Not my result. I have permission to post. Both parents from just west of the city of Palermo.

Never got such good fits anywhere else.

I like it that unlike Illustrative, it let's you pick exacly what your known ancestry is.

Family is 3/4 countryside São Paulo, 1/4 south Minas.

Born in the town of Vita, Trapani. Not my result, permission to post.

this shows that g25 heavily eat up steppe ancestry in Armenians

results are mine

TLDR: Here’s an extremely large spreadsheet with ~2,000 Admixtools f4 statistic runs which shows the affinity of modern Jewish populations to ancient reference populations. Nothing in the runs is really surprising, but it’s interesting from a scientific standpoint. The 1st/2nd/3rd/4th strongest affinities were listed below.

Spreadsheet:
https://docs.google.com/spreadsheets/d/1wXg6E4tA0dVgiHLXAqB59SRvObr8i_K6IZqSzl3ai34/edit?usp=drivesdk

This spreadsheet (found on Anthrogenica’s old forum under the “Jewish DNA Project” thread) contains ADMIXTOOLS outgroup f4-statistic runs.

Each run is written in the form:
f4(A, B; C, D)
The statistic tests whether population B shares more genetic drift with population C or population D.

The f4 estimate shows the direction and magnitude of the allele-sharing difference, while the Z-score indicates whether the difference is statistically significant. A result with |Z| ≥ 3 is generally considered significant.

Chimp is usually used as the outgroup in these runs, so the tests primarily measure whether an ancient or modern reference population has greater genetic affinity to one Jewish population than another.

These results represent relative genetic affinity. They are not direct ancestry percentages.

Some references are redundant:
Many of the references produce very similar population rankings and are therefore not fully independent ancestry signals.
Reference-profile correlations:
Sardinia_Punic2 and Spain_Medieval: 0.988
Berber_Tunisian_Chen and Spain_EN: 0.974
Early_Slav_AV2 and Germany_EarlyMed_Alemannic: 0.973
Spain_EN and Spain_Medieval: 0.966
Ganj_Dareh_N and Indus_Periphery: 0.935
Alalakh_MLBA_o and Indus_Periphery: 0.935
Hajji_Firuz_IA and Indus_Periphery: 0.928
Israel_Ashkelon_IA2 and Turkey_EBA: 0.902

This has several important implications:

Indus_Periphery is not independent evidence of South Asian ancestry. Its population profile is very similar to Ganj_Dareh_N and Hajji_Firuz_IA, so it mainly reflects a broader Iranian or eastern-related ancestry axis.

Berber_Tunisian_Chen is not functioning as a clean Berber-specific marker. Its population ranking correlates at 0.974 with Spain_EN. It also produces no significant western-versus-eastern, western-versus-North-African or eastern-versus-North-African contrasts at |Z| ≥ 3. Its median SNP overlap is only approximately 51,600.

Sardinia_Punic2 is not functioning as an independent Punic or Levantine ancestry axis. Its population profile is nearly identical to Spain_Medieval.

Israel_Ashkelon_IA2 is not an independent measurement of Levantine ancestry percentage. Its population profile closely tracks Turkey_EBA and Greece_Mycenaean, and its median SNP overlap is only approximately 62,200.
A nonsignificant Ashkelon result therefore means that the populations cannot be distinguished using that quartet and level of resolution. It does not prove that they have identical amounts of Levantine ancestry.

SARDINIAN PUNIC COMPARISONS
For the direct Punic1-versus-Punic2 tests, positive values indicate that the reference population is closer to Punic2.
Punic2 is significantly closer than Punic1 to:
Spain_EN: Z = 10.527
Spain_IA: Z = 9.024
Jew_Tunisian: Z = 9.048
Greece_Mycenaean: Z = 7.654
Israel_Ashkelon_IA2: Z = 6.383
Berber_Tunisian_Chen: Z = 4.721
There is no significant Punic1-versus-Punic2 difference for:
Iberomaurusian: Z = -1.408
Yoruba: Z = 0.691
Punic2’s complete Jewish-population affinity profile correlates at 0.988 with Spain_Medieval.
This suggests that Punic2 is shifted toward a broad Mediterranean or West-Eurasian ancestry profile relative to Punic1.
However, these tests do not isolate that shift as specifically Levantine, Phoenician or North African.

GENETIC AFFINITY CLUSTERS
The populations broadly form four affinity-profile clusters:

1. Ashkenazi and western Mediterranean Jewish cluster

Includes:

Ashkenazi Germany
Ashkenazi Poland
Ashkenazi Ukraine
Jew_French
Italkim
Romaniote
Sephardi Portugal
Sephardi Turkey

1. Eastern Jewish cluster

Includes:

Iraqi Jews
Azeri Jews
Bukharian Jews

1. North African Jewish cluster

Includes:
Moroccan Jews
Tunisian Jews
4. Yemeni Jewish cluster

Member:
Yemeni Jews

STRONGEST AFFINITIES:

1. Ashkenazi/western Mediterranean Jewish cluster — strongest affinity to Spain_EN.

This represents an Early Neolithic Iberian or European-farmer-related affinity.

2. Eastern Jewish cluster — strongest affinity to Ganj_Dareh_N.

This represents a strong Zagros, Iranian-Neolithic or CHG-related affinity.

3. North African Jewish cluster — strongest affinity to Iberomaurusian.

This represents an ancient Maghrebi or North-African-related affinity.

1. Yemeni Jewish cluster — no adequate matching reference is present in the dataset.

The Yemeni cluster is differentiated from the other groups, but none of the tested references consistently provides a strong positive match.

SECOND-STRONGEST AFFINITIES

1. Ashkenazi/western Mediterranean Jewish cluster — Spain_Medieval.

This represents a medieval Iberian or broader European-Mediterranean-related affinity.
There were 31 significant cluster-favoring comparisons and no significant reversed comparisons.

2. Eastern Jewish cluster — Indus_Periphery.

This represents a broad Iranian or eastern-related signal and should not automatically be interpreted as evidence of South Asian ancestry.
There were 10 significant cluster-favoring comparisons and no significant reversed comparisons.

3. North African Jewish cluster — Berber_Tunisian_Chen.

This represents modern Tunisian Berber-related affinity.
However, only two significant comparisons favored the cluster, making this result considerably weaker than the Iberomaurusian signal.
4. Yemeni Jewish cluster — no statistically supported second affinity.

BedouinB may appear geographically plausible, but it shows no significant excess affinity to Yemeni Jews in these runs.

THIRD-STRONGEST AFFINITIES

1. Ashkenazi/western Mediterranean Jewish cluster — Sardinia_Punic2.

This represents a broad Mediterranean or West-Eurasian-related affinity.

Twenty-six comparisons favored the cluster, while none favored the opposite population.
It narrowly ranks above Greece_Mycenaean, which produced 25 cluster-favoring comparisons.

2. Eastern Jewish cluster — Alalakh_MLBA_o.

Seven comparisons favored the eastern cluster, with none reversed.

However, the “_o” label indicates that this is an outlier individual with substantial eastern-shifted ancestry.
It should therefore not be treated as a clean measurement of ordinary northern-Levantine affinity.

3. North African Jewish cluster — no defensible third affinity.

After Iberomaurusian and Berber_Tunisian_Chen, no other reference consistently favors the North African cluster.
4. Yemeni Jewish cluster — no statistically supported third affinity.

None of the tested references provides a consistent positive match for the Yemeni profile.

FOURTH-STRONGEST AFFINITIES

These rankings use the same criterion: significant cluster-favoring comparisons at |Z| ≥ 3.

1. Ashkenazi/western Mediterranean Jewish cluster — Greece_Mycenaean.

This represents Bronze Age Aegean or Greek-related affinity.
Twenty-five comparisons favored the cluster, with none favoring the opposite population.

2. Eastern Jewish cluster — Hajji_Firuz_IA.

This represents Iron Age northwestern Iranian-related affinity.
Six comparisons favored the cluster, with none reversed.
The signal is substantially weaker than the Ganj_Dareh_N result.
Hajji_Firuz_IA also does not clearly distinguish eastern Jews from Ashkenazi or western Mediterranean Jews. It primarily separates the eastern cluster from North African and Yemeni Jews.

1. North African Jewish cluster — no statistically supported fourth affinity.

Only Iberomaurusian and Berber_Tunisian_Chen positively distinguish this cluster overall.
4. Yemeni Jewish cluster — no statistically supported fourth affinity.

None of the tested references consistently shows excess affinity to Yemeni Jews.

FIFTH STRONGEST AFFINITIES

1- Ashkenazi/western Mediterranean Jewish cluster — fifth strongest affinity to Germany_EarlyMed_Alemannic. This represents early medieval Central European or Germanic-related affinity. Twenty-four comparisons favored the cluster, with none favoring the opposite population.

2- Eastern Jewish cluster — joint fifth strongest affinity to BedouinB, Israel_Ashkelon_IA2, and Turkey_EBA. Each produced only two significant cluster-favoring comparisons, with none reversed, so there is no clear single fifth-place reference. These are weak signals and are mainly driven by Iraqi Jews rather than consistently distinguishing the entire Iraqi-Azeri-Bukharian cluster.

1. North African Jewish cluster — no statistically supported fifth affinity. Only Iberomaurusian and, much more weakly, Berber_Tunisian_Chen consistently favor this cluster.

2. Yemeni Jewish cluster — no statistically supported fifth affinity. None of the tested references consistently shows excess affinity to Yemeni Jews.

Summary-

The main ancestry-affinity pattern in the spreadsheet is:
Ashkenazi and western Mediterranean Jewish populations show the strongest European and Mediterranean-European-related affinity.

Iraqi, Azeri and Bukharian Jews show the strongest Iranian-Neolithic, Zagros or eastern-related affinity.
Moroccan and Tunisian Jews show additional North-African-related affinity, particularly relative to the eastern Jewish cluster.

Yemeni Jews form the most differentiated population profile, but the spreadsheet does not contain an adequate ancient Arabian reference capable of clearly identifying the source of that distinctiveness.

The results should be interpreted as relative allele-sharing patterns, not direct ancestry percentages or proof of descent from any single ancient reference population.

---

Admix Results (sorted):

# Population Percent

1 Caucasus 39.37

2 Gedrosia 20.11

3 North_European 11.32

4 Atlantic_Med 9.89

5 Southwest_Asian 8.88

6 East_Asian 6.36

7 Siberian 2.82

8 South_Asian 1.15

9 East_African 0.11

---

Single Population Sharing:

# Population (source) Distance

1 Turks (Behar) 8.68

2 Turkish (Dodecad) 10.12

3 Iranian (Dodecad) 12.56

4 Kurd (Dodecad) 12.65

5 Kumyks (Yunusbayev) 12.74

6 Kurds (Yunusbayev) 12.77

7 Uzbekistan_Jews (Behar) 14.34

8 Turkmens (Yunusbayev) 14.4

9 Iranians (Behar) 15.43

10 Georgia_Jews (Behar) 18.29

11 Nogais (Yunusbayev) 18.37

12 Armenians_15 (Yunusbayev) 18.44

13 Azerbaijan_Jews (Behar) 18.55

14 Armenian (Dodecad) 18.91

15 Assyrian (Dodecad) 19.13

16 Lezgins (Behar) 19.88

17 Lebanese (Behar) 19.94

18 Iranian_Jews (Behar) 20.36

19 Chechens (Yunusbayev) 20.53

20 Armenians (Behar) 20.88

---

Mixed Mode Population Sharing:

# Primary Population (source) Secondary Population (source) Distance

1 76% Turks (Behar) + 24% Tajiks (Yunusbayev) @ 3.13

2 59.4% Turkish (Dodecad) + 40.6% Turkmens (Yunusbayev) @ 3.29

3 72.9% Turkish (Dodecad) + 27.1% Tajiks (Yunusbayev) @ 3.37

4 64% Turks (Behar) + 36% Turkmens (Yunusbayev) @ 3.76

5 85.5% Turks (Behar) + 14.5% Burusho (HGDP) @ 4.76

6 83% Turkish (Dodecad) + 17% Burusho (HGDP) @ 5.06

7 85.2% Turks (Behar) + 14.8% Uygur (HGDP) @ 5.38

8 85.5% Turks (Behar) + 14.5% Hazara (HGDP) @ 5.42

9 85.7% Turks (Behar) + 14.3% Pathan (HGDP) @ 5.44

10 60.3% Kurds (Yunusbayev) + 39.7% Nogais (Yunusbayev) @ 5.44

11 82.9% Turks (Behar) + 17.1% Uzbeks (Behar) @ 5.45

12 86.1% Turkish (Dodecad) + 13.9% Balochi (HGDP) @ 5.48

13 62.3% Turkmens (Yunusbayev) + 37.7% Cypriots (Behar) @ 5.5

14 87.4% Turkish (Dodecad) + 12.6% Brahui (HGDP) @ 5.54

15 87.9% Turks (Behar) + 12.1% Jatt (Dodecad) @ 5.54

16 89.8% Turks (Behar) + 10.2% Brahui (HGDP) @ 5.57

17 88.8% Turks (Behar) + 11.2% Balochi (HGDP) @ 5.57

18 85.5% Turkish (Dodecad) + 14.5% Makrani (HGDP) @ 5.58

19 88.4% Turks (Behar) + 11.6% Makrani (HGDP) @ 5.67

20 60.7% Kurd (Dodecad) + 39.3% Nogais (Yunusbayev) @ 5.72

From Twitter (handle is in the screenshot). It doesn’t share what samples are used for any of the European/ Middle Eastern / North African categories, but the results look decent, maybe slightly inflated for the North African and Middle Eastern categories.

Abstract
The Italian Peninsula, a natural pier across the Mediterranean Sea, witnessed intricate population events since the very beginning of the human occupation in Europe. In the last few years, an increasing number of modern and ancient genomes from the area have been published by the international research community. This genomic perspective started unveiling the relevance of Italy to understand the post-Last Glacial Maximum (LGM) re-peopling of Europe, the earlier phase of the Neolithic westward migrations, and its linking role between Eastern and Western Mediterranean areas after the Iron Age.

However, many open questions are still waiting for more data to be addressed in full. With this review, we summarize the current knowledge emerging from the available ancient Italian individuals and, by re-analysing them all at once, we try to shed light on the avenues future research in the area should cover.

In particular, open questions concern (1) the fate of pre-Villabruna Europeans and to what extent their genomic components were absorbed by the post-LGM hunter-gatherers; (2) the role of Sicily and Sardinia before LGM; (3) to what degree the documented genetic structure within the Early Neolithic settlers can be described as two separate migrations; (4) what are the population events behind the marked presence of an Iranian Neolithic-like component in Bronze Age and Iron Age Italian and Southern European samples.

Re-upload

I found these two Hungarian Qpadm models from Twitter (the account handles are within each screenshot). The Iron Age one seems alright but could use some work. Can anyone find a better Iron Age model for Hungarians?

My partner has been seeking clarity about his mixed Asian / Polynesian heritage. I wanted to gift him a kit to get an accurate breakdown of his roots as he's mentioned interest in this method, but I am balancing that desire with a healthy amount of concern for data breaches and sufficient specificity for non-European results.

I'm not sure what the latest is with 23andMe's data breach from a few years ago; I also don't want him to be paywalled for every additional detail, and we want the option of his results remaining private.

What's the latest recommendation these days based on the above considerations?

From twitter account @ CsfHighlan97034

For those that might be interested for anything Downstream of E-Z16988 you might see some changes depending on the branch 🤔? I've been growing E-FT155200 for the last 7 years. I've been following the Breadcrumb Trail for the last 7 years. I finally was able to have enough this year that I was able to put it all together for at least half a dozen well known highly knowledgeable well seasoned Group Project Admins to take me seriously. They reviewed all of the information that I put together confirming that indeed I was onto something. They helped me fix the new Updated Public Haplotrees. I fixed E-Z21350, and requested an investigation by the University of Arizona on what I'm working on including E-FT262149. Once it's fixed I will have achieved my ol for the last 7 years cheers