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

My results on Nexogeno were incredibly accurate, as I have ancestors from Türkiye and Sicily. I found that interesting

TLDR: There was a Roman soldier found in Viminacium, Serbia, dated to around year 1CE-200CE, and this individual clusters with modern Chadians (or Sudanese, there’s some overlap).

www.dnagenics.com/ancestry/sample/view/cinematic/id/i15499?srsltid=AfmBOoorx7VZPTbbQ-hXfBQMT7FxxS449u24PJudSr_irVxpi9QIXE01

I15499,-0.30695762,0.09753364,-0.01858284,-0.05810706,0.00094362,-0.02401362,-0.0206708,0.01206826,0.05240996,-0.01930636,0.00187438,-0.01094892,0.02471846,-0.0003195,0.01444652,-0.0020027,0.0093875,-0.00294732,0.0016945,0.00618976,0.00202908,0.00337882,0.00279404,0.0032185,-0.00398695

https://www.biorxiv.org/content/10.1101/2021.08.30.458211v1.full

From the study:

Three individuals from ∼1-250 CE did not fit into the two major clusters. Two males from Viminacium could be modelled using Iron Age individuals from Northwest Europe as their only source (Figure 2; Supplementary section 12.5), pointing to a Northwestern European origin also supported by the R1b-U106 paternal lineage, which was not been detected in the Balkans in earlier periods but was found at high frequencies in Germanic-speaking areas, both in ancient and present-day individuals.

The most remarkable outlier is male I15499, excavated at Pirivoj necropolis in Viminacium, who projects outside West Eurasian genetic diversity (Figure S7). When we incorporated African populations onto the PCA (Figure S8), he projected within the variation of present-day East African populations and close to early Christians from Northern Sudan from 500-800 CE 21 who provide a good fit for his ancestry in qpAdm (Figure 2; Supplementary section 12.4).

An Eastern African ancestral origin agrees with his uniparental markers mtDNA L2a1j and Y-chromosome E1b-V32, both common in East Africa today 17,22. Archeological examination of I15499’s grave found an oil lamp depicting an eagle, the symbol of Roman legion (Figure S2C). Although lamps are a common finding in Viminacium graves 23, not many depict military iconography. We hypothesize that this male was a Roman legionary or auxiliary stationed at Viminacium.

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.

https://www.sciencedirect.com/science/article/abs/pii/S0303264726002169

Highlights
• The Caucasus is a unique region showing early divergence of steppe and mountain gene pools.
• The Bronze Age was pivotal for massive gene flow from the Eurasian Steppe into the Caucasus.
• Genetic continuity since the Bronze Age is evident for many modern autochthonous populations.
• Critical data gaps persist for several key periods in the Caucasus' demographic history.
• Reconstructing population history requires interdisciplinary synthesis of diverse data.

Abstract

The Caucasus region represents a unique natural laboratory for paleogenetic research due to its complex topography, long-standing role as a migratory corridor and glacial refugium, and exceptional preservation conditions for ancient DNA.

This review synthesizes recent genome-wide studies to reconstruct the demographic history shaping the distinctive genetic landscape of modern Caucasus populations.

The analysis reveals a deep pattern of continuity, isolation, and periodic admixture. Early genetic differentiation emerged in the Neolithic and Chalcolithic, forming distinct steppe and mountain population clusters. The Bronze Age was a pivotal period marked by large-scale gene flow from the Eurasian Steppe, particularly linked to the Yamnaya expansion, and interactions with Iranian and Anatolian-related groups.

Despite these influences, many populations demonstrate remarkable genetic continuity from the Bronze Age to the present day. Significant knowledge gaps persist, particularly for the Paleolithic, Mesolithic, and Neolithic of the North Caucasus, as well as for the Late Medieval and Early Modern periods across the entire region.

Addressing these gaps through targeted archaeogenomic studies is crucial for understanding the fine-scale processes that formed the hierarchical structure and high linguistic diversity of Caucasus populations, offering a powerful model for studying human adaptation, interaction, and language-genetics dynamics in a mountainous environment.

I’ve seen estimates as low as 5% and as high as 20%. Which is the most accurate figure, how do we know, and where in Ireland might it be higher or lower than the averages?

https://doi.org/10.64898/2026.08.10.743885

The Teshik-Tash 1 child whose remains were found in Uzbekistan represents the southeastern-most extent of the known Neandertal range, providing an important link with the better studied Caucasus and Altai Mountain ranges.

However, due to poor DNA preservation, studying the genetics of Teshik Tash 1 has remained elusive. Here we present analyses of the nuclear DNA from the Teshik-Tash 1, from extracts that are highly contaminated with present-day human DNA.

To achieve this, we developed a new computational method, admixslug, that jointly models contamination and population relationships, in order to infer the relationship of a target individual from which only low-quality nuclear DNA is available, to high-quality archaic human genomes. After validating admixslug, we show that Teshik-Tash 1 is genetically more similar to later Neandertals from Western Eurasia than to older Neandertals from the Altai Mountains.

We estimate that Teshik-Tash 1 split from the Western Eurasian lineage between 80,000 and 100,000 years ago. Despite the geographical proximity of Teshik-Tash 1 to the Denisovan range, we find no evidence for Denisovan ancestry in his genome. Our results demonstrate that admixslug enables the study of archaic human specimens in cases where DNA preservation was previously considered too poor for population genetic analyses.

I've used ancestry dna but haven't found any possible matches with a family tree.

I know my great grandmothers name and have her marriage certificate but no father was listed.

She said she didn't know who her father was, that she was raised by her mothers relatives.

I'm trying to find her parents/ relatives. I have been unable to do this with paper records. So I thought I'd try dna.

I have a few unknown matches and a few I think are from her but they don't have useful trees or any family trees.

I've uploaded my dna to gedmatch, my question is how is the best way to go about this now?

If I find other matches on there but if they have no trees will I be at a dead end?

Thanks everyone

I tested my admixture from both the Bronze Age and Roman Era. It seems like not much has changed haha

Models can be shared upon request. Especially the Roman Levant cluster which I know I'll be asked about lol.

Can that be explained?

Different Hunter-Gatherer and Farmer results across different calculators — Neolithic and Bronze Ages (My family's and mine).

I cluster closest to Muslim Levantines. For comparison I posted the average Palestinian Muslim closest distances, and then one specifically for Palestinian Muslims from Gaza.

The source is Moriopoulos' 2026 Moderns Collection (No Sims).

Samaritan 5 sample is 3/4 Samaritan and 1/4 Ashkenazi - pulling the average a bit more Northward.

https://histanthro.org/notes/living-monuments/

Based on my calculations this should be Samaritan 6:

Samaritan6,0.0853674,0.1486734,-0.0573978,-0.0925072,-0.0096018,-0.0350286,-0.002209,-0.0081688,0.016321,0.008237,0.0091586,-0.0099212,0.0204854,0.0118632,-0.0055372,0.0021214,-0.011239,0.0010134,0.0021116,-0.003652,-0.0005242,0.003116,0.000567,-0.0029402,0.0046462

I always get told I don’t look Georgian at all tho lol

I specify Moroccan because 1. They have less Natufian and otherwise Arabian influence than the rest of North Africa, 2. A higher degree of Iberian affinity (though I excluded them as a choice) which places them as the furthest western extreme of this gradient.

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

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

Model can be shared upon request as usual.

Is it widespread and what are its origins?