r/DNAAncestry 10d 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]

Thumbnail
gallery
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 16d ago

Qpadm / G25 / Other Qpadm: Mainland Southeast Asia and Island Southeast Asia

Thumbnail
gallery
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 13h ago

Do I look like my results?

Thumbnail
gallery
258 Upvotes

r/DNAAncestry 1h ago

Not suprised at these results 😅

Thumbnail
gallery
Upvotes

r/DNAAncestry 8h ago

An Australian born to parents who weren’t sure of their lineage - my results definitely surprised me

Thumbnail
gallery
24 Upvotes

r/DNAAncestry 12h ago

Do I look like my results?

Thumbnail
gallery
53 Upvotes

r/DNAAncestry 9h ago

I wonder if there’s anyone similar to me

Thumbnail
gallery
10 Upvotes

r/DNAAncestry 3h ago

Excited about the enlarged borders of the Basque region in the upcoming AncestryDNA update as a person of Basque descent (old borders on the left/new borders on the right) !!!

Thumbnail
gallery
3 Upvotes

As you can see in the second screenshot, my results got completely botched in last year's update: I went from 70% Basque / 30% Spain in the Jul 2024 update to 38% Basque / 33% Spain / 24% Northern Spain / 2% Northern Wales & North West England / 1% Connacht, Ireland / 1% Leinster, Ireland / 1% North East England in the Oct 2025 update 😭😭😭😭😭😭

Hopefully this will get fixed in the upcoming update, especially with these enlarged borders of the Basque region!!

The third screenshot shows my 23AndMe results, &, while overall speaking, I'm MUCH more satisfied with my current 23AndMe results than with my current AncestryDNA ones, I have a pretty big pet peeve with 23AndMe that I don't have with AncestryDNA:

23AndMe is OBSESSED with strictly adhering to formal administrative borders, so, in 23AndMe, the borders of the Basque region are strictly limited to the formal administrative borders of the Spanish Basque Country, fully excluding the French Basque Country, which is just flat-out wrong, French Basques are still to this very day very much genetically Basque in terms of genetic admixture!!!

AncestryDNA on the other hand is not afraid to include not just both the Spanish Basque Country AND the French Basque Country but also even regions that haven't been considered Basque in millennia but which are still to this very day very much genetically Basque in terms of genetic admixture, like the non-Basque Northern Spanish regions of La Rioja & North West Aragon https://en.wikipedia.org/wiki/La_Rioja https://en.wikipedia.org/wiki/Aragon & the non-Basque South West French region of Gascon Occitania https://en.wikipedia.org/wiki/Gascony https://en.wikipedia.org/wiki/Occitania

I appreciate how, unlike 23AndMe, AncestryDNA is not afraid to completely disregard formal administrative borders, which, especially in areas like Northern Spain & South West France that haven't been subjected to settler colonialism in at the very least the most recent last several centuries, tend to align very little with actual genetic admixture.

The fourth screenshot shows my LivingDNA results, which suffer from even more unserious borders than 23AndMe somehow lol although at the very least they do include the French Basque Country in the Basque region!!


r/DNAAncestry 6h ago

Results - Wasian 27M

Thumbnail gallery
2 Upvotes

r/DNAAncestry 3h ago

From Western Gujarat. How did I get South West African genes.

Post image
1 Upvotes

r/DNAAncestry 3h ago

Scientific Paper / Article / Study Study: (Ghost Archaic, Super Archaic found in modern humans) Recovering signatures of archaic hominin introgression using ancestral recombination graphs

1 Upvotes

TLDR: Researchers found Super Archaic and Ghost Archaic DNA at trace amounts in modern humans along with Neanderthal and Denisovan DNA. The Ghost Archaic ancestry split ~830,000 years ago from the ancestors of modern humans. The Super Archaic split 900,000-1.4 million years ago from the ancestors of modern humans. The Ghost Archaic is found in all people since it introgressed into modern humans while we were in Africa. The Super Archaic is only found in Denisovan DNA segments which peak in Oceanian populations, and found at low levels in parts of Asia.

https://www.science.org/doi/10.1126/science.aef8874

We introduce TRACE (Tracking Archaic Contributions Via ARG Estimation), a method for identifying footprints of archaic ancestry in modern human genomes by leveraging features of ARGs constructed from contemporary genomes alone, requiring neither an archaic reference genome nor an unadmixed outgroup population. We validate TRACE by performing extensive simulations and show that it has high precision and low false discovery rate across a range of demographic scenarios and parameters. Applied to data from the 1000 Genomes Project, TRACE recovers the known signatures of Neanderthal introgression in all non-Africans and Denisovan introgression in Asians and Oceanians, with population-level patterns consistent with prior estimates.
Beyond these established signals, TRACE identifies extensive segments of ghost archaic ancestry—from a previously uncharacterized hominin lineage—in both African and non-African populations. We find ghost ancestry persists within Neanderthal and Denisovan ancestry deserts, genomic regions previously interpreted as depleted of archaic introgression and thought to be specific to Homo sapiens. This challenges the prevailing interpretation of these regions and suggests that selection acting against introgressed ancestry has not been uniform across all archaic sources. In Oceanian populations, TRACE detects a substantial enrichment of unusually deep coalescent lineages within Denisovan ancestry segments compared with Neanderthal segments, consistent with a model of super-archaic gene flow into modern humans through Denisovans. Together, these results point to multiple layers of introgression—both direct and mediated through other archaic groups—that have shaped modern human genomes.

Most non-Africans living today possess 1 to 2% Neanderthal ancestry, whereas Asians and Oceanians harbor ~0.1 to 5% Denisovan ancestry

Genetic analyses of present-day African populations have identified highly divergent haplotypes that cannot be explained by known demographic histories, hinting at “ghost” introgression (1729, 31). At deeper timescales, analysis of the Altai Denisovan genome suggests that they harbor ancestry from a deeply divergent population, referred to as “super-archaic”, that split from modern humans around 0.9 to 1.4 million years ago (Mya)

In non-African populations, TRACE identifies 0.8 to 1% Neanderthal ancestry per individual in Europeans, East Asians, and South Asians (Fig. 2A). We recovered minimal Denisovan ancestry in Europeans (0.03%), with higher levels in East and South Asians (0.10% each), consistent with published results

In both West and East Africans, TRACE detects less than 0.1% combined Neanderthal and Denisovan ancestry per individual.

We note that TRACE recovers less total Neanderthal and Denisovan ancestry compared with some previous studies (33, 54, 55), owing to the lower recall with inferred ARGs

TRACE revealed “ghost” archaic ancestry—introgression from an archaic lineage more distantly related to Neanderthals and Denisovans—in all modern human populations studied. We detected 0.5 to 1.1% of ghost ancestry on average across populations (Fig. 2A). Most ghost ancestry segments found in non-Africans are shared with sub-Saharan Africans, while both East and West Africans harbor a greater diversity of distinct ghost segments consistent with the reduction in genetic diversity in non-Africans caused by the OOA bottleneck (Fig. 2B and figs. S23 and S24). Ghost segments exhibit deep divergence in marginal trees and show nearly identical genetic affinity to both sequenced Neanderthal and Denisovan genomes, indicating that they originated from an unsequenced lineage equally related to both archaic groups (Fig. 2C and figs. S17 to S19). The average coalescence time between the ghost and modern human segments, inferred from introgressed segments, is approximately 0.83 Mya (95% CI: 0.83 to 0.84 Mya, table S6).

Several additional lines of evidence support our results of ghost ancestry in modern humans. First, we find that genomic regions harboring ghost ancestry exhibit elevated heterozygosity levels, a pattern also observed for Neanderthal and Denisovan segments (fig. S25). The elevation in heterozygosity is consistent with a model of introgression from a deeply divergent lineage (56) and, notably, suggests that the ghost ancestry signal is not an artifact of ARG inference. Second, across all tested populations, ghost ancestry segments are shorter than Neanderthal and Denisovan segments, reflecting a more ancient introgression event predating Neanderthal gene flow (fig. S26).

While Neanderthal and Denisovan segments show the expected “U-shape” (17), ghost segments display distinct SFS and cSFS patterns that are consistent with a model of pre–OOA ghost introgression in simulations (fig. S29 and S30).
Together, these findings suggest that an unknown archaic population, which diverged more than 500,000 years ago, introgressed into the common ancestors of all modern humans prior to the OOA migration, resulting in similar patterns of ghost ancestry in non-Africans and Africans.

Across all ghost haplotypes identified in all populations (sub-Saharan Africans and non-Africans) by TRACE, we recovered 1548.75 Mbp or 71.54% of the accessible genome (Fig. 3, supplementary text section S6). Broadly, the genome-wide patterns of ghost ancestry segments resemble those expected from hybridization between deeply divergent lineages (5759).

we identified 1932 peaks of ghost ancestry (average length 61 kbp, SD 64 kbp) (supplementary text section S6.2, Fig. 3, and table S13). This result contrasts with the smaller number of 1155 and 160 peaks of Neanderthal and Denisovan ancestry, respectively (average length 84.97 and 76.54 kbp). We find that ghost ancestry peaks are overrepresented in sub-Saharan African populations relative to non-African populations, which is consistent with the lower genetic diversity in non-Africans (figs. S33 and S34). Several genes intersect with high-frequency peaks of ghost ancestry, such as CSMD1 and RBFOX1, with an overall functional enrichment for immune and metabolic complexes, including the major histocompatibility and lipoprotein complexes (P < 10−5 using binomial test; figs. S35 and S36).

a fraction of super-archaic ancestry may have been inherited through Denisovan gene flow and may persist within Denisovan-introgressed segments in present-day Oceanians

In Oceanian individuals, TRACE inferred an average of 0.73% Neanderthal, 0.66% Denisovan, and 0.33% ghost ancestry (Fig. 4A), lower than estimates from previous studies.

ghost segments in Oceanians overlapped with those detected in 1000G populations (fig. S50), consistent with a shared origin in modern humans.

we infer that the super-archaic segments embedded within Denisovan ancestry tracts range between 20 to 83 kbp (average: 37.6 kbp) and contribute approximately 0.3% of the total detected Denisovan ancestry in Oceanians (table S18 and fig. S51). This estimate constitutes a very conservative lower bound on the true fraction of super-archaic ancestry

we estimate the coalescence time between super-archaic and modern human lineages to be approximately 1.77 Mya (95% CI: 1.69 to 1.83 Mya), consistent with earlier reports (2, 32).

We evaluated the potential functional effects of super-archaic segments recovered from Oceanian genomes by characterizing common gene annotations and patterns of functional enrichment (Fig. 4B). Several genic regions harbor high proportions (>70%) of super-archaic tracts such as RNF39, PPP1R11, and POLR1Hwithin the major histocompatibility complex (MHC) (Fig. 4B and fig. S53) and CYP24A1, which is a part of the cytochrome P450 family and a critical regulator of vitamin D degradation in humans (fig. S54)


r/DNAAncestry 3h ago

Question

1 Upvotes

Since AncestryDNA removed like 200.000 snps (insane number), do you still think they produce better RAW file compared to MyHeritage RAW dna file?

I want to try illustrativeDNA for ancient DNA breakdown, but not sure which RAW file to choose.


r/DNAAncestry 9h ago

Qpadm / G25 / Other G25: Italian (Iron Age) samples compared to modern samples

Thumbnail
gallery
3 Upvotes

Italy_Etruscan_Monteriggioni_IA:EV7A__BC_600__Cov_21.86%,0.10927,0.145221,0.039598,-0.007429,0.054164,-0.00502,0.00611,0.009461,0.036201,0.046106,-0.002761,0.010191,-0.014123,-0.008257,0.001221,-0.00305,-0.00665,0.011909,0.008547,0.004377,-0.00287,0.010758,0.002465,-0.014098,-0.002634
Italy_Etruscan_Monteriggioni_IA:EV16D1__BC_600__Cov_10.57%,0.104717,0.158423,0.032055,-0.011305,0.055395,-0.007809,0.00376,-0.000462,0.033951,0.046835,0.008119,0.008842,-0.02334,-0.019267,-0.00095,-0.007292,-0.00678,-0.011909,-0.006536,0.00075,-0.000998,-0.006677,0.005546,-0.002651,-0.002155
Italy_Pesaro_LateAntiquity:PF1__AD_600__Cov_9.85%,0.046667,0.149283,0.001886,-0.059109,0.038469,-0.023427,-0.035722,0.003692,0.025156,0.01549,0.008119,-0.003447,0.003865,0.003303,0.0076,-0.016971,-0.012126,-0.006461,-0.010684,-0.012756,-0.024332,-0.00507,0.011216,-0.004217,0.007664
Italy_Pesaro_LateAntiquity:PF11__AD_600__Cov_17.10%,0.113823,0.153345,0.004148,-0.021641,0.034468,-0.010319,-0.010575,0.002077,0.007567,0.035354,-0.002761,-0.006294,-0.002825,-0.007982,-0.002986,-0.000796,-0.001173,0.00114,-0.00088,-0.001126,-0.006863,0,0.000246,0.004579,-0.003473
Italy_Pesaro_LateAntiquity:PF19__AD_600__Cov_15.19%,0.094473,0.150298,0.004525,-0.032623,0.025851,-0.013666,0.003995,-0.001154,0.016975,0.024055,-0.001137,0.004796,-0.010555,-0.010322,-0.017779,0.009679,0.018906,-0.001394,0.001885,0.005127,0.002246,0.001237,0.006039,0.006507,-0.010298
Italy_Pesaro_LateAntiquity:PF24__AD_600__Cov_29.11%,0.117238,0.136081,0.007165,-0.030039,0.017849,-0.013387,0.00564,-0.006461,0.011862,0.032985,0.005359,0.009591,-0.009663,-0.01913,-0.00665,-0.001326,-0.003651,0.005321,0.002765,-0.014882,0.00262,-0.000371,0.003081,0.003133,-0.003712
Italy_Pesaro_LateAntiquity:PF28__AD_600__Cov_24.81%,0.105855,0.145221,-0.028661,-0.070414,0.007694,-0.023985,-0.0094,0.000692,-0.007567,0.031891,0.00406,0.004196,-0.023191,-0.004129,-0.031216,0.010209,0.015646,0.003674,0.003268,-0.000875,-0.000499,-0.004204,0.000493,-0.003976,-0.005748
Italy_Pesaro_LateAntiquity:PF36__AD_600__Cov_10.61%,0.104717,0.129988,-0.000377,-0.026809,0.014156,-0.006693,0.012691,-0.010384,-0.001023,0.033349,0.004547,0.004496,-0.011596,-0.004954,-0.000679,0.011933,0.005867,-0.011782,0.003771,-0.004002,0.001622,0.002102,-0.007025,0.00723,-0.006466
Italy_Picene_Novilara_IA:PN20__BC_625__Cov_15.30%,0.118376,0.149283,0.031301,-0.023579,0.045547,-0.003347,0.011281,0.010615,0.016157,0.036812,0.005196,0.013938,-0.015461,-0.022295,-0.002714,0.006232,-0.000782,0.008868,0.019357,-0.025012,0.014974,-0.006059,0.002465,-0.01699,0.002994
Italy_Picene_Novilara_IA:PN24__BC_675__Cov_22.18%,0.119514,0.161469,0.042615,0.002261,0.0437,-0.001394,-0.00517,0,0.002863,0.03426,0.006496,0.008842,-0.017839,-0.010597,0.003664,0.002254,0.009127,0.005068,0.004399,-0.003377,-0.000374,0.005564,-0.009244,0.002289,-0.010897
Italy_Picene_Novilara_IA:PN35__BC_725__Cov_22.42%,0.119514,0.142174,0.036581,0.003876,0.030159,-0.001673,0.00235,-0.011076,0.011249,0.029522,-0.011205,-0.007044,-0.01665,-0.011836,-0.006786,0.000398,0.002217,0.000127,0.009804,-0.001501,0.004742,0.004946,0.004067,0.010363,-0.012813
Italy_Picene_Novilara_IA:PN41__BC_625__Cov_19.22%,0.1161,0.139128,0.029793,-0.007752,0.02739,-0.014781,-0.00329,0.010153,-0.002454,0.043372,-0.004709,0.02248,-0.023191,-0.015551,-0.005157,-0.003182,0.006389,-0.007601,0.008673,-0.003377,-0.006988,-0.006677,0.002342,-0.000482,-0.011735
Italy_Picene_Novilara_IA:PN42__BC_700__Cov_13.96%,0.136588,0.126941,0.037712,0.008721,0.042469,0.002789,-0.011751,0.011076,0.003681,0.016037,-0.000325,0.005245,-0.014569,-0.023258,0.002307,0.006364,0.011213,0,0.001006,-0.017383,-0.002121,0.005688,-0.001479,-0.002651,-0.00946
Italy_Picene_Novilara_IA:PN44__BC_650__Cov_21.60%,0.120652,0.137096,0.034318,-0.005814,0.031083,-0.003347,-0.003525,0.007615,0.00859,0.022233,-0.008769,0.007044,-0.013082,-0.002064,0.005836,0.012198,0.02373,-0.008235,0.012696,-0.009379,-0.011105,0.00371,-0.016515,0.006145,-0.001078
Italy_Picene_Novilara_IA:PN50__BC_650__Cov_21.27%,0.121791,0.150298,0.027153,0.003876,0.045547,0.003068,0.000705,0.000462,0.018816,0.026424,-0.004872,-0.002997,-0.035381,0.00578,0.003529,0.001193,-0.002999,0.003041,-0.004525,-0.007003,0.007237,0.004204,-0.003328,0.005543,-0.006586
Italy_Picene_Novilara_IA:PN51__BC_675__Cov_11.58%,0.120652,0.141159,0.02753,-0.015181,0.029544,-0.00502,-0.00047,0.012692,0.022702,0.039181,0.003897,0.017684,-0.012636,-0.013349,0.002036,-0.003447,0.022296,-0.008995,-0.004274,-0.001876,-0.010107,0.007914,0.007148,-0.006145,0.00012
Italy_Picene_Novilara_IA:PN78__BC_700__Cov_23.76%,0.122929,0.146236,0.037335,0.00323,0.044008,0.005578,0.00235,-0.001615,0.004909,0.018588,-0.001461,0.005245,-0.013974,-0.013487,0.008143,0.002121,0.000913,-0.007221,0.014078,-0.001501,-0.001123,0.006306,-0.00493,-0.001687,0.000599
Italy_Picene_Novilara_IA:PN79__BC_675__Cov_23.59%,0.124067,0.141159,0.037712,0.005168,0.042469,0.014502,-0.001175,-0.003231,0.009408,0.020775,-0.001299,0.009741,-0.01219,0.000413,0.0076,-0.00769,-0.001825,-0.011022,0.012821,0.001,0.002745,-0.00507,0.004314,0.005543,-0.001197
Italy_Picene_Novilara_IA:PN85__BC_625__Cov_27.04%,0.126344,0.155376,0.046009,0.001938,0.029236,0.000837,0.00611,0.002308,0.006136,0.025878,-0.006496,0.008992,-0.020812,0.000688,-0.013843,-0.01127,-0.000782,0.005828,0.007542,-0.00963,0.002745,0.007914,0.00037,-0.005663,0.005029
Italy_Picene_Novilara_IA:PN87__BC_650__Cov_15.50%,0.113823,0.139128,0.024513,0.010013,0.028005,0.001952,0.00282,-0.003,-0.002454,0.018588,0.001786,0.014537,-0.018583,-0.004542,-0.005022,-0.00769,0.004694,0.002787,-0.002514,-0.011631,-0.012478,0.006925,0.005546,-0.001325,-0.013771
Italy_Picene_Novilara_IA:PN90__BC_675__Cov_19.88%,0.1161,0.150298,0.02753,0.002584,0.037853,-0.004462,0.005405,-0.003461,0.006545,0.032074,0.00065,0.018434,-0.012933,-0.029038,-0.006515,0.005038,0.014473,0.005954,0.007039,0.003377,-0.018218,0.009645,-0.003697,-0.013014,-0.007065
Italy_Picene_Novilara_IA:PN91__BC_625__Cov_19.60%,0.113823,0.140143,0.029038,0.00323,0.032621,-0.000558,0.00235,0.003461,0.012271,0.035172,0.006496,-0.008692,-0.013677,-0.004542,-0.006379,-0.012331,-0.004563,0.007095,0.006034,-0.008004,-0.016097,-0.000247,0.006039,0.005061,0
Italy_Picene_Novilara_IA:PN101__BC_650__Cov_22.21%,0.119514,0.14319,0.032055,0.003876,0.048317,-0.013108,0.00141,0.002538,0.009817,0.031709,-0.001786,0.011839,-0.028692,-0.009909,-0.010179,0.002254,0.005607,0.000127,0.004777,-0.019384,-0.00836,0.003091,0.010106,0.006386,-0.003473
Italy_Picene_Novilara_IA:PN105__BC_650__Cov_12.49%,0.119514,0.170609,0.042992,-0.00646,0.034776,-0.013108,0.00094,-0.011307,0.019225,0.035901,0.000812,-0.002997,-0.027354,-0.013212,-0.005022,-0.030363,-0.000522,0.01875,-0.002514,-0.011881,-0.00574,0.006059,-0.007272,0.006266,0.00455
Italy_Picene_Novilara_IA:PN125__BC_625__Cov_14.66%,0.121791,0.153345,0.044877,0.000969,0.037238,-0.010877,-0.00094,0.002308,0.023316,0.032256,0.002273,0.002098,-0.004906,-0.009358,-0.000814,0.008751,0.004824,0.006461,0.019106,-0.003877,0.000749,-0.000247,0.005669,0.00012,-0.002515
Italy_Picene_Novilara_IA:PN135__BC_725__Cov_16.74%,0.117238,0.144205,0.037335,0.008075,0.042469,-0.003904,0.00188,0.006692,0.012885,0.014761,0.007307,0.006444,-0.017988,0.005918,-0.007465,0.001458,0.016559,0.005068,0.007416,-0.004627,-0.001248,-0.008779,-0.003821,0.003856,-0.012813
Italy_Picene_Novilara_IA:PN138__BC_700__Cov_26.85%,0.122929,0.144205,0.034695,-0.010336,0.028621,0.00502,-0.003995,0.004846,0.01268,0.029887,-0.003248,0.006894,-0.015461,-0.011285,-0.004207,0.001989,0.002999,-0.001647,0.005405,0.002876,0.000624,0.004328,-0.005176,-0.002771,-0.006706
Italy_Picene_Novilara_IA:PN141__BC_700__Cov_15.02%,0.135449,0.142174,0.031678,-0.003553,0.045239,-0.01506,0.00329,0.005077,0.010431,0.037358,0.006008,0.001948,-0.014123,-0.002615,-0.0019,-0.003845,0.009779,0.00228,0.008547,-0.014007,0.002371,0.005317,-0.014297,-0.000964,-0.006826
Italy_Picene_Novilara_IA:PN146__BC_700__Cov_17.54%,0.120652,0.153345,0.032432,-0.005168,0.055703,-0.00502,-0.015041,0.004384,0.01309,0.031891,-0.000487,0.008692,-0.021407,-0.017065,0.000136,-0.000398,0.004824,0.000507,0.006034,-0.006003,0.001373,0.007172,-0.000863,-0.007109,0.00491
Italy_Picene_Novilara_IA:PN157__BC_700__Cov_21.37%,0.118376,0.151314,0.037712,0.00969,0.035391,-0.010598,-0.00329,0.001385,0.009817,0.030433,0.009256,0.014987,-0.020961,-0.013487,-0.001357,-0.002784,0.007693,-0.006588,0.000754,-0.006878,0.001248,0.000618,-0.005793,-0.003615,0.003592
Italy_Picene_Novilara_IA:PN158__BC_700__Cov_11.73%,0.127482,0.125926,0.038843,0,0.0397,-0.006693,0.002115,0.003231,0.005113,0.032985,-0.005521,0.015137,-0.020664,-0.011836,-0.000271,0.002784,0.008214,0.003421,0.020237,-0.009004,0.001747,0.002968,0.003204,0.001566,0.000479
Italy_Picene_Novilara_IA:PN162__BC_700__Cov_25.84%,0.126344,0.156392,0.04714,0.008075,0.038469,-0.004741,-0.00235,0.005538,0.011044,0.029887,-0.000487,0.008992,-0.02334,-0.001927,-0.002579,0.001193,0.01369,0.002154,-0.002765,-0.006503,0.00025,0.006677,-0.011585,-0.00494,0.009939
Italy_Picene_Novilara_IA:PN172__BC_700__Cov_12.59%,0.125205,0.138112,0.050534,0.010013,0.03693,-0.005299,-0.008225,-0.000692,0.02127,0.022415,-0.005359,-0.006145,-0.006838,-0.015689,0.005157,0.01432,0.007302,-0.009122,0.004525,-0.01113,-0.00836,0.00136,-0.007518,0.00735,0.013412
Italy_Picene_Novilara_IA:PN174__BC_650__Cov_9.97%,0.124067,0.133034,0.033564,0.002907,0.048624,-0.014781,-0.00658,-0.011769,0.008999,0.02606,0.008769,-0.015436,-0.015461,0.007294,0.009636,-0.018828,-0.022556,0.003801,0.007542,0.01113,-0.004492,-0.003957,0.003821,-0.004097,-0.008382
Italy_Picene_Novilara_IA:PN177__BC_675__Cov_12.00%,0.118376,0.151314,0.032809,-0.018411,0.02739,-0.01255,0.001175,-0.003,0.020861,0.024602,0.009419,-0.009591,-0.024232,-0.007156,0.000543,-0.002254,-0.017863,0.008615,-0.002263,-0.007504,-0.000873,-0.001113,0.01479,0.008073,-0.002036
Italy_Picene_Novilara_IA:PN179__BC_675__Cov_10.95%,0.125205,0.157407,0.020365,-0.007429,0.03416,-0.003626,0.000235,-0.009923,0.005522,0.039545,0.006496,0.016036,-0.000297,-0.034956,-0.016558,0.011668,0.017602,0.000507,0.01081,0.001126,-0.008485,0.007666,-0.005916,0.007832,-0.00467
Italy_Picene_Novilara_IA:PN180__BC_650__Cov_13.10%,0.125205,0.149283,0.035449,0.012274,0.046778,0.008088,-0.00611,0.011769,0.011862,0.038816,0.004222,0.005245,-0.019772,0.001101,0.001086,-0.016309,-0.00678,0.000127,-0.01169,-0.003252,-0.000873,-0.000495,-0.005053,-0.002048,0.000958
Italy_Picene_Sirolo/Numana_IA:PNU76__BC_600__Cov_11.71%,0.124067,0.15436,0.050157,-0.013889,0.046778,0.000837,-0.00094,-0.004154,0.032315,0.030251,0.003248,0.003297,-0.009663,-0.010046,0.004343,-0.008088,-0.013299,0.012035,-0.01169,-0.01038,-0.007736,-0.003462,0.002958,-0.000964,0
Italy_Picene_Sirolo/Numana_IA:PSD2__BC_600__Cov_10.30%,0.125205,0.14319,0.041106,0.000646,0.032314,-0.003904,-0.011751,0.009692,0.012885,0.030433,-0.001624,-0.001049,-0.008325,-0.014863,-0.010179,-0.002519,0.019688,0.004941,-0.000754,0.017884,-0.007362,0.012736,0.010106,0.008796,0.00012


r/DNAAncestry 14h ago

Nexo Geno Results

Thumbnail
gallery
4 Upvotes

r/DNAAncestry 7h ago

8 Rajasthani community samples — HarappaWorld admixture results

Thumbnail gallery
1 Upvotes

r/DNAAncestry 15h ago

DNA results

Thumbnail
gallery
3 Upvotes

Hey guys, I just got my results and I wanted to share.


r/DNAAncestry 14h ago

I've got a unique mix

Post image
3 Upvotes

Guess my 3 way mix (50% +25%+25%)


r/DNAAncestry 19h ago

Hey guys, I just got the results.

Thumbnail
gallery
5 Upvotes

r/DNAAncestry 14h ago

Sicilian clusters

Thumbnail
gallery
2 Upvotes

Reposted because they removed my post


r/DNAAncestry 11h ago

Welsh: Genetic Proximity Heatmap tool result

Thumbnail gallery
1 Upvotes

r/DNAAncestry 13h ago

Interesting test, didn’t expect this at all!

Thumbnail gallery
1 Upvotes

r/DNAAncestry 21h ago

NW Indian (Khatri) - 23andme results + G25 mix/distances + pic

Thumbnail gallery
2 Upvotes

r/DNAAncestry 1d ago

Do my features match my results?

Thumbnail
gallery
45 Upvotes

r/DNAAncestry 2d ago

My results as an old white man from New York City (Brooklyn)

Thumbnail
gallery
1.8k Upvotes

r/DNAAncestry 1d ago

Updated ancestralgenome results (southern Italian) with genetic heatmap

Thumbnail
gallery
20 Upvotes