G25 The Moriopoulos G25 Collection - 2026 Edition

[Tomenable]

The updated collection is here thanks to hard work by Michalis Moriopoulos!:

https://genarchivist.net/showthread.php?tid=2687&pid=80167#pid80167

For the 250th anniversary of the USA there are many new American averages!

 

[Power]

i have the feeling that i am Kurdish

Distance to: fuckeeeeer_scaled

0.02273552 Kurdish_Kurmanji_Turkey_Mardin_(n=21)
0.02314864 Kurdish_Sorani_Iraq_Sulaymaniyah_(n=13)
0.02364977 Lak_Turkey_(n=3)
0.02376094 Kurdish_Kurmanji_Syria_Afrin_(n=16)
0.02379049 Yazidi_Iraq_(n=4)
0.02400040 Kurdish_Kurmanji_Turkey_(n=21)
0.02403762 Kurdish_Zaza_Sanliurfa_(n=3)
0.02410778 Bukharian_Jew_(n=31)
0.02421137 Kurdish_Kurmanji_Syria_Qamishli_(n=9)
0.02480274 Kurdish_Kurmanji_Turkey_Elazig_(n=6)
0.02480637 Kurdish_Kurmanji_Turkey_Malatya_(n=24)
0.02502419 Kurdish_Kurmanji_Turkey_Siirt_(n=2)
0.02504851 Kurdish_Zaza_Dersim_(n=21)
0.02513854 Kurdish_Zaza_Erzincan_(n=9)
0.02534665 Kurdish_Iraq_(n=7)
0.02544535 Kurdish_Kurmanji_Syria_Al-Hasakah_(n=6)
0.02550646 Kurdish_Kurmanji_Turkey_Sanliurfa_(n=8)
0.02552520 Kurdish_Kurmanji_Turkey_Konya_(n=29)
0.02553991 Kurdish_Kurmanji_Turkey_Kahramanmaras_(n=23)
0.02563955 Kurdish_Zaza_(n=10)
0.02568303 Kurdish_Zaza_Mardin_(n=2)
0.02628119 Kurdish_Sorani_Iran_West_Azerbaijan_(n=8)
0.02633558 Kurdish_Kurmanji_Turkey_Diyarbakir_(n=9)
0.02636033 Kurdish_Sorani_Iraq_Erbil_(n=6)
0.02659172 Kurdish_Zaza_Diyarbakir_(n=8)

 

[Duarte]

The updated collection is here thanks to the hard work of Michalis Moriopoulos!

https://genarchivist.net/showthread.php?tid=2687&pid=80167#pid80167

To celebrate the 250th anniversary of the USA, there are many new American media!

When comparing my actual (non-simulated) coordinates with all target coordinates from the "Moderns Only Averages (No Sims)" file, good distances were obtained only in the 2-way admixture mode. In the 1-way mode, distances ranged from moderate to poor, with poor results for all groups classified as White Brazilians (Luso-Brazilians [n=4], Euro-Brazilians [n=18], and Brazilian_Branco [n=209]). I think that these combinations with Euro ≥ 95% and SSA ≤ 5% stood out favorably:
0.02606 Duarte_scaled = 95% Spanish_Valencia_Alicante_(Valencian)_(n=8) + 5% Ngumba_(n=2)
0.02639 Duarte_scaled = 95% Spanish_Asturias_(Asturian)_(n=4) + 5% Ngumba_(n=2)
0.02663 Duarte_scaled = 96% Portuguese_(n=54) + 4% Ngumba_(n=2)
0.02747 Duarte_scaled = 95% Spanish_Murcia_(Murcian)_(n=8) + 5% Ngumba_(n=2)
0.02749 Duarte_scaled = 95% Luso-Brazilian_(n=4) + 5% Ngumba_(n=2)
0.02779 Duarte_scaled = 95% Spanish_Galicia_(Galician)_(n=19) + 5% Ngumba_(n=2)
0.02788 Duarte_scaled = 96% Spanish_Extremadura_(Extremaduran)_(n=7) + 4% Ngumba_(n=2)
0.02793 Duarte_scaled = 98% Spanish_Canarias_La_Palma_(Canarian)_(n=34) + 2% Ngumba_(n=2)
0.02825 Duarte_scaled = 95% Spanish_Andalusia_(Andalusian)_(n=33) + 5% Ngumba_(n=2)
0.02859 Duarte_scaled = 95% Spanish_Castile_and_Leon_(Castilian)_(n=7) + 5% Ngumba_(n=2)
0.02886 Duarte_scaled = 98% Cuban_Blanco_(n=11) + 2% Ngumba_(n=2)
0.02970 Duarte_scaled = 95% Spanish_Castilla-La_Mancha_(Castilian)_(n=8) + 5% Ngumba_(n=2)
Still beliving that I am a White Brazilian (at least in the phenotype) althoug having a bad fit with all groups classified as "pure" White Brazilians in these archives of Mariopoulos.

DISTANCE	GOOD	MODERATE	BAD
COLOR	XXXXXXXXXXXXXXXXXXXXXXXX	XXXXXXXXXXXXXXXXXXXXXXXX	XXXXXXXXXXXXXXXXXXXXXXXX

0.02143 Duarte_scaled = 94% Spanish_Catalonia_(Catalan)_(n=9) + 6% Ngumba_(n=2)
0.02368 Duarte_scaled = 94% Spanish_Valencia_Castellon_(Valencian)_(n=7) + 6% Ngumba_(n=2)
0.02402 Duarte_scaled = 94% Spanish_Catalonia_Girona_(Catalan)_(n=11) + 6% Ngumba_(n=2)
0.02472 Duarte_scaled = 94% Spanish_Valencia_(Valencian)_(n=17) + 6% Ngumba_(n=2)
0.02566 Duarte_scaled = 94% Spanish_Catalonia_Peri-Barcelona_(Catalan)_(n=10) + 6% Ngumba_(n=2)
0.02571 Duarte_scaled = 94% Spanish_Cantabria_(Cantabrian)_(n=15) + 6% Ngumba_(n=2)
0.02594 Duarte_scaled = 93% Spanish_Catalonia_Lleida_(Catalan)_(n=9) + 7% Ngumba_(n=2)
0.02606 Duarte_scaled = 95% Spanish_Valencia_Alicante_(Valencian)_(n=8) + 5% Ngumba_(n=2)
0.02639 Duarte_scaled = 95% Spanish_Asturias_(Asturian)_(n=4) + 5% Ngumba_(n=2)
0.02645 Duarte_scaled = 93% Spanish_Catalonia_Central_(Catalan)_(n=9) + 7% Ngumba_(n=2)
0.02660 Duarte_scaled = 94% Spanish_Aragon_(Aragonese)_(n=5) + 6% Ngumba_(n=2)
0.02663 Duarte_scaled = 96% Portuguese_(n=54) + 4% Ngumba_(n=2)
0.02679 Duarte_scaled = 94% Spanish_Catalonia_Girona_(Catalan)_(low_res)_(n=139) + 6% Ngumba_(n=2)
0.02698 Duarte_scaled = 94% Spanish_Catalonia_Penedes_(Catalan)_(n=11) + 6% Ngumba_(n=2)
0.02700 Duarte_scaled = 93% Spanish_Catalonia_Alt_Pirineu_(Catalan)_(n=6) + 7% Ngumba_(n=2)
0.02723 Duarte_scaled = 93% Spanish_Castile_and_Leon_Soria_(Castilian)_(n=2) + 7% Ngumba_(n=2)
0.02745 Duarte_scaled = 93% Spanish_La_Rioja_(Riojan)_(n=11) + 7% Ngumba_(n=2)
0.02746 Duarte_scaled = 93% Spanish_Catalonia_Central_(Catalan)_(low_res)_(n=67) + 7% Ngumba_(n=2)
0.02747 Duarte_scaled = 95% Spanish_Murcia_(Murcian)_(n=8) + 5% Ngumba_(n=2)
0.02749 Duarte_scaled = 95% Luso-Brazilian_(n=4) + 5% Ngumba_(n=2)
0.02760 Duarte_scaled = 93% Spanish_Catalonia_Alt_Pirineu_(Catalan)_(low_res)_(n=165) + 7% Ngumba_(n=2)
0.02768 Duarte_scaled = 94% Spanish_Baleares_Mallorca_(Catalan)_(n=9) + 6% Ngumba_(n=2)
0.02779 Duarte_scaled = 95% Spanish_Galicia_(Galician)_(n=19) + 5% Ngumba_(n=2)
0.02788 Duarte_scaled = 96% Spanish_Extremadura_(Extremaduran)_(n=7) + 4% Ngumba_(n=2)
0.02792 Duarte_scaled = 94% Spanish_Baleares_(Catalan)_(n=5) + 6% Ngumba_(n=2)
0.02793 Duarte_scaled = 98% Spanish_Canarias_La_Palma_(Canarian)_(n=34) + 2% Ngumba_(n=2)
0.02825 Duarte_scaled = 95% Spanish_Andalusia_(Andalusian)_(n=33) + 5% Ngumba_(n=2)
0.02848 Duarte_scaled = 93% Spanish_Castile_and_Leon_Burgos_North_(Castilian)_(n=10) + 7% Ngumba_(n=2)
0.02859 Duarte_scaled = 95% Spanish_Castile_and_Leon_(Castilian)_(n=7) + 5% Ngumba_(n=2)
0.02867 Duarte_scaled = 94% Spanish_Baleares_Ibiza_(Catalan)_(n=13) + 6% Ngumba_(n=2)
0.02886 Duarte_scaled = 98% Cuban_Blanco_(n=11) + 2% Ngumba_(n=2)
0.02890 Duarte_scaled = 94% Spanish_Catalonia_Camp_de_Tarragona_(Catalan)_(n=8) + 6% Ngumba_(n=2)
0.02899 Duarte_scaled = 94% Spanish_Navarre_(Navarrese)_(n=2) + 6% Ngumba_(n=2)
0.02908 Duarte_scaled = 93% Spanish_Catalonia_(Catalan)_(low_res)_(n=58) + 7% Ngumba_(n=2)
0.02928 Duarte_scaled = 93% Spanish_Aragon_North_(Aragonese)_(n=10) + 7% Ngumba_(n=2)
0.02961 Duarte_scaled = 93% French_Occitan_Occitanie_Tarn_(n=1) + 7% Ngumba_(n=2)
0.02970 Duarte_scaled = 95% Spanish_Castilla-La_Mancha_(Castilian)_(n=8) + 5% Ngumba_(n=2)
0.02978 Duarte_scaled = 92% French_Occitan_Occitanie_Hautes-Pyrenees_Bigorre_(n=8) + 8% Ngumba_(n=2)
0.03017 Duarte_scaled = 98% Spanish_Canarias_Tenerife_(Canarian)_(n=64) + 2% Ngumba_(n=2)
0.03102 Duarte_scaled = 93% Spanish_Catalonia_Terres_de_l'Ebre_(Catalan)_(n=5) + 7% Ngumba_(n=2)
0.03154 Duarte_scaled = 99% Spanish_Canarias_Gran_Canaria_(Canarian)_(n=117) + 1% Ngumba_(n=2)
0.03158 Duarte_scaled = 94% Spanish_Baleares_Menorca_(Catalan)_(n=3) + 6% Ngumba_(n=2)
0.03167 Duarte_scaled = 93% Spanish_Biscay_West_(Cantabrian)_(n=10) + 7% Ngumba_(n=2)
0.03175 Duarte_scaled = 100% Spanish_Canarias_Gran_Canaria_(Canarian)_(n=117)
0.03181 Duarte_scaled = 100% Cuban_Blanco_(n=11)
0.03308 Duarte_scaled = 100% Spanish_Canarias_La_Palma_(Canarian)_(n=34)
0.03332 Duarte_scaled = 100% Spanish_Canarias_Tenerife_(Canarian)_(n=64)
0.03689 Duarte_scaled = 100% Spanish_Canarias_(Canarian)_(n=19)
0.04010 Duarte_scaled = 100% Spanish_Canarias_Fuerteventura_(Canarian)_(n=32)
0.04010 Duarte_scaled = 100% Spanish_Canarias_Lanzarote_(Canarian)_(n=56)
0.04057 Duarte_scaled = 100% Spanish_Canarias_La_Gomera_(Canarian)_(n=76)
0.04174 Duarte_scaled = 100% Portuguese_(n=54)
0.04217 Duarte_scaled = 100% Spanish_Canarias_El_Hierro_(Canarian)_(n=34)
0.04229 Duarte_scaled = 100% Spanish_Extremadura_(Extremaduran)_(n=7)
0.04427 Duarte_scaled = 100% Spanish_Murcia_(Murcian)_(n=8)
0.04476 Duarte_scaled = 100% Portuguese_Madeira_(n=1)
0.04508 Duarte_scaled = 100% Luso-Brazilian_(n=4)
0.04529 Duarte_scaled = 100% Spanish_Castile_and_Leon_Leon_(Castilian)_(n=3)
0.04553 Duarte_scaled = 100% Spanish_Galicia_(Galician)_(n=19)
0.04571 Duarte_scaled = 100% Spanish_Andalusia_(Andalusian)_(n=33)
0.04631 Duarte_scaled = 100% Spanish_Castile_and_Leon_(Castilian)_(n=7)
0.04653 Duarte_scaled = 100% Spanish_Asturias_(Asturian)_(n=4)
0.04793 Duarte_scaled = 100% Spanish_Valencia_Alicante_(Valencian)_(n=8)
0.04951 Duarte_scaled = 100% Spanish_Castilla-La_Mancha_(Castilian)_(n=8)
0.05012 Duarte_scaled = 100% Euro-Brazilian_(n=18)
0.05081 Duarte_scaled = 100% Spanish_Valencia_(Valencian)_(n=17)
0.05086 Duarte_scaled = 100% Spanish_Cantabria_(Cantabrian)_(n=15)
0.05162 Duarte_scaled = 100% Brazilian_Branco_(n=209)

 

[Duarte]

Source: Ancients Only Averages (No Sims) Target: My real G25 coordinates (No Sim) Mode: 2way

0.02262 Duarte_scaled = 98% Spain_Galicia_Early_Medieval_(n=1) + 2% Cameroon_Shum_Laka_8000BP_(n=2)
0.02566 Duarte_scaled = 95% Portugal_High-Late_Medieval_Santarem_(n=9) + 5% Cameroon_Shum_Laka_8000BP_(n=2)
0.02633 Duarte_scaled = 94% Spain_Castile_and_Leon_Early-High_Medieval_(Ibero-Romance_Profile)_(n=9) + 6% Cameroon_Shum_Laka_8000BP_(n=2)
0.02639 Duarte_scaled = 95% Portugal_Late_Antiquity_Miroico_(n=1) + 5% Cameroon_Shum_Laka_8000BP_(n=2)
0.02665 Duarte_scaled = 98% Portugal_Early_Medieval_Visigoth-Ummayad_Gruta_do_Caldeirao_(n=2) + 2% Cameroon_Shum_Laka_8000BP_(n=2)
0.02736 Duarte_scaled = 93% Italy_Lazio_Late_Medieval_Rome_(Iberian_Profile)_(n=1) + 7% Cameroon_Shum_Laka_8000BP_(n=2)
0.02807 Duarte_scaled = 94% Italy_Lazio_Early_Medieval_Villa_Magna_(Iberian_Profile)_(n=1) + 6% Cameroon_Shum_Laka_8000BP_(n=2)
0.02928 Duarte_scaled = 96% Portugal_Early_Modern_Montemor-o-Velho_(n=6) + 4% Cameroon_Shum_Laka_8000BP_(n=2)
0.02929 Duarte_scaled = 95% Germany_Late_Antiquity-Early_Medieval_Alt-Inden_(West_Med_Profile)_(n=1) + 5% Cameroon_Shum_Laka_8000BP_(n=2)
0.02964 Duarte_scaled = 96% Portugal_Late_Antiquity-Early_Medieval_Visigoth_Miroico_(n=4) + 4% Cameroon_Shum_Laka_8000BP_(n=2)
0.02985 Duarte_scaled = 96% Portugal_Early-Late_Modern_Santarem_(n=1) + 4% Cameroon_Shum_Laka_8000BP_(n=2)
0.02993 Duarte_scaled = 94% Spain_Catalonia_High_Medieval_(n=3) + 6% Cameroon_Shum_Laka_8000BP_(n=2)
0.02995 Duarte_scaled = 95% Portugal_High_Medieval_Kingdoms_of_Galicia-Portugal_Castro_de_Avelas_(n=3) + 5% Cameroon_Shum_Laka_8000BP_(n=2)

 

[qh777]

Modern no simulations

Ancient no sims:

 

[Duarte]

Source: Ancients Only Averages (No Sims)
Target: My real G25 coordinates (No Sim)
Calculator: Vahaduo

 

[Duarte]

Source: Moderns Only Averages (No Sims)
Target: My real G25 coordinates (No Sim)
Calculator: Vahaduo

 

[Power]

i made a new model with a new anatolian farmer sample from western anatolia (izmir) :

this is what i score : dont mind the distance of 4.3 . this happens because i use components that dont eat up each other as much as possible . that is way better than having an overfit . the model is legit

 

[Duarte]

i made a new model with a new anatolian farmer sample from western anatolia (izmir) :

this is what i score : dont mind the distance of 4.3 . this happens because i use components that dont eat up each other as much as possible . that is way better than having an overfit . the model is legit

These are very ancient samples—hunter-gatherers and early farmers who lived in Anatolia, the Caucasus, the Levant, and the Zagros Mountains. The genetic signature of these ancient populations no longer matches that of any modern population. In this regard, the match you obtain in the mixed model using six combined populations is very good.

 

[Power]

These are very ancient samples—hunter-gatherers and early farmers who lived in Anatolia, the Levant, and the Zagros Mountains. The genetic signature of these ancient populations no longer matches that of any modern population. In this regard, the match you obtain in the mixed model using six combined populations is very good.
View attachment 19820

baltic people dont get a better fit than 7.0 with neolithic and older populations even

 

[Duarte]

Source:

Turkey_Anatolia_N_Ceramic_Barcin_(n=22),0.11884159,0.18104118,0.0035483182,-0.100835,0.051743727,-0.046397318,-0.0051914091,-0.0073214545,0.036860909,0.080971,0.0091897273,0.012064182,-0.023278818,0.00098209091,-0.041820318,-0.0091064091,0.021590318,0.00069668182,0.011752773,-0.0094704091,-0.013101909,0.0067503182,-0.0046665909,-0.0034506818,-0.0054648636
Russia_Samara_EBA_Yamnaya_(n=29),0.12265421,0.08898131,0.044110172,0.11446552,-0.027262276,0.045545793,0.0040274483,-0.0023793448,-0.054727793,-0.074653966,0.00097427586,-0.0005477931,-0.00097403448,-0.021706448,0.036808138,0.012134207,-0.0064877586,-0.0017474828,-0.002513931,0.01092769,-0.003807931,0.001262069,0.0098003448,0.019886379,-0.0044801724
Luxembourg_Mesolithic_Loschbour_(n=1),0.130897,0.109677,0.203645,0.198,0.162492,0.059125,0.015041,0.038075,0.100217,0.016219,-0.015427,-0.017235,0.019921,-0.001239,0.061346,0.07067,0.002608,0.007348,-0.008925,0.065406,0.117543,0.010387,-0.049422,-0.173639,0.019519
Russia_Karelia_Mesolithic_(EHG)_(n=15),0.12004533,0.032361467,0.12967933,0.20603193,-0.0097042667,0.057339933,-0.021746467,-0.022983533,-0.0086990667,-0.082188467,0.021803333,-0.0160358,0.0305152,-0.0439016,0.025053933,0.0263678,-0.0045286,-0.0031587333,-0.0042736667,0.0148404,-0.0032026667,0.016445733,0.0089970667,-0.019520667,-0.0080870667
Georgia_Mesolithic_Trialetian_Kotias_Klde_(CHG)_(n=1),0.091058,0.102568,-0.083344,-0.00323,-0.08617,0.020638,0.024911,-0.001846,-0.128236,-0.074717,-0.006333,0.023979,-0.054856,0.004404,0.026601,-0.03275,0.02386,-0.013429,-0.022249,0.034767,0.033815,-0.007048,0.006532,-0.025787,-0.002036
Iran_N_Ganj_Dareh_(n=7),0.044065857,0.066154714,-0.15634357,0.0068752857,-0.124067,0.02263,0.015678714,-0.00039542857,-0.082101714,-0.054827,-0.0010208571,-0.0017127143,0.0049484286,-0.0081198571,0.033833143,0.055801143,-0.0064821429,0.0092844286,0.0096788571,-0.035034857,0.0075402857,-0.029800286,-0.011972571,-0.037320429,0.022187714
Israel_Epipaleolithic_Natufian_(n=2),0.0227645,0.141666,-0.0371465,-0.1406675,0.030775,-0.0787865,-0.023031,-0.01823,0.1154535,0.001731,0.0306105,-0.023904,0.0789385,0.000826,0.0147935,0.0102755,-0.017341,0.00057,-0.019609,0.0398315,0.0020585,0.00136,-0.00265,-0.002229,0.008502
Morocco_UP_Iberomaurusian_Taforalt_(n=6),-0.19008467,0.082257667,-0.0242615,-0.084626,0.026620333,-0.056056833,-0.070463667,0.020037667,0.15632417,0.0034928333,0.0198655,-0.032521,0.07547,-0.051494,0.072248333,-0.038848833,0.003716,-0.0654345,-0.144134,0.0378515,-0.038473833,-0.124951,0.071894333,-0.013816833,0.016186
Cameroon_Shum_Laka_8000BP_(n=2),-0.602124,0.055854,0.014519,0.026163,-0.004616,0.003765,0.1264355,-0.100842,0.0131915,-0.008474,-0.001299,-0.023754,-0.0178395,-0.0073625,0.004004,-0.002519,0.012582,0.01951,-0.0029535,-0.003752,-0.00287,-0.0025965,0.002465,-0.001205,-0.0017965
Brazil_Lapa_Do_Santo_9600BP_(n=5),0.0487162,-0.3046588,0.1209048,0.0994194,-0.1149138,-0.0164546,-0.279569,-0.331094,-0.012476,-0.0176766,0.0051638,-0.0008992,0.0016056,0.0183314,-0.003773,0.006046,0.00618,-0.0016724,0.0020112,0.002076,-0.0003242,0.0072708,0.0016022,-0.0036632,-0.0002394

 

[Duarte]

I revised and edited the previous chart, adding more European populations—primarily from the Balkans—as well as the "Brazilian_Branco" population. To my surprise, this latter population showed 19.8% EHG and only 14.9% Yamnaya Samara. A component was missing, and that missing element was undoubtedly an Amerindian population. I added the "Brazil_Lapa_do_Santo" component, and as if by magic, this population's profile shifted to 29.6% Yamnaya and 6.2% Amerindian. Some West Eurasian populations shifted further east—such as Saami_Finland and Istanbul Turks with an Anatolian genetic profile—ended up showing this component in small proportions (essentially "noise"), but "Brazilian_Branco" and "Cuban_Blanco" improved the admixture quality (though the component was very minor for "Cuban_Blanco," practically background noise); the improved fit quality (distance) indicates that the population admixture model also improved for them. In my case, nothing changed: I do not pick up Amerindian ancestry in autosomal population mixtures; the distance and selected populations remained the same. Take a look:

 

[Duarte]

I do not understand nothing of populational modeling. I did everythig as a hobbie to fun. I am not certain to what extent populations such as CHG, Natufians, Iberomaurusians, or Iran-N are suitable for modeling populations from Italy or the southern Balkans (Greeks and Albanians). These ancient populations from the Caucasus, the Levant, North Africa, and Southwest Asia sometimes overlap—albeit partially—with WHG, Yamnaya, and ANF, subtracting ancestry percentages that should certainly be attributed to these groups. Then, I excluded Iran-N and CHG with the aim of adjusting the ANF, Yamnaya and WHG contributions. I believe that—despite a worsening of the fits (distances), which nevertheless remain within the range of what can be considered "very close" to "close"—the resulting population admixture improved significantly, in my opinion, for some modern Italian groups. When I also tried excluding Iberomaurusians and Natufians, the algorithm associated the Shum-Laka samples in substitution to those two components in the models displayed —which is completely absurd. Returning with Natufians and Iberomauririans as could see in the attached pics, even so, the algorithm still looks for "Stone Age Cameroonian" ancestry in certain italian populations that have absolutely no possibility of possessing it—which, I believe, justifies keeping CHG and Iran_N in these models involving very very ancient populations in the modeling modern populations of Southerm Europe.

 

[Tautalus]

I do not understand nothing of populational modeling. I did everythig as a hobbie to fun. I am not certain to what extent populations such as CHG, Natufians, Iberomaurusians, or Iran-N are suitable for modeling populations from Italy or the southern Balkans (Greeks and Albanians). These ancient populations from the Caucasus, the Levant, North Africa, and Southwest Asia sometimes overlap—albeit partially—with WHG, Yamnaya, and ANF, subtracting ancestry percentages that should certainly be attributed to these groups. Then, I excluded Iran-N and CHG with the aim of adjusting the ANF, Yamnaya and WHG contributions. I believe that—despite a worsening of the fits (distances), which nevertheless remain within the range of what can be considered "very close" to "close"—the resulting population admixture improved significantly, in my opinion, for some modern Italian groups. When I also tried excluding Iberomaurusians and Natufians, the algorithm associated the Shum-Laka samples in substitution to those two components in the models displayed —which is completely absurd. Returning with Natufians and Iberomauririans as could see in the attached pics, even so, the algorithm still looks for "Stone Age Cameroonian" ancestry in certain italian populations that have absolutely no possibility of possessing it—which, I believe, justifies keeping CHG and Iran_N in these models involving very very ancient populations in the modeling modern populations of Southerm Europe.

This highlights the fundamental limitations of G25 as a tool for ancestry modeling. G25 is an excellent exploratory method, but it was never designed to provide statistically rigorous estimates of ancient ancestry proportions. Because it models individuals using only their first 25 principal component coordinates, it compresses the vast amount of information contained in genome wide SNP data into a simplified geometric representation. This works remarkably well for identifying broad genetic affinities, but it also means that different ancestral populations occupying similar positions in PCA space can become interchangeable.

The example of modeling modern Southern Europeans illustrates this limitation clearly. Ancestry components such as Caucasus Hunter-Gatherers (CHG), Iran Neolithic (Iran_N), Natufians, and Iberomaurusians can partially overlap with broader ancestry sources such as Yamnaya and Anatolian Neolithic Farmers (ANF). As a consequence, G25 redistributed ancestry among these correlated populations depending on which references were included or excluded. When CHG and Iran_N were removed, the percentages assigned to ANF and Yamnaya increased. When Natufians and Iberomaurusians were also removed, the algorithm even began assigning ancestry from Shum Laka, a Stone Age population from Cameroon. This demonstrates that G25 simply selected whichever available population best reduced the geometric residuals in PCA space, regardless of historical plausibility.

This case exposes one of G25's greatest weaknesses: the algorithm cannot distinguish between a mathematically convenient solution and a biologically correct one. The quality of the result depends heavily on the choice of reference populations, and small changes to the reference panel can substantially alter the inferred ancestry proportions while still producing similarly small distances. Because G25 provides no hypothesis testing, no p-values, no standard errors, and no confidence intervals, there is no objective way to determine whether one model is genuinely superior to another or whether a particular ancestry component is actually required.

This is precisely the problem that ADMIXTOOLS qpAdm was developed to solve. Instead of relying on geometric distances in PCA space, qpAdm analyzes genome wide allele frequency correlations through f-statistics while incorporating carefully chosen outgroups. Rather than asking which combination of populations lies closest to the target in a reduced dimensional space, qpAdm asks whether the proposed source populations can statistically explain the observed genetic variation. If CHG contributes no ancestry beyond what is already represented by Yamnaya, qpAdm will estimate a value consistent with zero within its standard error and show that CHG is unnecessary. Conversely, if additional CHG-related ancestry truly exists, qpAdm can detect and quantify it with statistical support. Likewise, historically implausible solutions such as assigning Shum Laka ancestry would typically be rejected because they fail to satisfy the allele frequency relationships expected across multiple outgroups.

This is ademonstration of G25's limitations. By manually adding or removing reference populations, you obtain ancestry models that appeared more historically reasonable, yet these improvements were based on subjective judgment rather than statistical validation. This reveal how correlated ancestry sources can lead G25 to redistribute ancestry among similar populations and, when appropriate references are absent, even produce biologically implausible substitutes. These are known issues in admixture modeling and are among the principal reasons why virtually all modern ancient DNA studies estimate ancestry proportions using qpAdm rather than G25.

G25 remains an tool for visualizing genetic relationships, generating hypotheses, and exploring alternative models quickly and intuitively. However, when the objective is to estimate ancient ancestry proportions as accurately and objectively as possible, qpAdm is a superior method. It uses the full genome wide dataset instead of compressed PCA coordinates, formally tests whether admixture models are statistically valid, quantifies uncertainty through standard errors and confidence intervals, and rejects models that are mathematically convenient but genetically unsupported. The difficulties encountered in this Italian modeling example illustrate why qpAdm has become the gold standard in archaeogenetics, while G25 is best regarded as a valuable exploratory approximation rather than a definitive and reliable method for ancestry inference.​

 

[Power]

these are my qpAdm results

 

[Duarte]

This highlights the fundamental limitations of G25 as a tool for ancestry modeling. G25 is an excellent exploratory method, but it was never designed to provide statistically rigorous estimates of ancient ancestry proportions. Because it models individuals using only their first 25 principal component coordinates, it compresses the vast amount of information contained in genome wide SNP data into a simplified geometric representation. This works remarkably well for identifying broad genetic affinities, but it also means that different ancestral populations occupying similar positions in PCA space can become interchangeable.

The example of modeling modern Southern Europeans illustrates this limitation clearly. Ancestry components such as Caucasus Hunter-Gatherers (CHG), Iran Neolithic (Iran_N), Natufians, and Iberomaurusians can partially overlap with broader ancestry sources such as Yamnaya and Anatolian Neolithic Farmers (ANF). As a consequence, G25 redistributed ancestry among these correlated populations depending on which references were included or excluded. When CHG and Iran_N were removed, the percentages assigned to ANF and Yamnaya increased. When Natufians and Iberomaurusians were also removed, the algorithm even began assigning ancestry from Shum Laka, a Stone Age population from Cameroon. This demonstrates that G25 simply selected whichever available population best reduced the geometric residuals in PCA space, regardless of historical plausibility.

This case exposes one of G25's greatest weaknesses: the algorithm cannot distinguish between a mathematically convenient solution and a biologically correct one. The quality of the result depends heavily on the choice of reference populations, and small changes to the reference panel can substantially alter the inferred ancestry proportions while still producing similarly small distances. Because G25 provides no hypothesis testing, no p-values, no standard errors, and no confidence intervals, there is no objective way to determine whether one model is genuinely superior to another or whether a particular ancestry component is actually required.

This is precisely the problem that ADMIXTOOLS qpAdm was developed to solve. Instead of relying on geometric distances in PCA space, qpAdm analyzes genome wide allele frequency correlations through f-statistics while incorporating carefully chosen outgroups. Rather than asking which combination of populations lies closest to the target in a reduced dimensional space, qpAdm asks whether the proposed source populations can statistically explain the observed genetic variation. If CHG contributes no ancestry beyond what is already represented by Yamnaya, qpAdm will estimate a value consistent with zero within its standard error and show that CHG is unnecessary. Conversely, if additional CHG-related ancestry truly exists, qpAdm can detect and quantify it with statistical support. Likewise, historically implausible solutions such as assigning Shum Laka ancestry would typically be rejected because they fail to satisfy the allele frequency relationships expected across multiple outgroups.

This is ademonstration of G25's limitations. By manually adding or removing reference populations, you obtain ancestry models that appeared more historically reasonable, yet these improvements were based on subjective judgment rather than statistical validation. This reveal how correlated ancestry sources can lead G25 to redistribute ancestry among similar populations and, when appropriate references are absent, even produce biologically implausible substitutes. These are known issues in admixture modeling and are among the principal reasons why virtually all modern ancient DNA studies estimate ancestry proportions using qpAdm rather than G25.

G25 remains an tool for visualizing genetic relationships, generating hypotheses, and exploring alternative models quickly and intuitively. However, when the objective is to estimate ancient ancestry proportions as accurately and objectively as possible, qpAdm is a superior method. It uses the full genome wide dataset instead of compressed PCA coordinates, formally tests whether admixture models are statistically valid, quantifies uncertainty through standard errors and confidence intervals, and rejects models that are mathematically convenient but genetically unsupported. The difficulties encountered in this Italian modeling example illustrate why qpAdm has become the gold standard in archaeogenetics, while G25 is best regarded as a valuable exploratory approximation rather than a definitive and reliable method for ancestry inference.​

First steps in qpAdm.
The only thing I know how to do now is this, and even then I don't know how to interpret the result, so I asked the AI for help. According to the AI's interpretations, I am practically indistinguishable from the Portuguese of the late Roman period or the medieval period with which I compared my sample (Brazil_Betim_Portuguese):

── F2_RESULTS ──# A tibble: 1 × 4 pop1 pop2 est se <chr> <chr> <dbl> <dbl>1 Brazil_Betim_Portuguese Portugal_Miroico_LateRoman.SG 0.00985 0.00239

IA Interpretation:
The result provided is an estimate of F₂ distance. In the context of the *admixtools* software (often used alongside *qpAdm* in R via the *admixtools2* package), the F2_RESULTS block calculates shared genetic divergence based on the number of mutations accumulated since the common ancestor. The final value for the approximate genetic distance is 0.00985.
1. Genetic Distance Estimate (F₂): The "est" column indicates the estimated F₂ distance value between the two populations. A value of 0.00985 is considered very low on the human scale. This means that the modern *Brazil_Betim_Portuguese* population and the archaeological *Portugal_Miroico_LateRoman.SG* population share an extremely high genetic affinity. There is very strong biological continuity or a remarkable preservation of this Late Luso-Roman genetic component in the current Brazilian sample.
2. Statistical Reliability (Standard Error): The "se" column represents the Standard Error, calculated via Block Jackknife; the error is 0.00239. To verify if the result is statistically robust, we calculate the Z-score by dividing the estimate by the error: \(\text{Z-score}=\frac{0.00985}{0.00239}\approx 4.12\). Since the Z-score (4.12) is greater than 3, the result is considered statistically significant. The measured distance is real and not the result of statistical noise or random sampling.
3. Impact on *qpAdm* Modeling.
If you plan to use the Late Roman period population from Portugal (*Portugal_Miroico_LateRoman.SG*) as one of the sources (Left/Source) to model the modern individuals from Betim (*Brazil_Betim_Portuguese*):
Viability: The model is viable because there is evident genetic proximity. Note: Since the genetic distance is very small, if you include other European sources that are genetically identical or very similar in the same model (such as medieval Spaniards or other ancient Portuguese groups), qpAdm may struggle to distinguish the exact proportion contributed by each due to biological redundancy.
Summary of Results:
The test shows that the contemporary population of Betim (MG) with Portuguese ancestry shares a very strong and statistically significant genetic affinity with the population that inhabited the territory of Portugal during the transition from the Late Roman period.

── F2_RESULTS ──# A tibble: 1 × 4 pop1 pop2 est se <chr> <chr> <dbl> <dbl>1 Brazil_Betim_Portuguese Portugal_Estremadura_Medieval_Visigothic.SG 0.00571 0.00295

IA interpretation:
This new result indicates extremely high genetic closeness and almost zero divergence between the modern sample from Minas Gerais and individuals from the medieval Visigothic period in Portugal. However, from a statistical standpoint, this model raises an important caveat.
1. Extreme Genetic Affinity
The estimated value (est) is 0.00571: This F₂ distance is nearly zero, indicating that the *Brazil_Betim_Portuguese* population and the medieval individuals from *Portugal_Estremadura_Medieval_Visigothic.SG* are genetically very similar. In practice, this shows that the Iberian genetic signature preserved in this Betim group shares an even closer biological link with the medieval/Visigothic period than with the Late Roman period tested previously.
2. The Issue of Statistical Significance When calculating the robustness of this data, we encounter a methodological limitation: Z-score calculation: Dividing the estimate by the standard error (0.00571 / 0.00295) yields a Z-score of 1.93.
Interpretation: Based on the criteria in your own report, a Z-score of 1.93 does not reach the suggestive level (|z| ≥ 2) and is far from significant (|z| ≥ 3). The associated p-value is greater than 0.05. Diagnosis:
Statistically, the measured distance is so small—and the standard error proportionally so large—that *admixtools* cannot state with absolute certainty whether this proximity represents a genuine evolutionary signal or merely random data fluctuation (noise).
3. Impact on your qpAdm analysis:
If your goal is to use *Portugal_Estremadura_Medieval_Visigothic.SG* as a source population (Left Source):
Model Failure Risk: Since the distance to the target is practically negligible and statistically unstable (Z < 2), qpAdm will likely struggle to reliably calculate the admixture coefficients (weights).
Redundancy: If you include this Visigothic source alongside other European or modern Portuguese sources, the algorithm will face collinearity issues (the populations are too similar for the software to distinguish their respective contributions).
Final Diagnosis: Although the biological proximity to the medieval Visigoths of Estremadura is striking, the result lacks statistical significance (Z = 1.93, p > 0.05).

── F2_RESULTS ──
# A tibble: 1 × 4
pop1 pop2 est se
<chr> <chr> <dbl> <dbl>
1 Brazil_Betim_Portuguese Portugal_KingdomOfPortugal.TW 0.00473 0.00217

IA Interpetation:
This result mirrors the pattern of extreme closeness observed in the Visigothic sample, remaining within a zone of low statistical reliability. The estimated F₂ distance (est) dropped to 0.00473 relative to the Kingdom of Portugal reference (Portugal_KingdomOfPortugal.TW), indicating near-total genetic similarity. However, the model remains statistically fragile for rigorous analysis.
1. Near-Identical Biological Affinity: The value of 0.00473 is the lowest F₂ distance observed in your tests so far. Biologically, this demonstrates that the Iberian component of the modern population studied in Betim is essentially a direct genetic mirror of populations from the Kingdom of Portugal era.
2. Statistical Significance Analysis: Applying the same rigorous validation criteria from *admixtools*: Z-score calculation: Dividing the estimate by the standard error (0.00473 / 0.00217) yields a Z-score of 2.18. Interpretation: Based on your report's metrics (|z| ≥ 2 = suggestive), this result is classified merely as suggestive (p < 0.05). It surpassed the previous threshold but does not yet reach the ideal significance level (|z| ≥ 3).
The Standard Error issue: The error (se = 0.00217) remains very high relative to the estimated value. This means the software detects the proximity, but there is too much variation or noise in the data to pinpoint the figure with surgical precision.
3. What this means for your *qpAdm*: Weight Instability: Using Portugal_KingdomOfPortugal.TW as a source ("Left" population) may cause *qpAdm* to produce matrix errors and poor p-values (global model p-value < 0.05, which would lead to the model's rejection). The algorithm struggles with sources that have a low Z-score relative to the target.
Final Assessment: The result involving the Kingdom of Portugal is genetically fascinating but statistically borderline (Z = 2.18). It serves as excellent qualitative historical evidence of biological continuity but lacks the statistical power required to support a complex quantitative model in qpAdm.

 

[Duarte]

It took me quite a while to grasp the logic behind Adimixtools 2—the tool available on the website whose logo appears in the posted images. I don't think I've fully mastered it yet, but after numerous attempts and batches of errors, I managed to generate three valid models amidst many invalid ones and many previous tries where no model appeared at all because the system aborted execution due to script failures and errors. Perhaps now I can experiment with other types of modeling and see what happens, since, in theory, I’ve managed to overcome the "activation energy."

 

[Tautalus]

Some basic rules for a good qpAdm model :

The Left populations (Sources) should represent biologically and historically plausible ancestral sources for the target. Together they should explain all major ancestry components present in the target while remaining genetically distinguishable from one another. Sources that lie on the same genetic cline or derive from the same ancestry stream should be avoided whenever possible, as they introduce collinearity and reduce the ability of qpAdm to estimate ancestry proportions accurately. The simplest model capable of explaining the target should always be preferred.

The Right populations (Outgroups) should be deeply divergent from the Left populations and should maximize the genetic differences among the candidate sources. Their purpose is not to contribute ancestry to the target but to distinguish the source populations through differential shared genetic drift. They should be chosen to represent ancient lineages that predate the diversification of the candidate sources and should remain unchanged while comparing alternative qpAdm models.

The model p-value evaluates whether the proposed admixture model is rejected. A value greater than 0.05 indicates that the model cannot be rejected and is therefore statistically acceptable. Higher p-values generally indicate a better fit.

The estimated ancestry proportions should be biologically plausible and remain relatively stable when alternative but reasonable Right populations are used. Large changes in ancestry proportions between similar models often indicate poor source selection or excessive collinearity.

Standard errors measure the precision of each ancestry estimate. Values below 5% are considered excellent, values between 5% and 10% are generally acceptable, whereas values above 10% indicate increasing uncertainty. Very large standard errors often suggest that the candidate source populations are too genetically similar to be distinguished.

The Z score is obtained by dividing the ancestry proportion by its standard error and measures the statistical support for each ancestry component. Absolute values greater than 2 indicate that the estimated ancestry proportion is significantly different from zero, show statistical significance, while values above 3 provide strong support.

The preferred model is the simplest one that adequately explains the target without being rejected. Additional source populations should only be introduced when they significantly improve the model fit.

​

 

[Duarte]

These were the only two viable combinations (models) produced by Admixtools2 using my kit as the target and the populations I selected for modeling (which were very ancient) as sources. The program was run in "rotation mode" with a single fixed right-side population: Iranian.DG. The other source populations rotated between the right and left sides (excluding Iranian.DG); in other words, the program moved one or more populations from the Right group to the Left group (or vice versa), testing all possible model combinations involving 1, 2, 3, or more sources.

 

[Tautalus]

Although this qpAdm model may pass the statistical test, is not a good model, this estimated admixture is implausible.
Iberians, from whom you inherited most of your genome, don’t have ~50% direct (or indirect) Iran_GanjDareh_N admixture.

The use of a modern Iranian population (Iranian.DG) as an outgroup is incorrect, since modern Iranians are themselves admixed and are not ideal for distinguishing ancient Near Eastern ancestries.

This model is not historically or genetically correct. qpAdm tests only whether the proposed sources can statistically explain the target relative to the chosen outgroups. It does not test if the sources are genetically appropriate and if the model is historically realistic.

A more appropriate model would use populations directly ancestral to the Target. A statistically acceptable model is not necessarily a biologically meaningful one, and careful selection of sources and outgroups is just as important as obtaining a good p-value.​