SG reich.1240k.k13.mds.ave)
Mari.SG (6.687):
72% Finland
20% Japan_Tokyo
5% Peru_Lima
2% USA_Gujaratis_Houston_Texas
1% Nigeria_Yoruba_Ibadan

I think the reason why Maris get so much South_Asian might be because their Caucasoid side has higher ANE than IE Europeans, because even Mansi and Selkups get around 5% South_Asian, and WSHGs get around 11-12% South_Asian. But it could also be because none of the European components is a good match for the Caucasoid ancestry of Maris, because VURians also get high West_Asian, but even Mansi get 5% West_Asian, and MA1 gets 10% West_Asian.

https://i.ibb.co/g77LZCb/k13-admix.png

[Gallop]

Target: Gallop
Distance: 0.3516% / 0.35157122
69.2 IBS.SG
14.6 TSI.SG
9.7 FIN.SG
6.0 GBR.SG
0.5 CEU.SG


Target: Father
Distance: 1.4560% / 1.45597795
82.1 IBS.SG
9.1 FIN.SG
8.8 TSI.SG

[Dušan]

Very close distance

Target: Dušan
Distance: 0.5367% / 0.53667712
50.2 TSI.SG
34.6 FIN.SG
15.2 CEU.SG

[Voskos]

Target: voskos
Distance: 46.4346% / 0.46434590
51.8 TSI.SG
16.4 3DT26_England_Roman_MiddleEast.SG_1750_ybp
10.8 ANCIENT_ARM002_Kaps_Kura_Araxes_culture_5148_years _old_
9.8 I2062_Israel_MLBA_3197_ybp
4.0 IBS.SG
3.8 FIN.SG
3.2 CEU.SG
0.2 GBR.SG

[gixajo]

Here's averages for all samples from 1000 Genomes. The samples from Spain and UK are divided into different regions, but the samples from Finland are not.

g]

Fantastic Komintasavalta , I also have many things to criticize about those averages that you show us, and about many other different things ,including some of which many other member forums do here and in other forums, but everything you show us you can do it in your own thread, this thread is about a specific model and to show our results.

Thanks.:thumb001:

[gixajo]

Here's averages for all samples from 1000 Genomes. The samples from Spain and UK are divided into different regions, but the samples from Finland are not.

]

In the list of samples avalaible in the Reich datasheet they are more individual samples apart from those of 1000genomes project, including Siberians.

[Komintasavalta]

I think some of the samples from the UK suffer from the calculator effect, because they get over 80% North_Atlantic, and two samples get even over 90% North_Atlantic:


$ curl -Lso reich.1240k.k13 'https://drive.google.com/uc?export=download&id=15Mvba7Bw07VtixiBO_EctOhPPECGzZC9';grep GBR reich.1240k.k13|awk -F, '{print$2,$1}'|sort -rn|head
91.30 GBR.SG:HG00145.SG
91.14 GBR.SG:HG00137.SG
89.93 GBR.SG:HG00105.SG
88.77 GBR.SG:HG02215.SG
88.49 GBR.SG:HG00103.SG
85.26 GBR.SG:HG00265.SG
83.08 GBR.SG:HG00253.SG
81.03 GBR.SG:HG00236.SG
80.42 GBR.SG:HG00232.SG
76.23 GBR.SG:HG00252.SG

[chrisbab]

Target: chrisbabmomk13
Distance: 311.9391% / 3.11939061
64.8 TSI.SG
35.2 FIN.SG

Target: chrisbabdadk13
Distance: 344.4851% / 3.44485059
74.8 TSI.SG
25.2 FIN.SG

Target: chrisbabk13
Distance: 378.9839% / 3.78983918
73.0 TSI.SG
27.0 FIN.SG

[celticdragongod]

Distance to: CDG
3.50789110 GBR.SG:HG00096.SG
4.38379972 GBR.SG:HG00121.SG
4.38597766 GBR.SG:HG00117.SG
4.42216011 GBR.SG:HG00107.SG
4.55330649 CEU.SG:NA12760.SG
4.68943493 GBR.SG:HG00101.SG
4.98329208 CEU.SG:NA12156.SG
4.98408467 CEU.SG:NA12763.SG
4.98802566 CEU.SG:NA11933.SG
4.99616853 GBR.SG:HG00133.SG
5.09067775 GBR.SG:HG00110.SG
5.21246583 GBR.SG:HG00124.SG
5.37344396 GBR.SG:HG00119.SG
5.57351774 GBR.SG:HG00259.SG
5.67666275 GBR.SG:HG00109.SG
5.76736508 GBR.SG:HG00116.SG
5.79730110 GBR.SG:HG00112.SG
5.97299757 CEU.SG:NA06989.SG
5.99704927 CEU.SG:NA11932.SG
6.10288456 CEU.SG:NA11894.SG
6.11313340 GBR.SG:HG00120.SG
6.36604273 GBR.SG:HG00111.SG
6.41144290 CEU.SG:NA12004.SG
6.44358596 CEU.SG:NA11843.SG
6.46469644 CEU.SG:NA10847.SG

Target: CDG
Distance: 0.0615% / 0.06151501
67.2 GBR.SG
14.0 FIN.SG
11.0 CEU.SG
5.0 TSI.SG
2.8 IBS.SG

[celticdragongod]

Here's averages for all samples from 1000 Genomes. The samples from Spain and UK are divided into different regions, but the samples from Finland are not.


$ curl -Lso reich.1240k.k13 'https://drive.google.com/uc?export=download&id=15Mvba7Bw07VtixiBO_EctOhPPECGzZC9'
$ curl -LsO https://reichdata.hms.harvard.edu/pub/datasets/amh_repo/curated_releases/V50/V50.0/SHARE/public.dir/v50.0_1240K_public.anno
$ grep 1KGPhase3 v50.0_1240K_public.anno |awk -F\\t '{print$2"\t"$15" "$14}'|sed 's/ \.\.//;s/[, ]\{1,\}/_/g'|tr \\t ,|awk 'NR==FNR{a[$1]=$2;next}$1 in a{$1=a[$1];print}' {,O}FS=, - <(cut -d: -f2 reich.1240k.k13)|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("%.2f",a[i,j]/n[i]);print o}}' FS=,|sort|tee 1kg
Bangladesh_Bengalis,1.62,2.65,1.20,14.21,0.53,0.47 ,64.11,9.69,2.22,1.00,1.40,0.46,0.44
Barbados_Afro-Caribbeans,5.46,2.07,1.74,0.89,0.95,0.81,0.67,0.42 ,0.32,0.40,0.31,4.24,81.72
China_Dai_Xishuangbanna,0.26,0.89,0.24,0.13,0.16,0 .18,3.05,89.42,3.57,0.56,1.12,0.18,0.22
China_Han_Beijing,0.19,0.15,0.18,0.23,0.15,0.12,0. 16,76.46,21.60,0.38,0.19,0.11,0.09
China_Southern_Han,0.18,0.34,0.18,0.14,0.10,0.11,0 .34,83.55,14.03,0.39,0.32,0.14,0.17
Colombia_Medellin,25.11,5.57,19.30,2.25,8.75,2.46, 0.73,0.70,0.67,25.91,0.45,1.57,6.55
Finland,33.04,50.09,3.67,1.23,1.17,0.67,1.21,0.68, 6.25,1.04,0.52,0.26,0.19
Gambia_Western_Divisions,0.25,0.24,0.62,0.11,0.42, 1.35,0.39,0.18,0.27,0.21,0.40,8.25,87.32
India_Telugu_sampled_from_United_Kingdom,0.45,0.69 ,0.40,20.08,1.20,0.63,71.24,2.02,0.70,0.64,1.22,0. 38,0.35
Italy_Tuscany,27.74,8.89,26.90,9.17,23.95,1.60,0.3 1,0.30,0.23,0.30,0.30,0.15,0.15
Japan_Tokyo,0.05,0.02,0.16,0.04,0.10,0.13,0.50,65. 83,31.66,0.64,0.58,0.11,0.18
Kenya_Webuye,0.15,0.36,0.19,0.49,0.11,0.30,0.30,0. 14,0.12,0.26,0.26,24.72,72.59
Nigeria_Esan,0.10,0.16,0.13,0.13,0.17,0.45,0.30,0. 06,0.22,0.20,0.22,2.95,94.90
Nigeria_Yoruba_Ibadan,0.10,0.13,0.19,0.11,0.27,0.7 7,0.25,0.22,0.17,0.22,0.21,1.05,96.30
Pakistan,3.04,5.21,0.71,26.38,1.91,0.45,56.94,1.36 ,1.35,1.06,0.80,0.32,0.47
Peru_Lima,6.06,1.00,9.18,1.26,1.78,0.76,0.34,1.05, 0.64,74.77,0.25,0.49,2.41
Puerto_Rico,26.64,7.05,20.85,2.66,9.79,3.56,0.58,0 .53,0.53,13.73,0.53,2.09,11.46
Sierra_Leone_Mende,0.18,0.25,0.28,0.09,0.18,0.53,0 .30,0.17,0.12,0.20,0.31,5.60,91.80
Spain,41.99,10.59,24.47,2.90,13.73,2.79,0.69,0.33, 0.68,0.25,0.44,0.72,0.42
Spain_Andalucia,38.12,6.88,29.76,3.70,15.12,1.33,0 .38,0.77,0.68,0.49,0.76,1.95,0.07
Spain_Aragon,43.80,7.41,32.30,1.38,10.65,1.16,1.43 ,0.05,0.25,0.30,0.49,0.71,0.08
Spain_Baleares,38.47,9.13,30.79,2.66,13.94,2.36,0. 32,0.39,0.39,0.29,0.16,0.65,0.44
Spain_Canarias,40.70,1.13,34.48,3.68,7.96,3.61,2.4 8,0.49,0.00,0.35,1.15,3.92,0.06
Spain_Cantabria,46.24,8.31,31.81,1.14,8.16,1.14,0. 52,0.16,0.19,0.52,0.63,0.62,0.56
Spain_Castilla_la_Mancha,42.50,9.34,27.14,1.44,11. 30,4.49,0.94,0.40,0.13,0.62,0.16,0.92,0.61
Spain_Castilla_y_Leon,44.27,5.58,31.80,1.12,9.08,3 .60,0.50,0.52,0.36,0.21,0.39,1.56,1.00
Spain_Cataluna,42.89,11.22,27.77,1.95,10.90,1.75,1 .09,0.16,0.20,0.63,0.17,0.71,0.56
Spain_Extremadura,38.47,10.08,30.96,2.05,10.05,3.5 8,0.95,0.07,0.40,0.44,0.34,1.10,1.52
Spain_Galicia,39.74,11.25,30.61,0.92,9.94,3.40,0.8 0,0.23,0.36,0.21,0.16,1.24,1.16
Spain_Murcia,37.61,7.80,30.05,1.74,13.42,4.01,0.44 ,1.17,0.34,0.57,0.17,1.56,1.10
Spain_Pais_Vasco,57.08,1.11,39.13,0.04,0.49,0.51,0 .16,0.32,0.29,0.29,0.24,0.16,0.18
Spain_Valencia,43.01,7.53,32.56,2.33,8.91,2.17,0.7 6,0.43,0.07,0.39,0.43,0.75,0.66
Sri_Lanka_Tamils_sampled_from_United_Kingdom,0.47, 0.48,0.84,17.56,1.59,0.88,71.26,2.41,1.10,0.61,1.6 4,0.55,0.60
USA_African-Americans,10.70,4.56,1.96,1.18,0.88,1.07,0.87,0.65 ,0.68,3.13,0.35,3.31,70.67
USA_Gujaratis_Houston_Texas,1.90,3.48,0.34,24.88,0 .99,0.46,64.85,0.54,0.80,0.86,0.51,0.21,0.18
USA_Mexican-American_Los_Angeles,15.75,4.02,16.99,2.23,4.92,1. 71,1.18,1.17,1.75,45.87,0.42,0.82,3.19
USA_Utah,59.83,20.33,7.83,4.11,3.81,1.06,0.90,0.49 ,0.30,0.43,0.36,0.29,0.26
United_Kingdom_England_Cornwall,67.04,16.20,7.10,3 .05,3.25,0.80,0.77,0.36,0.16,0.27,0.43,0.25,0.31
United_Kingdom_England_Cornwall_and_Devon,85.26,5. 57,0.67,0.71,4.08,0.00,1.61,0.41,0.00,1.22,0.32,0. 00,0.15
United_Kingdom_England_Kent,64.35,17.51,8.51,3.46, 2.67,0.97,0.52,0.28,0.27,0.52,0.52,0.29,0.14
United_Kingdom_Scotland_Argyll_and_Bute,84.13,5.23 ,0.25,5.89,0.76,0.70,0.24,0.14,1.01,0.36,0.50,0.79 ,0.00
United_Kingdom_Scotland_Orkney,51.63,24.04,11.56,6 .62,2.54,0.53,0.95,0.32,0.36,0.84,0.34,0.05,0.21
Vietnam_Kinh_Ho_Chi_Minh_City,0.23,1.22,0.30,0.15, 0.20,0.29,4.01,88.55,3.06,0.61,0.98,0.18,0.23


Distance to: CDG
2.66232981 United_Kingdom_Scotland_Orkney
12.20143844 USA_Utah
17.11366121 United_Kingdom_England_Kent
20.34691623 United_Kingdom_England_Cornwall
23.81159591 Spain
24.04268080 Spain_Cataluna
25.50196855 Spain_Castilla_la_Mancha
27.10415282 Spain_Galicia
27.32713853 Spain_Cantabria
28.33532424 Spain_Extremadura
28.98477359 Spain_Valencia
29.15480406 Spain_Aragon
29.68069069 Spain_Baleares
29.84874537 Spain_Castilla_y_Leon
30.51497501 Spain_Murcia
30.84342718 Spain_Andalucia
31.99961719 Finland
35.18409300 Spain_Canarias
36.74758904 Puerto_Rico
37.63919898 Spain_Pais_Vasco
37.82414044 Italy_Tuscany
41.55907482 United_Kingdom_Scotland_Argyll_and_Bute
42.49596334 United_Kingdom_England_Cornwall_and_Devon
42.75483248 Colombia_Medellin
61.48034401 USA_Mexican-American_Los_Angeles

Target: CDG
Distance: 1.1443% / 1.14430843
84.3 United_Kingdom_Scotland_Orkney
8.8 Finland
2.3 Spain_Pais_Vasco
2.2 Italy_Tuscany
1.5 United_Kingdom_England_Cornwall
0.9 Spain_Aragon

[calxpal]

Target: cpal
Distance: 12.4918% / 0.12491769
32.2 TSI.SG
24.2 FIN.SG
21.6 GBR.SG
21.4 CEU.SG
0.6 IBS.SG

Distance to: cpal
4.55411901 CEU.SG:NA12413.SG
5.74585938 CEU.SG:NA12273.SG
7.01255303 CEU.SG:NA06985.SG
7.24526052 GBR.SG:HG00108.SG
9.44176361 GBR.SG:HG00117.SG
9.47614373 CEU.SG:NA11840.SG
9.50987382 CEU.SG:NA11994.SG
10.15511694 GBR.SG:HG00116.SG
10.19685736 GBR.SG:HG00126.SG
10.29743657 GBR.SG:HG00122.SG
10.34353905 GBR.SG:HG00107.SG
11.43662100 CEU.SG:NA12287.SG
11.44336926 CEU.SG:NA12760.SG
11.91183865 CEU.SG:NA11932.SG
12.04288587 CEU.SG:NA12156.SG
12.27050529 CEU.SG:NA12004.SG
12.72572984 GBR.SG:HG00259.SG
12.90590562 GBR.SG:HG00112.SG
12.91017816 GBR.SG:HG00124.SG
12.91448799 GBR.SG:HG00115.SG
12.97138389 CEU.SG:NA12763.SG
12.99780751 GBR.SG:HG00133.SG
13.03418198 GBR.SG:HG00155.SG
13.07988914 CEU.SG:NA06994.SG
13.12773400 CEU.SG:NA12272.SG

[Grace O'Malley]

Target: Grace
Distance: 22.6226% / 0.22622610
55.4 GBR.SG
28.0 CEU.SG
11.8 FIN.SG
4.8 TSI.SG

Target: Grace
Distance: 62.0484% / 0.62048411 | ADC: 0.25x RC
66.4 GBR.SG
29.6 CEU.SG
4.0 FIN.SG

Target: Grace
Distance: 196.3642% / 1.96364209
82.4 United_Kingdom_Scotland_Orkney
9.6 Finland
7.4 United_Kingdom_Scotland_Argyll_and_Bute
0.6 Peru_Lima

[Komintasavalta]

Distance to: CDG
2.66232981 United_Kingdom_Scotland_Orkney
12.20143844 USA_Utah
17.11366121 United_Kingdom_England_Kent
20.34691623 United_Kingdom_England_Cornwall
23.81159591 Spain
24.04268080 Spain_Cataluna
25.50196855 Spain_Castilla_la_Mancha
27.10415282 Spain_Galicia
27.32713853 Spain_Cantabria
28.33532424 Spain_Extremadura
28.98477359 Spain_Valencia
29.15480406 Spain_Aragon
29.68069069 Spain_Baleares
29.84874537 Spain_Castilla_y_Leon
30.51497501 Spain_Murcia
30.84342718 Spain_Andalucia
31.99961719 Finland
35.18409300 Spain_Canarias
36.74758904 Puerto_Rico
37.63919898 Spain_Pais_Vasco
37.82414044 Italy_Tuscany
41.55907482 United_Kingdom_Scotland_Argyll_and_Bute
42.49596334 United_Kingdom_England_Cornwall_and_Devon
42.75483248 Colombia_Medellin
61.48034401 USA_Mexican-American_Los_Angeles

Target: CDG
Distance: 1.1443% / 1.14430843
84.3 United_Kingdom_Scotland_Orkney
8.8 Finland
2.3 Spain_Pais_Vasco
2.2 Italy_Tuscany
1.5 United_Kingdom_England_Cornwall
0.9 Spain_Aragon

It's probably better to multiply the matrix of admixture percentages with an MDS matrix of the FST matrix, even though it also amplifies noise-level admixture of minor components. The code below uses a simplified version of nMonte3.R (https://www.dropbox.com/sh/1iaggxyc2alafow/AACIjLtnkuaNNsJ5oKME_3XHa) to do the models so it accounts for FST. You can run the code here: https://rdrr.io/snippets/.


t=read.csv(h=F,r=1,text="celticdragongod,50.06,25.58,12.24,6.16,3.27,0,1.18 ,0.87,0.31,0.23,0.05,0.03,0.01
Bangladesh_Bengalis,1.62,2.65,1.20,14.21,0.53,0.47 ,64.11,9.69,2.22,1.00,1.40,0.46,0.44
Barbados_Afro-Caribbeans,5.46,2.07,1.74,0.89,0.95,0.81,0.67,0.42 ,0.32,0.40,0.31,4.24,81.72
China_Dai_Xishuangbanna,0.26,0.89,0.24,0.13,0.16,0 .18,3.05,89.42,3.57,0.56,1.12,0.18,0.22
China_Han_Beijing,0.19,0.15,0.18,0.23,0.15,0.12,0. 16,76.46,21.60,0.38,0.19,0.11,0.09
China_Southern_Han,0.18,0.34,0.18,0.14,0.10,0.11,0 .34,83.55,14.03,0.39,0.32,0.14,0.17
Colombia_Medellin,25.11,5.57,19.30,2.25,8.75,2.46, 0.73,0.70,0.67,25.91,0.45,1.57,6.55
Finland,33.04,50.09,3.67,1.23,1.17,0.67,1.21,0.68, 6.25,1.04,0.52,0.26,0.19
Gambia_Western_Divisions,0.25,0.24,0.62,0.11,0.42, 1.35,0.39,0.18,0.27,0.21,0.40,8.25,87.32
India_Telugu_sampled_from_United_Kingdom,0.45,0.69 ,0.40,20.08,1.20,0.63,71.24,2.02,0.70,0.64,1.22,0. 38,0.35
Italy_Tuscany,27.74,8.89,26.90,9.17,23.95,1.60,0.3 1,0.30,0.23,0.30,0.30,0.15,0.15
Japan_Tokyo,0.05,0.02,0.16,0.04,0.10,0.13,0.50,65. 83,31.66,0.64,0.58,0.11,0.18
Kenya_Webuye,0.15,0.36,0.19,0.49,0.11,0.30,0.30,0. 14,0.12,0.26,0.26,24.72,72.59
Nigeria_Esan,0.10,0.16,0.13,0.13,0.17,0.45,0.30,0. 06,0.22,0.20,0.22,2.95,94.90
Nigeria_Yoruba_Ibadan,0.10,0.13,0.19,0.11,0.27,0.7 7,0.25,0.22,0.17,0.22,0.21,1.05,96.30
Pakistan,3.04,5.21,0.71,26.38,1.91,0.45,56.94,1.36 ,1.35,1.06,0.80,0.32,0.47
Peru_Lima,6.06,1.00,9.18,1.26,1.78,0.76,0.34,1.05, 0.64,74.77,0.25,0.49,2.41
Puerto_Rico,26.64,7.05,20.85,2.66,9.79,3.56,0.58,0 .53,0.53,13.73,0.53,2.09,11.46
Sierra_Leone_Mende,0.18,0.25,0.28,0.09,0.18,0.53,0 .30,0.17,0.12,0.20,0.31,5.60,91.80
Spain,41.99,10.59,24.47,2.90,13.73,2.79,0.69,0.33, 0.68,0.25,0.44,0.72,0.42
Spain_Andalucia,38.12,6.88,29.76,3.70,15.12,1.33,0 .38,0.77,0.68,0.49,0.76,1.95,0.07
Spain_Aragon,43.80,7.41,32.30,1.38,10.65,1.16,1.43 ,0.05,0.25,0.30,0.49,0.71,0.08
Spain_Baleares,38.47,9.13,30.79,2.66,13.94,2.36,0. 32,0.39,0.39,0.29,0.16,0.65,0.44
Spain_Canarias,40.70,1.13,34.48,3.68,7.96,3.61,2.4 8,0.49,0.00,0.35,1.15,3.92,0.06
Spain_Cantabria,46.24,8.31,31.81,1.14,8.16,1.14,0. 52,0.16,0.19,0.52,0.63,0.62,0.56
Spain_Castilla_la_Mancha,42.50,9.34,27.14,1.44,11. 30,4.49,0.94,0.40,0.13,0.62,0.16,0.92,0.61
Spain_Castilla_y_Leon,44.27,5.58,31.80,1.12,9.08,3 .60,0.50,0.52,0.36,0.21,0.39,1.56,1.00
Spain_Cataluna,42.89,11.22,27.77,1.95,10.90,1.75,1 .09,0.16,0.20,0.63,0.17,0.71,0.56
Spain_Extremadura,38.47,10.08,30.96,2.05,10.05,3.5 8,0.95,0.07,0.40,0.44,0.34,1.10,1.52
Spain_Galicia,39.74,11.25,30.61,0.92,9.94,3.40,0.8 0,0.23,0.36,0.21,0.16,1.24,1.16
Spain_Murcia,37.61,7.80,30.05,1.74,13.42,4.01,0.44 ,1.17,0.34,0.57,0.17,1.56,1.10
Spain_Pais_Vasco,57.08,1.11,39.13,0.04,0.49,0.51,0 .16,0.32,0.29,0.29,0.24,0.16,0.18
Spain_Valencia,43.01,7.53,32.56,2.33,8.91,2.17,0.7 6,0.43,0.07,0.39,0.43,0.75,0.66
Sri_Lanka_Tamils_sampled_from_United_Kingdom,0.47, 0.48,0.84,17.56,1.59,0.88,71.26,2.41,1.10,0.61,1.6 4,0.55,0.60
USA_African-Americans,10.70,4.56,1.96,1.18,0.88,1.07,0.87,0.65 ,0.68,3.13,0.35,3.31,70.67
USA_Gujaratis_Houston_Texas,1.90,3.48,0.34,24.88,0 .99,0.46,64.85,0.54,0.80,0.86,0.51,0.21,0.18
USA_Mexican-American_Los_Angeles,15.75,4.02,16.99,2.23,4.92,1. 71,1.18,1.17,1.75,45.87,0.42,0.82,3.19
USA_Utah,59.83,20.33,7.83,4.11,3.81,1.06,0.90,0.49 ,0.30,0.43,0.36,0.29,0.26
United_Kingdom_England_Cornwall,67.04,16.20,7.10,3 .05,3.25,0.80,0.77,0.36,0.16,0.27,0.43,0.25,0.31
United_Kingdom_England_Cornwall_and_Devon,85.26,5. 57,0.67,0.71,4.08,0.00,1.61,0.41,0.00,1.22,0.32,0. 00,0.15
United_Kingdom_England_Kent,64.35,17.51,8.51,3.46, 2.67,0.97,0.52,0.28,0.27,0.52,0.52,0.29,0.14
United_Kingdom_Scotland_Argyll_and_Bute,84.13,5.23 ,0.25,5.89,0.76,0.70,0.24,0.14,1.01,0.36,0.50,0.79 ,0.00
United_Kingdom_Scotland_Orkney,51.63,24.04,11.56,6 .62,2.54,0.53,0.95,0.32,0.36,0.84,0.34,0.05,0.21
Vietnam_Kinh_Ho_Chi_Minh_City,0.23,1.22,0.30,0.15, 0.20,0.29,4.01,88.55,3.06,0.61,0.98,0.18,0.23")

fst=as.matrix(as.dist(read.csv(h=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,")))
fst=fst/mean(fst)

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

nbatch=500
cycles=1000
pen=0

do_algorithm=function(source,target){
source=as.matrix(source,rownames.force=NA)
target=as.matrix(target,rownames.force=NA)
dist=sweep(source,2,target,"-")
nrow=nrow(dist)
ncol=ncol(dist)
matpop=sample(1:nrow,nbatch,replace=T)
matadmix=dist[matpop,]
colm1=colMeans(matadmix)
eval1=(1+pen)*sum(colm1^2)
for(c in 1:cycles){
dumpop=sample(1:nrow,nbatch,replace=T)
dumadmix=dist[dumpop,]
for(b in 1:nbatch){
store=matadmix[b,]
matadmix[b,]=dumadmix[b,]
colm2=colMeans(matadmix)
eval2=sum(colm2^2)+pen*sum(matadmix[b,]^2)
if(eval2<=eval1){
matpop[b]=dumpop[b]
colm1=colm2
eval1=eval2
}else{matadmix[b,]=store}
}
}
fitted=t(colMeans(matadmix)+target[1,])
popl=sapply(strsplit(rownames(dist)[matpop],";"),function(x)x[1])
return(list("estimated"=fitted,"pops"=factor(popl)))
}

writeLines(paste0("Distance to ",rownames(t)[1],":"))
s=sort(as.matrix(dist(mult))[1,])
writeLines(paste(sprintf("%.2f",s),names(s)))
writeLines("")

source=mult[-1,]
target=mult[1,]
result=do_algorithm(source,target)
dist=sqrt(sum((result$estimated-target)^2))
cat(paste0("Target: ",rownames(t)[1]," (d=",sprintf("%.3f",dist),")\n"))
pct=as.matrix(100*sort(table(result$pops),decreasi ng=T)/nbatch)
writeLines(paste(sprintf("%.1f",pct),row.names(pct)))

Output:

Distance to celticdragongod:
0.00 celticdragongod
0.83 United_Kingdom_Scotland_Orkney
1.85 USA_Utah
2.23 United_Kingdom_England_Kent
2.57 United_Kingdom_England_Cornwall
5.03 United_Kingdom_Scotland_Argyll_and_Bute
5.10 United_Kingdom_England_Cornwall_and_Devon
6.98 Spain_Cataluna
7.35 Spain
7.55 Spain_Cantabria
8.10 Spain_Aragon
8.18 Spain_Castilla_la_Mancha
8.41 Spain_Valencia
8.71 Spain_Galicia
8.72 Spain_Baleares
9.02 Spain_Extremadura
9.06 Spain_Andalucia
9.14 Spain_Pais_Vasco
9.52 Spain_Castilla_y_Leon
9.74 Finland
9.80 Spain_Murcia
9.93 Italy_Tuscany
11.49 Spain_Canarias
25.04 Puerto_Rico
34.09 Colombia_Medellin
39.99 Pakistan
42.76 USA_Gujaratis_Houston_Texas
48.03 India_Telugu_sampled_from_United_Kingdom
49.06 Sri_Lanka_Tamils_sampled_from_United_Kingdom
51.39 Bangladesh_Bengalis
60.16 USA_Mexican-American_Los_Angeles
97.94 Peru_Lima
100.07 Vietnam_Kinh_Ho_Chi_Minh_City
101.11 China_Dai_Xishuangbanna
101.37 Japan_Tokyo
102.14 China_Han_Beijing
102.25 USA_African-Americans
102.65 China_Southern_Han
118.54 Barbados_Afro-Caribbeans
128.74 Kenya_Webuye
130.76 Gambia_Western_Divisions
133.81 Sierra_Leone_Mende
134.99 Nigeria_Yoruba_Ibadan
135.11 Nigeria_Esan

Target: celticdragongod (d=0.713)
88.8 United_Kingdom_Scotland_Orkney
6.8 Spain_Pais_Vasco
4.4 Finland

I think the reason why you're so far from Scotland_Argyll_and_Bute and England_Cornwall_and_Devon is that they include samples that suffer from the calculator effect, so they have really high North_Atlantic.

[Grace O'Malley]

It's probably better to multiply the matrix of admixture percentages with an MDS matrix of the FST matrix, even though it also amplifies noise-level admixture of minor components. The code below uses a simplified version of nMonte3.R (https://www.dropbox.com/sh/1iaggxyc2alafow/AACIjLtnkuaNNsJ5oKME_3XHa) to do the models. You can run the code here: https://rdrr.io/snippets/.


t=read.csv(h=F,r=1,text="celticdragongod,50.06,25.58,12.24,6.16,3.27,0,1.18 ,0.87,0.31,0.23,0.05,0.03,0.01
Bangladesh_Bengalis,1.62,2.65,1.20,14.21,0.53,0.47 ,64.11,9.69,2.22,1.00,1.40,0.46,0.44
Barbados_Afro-Caribbeans,5.46,2.07,1.74,0.89,0.95,0.81,0.67,0.42 ,0.32,0.40,0.31,4.24,81.72
China_Dai_Xishuangbanna,0.26,0.89,0.24,0.13,0.16,0 .18,3.05,89.42,3.57,0.56,1.12,0.18,0.22
China_Han_Beijing,0.19,0.15,0.18,0.23,0.15,0.12,0. 16,76.46,21.60,0.38,0.19,0.11,0.09
China_Southern_Han,0.18,0.34,0.18,0.14,0.10,0.11,0 .34,83.55,14.03,0.39,0.32,0.14,0.17
Colombia_Medellin,25.11,5.57,19.30,2.25,8.75,2.46, 0.73,0.70,0.67,25.91,0.45,1.57,6.55
Finland,33.04,50.09,3.67,1.23,1.17,0.67,1.21,0.68, 6.25,1.04,0.52,0.26,0.19
Gambia_Western_Divisions,0.25,0.24,0.62,0.11,0.42, 1.35,0.39,0.18,0.27,0.21,0.40,8.25,87.32
India_Telugu_sampled_from_United_Kingdom,0.45,0.69 ,0.40,20.08,1.20,0.63,71.24,2.02,0.70,0.64,1.22,0. 38,0.35
Italy_Tuscany,27.74,8.89,26.90,9.17,23.95,1.60,0.3 1,0.30,0.23,0.30,0.30,0.15,0.15
Japan_Tokyo,0.05,0.02,0.16,0.04,0.10,0.13,0.50,65. 83,31.66,0.64,0.58,0.11,0.18
Kenya_Webuye,0.15,0.36,0.19,0.49,0.11,0.30,0.30,0. 14,0.12,0.26,0.26,24.72,72.59
Nigeria_Esan,0.10,0.16,0.13,0.13,0.17,0.45,0.30,0. 06,0.22,0.20,0.22,2.95,94.90
Nigeria_Yoruba_Ibadan,0.10,0.13,0.19,0.11,0.27,0.7 7,0.25,0.22,0.17,0.22,0.21,1.05,96.30
Pakistan,3.04,5.21,0.71,26.38,1.91,0.45,56.94,1.36 ,1.35,1.06,0.80,0.32,0.47
Peru_Lima,6.06,1.00,9.18,1.26,1.78,0.76,0.34,1.05, 0.64,74.77,0.25,0.49,2.41
Puerto_Rico,26.64,7.05,20.85,2.66,9.79,3.56,0.58,0 .53,0.53,13.73,0.53,2.09,11.46
Sierra_Leone_Mende,0.18,0.25,0.28,0.09,0.18,0.53,0 .30,0.17,0.12,0.20,0.31,5.60,91.80
Spain,41.99,10.59,24.47,2.90,13.73,2.79,0.69,0.33, 0.68,0.25,0.44,0.72,0.42
Spain_Andalucia,38.12,6.88,29.76,3.70,15.12,1.33,0 .38,0.77,0.68,0.49,0.76,1.95,0.07
Spain_Aragon,43.80,7.41,32.30,1.38,10.65,1.16,1.43 ,0.05,0.25,0.30,0.49,0.71,0.08
Spain_Baleares,38.47,9.13,30.79,2.66,13.94,2.36,0. 32,0.39,0.39,0.29,0.16,0.65,0.44
Spain_Canarias,40.70,1.13,34.48,3.68,7.96,3.61,2.4 8,0.49,0.00,0.35,1.15,3.92,0.06
Spain_Cantabria,46.24,8.31,31.81,1.14,8.16,1.14,0. 52,0.16,0.19,0.52,0.63,0.62,0.56
Spain_Castilla_la_Mancha,42.50,9.34,27.14,1.44,11. 30,4.49,0.94,0.40,0.13,0.62,0.16,0.92,0.61
Spain_Castilla_y_Leon,44.27,5.58,31.80,1.12,9.08,3 .60,0.50,0.52,0.36,0.21,0.39,1.56,1.00
Spain_Cataluna,42.89,11.22,27.77,1.95,10.90,1.75,1 .09,0.16,0.20,0.63,0.17,0.71,0.56
Spain_Extremadura,38.47,10.08,30.96,2.05,10.05,3.5 8,0.95,0.07,0.40,0.44,0.34,1.10,1.52
Spain_Galicia,39.74,11.25,30.61,0.92,9.94,3.40,0.8 0,0.23,0.36,0.21,0.16,1.24,1.16
Spain_Murcia,37.61,7.80,30.05,1.74,13.42,4.01,0.44 ,1.17,0.34,0.57,0.17,1.56,1.10
Spain_Pais_Vasco,57.08,1.11,39.13,0.04,0.49,0.51,0 .16,0.32,0.29,0.29,0.24,0.16,0.18
Spain_Valencia,43.01,7.53,32.56,2.33,8.91,2.17,0.7 6,0.43,0.07,0.39,0.43,0.75,0.66
Sri_Lanka_Tamils_sampled_from_United_Kingdom,0.47, 0.48,0.84,17.56,1.59,0.88,71.26,2.41,1.10,0.61,1.6 4,0.55,0.60
USA_African-Americans,10.70,4.56,1.96,1.18,0.88,1.07,0.87,0.65 ,0.68,3.13,0.35,3.31,70.67
USA_Gujaratis_Houston_Texas,1.90,3.48,0.34,24.88,0 .99,0.46,64.85,0.54,0.80,0.86,0.51,0.21,0.18
USA_Mexican-American_Los_Angeles,15.75,4.02,16.99,2.23,4.92,1. 71,1.18,1.17,1.75,45.87,0.42,0.82,3.19
USA_Utah,59.83,20.33,7.83,4.11,3.81,1.06,0.90,0.49 ,0.30,0.43,0.36,0.29,0.26
United_Kingdom_England_Cornwall,67.04,16.20,7.10,3 .05,3.25,0.80,0.77,0.36,0.16,0.27,0.43,0.25,0.31
United_Kingdom_England_Cornwall_and_Devon,85.26,5. 57,0.67,0.71,4.08,0.00,1.61,0.41,0.00,1.22,0.32,0. 00,0.15
United_Kingdom_England_Kent,64.35,17.51,8.51,3.46, 2.67,0.97,0.52,0.28,0.27,0.52,0.52,0.29,0.14
United_Kingdom_Scotland_Argyll_and_Bute,84.13,5.23 ,0.25,5.89,0.76,0.70,0.24,0.14,1.01,0.36,0.50,0.79 ,0.00
United_Kingdom_Scotland_Orkney,51.63,24.04,11.56,6 .62,2.54,0.53,0.95,0.32,0.36,0.84,0.34,0.05,0.21
Vietnam_Kinh_Ho_Chi_Minh_City,0.23,1.22,0.30,0.15, 0.20,0.29,4.01,88.55,3.06,0.61,0.98,0.18,0.23")

fst=as.matrix(as.dist(read.csv(h=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,")))
fst=fst/mean(fst)

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

s=sort(as.matrix(dist(mult))[1,])
cat(paste(sprintf("%.2f",s),names(s)),sep="\n")

nbatch=500
cycles=1000
pen=0

do_algorithm=function(source,target){
source=as.matrix(source,rownames.force=NA)
target=as.matrix(target,rownames.force=NA)
dist=sweep(source,2,target,"-")
nrow=nrow(dist)
ncol=ncol(dist)
matpop=sample(1:nrow,nbatch,replace=T)
matadmix=dist[matpop,]
colm1=colMeans(matadmix)
eval1=(1+pen)*sum(colm1^2)
for(c in 1:cycles){
dumpop=sample(1:nrow,nbatch,replace=T)
dumadmix=dist[dumpop,]
for(b in 1:nbatch){
store=matadmix[b,]
matadmix[b,]=dumadmix[b,]
colm2=colMeans(matadmix)
eval2=sum(colm2^2)+pen*sum(matadmix[b,]^2)
if(eval2<=eval1){
matpop[b]=dumpop[b]
colm1=colm2
eval1=eval2
}else{matadmix[b,]=store}
}
}
fitted=t(colMeans(matadmix)+target[1,])
popl=sapply(strsplit(rownames(dist)[matpop],";"),function(x)x[1])
return(list("estimated"=fitted,"pops"=factor(popl)))
}

source=mult[-1,]
target=as.data.frame(mult)[1,]

for(i in 1:nrow(target)){
row=target[i,]
result=do_algorithm(source,row)

if(i>=2)cat("\n")
dist=sqrt(sum((result$estimated-row)^2))
cat(paste0("Target: ",row.names(row)," (d=",sprintf("%.4f",dist),")\n"))

pct=as.matrix(100*sort(table(result$pops),decreasi ng=T)/nbatch)
cat(paste(sprintf("%.1f",pct),row.names(pct)),sep="\n")
}

Output:

0.00 celticdragongod
0.83 United_Kingdom_Scotland_Orkney
1.85 USA_Utah
2.23 United_Kingdom_England_Kent
2.57 United_Kingdom_England_Cornwall
5.03 United_Kingdom_Scotland_Argyll_and_Bute
5.10 United_Kingdom_England_Cornwall_and_Devon
6.98 Spain_Cataluna
7.35 Spain
7.55 Spain_Cantabria
8.10 Spain_Aragon
8.18 Spain_Castilla_la_Mancha
8.41 Spain_Valencia
8.71 Spain_Galicia
8.72 Spain_Baleares
9.02 Spain_Extremadura
9.06 Spain_Andalucia
9.14 Spain_Pais_Vasco
9.52 Spain_Castilla_y_Leon
9.74 Finland
9.80 Spain_Murcia
9.93 Italy_Tuscany
11.49 Spain_Canarias
25.04 Puerto_Rico
34.09 Colombia_Medellin
39.99 Pakistan
42.76 USA_Gujaratis_Houston_Texas
48.03 India_Telugu_sampled_from_United_Kingdom
49.06 Sri_Lanka_Tamils_sampled_from_United_Kingdom
51.39 Bangladesh_Bengalis
60.16 USA_Mexican-American_Los_Angeles
97.94 Peru_Lima
100.07 Vietnam_Kinh_Ho_Chi_Minh_City
101.11 China_Dai_Xishuangbanna
101.37 Japan_Tokyo
102.14 China_Han_Beijing
102.25 USA_African-Americans
102.65 China_Southern_Han
118.54 Barbados_Afro-Caribbeans
128.74 Kenya_Webuye
130.76 Gambia_Western_Divisions
133.81 Sierra_Leone_Mende
134.99 Nigeria_Yoruba_Ibadan
135.11 Nigeria_Esan

Target: celticdragongod (d=.7132)
88.8 United_Kingdom_Scotland_Orkney
6.8 Spain_Pais_Vasco
4.4 Finland

I think the reason why you're so far from United_Kingdom_Scotland_Argyll_and_Bute and United_Kingdom_England_Cornwall_and_Devon is that they include samples that suffer from the calculator effect, so they have really high North_Atlantic.

I've just looked at my distances to compare to CDG. I'm similar in that I'm really only close to Orkney and then there is a big jump.

Distance to: Grace
2.85996503 United_Kingdom_Scotland_Orkney
10.27346582 USA_Utah
15.17891959 United_Kingdom_England_Kent
18.36918071 United_Kingdom_England_Cornwall
26.41307063 Spain
26.79733755 Spain_Cataluna
27.96163443 Spain_Castilla_la_Mancha
29.76196734 Spain_Cantabria
29.93362157 Spain_Galicia
31.10582261 Spain_Extremadura
31.50690559 Spain_Valencia
31.78271543 Spain_Aragon
32.13330982 Spain_Castilla_y_Leon
32.51024762 Spain_Baleares
32.93167928 Finland
33.18887314 Spain_Murcia
33.56228538 Spain_Andalucia
37.41215311 Spain_Canarias
38.43974636 Puerto_Rico
39.05070934 Spain_Pais_Vasco
39.09155791 United_Kingdom_Scotland_Argyll_and_Bute
40.36004088 United_Kingdom_England_Cornwall_and_Devon
40.59781768 Italy_Tuscany
43.88825241 Colombia_Medellin
62.04231056 USA_Mexican-American_Los_Angeles

There is something wrong here because look at the big difference between the United Kingdom Scotland Argyll and Bute and Cornwall and Devon. That's not correct.

[Grace O'Malley]

It's probably better to multiply the matrix of admixture percentages with an MDS matrix of the FST matrix, even though it also amplifies noise-level admixture of minor components. The code below uses a simplified version of nMonte3.R (https://www.dropbox.com/sh/1iaggxyc2alafow/AACIjLtnkuaNNsJ5oKME_3XHa) to do the models. You can run the code here: https://rdrr.io/snippets/.


t=read.csv(h=F,r=1,text="celticdragongod,50.06,25.58,12.24,6.16,3.27,0,1.18 ,0.87,0.31,0.23,0.05,0.03,0.01
Bangladesh_Bengalis,1.62,2.65,1.20,14.21,0.53,0.47 ,64.11,9.69,2.22,1.00,1.40,0.46,0.44
Barbados_Afro-Caribbeans,5.46,2.07,1.74,0.89,0.95,0.81,0.67,0.42 ,0.32,0.40,0.31,4.24,81.72
China_Dai_Xishuangbanna,0.26,0.89,0.24,0.13,0.16,0 .18,3.05,89.42,3.57,0.56,1.12,0.18,0.22
China_Han_Beijing,0.19,0.15,0.18,0.23,0.15,0.12,0. 16,76.46,21.60,0.38,0.19,0.11,0.09
China_Southern_Han,0.18,0.34,0.18,0.14,0.10,0.11,0 .34,83.55,14.03,0.39,0.32,0.14,0.17
Colombia_Medellin,25.11,5.57,19.30,2.25,8.75,2.46, 0.73,0.70,0.67,25.91,0.45,1.57,6.55
Finland,33.04,50.09,3.67,1.23,1.17,0.67,1.21,0.68, 6.25,1.04,0.52,0.26,0.19
Gambia_Western_Divisions,0.25,0.24,0.62,0.11,0.42, 1.35,0.39,0.18,0.27,0.21,0.40,8.25,87.32
India_Telugu_sampled_from_United_Kingdom,0.45,0.69 ,0.40,20.08,1.20,0.63,71.24,2.02,0.70,0.64,1.22,0. 38,0.35
Italy_Tuscany,27.74,8.89,26.90,9.17,23.95,1.60,0.3 1,0.30,0.23,0.30,0.30,0.15,0.15
Japan_Tokyo,0.05,0.02,0.16,0.04,0.10,0.13,0.50,65. 83,31.66,0.64,0.58,0.11,0.18
Kenya_Webuye,0.15,0.36,0.19,0.49,0.11,0.30,0.30,0. 14,0.12,0.26,0.26,24.72,72.59
Nigeria_Esan,0.10,0.16,0.13,0.13,0.17,0.45,0.30,0. 06,0.22,0.20,0.22,2.95,94.90
Nigeria_Yoruba_Ibadan,0.10,0.13,0.19,0.11,0.27,0.7 7,0.25,0.22,0.17,0.22,0.21,1.05,96.30
Pakistan,3.04,5.21,0.71,26.38,1.91,0.45,56.94,1.36 ,1.35,1.06,0.80,0.32,0.47
Peru_Lima,6.06,1.00,9.18,1.26,1.78,0.76,0.34,1.05, 0.64,74.77,0.25,0.49,2.41
Puerto_Rico,26.64,7.05,20.85,2.66,9.79,3.56,0.58,0 .53,0.53,13.73,0.53,2.09,11.46
Sierra_Leone_Mende,0.18,0.25,0.28,0.09,0.18,0.53,0 .30,0.17,0.12,0.20,0.31,5.60,91.80
Spain,41.99,10.59,24.47,2.90,13.73,2.79,0.69,0.33, 0.68,0.25,0.44,0.72,0.42
Spain_Andalucia,38.12,6.88,29.76,3.70,15.12,1.33,0 .38,0.77,0.68,0.49,0.76,1.95,0.07
Spain_Aragon,43.80,7.41,32.30,1.38,10.65,1.16,1.43 ,0.05,0.25,0.30,0.49,0.71,0.08
Spain_Baleares,38.47,9.13,30.79,2.66,13.94,2.36,0. 32,0.39,0.39,0.29,0.16,0.65,0.44
Spain_Canarias,40.70,1.13,34.48,3.68,7.96,3.61,2.4 8,0.49,0.00,0.35,1.15,3.92,0.06
Spain_Cantabria,46.24,8.31,31.81,1.14,8.16,1.14,0. 52,0.16,0.19,0.52,0.63,0.62,0.56
Spain_Castilla_la_Mancha,42.50,9.34,27.14,1.44,11. 30,4.49,0.94,0.40,0.13,0.62,0.16,0.92,0.61
Spain_Castilla_y_Leon,44.27,5.58,31.80,1.12,9.08,3 .60,0.50,0.52,0.36,0.21,0.39,1.56,1.00
Spain_Cataluna,42.89,11.22,27.77,1.95,10.90,1.75,1 .09,0.16,0.20,0.63,0.17,0.71,0.56
Spain_Extremadura,38.47,10.08,30.96,2.05,10.05,3.5 8,0.95,0.07,0.40,0.44,0.34,1.10,1.52
Spain_Galicia,39.74,11.25,30.61,0.92,9.94,3.40,0.8 0,0.23,0.36,0.21,0.16,1.24,1.16
Spain_Murcia,37.61,7.80,30.05,1.74,13.42,4.01,0.44 ,1.17,0.34,0.57,0.17,1.56,1.10
Spain_Pais_Vasco,57.08,1.11,39.13,0.04,0.49,0.51,0 .16,0.32,0.29,0.29,0.24,0.16,0.18
Spain_Valencia,43.01,7.53,32.56,2.33,8.91,2.17,0.7 6,0.43,0.07,0.39,0.43,0.75,0.66
Sri_Lanka_Tamils_sampled_from_United_Kingdom,0.47, 0.48,0.84,17.56,1.59,0.88,71.26,2.41,1.10,0.61,1.6 4,0.55,0.60
USA_African-Americans,10.70,4.56,1.96,1.18,0.88,1.07,0.87,0.65 ,0.68,3.13,0.35,3.31,70.67
USA_Gujaratis_Houston_Texas,1.90,3.48,0.34,24.88,0 .99,0.46,64.85,0.54,0.80,0.86,0.51,0.21,0.18
USA_Mexican-American_Los_Angeles,15.75,4.02,16.99,2.23,4.92,1. 71,1.18,1.17,1.75,45.87,0.42,0.82,3.19
USA_Utah,59.83,20.33,7.83,4.11,3.81,1.06,0.90,0.49 ,0.30,0.43,0.36,0.29,0.26
United_Kingdom_England_Cornwall,67.04,16.20,7.10,3 .05,3.25,0.80,0.77,0.36,0.16,0.27,0.43,0.25,0.31
United_Kingdom_England_Cornwall_and_Devon,85.26,5. 57,0.67,0.71,4.08,0.00,1.61,0.41,0.00,1.22,0.32,0. 00,0.15
United_Kingdom_England_Kent,64.35,17.51,8.51,3.46, 2.67,0.97,0.52,0.28,0.27,0.52,0.52,0.29,0.14
United_Kingdom_Scotland_Argyll_and_Bute,84.13,5.23 ,0.25,5.89,0.76,0.70,0.24,0.14,1.01,0.36,0.50,0.79 ,0.00
United_Kingdom_Scotland_Orkney,51.63,24.04,11.56,6 .62,2.54,0.53,0.95,0.32,0.36,0.84,0.34,0.05,0.21
Vietnam_Kinh_Ho_Chi_Minh_City,0.23,1.22,0.30,0.15, 0.20,0.29,4.01,88.55,3.06,0.61,0.98,0.18,0.23")

fst=as.matrix(as.dist(read.csv(h=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,")))
fst=fst/mean(fst)

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

s=sort(as.matrix(dist(mult))[1,])
cat(paste(sprintf("%.2f",s),names(s)),sep="\n")

nbatch=500
cycles=1000
pen=0

do_algorithm=function(source,target){
source=as.matrix(source,rownames.force=NA)
target=as.matrix(target,rownames.force=NA)
dist=sweep(source,2,target,"-")
nrow=nrow(dist)
ncol=ncol(dist)
matpop=sample(1:nrow,nbatch,replace=T)
matadmix=dist[matpop,]
colm1=colMeans(matadmix)
eval1=(1+pen)*sum(colm1^2)
for(c in 1:cycles){
dumpop=sample(1:nrow,nbatch,replace=T)
dumadmix=dist[dumpop,]
for(b in 1:nbatch){
store=matadmix[b,]
matadmix[b,]=dumadmix[b,]
colm2=colMeans(matadmix)
eval2=sum(colm2^2)+pen*sum(matadmix[b,]^2)
if(eval2<=eval1){
matpop[b]=dumpop[b]
colm1=colm2
eval1=eval2
}else{matadmix[b,]=store}
}
}
fitted=t(colMeans(matadmix)+target[1,])
popl=sapply(strsplit(rownames(dist)[matpop],";"),function(x)x[1])
return(list("estimated"=fitted,"pops"=factor(popl)))
}

source=mult[-1,]
target=as.data.frame(mult)[1,]

for(i in 1:nrow(target)){
row=target[i,]
result=do_algorithm(source,row)

if(i>=2)cat("\n")
dist=sqrt(sum((result$estimated-row)^2))
cat(paste0("Target: ",row.names(row)," (d=",sprintf("%.4f",dist),")\n"))

pct=as.matrix(100*sort(table(result$pops),decreasi ng=T)/nbatch)
cat(paste(sprintf("%.1f",pct),row.names(pct)),sep="\n")
}

Output:

0.00 celticdragongod
0.83 United_Kingdom_Scotland_Orkney
1.85 USA_Utah
2.23 United_Kingdom_England_Kent
2.57 United_Kingdom_England_Cornwall
5.03 United_Kingdom_Scotland_Argyll_and_Bute
5.10 United_Kingdom_England_Cornwall_and_Devon
6.98 Spain_Cataluna
7.35 Spain
7.55 Spain_Cantabria
8.10 Spain_Aragon
8.18 Spain_Castilla_la_Mancha
8.41 Spain_Valencia
8.71 Spain_Galicia
8.72 Spain_Baleares
9.02 Spain_Extremadura
9.06 Spain_Andalucia
9.14 Spain_Pais_Vasco
9.52 Spain_Castilla_y_Leon
9.74 Finland
9.80 Spain_Murcia
9.93 Italy_Tuscany
11.49 Spain_Canarias
25.04 Puerto_Rico
34.09 Colombia_Medellin
39.99 Pakistan
42.76 USA_Gujaratis_Houston_Texas
48.03 India_Telugu_sampled_from_United_Kingdom
49.06 Sri_Lanka_Tamils_sampled_from_United_Kingdom
51.39 Bangladesh_Bengalis
60.16 USA_Mexican-American_Los_Angeles
97.94 Peru_Lima
100.07 Vietnam_Kinh_Ho_Chi_Minh_City
101.11 China_Dai_Xishuangbanna
101.37 Japan_Tokyo
102.14 China_Han_Beijing
102.25 USA_African-Americans
102.65 China_Southern_Han
118.54 Barbados_Afro-Caribbeans
128.74 Kenya_Webuye
130.76 Gambia_Western_Divisions
133.81 Sierra_Leone_Mende
134.99 Nigeria_Yoruba_Ibadan
135.11 Nigeria_Esan

Target: celticdragongod (d=.7132)
88.8 United_Kingdom_Scotland_Orkney
6.8 Spain_Pais_Vasco
4.4 Finland

I think the reason why you're so far from United_Kingdom_Scotland_Argyll_and_Bute and United_Kingdom_England_Cornwall_and_Devon is that they include samples that suffer from the calculator effect, so they have really high North_Atlantic.

Thanks for the code as I ran this for myself.

0.00 Grace
1.18 United_Kingdom_Scotland_Orkney
1.88 USA_Utah
2.19 United_Kingdom_England_Kent
2.55 United_Kingdom_England_Cornwall
4.30 United_Kingdom_England_Cornwall_and_Devon
4.38 United_Kingdom_Scotland_Argyll_and_Bute
7.95 Spain_Cataluna
8.26 Spain
8.58 Spain_Cantabria
9.04 Spain_Castilla_la_Mancha
9.13 Spain_Aragon
9.29 Finland
9.42 Spain_Valencia
9.69 Spain_Baleares
9.70 Spain_Galicia
9.98 Spain_Extremadura
10.00 Spain_Andalucia
10.06 Spain_Pais_Vasco
10.47 Spain_Castilla_y_Leon
10.69 Spain_Murcia
10.80 Italy_Tuscany
12.42 Spain_Canarias
24.74 Puerto_Rico
33.24 Colombia_Medellin
39.95 Pakistan
42.72 USA_Gujaratis_Houston_Texas
48.02 India_Telugu_sampled_from_United_Kingdom
49.05 Sri_Lanka_Tamils_sampled_from_United_Kingdom
51.35 Bangladesh_Bengalis
59.13 USA_Mexican-American_Los_Angeles
96.86 Peru_Lima
100.01 Vietnam_Kinh_Ho_Chi_Minh_City
101.04 China_Dai_Xishuangbanna
101.20 Japan_Tokyo
102.01 China_Han_Beijing
102.55 China_Southern_Han
102.55 USA_African-Americans
118.89 Barbados_Afro-Caribbeans
129.10 Kenya_Webuye
131.12 Gambia_Western_Divisions
134.16 Sierra_Leone_Mende
135.33 Nigeria_Yoruba_Ibadan
135.46 Nigeria_Esan
Target: Grace (d=1.0073)
97.0 United_Kingdom_Scotland_Orkney
2.0 United_Kingdom_England_Cornwall_and_Devon
0.6 Peru_Lima
0.4 Finland

[oszkar07]

Target: Oszkar
Distance: 0.1548% / 0.15483643
28.0 GBR.SG
26.4 FIN.SG
24.7 TSI.SG
13.1 CEU.SG
7.8 IBS.SG


Target: Oszkar
Distance: 0.2019% / 0.20194703 | R3P
38.5 GBR.SG
31.7 TSI.SG
29.8 FIN.SG


without code
Target: Oszkar
Distance: 3.0086% / 3.00858147
41.3 Finland
29.8 United_Kingdom_Scotland_Orkney
23.4 Italy_Tuscany
5.5 Spain_Castilla_la_Mancha

Target: Oszkar
Distance: 3.0655% / 3.06550110 | R3P
40.0 Finland
33.5 United_Kingdom_Scotland_Orkney
26.5 Italy_Tuscany

[Komintasavalta]

Target: Oszkar
Distance: 0.1548% / 0.15483643
28.0 GBR.SG
26.4 FIN.SG
24.7 TSI.SG
13.1 CEU.SG
7.8 IBS.SG


I think you get high Finnish ancestry because the GBR and CEU averages include samples that suffer from the calculator effect. Even some of the CEU samples have over 70% North_Atlantic, even though in the original K13 spreadsheet, the highest North_Atlantic is 53% in West_Scottish. And also there's no other reference besides FIN.SG that has high Baltic. And if you do the models without accounting for FST, then it doesn't give enough weight to the distance between the Siberian component and European components.

[alnortedelsur]

Wow, I get an Iberian/Italian result to the core xD

Target: Alnortedelsur
Distance: 8.0443% / 8.04428753
61.9 IBS.SG
31.6 TSI.SG
6.5 FIN.SG

Target: Alnortedelsur
Distance: 8.0862% / 8.08622070 | ADC: 0.25x RC
54.6 IBS.SG
40.8 TSI.SG
4.6 FIN.SG

Target: Alnortedelsur
Distance: 8.2978% / 8.29775469 | ADC: 0.5x RC
70.6 IBS.SG
29.4 TSI.SG

Target: Alnortedelsur
Distance: 8.3886% / 8.38863172 | ADC: 1x RC
63.8 IBS.SG
36.2 TSI.SG

Target: Alnortedelsur
Distance: 9.6710% / 9.67101166 | ADC: 2x RC
100.0 IBS.SG



Distance to: Alnortedelsur
10.68724941 IBS.SG:HG02239.SG
10.87344472 IBS.SG:HG01768.SG
11.14772174 IBS.SG:HG01704.SG
11.41096403 IBS.SG:HG01620.SG
11.59251483 IBS.SG:HG01618.SG
11.62121766 IBS.SG:HG01537.SG
11.77345744 IBS.SG:HG01757.SG
11.86645693 TSI.SG:NA20516.SG
11.91819198 IBS.SG:HG01509.SG
11.98523675 IBS.SG:HG01619.SG
12.00717702 TSI.SG:NA20798.SG
12.20410996 TSI.SG:NA20511.SG
12.35214151 IBS.SG:HG02233.SG
12.38000808 TSI.SG:NA20536.SG
12.55374446 IBS.SG:HG02220.SG
12.61737691 TSI.SG:NA20818.SG
12.82485088 IBS.SG:HG01527.SG
13.00514898 TSI.SG:NA20767.SG
13.04377629 IBS.SG:HG01680.SG
13.05577267 TSI.SG:NA20530.SG
13.17836105 IBS.SG:HG02219.SG
13.23893878 IBS.SG:HG01607.SG
13.27421184 TSI.SG:NA20510.SG
13.32114860 TSI.SG:NA20783.SG
13.32407971 TSI.SG:NA20761.SG
13.32511914 TSI.SG:NA20587.SG
13.35242300 IBS.SG:HG01510.SG
13.35708052 IBS.SG:HG01624.SG
13.44334780 TSI.SG:NA20526.SG
13.79303085 IBS.SG:HG01699.SG
13.83038683 TSI.SG:NA20756.SG
13.86032467 TSI.SG:NA20772.SG
13.89665427 TSI.SG:NA20514.SG
13.95232597 IBS.SG:HG01767.SG
13.96604812 TSI.SG:NA20814.SG
14.13580560 TSI.SG:NA20518.SG
14.29918179 IBS.SG:HG01710.SG
14.41068007 IBS.SG:HG02238.SG
14.63847328 TSI.SG:NA20515.SG
14.68619079 TSI.SG:NA20585.SG

[RyoHazuki]

Target: RyoHazuki(English+French)
Distance: 0.2244% / 0.22438925
32.3 CEU.SG
27.9 GBR.SG
21.6 IBS.SG
11.9 FIN.SG
6.3 TSI.SG

Target: RyoHazuki(English+French)
Distance: 1.0720% / 1.07197571 | ADC: 0.25x RC
59.8 GBR.SG
20.4 CEU.SG
19.8 IBS.SG

Target: RyoHazuki(English+French)
Distance: 1.8507% / 1.85074483 | ADC: 0.5x RC
55.1 GBR.SG
44.9 CEU.SG

Target: RyoHazuki(English+French)
Distance: 0.4170% / 0.41701796 | R2P
75.9 CEU.SG
24.1 IBS.SG

Target: RyoHazuki(English+French)
Distance: 0.2952% / 0.29517735 | R3P
60.1 CEU.SG
29.4 IBS.SG
10.5 FIN.SG

[oszkar07]

I think you get high Finnish ancestry because the GBR and CEU averages include samples that suffer from the calculator effect. Even some of the CEU samples have over 70% North_Atlantic, even though in the original K13 spreadsheet, the highest North_Atlantic is 53% in West_Scottish. And also there's no other reference besides FIN.SG that has high Baltic. And if you do the models without accounting for FST, then it doesn't give enough weight to the distance between the Siberian component and European components.

it could be because my K13 baltic is around 31 and North Atlantic around 38, and Siberian 2.29.

[Lemminkäinen]

Target: Mm
Distance: 0.1506% / 0.15058596
74.2 FIN.SG
23.6 GBR.SG
1.3 IBS.SG
0.9 TSI.SG

Target: Mm
Distance: 0.1492% / 0.14923797
29.8 FIN.SG:HG00318.SG
19.7 GBR.SG:HG00123.SG
16.8 FIN.SG:HG00379.SG
10.9 FIN.SG:HG00343.SG
7.1 FIN.SG:HG00180.SG
4.1 FIN.SG:HG00281.SG
3.6 FIN.SG:HG00349.SG
3.5 GBR.SG:HG00119.SG
1.7 FIN.SG:HG00321.SG
1.0 IBS.SG:HG01673.SG
0.8 TSI.SG:NA20540.SG
0.6 CEU.SG:NA12004.SG
0.3 IBS.SG:HG01519.SG
0.1 FIN.SG:HG00308.SG

[Nanushka]

I hope I ran it correctly

Target: buusra
Distance: 39.2084% / 39.20843342 | ADC: 0.5x RC
61.4 TSI.SG
38.6 CEU.SG

Target: buusra
Distance: 39.1261% / 39.12605567 | ADC: 0.25x RC
72.5 TSI.SG
22.5 FIN.SG
5.0 CEU.SG

[gixajo]

I think some of the samples from the UK suffer from the calculator effect, because they get over 80% North_Atlantic, and two samples get even over 90% North_Atlantic:

[G[/code]

Thanks, this is actually useful, when I did the CEU average I see it too strange when comparing with the British sources that are in vahaduo.

I was advised at AG by another member that the same effect affected many individual Spanish samples, and I did check those but not GB.

I remove those samples and I will redo the CEU average.

[gixajo]

Sorry all, I have deleted the calculator , many samples were wrong.

I explain all in the first post.

[gixajo]

Here's averages for all samples from 1000 Genomes. The samples from Spain and UK are divided into different regions, but the samples from Finland are not.

]

Forgive me especially you, for ignoring and not giving importance to what you told me.

[Grace O'Malley]

Sorry, I didn´t check those samples before posting and many of them seems to be affected by the calculator effect and are wrong.

I was so excited to have so many samples in my possession that I trusted the source they came from and didn't even bother to check them.

I've been a bit naive making such a silly mistake, so I apologize for this mistake to all of you.

Next calculator will be better!!! (or maybe worse...)

Thank you for your indulgence and understanding.:rolleyes:

I have deleted all.

No apologies needed. I think most people are grateful that people like yourself make these calculators. It's all a learning curve. I've learned a couple of new things from this thread today. So some of these samples suffered from the calculator affect. That explains some of the strange positions distance wise. I'm also a bit more interested in Komintasavalta's work so I've really enjoyed the thread. :thumb001:

[gixajo]

No apologies needed. I think most people are grateful that people like yourself make these calculators. It's all a learning curve. I've learned a couple of new things from this thread today. So some of these samples suffered from the calculator affect. That explains some of the strange positions distance wise. I'm also a bit more interested in Komintasavalta's work so I've really enjoyed the thread. :thumb001:

Komintasavalta is a great R user without a doubt,the best if not the only one in TA and AG. Although in this case, it was only necessary to have looked at the references with some attention to have realized that they were wrong. :picard1:

At least it consoles me to know that my intention was good , something that is difficult to say about others.

Regardless of the fact that such a stupid mistake can be used as an argument against my already eroded credibility, what hurts me the most is having been so naive that I didn't even check the samples a bit.

But hey, I have to take responsibility for my mistake, and rectify, the fault is mine.

I will go and whip myself with a whip, but before doing it, I will have a coffe and the smoke a cigarrette, because I have just arrived home after a family meeting in my parents house.:thumb001:

[Lemminkäinen]

Sorry all, I have deleted the calculator , many samples were wrong.

I explain all in the first post.

No problem, I checked my results and they are ok.

[gixajo]

No apologies needed. I think most people are grateful that people like yourself make these calculators. It's all a learning curve. I've learned a couple of new things from this thread today. So some of these samples suffered from the calculator affect. That explains some of the strange positions distance wise. I'm also a bit more interested in Komintasavalta's work so I've really enjoyed the thread. :thumb001:

Komintasavalta is a great R user without a doubt,the best if not the only one in TA and AG. Although in this case, it was only necessary to have looked at the references with some attention to have realized that they were wrong. :picard1:

At least it consoles me to know that my intention was good , something that is difficult to say about others.

Regardless of the fact that such a stupid mistake can be used as an argument against my already eroded credibility, what hurts me the most is having been so naive that I didn't even check the samples a bit.

But hey, I have to take responsibility for my mistake, and rectify, the fault is mine.

I will go and whip myself with a whip, but before doing it, I will have a coffe and the smoke a cigarrette, because I have just arrived home after a family meeting in my parents house.:thumb001:

[Lemminkäinen]

Komintasavalta is a great R user without a doubt,the best if not the only one in TA and AG. Although in this case, it was only necessary to have looked at the references with some attention to have realized that they were wrong. :picard1:

At least it consoles me to know that my intention was good , something that is difficult to say about others.

Regardless of the fact that such a stupid mistake can be used as an argument against my already eroded credibility, what hurts me the most is having been so naive that I didn't even check the samples a bit.

But hey, I have to take responsibility for my mistake, and rectify, the fault is mine.

I will go and whip myself with a whip, but before doing it, I will have a coffe and the smoke a cigarrette, because I have just arrived home after a family meeting in my parents house.:thumb001:

Chin up and head towards new disappointmets. This is my motto �� Dienekes used my genome in his reference and never corrected my results.

[gixajo]

No problem, I checked my results and they are ok.

With so many references results can show as quite consistents, even having 10-15% invalid samples in sources.

My results are apparently ok too, better than using Spanish modern references that are just now in vahaduo K13, but better to do things correctly.

And thanks for your words.