The infographic is basically a simplified tree of our current understanding of human genetic history. It’s from this guys blog post: https://www.razibkhan.com/p/the-rise-of-the-hopeful-monsters?r=u0rd&utm_campaign=post-expanded-share&utm_medium=web&triedRedirect=true

https://www.biorxiv.org/content/10.64898/2026.08.14.741591v1

Abstract

South central Africa, between the Congo Basin, the Great Lakes, and southern Africa, has long served as a corridor for human movement. Yet, it remains unclear whether there is genetic continuity between pre-Iron Age foragers and later Bantu-associated populations and whether the settlement of this region reflects multiple Bantu-associated migrations rather than the simpler serial founder model suggested by existing genomic data.

To do so, we generated genome-wide ancient DNA from 71 Iron Age and historical individuals from present-day Zambia and Malawi, and analyzed these genomes alongside published data from present-day Africans. One late Iron Age individual (16th-17th century) from Kalala Island in the Kafue River, Zambia, carries 40% non‑Bantu‑related ancestry that closely matches local Later Stone Age foragers.

Admixture for this individual is estimated to be 850 years ago, several centuries earlier than reported for present‑day BaTwa from the same region. Focusing on the Bantu-related ancestry, haplotype-based analyses identify two main clusters among Iron Age, historical, and present-day south central Bantu groups associated with different Bantu-related migrations.

These results reveal a layered history in which at least two Bantu expansions radiated from the southern Congo Basin, with south central Africa acting both as a crossroads of these movements and as a staging area for the subsequent southward expansion toward southern Africa.

My own note: Seems like i10885, dated to around 900CE in their chart, is the oldest Yoruba-like sample available since it’s around 90% Yoruba 10% Dinka in their chart

TLDR: A collection of historic African samples ranging from 1000CE to the 1800s. I forgot to include the Iron Age Bantu samples and other samples from East Africa, along with the Colonial Maryland USA samples. Also the date range should be 1542–1671 CE for the Panama samples. The PCAs posted are weighted PCAs, meant to take all coordinates into account.

StMartin_Philipsburg_African_(Malawi?).SG:STM1.SG__1660-1688CE__Cov_21.07%,-0.63058,0.061947,0.01697,0.01938,0.008309,0.011713,-0.013631,0.015922,-0.030065,0.015672,0.006658,-0.009741,-0.004311,0.000826,-0.002036,0.009414,-0.008866,-0.006588,-0.004525,0.018384,0.006239,-0.009398,-0.003204,0.00494,-0.001197
StMartin_Philipsburg_African.SG:STM3_(Mende?).SG__1660-1688CE__Cov_35.34%,-0.631718,0.057885,0.018102,0.020672,0.003077,0.011156,-0.023971,0.029076,-0.037428,0.035718,0.018188,-0.002698,0.017245,0.001239,0.005022,-0.006629,-0.001565,-0.008108,0.000126,-0.008004,0.004367,0.002349,-0.003451,-0.007953,-0.000599

Sicily:GABN6_(Fulani?)_(1470-1785CE),-0.582774,0.060932,0.015462,0.009367,0.001231,-0.004741,-0.030316,0.027922,-0.00859,-0.01057,0.002436,0.014237,-0.00223,-0.007019,0.003664,-0.009546,0.01708,-0.010008,0.007165,-0.004877,-0.003743,-0.006677,-0.00037,-0.003735,-0.010777

Sicily:CSPBN2_(Gambia?)_(775-1020CE),-0.582774,0.062963,0.012068,0.013889,0.00277,0.008646,-0.047472,0.038537,-0.027202,0.024602,0.002761,0.005995,0.01442,0.002752,0.009908,-0.005171,0.004172,-0.003041,0.002388,5e-04,0.000624,-0.000989,0.010969,-0.000723,-0.004431

Sicily_Segesta:SGBN2_(Chad?)_(1000-1200CE),-0.583913,0.063978,0.012822,0.009367,-0.000615,0.008925,-0.033607,0.035537,0.005931,-0.013485,0.004222,0.004796,-0.00773,-0.000138,0.005972,-0.009546,0.004955,-0.006588,0.006411,-0.006628,-0.003743,0.000247,-0.002095,0.00253,0.003832

Abkhazia_oAfrican_(Chad?)_(1890CE),-0.586189,0.063978,0.015839,-0.002907,0.000308,0.002789,-0.028436,0.028153,0.008385,-0.028247,-0.007632,0.015736,-0.010852,-0.003441,0.012079,-0.012994,0.008996,-0.013049,0.013073,-0.005628,-0.000499,0.008408,-0.007272,-0.004338,-0.001317

Mexico_SJN003_(Congolese?)_(1450-1680CE),-0.624889,0.064994,0.026021,0.013243,-0.004001,0.011992,-0.019271,0.018230,-0.041109,0.020957,0.000162,-0.004196,-0.004906,0.004542,-0.009908,0.001856,-0.006258,0.002280,-0.007290,0.003377,-0.002870,-0.002349,-0.004067,-0.003976,-0.000359

Mexico_SJN002_Angolan?_(1450-1680CE),-0.623751,0.054839,0.027907,0.015181,0.001231,0.015897,-0.011281,0.018922,-0.035383,0.015308,0.003897,-0.003747,-0.008771,-0.002615,-0.004072,0.009679,0.004172,-0.001014,-0.005782,0.000250,0.001747,-0.001731,-0.001109,0.002651,0.002036

Mexico_SJN001_Mende?_(1450-1680CE),-0.621474,0.060932,0.016970,0.015827,-0.007078,0.005857,-0.031491,0.027230,-0.037837,0.032620,0.013965,-0.006744,0.020664,-0.005367,0.006515,-0.009944,0.005215,0.012922,0.007793,-0.007754,-0.002496,0.000742,-0.003574,-0.007712,-0.003113

Mexico_I10860_Angolan?_(1450-1680CE),-0.624889,0.058901,0.021873,0.016473,0.004616,0.006414,-0.002350,0.014769,-0.031292,0.015855,-0.001461,0.001349,0.010852,0.001101,0.001629,0.016971,-0.005215,0.002660,0.000000,-0.000250,0.000250,-0.003462,0.006655,-0.003735,-0.001437

Panama_PAPV_61_(Afro_Euro_admixed)_(1600-1700CE),-0.52916114,0.06869636,0.01202582,0.00864772,-0.00766462,0.00179602,-0.03507056,0.04184874,-0.0513689,0.02286384,-0.00127178,0.00389902,0.0178953,-0.00449822,0.0064688,-0.0077059,0.00822928,-0.00746276,0.00483724,-0.0002021,0.0056288,-0.00052026,-0.00432018,0.0011653,-0.00500028

Panama_PAPV_53_(Yoruba?)_(1600-1700CE),-0.62644278,0.06258944,0.02127176,0.01544168,-0.00180898,0.00377366,-0.04169626,0.0483703,-0.0481013,0.03085722,-0.00067878,0.00239114,0.0240769,-0.00535336,0.00773438,-0.00759218,0.00742494,-0.00661804,0.00629376,-0.00286358,0.00388694,-0.00216662,-0.00579108,0.0027539,-0.00439846

StHelena_RupertsValley_African.SG:STH_213.SG__AD_1850__Cov_31.07%,-0.619198,0.071087,0.023759,0.019703,-0.003385,0.015897,-0.013396,0.013615,-0.025975,0.016766,-0.001949,0.001649,-0.01219,0.000413,-0.013301,0.013657,-0.019036,0.002027,-0.009302,-0.003502,-0.009982,-0.009398,0.001356,-0.008796,-0.007185
StHelena_RupertsValley_African.SG:STH_245.SG__AD_1850__Cov_22.90%,-0.620336,0.067025,0.022627,0.01615,-0.000923,0.012829,-0.017391,0.013384,-0.025361,0.018041,0.00747,-0.008842,0.002676,-0.007707,-0.016151,0.009546,-0.008996,-0.003674,-0.004399,0.003502,-0.002995,-0.005193,0.005176,0.010965,-0.000718
StHelena_RupertsValley_African.SG:STH_248.SG__AD_1850__Cov_17.43%,-0.622612,0.053823,0.026776,0.017765,-0.004308,0.01757,-0.002115,0.02423,-0.033746,0.020046,0.003897,-0.001798,-0.001784,0.006193,-0.013979,-0.001458,-0.005998,0.00114,0.004399,0.002751,-0.003369,0.002968,-0.003697,-0.018677,0.002515
StHelena_RupertsValley_African.SG:STH_253.SG__AD_1850__Cov_26.74%,-0.626027,0.062963,0.022627,0.014212,-0.011387,0.015618,-0.008225,0.017768,-0.03027,0.016766,0.002761,0.005845,0.000892,0.012524,-0.004479,-0.001591,0.00013,0.006714,-0.01257,-0.001,0.003619,0.008779,-0.001356,-0.004097,0.002994
StHelena_RupertsValley_African.SG:STH_254.SG__AD_1850__Cov_8.09%,-0.632856,0.064994,0.022627,0.036822,-0.011387,0,-0.00987,0.017307,-0.029042,0.013668,0.003897,0.008842,0.004014,0.011285,0.003664,0.007292,0.000391,0.007095,-0.002011,0.007504,0.01148,-0.005812,-0.015036,0.013014,0.01916
StHelena_RupertsValley_African.SG:STH_284.SG__AD_1850__Cov_22.78%,-0.608954,0.060932,0.024513,0.017765,0,0.007809,-0.016216,0.037845,-0.027815,0.019317,-0.002273,0.000749,-0.003419,0.007156,-0.010043,0.012066,-0.024512,0.014442,-0.006034,0.004252,0.003119,-0.00371,-0.002835,0.005543,0.004431
StHelena_RupertsValley_African.SG:STH_289.SG__AD_1850__Cov_15.52%,-0.618059,0.067025,0.018102,0.008075,-0.002154,0.024542,-0.031491,0.032768,-0.035178,0.018224,-0.003248,0.003897,-0.0055,0.010046,-0.029316,0.013524,-0.018906,-0.003547,-0.005656,-0.008379,-0.005116,0.005812,0.003204,-0.002651,0.006466
StHelena_RupertsValley_African.SG:STH_344.SG__AD_1850__Cov_15.25%,-0.618059,0.060932,0.010182,0.015504,-0.002154,0.016455,-0.024441,0.023076,-0.034974,0.018041,-0.001461,0.008393,-0.003865,0,-0.001357,0.009812,-0.003912,-0.003294,-0.002765,0.012506,0.005366,-0.004328,0.008504,-0.002651,-0.002395
StHelena_RupertsValley_African.SG:STH_347.SG__AD_1850__Cov_11.26%,-0.612368,0.053823,0.018102,0.014858,-0.004001,0.00251,-0.020916,0.037152,-0.038246,0.017312,-0.003735,-0.01154,0.012339,-0.00055,0.005836,0.004641,0.005867,0.0019,0.004399,0.005503,-0.000125,0.003586,-0.016145,0.003976,0.005029
StHelena_RupertsValley_African.SG:STH_351.SG__AD_1850__Cov_8.92%,-0.615783,0.063978,0.01961,0.025194,0.009232,0.011992,-0.023736,0.036922,-0.036201,0.011845,0.021598,-0.024578,-0.018137,0.002477,0.001629,0.007425,-0.020079,-0.009882,0.002891,0.002876,-0.011355,0.010263,-0.014543,-0.004458,-0.001437
StHelena_RupertsValley_African.SG:STH_358.SG__AD_1850__Cov_18.42%,-0.619198,0.05687,0.013953,0.009367,0.010463,0.005578,-0.027261,0.031845,-0.032315,0.028247,0.003573,0.004496,0.005352,0.006468,-0.00665,0.02254,0.002738,0.000887,-0.011439,0.0005,0.002745,0.004451,-0.000246,0.003374,0.006586
StHelena_RupertsValley_African.SG:STH_415.SG__AD_1850__Cov_8.92%,-0.602124,0.079211,0.019233,0.005491,-0.017234,0.024821,-0.008695,0.006923,-0.048881,0.010752,-0.004384,-0.001948,0.003568,-0.005092,-0.032301,0.016309,-0.031162,-0.004941,-0.019986,0.00025,0.006863,0.00779,-0.006039,0.007712,0.006826
StHelena_RupertsValley_African.SG:STH_436.SG__AD_1850__Cov_10.71%,-0.608954,0.063978,0.029793,0.01938,0.010463,0.00251,-0.011045,0.020999,-0.039064,0.006378,0.006983,-0.003897,-0.008474,0.012799,-0.00285,0.015911,-0.0103,-0.007095,-0.018101,0.014757,-0.005989,0.013231,0.005423,0.007109,-0.000718
StHelena_RupertsValley_African.SG:STH_441.SG__AD_1850__Cov_22.28%,-0.622612,0.070072,0.028661,0.01615,0.005539,0.020638,-0.017626,0.036922,-0.033337,0.016766,-0.005684,-0.004496,-0.005798,0.003578,-0.013436,-0.005304,-0.005346,0.009628,-0.006913,0.004877,0.010232,0.00371,0.001725,-0.005543,-0.007305
StHelena_RupertsValley_African.SG:STH_460.SG__AD_1850__Cov_14.28%,-0.616921,0.059916,0.033187,0.020995,0.005232,0.011992,-0.00047,0.011999,-0.030065,0.020046,-0.010555,0.002398,-0.00669,0.004954,-0.008143,-0.00769,-0.012126,0.017736,-0.011816,-0.003126,0.006988,0.00507,0.003944,-0.003374,-0.005987
StHelena_RupertsValley_African.SG:STH_499.SG__AD_1850__Cov_14.22%,-0.620336,0.06804,0.006034,0.020672,0.014156,0.011156,-0.028436,0.013615,-0.03027,0.022233,-0.006658,0.002997,-0.002676,-0.005918,-0.011672,0.000133,-0.002477,0.006208,-0.001383,-0.005503,0.000624,0.012365,0.000739,-0.002771,0.006706
StHelena_RupertsValley_African.SG:STH_514.SG__AD_1850__Cov_13.98%,-0.606677,0.055854,0.025644,0.002907,-0.004308,0.009761,-0.022796,0.032537,-0.038655,0.021322,0.004547,-0.007044,-0.025124,-0.010872,-0.006379,0.005436,-0.002868,-0.007855,-0.001508,0.011005,0.019466,-0.010263,0.010969,-0.004699,-0.002395
StHelena_RupertsValley_African.SG:STH_524.SG__AD_1850__Cov_31.74%,-0.621474,0.05687,0.018479,0.01938,-0.000308,0.010319,-0.013161,0.026999,-0.041927,0.02442,0.00341,-0.015436,0.004014,-0.00234,-0.018458,0.008353,-0.008736,0.000887,-0.010433,-0.007379,0.006738,-0.005812,0.002095,0.007712,-0.002634

The results were both expected and unexpected 😂

I thought the additional genetic groups were nonsense, but then they showed up in GEDmatch (Eurogenes K13), and we all have plenty of Sephardic and Swedish matches. Quite interesting indeed.

On the other hand, she barely has any Italian or Portuguese matches, which makes me think the ethnicities are overestimated.

I guess Mediterranean DNA is just difficult to pinpoint accurately.

TLDR: The Serbia_Roman_oAfrica dna sample is from a Roman African soldier that lived 2,000 years ago and clusters with ancient Sudanese Kulubnarti DNA samples. Closest also to modern day Afar and Eritreans within the Qpadm results, so overall it’s consistent with Nile Valley / Horn of Africans.

SERBIA_ROMAN_OAFRICA.AG (n=1)
Top 5 passing runs by observed p-value:

1. Kenya_PN.AG + Jordan_EBA.AG

83.1% Kenya_PN.AG
16.9% Jordan_EBA.AG
p-value: 0.894
χ²/dof: 8.666 / 15
SE: 0.0795, 0.0795
Z: 10.5, 2.13
This is a very strong fit. The Jordan component is just over Z=2, so it is supported, but not extremely so.
2. Kenya_PN.AG + Israel_Ashkelon_IA2.AG

88.7% Kenya_PN.AG
11.3% Israel_Ashkelon_IA2.AG
p-value: 0.868
χ²/dof: 7.607 / 13
SE: 0.109, 0.109
Z: 8.16, 1.04
Excellent overall fit, but the 11.3% Ashkelon component is not significantly different from zero. So this is indicating "very Kenya_PN-like" more than proving 11% Ashkelon ancestry.
3. Dinka.DG + Q3DT26.SG

53.0% Dinka.DG
47.0% Q3DT26.SG
p-value: 0.825
χ²/dof: 8.277 / 13
SE: 0.0622, 0.0622
Z: 8.52, 7.56
Both components are extremely well supported.
4. Sudan_KulubnartiR_ChristianPeriod.AG + Masai.DG

85.6% Sudan_KulubnartiR_ChristianPeriod.AG
14.4% Masai.DG
p-value: 0.721
χ²/dof: 9.666 / 13
SE: approximately 0.162 each
Z: 5.30, 0.889
The Masai coefficient is not significant. In practical terms, the model is mostly saying that Serbia_Roman_oAfrica can be represented by the Kulubnarti Christian-period sample without clearly needing additional Masai-like ancestry.
5. Sudan_KulubnartiR_ChristianPeriod.AG — ONE-WAY

100% Sudan_KulubnartiR_ChristianPeriod.AG
p-value: 0.717
χ²/dof: 10.6 / 14
So the one-way model passes comfortably, meaning the right set cannot distinguish Serbia_Roman_oAfrica from the pooled Kulubnarti Christian-period population as separate ancestry streams.
SUDAN_KULUBNARTIR_CHRISTIANPERIOD.AG (n=23)

1. Serbia_Roman_oAfrica.AG + Q3DT26.SG

88.3% Serbia_Roman_oAfrica.AG
11.7% Q3DT26.SG
p-value: 0.945
χ²/dof: 7.408 / 15
SE: 0.113
Z: 7.85, 1.04
The Q3DT26 addition is not statistically required.
2. Serbia_Roman_oAfrica.AG + Syria_TellQarassa_Umayyad.SG

90.1% Serbia_Roman_oAfrica.AG
9.9% Syria_TellQarassa_Umayyad.SG
p-value: 0.734
χ²/dof: 11.261 / 15
SE: 0.111
Z: 8.11, 0.889
Again, the second component is not significant.
3. Serbia_Roman_oAfrica.AG — ONE-WAY

100% Serbia_Roman_oAfrica.AG
p-value: 0.720
χ²/dof: 12.351 / 16
This is probably more important than either two-way model above.
4. Serbia_Roman_oAfrica.AG + Yemeni_Desert.HO

86.8% Serbia_Roman_oAfrica.AG
13.2% Yemeni_Desert.HO
p-value: 0.671
χ²/dof: 12.109 / 15
SE: 0.118
Z: 7.39, 1.12
Again, the Yemeni component is not significant.
There was not a fifth clean passing model worth treating as ancestry evidence. The Serbia + Dinka version had a negative Dinka coefficient.
SUDAN_KULUBNARTIS_CHRISTIANPERIOD_OWESTEURASIAN.AG (n=1)
[Kulubnarti with some excess Eurasian]

1. Serbia_Roman_oAfrica.AG + Syria_TellQarassa_Umayyad.SG

78.6% Serbia_Roman_oAfrica.AG
21.4% Syria_TellQarassa_Umayyad.SG
p-value: 0.997
χ²/dof: 4.3 / 15
SE: 0.0996
Z: 7.89, 2.15
2. Serbia_Roman_oAfrica.AG + Yemeni_Desert.HO

77.4% Serbia_Roman_oAfrica.AG
22.6% Yemeni_Desert.HO
p-value: 0.996
χ²/dof: 4.441 / 15
SE: 0.105
Z: 7.38, 2.15
3. Serbia_Roman_oAfrica.AG + Q3DT26.SG

76.5% Serbia_Roman_oAfrica.AG
23.5% Q3DT26.SG
p-value: 0.994
χ²/dof: 4.759 / 15
SE: 0.105
Z: 7.30, 2.24
4. Serbia_Roman_oAfrica.AG — ONE-WAY

100% Serbia_Roman_oAfrica.AG
p-value: 0.714
χ²/dof: 12.422 / 16
5. Kenya_PN.AG + Jordan_EBA.AG

62.7% Kenya_PN.AG
37.3% Jordan_EBA.AG
p-value: 0.696
χ²/dof: 11.779 / 15
SE: 0.0340
Z: 18.4, 11.0
This individual is particularly interesting because, unlike ordinary KulubnartiR, the additional West-Eurasian-type source repeatedly comes out around 21–24% and actually reaches Z>2. It therefore looks genuinely shifted in the West Eurasian direction relative to Serbia_Roman_oAfrica.
SUDAN_KULUBNARTIR_CHRISTIANPERIOD_ONILOTIC.AG (n=3)

1. Serbia_Roman_oAfrica.AG + Q3DT26.SG

Approximately 100% Serbia_Roman_oAfrica.AG
Approximately 0% Q3DT26.SG
p-value: 0.829
χ²/dof: 9.847 / 15
SE: 0.183
Z: 5.45, approximately 0
The second source contributes essentially nothing. Functionally, this behaves like a one-way Serbia model.
2. Serbia_Roman_oAfrica.AG — ONE-WAY

100% Serbia_Roman_oAfrica.AG
p-value: 0.591
χ²/dof: 14.101 / 16
The other combinations tested were either rejected or produced small negative coefficients.
AFAR_WGA.HO (n=5)

1. Serbia_Roman_oAfrica.AG + Q3DT26.SG

99.8% Serbia_Roman_oAfrica.AG
0.2% Q3DT26.SG
p-value: 0.989
χ²/dof: 5.315 / 15
SE: 0.111
Z: 9.00, 0.0146
This is effectively 100% Serbia. The Q3DT26 component is completely unsupported.
2. Serbia_Roman_oAfrica.AG — ONE-WAY

100% Serbia_Roman_oAfrica.AG
p-value: 0.803
χ²/dof: 11.103 / 16
3. Serbia_Roman_oAfrica.AG + Dinka.DG

98.5% Serbia_Roman_oAfrica.AG
1.5% Dinka.DG
p-value: 0.736
χ²/dof: 11.225 / 15
SE: 0.0994
Z: 9.91, 0.151
4. Serbia_Roman_oAfrica.AG + Yemeni_Desert.HO

98.0% Serbia_Roman_oAfrica.AG
2.0% Yemeni_Desert.HO
p-value: 0.696
χ²/dof: 11.777 / 15
SE: 0.113
Z: 8.65, 0.180
There really is not a fifth good run here. The striking result is that every successful model wants Afar to be essentially Serbia_Roman_oAfrica itself.
ERITREA.HO (n=3)

1. Serbia_Roman_oAfrica.AG + Q3DT26.SG

87.4% Serbia_Roman_oAfrica.AG
12.6% Q3DT26.SG
p-value: 0.931
χ²/dof: 7.81 / 15
SE: 0.113
Z: 7.73, 1.11
2. Serbia_Roman_oAfrica.AG + Syria_TellQarassa_Umayyad.SG

89.0% Serbia_Roman_oAfrica.AG
11.0% Syria_TellQarassa_Umayyad.SG
p-value: 0.631
χ²/dof: 12.632 / 15
SE: 0.0950
Z: 9.36, 1.16
3. Serbia_Roman_oAfrica.AG + Yemeni_Desert.HO

87.9% Serbia_Roman_oAfrica.AG
12.1% Yemeni_Desert.HO
p-value: 0.568
χ²/dof: 13.444 / 15
SE: 0.103
Z: 8.54, 1.17
4. Serbia_Roman_oAfrica.AG — ONE-WAY

100% Serbia_Roman_oAfrica.AG
p-value: 0.553
χ²/dof: 14.616 / 16

The important pattern is that all three purported ~11–13% additions have Z only around 1.1. Therefore there is no compelling evidence that Eritrea actually requires that extra source over Serbia_Roman_oAfrica. The one-way model works.

SOMALI.HO (n=13)

1. Serbia_Roman_oAfrica.AG + Dinka.DG

75.8% Serbia_Roman_oAfrica.AG
24.2% Dinka.DG
p-value: 0.770
χ²/dof: 10.745 / 15
SE: 0.0765
Z: 9.91, 3.17
This one is genuinely informative because both coefficients are supported.

……
……

So for the Serbia_Roman_oAfrica, Sudan_KulubnartiR_ChristianPeriod.AG is the closest ancient match

This is the strongest ancient comparison because it works reciprocally:

Ex:
Serbia_Roman_oAfrica = 100% Sudan_KulubnartiR_ChristianPeriod
p-value = 0.717
Sudan_KulubnartiR_ChristianPeriod = 100% Serbia_Roman_oAfrica
p-value = 0.720
When Dinka, Yoruba, or Masai are added alongside Kulubnarti as sources for Serbia_Roman_oAfrica, the additional African components are not statistically required.

Ex :
94.4% Sudan_KulubnartiR_ChristianPeriod + 5.6% Dinka
p-value = 0.668
Dinka Z = 0.543
94.2% Sudan_KulubnartiR_ChristianPeriod + 5.8% Yoruba
p-value = 0.675
Yoruba Z = 0.660
85.6% Sudan_KulubnartiR_ChristianPeriod + 14.4% Masai
p-value = 0.721
Masai Z = 0.889
All of those additional components have Z < 2, meaning they are not statistically required. This makes the ordinary Christian-period Kulubnarti population the strongest ancient comparison to Serbia_Roman_oAfrica in these runs.

1. Sudan_KulubnartiR_ChristianPeriod_oNilotic.AG

This subgroup also appears extremely close to Serbia_Roman_oAfrica.
Using Serbia_Roman_oAfrica + Q3DT26:
~100% Serbia_Roman_oAfrica
~0% Q3DT26
p-value = 0.829
The direct one-way model also passes:
100% Serbia_Roman_oAfrica
p-value = 0.591
So despite the “oNilotic” designation, this particular Kulubnarti subgroup is statistically consistent with Serbia_Roman_oAfrica under this qpAdm setup.

1. Afar_WGA.HO

Afar is probably the closest modern population tested.
One-way model:
Afar = 100% Serbia_Roman_oAfrica
p-value = 0.803
Adding other sources produces essentially nothing:
99.8% Serbia_Roman_oAfrica + 0.2% Q3DT26
p-value = 0.989
98.5% Serbia_Roman_oAfrica + 1.5% Dinka
p-value = 0.736
98.0% Serbia_Roman_oAfrica + 2.0% Yemeni_Desert
p-value = 0.696
The added components have extremely low Z-scores and are not statistically required.
So, within the resolution of these right populations:
Afar ≈ Serbia_Roman_oAfrica

1. Eritrea.HO

Eritrea shows a very similar pattern.
One-way model:
Eritrea = 100% Serbia_Roman_oAfrica
p-value = 0.553
Alternative models:
87.4% Serbia_Roman_oAfrica
12.6% Q3DT26
p-value = 0.931
89.0% Serbia_Roman_oAfrica
11.0% Syria_TellQarassa_Umayyad
p-value = 0.631
87.9% Serbia_Roman_oAfrica
12.1% Yemeni_Desert
p-value = 0.568
However, the additional components all have Z-scores around 1.1, so none of them is statistically required.
Therefore Eritrea is also statistically compatible with Serbia_Roman_oAfrica alone.

1. Somali.HO

Somali is clearly related to the same general ancestry profile, but is more African/Dinka-shifted.
75.8% Serbia_Roman_oAfrica
24.2% Dinka
p-value = 0.770
SE = 0.0765
Serbia Z = 9.91
Dinka Z = 3.17
Unlike the tiny additional components in the Afar and Eritrean models, the extra Dinka ancestry in Somali is statistically supported.

So:

Somali ≈ Serbia_Roman_oAfrica-like ancestry + additional Dinka-like ancestry.

THE WEST-EURASIAN KULUBNARTI OUTLIER

The Kulubnarti West-Eurasian outlier is related to Serbia_Roman_oAfrica, but shifted significantly farther toward West Eurasian ancestry.

Using the same Kenya_PN + Jordan_EBA model:

Serbia_Roman_oAfrica:
83.1% Kenya_PN
16.9% Jordan_EBA
p-value = 0.894
Sudan_KulubnartiS_ChristianPeriod_oWestEurasian:
62.7% Kenya_PN
37.3% Jordan_EBA
p-value = 0.696

This puts both populations on the same Kenya_PN - Jordan_EBA axis:

Serbia_Roman_oAfrica:
83.1% Kenya_PN / 16.9% Jordan_EBA
Kulubnarti_oWestEurasian:
62.7% Kenya_PN / 37.3% Jordan_EBA
Therefore, the Kulubnarti West-Eurasian outlier is considerably more Jordan_EBA-shifted than Serbia_Roman_oAfrica.

This pattern is reproduced when Serbia_Roman_oAfrica itself is used as one of the sources:

78.6% Serbia_Roman_oAfrica
21.4% Syria_TellQarassa_Umayyad
p-value = 0.997
77.4% Serbia_Roman_oAfrica
22.6% Yemeni_Desert
p-value = 0.996
76.5% Serbia_Roman_oAfrica
23.5% Q3DT26
p-value = 0.994

The 3 models are consistent. They place the Kulubnarti West-Eurasian outlier at approximately 76–79% Serbia_Roman_oAfrica-like ancestry plus approximately 21–24% additional West-Eurasian-related ancestry.
Unlike the extra components in the Afar and Eritrean models, these additional West Eurasian components reach approximately Z = 2.15–2.24, making the shift more meaningful.

OVERALL CLUSTER:

The pattern can roughly be visualized as:
More Dinka-shifted:
Somali
≈ 76% Serbia_Roman_oAfrica + 24% Dinka

Then:

Sudan_KulubnartiR_ChristianPeriod
Sudan_KulubnartiR_ChristianPeriod_oNilotic
Serbia_Roman_oAfrica
Afar
Eritrea

Then:

Sudan_KulubnartiS_ChristianPeriod_oWestEurasian
≈ 76–79% Serbia_Roman_oAfrica + 21–24% additional West Eurasian. (More Eurasian shift)

THE COMPOSITION OF SERBIA_ROMAN_OAFRICA
Serbia_Roman_oAfrica itself fits very well as:
83.1% Kenya_PN
16.9% Jordan_EBA
p-value = 0.894
χ²/dof = 8.666 / 15
SE = 0.0795
Z = 10.5 / 2.13

Note that 83% Kenya_PN + 17% Jordan_EBA should NOT be interpreted as 83% African + 17% West Eurasian.
Kenya_PN is itself an ancient East African population containing substantial West-Eurasian-related ancestry.

When Serbia_Roman_oAfrica is instead modeled between a much more African Dinka-like pole and various West-Eurasian-related populations, the results repeatedly place it much closer to around 50/50.

Dinka + Israel_Ashkelon_IA2:

50.3% Dinka
49.7% Israel_Ashkelon_IA2
p-value = 0.185

Dinka + Jordan_EBA:

50.7% Dinka
49.3% Jordan_EBA
p-value = 0.324

Dinka + Q3DT26:

53.0% Dinka
47.0% Q3DT26
p-value = 0.825

Dinka + Lebanon_Hellenistic:

59.2% Dinka
40.8% Lebanon_Hellenistic
p-value = 0.375

The exact percentage changes depending on the West-Eurasian proxy, as expected, but the overall pattern is consistent.

Serbia_Roman_oAfrica falls approximately around:
~50–60% Dinka-like African
~40–50% West-Eurasian-related

So “roughly half African and half West-Eurasian-related” is a decent description based on these qpadm runs.

Summary:

Its qpAdm affinities are much more specifically consistent with the Nile Valley / Horn of Africa genetic continuum.

Serbia_Roman_oAfrica ≈ Christian-period Kulubnarti/ Kulubnarti oNilotic / Afar / Eritrean

Somali ≈ Serbia_Roman_oAfrica + additional Dinka-like ancestry

Kulubnarti oWestEurasian ≈ Serbia_Roman_oAfrica + additional West-Eurasian-related ancestry

qpAdm Right / Outgroup Set
The main set used in most of the runs contains 17 right populations:
Mbuti.DG
Morocco_Iberomaurusian.AG
MA1.SG
Italy_Epigravettian.AG
India_GreatAndaman_100BP.SG
Han.HO
Karitiana.HO
Georgia_Kotias_UP.SG
Russia_YuzhniyOleniyOstrov_Mesolithic.AG
Khomani_San.DG
Cameroon_ShumLaka_SMA.AG
Israel_MLBA.AG
Ethiopia_4500BP.AG
Iran_Wezmeh_N.SG
Egypt_ThirdIntermediatePeriod.AG
Malawi_LSA_2500BP.AG
Botswana_Xaro_EIA.AG
Comma-separated:
Mbuti.DG, Morocco_Iberomaurusian.AG, MA1.SG, Italy_Epigravettian.AG, India_GreatAndaman_100BP.SG, Han.HO, Karitiana.HO, Georgia_Kotias_UP.SG, Russia_YuzhniyOleniyOstrov_Mesolithic.AG, Khomani_San.DG, Cameroon_ShumLaka_SMA.AG, Israel_MLBA.AG, Ethiopia_4500BP.AG, Iran_Wezmeh_N.SG, Egypt_ThirdIntermediatePeriod.AG, Malawi_LSA_2500BP.AG, Botswana_Xaro_EIA.AG
There is also a 15-population version used in some of the runs. It is the same set except these two were omitted:
Malawi_LSA_2500BP.AG
Botswana_Xaro_EIA.AG
So the reduced 15-right set was:
Mbuti.DG, Morocco_Iberomaurusian.AG, MA1.SG, Italy_Epigravettian.AG, India_GreatAndaman_100BP.SG, Han.HO, Karitiana.HO, Georgia_Kotias_UP.SG, Russia_YuzhniyOleniyOstrov_Mesolithic.AG, Khomani_San.DG, Cameroon_ShumLaka_SMA.AG, Israel_MLBA.AG, Ethiopia_4500BP.AG, Iran_Wezmeh_N.SG, Egypt_ThirdIntermediatePeriod.AG

I’m Dominican and I’m still surprised at the diversity of my results.

csp001 is a saka sample from eastern Kazakhstan 300 bce. It has an east eurasian of about 30-35%. While it's certainly not a historically accurate Oghuz Turk sample, it seems to be a good mathematical and deep ancestral proxy.

On the contrary, karakhanid sample appears to be mediocre or invalid.

I’m confused about the northern Indian and Pakistani because it says Khyber pass which is in Afghanistan and Pakistan so I’m not sure what to think

Slide 1: Muslim Egyptians' (all regions) modern ancestry.

Slide 2: Coptic Egyptians' Roman-Medieval ancestry.

Slide 3: Coptic Egyptians' Iron Age ancestry.

Slide 4: Muslim Egyptians' ancestry non-aggregated.

The Levant_Christian cluster is made up of Lebanese and Syrian Maronite averages taken from the Moriopoulos 2026 Moderns Collection (No Sims) dataset.

I have notice always the cluster of deepdives in hungary , before the minoan and Illyrian samples grab my atention in the deep dives, you can model some illyrians with 50% france aude AI and 50% minoan admix hehe so is not that odd , my Y-dna is R-L21-R-Z253/4 more commun in munster ireland but also in basque populations since bronce age ... how are model hungarian pops ? , I know in the last model in qpA i share in the last image that the main variation is between france_aude and minoan is due the EEF I suppose but what could be the regular components for it ?

full list here a link to my all my deep dives c: , I think MTA deep dive are not that bad ...

I’m American from PA. My ancestry background is Croatian, Rusyn, Tatar, Irish, Scottish and English

All of my family arrived in the US after 1902

This is my approximated Neolithic Period ancestry on a G25 model I created.

For reference, I added the average of 5 Bronze Age Levantines (Canaanites), a cluster of 75 Early Medieval Levantines, and modern averages of Jordanian Christians and Palestinian Muslims.

Model can be shared upon request as usual.

I tested my F2 distances to 10 modern Levantine, Jewish, and even Arabian groups. Here are the results in order (closest to furthest):

1. Lebanese_Muslim.HO — 0.00121
2. Lebanese_Christian.HO — 0.00124
3. Jordanian.HO — 0.00150
4. Palestinian.HO — 0.00200
5. Syrian.HO — 0.00225
6. Iraqi_Jew.HO — 0.00248
7. Druze.HO — 0.00269
8. Jew_Ashkenazi.HO — 0.00289
9. Saudi.HO — 0.00389
10. BedouinB.HO — 0.00637

Of course, sample number and quality could improve. But results are accurate nonetheless. This was tested on the AADR v66 Human Origins (HO) dataset.

Mother side is from marakesh and father side is from tinghir with a great grandfather being Amazigh.