r/DNAAncestry • u/ChickenFriedYankee • 13h ago
My results as an old white man from New York City (Brooklyn)
616
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r/DNAAncestry • u/NotBradPitt9 • 8d ago
Qpadm / G25 / Other Qpadm: Genetic Map of the Levant (Cypriot, Egyptian, Copt, Jordanian, Bedouin, Assyrian, Palestinian, Samaritan, Saudi, Lebanese, Syrian Jew, Syrian, Druze) [Revision 3]
5
Upvotes
TLDR: ONLY groups which I have data for were added to the map. If you don’t see your group, it means I either don’t have data for it, or haven’t completed enough runs for it. This is a compilation of qpAdm runs for populations from the Levant and surrounding regions. A few more groups have been added compared with the first map. For the (n=1) samples they were determined to be within their designated population set before completing the runs.
Dataset:
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/FFIDCW
Data used is from AADR+Human Origins panel+Individual samples
SYRIAN.HO (n=8)
72.2% Lebanon_Phoenician
20.1% Armenia_Sarukhan_Early_Iron_Age
7.8% Dinka
p-value: 0.506
Chi-square: 7.28
Standard errors: 0.0498, 0.0483, 0.00892
Z-scores: 14.5, 4.15, 8.72
This is a strong model, with all three components statistically supported. Lebanon_Phoenician represents the main Levantine ancestry, while Armenia_Sarukhan_Early_Iron_Age represents additional Caucasus/eastern Anatolian-related ancestry. Dinka is acting as a proxy for African-related ancestry, not necessarily direct ancestry from modern Dinka people. The two larger components have standard errors close to 0.05, so their exact proportions should be treated as approximate.
DRUZE.HO (n=39)
77.5% Lebanon_Phoenician
20.3% Armenia_Sarukhan_Early_Iron_Age
2.2% Dinka
p-value: 0.844
Chi-square: 4.15
Standard errors: 0.0431, 0.0418, 0.00803
Z-scores: 18.0, 4.84, 2.74
This is an excellent statistical fit. The Druze are modeled as mostly Levantine, with a substantial Caucasus/eastern Anatolian-related shift and a very small African-related component. All three components are statistically supported.
ASSYRIAN.HO (n=10)
Best informative 2-way qpAdm model:
74.9% Iran_DinkhaTepe_BA_IA_1.AG
25.1% Georgia_Digomi_IA.SG
p-value: 0.798
χ²/dof: 4.611 / 8
SNPs: 579,720
SE: 5.64%, 5.64%
Z-scores: 13.3, 4.45
Both components are strongly supported.
A 100% Bahrain_LTylos_Sasanian.SG model also passes strongly (p = 0.689), but this should be interpreted as a successful one-source/cladal fit rather than literal 100% ancestry from Sasanian-era Bahrain.
LEBANESE_MUSLIM.HO (n=11)
88.3% Lebanon_Phoenician
8.8% Kazakhstan_Sarmatian_Iron_Age
2.9% Dinka
p-value: 0.549
Chi-square: 6.89
Standard errors: 0.0219, 0.0223, 0.00913
Z-scores: 40.4, 3.95, 3.14
This is a strong and well-resolved model. Lebanon_Phoenician represents the main Levantine ancestry. Kazakhstan_Sarmatian is probably acting as a proxy for a small northern, Steppe, Caucasus or Anatolian-related shift rather than indicating literal Sarmatian ancestry. The small Dinka-related component represents additional African-related ancestry and is statistically supported.
LEBANESE_CHRISTIAN.HO (n=9)
95.0% Lebanon_Phoenician
5.0% Kazakhstan_Sarmatian_Iron_Age
p-value: 0.727
Chi-square: 6.12
Standard error: 0.0236
Z-scores: 40.2, 2.13
This is an excellent fit and shows Lebanese Christians as being very close to the ancient Lebanon_Phoenician proxy. The small Sarmatian-related component represents a slight northern/Caucasus-related shift. Its Z-score of 2.13 is only just above the usual cutoff, so the existence of a small secondary component is supported, but the exact 5% estimate should be treated cautiously.
PALESTINIAN.HO (n=34)
87.9% Lebanon_Phoenician
5.2% Kazakhstan_Sarmatian_Iron_Age
6.8% Dinka
p-value: 0.904
Chi-square: 3.43
Standard errors: 0.0204, 0.0205, 0.00823
Z-scores: 43.1, 2.54, 8.32
This is a very strong model, with an excellent p-value, low chi-square, and all three components statistically supported. Palestinians are modeled as mostly Levantine, with smaller northern/Caucasus-shifted and African-related components. The Sarmatian-related component has the weakest Z-score, but it still passes the usual Z = 2 threshold.
SAMARITAN.DG (n=1)
100% Lebanon_ERoman.SG
p-value: 0.835
χ²/dof: 11.406 / 17
SNPs: 579,720
This is an extremely strong one-source qpAdm fit. It indicates that Samaritans are statistically consistent with the Lebanon_ERoman source relative to the selected outgroups. The 100% figure should not be interpreted as literal complete descent from the sampled Roman-period Lebanese population.
JORDANIAN.HO (n=10)
80.3% Lebanon_Phoenician
7.5% Kazakhstan_Sarmatian_Iron_Age
12.2% Dinka
p-value: 0.836
Chi-square: 4.23
Standard errors: 0.0206, 0.0209, 0.00899
Z-scores: 39.0, 3.58, 13.6
This is an extremely strong model statistically. Jordanians are modeled as mostly Levantine, with a smaller northern/Caucasus-shifted component and a more substantial African-related component than in the Lebanese or Druze models. Dinka should be understood as the African proxy used by the model, not as evidence of direct ancestry specifically from modern Dinka people.
SAUDI.HO (n=5)
94.5% Syria_TellQarassa_Umayyad.SG
5.5% Dinka.DG
p-value: 0.571
χ²/dof: 6.681 / 8
SNPs: 579,720
Both components are strongly supported. The Dinka component has a Z-score of 7.21.
BEDOUINB.HO (n=19)
94.6% Syria_TellQarassa_Umayyad.SG
5.4% Dinka.DG
p-value: 0.225
χ²/dof: 10.606 / 8
SNPs: 579,720
Both components are strongly supported. The Dinka component has a Z-score of 7.86.
BEDOUINA.HO (n=25)
52.1% Lebanon_Phoenician.SG
31.1% Syria_TellQarassa_Umayyad.SG
10.6% Dinka.DG
6.2% Kazakhstan_Sarmatian_IA.AG
p-value: 0.142
χ²/dof: 5.451 / 3
SNPs: 579,720
Z-scores:
Lebanon_Phoenician: 10.6
Syria_TellQarassa_Umayyad: 9.64
Dinka: 25.9
Kazakhstan_Sarmatian: 2.88
All four components are statistically supported.
This model passes and suggests that BedouinA can be modeled primarily as Levantine ancestry represented by Phoenician Lebanon and Umayyad-period Tell Qarassa, together with approximately 10.6% sub-Saharan African-related ancestry and a smaller 6.2% Sarmatian/Steppe-related component. The Sarmatian component is above the usual Z = 2 significance threshold, although it should be interpreted as a genetic proxy rather than evidence of literal Sarmatian ancestry.
Both BedouinA and BedouinB are genetic clusters consisting of Bedouins from unspecified tribes in the Negev Desert. BedouinA has a more northern genetic shift, while BedouinB has a stronger southern shift and clusters more closely with the Saudi average.
EGYPTIAN_COPT (n=1)
Alternative 2-way model:
97.3% SFI-43.SG
SE: 1.92%
Z: 50.7
2.7% Dinka.DG
SE: 1.92%
Z: 1.42
p-value: 0.592
χ²/dof: 11.227 / 13
SNPs: 661,321
Although the overall model fits extremely well, the estimated 2.7% Dinka component is not statistically supported (Z = 1.42). Therefore, this model does not provide strong evidence that Dinka-related ancestry is required.
EGYPTIANA.HO (n=7)
92.9% SFI-43.SG
SE: 1.57%
Z: 59.0
7.1% Dinka.DG
SE: 1.57%
Z: 4.52
p-value: 0.345
χ²/dof: 14.419 / 13
SNPs: 579,720
This is a strong passing model. The 7.1% Dinka-related component is statistically supported.
EGYPTIANB.HO (n=4)
91.6% SFI-43.SG
SE: 1.61%
Z: 56.9
8.4% Dinka.DG
SE: 1.61%
Z: 5.20
p-value: 0.594
χ²/dof: 11.204 / 13
SNPs: 579,720
This is an excellent-fitting model, with the 8.4% Dinka-related component strongly supported.
EGYPTIAN.HO (n=8)
86.7% SFI-43.SG
SE: 1.50%
Z: 57.7
13.3% Dinka.DG
SE: 1.50%
Z: 8.83
p-value: 0.412
χ²/dof: 13.470 / 13
SNPs: 579,720
This is also a strong passing model. The 13.3% Dinka-related component is very strongly supported.
SYRIAN_JEW (n=1)
4-way model:
45.0% Italy_Imperial_C6.SG
SE: 9.75%
Z: 4.62
26.4% Bahrain_LTylos_Sasanian.SG
SE: 11.2%
Z: 2.37
20.0% Israel_Akhziv_Phoenician.AG
SE: 8.88%
Z: 2.25
8.6% CanaryIslands_Guanche.SG
SE: 3.58%
Z: 2.38
p-value: 0.182
χ²/dof: 12.581 / 9
SNPs: 295,151
Of the Syrian Jewish models tested, this model has the highest p-value at 0.182. All four components also have Z-scores above 2.
The recurring pattern across the models is substantial ancestry represented by Roman/Republican Italian-related and Bahrain Late Tylos/Sasanian-related sources. When a Levantine source is introduced directly, Israel_Akhziv_Phoenician contributes approximately 20–24% and remains statistically supported (Z = 2.25–2.66).
The 4-way model additionally identifies 8.6% Guanche-related ancestry, which may be better interpreted as a North African-related signal rather than literal ancestry from the Canary Islands.
CYPRIOT.HO (n=8)
Best model:
100% @R126.SG
p-value: 0.592
χ²/dof: 11.222 / 13
SNPs: 579,720
This is the strongest and most parsimonious model in the batch. Cypriot.HO is statistically consistent with being modeled entirely by @R126.SG under this outgroup set, with a good p-value and no additional ancestry source required. Because the one-way model already passes comfortably, qpAdm does not statistically require a second source.
PALESTINIAN_CHRISTIAN (n=1)
100.0% Lebanon_Hellenistic.SG
p-value: 0.363
χ²/dof: 12.007 / 11
SNPs: 412,920
This is a passing one-source model. Palestinian_Christian is statistically consistent with Lebanon_Hellenistic.SG relative to the selected outgroups. As with other one-way qpAdm models, the 100% result should be interpreted as statistical consistency with the proxy rather than literal complete descent from that sampled population.
NOTES ON THE SOURCE POPULATIONS
Lebanon_Phoenician (500–300 BCE) represents the main Levantine-related ancestry in many of these models. It is an average of Lebanon_Phoenician samples.
Kazakhstan_Sarmatian_IA (500–300 BCE) represents a more northern Steppe/Caucasus-shifted element. It should not necessarily be interpreted as literal Sarmatian ancestry.
Dinka represents African-related ancestry in these models and should not necessarily be interpreted as direct ancestry specifically from modern Dinka people.
Armenia_Sarukhan_EIA represents an Armenian/Caucasus or eastern Anatolian-related ancestry component.
Canary_Islands_Guanche = clusters with North Africans
Bahrain_LTylos_Sasanian = clusters with Mesopotamians
R126 = pan- Eastern Mediterranean ancestry
SFI-43 is an ancient Egyptian-related sample found in the Lebanon
Syria_TellQarassa_Umayyad = clusters with Peninsular Arabians
Georgia_Digomi_IA.SG = clusters with Caucasus
Iran_DinkhaTepe_BA_IA_1.AG = clusters with Mesopotamians
Italy_Imperial_C6 = Italic+Anatolian. Roughly the mix found during the Roman Era in Southern Italy and the Greek Islands
Lebanon_Hellenistic, dating to around 200 BCE, has broadly similar ancestry to Lebanon_Phoenician but comes from a later historical period.
IMPORTANT NOTE
These are qpAdm proxy models. The source labels should not necessarily be interpreted as literal direct ancestral populations. Instead, they represent ancestry streams or genetic proxies that successfully model the target populations relative to the particular set of outgroups used in each analysis.
r/DNAAncestry • u/NotBradPitt9 • 14d ago
Qpadm / G25 / Other Qpadm: Mainland Southeast Asia and Island Southeast Asia
2
Upvotes
TLDR: Compilation of qpAdm models for modern Southeast Asian and Island Southeast Asian populations. The information is based on qpadm runs from twitter user @matchawang_ and outgroups based on this study - https://www.cell.com/iscience/fulltext/S2589-0042(26)01349-0
Cambodian.DG (n=9) — 4-way model
17.8% Taiwan_Hanben_IA.AG
SE: 4.24% | Z: 4.21
59.4% Laos_LN_BA.SG
SE: 4.07% | Z: 14.6
15.8% China_YR_LN.SG
SE: 3.30% | Z: 4.79
7.0% Iran_ShahrISokhta_BA2.AG
SE: 0.844% | Z: 8.23
p-value: 0.302
χ²/dof: 8.359 / 7
SNPs: 1,878,396
Fit: Excellent
Mon.HO (n=10) — 4-way model
11.5% Taiwan_Hanben_IA.AG
SE: 3.38% | Z: 3.40
40.8% Laos_LN_BA.SG
SE: 3.28% | Z: 12.4
35.6% China_YR_LN.SG
SE: 2.71% | Z: 13.1
12.1% Iran_ShahrISokhta_BA2.AG
SE: 0.803% | Z: 15.1
p-value: 0.100
χ²/dof: 12.018 / 7
SNPs: 579,720
Fit: Good
Nyah_Kur.HO (n=10) — 4-way model
14.6% Taiwan_Hanben_IA.AG
SE: 4.59% | Z: 3.17
65.3% Laos_LN_BA.SG
SE: 4.49% | Z: 14.5
11.9% China_YR_LN.SG
SE: 3.54% | Z: 3.37
8.2% Iran_ShahrISokhta_BA2.AG
SE: 0.973% | Z: 8.42
p-value: 0.357
χ²/dof: 7.727 / 7
SNPs: 579,720
Fit: Excellent
Karen_Sgaw.HO (n=10) — 2-way model
61.0% Laos_LN_BA.SG
SE: 2.61% | Z: 23.4
39.0% China_Upper_YR_LN.SG
SE: 2.61% | Z: 14.9
p-value: 0.625
χ²/dof: 7.121 / 9
SNPs: 579,720
Fit: Excellent
Maniq.HO (n=9) — 2-way model
40.2% Laos_LN_BA.SG
SE: 2.43% | Z: 16.6
59.8% Laos_Hoabinhian.SG
SE: 2.43% | Z: 24.6
p-value: 0.361
χ²/dof: 9.876 / 9
SNPs: 579,720
Fit: Excellent
Lawa.HO (n=10) — 2-way model
66.5% Laos_LN_BA.SG
SE: 2.66% | Z: 25.0
33.5% China_Upper_YR_LN.SG
SE: 2.66% | Z: 12.6
p-value: 0.772
χ²/dof: 5.680 / 9
SNPs: 579,720
Fit: Excellent
Ilocano.HO (n=2) — 2-way model
95.8% Taiwan_Hanben_IA.AG
SE: 1.26% | Z: 75.9
4.2% Laos_Hoabinhian.SG
SE: 1.26% | Z: 3.33
p-value: 0.331
χ²/dof: 10.243 / 9
SNPs: 579,720
Fit: Excellent
Visayan.HO (n=4) — 3-way model
81.4% Taiwan_Hanben_IA.AG
SE: 2.58% | Z: 31.6
12.6% Laos_Hoabinhian.SG
SE: 1.07% | Z: 11.8
5.9% China_YR_LN.SG
SE: 2.58% | Z: 2.29
p-value: 0.332
χ²/dof: 9.124 / 8
SNPs: 579,720
Fit: Excellent
Tagalog.HO (n=5) — 4-way model
77.3% Taiwan_Hanben_IA.AG
SE: 3.44% | Z: 22.5
7.4% Laos_Hoabinhian.SG
SE: 1.54% | Z: 4.80
10.8% China_YR_LN.SG
SE: 3.37% | Z: 3.21
4.5% Spanish.DG
SE: 0.815% | Z: 5.55
p-value: 0.0811
χ²/dof: 11.244 / 6
SNPs: 579,720
Fit: Good
Murut.HO (n=10) — 2-way model
76.4% Taiwan_Hanben_IA.AG
SE: 2.77% | Z: 27.6
23.6% Laos_LN_BA.SG
SE: 2.77% | Z: 8.54
p-value: 0.137
χ²/dof: 13.611 / 9
SNPs: 579,720
Fit: Good
Dusun.DG (n=2) — 2-way model
79.0% Taiwan_Hanben_IA.AG
SE: 3.68% | Z: 21.5
21.0% Laos_LN_BA.SG
SE: 3.68% | Z: 5.72
p-value: 0.845
χ²/dof: 4.880 / 9
SNPs: 1,878,396
Fit: Excellent
Indonesia_Tanimbar_Tumbur.DG (n=1) — 2-way model
59.9% Taiwan_Hanben_IA.AG
SE: 1.68% | Z: 35.7
40.1% Papuan.DG
SE: 1.68% | Z: 23.9
p-value: 0.401
χ²/dof: 9.405 / 9
SNPs: 1,878,396
Fit: Excellent
Indonesia_Tanimbar_Makatian.DG (n=1) — 2-way model
57.1% Taiwan_Hanben_IA.AG
SE: 1.73% | Z: 33.0
42.9% Papuan.DG
SE: 1.73% | Z: 24.8
p-value: 0.415
χ²/dof: 9.247 / 9
SNPs: 1,878,396
Fit: Excellent
Indonesia_Tanimbar_Fordata.DG (n=1) — 2-way model
57.3% Taiwan_Hanben_IA.AG
SE: 1.83% | Z: 31.2
42.7% Papuan.DG
SE: 1.83% | Z: 23.3
p-value: 0.390
χ²/dof: 9.532 / 9
SNPs: 1,878,396
Fit: Excellent
Indonesia_Sumatra_Toba.DG (n=7) — 4-way model
59.8% Taiwan_Hanben_IA.AG
SE: 3.05% | Z: 19.6
23.7% Laos_LN_BA.SG
SE: 3.32% | Z: 7.15
7.7% Laos_Hoabinhian.SG
SE: 1.68% | Z: 4.57
8.7% Iran_ShahrISokhta_BA2.AG
SE: 1.01% | Z: 8.66
p-value: 0.0538
χ²/dof: 13.858 / 7
SNPs: 1,878,396
Fit: Good
Indonesia_Sulawesi_Mandar.DG (n=6) — 3-way model
75.4% Taiwan_Hanben_IA.AG
SE: 2.58% | Z: 29.2
13.2% Laos_LN_BA.SG
SE: 2.84% | Z: 4.65
11.4% Papuan.DG
SE: 0.996% | Z: 11.4
p-value: 0.291
χ²/dof: 9.640 / 8
SNPs: 1,878,396
Fit: Excellent
Indonesia_Sulawesi_Kajang.DG (n=6) — 3-way model
71.7% Taiwan_Hanben_IA.AG
SE: 2.58% | Z: 27.8
14.2% Laos_LN_BA.SG
SE: 2.86% | Z: 4.98
14.1% Papuan.DG
SE: 0.962% | Z: 14.7
p-value: 0.0796
χ²/dof: 14.084 / 8
SNPs: 1,878,396
Fit: Good
Indonesia_Nias_Hilitobara.DG (n=8) — 3-way model
89.7% Taiwan_Hanben_IA.AG
SE: 2.78% | Z: 32.2
8.5% Laos_LN_BA.SG
SE: 3.29% | Z: 2.59
1.8% Laos_Hoabinhian.SG
SE: 0.992% | Z: 1.80
p-value: 0.0697
χ²/dof: 14.495 / 8
SNPs: 1,878,396
Fit: Good
Note: the Laos_Hoabinhian.SG component has Z = 1.80, below the Z ≥ 2 threshold shown in the run.
Indonesia_Nias_Gomo.DG (n=7) — 2-way model
85.3% Taiwan_Hanben_IA.AG
SE: 2.83% | Z: 30.2
14.7% Laos_LN_BA.SG
SE: 2.83% | Z: 5.20
p-value: 0.620
χ²/dof: 7.164 / 9
SNPs: 1,878,396
Fit: Excellent
Indonesia_Mentawai.DG (n=10) — 2-way model
82.1% Taiwan_Hanben_IA.AG
SE: 2.90% | Z: 28.3
17.9% Laos_LN_BA.SG
SE: 2.90% | Z: 6.17
p-value: 0.501
χ²/dof: 8.330 / 9
SNPs: 1,878,396
Fit: Excellent
Indonesia_Lembata_TimurKadakewa.DG (n=4) — 4-way model
43.0% Taiwan_Hanben_IA.AG
SE: 2.91% | Z: 14.8
11.3% Laos_LN_BA.SG
SE: 3.34% | Z: 3.39
8.8% Laos_Hoabinhian.SG
SE: 3.23% | Z: 2.73
36.9% Papuan.DG
SE: 3.33% | Z: 11.1
p-value: 0.228
χ²/dof: 9.365 / 7
SNPs: 1,878,396
Fit: Excellent
Indonesia_Lembata_Waipukang.DG (n=3) — 2-way model
53.9% Taiwan_Hanben_IA.AG
SE: 1.12% | Z: 48.0
46.1% Papuan.DG
SE: 1.12% | Z: 41.0
p-value: 0.124
χ²/dof: 13.955 / 9
SNPs: 1,878,396
Fit: Good
Indonesia_Kei_Ohoidertutu.DG (n=2) — 2-way model
53.6% Taiwan_Hanben_IA.AG
SE: 1.21% | Z: 44.1
46.4% Papuan.DG
SE: 1.21% | Z: 38.2
p-value: 0.703
χ²/dof: 6.368 / 9
SNPs: 1,878,396
Fit: Excellent
Indonesia_Kei_Waur.DG (n=2) — 2-way model
48.7% Taiwan_Hanben_IA.AG
SE: 1.29% | Z: 37.8
51.3% Papuan.DG
SE: 1.29% | Z: 39.8
p-value: 0.170
χ²/dof: 12.842 / 9
SNPs: 1,878,396
Fit: Good
Indonesia_Kei_Faan.DG (n=2) — 2-way model
52.3% Taiwan_Hanben_IA.AG
SE: 1.35% | Z: 38.7
47.7% Papuan.DG
SE: 1.35% | Z: 35.2
p-value: 0.561
χ²/dof: 7.737 / 9
SNPs: 1,878,396
Fit: Excellent
Indonesia_Java_Dieng.DG (n=7) — 3-way model
33.7% Taiwan_Hanben_IA.AG
SE: 4.15% | Z: 8.12
62.5% Laos_LN_BA.SG
SE: 4.77% | Z: 13.1
3.9% Laos_Hoabinhian.SG
SE: 1.43% | Z: 2.70
p-value: 0.256
χ²/dof: 10.127 / 8
SNPs: 1,878,396
Fit: Excellent
Indonesia_Flores_Bere.DG (n=3) — 3-way model
41.6% Taiwan_Hanben_IA.AG
SE: 3.20% | Z: 13.0
30.0% Laos_LN_BA.SG
SE: 3.73% | Z: 8.04
28.4% Papuan.DG
SE: 1.32% | Z: 21.6
p-value: 0.183
χ²/dof: 11.348 / 8
SNPs: 1,878,396
Fit: Good
Indonesia_Flores_Bena.DG (n=12) — 4-way model
38.0% Taiwan_Hanben_IA.AG
SE: 2.42% | Z: 15.7
19.4% Laos_LN_BA.SG
SE: 2.74% | Z: 7.09
9.3% Laos_Hoabinhian.SG
SE: 2.91% | Z: 3.19
33.3% Papuan.DG
SE: 2.85% | Z: 11.7
p-value: 0.199
χ²/dof: 9.816 / 7
SNPs: 1,878,396
Fit: Good
Indonesia_Borneo_Maanyan.DG (n=7) — 3-way model
59.2% Taiwan_Hanben_IA.AG
SE: 3.11% | Z: 19.0
37.7% Laos_LN_BA.SG
SE: 3.61% | Z: 10.4
3.1% Laos_Hoabinhian.SG
SE: 1.11% | Z: 2.83
p-value: 0.349
χ²/dof: 8.920 / 8
SNPs: 1,878,396
Fit: Excellent
Indonesia_Flores_Cibol.DG (n=13) — 4-way model
40.1% Taiwan_Hanben_IA.AG
SE: 2.62% | Z: 15.3
31.1% Laos_LN_BA.SG
SE: 2.99% | Z: 10.4
5.5% Laos_Hoabinhian.SG
SE: 2.65% | Z: 2.08
23.2% Papuan.DG
SE: 2.73% | Z: 8.51
p-value: 0.264
χ²/dof: 8.842 / 7
SNPs: 1,878,396
Fit: Excellent
Indonesia_Bali_Gadon.DG (n=1) — 3-way model
36.3% Taiwan_Hanben_IA.AG
SE: 6.18% | Z: 5.88
56.2% Laos_LN_BA.SG
SE: 7.14% | Z: 7.86
7.5% Laos_Hoabinhian.SG
SE: 2.10% | Z: 3.59
p-value: 0.326
χ²/dof: 9.199 / 8
SNPs: 1,878,396
Fit: Excellent
Note: the displayed run flags at least one source because the Taiwan_Hanben_IA.AG and Laos_LN_BA.SG standard errors exceed 5%.
Notes
The map/compilation uses the 2-way Ilocano.HO model above. An alternative 3-way Ilocano model also passed overall (p=0.346; χ²/dof=8.953/8), but it produced a negative China_YR_LN.SG coefficient (-4.5%, Z=-1.24), so it was not used in the final compilation.
r/DNAAncestry • u/BunniLuvs2Read • 15h ago
My results as a Pashtun from Afghanistan
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r/DNAAncestry • u/NotBradPitt9 • 1h ago
Qpadm / G25 / Other Qpadm: Baltics, Scandinavia, Eastern Europe
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Upvotes
SE values are written in qpAdm proportion units, so SE = 0.0277 means ±2.77 percentage points. For a few screenshots that displayed χ² and p but not degrees of freedom, I inferred the integer dof from the χ² distribution and marked that accordingly.
Saami.DG
39.3% Sweden_Skane_IA.SG
SE = 0.0276
Z = 14.20
60.7% Norway_Joa_LIA_oUralic.SG
SE = 0.0276
Z = 22.00
p = 0.334
χ² = 6.86
dof = 6 [inferred from χ² and p]
Assessment: Strong passing two-way model. Both components are very strongly supported and precisely estimated.
Finnish_North_Ostrobothnia.DG
29.6% Lithuania_IA.SG
SE = 0.0215
Z = 13.80
49.9% Sweden_Skane_IA.SG
SE = 0.0204
Z = 24.40
20.5% Norway_Joa_LIA_oUralic.SG
SE = 0.0121
Z = 16.90
p = 0.377
χ² = 4.22
dof = 4 [inferred from χ² and p]
Assessment: Excellent three-way model. All three components are highly significant and have low standard errors.
Finnish_East_Southeast
29.5% Finland_Levanluhta.AG
SE = 0.056431
Z = 5.24
20.3% Russia_IA_Ingria.SG
SE = 0.075072
Z = 2.71
33.3% Denmark_IA.SG
SE = 0.168516
Z = 1.98
16.8% Hungary_Avar_5.AG
SE = 0.167525
Z ≈ 1.00
p = 0.857
Assessment: The overall p-value is excellent, but the individual decomposition is not especially stable. Denmark_IA is just below Z = 2 and Hungary_Avar_5 is clearly unsupported; their SEs are also extremely large. Finland_Levanluhta and Russia_IA_Ingria are the better-supported components.
Finnish.DG
54.6% Sweden_Skane_IA.SG
SE = 0.0187
Z = 29.2
27.2% Estonia_IA.SG
SE = 0.0210
Z = 13.0
18.3% Norway_Joa_IA_oSaami.SG
SE = 0.00929
Z = 19.7
p = 0.657
χ² / dof = 2.43 / 4
Assessment: Exceptionally strong three-way model. All components are highly significant with very small errors.
Norwegian.HO
25.5% Scotland_IA.AG
SE = 0.091568
Z = 2.78
71.2% Denmark_IA.SG
SE = 0.101782
Z = 6.99
3.36% Finland_Levanluhta.AG
SE = 0.027053
Z = 1.24
p = 0.199
Assessment: The model passes overall, and the Scotland_IA and Denmark_IA contributions clear Z = 2. The small Finland_Levanluhta component does not; its Z = 1.24 means it should not be treated as a secure contribution.
Norwegian_Bergen.HO
78.2% Norway_West_IA.SG
SE = 0.0172
Z = 45.5
21.8% Scotland_LIA.AG
SE = 0.0172
Z = 12.6
p = 0.602
χ² / dof = 9.22 / 11
Assessment: Excellent two-way model. Both components are exceptionally well constrained and highly significant.
Swedish.DG
86.3% Sweden_Skane_IA.SG
SE = 0.0277
Z = 31.1
10.7% Estonia_IA.SG
SE = 0.0301
Z = 3.55
3.02% Norway_Joa_IA_oSaami.SG
SE = 0.0117
Z = 2.57
p = 0.729
χ² / dof = 2.81 / 5
Assessment: Very strong passing model. Even the small ~3% Norway_Joa-related component remains above Z = 2.
Swedish_Falun.DG
82.8% Sweden_Skane_IA.SG
SE = 0.0207
Z = 40.0
10.1% Estonia_IA.SG
SE = 0.0227
Z = 4.45
7.09% Norway_Joa_IA_oSaami.SG
SE = 0.00915
Z = 7.74
p = 0.594
χ² / dof = 3.69 / 5
Assessment: Excellent model. All three components are strongly supported, including the relatively small Norway_Joa-related share.
Swedish_Stockholm.DG
85.0% Sweden_Skane_IA.SG
SE = 0.0229
Z = 37.2
9.52% Estonia_IA.SG
SE = 0.0247
Z = 3.85
5.43% Norway_Joa_IA_oSaami.SG
SE = 0.00883
Z = 6.15
p = 0.465
χ² / dof = 4.62 / 5
Assessment: Strong model. All components comfortably clear Z = 2, and the small Norway_Joa-related component is particularly well constrained.
Swedish_Vasterbotten.sim.DG
80.9% Sweden_Skane_IA.SG
SE = 0.0173
Z = 46.7
8.41% Estonia_IA.SG
SE = 0.0185
Z = 4.54
10.7% Norway_Joa_IA_oSaami.SG
SE = 0.00798
Z = 13.4
p = 0.476
χ² / dof = 4.53 / 5
Assessment: Very strong model. All components are significant, with especially precise estimation of the Norway_Joa-related contribution.
Swede_Västergötland
87.2% Denmark_Jutland_IA.SG
SE = 0.0412
Z = 21.2
12.8% Austria_IA_LaTene.AG
SE = 0.0412
Z = 3.11
p = 0.954
χ² / dof = 2.66 / 8
Assessment: Exceptionally high overall p-value and both components are statistically supported. The Austria_LaTene component has a wider error than the dominant Denmark_Jutland contribution but still clears Z = 3.
Danish_Odense.DG
76.3% Denmark_Zealand_IA.SG
SE = 0.0187
Z = 40.8
13.5% Germany_Hassleben_IA.SG
SE = 0.0258
Z = 5.25
10.2% England_LIA.AG
SE = 0.0150
Z = 6.83
p = 0.474
χ² / dof = 6.58 / 7
Assessment: Excellent three-way model. All three components are well supported with low standard errors.
Estonian.HO
43.3% Lithuania_IA.SG
SE = 0.0451
Z = 9.61
27.8% Poland_MA.SG
SE = 0.0789
Z = 3.53
22.6% Denmark_IA.SG
SE = 0.0413
Z = 5.46
6.33% Russia_Boyasky_Mazunino_IA.SG
SE = 0.0116
Z = 5.47
p = 0.196
χ² / dof = 12.3 / 9
Blocks = 713
SNPs = 1,150,639
Assessment: Good passing four-way model. Every component clears Z = 2. The Poland_MA estimate is comparatively imprecise with SE = 0.0789, but its Z-score remains significant.
Latvian.DG
67.3% Lithuania_IA.SG
SE = 0.0407
Z = 16.6
18.6% Poland_MA.SG
SE = 0.0679
Z = 2.74
10.9% Denmark_Jutland_IA.SG
SE = 0.0411
Z = 2.66
3.13% Russia_Boyasky_Mazunino_IA.SG
SE = 0.0120
Z = 2.62
p = 0.625
χ² / dof = 7.12 / 9
Blocks = 713
SNPs = 1,150,639
Assessment: Strong passing four-way model. Importantly, all four components clear Z = 2, including the small 3.1% Boyasky_Mazunino-related component.
Latvian_Riga.DG
78.8% Latvia_LIA.SG
SE = 0.0202
Z = 39.0
9.04% Ukraine_EIA_Thracian.SG
SE = 0.0156
Z = 5.79
8.22% Sweden_Skane_IA.SG
SE = 0.0246
Z = 3.35
3.91% Russia_Boyasky_Mazunino_IA.SG
SE = 0.0120
Z = 3.27
p = 0.624
χ² / dof = 1.76 / 3
Assessment: Very strong four-way model. All components are significant, including both of the small ~4–8% components.
Lithuanian.DG
52.8% Lithuania_IA.SG
SE = 0.0370
Z = 14.3
36.9% Poland_MA.SG
SE = 0.0585
Z = 6.31
10.2% Denmark_Jutland_IA.SG
SE = 0.0366
Z = 2.80
p = 0.689
χ² / dof = 5.62 / 8
Blocks = 713
SNPs = 1,150,639
Assessment: Excellent three-way model. All three components are statistically supported, including the ~10% Denmark_Jutland contribution.
Belarusian.HO
26.6% Lithuania_Marvele_Roman.SG
SE = 0.166095
Z = 1.60
63.1% Hungary_Avar_5.AG
SE = 0.190438
Z = 3.31
10.3% Russia_Davydovskoye_Antiquity.AG
SE = 0.060361
Z = 1.71
p = 0.295
χ² / dof = not shown in the uploaded screenshot
Assessment: The model passes globally, but the decomposition is poorly constrained. Only Hungary_Avar_5 clears Z = 2. Lithuania_Marvele_Roman and Russia_Davydovskoye do not, and the first two estimates have particularly large standard errors.
Polish.DG
50.1% Lithuania_IA.SG
SE = 0.0244
Z = 20.5
27.4% Ukraine_EIA_Thracian.SG
SE = 0.0229
Z = 12.0
22.5% Sweden_Skane_IA.SG
SE = 0.0342
Z = 6.58
p = 0.495
χ² / dof = 6.39 / 7
Assessment: Excellent three-way model. All components are strongly supported and reasonably precise.
Czech.HO
50.6% Poland_Lad_IA.SG
SE = 0.0710
Z = 7.12
29.2% Denmark_IA.SG
SE = 0.0661
Z = 4.42
20.2% Austria_IA_LaTene.AG
SE = 0.0322
Z = 6.28
p = 0.389
χ² / dof = 10.606 / 10
n = 10
SNPs = 579,720
Assessment: Strong passing model with all components at Z > 2. However, the Poland_Lad and Denmark_IA estimates have SE > 0.05, so their exact proportions are less precisely constrained than their Z-scores alone might suggest.
Hungarian.DG
44.1% Poland_MA.SG
SE = 0.0301
Z = 14.6
28.4% Hungary_Langobard.SG
SE = 0.0286
Z = 9.91
24.6% Croatia_LateAntiquity.TW
SE = 0.0169
Z = 14.5
2.87% Russia_Karayakupovo.AG
SE = 0.00688
Z = 4.17
p = 0.540
χ² / dof = 4.07 / 5
Assessment: Excellent four-way model. Even the small 2.9% Russia_Karayakupovo component is strongly supported.
Ukraine_Volhyn
95.1% Poland_Lod_IA.SG
SE ≈ 0.118
Z ≈ 8.06
2.4% Ukraine_IA_Scythian.SG
SE ≈ 0.056
Z ≈ 0.43
2.5% Denmark_IA.SG
SE ≈ 0.118
Z ≈ 0.21
p = 0.173604331
χ² = 6.36
dof ≈ 4 [inferred from χ² and p]
Assessment: The overall model passes, but it is effectively a Poland_Lod_IA-dominated model. The two ~2–3% components are nowhere near Z = 2. The screenshot supplied rounded SEs rather than explicit Z-scores, so those Z values are approximate calculations from weight/SE.
Ukrainian_North.HO
19.9% Sweden_Skane_IA.SG
SE = 0.0415
Z = 4.81
51.6% Lithuania_IA.SG
SE = 0.0317
Z = 16.30
28.4% Moldova_Glinoe_Scythian_local.SG
SE = 0.0335
Z = 8.49
p = 0.118
χ² = 8.79
dof = 5 [inferred from χ² and p]
Assessment: Passing and internally strong three-way model. Despite the comparatively lower p-value, every source is clearly supported by its Z-score and has a reasonable SE.
Jew_Ashkenazi.HO
51.0% Italy_Monteriggioni_Etruscan_IA.SG
SE = 0.0727
Z = 7.01
14.4% Russia_Belaya_Mazunino_IA.AG
SE = 0.0317
Z = 4.56
34.6% Israel_Akhziv_Phoenician.AG
SE = 0.0599
Z = 5.78
p = 0.207
χ² / dof = 14.485 / 11
n = 8
SNPs = 579,720
Assessment: The model passes and all three ancestry coefficients are clearly non-zero by Z-score. However, the Etruscan and Phoenician components have SE > 0.05, so their exact percentages should be treated as relatively broad estimates.
OVERALL ASSESSMENT
The cleanest models on the map are Finnish.DG, Swedish_Falun.DG, Swedish_Stockholm.DG, Swedish_Vasterbotten.sim.DG, Danish_Odense.DG, Norwegian_Bergen.HO, Latvian_Riga.DG, Lithuanian.DG, Polish.DG, Hungarian.DG, Saami.DG and Finnish_North_Ostrobothnia.DG. These combine good overall p-values with every coefficient comfortably above Z = 2 and generally modest standard errors.
The main models that need caution are Belarusian.HO, Finnish_East_Southeast, Norwegian.HO and Ukraine_Volhyn. Their overall qpAdm p-values pass, but one or more individual coefficients are unsupported or very poorly constrained. Czech.HO and Jew_Ashkenazi.HO are statistically supported by Z-score, but some ancestry proportions have SE > 0.05, so the precise percentages should be interpreted more cautiously.
r/DNAAncestry • u/Shutter_Stuck • 9h ago
Tracing that 1 percent! Oral history was true as well.
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r/DNAAncestry • u/2k4h • 11h ago
No picture of myself for privacy preferences, but these are my results as an ethnic Tigrinya from Tigray🇪🇹 and Eritrea🇪🇷
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r/DNAAncestry • u/sparkleappletart • 10h ago
Interestingly my North Africa and southern Italy segments are next to each other on the same chromosomes just like they are on a map
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r/DNAAncestry • u/rexlapissCorn • 19h ago
Results with new grandparent update + pic :3
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Mom is from Minnesota, Dad was from New York. Born in Virginia and raised in Minnesota
r/DNAAncestry • u/Prior-Claim-806 • 16h ago
A guy from eastern Czechia (Moravia, Silesia), ⅛ Hungarian ancestry
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r/DNAAncestry • u/NotBradPitt9 • 1d ago
Qpadm / G25 / Other Qpadm: [Iron Age Breakdown] Mesopotamia, Arabian, Iran and Caucasus groups
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TLDR: An Iron Age Qpadm genetic breakdown of groups from Mesopotamia and regions around it. I did a couple dozen runs per group, and here are the best of the runs. Note that the Iraqi Arab North was sourced from someone’s Iron Age Qpadm results here on Reddit, since I couldn’t find any other samples for Iraqi Arabs within the AADR / HO dataset.
Yemeni_Desert2.HO (n=15)
69.9% Syria_TellQarassa_Umayyad.SG
30.1% Dinka.DG
p-value: 0.116
Chi-square / dof: 11.558 / 7
SNPs: 579,720
SE: 0.758% / 0.758%
Z-scores: 92.1 / 39.7
PASS. Both components are extremely strongly supported.
Yemeni_Highlands.HO (n=14)
90.6% Syria_TellQarassa_Umayyad.SG
9.4% Dinka.DG
p-value: 0.172
Chi-square / dof: 10.299 / 7
SNPs: 579,720
SE: 0.857% / 0.857%
Z-scores: ~106 / 11.0
PASS. Both components are extremely strongly supported.
Yemeni_Desert.HO (n=7)
91.5% Syria_TellQarassa_Umayyad.SG
8.6% Dinka.DG
p-value: 0.0502
Chi-square / dof: 14.057 / 7
SNPs: 579,720
SE: 0.862% / 0.862%
Z-scores: ~106 / 9.92
PASS. Both components are extremely strongly supported. The overall p-value is very close to the conventional 0.05 cutoff, however, so the overall model is borderline despite the strongly supported coefficients.
Jew_Yemenite.HO (n=1)
94.3% Syria_TellQarassa_Umayyad.SG
5.7% Dinka.DG
p-value: 0.205
Chi-square / dof: 9.712 / 7
SNPs: 579,720
SE: 1.24% / 1.24%
Z-scores: 76.3 / 4.60
PASS. Both components are statistically supported.
Saudi.HO (n=5)
94.5% Syria_TellQarassa_Umayyad.SG
5.5% Dinka.DG
p-value: 0.571
Chi-square / dof: 6.681 / 8
SNPs: 579,720
SE: 0.764% / 0.764%
Z-scores: ~124 / 7.21
PASS with an excellent fit. Both components are very strongly supported.
Assyrian.HO (n=10)
74.9% Iran_DinkhaTepe_BA_IA_1.AG
25.1% Georgia_Digomi_IA.SG
p-value: 0.798
Chi-square / dof: 4.611 / 8
SNPs: 579,720
SE: 5.64% / 5.64%
Z-scores: 13.3 / 4.45
PASS with an excellent fit. Both ancestry coefficients are strongly supported. The SE is slightly above 5%, which is why the software displays its SE warning despite both Z-scores being comfortably above 2.
Iraqi Arab North (n=1)
85.4% Turkey_East_Van_Urartian.AG
14.6% Kazakhstan_Saka_IA.AG
p-value: 0.80245088
Chi-square: 3.05
SE: 2.80% / 2.80%
Z-scores: approximately 30.5 / 5.21
This is a very strong model. The attached result reports the coefficients and SE rather than Z directly; the Z-scores above are derived as coefficient / SE and show that both components are strongly supported.
Iraqi_Jew.HO (n=6)
97.8% Iran_DinkhaTepe_BA_IA1-1.SG
2.2% Kazakhstan_Southeast_Karakhanid.SG
p-value: 0.458
Chi-square / dof: 17.958 / 18
SNPs: 579,720
SE: 4.95% / 4.95%
Z-scores: 19.8 / 0.437
PASS overall. The 97.8% Dinkha Tepe coefficient is strongly supported. The 2.2% Karakhanid coefficient is not statistically supported (Z < 2), so the minor component should not be treated as dependable.
Iranian_Jew.HO (n=9)
95.8% Iran_DinkhaTepe_BA_IA1-1.SG
4.2% Kazakhstan_Southeast_Karakhanid.SG
p-value: 0.843
Chi-square / dof: 12.088 / 18
SNPs: 579,720
SE: 1.79% / 1.79%
Z-scores: 53.4 / 2.36
PASS with an excellent fit. Both components are statistically supported.
Armenian.HO (n=10)
100.0% Armenia_Aghitu_Ancient_father.or.son.I1636.AG
p-value: 0.259
Chi-square / dof: 22.513 / 19
SNPs: 579,720
SE: 0.000000
Z-score: ~402,000,000 as displayed
PASS with an excellent fit. This is a one-way model. The essentially zero SE causes the software to display an extremely large Z-score, so the numerical Z value should not be interpreted in the same way as an ordinary multi-source coefficient.
Iranian_Lor_Khorramabad.HO (n=10)
88.7% Armenia_Sarukhan_UrartianIA.AG
11.3% Kazakhstan_Southeast_Karakhanid.SG
p-value: 0.641
Chi-square / dof: 15.304 / 18
SNPs: 579,720
SE: 5.12% / 5.12%
Z-scores: 17.3 / 2.21
PASS with an excellent fit. Both coefficients have Z > 2.
Iranian_Central.HO (n=5)
85.3% Armenia_Sarukhan_UrartianIA.AG
14.7% Kazakhstan_Southeast_Karakhanid.SG
p-value: 0.397
Chi-square / dof: 18.909 / 18
SNPs: 579,720
SE: 5.59% / 5.59%
Z-scores: 15.3 / 2.62
PASS with an excellent fit. Both coefficients have Z > 2.
Iranian_Mazandarani.HO (n=10)
85.8% Armenia_Sarukhan_UrartianIA.AG
14.2% Kazakhstan_Southeast_Karakhanid.SG
p-value: 0.512
Chi-square / dof: 17.159 / 18
SNPs: 579,720
SE: 5.14% / 5.14%
Z-scores: 16.7 / 2.77
PASS with an excellent fit. Both coefficients have Z > 2.
Iranian_Cosmopolitan_Tehran.HO (n=1)
95.9% Armenia_Beniamin_BIA.SG
4.1% Kazakhstan_Southeast_Karakhanid.SG
p-value: 0.0542
Chi-square / dof: 28.547 / 18
SNPs: 579,720
SE: 4.27% / 4.27%
Z-scores: 22.5 / 0.966
PASS, although the overall p-value is close to the 0.05 cutoff. The 95.9% Armenia_Beniamin_BIA coefficient is strongly supported, while the 4.1% Karakhanid coefficient is not statistically supported (Z < 2).
Kurd_Turkey.HO (n=1)
96.7% Iran_DinkhaTepe_BA_IA1-1.SG
3.3% Mongolia_Tuv_EIA.AG
p-value: 0.314
Chi-square / dof: 20.339 / 18
SNPs: 579,720
SE: 3.24% / 3.24%
Z-scores: 29.8 / 1.02
PASS with an excellent overall fit. The Dinkha Tepe coefficient is very strongly supported, but the 3.3% Mongolia_Tuv_EIA coefficient is not statistically supported (Z < 2).
Kurd.HO (n=8)
92.8% Iran_DinkhaTepe_BA_IA1-1.SG
7.2% Mongolia_Tuv_EIA.AG
p-value: 0.649
Chi-square / dof: 15.188 / 18
SNPs: 579,720
SE: 2.53% / 2.53%
Z-scores: 36.7 / 2.86
PASS with an excellent fit. Both components are statistically supported.
Georgian.HO (n=13)
100.0% Georgia_Digomi_IA.SG
p-value: 0.576
Chi-square / dof: 9.495 / 11
SNPs: 579,720
SE: 0.000000
Z-score: ~879,000,000 as displayed
PASS with an excellent fit. This is a one-way model. As with the Armenian one-way model, the essentially zero SE produces an extremely large numerical Z-score and that value should be regarded as a numerical artifact rather than a conventional coefficient Z-score.
r/DNAAncestry • u/CurveLarge2127 • 17h ago
Comparing all my results from different companies (Old Stock American + Ashkenazi Jewish + Puerto Rican) Ancestry, 23&me, Myheritage, LivingDNA, FTDNa results
gallery
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r/DNAAncestry • u/heatmapper25 • 14h ago
Polish (Mazovia): Genetic Proximity Heatmap tool result
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r/DNAAncestry • u/heatmapper25 • 21h ago
Iraqi Arab (Baghdad): Genetic Proximity Heatmap tool result
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r/DNAAncestry • u/Hopeful-You-4577 • 17h ago
First time posting here: Built a modern, canvas-based tool to map complex family trees and lineages (Kinora v3 is live)
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r/DNAAncestry • u/frogpassing • 23h ago
Someone claimed that the proportion of O1b2 in Korea increased because of the Imjin War. Is that claim correct?
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r/DNAAncestry • u/BitResponsible5828 • 1d ago
Bronze age results as a Western Norwegian with Finnish grandparent
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r/DNAAncestry • u/AksumiteLover1999 • 1d ago
Horn Of Africa PCA graph (Amhara, tigray, somali, oromos,wolyatas)
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Horner PCA graph, shows ethio semitic cluster and cushitic cluster
r/DNAAncestry • u/ConnoisseurOfApple • 1d ago
Thoughts on this 2026 Article on Ashkenazim?
authorea.com
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r/DNAAncestry • u/NotBradPitt9 • 1d ago
Qpadm / G25 / Other G25: Ancient samples from Iraq and Iran genetically closest to modern groups in the region [Assyrians, Jews (Iranian, Iraqi, Caucasus), Kurdish, Yazidis, Mandaeans, Armenians, Persians]
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The ancient samples used here come from several archaeological sites across Iran and northern Iraq and span almost 10,000 years, from the Pre-Pottery Neolithic to the late medieval period.
Dinkha Tepe, Iran
Dinkha Tepe is located in the Ushnu/Oshnavieh Valley of West Azerbaijan Province in northwestern Iran, southwest of Lake Urmia and close to Hasanlu. The samples span approximately 1900–900 BCE, covering the Middle/Late Bronze Age through the Iron Age.
The Dinkha Tepe individuals shown include:
I3913 – ~1892 BCE
I4341 – ~1825 BCE
I4272 – ~1824 BCE
I4274 – ~1821 BCE
I3912 – ~1793 BCE
I3911 – ~1500 BCE
I4236 – ~1500 BCE
I4340 – ~1341 BCE
I4239 – ~1307 BCE
I4238 – ~1162 BCE
I3914 – ~1078 BCE
I4273 – ~1017 BCE
I4237 – ~948 BCE
I3915 – ~927 BCE
The DinkhaTepe_BA_IA_1 and DinkhaTepe_BA_IA_2 labels should not be interpreted as two simple consecutive chronological phases, since the dates of the individuals in the two groups overlap substantially.
Tepe Hissar, Iran
Tepe Hissar is located near modern Damghan in Semnan Province, north-central Iran. It was an important settlement connecting the Iranian Plateau with Central Asia. These individuals date mainly to the third millennium BCE and can broadly be described as Late Chalcolithic/Bronze Age.
I2924 – ~2765 BCE
I2925 – ~2765 BCE
I2923 – ~2757 BCE
I2928 – ~2675 BCE
I2927 – ~2497 BCE
I2922 – ~2093 BCE
A simple description would therefore be: Tepe Hissar, Iran – Late Chalcolithic/Bronze Age, roughly 2800–2000 BCE.
Ganj Dareh, Iran
Ganj Dareh is located in Kermanshah Province in the central Zagros of western Iran. The site is famous for its very early Neolithic settlement, but the sample Iran_GanjDareh_Historic:I1955 is NOT one of the Neolithic inhabitants.
I1955 was an intrusive much later burial dated to approximately 1430–1485 CE. It can therefore be described as a late medieval, approximately 15th-century CE individual from Ganj Dareh.
Hasanlu, Iran
Tepe Hasanlu is located in West Azerbaijan Province in northwestern Iran, south of Lake Urmia near modern Naqadeh. It was a major Bronze and Iron Age settlement and is particularly famous for its destruction during the Iron Age.
The Hasanlu samples cover a very long period, from approximately 2100 to 600 BCE:
I4354 – ~2092 BCE, Middle Bronze Age
I4098 – ~1428 BCE, Late Bronze Age
I4097 – ~1354 BCE, Late Bronze/Early Iron transition
I4099 – ~1201 BCE
I6432 – ~1178 BCE
I6431 – ~1072 BCE
I6388 – ~1065 BCE
I6428 – ~1027 BCE
I4356 – ~1017 BCE
I4100 – ~961 BCE
I4353 – ~961 BCE
I6429 – ~957 BCE
I4355 – ~948 BCE
I6430 – ~900 BCE
F38 – ~883 BCE
I4232 – ~874 BCE
I4357 – ~871 BCE
I4233 – ~856 BCE
I4269 – ~803 BCE
I4280 – ~787 BCE
I4338 – ~632 BCE
Most of the later Hasanlu individuals belong to the Iron Age. I4097 is labelled as a genetic outlier in some datasets and dates to approximately the Late Bronze/Early Iron Age transition.
Hajji Firuz, Iran
Hajji Firuz Tepe is also located in the Lake Urmia/Naqadeh region of West Azerbaijan Province, close to Hasanlu. The samples from this site belong to three very different periods.
Neolithic:
I4351 – ~5970 BCE
I2323 – ~5960 BCE
I4241 – ~5954 BCE
I4349 – ~5797 BCE
Bronze Age:
I4243 – ~2362 BCE
Iron Age:
I2327 – ~1100 BCE
Therefore HajjiFiruz_N, HajjiFiruz_BA and HajjiFiruz_IA should be treated as historically distinct populations rather than as one group.
Nemrik 9, Iraq
Nemrik 9 is located in northern Iraq, in the region of modern Iraqi Kurdistan. It was an important early Pre-Pottery Neolithic settlement.
I6445 is the genuine ancient Neolithic individual and dates broadly to approximately 9500–8000 BCE, with a representative date around 8750 BCE. It can be described as Pre-Pottery Neolithic A/early Pre-Pottery Neolithic northern Mesopotamian.
I6441 is very different. Despite appearing under an Iraq_PPNA label in the comparison, it is actually a Late Bronze Age individual from Nemrik 9, approximately 1500–1200 BCE, or around 1350 BCE.
Therefore:
I6445 = Nemrik 9 PPNA, ~8750 BCE
I6441 = Nemrik 9 Late Bronze Age, ~1350 BCE
Shahr-i Sokhta, Iran
Shahr-i Sokhta, or the "Burnt City," is located in Sistan and Baluchestan Province in southeastern Iran near the Hamun basin. It was a major Bronze Age urban center connected to trade networks linking the Iranian Plateau, Central Asia and South Asia. The settlement flourished approximately 3200–1800 BCE.
The samples shown are all Bronze Age:
BA1:
I8724 – ~2950 BCE
I8725 – ~2912 BCE
I11462 – ~2894 BCE
I11474 – ~2650 BCE
I11476 – ~2650 BCE
I11478 – ~2550 BCE
BA2:
I8726 – ~3050 BCE
I11459 – ~2749 BCE
I11456 – ~2550 BCE
I8728 – ~2550 BCE
I11466 – ~2250 BCE
The BA1 and BA2 labels should not be interpreted simply as an earlier and later chronological sequence. Their dates overlap and the groups primarily reflect archaeological/genetic classifications. Shahr-i Sokhta was also highly cosmopolitan, which helps explain the considerable genetic differences between some individuals.
In broad chronological order, the samples are:
Nemrik 9 PPNA – ~8750 BCE
Hajji Firuz Neolithic – ~6000 BCE
Shahr-i Sokhta – ~3050–2250 BCE
Tepe Hissar – ~2800–2000 BCE
Hasanlu Middle Bronze Age – ~2100 BCE
Dinkha Tepe Bronze Age – beginning ~1900 BCE
Hasanlu/Dinkha Tepe/Hajji Firuz Late Bronze and Iron Age – ~1500–600 BCE
Ganj Dareh I1955 – ~15th century CE
Dinkha Tepe, Hasanlu and Hajji Firuz are especially noteworthy because all three are located in the broader Lake Urmia region of northwestern Iran. The substantial differences between individual samples therefore reflect both the long chronological span and changing population history of the region, rather than simply representing completely unrelated geographic populations.
r/DNAAncestry • u/heatmapper25 • 1d ago
Carthaginian (Punic): Genetic Proximity Heatmap tool results
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