Running Gedmatch calculators on the command line with stevenliuyi/admix

[Komintasavalta]

What do you mean by used to construct? G25 is not based on any specific dataset, you can use anything as long as you have the coordinates for that. Only Gedmatch was made up of artificial components based on modern samples (North Atlantic, Baltic, etc.).

I think the G25 datasheets consist of only projected samples, because projected samples plot differently from reference samples.

For example in the plot below, I took 8 random samples from different populations, I used half of samples from each population as references, which are indicated by a triangle, and I projected the other half of samples, which are indicated by a circle (https://anthrogenica.com/showthread....l=1#post806356). The reference samples plot further away from the center, in the same way that in ADMIXTURE, reference samples get higher percentages of their main components than projected samples:



Davidski refers to this phenomenon as "projection bias" (https://eurogenes.blogspot.com/2017/...-r1a-z645.html):

Speaking of projection bias, I'm quite certain that their Principal Component Analysis (PCA) suffers from it. The ancient samples look like they're being pulled into the middle of the plot, so much so that one of the foragers basically clusters with modern-day Lithuanians, while the CWC individuals appear too western. They need to fix this.

In SMARTPCA, you can use the `poplistname` option to do projection, and in PLINK 2, you can use `--score`:

https://compvar-workshop.readthedocs...-vs-projection
https://www.cog-genomics.org/plink/2...re#pca_project
https://groups.google.com/g/plink2-u...m/b_o3JMrxAwAJ

[Leto]

Anyway, if there is a third study out there with Tajik samples in it (other than Yunus and Jeong), please let me know. I need them for my Steppe-centric Central Asian model.

[Leto]

Sorry, that stuff is kind of hard for me to understand.

[vbnetkhio]

Does G25 have components? There are no fixed calculators on G25 unlike on Gedmatch. When I go to Vahaduo G25, I see a lot of shit made up by TA users and other folks plus David's standard model.

there are the 25 principal components (coordinates), the difference is that they don't range from 0% to 100% like the gedmatch components, for example the first one peaks in Lithuanians 0.14 and is lowest in Mbuti at -0.65.

[Komintasavalta]

Anyway, if there is a third study out there with Tajik samples in it (other than Yunus and Jeong), please let me know. I need them for my Steppe-centric Central Asian model.

There's G25 coordinates for some Tajik samples in Cardona et al. 2014, "Genome-Wide Analysis of Cold Adaptation in Indigenous Siberian Populations": https://anthrogenica.com/showthread....Sample-for-G25, https://pastebin.com/raw/MhkaSSgD.

In the stock G25, the average of the Tajik samples from Cardona is actually closer to Turkmens and Uzbeks than to Tajiks:

$ dist()(awk -F, 'NR==FNR{for(i=2;i<=NF;i++)a[i]=$i;next}$1{s=0;for(i=2;i<=NF;i++)s+=($i-a[i])^2;printf"%f %s\n",s^.5,$1}' "$2" "$1"|sort -n|awk '{printf"%."x"f %s\n",$1,$2}' "x=${3-3}"|sed s,^0,,)
$ tav()(awk '{n[$1]++;for(i=2;i<=NF;i++){a[$1][i]+=$i}}END{for(i in a){o=i;for(j=2;j<=NF;j++)o=o FS sprintf("%f",a[i][j]/n[i]);print o}}' "FS=${1-$'\t'}")
$ curl -Ls https://pastebin.com/raw/MhkaSSgD|grep Tajik|sed 's/:[^,]*//'|tav ,|dist <(curl -Ls 'https://drive.google.com/uc?export=download&id=1wZr-UOve0KUKo_Qbgeo27m-CQncZWb8y') -|head -n8
.028 Turkmen_Uzbekistan
.029 Turkmen
.061 Uzbek
.067 Tatar_Crimean_steppe
.071 Tajik
.076 Sarikoli_China
.088 Tatar_Lipka
.092 Tajik_Badakshan

[Leto]

There's G25 coordinates for some Tajik samples in Cardona et al. 2014, "Genome-Wide Analysis of Cold Adaptation in Indigenous Siberian Populations": https://anthrogenica.com/showthread....Sample-for-G25, https://pastebin.com/raw/MhkaSSgD.

In the stock G25, the average of the Tajik samples from Cardona is actually closer to Turkmens and Uzbeks than to Tajiks:

$ dist()(awk -F, 'NR==FNR{for(i=2;i<=NF;i++)a[i]=$i;next}$1{s=0;for(i=2;i<=NF;i++)s+=($i-a[i])^2;printf"%f %s\n",s^.5,$1}' "$2" "$1"|sort -n|awk '{printf"%."x"f %s\n",$1,$2}' "x=${3-3}"|sed s,^0,,)
$ tav()(awk '{n[$1]++;for(i=2;i<=NF;i++){a[$1][i]+=$i}}END{for(i in a){o=i;for(j=2;j<=NF;j++)o=o FS sprintf("%f",a[i][j]/n[i]);print o}}' "FS=${1-$'\t'}")
$ curl -Ls https://pastebin.com/raw/MhkaSSgD|grep Tajik|sed 's/:[^,]*//'|tav ,|dist <(curl -Ls 'https://drive.google.com/uc?export=download&id=1wZr-UOve0KUKo_Qbgeo27m-CQncZWb8y') -|head -n8
.028 Turkmen_Uzbekistan
.029 Turkmen
.061 Uzbek
.067 Tatar_Crimean_steppe
.071 Tajik
.076 Sarikoli_China
.088 Tatar_Lipka
.092 Tajik_Badakshan

Wow, thank you so much! They do seem to be more Mongoloid than the ones I previously had. Probably from Northern/Northwestern TJK (Sughd region). Some are well over 25% East Eurasian.

Their BA Steppe average is 30.9 percent (three samples are below 30%, the highest value is 34.9%). Don't know what they would score on Gedmatch though.

[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. There's many samples that suffer from the calculator effect, because I didn't bother removing samples that were used as references in K13.

The code below selects populations that include at least one sample whose latitude is over 50, and it adds up the percentage of the Siberian, East Asian, and American components:

$ curl 'https://drive.google.com/uc?export=download&id=15Mvba7Bw07VtixiBO_EctOhPPECGzZC9' -Lso reich.k13
$ curl https://reichdata.hms.harvard.edu/pub/datasets/amh_repo/curated_releases/V50/V50.0/SHARE/public.dir/v50.0_HO_public.anno|iconv -f macintosh -t utf-8 >ho.anno
$ igno()(grep -Ev '\.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')
$ 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 1d reich.k13|igno|sed 's/:[^,]*//'|tav ,|sort>reich.k13.ave
$ awk -F\\t '$10>=50{print$7}' ho.anno|igno|awk -F, 'NR==FNR{a[$0];next}$1 in a' - reich.k13.ave|awk -F, '{print$9+$10+$11,$1}'|sort -n|awk '{$1=sprintf("%.0f",$1)}1'
0 Czech_Baalberge
0 Czech_Bohemia_Baden_N
0 Czech_Bohemia_CordedWare_o1
0 Czech_Bohemia_Jordanow_N
0 Czech_Bohemia_Rivnac_N_oAnatolia
0 Czech_Bohemia_Rivnac_N_oWHG
0 Czech_C_Baalberge_o1
0 Czech_C_Baalberge_o2
0 Czech_EarlySlav.SG
0 Czech_Eneolithic
0 Czech_MN
0 Czech_N
0 England_IA_ERoman.SG
0 England_IA_Roman_oMiddleEast.SG
0 England_Mesolithic.SG
0 England_Mesolithic_o1
0 England_Mesolithic_o1.SG
0 England_N_published
0 England_Trumpington_N.SG
0 Faroes_EarlyModern_o2.SG
0 France_HautsDeFrance_MN.SG
0 Germany_Blatterhohle_MN_oWHG
0 Germany_CordedWare_o
0 Germany_LN_oWHG
0 Germany_MN_Esperstedt
0 Germany_MN_Salzmuende
0 Germany_N
0 Germany_Tollense_BA_o1.SG
0 Iceland_Early_Christian_o.SG
0 Ireland_EN.SG
0 Ireland_Mesolithic.SG
0 Ireland_N.SG
0 Poland_BKG.SG
0 Poland_BKG_o2.SG
0 Poland_GAC.SG
0 Poland_Globular_Amphora_published
0 Poland_Medieval_1.SG
0 Poland_Mierzanowice_GAC.SG
0 Poland_TRB_o.SG
0 Poland_Wilczyce_GAC.SG
0 Russia_EasternScythian_SouthernUrals_o.SG
0 Scotland_MBA_published
0 Scotland_Megalithic.SG
0 Scotland_N_lowEEF_all.SG
0 Scotland_N_mediumlowEEF
0 Scotland_N_published
0 Sweden_BA.SG
0 Sweden_FBC.SG
0 Sweden_Gotland_Vasterbjers_PittedWare_BattleAxe_o.SG
0 Sweden_TRB_MN
0 Wales_Mesolithic
0 Wales_Mesolithic.SG
0 Wales_N_all.SG
0 England_Mesolithic
0 Czech_C_Baalberge
0 Czech_Bohemia_Rivnac_N
0 Germany_Tollense_BA_o2.SG
0 Poland_TRB.SG
0 Germany_EN_LBK_published
0 Ireland_MN.SG
0 Poland_BKG_o1.SG
0 Denmark_MN_B.SG
0 Ireland_EN_MN.SG
0 Poland_Globular_Amphora
0 English.DG
0 England_N.SG
0 Germany_Blatterhohle_MN
0 Scotland_N
0 Poland_Koszyce_GAC.SG
0 Czech_Bohemia_CordedWare_o3
0 England_N_all.SG
0 Ireland_LN.SG
0 Scotland_N_lowEEF.SG
0 England_N
0 Czech_Bohemia_GlobularAmphorae_N
0 Germany_MN_Baalberge
0 Sweden_EarlyViking.SG
0 Scotland_N.SG
0 Germany_LBA_Halberstadt_published
0 Ukraine_Medieval.SG
0 England_Mesolithic_all.SG
0 Scotland_N_lowEEF
0 England_MBA_highEEF
0 Germany_EN_LBK
0 Sweden_Ansarve_Megalithic.SG
1 English
1 Poland_Sandomierz_GAC.SG
1 Faroes_EarlyModern_o1.SG
1 Sweden_LNBA
1 Czech.DG
1 Wales_MBA_published
1 England_BellBeaker_highWHG_published
1 Russia_MLBA_Sintashta_published
1 Polish.DG
1 Wales_N
1 Czech_Bohemia_BellBeaker_oAnatolia1
1 Sweden_IA_2.SG
1 Czech_Bohemia_FunnelBeaker_N
1 Ireland_Megalithic.SG
1 Norway_IA.SG
1 Czech_MN.SG
1 Denmark_Viking_o1.SG
1 Czech_Bohemia_CordedWare_o2
1 Czech_EBA_Starounetice
1 England_EarlyMedieval_Saxon.SG
1 Lithuanian
1 England_BellBeaker_mediumEEF
1 Wales_N.SG
1 Germany_LN_Alberstedt
1 French
1 Orcadian.SDG
1 Kazakhstan_Chanchar_MBA_published
1 England_MBA_lowEEF
1 Germany_BellBeaker_published
1 Orcadian
1 Denmark_Viking_o2.SG
1 Poland_Ksiaznice_GAC.SG
1 Orcadian.DG
1 Ireland_Viking.SG
1 Icelandic.DG
1 Latvia_BA
1 Icelandic
1 Scotland_Viking.SG
1 Russia_IA_Ingria.SG
1 Poland_CWC_1.SG
1 Denmark_Viking.SG
1 Norway_Viking_o2.SG
1 Poland_Medieval_2.SG
1 Czech
1 England_IA.SG
1 Poland_ChopiceVeseleCulture
1 Lithuania_EMN_Narva
1 Iceland_Pre_Christian.SG
1 Sweden_BattleAxe.SG
1 Poland_BellBeaker_published
2 Faroes_EarlyModern.SG
2 England_IA_o.SG
2 Norwegian
2 Greenland_EarlyNorse.SG
2 Iceland_Early_Christian.SG
2 Russia_MLBA_Sintashta.SG
2 Russia_Ivanovo_Fatyanovo_BA.SG
2 Scotland_C_EBA_mediumhighEEF
2 Sweden_Gotland_Vasterbjers_PittedWare_BattleAxe_o_minus.SG
2 Scotland_C_EBA_mediumhighEEF_published
2 Germany_Tollense_BA.SG
2 Russia_MBA_Poltavka_oEEF
2 Russia_Moscow_Fatyanovo_BA.SG
2 Ukraine_Viking_o.SG
2 Germany_EBA_Unetice_published
2 England_IA_Roman.SG
2 Sweden_Viking_o2.SG
2 Scotland_Viking_o.SG
2 Russia_Tver_Fatyanovo_BA.SG
2 Estonia_EarlyViking.SG
2 Scotland_LBA
2 Norway_Medieval.SG
2 Sweden_Late_N.SG
2 Czech_Bohemia_BellBeaker
2 Latvia_MN_o1.SG
2 Netherlands_BellBeaker
2 Denmark_EarlyViking.SG
2 Denmark_IA.SG
2 Russia_Yaroslavl_Fatyanovo_BA.SG
2 Lithuania_LN_o
2 Sweden_LN.SG
2 Germany_EBA_Unetice
2 Sweden_IA.SG
2 Czech_BellBeaker
2 Czech_Bohemia_Unetice_EBA
2 Estonia_CordedWare
2 England_MBA
2 Kazakhstan_Maitan_MLBA_Alakul
2 England_C_EBA
2 Belarusian
2 Sweden_Viking.SG
2 Ireland_EBA.SG
2 England_Viking_o.SG
2 Wales_C_EBA
2 Jew_Ashkenazi
2 Estonia_IdaViru_CordedWare_Neolithic.SG
2 Scotland_MBA
2 Ukrainian
2 England_BellBeaker_highEEF
2 Czech_EBA
2 Ukrainian_North
2 Greenland_EarlyNorse_o1.SG
2 Estonia_BA.SG
2 Scottish
2 Scotland_Mesolithic_all.SG
2 Estonian.DG
2 England_LBA
2 Czech_Bohemia_BellBeaker_oAnatolia2
2 Germany_BellBeaker
2 Latvia_HG.SG
2 Lithuania_BA
2 Netherlands_BA
2 Germany_BenzigerodeHeimburg_LN
2 Sweden_PWC.SG
2 Russia_Viking.SG
2 Germany_Mesolithic
2 Poland_Southeast_BellBeaker.SG
2 England_Viking.SG
2 England_BellBeaker
2 Estonia_CordedWare.SG
3 Estonia_CordedWare.SG_o1
3 Faroes_Viking.SG
3 Poland_Viking.SG
3 Sweden_IA_1.SG
3 Sweden_Motala_HG.SG
3 Scotland_C_EBA
3 England_C_EBA_lowEEF
3 Lithuania_Mesolithic
3 Russia_MLBA_Sintashta
3 Czech_BA_Veterov_1
3 England_C_EBA_highEEF
3 Finland_Levanluhta_B
3 Lithuania_EMN_Narva_o
3 Denmark_LN_BA.SG
3 Kazakhstan_MLBA_Alakul_Lisakovskiy
3 Latvia_HG
3 Czech_IA_Hallstatt.SG
3 Estonia_CordedWare.SG_o2
3 Poland_BellBeaker
3 Sweden_Gotland_Hemmor_PittedWare_BattleAxe_minus.SG
3 Poland_EBA
3 Iceland_Viking.SG
3 Czech_Bohemia_CordedWare
3 Germany_LN_Karsdorf
3 Denmark_Djursland_SingleGraveCulture.SG
3 Greenland_LateNorse.SG
3 Sweden_TRB_MN.SG
3 Sweden_Gotland_Ajvide_PittedWare_BattleAxe.SG
3 Poland_EBA.SG
3 Poland_EBA_Unetice.SG
3 Estonia_IA.SG
3 Sweden_Gotland_Vasterbjers_PittedWare_BattleAxe.SG
3 Russia_Andronovo.SG
3 England_LBA_lowEEF
3 Sweden_BAC.SG
3 Russia_SaltovoMayaki.SG
3 England_BellBeaker_lowEEF
3 Germany_CordedWare.SG
3 Russia_Srubnaya
3 Estonian
3 Norway_Medieval_o.SG
3 Sweden_Gotland_Hemmor_PittedWare_BattleAxe.SG
3 Norway_Viking.SG
4 Sweden_Gotland_Vasterbjers_PittedWare_BattleAxe_minus.SG
4 Denmark_BA.SG
4 Sweden_Viking_o1.SG
4 Latvia_MN
4 Poland_CWC_3.SG
4 Germany_CordedWare
4 Ukraine_IA_WesternScythian_o1.SG
4 Scotland_BellBeaker
4 Czech_CordedWare
4 IsleOfMan_Viking.SG
4 Russia_Srubnaya_Alakul.SG
4 Germany_CordedWare_published_o1
4 Russia_Viking_o.SG
4 Estonia_CWC.SG
4 Poland_Southeast_CordedWare.SG
4 Sweden_Mesolithic.SG
4 Kazakhstan_LBA_Guruldek_published
4 Russia_Potapovka_o2
4 Estonia_EMN_Narva
4 Russia_Sunghir_Medieval.SG
5 Sweden_Motala_HG
5 Kazakhstan_Georgievsky_MBA_published
5 Sweden_Mesolithic_o.SG
5 Lithuania_LN
5 Sweden_HG.SG
5 Poland_CWC.SG
5 Russian
5 Kazakhstan_Andronovo.SG
5 Russia_Afanasievo
5 Russia_MBA_Poltavka
5 Russia_Samara_EBA_Yamnaya
5 Russia_Petrovka
5 Czech_Bohemia_Jordanow_Michelsberg_N
5 Ukraine_Viking.SG
5 Russia_Samara_EBA_Yamnaya_published
5 England_C_EBA_published
5 Latvia_LN_CordedWare.SG
6 Russia_MLBA_Krasnoyarsk
6 Estonia_Medieval.SG
6 Russia_Afanasievo.SG
6 Czech_Bohemia_BellBeaker_oSteppe
6 Russia_MBA_Poltavka_published
6 Russia_MLBA_Sintashta_o2
6 Finnish.DG
6 Russia_Samara_EBA_Yamnaya_published2
6 Latvia_LN_CordedWare
6 Kazakhstan_MLBA_Sintashta_o.SG
6 Russia_MBA_Poltavka_o2
6 Poland_CordedWare_ProtoUnetice.SG
7 Belgium_UP_Magdalenian_udg
7 Kazakhstan_Maitan_MLBA_Alakul_o1
7 Kazakhstan_Shoendykol_MLBA_Fedorovo
7 Norway_N_HG.SG
7 Denmark_LN.SG
7 Kazakhstan_Mereke_MBA_o2
7 Sweden_PWC_o.SG
7 Russia_MLBA_Potapovka
7 FIN_o
7 Estonia_BA_o.SG
8 Kazakhstan_Zevakinskiy_BA
8 Russian.DG
8 Lithuania_Late_Antiquity.SG
8 Latvia_MN_o2
8 Russia_IA_EarlySarmatian
8 Kazakhstan_CentralSaka_o2.SG
8 Mordovian
8 Finnish
8 Norway_Mesolithic.SG
8 Russian.SDG
8 Russia_Yaroslavl_VolosovoLyalovo_N.SG
9 Kazakhstan_Sarmatian.SG
9 Russia_Popovo_HG
9 Karelian
9 Estonia_N_CombCeramic.SG
9 Russia_Kostenki14.SG
9 Belgium_UP_GoyetQ116_1_published
9 Denmark_LBA.SG
9 Russian_Archangelsk_Krasnoborsky
10 Russia_Sunghir3.SG
10 Russia_Sunghir4.SG
10 Belgium_UP_Magdalenian
10 Russia_Kostenki14
10 Belgium_UP_GoyetQ116_1_published_all
10 Norway_LN_BA.SG
10 Estonia_MN_CCC_1
10 Lithuania_LBA.SG
11 Netherlands_BellBeaker_published
11 Russia_MiddleSarmatian_SouthernUrals.SG
11 Russia_Sunghir2.SG
11 Veps
11 Kazakhstan_Sarmatian_IA
11 Russia_Potapovka
11 Estonia_MN_CCC_2
11 Ukraine_IA_WesternScythian.SG
11 Russia_Khvalynsk_Eneolithic
11 Russian_Archangelsk_Pinezhsky
12 Russia_Sunghir1.SG
12 Russia_HG_Karelia
12 Kazakhstan_Birlik_EIA.SG
12 Russia_LateSarmatian.SG
12 Russia_EarlySarmatian.SG
12 Russia_Arkhangelsk_Veretye_Mesolithic.SG
13 Kazakhstan_IA_Chanchar_published
13 Kazakhstan_LIA_Georgievsky_published
13 Russia_EarlySarmatian_SouthernUrals.SG
13 Russia_IA_Scythian_questionable
13 Russia_HG_Samara
13 Russia_Srubnaya_o1
14 Latvia_MN_o3
14 Russia_Vologda_Veretye_Mesolithic.SG
14 Russia_Sidelkino_HG.SG
14 Latvia_MN_Comb_Ware.SG
14 Russia_AfontovaGora2.SG
14 Russia_Kostenki12
15 Kazakhstan_Nomad_IA_o.SG
15 Scotland_C_EBA_published
15 Russian_Archangelsk_Leshukonsky
15 Kazakhstan_Maitan_MLBA_Alakul_o2
15 Russia_MLBA_Sintashta_o1
16 Russia_Potapovka_o1
16 Russia_EHG
16 Kazakhstan_Zevakinskiy_LBA_o
16 Russia_Mezhovskaya.SG
16 Tatar_Mishar
18 Russia_HG_Karelia.SG
18 Belgium_UP_GoyetQ376-19_published
18 Russia_LBA_Priobrazhenka
18 Kazakhstan_Mereke_MBA
19 Kazakhstan_MLBA_Zevakinskiy
19 Russia_Ust_Ishim.DG
19 Russia_Ust_Ishim_HG_published.DG
19 Russia_Andronovo_o.SG
20 Russia_Tagar.SG
20 Russia_MLBA_Sintashta_o3
20 Russia_Yana_UP.SG
21 Tatar_Kazan
21 Russia_MA1_HG.SG
21 Kazakhstan_Zevakinskiy_LBA
21 Russia_LBA_1.SG
23 Chuvash
23 Besermyan
24 Russia_Karasuk_oRISE.SG
25 Saami.WGA
25 Saami.DG
25 Russia_Chalmny_Varre
26 Russia_AfontovaGora3
26 Udmurt
27 Kazakstan_Sargat_IA
28 Russia_Gorokhov_IA_2
29 Russia_HG_Sosnoviy
30 Kazakhstan_Nomad_IA.SG
31 Finland_Levanluhta
32 Russia_Sargat_IA
33 Bashkir
33 Finland_Saami_IA.SG
34 Russia_HG_Tyumen
34 Russia_MLBA_Krasnoyarsk_o
34 Aleut_o1
34 Aleut_o1.DG
35 Kazakhstan_Botai_Eneolithic.SG
35 Norway_Viking_o1.SG
35 Russia_EasternScythian_SouthernUrals.SG
35 Kazakhstan_Botai_Eneolithic
36 Russia_Gorokhov_IA_3
37 Aleut
39 Russia_Tuva_IA_AldyBel
39 Russia_IA_3.SG
41 Kazakhstan_Tasmola_EIA
41 Kazakhstan_Central_Saka.SG
43 Russia_Siberia_Lena_EBA_o
43 Russia_KusnarenkovoKarajakupovo_Medieval.SG
46 Tatar_Siberian
47 Russia_BA_Okunevo.SG
47 Yukagir_Forest
47 Russia_Bolshoy
49 Kazakhstan_Central_Steppe_EMBA.SG
49 Tatar_Siberian_Zabolotniye
49 Kazakhstan_Kimak.SG
51 Mansi
51 Mansi.DG
52 Chukchi.DG
52 USA_AK_Prehistoric.SG
52 Aleut_o
52 Aleut_o.DG
55 Altaian_Chelkan
55 Kazakhstan_ZevakinoChilikta_IA_2.SG
55 Russia_Gorokhov_IA_1
56 Tlingit
58 Khakass_outlier
59 Tubalar
60 Canada_MDorset.SG
60 Kazakhstan_Kipchak2.SG
60 Shor_Khakassia
60 Russia_IA_2.SG
60 Shor_Mountain
61 Aleut.DG
61 Tubalar.DG
61 Kazakh
64 Selkup
64 Cree1.DG
66 Khakass
66 Ket
66 Even
67 Russia_Siberia_Tenisei_EBA
68 Russia_LBA_2.SG
68 Russia_LenaRiver_LUP.SG
72 Cree2.DG
72 Khakass_Kachin
72 Altaian
74 Altaian.DG
75 Kazakhstan_Hun_Elite_LIA
75 Enets
76 Russia_AngaraRiver_Medieval.SG
76 USA_Alaska_TrailCreek_9000BP.SG
80 Mongolia_LBA_CenterWest_4
80 Russia_UstIda_LN.SG
82 Russia_Kurma_EBA_o.SG
82 Russia_UstBelaya_Angara_Medieval
82 Tuvinian
82 Russia_Karasuk_o1.SG
83 Even_o.DG
83 Even_o
83 USA_Ancient_Beringian.SG
83 Evenk_FarEast
83 Russia_Kolyma_M.SG
84 Russia_UstBelaya_Angara_published
84 Tofalar
84 Kazakhstan_Birlik_Tasmola_EIA
84 Russia_UstBelaya_Angara_o_published
84 Russia_LenaRiver_N.SG
85 Russia_AngaraRiver_N.SG
85 Russia_UstIda_EBA.SG
85 Russia_UstBelaya_MED.SG
85 Russia_LakeBaikal_N.SG
85 Canada_LateDorset.SG
86 Todzin
86 Russia_UstBelaya_Angara
86 Russia_AngaraRiver_BA.SG
86 Buryat
86 Russia_Shamanka_EBA.SG
86 Russia_Kurma_EBA.SG
87 Russia_Siberia_Lena_EBA
87 Dolgan
87 Russia_Siberia_UP
87 Russia_Buryatia_M.SG
87 Kazakhstan_Nomad_HP.SG
87 Russia_UstBelaya_Angara.SG
87 Russia_LenaRiver_BA.SG
88 Russia_Buryatia_EIA
88 Khamnegan
88 Russia_Yana_Medieval.SG
89 Russia_LenaRiver_MiddleN.SG
89 Greenland_Saqqaq.SG
89 USA_AK_PaleoAleut_published
89 Mongolia_Khuvsgul_LateMedieval
89 Russia_LakeBaikal_BA.SG
89 Kazakhstan_Korgantas_IA
89 Russia_UstBelaya_EBA.SG
90 Yakut
90 Yakut.SDG
90 Russia_Uelen_IA.SG
90 Kazakhstan_Nomad_Hun_Sarmatian.SG
90 Russia_Siberia_Lena_EN
91 Russia_LenaRiver_EN.SG
91 Russia_Siberia_Angara_EN
91 Russia_Buryatia_Xiongnu
91 Russia_KuengaRiver_N_1.SG
91 Mongolia_EIA_3
91 Canada_MDorset_published
92 Yakut.DG
92 Itelmen.DG
92 Russia_Siberia_Irkutsk_EBA
92 Russia_Ekven_IA.SG
92 Itelmen
93 Canada_6500BP.SG
93 Russia_Uelen_OldBeringSea
93 Russia_Ekven_OldBeringSea.SG
93 Russia_Buryatia_PreBronze
93 Koryak
93 Chukchi1
93 Canada_BigBar_5700BP.SG
93 Russia_Uelen_OldBeringSea_published
93 USA_AK_PaleoAleut.SG
94 Russia_Ekven_OldBeringSea
94 Chipewyan.DG
94 USA_AK_Ancient_Athabaskan_1100BP.DG
94 Eskimo_Naukan.DG
94 USA_AK_Ancient_Athabaskan_1100BP.SG
94 USA_AK_NeoAleut
94 Eskimo_Naukan
94 USA_AK_PaleoAleut
94 Mongolia_EIA_SlabGrave_1
94 Chukchi
94 Canada_Thule.SG
94 Eskimo_ChaplinSireniki
94 Russia_Lokomotiv_Eneolithic.SG
95 Evenk_Transbaikal
95 USA_AK_Ancient_Athabaskan_1100BP
95 Yukagir_Tundra
95 Russia_Shamanka_Eneolithic.SG
95 Russia_CentralYakutia_LN.SG
95 Russia_AginBuryat_N.SG
95 Russia_AngaraRiver_EN.SG
95 USA_AK_NeoAleut_published
95 Eskimo_Sireniki.DG
96 USA_AK_Athabskan.SG
96 Russia_UstBelaya_Angara_o.SG
96 Eskimo_Chaplin.DG
96 Nganasan
96 Even.DG
96 Eskimo_Sireniki
97 Russia_KolymaRiver_LN.SG
97 Russia_Krasnoyarsk_BA.SG
97 Russia_Chita_BA.SG
98 Russia_KuengaRiver_N_2.SG
98 Oroqen
98 Russia_KadalinkaRiver_N.SG
98 Oroqen.SDG
99 Russia_CentralYakutia_IA.SG
99 Russia_ArgunRiver_M.SG
99 Nivh
99 Ulchi.DG
99 Oroqen.DG
99 Ulchi
100 Negidal

This finds populations that are closest to the Mari average when multiplied by MDS of FST:

$ 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 't=read.csv("reich.k13.ave",h=F,r=1);fst=as.matrix(as.dist(read.csv("k13fst",h=F)));fst=fst/mean(fst);t2=as.matrix(t)%*%cmdscale(fst,ncol(fst)-1);write.table(round(t2,6),"reich.k13.ave.mds",sep=",",quote=F,col.names=F)'
$ grep Mari.SG reich.k13.ave.mds|awk -F, 'NR==FNR{for(i=2;i<=NF;i++)a[i]=$i;next}$1{s=0;for(i=2;i<=NF;i++)s+=($i-a[i])^2;print s^.5,$1}' - reich.k13.ave.mds|sort -n|awk '{$1=sprintf("%.2f",$1)}1'|head -n16
0.00 Mari.SG
2.70 Udmurt
3.64 Bashkir.SG
4.05 Kazakstan_Sargat_IA
4.70 Finland_Levanluhta
5.28 Bashkir
5.35 Russia_Sargat_IA
5.65 Russia_Chalmny_Varre
5.88 Besermyan
6.03 Russia_Karasuk_oRISE.SG
6.41 Kazakhstan_Tasmola_Saka_IA
6.73 Kyrgyzstan_AlaiNura_IA
7.01 Saami.DG
7.23 Saami.WGA
7.25 Kyrgyzstan_Saka_IA
7.40 Kyrgyzstan_TianShan_Saka_o1.SG

Here's PCAs of samples where the combined percentage of the last four components is less than 20%:

awk -F\\t '$5==0{print$7}' ho.anno|igno|grep -Fv .|grep -v _o|awk -F: 'NR==FNR{a[$0];next}$1 in a' - reich.k13 >modern
awk -F\\t '$5>0{print$7}' ho.anno|igno|grep -v _o|awk -F: 'NR==FNR{a[$0];next}$1 in a' - reich.k13 >ancient

library(tidyverse)
library(ggforce)

t=read.csv("modern",header=F,row.names=1)

t=t[rowSums(t[,10:13])<=20,]

fst=as.matrix(as.dist(read.csv("k13fst",header=F)))
t2=as.matrix(t)%*%cmdscale(fst,ncol(fst)-1)

p0=prcomp(t2)
pct=paste0(colnames(p0$x)," (",sprintf("%.1f",100*p0$sdev/sum(p0$sdev)),"%)")
p=as.data.frame(p0$x)

p[,1]=-p[,1]
p=p/sd(p[,1])

pop=sub(":.*","",rownames(t))
pop=sub("\\.(SG|DG|SDG|WGA)","",pop)

set.seed(1)
color=as.factor(sample(seq(1,length(unique(pop)))))
col=rbind(c(60,80),c(25,95),c(100,70),c(30,70),c(70,50),c(60,100),c(20,50),c(15,40))
hues=max(ceiling(length(color)/nrow(col)),7)
pal1=as.vector(apply(col,1,function(x)hcl(seq(15,375,length=hues+1)[1:hues],x[1],x[2])))
pal2=as.vector(apply(col,1,function(x)hcl(seq(15,375,length=hues+1)[1:hues],ifelse(x[2]>50,.8*x[1],.2*x[1]),ifelse(x[2]>50,.3*x[2],100))))

i=1
xpc=sym(paste0("PC",i))
ypc=sym(paste0("PC",i+1))

p[,i]=p[,i]*diff(range(p[,i+1]))/diff(range(p[,i]))

centers=data.frame(aggregate(p,list(pop),mean),row.names=1)

ranges=apply(p,2,function(x)abs(max(x)-min(x)))
maxrange=max(ranges[c(i,i+1)])

ggplot(p,aes(!!xpc,!!ypc,group=0))+
ggforce::geom_voronoi_tile(aes(x=!!xpc,y=!!ypc,fill=color[as.factor(!!pop)],color=color[as.factor(!!pop)]),size=.07,max.radius=maxrange/35)+
geom_label(data=centers,aes(x=!!xpc,y=!!ypc,label=rownames(centers)),color=pal2[color],fill=pal1[color],alpha=.7,size=2,label.r=unit(.1,"lines"),label.padding=unit(.1,"lines"),label.size=.1)+
labs(x=pct[i],y=pct[i+1])+
coord_fixed()+
scale_x_continuous(expand=expansion(.03))+
scale_y_continuous(expand=expansion(.03))+
scale_fill_manual(values=pal1)+
scale_color_manual(values=pal2)+
theme(
  axis.text=element_blank(),
  axis.ticks=element_blank(),
  axis.ticks.length=unit(0,"pt"),
  axis.title=element_text(color="black",size=8),
  legend.position="none",
  panel.background=element_rect(fill="white"),
  panel.border=element_rect(color="gray90",fill=NA,size=.4),
  panel.grid=element_blank(),
  plot.background=element_rect(fill="white",color=NA)
)

ggsave(paste0(i,".png"),width=8,height=8)

Finally below is a heatmap of modern populations with no suffix like .SG or .DG. There are many samples that suffer from the calculator effect, so for example the Scottish average gets 85% North_Atlantic. In order to rearrange the branches of the clustering tree, I used MDS on the FST matrix of K13 in order to plot the 13 components of K13 in 12-dimensional space, and I then plotted the populations in 12-dimensional space by multiplying their component percentages with the matrix produced by MDS, and I used the value of the first dimension as a weight for the function `reorder.hclust`.

library(pheatmap)
library(vegan) # for reorder.hclust
library(colorspace) # for hex

t=read.csv("modern",row.names=1,header=F)
colnames(t)=c("North_Atlantic","Baltic","West_Med","West_Asian","East_Med","Red_Sea","South_Asian","East_Asian","Siberian","Amerindian","Oceanian","Northeast_African","Sub-Saharan")

ave=data.frame(aggregate(t,list(sub(":.*","",rownames(t))),mean),row.names=1)
ave=ave[rownames(ave)%in%readLines("pop"),]

fst=as.matrix(as.dist(read.csv("k13fst",header=F,check.names=F)))
ave2=as.matrix(ave)%*%cmdscale(fst,ncol(fst)-1)
hc=hclust(dist(ave2))
hc=reorder(hc,prcomp(ave2)$x[,1])

pheatmap::pheatmap(
  ave,
  filename="1.png",
  cluster_cols=F,
  clustering_callback=function(...)hc,
  legend=F,
  cellwidth=16,
  cellheight=16,
  treeheight_row=150,
  treeheight_col=80,
  fontsize=8,
  border_color=NA,
  display_numbers=T,
  number_format="%.0f",
  fontsize_number=7,
  number_color="black",
  colorRampPalette(hex(HSV(c(210,210,130,60,40,20,0),c(0,rep(.5,6)),1)))(256)
)

[vbnetkhio]

I'm looking for a study on modern DNA, whose G25 results were also published. It included a few hundred modern samples, including a dozen Slovenians. Does anybody know what it is?

I thought it was this one, but I can't find those Slovenians in the list anymore?
https://anthrogenica.com/showthread....Sample-for-G25

[JamesBond007]

AFAIK, DIYDodecad only has Linux and Windows binaries, and I didn't want to install a VM, so I wasn't able to use it before. But I now found this Python-based alternative to it: https://github.com/stevenliuyi/admix.

So, you would not run binaries in your main system outside a VM but you will run open source pip python packages, blindly trusting them, without code auditing it for security when it is not even an official debian etc... package etc ?

Are you trusting open source blindly? Then you're in for a world of hurt!

Published on 2021-02-10.

So, you normally do pip install foo, or composer install foo, or npm install foo, or perhaps go get foo, and you never read the source code of the package you just pulled down? Well guess what, that's one (almost) sure way to blow up your project!

Pulling down open source code as a dependency without ever reading the code and verifying that it doesn't contain any backdoors or other malicious content has become one of the easiest ways to introduce malicious content into a code base.

All you have to do is this:

Fix some code and create a pull request.
Fix some more code, perhaps add a new feature, and create more pull requests.
Upstream "rewards" you with commit access.
Keep a low profile for a while longer.
Make a few mistake to check how fast "mistakes" are discovered.
Create some malicious code disguised as a bug, an honest programming mistake.
Repeat.

Of course you cannot validate every single line of code in every open source projects you might use, but I cannot fathom how just about everyone today are completely and blindly trusting every package out there. This is a madness and level of ignorance and naivety in the software industry not previously seen.

...

https://www.unixsheikh.com/articles/...d-of-hurt.html

Don't get me wrong the code could be fine but I don't care enough to code audit it when I can just use GEDmatch, vahaduo , DNAgenics, Genoplot instead but your reasoning sounds retarded.

[Leto]

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

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

Siberian genetic diversity reveals complex origins of the Samoyedic-speaking populations
Tatiana M. Karafet, Ludmila P. Osipova, Olga V. Savina, Brian Hallmark, Michael F. Hammer