Genética de españoles e hispanoamericanos

[Ruderico]

I see it , your paper speaks about 2 routes of entry of Neolithic in Europe, with 3 fonal different clusters among which we are included in the same cluster with Sardinians and Frenchs(and...Sweden?).

Yes, but these are exclusively ancient samples, not modern individuals. That said you can still see this difference in G25, Iberians strongly prefer Western EEFs (especially those from Iberia) than other Europeans do. My ancient models always benefit a lot from using Iberian EEF over Barcin or LBK.




Edit: You can try splitting these into two different groups, odds are most of us here will massively prefer Iberia_EEF over LBK.

LBK:HUN_LBK_MN,0.123498,0.1807643,0.0126335,-0.0942352,0.0580878,-0.0403695,-0.0029375,-0.003,0.0397798,0.084603,0.008769,0.0125138,-0.02111,0.0004818,-0.042107,-0.0149492,0.01105,-0.0005382,0.012821,-0.0153825,-0.013757,0.0095827,-0.0092128,-0.0026512,-0.002934
LBK:DEU_LBK_HBS,0.1244086,0.182592,0.0124072,-0.0896648,0.0641812,-0.0429073,-0.0036896,-0.0041883,0.0414365,0.0872364,0.0092805,0.0126936,-0.023927,0.0001514,-0.038877,-0.0078295,0.0197988,0.00515,0.0125886,-0.0122746,-0.0098015,0.0061826,-0.0089292,-0.0052416,-0.0037063
LBK:DEU_LBK_KD,0.127482,0.183811,0.023381,-0.086564,0.079707,-0.043228,-0.010575,0.021691,0.052767,0.091118,0.011205,0.018583,-0.015461,-0.006881,-0.044109,-0.020419,0.002217,0.015836,0.015587,-0.021635,-0.004243,0.022999,-0.025882,0.010363,-0.002994
LBK:DEU_LBK_SCH,0.1142784,0.1787332,0.0100314,-0.0908276,0.0607496,-0.0401042,0.000329,-0.0073844,0.0417638,0.0824072,0.009776,0.0103108,-0.0229828,0.0046242,-0.0385172,-0.0069476,0.018019,0.00451,0.0144304,-0.0110304,-0.0054652,0.0019288,-0.0053244,-0.0122186,-0.0034966
LBK:DEU_LBK_SMH,0.1203421,0.1797025,0.015942,-0.0828642,0.0596754,-0.0407307,-0.0022112,-0.0044264,0.0449487,0.0840605,0.0068277,0.011417,-0.0237046,0.0007444,-0.03699,-0.0066837,0.0194035,0.0016067,0.0141925,-0.0104368,-0.0090976,0.0036702,-0.0058822,-0.0043379,-0.0037395
LBK:DEU_LBK_UW,0.129758,0.178733,0.01961,-0.081719,0.068013,-0.042949,-0.00282,-0.006692,0.037837,0.082553,0.002923,0.019033,-0.022002,0.006193,-0.042616,-0.006762,0.026859,0.006841,0.015209,-0.008254,-0.015972,0.016075,0.000493,-0.005904,-0.007784
LBK:AUT_LBK_N,0.1220754,0.1839379,0.015792,-0.0885424,0.0642426,-0.0428098,-0.0023206,-0.0037789,0.0449952,0.0860381,0.0079368,0.0105094,-0.0204036,0.0025289,-0.0382054,-0.0129606,0.0148475,0.0011245,0.0146436,-0.0096453,-0.006738,0.0098304,-0.0058542,-0.0064919,-0.0058527
Iberia_EEF:Iberia_Southwest_EN,0.129758,0.165531,0.043369,-0.062662,0.080015,-0.031515,-0.012456,0.007846,0.069129,0.094763,0.003735,0.018733,-0.031665,-0.006055,-0.016558,-0.013789,-0.004042,0.010262,0.007039,-0.009755,0.001373,0.001237,-0.016638,-0.015183,0.002874
Iberia_EEF:Iberia_Southwest_MLN,0.1225493,0.1648543,0.0487743,-0.041021,0.085452,-0.0256577,-0.004935,-0.0016923,0.065993,0.093001,0.002057,0.0125387,-0.0256193,-0.0159183,-0.0060623,0.0037567,0.012517,0.0048143,0.000545,-0.003585,0.016596,0.0056057,-0.0175427,-0.0430987,0.000359
Iberia_EEF:Iberia_Southeast_MLN,0.1261811,0.1677073,0.0457934,-0.042959,0.0838399,-0.0274906,-0.0071173,-0.0006594,0.0686909,0.0943201,0.0008583,0.0181337,-0.0329389,-0.0142144,-0.0076001,-0.0033906,0.0013783,0.002968,0.0018857,-0.0046451,0.0077719,0.0038863,-0.0161279,-0.037768,0.0005473
Iberia_EEF:Iberia_North_MLN,0.1300429,0.1642619,0.069296,-0.0196222,0.0942868,-0.0192784,-0.0060512,0.0041826,0.0651152,0.0902069,-0.0032476,0.0134692,-0.0308842,-0.0175298,-0.0036982,0.0054198,0.008263,0.0004276,0.0001414,-0.0005314,0.0160184,0.0039721,-0.0140041,-0.0431538,0.0040265
Iberia_EEF:Iberia_Northeast_MLN,0.1258561,0.1704637,0.0529047,-0.039406,0.0901707,-0.0212354,-0.0049016,0.0039559,0.0634316,0.0944244,-0.0020646,0.0167853,-0.0307091,-0.0175763,-0.005138,0.0010987,0.0051967,0.0032034,-0.0005209,-0.004127,0.0115331,0.0051229,-0.0170083,-0.0366319,0.0007699
Iberia_EEF:Iberia_MN,0.1212218,0.1627387,0.0523252,-0.033592,0.0853235,-0.0214745,-0.0098702,-0.0010385,0.0643228,0.0923028,-0.003004,0.0138628,-0.032854,-0.0157232,-0.0081772,-0.0003315,0.0109522,0.004244,-0.001257,-0.00544,0.0181868,0.0046368,-0.0155598,-0.0405782,0.0024248
Iberia_EEF:Iberia_N,0.1239407,0.1755737,0.0341921,-0.0626261,0.0768007,-0.0339317,-0.004439,-0.0011793,0.0618117,0.0996021,0.0021292,0.015586,-0.0343077,-0.0108723,-0.0189858,-0.0058929,0.0074029,0.001844,0.0048463,-0.0070036,0.002468,0.0035996,-0.008148,-0.0235909,9.3e-05
Yamnaya_RUS_Samara,0.1255849,0.089028,0.0426986,0.1153479,-0.0287232,0.0450564,0.0036033,-0.0025642,-0.0559032,-0.0728943,0.0018222,3.32e-05,-0.0026924,-0.0233041,0.0366141,0.0157633,-0.0012316,-0.0017879,-0.0038408,0.0137704,-0.0031749,0.0007557,0.0110649,0.0186102,-0.004537
WHG,0.1246365,0.116278,0.184789,0.189279,0.1546445,0.0464355,0.0131605,0.0372675,0.0890705,0.017768,-0.0153455,-0.015811,0.0159065,-0.0030275,0.053338,0.0582065,0.00502,0.016343,-0.0093015,0.055589,0.0944585,0.0111905,-0.049607,-0.160866,0.0170045
MAR_EN,-0.1735805,0.0919055,-0.0258325,-0.083657,0.0283125,-0.0596825,-0.079316,0.021461,0.1500185,0.0043735,0.0222475,-0.0264515,0.075148,-0.0461725,0.069353,-0.03381,0.0171455,-0.05549,-0.1487635,0.0340785,-0.038245,-0.118212,0.0826995,-0.009941,0.021615
Levant_Natufian,0.020488,0.1431895,-0.0377125,-0.1387295,0.030775,-0.079484,-0.025616,-0.0175375,0.114329,0.002005,0.0332085,-0.0222555,0.076486,0.002133,0.0153365,0.009016,-0.0154505,-0.001014,-0.02206,0.040832,0.001497,0.0001235,-0.003636,-0.0044585,0.006287

[Damião de Góis]

These aren't Iron Age at all, they postdate Romans by at ~500 years, the IA label is exclusively used for pre-Roman samples. Roman samples from Urbino are closer in time to the Roman conquest and they already show a completely East Med profile because of colonisation. You could likewise say North African and East Med ancestry was already present in "Portugal" in the "Iron Age".
That's like getting a genetic sample from a modern Brazilian and saying the indigenous people pre-1500 already had European ancestry.

PS: We have two samples from ~800BC from nearby (Arrifana, Coimbra). And yes, they are Basque-like. One appears to have steppe similar to modern individuals, the other one only 2/3s of that. That said the samples are of poor quality.


I suggest you look into qpAdm models made for modern and IA Iberians in "Large-scale migration into Britain during the Middle to Late Bronze Age"
https://reich.hms.harvard.edu/sites/...ipt_2021_3.pdf

The bulk of ancient IA Iberians has similar levels steppe ancestry in 3-way models to Basques, but usually less than the average non-Basque Spanish. The exception are IA samples from Cataluña, particularly Girona which is close to the French border, plenty of which have similar or suprior levels to modern Spanish. Obviously the vector for steppe ancestry in Iberia is through the Pyrenees, it's not surprising pre-Romans from that area are showing higher levels than those further away.

Earliest Portuguese sample is dated to around 337 AD. One problem with this paper is that they talk about the Iron Age and when the vast majority of samples are from the Roman period.

Eurogenes K13

Portugal_Conimbriga:R10487___AD_337___Coverage_66.13%,38.73,16.47,28.87,2.20,9.76,0.72,1.13,0,0.20,0,0.20,1.69,0.03
Portugal_Miroico:R10499___AD_331___Coverage_81.68%,35,10.38,32.24,1.54,18.69,1.46,0,0.69,0,0,0,0,0
Portugal_Miroico:R10501___AD_426___Coverage_64.40%,34.31,10.92,27.09,3.01,15.41,2.89,1.91,1.05,0.54,0.23,0.06,0.76,1.82
Portugal_Miroico:R10502___AD_426___Coverage_71.97%,34.91,10.53,26.33,2.91,15.51,5.11,0.03,0.19,0,0.23,1.41,1.40,1.45
Portugal_Miroico:R10503___AD_518___Coverage_88.61%,26.55,5.38,27.92,0,17.32,10.84,0.01,0,0.76,0.74,0.46,5.27,4.75
Portugal_Miroico:R10507___AD_426___Coverage_50.92%,38.63,11.65,31.01,0.01,9.03,4.08,0.01,0.65,0,1.61,0,1.86,1.49
Portugal_Miroico:R10508___AD_337___Coverage_66.89%,34.88,7.41,31.31,2.22,15.35,5.34,0,0.10,0,0.09,0.98,0.88,1.44

SAMPLE	DATE	COVERAGE	LATITUDE	LONGITUDE	LOCALITY	COUNTRY
R10499	331	81,68%	38,7261	-9,3653	Miroico	Portugal
R10508	337	66,89%	38,7261	-9,3653	Miroico	Portugal
R10487	337	66,13%	40,099444	-8,490556	Conimbriga	Portugal
R10488	337	57,83%	40,099444	-8,490556	Conimbriga	Portugal
R10500	426	53,65%	38,7261	-9,3653	Miroico	Portugal
R10501	426	64,40%	38,7261	-9,3653	Miroico	Portugal
R10502	426	71,97%	38,7261	-9,3653	Miroico	Portugal
R10506	426	77,69%	38,7261	-9,3653	Miroico	Portugal
R10507	426	50,92%	38,7261	-9,3653	Miroico	Portugal
R10503	518	88,61%	38,7261	-9,3653	Miroico	Portugal
R10494	781	86,54%	38,8803	-7,1637	MontedaNora	Portugal
R10496	829	85,03%	38,8803	-7,1637	MontedaNora	Portugal
R10491	832	82,74%	38,8803	-7,1637	MontedaNora	Portugal

Yes i see i made a mistake, i may have been misguided by the study's name. Either way these don't look like roman soldiers and i don't know which sorts of mixes would turn a basque into those samples. I would still guess they shouldn't diverge that much from the original source. If everyone everywhere was basque before the romans, i would be curious to know what was the process that changed people from basques to what we are now.

@Ajeje Brazorf, thanks for extracting information on these samples btw.

Until there is a significant number of pre-Roman samples from further west that unequivocally show high levels of steppe ancestry and north African ancestry no one can contradict Olalde's statement. For the time being it does seem your high steppe ancestry is not because of indigenous western Iberians, but from colonists who made their way here after the Roman conquest. Likewise for the non-West Euro things we all carry.
https://i.postimg.cc/tg31S1qD/sadsadsad.png

Where would you guess the colonists to be from? I don't think Y-DNA in Portugal reflects any sort of those movements.

[mitalit]

Se me ocurren muchas posibles razones, porque en realidad no lo sé.

Realmente los que habría que quitar son los que sean de después del 711, independientemente de que ploteen un poco raros, o tanto los que ploteen rarunos como los de fechas posteriores al 711.

Pero bueno, para ir divirtiéndonos, con el primer average que hiciste:

West_Iberia_LIA,0.0992,0.146075,0.0342125,-0.013775,0.0435625,-0.008375,-0.00425,0.0018625,0.0261125,0.0337625,-0.0008375,0.00565,-0.0100625,-0.009275,0.0043125,-0.000775,0.0016,-0.0030875,-0.0039,-0.000925,0.000125,-0.002825,0.0002125,-0.0061125,-0.0003875
North_Iberia_IA,0.125205,0.1431895,0.065053,0.0114665,0.058011,0.003347,-0.0034075,0.0045,0.026077,0.034989,-0.002192,0.0088425,-0.0169475,-0.0085325,0.0107215,6.65e-05,-0.008279,0.0008865,-0.000126,-0.00075,0.0082355,0.0023495,-0.0070865,-0.0118085,-0.0061075
North_Iberia_LIA,0.1257745,0.155376,0.0631675,0.005814,0.052933,-0.000697,-0.001175,0.0074995,0.032826,0.0429165,-0.0010555,0.008842,-0.0237115,-0.0187855,0.013572,0.0043755,-0.004759,0.0051945,-0.0022625,-0.0090665,0.0131645,0.000371,-0.0077645,-0.0163875,-0.000778
Northeast_Iberia_LIA,0.122929,0.1412094,0.0456504,0.018411,0.0412382,0.0034167,0.0027378,0.0059997,0.0162905,0.019244,-0.0069746,0.0078154,-0.0095364,-0.0064202,0.009161,0.0037191,-0.0010755,0.0039592,-0.0010431,0.0037894,0.0030444,0.0005132,-0.0041782,0.0024823,-0.0025866

EL NE_Iberia_LIA son el NE_Iberia son dos averages, el NE_Iberia(6CE)+NE_Iberia(6-8CE), sería equiparable al tuyo, porque mezcla de justo antes del 711, y de un poco después, pero ha sido un modelito apresurado.

A mi se me ocurren solo dos. Una que las muestras sean de sitios muy específicos y poco representativos, y la otra es que en ese periodo se estuviese experimentando un cambio demográfico razonablemente grande a causa de una gran inmigración.
Si estas muestras son representativas, los "outliers" serian bastante comunes (aunque como bien dices, hay que separarlas por épocas)

[Ruderico]

Where would you guess the colonists to be from? I don't think Y-DNA in Portugal reflects any sort of those movements.

It does though, yDNA used to be overwhelmingly R1b in the past, today it's at ~55% and part of those should have their origins abroad (although some non-R1b might be local as well), I think this really does suggest that there was a massive turnover in Iberia starting with Roman occupation.

I have no idea why, but as I said in AG my suspicion lies with the abundance of mines in Iberia, maybe the Romans needed a lot of manpower to operate them and resorted to people (and slaves) from surrounding areas such as Mauritania and Gaul, in addition to Imperial Romans who colonised the territory. The famous Seventh Legion, which spent centuries in northwestern Iberia, also had Gallic auxiliaries, maybe some of them ended up settling in Hispania after they were released from service. Just speculation, they may be totally irrelevant.





I still find it hard to believe the Germanic period isn't necessary to explain some of these changes though, in the end with the exception of two samples (R10487 and R10507) all of these seem very Mediterranean. That said we haven't seen samples from Roman Braga, which should theoretically more important for Portuguese (and Galician) ethnogenesis, maybe one day we'll have them and this Mediterranean profile will end up being less common, with the majority of samples closer to us. Or maybe not, who knows.

[Damião de Góis]

Edit: You can try splitting these into two different groups, odds are most of us here will massively prefer Iberia_EEF over LBK.

Target: Gil_Vicente
Distance: 2.9547% / 0.02954739
37.2 Yamnaya_RUS_Samara
30.8 Iberia_EEF
25.8 LBK
6.2 MAR_EN

[Ruderico]

Target: Gil_Vicente
Distance: 2.9547% / 0.02954739
37.2 Yamnaya_RUS_Samara
30.8 Iberia_EEF
25.8 LBK
6.2 MAR_EN

Yeah I was expecting something like that from you because of all the steppe you have, I barely score LBK

Target: Ruderico_scaled
Distance: 3.4848% / 0.03484773
52.6 Iberia_EEF
32.2 Yamnaya_RUS_Samara
8.6 LBK
6.6 MAR_EN



Target: MrsRuderico_scaled
Distance: 2.1762% / 0.02176182
49.2 Iberia_EEF
32.8 Yamnaya_RUS_Samara
9.6 LBK
6.8 MAR_EN
1.6 Levant_Natufian

[gixajo]

Yes, but these are exclusively ancient samples, not modern individuals. That said you can still see this difference in G25, Iberians strongly prefer Western EEFs (especially those from Iberia) than other Europeans do. My ancient models always benefit a lot from using Iberian EEF over Barcin or LBK.




Edit: You can try splitting these into two different groups, odds are most of us here will massively prefer Iberia_EEF over LBK.

LBK:HUN_LBK_MN,0.123498,0.1807643,0.0126335,-0.0942352,0.0580878,-0.0403695,-0.0029375,-0.003,0.0397798,0.084603,0.008769,0.0125138,-0.02111,0.0004818,-0.042107,-0.0149492,0.01105,-0.0005382,0.012821,-0.0153825,-0.013757,0.0095827,-0.0092128,-0.0026512,-0.002934
LBK:DEU_LBK_HBS,0.1244086,0.182592,0.0124072,-0.0896648,0.0641812,-0.0429073,-0.0036896,-0.0041883,0.0414365,0.0872364,0.0092805,0.0126936,-0.023927,0.0001514,-0.038877,-0.0078295,0.0197988,0.00515,0.0125886,-0.0122746,-0.0098015,0.0061826,-0.0089292,-0.0052416,-0.0037063
LBK:DEU_LBK_KD,0.127482,0.183811,0.023381,-0.086564,0.079707,-0.043228,-0.010575,0.021691,0.052767,0.091118,0.011205,0.018583,-0.015461,-0.006881,-0.044109,-0.020419,0.002217,0.015836,0.015587,-0.021635,-0.004243,0.022999,-0.025882,0.010363,-0.002994
LBK:DEU_LBK_SCH,0.1142784,0.1787332,0.0100314,-0.0908276,0.0607496,-0.0401042,0.000329,-0.0073844,0.0417638,0.0824072,0.009776,0.0103108,-0.0229828,0.0046242,-0.0385172,-0.0069476,0.018019,0.00451,0.0144304,-0.0110304,-0.0054652,0.0019288,-0.0053244,-0.0122186,-0.0034966
LBK:DEU_LBK_SMH,0.1203421,0.1797025,0.015942,-0.0828642,0.0596754,-0.0407307,-0.0022112,-0.0044264,0.0449487,0.0840605,0.0068277,0.011417,-0.0237046,0.0007444,-0.03699,-0.0066837,0.0194035,0.0016067,0.0141925,-0.0104368,-0.0090976,0.0036702,-0.0058822,-0.0043379,-0.0037395
LBK:DEU_LBK_UW,0.129758,0.178733,0.01961,-0.081719,0.068013,-0.042949,-0.00282,-0.006692,0.037837,0.082553,0.002923,0.019033,-0.022002,0.006193,-0.042616,-0.006762,0.026859,0.006841,0.015209,-0.008254,-0.015972,0.016075,0.000493,-0.005904,-0.007784
LBK:AUT_LBK_N,0.1220754,0.1839379,0.015792,-0.0885424,0.0642426,-0.0428098,-0.0023206,-0.0037789,0.0449952,0.0860381,0.0079368,0.0105094,-0.0204036,0.0025289,-0.0382054,-0.0129606,0.0148475,0.0011245,0.0146436,-0.0096453,-0.006738,0.0098304,-0.0058542,-0.0064919,-0.0058527
Iberia_EEF:Iberia_Southwest_EN,0.129758,0.165531,0.043369,-0.062662,0.080015,-0.031515,-0.012456,0.007846,0.069129,0.094763,0.003735,0.018733,-0.031665,-0.006055,-0.016558,-0.013789,-0.004042,0.010262,0.007039,-0.009755,0.001373,0.001237,-0.016638,-0.015183,0.002874
Iberia_EEF:Iberia_Southwest_MLN,0.1225493,0.1648543,0.0487743,-0.041021,0.085452,-0.0256577,-0.004935,-0.0016923,0.065993,0.093001,0.002057,0.0125387,-0.0256193,-0.0159183,-0.0060623,0.0037567,0.012517,0.0048143,0.000545,-0.003585,0.016596,0.0056057,-0.0175427,-0.0430987,0.000359
Iberia_EEF:Iberia_Southeast_MLN,0.1261811,0.1677073,0.0457934,-0.042959,0.0838399,-0.0274906,-0.0071173,-0.0006594,0.0686909,0.0943201,0.0008583,0.0181337,-0.0329389,-0.0142144,-0.0076001,-0.0033906,0.0013783,0.002968,0.0018857,-0.0046451,0.0077719,0.0038863,-0.0161279,-0.037768,0.0005473
Iberia_EEF:Iberia_North_MLN,0.1300429,0.1642619,0.069296,-0.0196222,0.0942868,-0.0192784,-0.0060512,0.0041826,0.0651152,0.0902069,-0.0032476,0.0134692,-0.0308842,-0.0175298,-0.0036982,0.0054198,0.008263,0.0004276,0.0001414,-0.0005314,0.0160184,0.0039721,-0.0140041,-0.0431538,0.0040265
Iberia_EEF:Iberia_Northeast_MLN,0.1258561,0.1704637,0.0529047,-0.039406,0.0901707,-0.0212354,-0.0049016,0.0039559,0.0634316,0.0944244,-0.0020646,0.0167853,-0.0307091,-0.0175763,-0.005138,0.0010987,0.0051967,0.0032034,-0.0005209,-0.004127,0.0115331,0.0051229,-0.0170083,-0.0366319,0.0007699
Iberia_EEF:Iberia_MN,0.1212218,0.1627387,0.0523252,-0.033592,0.0853235,-0.0214745,-0.0098702,-0.0010385,0.0643228,0.0923028,-0.003004,0.0138628,-0.032854,-0.0157232,-0.0081772,-0.0003315,0.0109522,0.004244,-0.001257,-0.00544,0.0181868,0.0046368,-0.0155598,-0.0405782,0.0024248
Iberia_EEF:Iberia_N,0.1239407,0.1755737,0.0341921,-0.0626261,0.0768007,-0.0339317,-0.004439,-0.0011793,0.0618117,0.0996021,0.0021292,0.015586,-0.0343077,-0.0108723,-0.0189858,-0.0058929,0.0074029,0.001844,0.0048463,-0.0070036,0.002468,0.0035996,-0.008148,-0.0235909,9.3e-05
Yamnaya_RUS_Samara,0.1255849,0.089028,0.0426986,0.1153479,-0.0287232,0.0450564,0.0036033,-0.0025642,-0.0559032,-0.0728943,0.0018222,3.32e-05,-0.0026924,-0.0233041,0.0366141,0.0157633,-0.0012316,-0.0017879,-0.0038408,0.0137704,-0.0031749,0.0007557,0.0110649,0.0186102,-0.004537
WHG,0.1246365,0.116278,0.184789,0.189279,0.1546445,0.0464355,0.0131605,0.0372675,0.0890705,0.017768,-0.0153455,-0.015811,0.0159065,-0.0030275,0.053338,0.0582065,0.00502,0.016343,-0.0093015,0.055589,0.0944585,0.0111905,-0.049607,-0.160866,0.0170045
MAR_EN,-0.1735805,0.0919055,-0.0258325,-0.083657,0.0283125,-0.0596825,-0.079316,0.021461,0.1500185,0.0043735,0.0222475,-0.0264515,0.075148,-0.0461725,0.069353,-0.03381,0.0171455,-0.05549,-0.1487635,0.0340785,-0.038245,-0.118212,0.0826995,-0.009941,0.021615
Levant_Natufian,0.020488,0.1431895,-0.0377125,-0.1387295,0.030775,-0.079484,-0.025616,-0.0175375,0.114329,0.002005,0.0332085,-0.0222555,0.076486,0.002133,0.0153365,0.009016,-0.0154505,-0.001014,-0.02206,0.040832,0.001497,0.0001235,-0.003636,-0.0044585,0.006287

I believe you, don´t worry, I was just explainning why I did what I did.

Anyway,even getting better distances with Iberia_EEF, WHG practically dissappears and some strange Natufian apperas in my results.

With Iberia_EEF

Target: gixajo_scaled
Distance: 2.2414% / 0.02241432
61.4 Iberia_EEF
34.2 Yamnaya_RUS_Samara
3.6 Levant_Natufian
0.8 MAR_EN

Target: gixajo_dad_scaled
Distance: 2.3139% / 0.02313912
63.0 Iberia_EEF
32.4 Yamnaya_RUS_Samara
3.4 MAR_EN
1.2 Levant_Natufian

Target: gixajo_mom_scaled
Distance: 2.7161% / 0.02716124
61.2 Iberia_EEF
34.6 Yamnaya_RUS_Samara
2.8 WHG
1.4 MAR_EN

With LBKs

Target: gixajo_scaled
Distance: 3.0693% / 0.03069291
55.4 LBK
31.2 Yamnaya_RUS_Samara
10.4 WHG
3.0 MAR_EN

Target: gixajo_dad_scaled
Distance: 2.7760% / 0.02776006
58.8 LBK
29.2 Yamnaya_RUS_Samara
7.2 WHG
4.8 MAR_EN

Target: gixajo_mom_scaled
Distance: 3.5820% / 0.03582031
52.6 LBK
32.6 Yamnaya_RUS_Samara
11.8 WHG
3.0 MAR_EN

With both at the same time, as you said, algortim prefers Iberia_EEf as solution:

Target: gixajo_scaled
Distance: 2.2491% / 0.02249126
53.0 Iberia_EEF
34.0 Yamnaya_RUS_Samara
10.4 LBK
2.2 MAR_EN
0.4 WHG

Target: gixajo_dad_scaled
Distance: 2.1401% / 0.02140143
40.2 Iberia_EEF
31.4 Yamnaya_RUS_Samara
24.0 LBK
4.4 MAR_EN

Target: gixajo_mom_scaled
Distance: 2.6947% / 0.02694653
53.4 Iberia_EEF
34.4 Yamnaya_RUS_Samara
7.0 LBK
3.6 WHG
1.6 MAR_EN

And again, when I post that model using LBK, the model was for every European, not just for Iberians, so probably what I did was better option because our Iberian_EEF could have created "interferences" reading the Neolithic+WHG of Northern Europeans as Iberia_EEF.

For example:

Target: Norwegian
Distance: 3.4021% / 0.03402114
52.6 Yamnaya_RUS_Samara
24.8 Iberia_EEF
12.8 LBK
9.8 WHG

Target: German
Distance: 2.8730% / 0.02873033
49.0 Yamnaya_RUS_Samara
30.0 LBK
12.4 Iberia_EEF
8.6 WHG

Target: English
Distance: 3.2677% / 0.03267709
50.2 Yamnaya_RUS_Samara
29.8 Iberia_EEF
12.6 LBK
7.4 WHG

[Ruderico]

Yeah it's all as expected, particularly your mum benefiting even more from the Iberian EEF references.
It's interesting that using Iberian EEFs is making steppe ancestry higher than using LBK, I have no idea why the algorithm does this, nor if it's any more valid.

[Brás Garcia de Mascarenhas]

Target: Viriato_scaled
Distance: 2.7569% / 0.02756924
46.0 Iberia_EEF
34.2 Yamnaya_RUS_Samara
12.6 LBK
6.0 MAR_EN
1.2 WHG

Target: Viriato_Wife_scaled
Distance: 2.5882% / 0.02588185
46.2 Iberia_EEF
34.4 Yamnaya_RUS_Samara
11.8 LBK
6.2 MAR_EN
1.4 Levant_Natufian

Target: Viriato_Father_scaled
Distance: 1.5579% / 0.01557927
38.0 Yamnaya_RUS_Samara
30.2 LBK
25.4 Iberia_EEF
6.2 MAR_EN
0.2 WHG

[gixajo]

Yeah it's all as expected, particularly your mum benefiting even more from the Iberian EEF references.
It's interesting that using Iberian EEFs is making steppe ancestry higher than using LBK, I have no idea why the algorithm does this, nor if it's any more valid.

Maybe using unscaled coordinates with some penalization could give more stable or accurate results.

Anyway, all those papers don´t use G25 to show their samples genetic admixtures (as far as I know), so it´s difficult to get exactly all those results with our models.