u/NotBradPitt9

From Twitter user @Light3675

From twitter user @m_izanami_

[Queue] Added Standard qpAdm job (1 targets). Position: 1
---
Done by weathertobereported - Run started at 2026-05-17 14:30:10.527906
Target: Turkey_Marmara_Barcin_N.AG (1/1)
Lefts: Turkey_Central_Pinarbasi_Epipaleolithic.AG, Georgia_Kotias_Mesolithic.SG, Iran_TepeGuran_N.SG, Israel_Natufian_I0861.AG
Rights: Mbuti.DG, Russia_MA1_UP.SG, Iran_Wezmeh_N.SG, Georgia_Dzudzuana_UP.AG, Israel_Natufian_I1072.AG, Georgia_Satsurblia_LateUP.SG, Russia_Kostenki14_UP.DG
Weights:
# A tibble: 4 × 5
target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Turkey_Marmara_Barcin_N.AG Turkey_Central_Pinarbasi_Epipa… 0.737 0.0497 14.9
2 Turkey_Marmara_Barcin_N.AG Georgia_Kotias_Mesolithic.SG 0.0641 0.0349 1.83
3 Turkey_Marmara_Barcin_N.AG Iran_TepeGuran_N.SG 0.0850 0.0318 2.68
4 Turkey_Marmara_Barcin_N.AG Israel_Natufian_I0861.AG 0.113 0.0265 4.29

Popdrop:
# A tibble: 15 × 15
pat wt dof chisq p f4rank Turkey_Central_Pinarbasi_Epipale…¹
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0000 0 3 2.69 4.42e- 1 3 0.737
2 0001 1 4 17.1 1.82e- 3 2 0.937
3 0010 1 4 9.20 5.62e- 2 2 0.802
4 0100 1 4 6.21 1.84e- 1 2 0.784
5 1000 1 4 193. 1.08e- 40 2 NA
6 0011 2 5 18.3 2.61e- 3 1 0.949
7 0101 2 5 17.8 3.23e- 3 1 0.946
8 0110 2 5 19.4 1.61e- 3 1 0.965
9 1001 2 5 472. 1.05e- 99 1 NA
10 1010 2 5 209. 3.52e- 43 1 NA
11 1100 2 5 234. 1.77e- 48 1 NA
12 0111 3 6 21.4 1.55e- 3 0 1
13 1011 3 6 1095. 3.11e-233 0 NA
14 1101 3 6 973. 4.84e-207 0 NA
15 1110 3 6 411. 1.16e- 85 0 NA
# ℹ abbreviated name: ¹​Turkey_Central_Pinarbasi_Epipaleolithic.AG
# ℹ 8 more variables: Georgia_Kotias_Mesolithic.SG <dbl>,
# Iran_TepeGuran_N.SG <dbl>, Israel_Natufian_I0861.AG <dbl>, feasible <lgl>,
# best <lgl>, dofdiff <dbl>, chisqdiff <dbl>, p_nested <dbl>

Total: 713 Blocks, 1150639 SNPs

From twitter user @Vamscape

From twitter user @ Vamscape

Yoruba.DG
Model: Yoruba.DG
Sources: Tanzania_Pemba_600BP 90.7%, Morocco_Iberomaurusian 9.3%
p = 0.163
chisq = 16.7
Status: PASS
This passes, and the Morocco_Iberomaurusian component is supported with Z = 2.90, but the overall fit is only moderate. p = 0.163 is decent.

Gambian.DG
Model: Gambian.DG
Sources: Tanzania_Pemba_600BP 88.3%, Morocco_Iberomaurusian 11.7%
p = 0.579
chisq = 10.4
Status: PASS
This one looks good. The p-value is solid, chisq is reasonable, and the Morocco_Iberomaurusian component has Z = 3.07, so it is meaningfully supported. This is the best of these three.

Kongo.DG
Model: Kongo.DG
Sources: Tanzania_Pemba_600BP 96.2%, Morocco_Iberomaurusian 3.8%
p = 0.161
chisq = 16.7
Status: PASS
This passes, but it is not very strong. The Morocco_Iberomaurusian component is only 3.8% with Z = 0.928, so it is not clearly required.

It should be noted that Tanzania_Pemba_600BP is modeled as 100% Bantu South Africa Ovambo in another study. ( https://www.researchgate.net/figure/Admixture-history-of-ancient-African-populations-A-Overview-of-coexistence-of-distinct_fig3_342135975 )
Mostly Bantu genetically. I tried several other source combinations but couldn’t get any to work as well as this one.

The Taforalt ancestry within West Africans is based on Figure 2 of this study: https://www.biorxiv.org/content/10.1101/423079v1.full

TLDR: Qpadm was used to model the Jew_Iranian.HO sample from the Human Origins dataset. Around 70 runs total were completed. These are the best of those runs. The statistically strongest models were achieved with the two way models you see below. Note that the Bahrain genetic samples used in the models are genetically closest to ancient Mesopotamian samples. The runs with Levantine components were not as strongly supported as the two way models below.

Two way models (statistically the strongest):

Model: Jew_Iranian.HO
Sources: Bahrain_LTyLos_Sasanian 84.1%, Turkey_Central_Kalehoyuk_AssyrianColonyPeriod 15.9%
p = 0.786
chisq = 7.99
Status: PASS
Analysis: It has a very high p-value, good chisq, and both components are supported. It is also simple because it is only a 2-way model. The Bahrain/Tylos source captures the main ancestry, while the Kalehoyuk Assyrian Colony source adds a meaningful secondary Anatolian/Northern Mesopotamian-like component.

Model: Jew_Iranian.HO
Sources: Bahrain_LTyLos_Sasanian 85.7%, Turkey_BlackSea_Samsun_Ancient_A 14.3%
p = 0.786
chisq = 7.99
Status: PASS
Analysis: This is statistically a good 2-way model. The Turkey_BlackSea_Samsun source is a little less directly interpretable for Jew_Iranian than the Assyrian Colony Period source. Still, it is a strong model.

Model: Jew_Iranian.HO
Sources: Bahrain_LTyLos_Sasanian 85.4%, Turkey_Central_Kalehoyuk_OldHittitePeriod 14.6%
p = 0.672
chisq = 9.36
Status: PASS
Analysis: This passes comfortably, and the secondary Turkey/Kalehoyuk Old Hittite component is meaningful. It is usable, but not the best of the batch.

Three way models: Statistically weaker than the two way models. The smaller components (Levant, Armenia, Georgia) are not strongly supported.

Model: Jew_Iranian.HO
Sources: Bahrain_EMTylos_SeleucidCharacene 65.2%, Georgia_Nazarlebi_LBA_lc 12.3%, Lebanon_IA2 22.5%
p = 0.723
chisq = 5.32
Status: PASS
Analysis: The p-value is high and the chisq is low, so the statistical fit is strong. The main caution is that the smaller Georgia and Lebanon components are not strongly supported, so the exact 12.3% and 22.5% values should be treated cautiously.

Model: Jew_Iranian.HO
Sources: Bahrain_EMTylos_SeleucidCharacene 57.2%, Armenia_Dzori_Gekh_LBA 23.9%, Lebanon_EjJaouze_Phoenician 18.9%
p = 0.702
chisq = 8.13
Status: PASS
Analysis: This is a good 3-way model and gives a more detailed breakdown: Bahrain/Characene-like, Armenian/Caucasus-like, and Phoenician/Levantine-like. The fit is good, but the Lebanon_EjJaouze component is not as strongly supported as the main Bahrain component, so the exact percentage should be treated cautiously.

Model: Jew_Iranian.HO
Sources: Bahrain_EMTylos_SeleucidCharacene 71.9%, Georgia_Nazarlebi_LBA_lc 14.7%, Israel_Ashkelon_IA2 13.4%
p = 0.777
chisq = 7.27
Status: PASS
Analysis: This has one of the highest p-values in the batch, so the raw fit is strong. However, the Georgia and Ashkelon components are weaker than the main Bahrain component, especially the Ashkelon component.

In qpAdm, the p-value indicates whether the model is rejected or not; generally, p > 0.05 is considered a passing model, while a higher p-value means the model is a more comfortable fit.

The chisq value measures how much mismatch there is between the proposed model and the genetic data, so lower chisq is generally better when comparing similar models using the same Right/outgroup set. The weights are the estimated ancestry proportions assigned to each source population.

The SE, or standard error, shows how uncertain each weight is; smaller SE means the percentage is more stable, while large SE means the exact percentage should be treated cautiously.

The Z-score is roughly the weight divided by its SE, and it shows how strongly that source is supported in the model; higher Z-scores, especially around 2 or above, suggest the component is more meaningful, while very low Z-scores can mean the source is weak, unstable, or not really required.

These qpAdm results should still be read as proxy models rather than literal ancestry percentages, because ancient samples are being used to represent broader ancestry streams rather than exact direct ancestors.

Edit: The images for these runs are best viewed within the Reddit app on a smartphone. The resolution is poor on desktop view using laptop or Chrome/ Firefox.

Armenian (n=58) qpAdm model
34% Lchashen-Metsamor Culture (LBA Armenia)
66% Mesopotamian like (LBA Iraq)

Source: @light3675 on twitter

Source: @Light3675 on Twitter

Model: Jew_Iraqi.DG
Sources: Bahrain_EMTylos_SeleucidCharacene 86.1%, Dinka 0.6%, Lebanon_Hellenistic 13.3%
p = 0.753
chisq = 2.65
Status: PASS

Model: Jew_Iraqi.DG
Sources: Bahrain_EMTylos_SeleucidCharacene 71.6%, Sudan_Kulubnarti_R_ChristianPeriod 4.1%, Lebanon_IA2 24.4%
p = 0.637
chisq = 0.903
Status: PASS

Model: Jew_Iraqi.DG
Sources: Bahrain_EMTylos_SeleucidCharacene 98.0%, Dinka 1.0%, Israel_Akhziv_Phoenician 1.0%
p = 0.727
chisq = 3.63
Status: PASS

Summary:

The best raw p-value was the Bahrain_EMTylos + Lebanon_Hellenistic + Dinka model, with p = 0.753 and chisq = 2.65.

However, the best balanced model may be the Bahrain_EMTylos + Lebanon_IA2 + Sudan_Kulubnarti model, since it has a very low chisq of 0.903 while still passing comfortably with p = 0.637.

The Akhziv/Phoenician version also passes with p = 0.727, and most of the ancestry is expressed as Bahrain_EMTylos at 98%. Statistically this is good but it has a slightly lower p value and a slightly higher chisq than the Lebanon_Hellenistic model, thus making the Lebanon_Hellenistic model slightly better.

Overall, these models suggest Jew_Iraqi.DG is mainly captured by a Bahrain/Characene-like Mesopotamian/Gulf proxy, with a possible smaller Levantine-related component depending on the source used.

The qpAdm models posted here were selected based on the best overall fit parameters among the tested runs: passing p-values, lower chisq values, feasible positive weights, reasonable SE values, and source components that stayed meaningful rather than collapsing to zero or becoming unstable. The p-value indicates whether qpAdm rejects the model; generally, p > 0.05 is considered a pass, with higher values usually meaning a more comfortable fit. The chisq value measures mismatch between the model and the data, so lower chisq is generally better when comparing similar models with the same Right/outgroup set. The weights are the estimated ancestry proportions assigned to each source population. The SE, or standard error, shows how uncertain each weight is; smaller SE values mean the estimate is more stable, while very large SE values mean the exact percentage should be treated cautiously. These models should still be understood as proxy models rather than literal ancestry percentages, since sources like Natufian, Dinka, Kenya Pastoral Neolithic, Syria Umayyad, Yemen/Socotri, etc. may represent broader ancestral streams rather than direct ancestors.

It seems the images aren’t viewable on desktop but it works on the Reddit app on iPhone.

Here’s the plain text version for all of them:

Model: Yemen_Medieval_Soqotri.AG
Sources: Syria_TellQarassa_Umayyad 92.0%, Ethiopia_4500BP 8.0%
p = 0.268
chisq = 9.95
Status: PASS

Model: Kenya_IA_Pastoral.AG
Sources: Israel_PPNB 22.2%, Dinka 77.8%
p = 0.368
chisq = 8.73
Status: PASS

Model: Somali.HO
Sources: Israel_Natufian 39.3%, Dinka 45.9%, Ethiopia_4500BP 14.7%
p = 0.572
chisq = 2.92
Status: PASS

Model: Ethiopia_BetaIsrael.HO
Sources: Kenya_IA_Pastoral 59.6%, Syria_TellQarassa_Umayyad 40.4%
p = 0.451
chisq = 8.85
Status: PASS

Model: Eritrean.HO
Sources: Kenya_IA_Pastoral 50.0%, Yemen_Medieval_Soqotri 50.0%
p = 0.528
chisq = 8.06
Status: PASS

Model: Somali.HO
Sources: Kenya_IA_Pastoral 76.1%, Yemen_Medieval_Soqotri 23.9%
p = 0.164
chisq = 13.0
Status: PASS

Model: Ethiopia_BetaIsrael.HO
Sources: Kenya_IA_Pastoral 58.8%, Israel_Ashkelon_IA2 41.2%
p = 0.245
chisq = 11.5
Status: PASS

Model: Eritrean.HO
Sources: Kenya_IA_Pastoral 47.8%, Israel_Ashkelon_IA2 52.2%
p = 0.333
chisq = 10.2
Status: PASS

Model: Eritrean.HO
Sources: Dinka 41.9%, Turkey_Marmara_Barcin_N 17.1%, Iran_GanjDareh_N 6.5%, Israel_Natufian 34.6%
p = 0.135
chisq = 13.7
Status: PASS

The qpAdm models posted here were selected based on the best overall fit parameters among the tested runs: passing p-values, lower chisq values, feasible positive weights, reasonable SE values, and source components that stayed meaningful rather than collapsing to zero or becoming unstable. The p-value indicates whether qpAdm rejects the model; generally, p > 0.05 is considered a pass, with higher values usually meaning a more comfortable fit. The chisq value measures mismatch between the model and the data, so lower chisq is generally better when comparing similar models with the same Right/outgroup set. The weights are the estimated ancestry proportions assigned to each source population. The SE, or standard error, shows how uncertain each weight is; smaller SE values mean the estimate is more stable, while very large SE values mean the exact percentage should be treated cautiously. These models should still be understood as proxy models rather than literal ancestry percentages, since sources like Natufian, Dinka, Kenya Pastoral Neolithic, Syria Umayyad, Yemen/Socotri, etc. may represent broader ancestral streams rather than direct ancestors.

It seems the images aren’t viewable on desktop but it works on the Reddit app on iPhone.

Here’s the plain text version for all of them:

Model: Yemen_Medieval_Soqotri.AG
Sources: Syria_TellQarassa_Umayyad 92.0%, Ethiopia_4500BP 8.0%
p = 0.268
chisq = 9.95
Status: PASS

Model: Kenya_IA_Pastoral.AG
Sources: Israel_PPNB 22.2%, Dinka 77.8%
p = 0.368
chisq = 8.73
Status: PASS

Model: Somali.HO
Sources: Israel_Natufian 39.3%, Dinka 45.9%, Ethiopia_4500BP 14.7%
p = 0.572
chisq = 2.92
Status: PASS

Model: Ethiopia_BetaIsrael.HO
Sources: Kenya_IA_Pastoral 59.6%, Syria_TellQarassa_Umayyad 40.4%
p = 0.451
chisq = 8.85
Status: PASS

Model: Eritrean.HO
Sources: Kenya_IA_Pastoral 50.0%, Yemen_Medieval_Soqotri 50.0%
p = 0.528
chisq = 8.06
Status: PASS

Model: Somali.HO
Sources: Kenya_IA_Pastoral 76.1%, Yemen_Medieval_Soqotri 23.9%
p = 0.164
chisq = 13.0
Status: PASS

Model: Ethiopia_BetaIsrael.HO
Sources: Kenya_IA_Pastoral 58.8%, Israel_Ashkelon_IA2 41.2%
p = 0.245
chisq = 11.5
Status: PASS

Model: Eritrean.HO
Sources: Kenya_IA_Pastoral 47.8%, Israel_Ashkelon_IA2 52.2%
p = 0.333
chisq = 10.2
Status: PASS

Model: Eritrean.HO
Sources: Dinka 41.9%, Turkey_Marmara_Barcin_N 17.1%, Iran_GanjDareh_N 6.5%, Israel_Natufian 34.6%
p = 0.135
chisq = 13.7
Status: PASS

The qpAdm models posted here were selected based on the best overall fit parameters among the tested runs: passing p-values, lower chisq values, feasible positive weights, reasonable SE values, and source components that stayed meaningful rather than collapsing to zero or becoming unstable. The p-value indicates whether qpAdm rejects the model; generally, p > 0.05 is considered a pass, with higher values usually meaning a more comfortable fit. The chisq value measures mismatch between the model and the data, so lower chisq is generally better when comparing similar models with the same Right/outgroup set. The weights are the estimated ancestry proportions assigned to each source population. The SE, or standard error, shows how uncertain each weight is; smaller SE values mean the estimate is more stable, while very large SE values mean the exact percentage should be treated cautiously. These models should still be understood as proxy models rather than literal ancestry percentages, since sources like Natufian, Dinka, Kenya Pastoral IA, Syria Umayyad, Yemen/Socotri, etc. may represent broader ancestral streams rather than direct ancestors.

It seems the images aren’t viewable on desktop but it works on the Reddit app on iPhone.

Here’s the plain text version for all of them:

Model: Yemen_Medieval_Soqotri.AG
Sources: Syria_TellQarassa_Umayyad 92.0%, Ethiopia_4500BP 8.0%
p = 0.268
chisq = 9.95
Status: PASS

Model: Kenya_IA_Pastoral.AG
Sources: Israel_PPNB 22.2%, Dinka 77.8%
p = 0.368
chisq = 8.73
Status: PASS

Model: Somali.HO
Sources: Israel_Natufian 39.3%, Dinka 45.9%, Ethiopia_4500BP 14.7%
p = 0.572
chisq = 2.92
Status: PASS

Model: Ethiopia_BetaIsrael.HO
Sources: Kenya_IA_Pastoral 59.6%, Syria_TellQarassa_Umayyad 40.4%
p = 0.451
chisq = 8.85
Status: PASS

Model: Eritrean.HO
Sources: Kenya_IA_Pastoral 50.0%, Yemen_Medieval_Soqotri 50.0%
p = 0.528
chisq = 8.06
Status: PASS

Model: Somali.HO
Sources: Kenya_IA_Pastoral 76.1%, Yemen_Medieval_Soqotri 23.9%
p = 0.164
chisq = 13.0
Status: PASS

Model: Ethiopia_BetaIsrael.HO
Sources: Kenya_IA_Pastoral 58.8%, Israel_Ashkelon_IA2 41.2%
p = 0.245
chisq = 11.5
Status: PASS

Model: Eritrean.HO
Sources: Kenya_IA_Pastoral 47.8%, Israel_Ashkelon_IA2 52.2%
p = 0.333
chisq = 10.2
Status: PASS

Model: Eritrean.HO
Sources: Dinka 41.9%, Turkey_Marmara_Barcin_N 17.1%, Iran_GanjDareh_N 6.5%, Israel_Natufian 34.6%
p = 0.135
chisq = 13.7
Status: PASS

https://www.biorxiv.org/content/10.1101/423079v1

From Twitter user @cicikus. Outgroups are found in the second slide. Russia_Karavaikha within the sources list is Eastern European Hunter Gatherer.

From Twitter @sapphiregnm

Expressed in terms of Iron Age populations.

From Twitter: @CsfHighlan97034

From @madailabs on Twitter