u/NotBradPitt9

TLDR: This is somewhat of a random assortment of results, but basically the ancient ones were used in the modern qpadm samples’ runs, so I figured I’d include them in this post as well. All runs are sourced from Twitter user @ r7f001

Turkey_Aegean_Mugla_Capalibag_Medieval.AG — Model 1
Çapalıbağ Ottomans (n=12)
57.0% Turkey_Aegean_Mugla_Samantas_Byzantine.AG
SE: 2.56% | Z: 22.30
20.0% Uzbekistan_SurxondaryoRegion_Bactrian_IA.AG
SE: 4.87% | Z: 4.11
16.0% Mongolia_EIA_Xiongnu_7.AG
SE: 0.61% | Z: 26.10
7.0% Kazakhstan_Sarmatian_IA.AG
SE: 2.97% | Z: 2.34
p-value: 0.643
χ²: 6.04

Outgroups:
Russia_UstIshim_IUP_snpAD.DG, Mongolia_EIA_SlabGrave_1.AG, China_YR_LN.SG, Russia_Krasnoyarsk_BA.SG, Uzbekistan_SappaliTepe_BA.AG, Kazakhstan_Botai_Eneolithic.SG, Russia_MLBA_Sintashta.AG, Turkey_BlackSea_Amasya_EBA.AG, Turkey_Central_Ovaoren_EBA_II.SG, Russia_Samara_EBA_Yamnaya.AG, Italy_Sicily_Himera_480BCE_Greek.AG, Turkey_Aegean_Mugla_Degirmendere_Ancient.AG

Turkey_Aegean_Mugla_Capalibag_Medieval.AG — Model 2
Çapalıbağ Ottomans (n=12)
54.6% Turkey_Aegean_Mugla_Samantas_Byzantine.AG
SE: 2.27% | Z: 24.10
29.2% Russia_Shekshovo2_Medieval.AG
SE: 1.26% | Z: 23.20
16.2% Uzbekistan_SurxondaryoRegion_Bactrian_IA.AG
SE: 2.92% | Z: 5.54
p-value: 0.222
χ²: 14.20

Outgroups:
Russia_UstIshim_IUP_snpAD.DG, Mongolia_EIA_SlabGrave_1.AG, China_YR_LN.SG, Russia_Krasnoyarsk_BA.SG, Uzbekistan_SappaliTepe_BA.AG, Kazakhstan_Botai_Eneolithic.SG, Russia_MLBA_Sintashta.AG, Turkey_BlackSea_Amasya_EBA.AG, Turkey_Central_Ovaoren_EBA_II.SG, Russia_Samara_EBA_Yamnaya.AG, Greece_Manika_EBA.SG, Greece_Mycenaean_BA.AG, Italy_Sicily_Himera_480BCE_Greek.AG, Turkey_Aegean_Mugla_Degirmendere_Ancient.AG
Armenia_Beniamin_Ancient.SG

Late Antiquity Armenians from Beniamin (n=8)
53.1% Armenia_Beniamin_LBA.SG
SE: 3.30% | Z: 16.10
46.9% Turkey_Southeast_Sirnak_BA.AG
SE: 3.30% | Z: 14.20
p-value: 0.809
χ²: 2.28

Outgroups:
Russia_UstIshim_IUP_snpAD.DG, Armenia_KuraAraxes_EBA.AG, Russia_Samara_EBA_Yamnaya.AG, Czechia_EBA_CordedWare.AG, Turkey_Southeast_Sirnak_Chalcolithic_B.AG, Turkey_Southeast_Sirnak_Chalcolithic_C.AG, Israel_C.AG
Mongolia_EIA_Xiongnu_7.AG

Xiongnu sample from Uvs, Mongolia
61.2% Mongolia_EIA_SlabGrave_1.AG
SE: 3.73% | Z: 16.40
30.1% China_YR_LBIA.SG
SE: 3.52% | Z: 8.54
8.7% Russia_Afanasievo.AG
SE: 1.14% | Z: 7.68
p-value: 0.709
χ²: 2.15

Outgroups:
Russia_UstIshim_IUP_snpAD.DG, Mongolia_North_N.AG, Mongolia_East_N.AG, Russia_MA1_UP.SG, China_YR_MN.SG, China_YR_LN.SG, Russia_Samara_EBA_Yamnaya.AG
Kazakhstan_Kangju.SG
Kangju
55.8% Russia_MLBA_Sintashta.AG
SE: 1.89% | Z: 29.50
25.6% Uzbekistan_SappaliTepe_BA.AG
SE: 1.61% | Z: 15.90
10.5% Russia_Krasnoyarsk_BA.SG
SE: 0.87% | Z: 12.10
8.1% Kazakhstan_Botai_Eneolithic.SG
SE: 1.51% | Z: 5.38
p-value: 0.796
χ²: 2.37

Outgroups:
Russia_UstIshim_IUP_snpAD.DG, Czechia_EBA_CordedWare.AG, Czechia_N_GlobularAmphora.AG, Russia_Tyumen_HG.SG, Turkmenistan_C_Geoksyur.AG, Turkmenistan_C_Parkhai.AG, Tajikistan_C_Sarazm.AG, Russia_DevilsCave_N.SG, Russia_MA1_UP.SG
Turkey_Central_Kalehoyuk_Ottoman_A.SG

Possible Medieval Oghuz / Turkic proxy
54.6% Kazakhstan_Kangju.SG
SE: 1.82% | Z: 29.90
45.4% Mongolia_EIA_Xiongnu_7.AG
SE: 1.82% | Z: 24.90
p-value: 0.251
χ²: 7.82

Outgroups:
Russia_UstIshim_IUP_snpAD.DG, Russia_MLBA_Sintashta.AG, Uzbekistan_SappaliTepe_BA.AG, Kazakhstan_Botai_Eneolithic.SG, Russia_Krasnoyarsk_BA.SG, Mongolia_EIA_SlabGrave_1.AG, China_YR_LN.SG, Russia_Samara_EBA_Yamnaya.AG

Turkmen.SG (n=2)
83.6% Turkey_Central_Kalehoyuk_Ottoman_A.SG
SE: 3.21% | Z: 26.00
16.4% Iran_Naqadeh-Oshnavieh_IA
SE: 3.21% | Z: 5.12
p-value: 0.420
χ²: 8.14

Outgroups:
Russia_UstIshim_IUP_snpAD.DG, Mongolia_EIA_SlabGrave_1.AG, China_YR_LN.SG, Russia_Krasnoyarsk_BA.SG, Russia_Samara_EBA_Yamnaya.AG, Russia_MLBA_Sintashta.AG, Uzbekistan_SappaliTepe_BA.AG, Kazakhstan_Botai_Eneolithic.SG, Iran_DinkhaTepe_BA_IA_1.AG, Iran_Hasanlu_IA.AG

Uzbek.SG (n=3)
84.2% Turkey_Central_Kalehoyuk_Ottoman_A.SG
SE: 3.24% | Z: 26.00
15.8% Iran_Naqadeh-Oshnavieh_IA
SE: 3.24% | Z: 4.86
p-value: 0.709
χ²: 5.44

Outgroups:
Russia_UstIshim_IUP_snpAD.DG, Mongolia_EIA_SlabGrave_1.AG, China_YR_LN.SG, Russia_Krasnoyarsk_BA.SG, Russia_Samara_EBA_Yamnaya.AG, Russia_MLBA_Sintashta.AG, Uzbekistan_SappaliTepe_BA.AG, Kazakhstan_Botai_Eneolithic.SG, Iran_DinkhaTepe_BA_IA_1.AG, Iran_Hasanlu_IA.AG

Kazakh.SG (n=3)
67.8% Turkey_Central_Kalehoyuk_Ottoman_A.SG
SE: 3.49% | Z: 19.40
32.2% Mongolia_Medieval_Mongol.AG
SE: 3.49% | Z: 9.24
p-value: 0.472
χ²: 5.58

Outgroups:
Russia_UstIshim_IUP_snpAD.DG, Mongolia_EIA_SlabGrave_1.AG, China_YR_LN.SG, Russia_Krasnoyarsk_BA.SG, Russia_Samara_EBA_Yamnaya.AG, Russia_MLBA_Sintashta.AG, Uzbekistan_SappaliTepe_BA.AG, Kazakhstan_Botai_Eneolithic.SG

Kyrgyz_Kyrgyzstan.SG (n=2)
57.7% Mongolia_Medieval_Mongol.AG
SE: 2.98% | Z: 19.40
42.3% Kazakhstan_Southeast_Karakhanid.SG
SE: 2.98% | Z: 14.20
p-value: 0.306
χ²: 7.16

Outgroups:
Russia_UstIshim_IUP_snpAD.DG, Mongolia_EIA_SlabGrave_1.AG, China_YR_LN.SG, Russia_Krasnoyarsk_BA.SG, Russia_Samara_EBA_Yamnaya.AG, Russia_MLBA_Sintashta.AG, Uzbekistan_SappaliTepe_BA.AG, Kazakhstan_Botai_Eneolithic.SG

Notes:
Turkey_Central_Kalehoyuk_Ottoman_A.SG is used as the Medieval Turkic / Oghuz-related proxy in the Turkmen, Uzbek, and Kazakh models.

The Çapalıbağ target has two separate acceptable models shown here. Model 1 uses Byzantine Anatolian, Xiongnu, Bactrian IA, and Sarmatian sources, while Model 2 uses Byzantine Anatolian, Shekshovo2 Medieval, and Bactrian IA.

qpAdm: Southeast Asia & Island Southeast Asia

Compilation of qpAdm models for modern Southeast Asian and Island Southeast Asian populations.

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%.

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.

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.

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.

TLDR: This is a qpadm map of Iberia. The data from these runs is sourced from data from qpadm runs by @ m_izanami_ on Twitter. The data for the Canary Islands is sourced from a run by user @ devilscave24153. The data for Portugal North is sourced from a run by user @ drawinghistory6. The proxies of Iron Age Czechia and Denmark are just what best fit within the runs to represent Continental Celtic admixture.

1. IBS.DG_Castile_Mancha

Model: 3-way
p-value: 0.718
χ²/dof: 11.50 / 15
Pattern: 000
wt: 0
f4rank: 2

76.10% Spain_IA_Celt.DG
SE: 3.72% | Z: 20.50

18.30% Italy_Imperial_oLevant.SG
SE: 5.05% | Z: 3.62

5.57% Tunisia_Kerkouane_Punic_oAfrica1.SG
SE: 2.66% | Z: 2.09

2. IBS.DG_Aragon

Model: 2-way
p-value: 0.467
χ²/dof: 15.80 / 16
Pattern: 00
wt: 0
f4rank: 1

78.80% Spain_IA_Celt.DG
SE: 3.72% | Z: 21.20

21.20% Italy_Imperial_oLevant.SG
SE: 3.72% | Z: 5.71

3. IBS.DG_Catalonia

Model: 2-way
p-value: 0.722
χ²/dof: 12.30 / 16
Pattern: 00
wt: 0
f4rank: 1

78.70% Spain_IA_Celt.DG
SE: 3.71% | Z: 21.20

21.30% Italy_Imperial_oLevant.SG
SE: 3.71% | Z: 5.74

4. IBS.DG_Murcia

Model: 3-way
p-value: 0.299
χ²/dof: 17.30 / 15
Pattern: 000
wt: 0
f4rank: 2

74.60% Spain_IA_Celt.DG
SE: 3.87% | Z: 19.30

18.40% Italy_Imperial_oLevant.SG
SE: 5.12% | Z: 3.59

7.00% Tunisia_Kerkouane_Punic_oAfrica1.SG
SE: 2.69% | Z: 2.60

5. IBS.DG_Valencia

Model: 3-way
p-value: 0.527
χ²/dof: 14.00 / 15
Pattern: 000
wt: 0
f4rank: 2

81.80% Spain_IA_Celt.DG
SE: 3.69% | Z: 22.20

16.00% Italy_Imperial_oLevant.SG
SE: 4.79% | Z: 3.34

2.18% Tunisia_Kerkouane_Punic_oAfrica1.SG
SE: 2.08% | Z: 1.05

6. IBS.DG_Basque

Model: 2-way
p-value: 0.949
χ²/dof: 8.01 / 16
Pattern: 00
wt: 0
f4rank: 1

93.40% Spain_IA_Celt.DG
SE: 4.00% | Z: 23.30

6.59% Italy_Imperial_oLevant.SG
SE: 4.00% | Z: 1.65

7. IBS.DG_Extremadura

Model: 3-way
p-value: 0.638
χ²/dof: 12.50 / 15
Pattern: 000
wt: 0
f4rank: 2

75.90% Spain_IA_Celt.DG
SE: 3.62% | Z: 21.00

18.30% Italy_Imperial_oLevant.SG
SE: 4.75% | Z: 3.85

5.79% Tunisia_Kerkouane_Punic_oAfrica1.SG
SE: 2.53% | Z: 2.29

8. IBS.DG_Cantabria

Model: 2-way
p-value: 0.214
χ²/dof: 20.10 / 16
Pattern: 00
wt: 0
f4rank: 1

80.90% Spain_IA_Celt.DG
SE: 3.92% | Z: 20.70

19.10% Italy_Imperial_oLevant.SG
SE: 3.92% | Z: 4.89

9. IBS.DG_Andalusia

Model: 3-way
p-value: 0.610
χ²/dof: 12.90 / 15
Pattern: 000
wt: 0
f4rank: 2

73.70% Spain_IA_Celt.DG
SE: 4.04% | Z: 18.20

21.70% Italy_Imperial_oLevant.SG
SE: 5.24% | Z: 4.14

4.59% Tunisia_Kerkouane_Punic_oAfrica1.SG
SE: 2.72% | Z: 1.69

10. IBS.DG_Castile_Leon

Model: 3-way
p-value: 0.231
χ²/dof: 18.60 / 15
Pattern: 000
wt: 0
f4rank: 2

76.10% Spain_IA_Celt.DG
SE: 3.72% | Z: 20.40

16.00% Italy_Imperial_oLevant.SG
SE: 4.92% | Z: 3.25

7.96% Tunisia_Kerkouane_Punic_oAfrica1.SG
SE: 2.59% | Z: 3.07

11. IBS.DG_Baleares

Model: 3-way
p-value: 0.297
χ²/dof: 17.40 / 15
Pattern: 000
wt: 0
f4rank: 2

77.10% Spain_IA_Celt.DG
SE: 3.86% | Z: 20.00

20.30% Italy_Imperial_oLevant.SG
SE: 4.83% | Z: 4.19

2.62% Tunisia_Kerkouane_Punic_oAfrica1.SG
SE: 2.17% | Z: 1.21

12. IBS.DG_Galicia

Model: 3-way
p-value: 0.423
χ²/dof: 15.40 / 15
Pattern: 000
wt: 0
f4rank: 2

76.40% Spain_IA_Celt.DG
SE: 3.78% | Z: 20.20

14.00% Italy_Imperial_oLevant.SG
SE: 4.97% | Z: 2.81

9.63% Tunisia_Kerkouane_Punic_oAfrica1.SG
SE: 2.67% | Z: 3.60

13. IBS_CanaryIslands.DG

Model: 4-way
p-value: 0.616
χ²/dof: 7.20 / 9

24.20% Denmark_IA.SG_Germanic
SE: 4.07% | Z: 5.94

19.40% Turkey_Central_Ovaoren_EBA_II.SG
SE: 3.80% | Z: 5.11

33.40% Spain_IA.AG
SE: 3.62% | Z: 9.22

23.00% Tunisia_Kerkouane_Punic_oAfrica1.SG
SE: 2.12% | Z: 10.90

14. Portugal_North

Model: 4-way
p-value: 0.104
χ²/dof: 11.90 / 7

30.10% Iberia_IA.AG
SE: 6.96% | Z: 4.32

41.00% Czechia_IA_Hallstatt.SG
SE: 8.23% | Z: 4.98

14.60% Italy_Imperial_oEast.SG
SE: 5.78% | Z: 2.52

14.40% Tunisia_Berber.SG
SE: 2.46% | Z: 5.84

These qpAdm models use Iron Age reference populations as ancestry proxies. The percentages below are qpAdm mixture coefficients from the selected source populations and should not be interpreted as literal percentages of historically defined ethnic ancestry.

English_Kent.DG (n=32)
Model: 3-way qpAdm
Status: PASS / Feasible
Fit: Excellent
p-value: 0.234
χ²/dof: 11.648 / 9
SNPs: 579,720
42.9% Denmark_Jutland_IA.SG
SE: 3.95%
Z: 10.9
33.1% France_IA_Hallstatt.SG
SE: 3.92%
Z: 8.43
24.1% Scotland_IA.AG
SE: 4.23%
Z: 5.68
All three components are statistically well supported, with Z > 2 and SE < 5%. This is one of the better-resolved models in the set.

English_EastMidlands.DG
Model: 3-way qpAdm
p-value: 0.258
χ²: 13.60
47.6% Denmark_Jutland_IA.SG
SE: 3.33%
Z: 14.30
29.1% France_IA_Hallstatt.SG
SE: 4.46%
Z: 6.52
23.4% Scotland_IA.AG
SE: 3.91%
Z: 5.98
All three components are strongly statistically supported and have SE values below 5%. The East Midlands target has the highest Denmark_Jutland_IA-related proportion among the English regional targets shown here.
Target sample count, SNP count, degrees of freedom, and the complete right-population set were not shown in the supplied East Midlands source chart.

English_Cornwall.DG (n=31)
Model: 3-way qpAdm
Status: PASS / Feasible
Fit: Good
p-value: 0.150
χ²/dof: 13.288 / 9
SNPs: 579,720
37.2% Denmark_Jutland_IA.SG
SE: 4.86%
Z: 7.66
32.0% France_IA_Hallstatt.SG
SE: 4.50%
Z: 7.11
30.7% Scotland_IA.AG
SE: 4.81%
Z: 6.38
All three coefficients are statistically supported and have SE values below 5%. Cornwall has a lower Denmark_Jutland_IA proportion and a higher Scotland_IA proportion than Kent and the East Midlands in this source framework.

English.DG (n=2)
Model: 3-way qpAdm
Status: PASS / Feasible
Fit: Excellent
p-value: 0.715
χ²/dof: 6.245 / 9
SNPs: 579,720
41.2% Denmark_Jutland_IA.SG
SE: 9.05%
Z: 4.56
38.2% France_IA_Hallstatt.SG
SE: 8.19%
Z: 4.66
20.6% Scotland_IA.AG
SE: 8.92%
Z: 2.30
The overall model has a strong fit and all three coefficients have Z > 2, but the relatively large standard errors mean that the precise proportions are considerably less well resolved than in the larger regional English samples.

French_Brittany.HO (n=9)
Model: 3-way qpAdm
Status: PASS / Feasible
Fit: Good
p-value: 0.0572
χ²/dof: 16.498 / 9
SNPs: 579,720
34.0% Denmark_Jutland_IA.SG
SE: 6.39%
Z: 5.32
31.4% France_IA_Hallstatt.SG
SE: 5.70%
Z: 5.51
34.6% Scotland_IA.AG
SE: 6.56%
Z: 5.28
All three coefficients have strong Z-scores, but their standard errors are moderately above 5%. The p-value of 0.0572 is also relatively close to the conventional 0.05 rejection threshold, so this model should be interpreted somewhat more cautiously.

Scottish.HO (n=1)
Model: 3-way qpAdm
Status: PASS / Feasible
Fit: Excellent
p-value: 0.504
χ²/dof: 8.301 / 9
SNPs: 579,720
35.2% Denmark_Jutland_IA.SG
SE: 12.9%
Z: 2.73
18.5% France_IA_Hallstatt.SG
SE: 12.8%
Z: 1.44
46.3% Scotland_IA.AG
SE: 12.8%
Z: 3.63

Icelandic.DG (n=2)
Model: 2-way qpAdm
Status: PASS / Feasible
Fit: Excellent
p-value: 0.391
χ²/dof: 24.241 / 23
SNPs: 579,720
82.6% Denmark_Funen_IA.SG (n=5)
SE: 8.89%
Z: 9.29
17.4% Wales_IA.AG (n=2)
SE: 8.89%
Z: 1.96
The Denmark_Funen_IA coefficient is strongly supported. The Wales_IA coefficient is borderline at Z=1.96, just below the conventional Z=2 threshold.

Orcadian.DG (n=17)
Model: 3-way qpAdm
Status: PASS / Feasible
Fit: Excellent
p-value: 0.593
χ²/dof: 21.774 / 24
SNPs: 579,720
23.3% Norway_Fore_IA.SG (n=2)
SE: 6.01%
Z: 3.88
48.0% Scotland_LIA.SG (n=2)
SE: 7.22%
Z: 6.64
28.7% Austria_IA_Hallstatt.SG (n=6)
SE: 5.95%
Z: 4.82
All three coefficients have Z > 2, so each is statistically supported in that sense. However, the SE values range from about 6% to 7%, meaning the precise proportions are only moderately resolved.

Shared source populations for the Kent, English.DG, Cornwall, Brittany and Scottish 3-way models:
Denmark_Jutland_IA.SG (n=48)
France_IA_Hallstatt.SG (n=38)
Scotland_IA.AG (n=1)

Shared right populations / outgroups for those models:
Mbuti.DG
Poland_GAC.SG
Czechia_EBA_Unetice.AG
Sweden_LM_Ertebolle.SG
England_MBA.AG
Czechia_LBA_Knoviz.AG
Croatia_Jagodnjak_MBA.SG
Czechia_MBA_Tumulus.AG
Sweden_LN_BA.SG
France_MN.AG
England_N.AG
England_BellBeaker.AG
Icelandic.DG right populations / outgroups:
Mbuti.DG
Turkey_Marmara_Barcin_N.AG
Georgia_Kotias_Mesolithic.SG
Iran_Wezmeh_N.SG
__ind_Loschbour.AG
__ind_MA1.SG
Czechia_EBA_Unetice.AG
Russia_EBA_Yamnaya.AG
Denmark_SouthScandinavia_EBA.SG
England_BellBeaker.AG
Sweden_south_Pre_Viking.SG
Poland_Weklice_WielbarkCulture_Roman.SG
Sweden_LN_EBA.AG
Scotland_LBA.DG
England_Mesolithic.AG
Scotland_EIA.AG
England_MBA.AG
England_LBA.AG
Norway_North_Pre_Viking.SG
England_IA.SG
Sweden_IA_2.SG
Denmark_SouthScandinavia_LN.SG
Germany_BellBeaker.SG
Han.HO
India_GreatAndaman_100BP.SG
Orcadian.DG right populations / outgroups:
Mbuti.DG
Turkey_Marmara_Barcin_N.AG
Han.HO
India_GreatAndaman_100BP.SG
Karitiana.HO
Georgia_Kotias_Mesolithic.SG
Iran_Wezmeh_N.SG
__ind_Loschbour.AG
__ind_MA1.SG
Czechia_EBA_Unetice.AG
Russia_EBA_Yamnaya.AG
Denmark_SouthScandinavia_EBA.SG
England_BellBeaker.AG
Sweden_south_Pre_Viking.SG
Poland_Weklice_WielbarkCulture_Roman.SG
Sweden_LN_EBA.AG
Scotland_LBA.DG
England_Mesolithic.AG
Scotland_EIA.AG
England_MBA.AG
Norway_IA.SG
England_LBA.AG
Norway_North_Pre_Viking.SG
England_IA.SG
Sweden_IA_2.SG
Denmark_SouthScandinavia_LN.SG
Germany_BellBeaker.SG

Overall, the English regional targets show a broadly similar three-way profile, with approximately 37–48% Denmark_Jutland_IA-related ancestry, 29–38% France_IA_Hallstatt-related ancestry, and 21–31% Scotland_IA-related ancestry. The East Midlands has the highest Denmark_Jutland_IA proportion in this comparison, while Cornwall has the highest Scotland_IA proportion among the English regional samples.
The exact division between the Hallstatt-related and insular British Iron Age-related components should be treated as proxy-dependent. These are qpAdm mixture coefficients from the selected ancient reference populations, not literal percentages of historically defined “Germanic,” “Celtic,” “Gaelic,” or other ethnic ancestry.

https://www.cell.com/cell/fulltext/S0092-8674(26)00876-7

Highlights
•
Papuan-related ancestry reached western Fiji in at least two pulses
•
Papuan-related ancestry spread more slowly than the initial Southeast Asian-related dispersal
•
Polynesian gene flow shaped outlier communities in southern Vanuatu ∼1,000 years ago
•
Early Fijians show non-unilineal descent and wealth preservation in a genetic kin group

Summary

The settlement of western Remote Oceania involved interactions among deeply divergent populations. Initial settlement ∼3,000 years ago by Lapita-associated, Southeast Asian-related groups was followed by Papuan-related expansions and later sea voyages from Polynesia. We present genome-wide data spanning the past three millennia from the eastern Solomon Islands, Vanuatu, Fiji, and Tonga.

We find that the Papuan-related expansion proceeded at a slower pace than the earlier Lapita-associated expansion and reached Fiji through at least two distinct human dispersals, centuries earlier than previously suggested.

We provide direct evidence of Polynesian-related gene flow at around 1,000 years ago, giving rise to Polynesian Outlier communities in southern Vanuatu. Finally, we examine an early Fijian community, revealing social practices that preserved wealth/status within a biological kin group with elevated Southeast Asian-related ancestry. Our findings refine the settlement model of western Remote Oceania and highlight the complex interplay of past migrations and community structure.

TLDR: This is a visualization of data sourced from a North Africa Iron Age qpadm run chart by @devilscave24153 (X).

All runs:

Saharawi.DG (n=2; 1240K)
72.6% Tunisia_Berber.SG [IA Maghreb]
SE: 4.61% | Z: 15.7
21.7% Syria_TellQarassa_Umayyad.SG [Peninsular Arab]
SE: 3.73% | Z: 5.82
5.71% Gambian.DG [SSA]
SE: 1.36% | Z: 4.19
p-value: 0.483
χ²: 8.52

Saharawi.HO (n=6; HO)
77.9% Tunisia_Berber.SG [IA Maghreb]
SE: 5.03% | Z: 15.5
15.3% Syria_TellQarassa_Umayyad.SG [Peninsular Arab]
SE: 4.02% | Z: 3.81
6.77% Gambian.DG [SSA]
SE: 1.46% | Z: 4.64
p-value: 0.876
χ²: 4.49

Libyan.HO (n=5; HO)
32.8% Italy_Imperial_oEast.SG [East Med]
SE: 4.90% | Z: 6.69
30.9% Tunisia_Berber.SG [IA Maghreb]
SE: 3.06% | Z: 10.1
26.8% Syria_TellQarassa_Umayyad.SG [Peninsular Arab]
SE: 5.55% | Z: 4.83
9.58% Gambian.DG [SSA]
SE: 0.889% | Z: 10.8
p-value: 0.632
χ²: 8.89

Moroccan.HO (n=10; HO)
56.5% Tunisia_Berber.SG [IA Maghreb]
SE: 3.81% | Z: 14.8
20.9% Spain_Roman_HalfHalf.AG [Spain Roman]
SE: 5.32% | Z: 3.93
12.2% Gambian.DG [SSA]
SE: 1.11% | Z: 11.0
10.4% Syria_TellQarassa_Umayyad.SG [Peninsular Arab]
SE: 5.67% | Z: 1.83
p-value: 0.182
χ²: 13.8

Moroccan.DG (n=10; Custom)
61.1% Tunisia_Berber.SG [IA Maghreb]
SE: 3.55% | Z: 17.2
15.8% Spain_Roman_HalfHalf.AG [Spain Roman]
SE: 5.24% | Z: 3.01
13.8% Gambian.DG [SSA]
SE: 0.981% | Z: 14.0
9.4% Syria_TellQarassa_Umayyad.SG [Peninsular Arab]
SE: 5.96% | Z: 1.58
p-value: 0.827
χ²: 5.86

Mozabite.DG (n=24; 1240K)
60.9% Tunisia_Berber.SG [IA Maghreb]
SE: 3.10% | Z: 19.7
30.7% Tunisia_Kerkouane_Punic.SG [Punic-like]
SE: 2.43% | Z: 12.6
8.42% Gambian.DG [SSA]
SE: 0.943% | Z: 8.93
p-value: 0.754
χ²: 4.22

Berber_WGA.HO (n=5; HO)
75.1% Tunisia_Berber.SG [IA Maghreb]
SE: 4.85% | Z: 15.5
20.4% Tunisia_Kerkouane_Punic.SG [Punic-like]
SE: 3.73% | Z: 5.46
4.48% Gambian.DG [SSA]
SE: 1.49% | Z: 3.01
p-value: 0.895
χ²: 2.89

Tunisian.HO (n=8; HO)
43.7% Tunisia_Berber.SG [IA Maghreb]
SE: 3.40% | Z: 12.9
35.3% Tunisia_Kerkouane_Punic.SG [Punic-like]
SE: 4.05% | Z: 8.70
10.7% Gambian.DG [SSA]
SE: 0.97% | Z: 11.0
10.3% Syria_TellQarassa_Umayyad.SG [Peninsular Arab]
SE: 4.47% | Z: 2.31
p-value: 0.226
χ²: 14.1

Algerian.HO (n=7; HO)
50.1% Tunisia_Berber.SG [IA Maghreb]
SE: 3.80% | Z: 13.2
29.0% Spain_Medieval.AG [Spain Medieval]
SE: 4.75% | Z: 6.10
10.6% Syria_TellQarassa_Umayyad.SG [Peninsular Arab]
SE: 5.19% | Z: 2.05
10.3% Gambian.DG [SSA]
SE: 1.13% | Z: 9.16
p-value: 0.417
χ²: 10.3

Riffian_Possible.MA (n=1; Custom)
66.7% Tunisia_Berber.SG [IA Maghreb]
SE: 3.27% | Z: 20.4
33.3% Spain_Medieval.AG [Spain Medieval]
SE: 3.27% | Z: 10.2
p-value: 0.603
χ²: 5.47

Riffian_Berber.MA (n=1; Custom)
65.9% Tunisia_Berber.SG [IA Maghreb]
SE: 3.36% | Z: 19.6
34.1% Italy_Imperial_oEast.SG [East Med]
SE: 3.36% | Z: 10.1
p-value: 0.666
χ²: 5.84

Bejala_Kabyle.MA (n=1; Custom)
57.4% Tunisia_Berber.SG [IA Maghreb]
SE: 5.00% | Z: 11.5
39.2% Spain_Roman_HalfHalf.AG [Spain Roman]
SE: 3.91% | Z: 10.0
3.46% Gambian.DG [SSA]
SE: 1.61% | Z: 2.16
p-value: 0.745
χ²: 2.71

Tunisian_Berber.MA (n=1; Custom)
71.1% Tunisia_Berber.SG [IA Maghreb]
SE: 3.40% | Z: 20.9
28.9% Italy_Imperial_oEast.SG [East Med]
SE: 3.40% | Z: 8.49
p-value: 0.596
χ²: 6.46

Category labels used in the summary chart:
IA Maghreb = Tunisia_Berber.SG
Peninsular Arab = Syria_TellQarassa_Umayyad.SG
Spain Roman = Spain_Roman_HalfHalf.AG
Spain Medieval = Spain_Medieval.AG
East Med = Italy_Imperial_oEast.SG
Punic-like = Tunisia_Kerkouane_Punic.SG
SSA = Gambian.DG
Note: Moroccan.HO uses Spain_Roman_HalfHalf.AG rather than Spain_Medieval.AG in the author's corrected version. Its p-value remains 0.182.

TLDR (please read first) : This is just a map of the results from several qpadm runs. I’m hoping to add more to it in the future. This isn’t by any means a full list of all populations, it’s just meant to give a visual depiction of the qpadm runs, along with the results of the runs themselves in the other slides.

These qpAdm models use China_Qinghai_Dacaozisite_IA as an Upper Yellow River/northern East Asian-rich source and Thailand_IA as an ancient mainland Southeast Asian source. Thailand_IA should not be interpreted as literal Thai ancestry; it represents a mixture dominated by southern East Asian farmer-related ancestry with additional Hoabinhian-related ancestry. The results are therefore best understood as positions along a northern East Asian–mainland Southeast Asian genetic cline.

1. Hmong = 83.6% China_Qinghai_Dacaozisite_IA + 16.4% Thailand_IA

p-value: 0.945
Both components are strongly supported. The Hmong are modeled primarily with northern East Asian-rich ancestry, plus a smaller mainland Southeast Asian-related contribution.
2. Kinh = 77.4% China_Qinghai_Dacaozisite_IA + 22.6% Thailand_IA
p-value: 0.948

Both components are strongly supported. The Kinh show a larger mainland Southeast Asian-related contribution than the Hmong, consistent with their more southerly position.
3. Thai = 35.5% China_Qinghai_Dacaozisite_IA + 64.5% Thailand_IA
p-value: 0.292

The overall model passes, but the coefficients have a high standard error of 9.39%, so the exact percentages are less dependable. The result still clearly places the Thai much closer to the ancient mainland Southeast Asian side of the cline.
4. Dai = 78.7% China_Qinghai_Dacaozisite_IA + 21.3% Thailand_IA
p-value: 0.762

Both components are strongly supported. The Dai are modeled as mainly northern/southern Chinese-derived East Asian ancestry with a smaller ancient mainland Southeast Asian-related contribution.
5. Lahu = 74.7% China_Qinghai_Dacaozisite_IA + 25.3% Thailand_IA
p-value: 0.823

Both components are strongly supported. The Lahu have the largest Thailand_IA-related proportion among the non-Thai populations shown here.
6. Han = 92.0% China_Qinghai_Dacaozisite_IA + 8.0% Thailand_IA
p-value: 0.923

The Han are overwhelmingly modeled with the Upper Yellow River-related source, with a small but statistically supported southern/mainland Southeast Asian shift.
Thailand_IA-related proportions:
Han: 8.0%
Hmong: 16.4%
Dai: 21.3%
Kinh: 22.6%
Lahu: 25.3%
Thai: 64.5%
These percentages should not be interpreted as literal ancestry from Iron Age Qinghai and Thailand. The ancient samples are proxies for broader northern East Asian and ancient mainland Southeast Asian ancestry.
….
….
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China_Qinghai_Dacaozisite_IA:
An Iron Age population from the Dacaozi site in Qinghai, northwestern China, near the Upper Yellow River. Genetically, it was composed overwhelmingly of northern East Asian ancestry related to ancient Yellow River millet farmers, with a smaller southern East Asian-related shift already present in later Yellow River populations. It therefore works as the northern or Upper Yellow River-rich component in these models, although it is not a completely unmixed northern source. (Nature)
Thailand_IA:
An Iron Age individual from Ban Chiang in northeastern Thailand. Genetically composed of a mixed ancient mainland Southeast Asian population, with mainly a southern East Asian farmer-related ancestry together with a substantial Hoabinhian hunter-gatherer-related component. A rough interpretation is approximately 70% southern East Asian farmer-related and 30% Hoabinhian-related ancestry, but the exact proportions vary depending on the sources and outgroups used. It should not be interpreted as literal modern Thai ancestry, because it predates the later spread of Tai-speaking populations into Thailand. (Nature)
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Note: the reason I chose these is because there wasn’t much else to choose from for the Iron Age. For China there was a bunch but not for Southeast Asia. I tried Taiwan Hanben IA but that didn’t work. There’s Cambodia IA but that has a large amount of South Asian ancestry, so the Thailand_IA seemed to be the best one. There may be some Southern Chinese ones to try as a proxy for southeast Asian ancestry but I haven’t found it.

TLDR (please read first) : This is just a map of the results from several qpadm runs. I’m hoping to add more to it in the future. This isn’t by any means a full list of all populations, it’s just meant to give a visual depiction of the qpadm runs, along with the results of the runs themselves in the other slides.

These qpAdm models use Iron Age reference populations as ancestry proxies. Denmark_IA.SG represents a Germanic-related proxy, Scotland_IA.AG an insular Celtic-related proxy, Czechia_IA_Hallstatt.SG a continental Celtic-related proxy, and Gael.SG a Gaelic-related proxy.

English.HO
42.55% Denmark_IA.SG
29.61% Scotland_IA.AG
27.84% Czechia_IA_Hallstatt.SG

English_Cornwall.DG
34.17% Denmark_IA.SG
35.68% Scotland_IA.AG
30.16% Czechia_IA_Hallstatt.SG
p-value: 0.338
χ²: 18.84

English_Kent.DG
40.98% Denmark_IA.SG
30.70% Scotland_IA.AG
28.33% Czechia_IA_Hallstatt.SG
p-value: 0.704
χ²: 13.48

Scottish_Argyll.DG
81.85% Gael.SG
18.15% Denmark_IA.SG
p-value: 0.626
χ²: 13.63

Irish.DG
76.02% Gael.SG
23.98% Denmark_IA.SG
p-value: 0.582
χ²: 14.23

Overall, the English samples fall along a relatively similar three-way profile, with roughly 34–43% Denmark Iron Age-related, 30–36% Scotland Iron Age-related, and 28–30% Hallstatt-related ancestry. Kent has the highest Denmark_IA proportion among the regional English samples shown, while Cornwall has the highest Scotland_IA proportion.

The Scottish Argyll and Irish models use a different two-way framework. They are predominantly modeled with Gael.SG, at about 76–82%, with approximately 18–24% Denmark_IA.SG. Argyll has the highest Gael.SG proportion.

These percentages should be interpreted as qpAdm proportions from the selected ancient proxy populations, rather than literal percentages of historically defined “Germanic” or “Celtic” ethnic ancestry.

TLDR: Iron Age / medieval-source qpAdm models for several modern German and Dutch regional populations.

Main-Kinzig Hessian
60.4% Denmark_IA.SG
39.6% Austria_IA_LaTene.AG
p-value: 0.615
Chi-squared: 11.00

German Alsatian (Bas-Rhin)
60.0% Denmark_IA.SG
40.0% Austria_IA_LaTene.AG
p-value: 0.384
Chi-squared: 13.90

Swabian
52.3% Denmark_IA.SG
47.7% Austria_IA_LaTene.AG
p-value: 0.639
Chi-squared: 14.40

Odenwald Hessian
56.6% Denmark_IA.SG
43.4% Austria_IA_LaTene.AG
p-value: 0.733
Chi-squared: 7.78

Southeast Bavarian
31.3% Denmark_IA.SG
31.3% Austria_IA_LaTene.AG
37.4% Russia_Sunghir_Medieval.SG
p-value: 0.979
Chi-squared: 10.70

Mennonite Prussian
40.6% Denmark_IA.SG
24.9% Austria_IA_LaTene.AG
34.5% Russia_Sunghir_Medieval.SG
p-value: 0.923
Chi-squared: 11.00

North Brabant / North Holland Dutch
70.0% Denmark_IA.SG
30.0% Austria_IA_LaTene.AG
p-value: 0.685
Chi-squared: 10.10

German Northwest
79.6% Denmark_IA.SG
20.4% Austria_IA_LaTene.AG
p-value: 0.716
Chi-squared: 9.73

Overall, the western and northwestern German/Dutch targets are modeled with relatively higher Denmark_IA.SG-related ancestry, while the southwestern German groups show more Austria_IA_LaTene.AG-related ancestry. Southeast Bavarian and Mennonite Prussian require a third Russia_Sunghir_Medieval.SG-related component in these models.

TLDR: I’ll be adding more populations to a future revised version, I only added the ones which enough qpadm runs have been completed for. This is a compilation of qpAdm runs for populations from the Levant region. A few more groups were added compared to the first map. The samples used are from the following dataset : https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/FFIDCW

Syrian.HO

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 and 0.00892
Z-scores: 14.5, 4.15 and 8.72

This is a strong model with all three components statistically supported. The Lebanon Phoenician source represents the main Levantine ancestry, while Armenia Sarukhan Early Iron Age represents additional Caucasus or 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 percentages have standard errors close to 0.05, so the exact proportions should be treated as approximate.

Druze.HO

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 and 0.00803
Z-scores: 18.0, 4.84 and 2.74

This is an excellent statistical fit. It describes the Druze as mostly Levantine, with a substantial Caucasus or eastern Anatolian-related shift and a very small African-related component. All three percentages are statistically supported.

Assyrian.HO

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
Z-scores: 13.3 / 4.45
SE: 5.64% / 5.64%

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% Sasanian Bahrain ancestry.

Cypriot.HO

82.4% Italy_Imperial_oAnatoliaCaucasus.SG
17.6% Serbia_Sirmium_Ottoman.SG
p-value: 0.955
χ²/dof: 5.729 / 13
SNPs: 579,720
Italy_Imperial_oAnatoliaCaucasus: 82.4% ± 7.49%, Z = 11.0
Serbia_Sirmium_Ottoman: 17.6% ± 7.49%, Z = 2.35
This is an exceptionally good statistical fit and has a substantially higher p-value than the one-way model. Both ancestry coefficients have Z > 2, although the ±7.49% standard errors are relatively large, so the exact proportions should be treated as approximate.

Lebanese_Muslim.HO

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 and 0.00913
Z-scores: 40.4, 3.95 and 3.14
This is a strong and well-resolved model. The Lebanon Phoenician source 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

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 and 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 or Caucasus-related shift. Its Z-score of 2.13 is only just above the normal cutoff, so the existence of a small secondary component is supported, but the exact 5% figure should be treated cautiously.

Palestinian.HO

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 and 0.00823
Z-scores: 43.1, 2.54 and 8.32
This is a strong model, and has an excellent p-value, a low chi-square with all three components are statistically supported. The model describes Palestinians as mostly Levantine, with smaller northern or Caucasus-shifted and African-related components. The Sarmatian-related percentage has the weakest Z-score, but it still passes the usual Z = 2 threshold.

Samaritan.DG

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 Lebanese population.

Jordanian.HO

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 and 0.00899
Z-scores: 39.0, 3.58 and 13.6
This is an extremely strong model statistically. Jordanians are modeled as mostly Levantine, with a smaller northern or Caucasus-shifted component and a more substantial African-related component than the Lebanese or Druze models. Dinka should be understood as the African proxy used by the model, not as evidence of direct Dinka ancestry.

Egyptian.HO

44.3% 3DT26.SG
38.5% Lebanon_Hellenistic.SG
17.2% Dinka.DG
p-value: 0.513
χ²/dof: 13.172 / 14
SNPs: 579,720
Z-scores: 4.43 / 4.06 / 14.3
SE: 9.99% / 1.21% / 9.49%
This is a strong passing model. All three ancestry components are well supported, with the Dinka-related component particularly precisely estimated.

EgyptianA.HO

46.9% 3DT26.SG
40.7% Lebanon_Hellenistic.SG
12.4% Dinka.DG
p-value: 0.223
χ²/dof: 17.654 / 14
SNPs: 579,720
Z-scores: 3.70 / 3.38 / 9.64
SE: 12.7% / 12.1% / 1.29%
The model passes and all three components are supported, although the estimates for 3DT26 and Lebanon_Hellenistic have relatively large standard errors.

EgyptianB.HO

43.7% 3DT26.SG
42.6% Lebanon_Hellenistic.SG
13.6% Dinka.DG
p-value: 0.745
χ²/dof: 10.226 / 14
SNPs: 579,720
Z-scores: 4.92 / 5.07 / 10.5
SE: 8.89% / 8.40% / 1.30%
All three components are strongly supported. The Dinka-related component is especially precisely estimated, while the exact proportions assigned to 3DT26 and Lebanon_Hellenistic have somewhat wider uncertainty.

Saudi.HO

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 (Dinka Z = 7.21).

BedouinB.HO

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 (Dinka Z = 7.86).

BedouinA.HO

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
All four components are statistically supported:
Lebanon Phoenician: Z = 10.6
Tell Qarassa Umayyad: Z = 9.64
Dinka: Z = 25.9
Kazakhstan Sarmatian: Z = 2.88

This model passes and suggests BedouinA can be modeled primarily as Levantine ancestry represented by Phoenician Lebanon and Umayyad-period Tell Qarassa, together with ~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.

These are qpAdm proxy models, so the source labels should not necessarily be interpreted as literal direct ancestral populations; they represent ancestry streams that fit the targets relative to the chosen outgroups.
Both Bedouin A and B genetic clusters are Bedouins from unspecified tribes in the Negev desert, with the Bedouin A group having a more northern shift and the Bedouin B subgroup having a strong southern genetic shift and clustering with the Saudi average.

Lebanon_Phoenician (500-300 BCE) represents the main Levantine-related ancestry. It’s an average of Lebanon_Phoenician samples.

Kazakhstan_Sarmatian_IA (500-300 BCE) represents a more northern Steppe/Caucasus-shifted element, not necessarily literal Sarmatian ancestry.
Dinka represents African-related ancestry, not direct ancestry specifically from modern Dinka people.

Armenia_Sarukhan_EIA represents an Armenian/Caucasus or eastern Anatolian-related element.

Serbia_Sirmium_Ottoman is roughly 80% Slavic, 20% Anatolian, and represents the Southern Slavic input in the Balkans.

3DT26 is an ancient Egyptian sample found in the UK (United Kingdom, England_IA_Roman_oMiddleEast) from around 200AD.

Lebanon_Hellenistic (200 BCE) has basically the same composition as the Lebanon_Phoenician it’s just from a later time period.

TLDR: I did a few qpadm runs for Northwestern Europeans, do you have any tips for improving any of the specific models to get them accurately in terms of Iron Age components?

English_Kent.DG (n=32)

Model: 3-way qpAdm
Status: PASS / Feasible
Fit: Excellent
p-value: 0.234
χ²/dof: 11.648 / 9
SNPs: 579,720
42.9% Denmark_Jutland_IA.SG
SE: 0.0395 (±3.95%)
Z-score: 10.9
33.1% France_IA_Hallstatt.SG
SE: 0.0392 (±3.92%)
Z-score: 8.43
24.1% Scotland_IA.AG
SE: 0.0423 (±4.23%)
Z-score: 5.68
Note: All sources meet Z ≥ 2 and SE ≤ 0.05. The individual source coefficients are therefore comparatively well resolved.

English.DG (n=2)
Model: 3-way qpAdm
Status: PASS / Feasible
Fit: Excellent
p-value: 0.715
χ²/dof: 6.245 / 9
SNPs: 579,720
41.2% Denmark_Jutland_IA.SG
SE: 0.0905 (±9.05%)
Z-score: 4.56
38.2% France_IA_Hallstatt.SG
SE: 0.0819 (±8.19%)
Z-score: 4.66
20.6% Scotland_IA.AG
SE: 0.0892 (±8.92%)
Z-score: 2.30
Note: The model passes statistically, but the source proportions have relatively large SEs (>0.05), so the individual percentages are not considered especially precise.

Icelandic.DG (n=2)
Model: 3-way qpAdm
Status: PASS / Feasible
Fit: Excellent
p-value: 0.938
χ²/dof: 3.567 / 9
SNPs: 579,720
66.9% Denmark_Jutland_IA.SG
SE: 0.0847 (±8.47%)
Z-score: 7.90
1.7% France_IA_Hallstatt.SG
SE: 0.0778 (±7.78%)
Z-score: 0.216
31.4% Scotland_IA.AG
SE: 0.0763 (±7.63%)
Z-score: 4.12
Note: Although the overall model has an excellent p-value, the 1.7% Hallstatt component is not statistically supported (Z = 0.216), and the SEs are relatively large.

English_Cornwall.DG (n=31)
Model: 3-way qpAdm
Status: PASS / Feasible
Fit: Good
p-value: 0.150
χ²/dof: 13.288 / 9
SNPs: 579,720
37.2% Denmark_Jutland_IA.SG
SE: 0.0486 (±4.86%)
Z-score: 7.66
32.0% France_IA_Hallstatt.SG
SE: 0.0450 (±4.50%)
Z-score: 7.11
30.7% Scotland_IA.AG
SE: 0.0481 (±4.81%)
Z-score: 6.38
Note: All three sources meet Z ≥ 2 and SE ≤ 0.05. This is one of the more statistically well-resolved models in the set.

French_Brittany.HO (n=9)
Model: 3-way qpAdm
Status: PASS / Feasible
Fit: Good
p-value: 0.0572
χ²/dof: 16.498 / 9
SNPs: 579,720
34.0% Denmark_Jutland_IA.SG
SE: 0.0639 (±6.39%)
Z-score: 5.32
31.4% France_IA_Hallstatt.SG
SE: 0.0570 (±5.70%)
Z-score: 5.51
34.6% Scotland_IA.AG
SE: 0.0656 (±6.56%)
Z-score: 5.28
Note: All components have strong Z-scores, but all three SEs are slightly above 0.05. The overall p-value of 0.0572 is also fairly close to the conventional 0.05 rejection threshold.

Scottish.HO (n=1)
Model: 3-way qpAdm
Status: PASS / Feasible
Fit: Excellent
p-value: 0.504
χ²/dof: 8.301 / 9
SNPs: 579,720
35.2% Denmark_Jutland_IA.SG
SE: 0.129 (±12.9%)
Z-score: 2.73
18.5% France_IA_Hallstatt.SG
SE: 0.128 (±12.8%)
Z-score: 1.44
46.3% Scotland_IA.AG
SE: 0.128 (±12.8%)
Z-score: 3.63
Note: The overall model fits well, but the proportions are poorly resolved because of the very large SEs. The 18.5% Hallstatt component is also below Z = 2 and therefore is not individually statistically supported.

Sources / Left populations used for all models:
Denmark_Jutland_IA.SG (n=48)
France_IA_Hallstatt.SG (n=38)
Scotland_IA.AG (n=1)

Outgroups / Right populations used for all models:
Mbuti.DG
Poland_GAC.SG
Czechia_EBA_Unetice.AG
Sweden_LM_Ertebolle.SG
England_MBA.AG
Czechia_LBA_Knoviz.AG
Croatia_Jagodnjak_MBA.SG
Czechia_MBA_Tumulus.AG
Sweden_LN_BA.SG
France_MN.AG
England_N.AG
England_BellBeaker.AG

Here are some 3 way qpadm models for English, Scottish, Orcadian, Icelandic, Norwegian. The breakdown is in terms of : Yamnaya, ANF, WHG .

Also included a Scottish Hebrides “Iron Age / early medieval” breakdown.

Sourced from the pastebins of @ 3figuresof9 on X