r/illustrativeDNA

How European are Mixed People. A Short Analysis
▲ 4 r/illustrativeDNA+3 crossposts

How European are Mixed People. A Short Analysis

This is from a twitter user and addresses an interesting point about mixed peoples genetic relation to europe.

It's a pretty common and simple concept that many people here already understand. If you mix two people of more or less related ethnicities it affects how genetically close they are to said ethnicity. In the case here when taking european ethnicities, they are most closely related to west asians and very european shifted central asians, followed by north africans. They are further from noticeably mixed(as in 20%+ east eurasian including ancient ancestral south indian and sub saharan african admixture) part west eurasian ethnicities and groups such as south asians, horn africans, central asians, and most modern latinos(who are mixes of european, sub saharan african, and indigenous amerindian). Europeans are very far from east eurasians such as east and southeast asians, aboriginal and papuan people, polynesians, and amerindians, and the farthest from sub saharan africans.

Therefore if someone is a mix between west asians or north africans for example, they will plot noticeably close to european ethnicities on a pca. Ashkenazi jews and north caucasian(as in the caucasus mountains, not scandinavian people, scandinavians are very much europeans) ethnicities are great examples of this, both are mixes of west asian and european groups and are still quite close with european ethnicities on a pca. The image here uses a moern example of a spaniard for european and syrian for west asian, and they also plot among the border area of the southern italian and greek mediteranean island areas.

On the other hand as illustrated in the image above, european and say amerindian or mulato/lightskin(sub saharan african and european) mixed people will be noticeably more distant from europeans genetically. Even a small amount of admixture from these groups can shift someone noticeably away from europe genetically due to how distant these people are from europe.

Lastly I don't want to turn this into a purity debate or anything of the sorts, especially since phenotype does not equal genotype, although there is a strong correlation. This is often why you will see many europeans mixed with west asian or north african ethnicities just "appear european" or white, but that tends not to hold up for someone mixed with east eurasian or sub saharan african ethnicities. For example looking at many celebrities, mainly european mother west asia or north african father, like Bella Hadid, or even Zayn Malik(european and part south asian) nearly all of them are "white/european appearing" especially if their non european parent is as well. On the other hand the average mexican is somewhere around 40% european and 60% amerindian, but no one would say that the average mexican looks european, you'd be hard pressed to even find northern mexicans with higher european admixture, where people would say that.

u/kingjohngreen — 11 hours ago
▲ 0 r/illustrativeDNA+1 crossposts

Part 2: South Asian people getting flagged as Romani

Yeah, part 2 is here. After AncestryDNA wrongfully labeled me as 23% Eastern European Romani - while I believed I have Punjabi ancestry - and I have led countless arguments with prople who insisted I actually have Romani ancestry (as if there were no Indians in Europe, ever), had to explain I have extensive numbers of meaningful Hindu/Sikh matches and a consistent Punjabi/Haryanvi visual phenotype in my family, and lost my nerves a few times while doing so, I finally have a conclusion.

One of those people arguing with me sent me whitepapers written by AncestryDNA in years 2023 and 2025. They did it to support their own claim that Eastern European Roma is detected by AncestryDNA with 100% precision and therefor I must be Roma (yeah, because having 2nd and 3rd cousins from India and sharing 20-40 cM of DNA with them apparently means nothing at all according to those people).

2023:
https://archive.org/details/AncestryDNA-ethnicity2023whitepaper

2025:
https://archive.org/details/AncestryDNA-2025ancestralregionswhitepaper

Between those two revisions, I noticed AncestryDNA completely deleted any metrics of their confidence with Indian ancestry, and deleted their recall rates of Eastern European Roma. This observation further supports my theory that AncestryDNA has problem distinguishing between those two categories.

The root cause of what happened to me is (and this is only my hypothesis, that has very real foundations however):

Eastern European Roma (~95% precision, ~95% recall - based on the 2023 whitepaper) has ancestry originating between modern day Punjab, Rajasthan (possibly Haryana?), but AncestryDNA also attempts to distinguish them from modern day Northern Indians (but with shocking ~70% precision and ~70% recall - based on the 2023 whitepaper), which has a couple of problems:

- North India is too broad to confidently catch anything as specific as Punjab

- The claimed precision of 70% doesn’t help either

- They have only 925 samples for India, but India has 1.5 Billion people, and there is countless distinct groups within India

- Since Eastern European Roma have very geographically specific signal in their genome coming from Punjab or neighbouring locations (and precision and recall of ~95% - based on the 2023 whitepaper, and even 100% precision based on the 2025 whitepaper), AncestryDNA just confidently flags you as Eastern European Roma if you happen to have some of the same signature in your DNA - and I think that happens specifically because the category for modern Northern Indians is so immensely broad and uncertain in comparison to Eastern European Roma.

But since they also deleted any confidence metrics on their Indian ethnicity estimates, and at the same time the recall metrics for Eastern European Romani, it is reasonable to assume that AncestryDNA knew about the problem but decided not to disclose it.

TL;DR: AncestryDNA doesn’t publish their certainty with Indian ancestry anymore, which points to the possibility that the most accurate and confident reference they have for Punjab and neighbouring populations is Eastern European Roma, making it possible even for people born in India being classified as Eastern European Romani by AncestryDNA’s algorithms.

This didn’t happen only to me, but also 30+ of my matches (which are the ones I discovered so far in the ~10% of all of my matches that I manually checked).

Since majority of readers come from the US and Canada: Yes, we do have Indians in Europe. Yes, they are having kids. No, I am not 300 years old - Indians were certainly already here when my grandma was born - although in sparse numbers.

u/import_Cyborg — 9 hours ago

Turkish from Central Anatolia - Sivas Illustrative

Comments are yours. How turkic I am. At least I have some. I dont know I always found mongolian and georgian music impressive also slavic ones :D

u/Lopsided_Offer_9889 — 16 hours ago

Northwestern europe + Hungary results (& gedmatch)

Thought i’d post my results in full with gedmatch. I am mostly northwestern European (British isles, some French) with a bit of Hungarian. I think my Hungarian ancestry is partially german hence the lack of slavic.

I added some of the time periods with the 5 limit or 3 limit populations to compare.

u/JuggernautNaive179 — 15 hours ago

Who is closest people to EU(N&S) outside EU?

Which most closest people to europeans ( both north and south) outside of Europe.1) levant 2) anatolian 3) cauacasian 4) central asians 5)gulf arab& north Africans 6) south asians

reddit.com

Who are the least slavic balkan slavs?

Hey everyone, I saw some genetic results of north Macedonians and sandzak bosniaks, and both seem to be under 40 percent slavic. Are there other slavs in the balkans that are under 40% slavic? And are there any romanians that are genetically the same as fyromians and sandzak bosniaks?

reddit.com
u/languagelover1998 — 1 day ago

Qpadm: Southeast Asia

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.

u/NotBradPitt9 — 23 hours ago

Updated results (Admixture + Periodical)

I’m American from PA. My background is Croatian, Rusyn, Tatar (Kryashen/Chuvash), Irish, Scottish and English

All of my ancestors arrived in the US after 1902

u/Hrvaturk — 1 day ago

Can anyone help me understand my illustrativeDNA

sorry I‘m new to this and this might seem like a dumb question, but I can’t seem to understand why the test appear like it does.

I asked my mum about our family tree and she said we have mostly ancestors in Palestine/Lebanon, however I do have a great grandmother who is Turkish from Cyprus, and perhaps ancestors from Morocco from a long time ago.

but why does it show such high percentage of Egyptian and Jew?

So I dont quite understand the test. and there was a some more background mostly from Central + Southasian continent but it was smaller percentages

thank you in advance

edit: I put the test on Global

edit: I put the test on the Levant and it showed I was overwhelmingly from the area (Canaanite, Phoenician, Levantine with some Anatolian, and small amount of Arab Peninsula and Sub Saharan African)

u/Negative-Ad4009 — 1 day ago

Turkish ydna and mtdna result

I have some Kurdish roots, I’m from Semsûr / Adiyaman that’s why, can you help me to understand these results ?

u/Bronze_Balance — 1 day ago
▲ 8 r/illustrativeDNA+1 crossposts

Interesting Results - Indian

Bronze Age | Iron Age | Late Antiquity | Middle Ages | Hunter Gatherer - Illustrative DNA Results

u/pastoralistnomadic — 3 days ago

Is there a south eurasian?

The reason I ask this is becuase east asia genetically are closer to amerindian than they are to oceanian population like papuans even though amerindians are 40 percent west eurasian and papuan is supposedly east eurasian with no other admixture and is it true that east asian are closer to europeans than papuans

reddit.com
u/PianoNice4180 — 2 days ago
▲ 15 r/illustrativeDNA+3 crossposts

Genetic Map of the Levant (Revised)

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.

u/Miserable_Win_1239 — 3 days ago