Running Gedmatch calculators on the command line with stevenliuyi/admix

Quote:

Originally Posted by Leto

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:

https://i.ibb.co/m6nKLG2/a.png

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.

https://4.bp.blogspot.com/-PeuXkrFay...Saag_Fig_2.png

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

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.

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

Quote:

Originally Posted by Leto

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.

Quote:

Originally Posted by 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.

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:

Code:

$ 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

Quote:

Originally Posted by Komintasavalta

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:

Code:

$ 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.

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:

Code:

$ 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:

Code:

$ 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%:

https://i.ibb.co/x8wVLJg/1.png
https://i.ibb.co/crX5FbM/a.png

Code:

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

Code:

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`.

https://i.ibb.co/wcgGkmW/h.png

Code:

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) )

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

Quote:

Originally Posted by Komintasavalta

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.

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

Quote:

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