UNscaled Steppe-Farmer-HG map tool

[ScandinavianCelt]

https://www.exploreyourdna.com/eurocentric.aspx

With official averaged UNscaled coordinates:

https://i.postimg.cc/hGCCq4QV/unscaled-g25-map.jpg (https://postimg.cc/B8K5T0gv)


Post your map from your best set of data!

My results:

Steppe: Eurasian-Steppe: 47.41%
Anatolia-Barcin: 33.76%
Loschbour: 7.95%
Iberomaurasian: 3.54%
Indian/Iranian: Shahr-I-Sokhta: 2.89%
Siberian: 2.62%
Levant: 1.82%

----------------------------------------

Centromeres & LCR, using K36 Gedmatch Superkit coordinates converted to UNscaled:

https://i.postimg.cc/mZMn65Gj/unscaled-steppe-k36centromeres-lcr-snip.jpg (https://postimg.cc/kDJw2wGR)

[Beowulf]

https://i.postimg.cc/x8kDFzvK/2a7435be-6e7d-45c2-ab3a-3b6b1177d708.png (https://postimg.cc/ctNbv65H)

[AndreiDNA]

138101
Kinda goofy

[R1b-L51]

https://i.postimg.cc/bJ5Jt01S/285c3450-cabb-4da1-babc-ee5e63e14a53.png (https://postimg.cc/R6c4rHsv)

[ScandinavianCelt]

https://i.postimg.cc/bJ5Jt01S/285c3450-cabb-4da1-babc-ee5e63e14a53.png (https://postimg.cc/R6c4rHsv)

Very heavy WHG. Nicely done.

[R1b-L51]

https://i.postimg.cc/bJ5Jt01S/285c3450-cabb-4da1-babc-ee5e63e14a53.png (https://postimg.cc/R6c4rHsv)


Very heavy WHG. Nicely done.

AND NOW THE UNSCALED GOOD ONES!!! HAHHA

https://i.postimg.cc/rF1rvbSh/6e9e6a55-2aa0-4328-ac76-c6cbd1fe8556.png (https://postimg.cc/RN0htgpf)

[ScandinavianCelt]

138101
Kinda goofy

did you use correct coordinates?

[ScandinavianCelt]

AND NOW THE UNSCALED GOOD ONES!!! HAHHA

https://i.postimg.cc/rF1rvbSh/6e9e6a55-2aa0-4328-ac76-c6cbd1fe8556.png (https://postimg.cc/RN0htgpf)

what's different in the coordinates you used from #1 to 2?

[R1b-L51]

what's different in the coordinates you used from #1 to 2?

I accidentally used the scaled ones on my first try (and accidentally turned into a Gallop).
On my second try, I used the unscaled ones.

Sorry

[ScandinavianCelt]

I accidentally used the scaled ones on my first try (and accidentally turned into a Gallop).
On my second try, I used the unscaled ones.

Sorry

No problem. It's late there. You are still heavy WHG. I thought Spaniards were supposed to have less WHG and not more? Weird.

[ScandinavianCelt]

Iberomaurasian is 2/3 Natufian, 1/3 SSA

[R1b-L51]

No problem. It's late there. You are still heavy WHG. I thought Spaniards were supposed to have less WHG and not more? Weird.

According to Euroafricanid and some priests, we Iberians have the highest WHG of all Mediterraneans. We're creative like an Italian, but disciplined like a German, and apparently this WHG comes from the first Anatolian farmers.

I don't think anything special is happening in my case, in fact surely some Paleo-Atlantids like Gallop or some baskids as Gixajo could have even several points more.


ROBERTOFERN_scaled,0.100164,0.140143,0.035449,-0.000323,0.041854,-0.005578,0.00282,0.002538,0.020861,0.030616,-0.00406,0.006894,-0.013528,-0.009221,0.008007,-0.005967,-0.006258,-0.000253,-0.011816,-0.002626,0.004742,-0.012118,-0.010599,-0.003253,0.001437

ROBERTOFERN,0.0088,0.0138,0.0094,-0.0001,0.0136,-0.002,0.0012,0.0011,0.0102,0.0168,-0.0025,0.0046,-0.0091,-0.0067,0.0059,-0.0045,-0.0048,-0.0001997,-0.0094,-0.0021,0.0038,-0.0098,-0.0086,-0.0027,0.0012

[R1b-L51]

CORDED WARE GYVAKARAI (INDO ARYAN ANCESTOR OF SEVERAL EUROPEANS)

Gyvakarai1_10bp,0.12244312,0.0867149,0.05225324,0. 06092556,0.01237556,0.0244885,0.00791422,0.0106243 6,-0.00737058,-0.02484582,0.00043062,-0.0020716,0.00778678,-0.00232034,0.0023492,-0.00268724,-0.00679982,-0.00176806,-0.00297244,-0.00478052,0.00155092,0.00128408,-0.0059949,0.00726656,-0.0019295

https://i.postimg.cc/Yq95VKK7/454d9b93-5900-47bc-acef-666b53798800.png (https://postimg.cc/bd7VD5wV)

[R1b-L51]

CORDED WARE GYVAKARAI (INDO ARYAN ANCESTOR OF SEVERAL EUROPEANS)

Gyvakarai1_10bp,0.12244312,0.0867149,0.05225324,0. 06092556,0.01237556,0.0244885,0.00791422,0.0106243 6,-0.00737058,-0.02484582,0.00043062,-0.0020716,0.00778678,-0.00232034,0.0023492,-0.00268724,-0.00679982,-0.00176806,-0.00297244,-0.00478052,0.00155092,0.00128408,-0.0059949,0.00726656,-0.0019295

https://i.postimg.cc/Yq95VKK7/454d9b93-5900-47bc-acef-666b53798800.png (https://postimg.cc/bd7VD5wV)

This is a bit weird, it should have more Indian or Pakistani markers.

[AndreiDNA]

did you use correct coordinates?

AndreiKharchenko_scaled,0.129758,0.09546,0.084852, 0.062016,0.032621,0.025937,0.00564,0.010153,-0.006545,-0.024966,0.000487,-0.004496,0.014123,0.012799,-0.015744,0.012861,0.015516,-0.00076,-0.001508,0.002751,-0.002496,0.001978,0.008381,0.003494,-0.00467
AndreiKharchenko,0.0114,0.0094,0.0225,0.0192,0.010 6,0.0093,0.0024,0.0044,-0.0032,-0.0137,0.0003,-0.003,0.0095,0.0093,-0.0116,0.0097,0.0119,-0.0006,-0.0012,0.0022,-0.002,0.0016,0.0068,0.0029,-0.0039

Run them yourself if you want to

[ScandinavianCelt]

AndreiKharchenko_scaled,0.129758,0.09546,0.084852, 0.062016,0.032621,0.025937,0.00564,0.010153,-0.006545,-0.024966,0.000487,-0.004496,0.014123,0.012799,-0.015744,0.012861,0.015516,-0.00076,-0.001508,0.002751,-0.002496,0.001978,0.008381,0.003494,-0.00467
AndreiKharchenko,0.0114,0.0094,0.0225,0.0192,0.010 6,0.0093,0.0024,0.0044,-0.0032,-0.0137,0.0003,-0.003,0.0095,0.0093,-0.0116,0.0097,0.0119,-0.0006,-0.0012,0.0022,-0.002,0.0016,0.0068,0.0029,-0.0039

Run them yourself if you want to

I believed you were, but mistakes happen. Is your result similar to those from your area/s?

High Levant for sure. Heavy Shamanka too.

[ScandinavianCelt]

AndreiKharchenko_scaled,0.129758,0.09546,0.084852, 0.062016,0.032621,0.025937,0.00564,0.010153,-0.006545,-0.024966,0.000487,-0.004496,0.014123,0.012799,-0.015744,0.012861,0.015516,-0.00076,-0.001508,0.002751,-0.002496,0.001978,0.008381,0.003494,-0.00467
AndreiKharchenko,0.0114,0.0094,0.0225,0.0192,0.010 6,0.0093,0.0024,0.0044,-0.0032,-0.0137,0.0003,-0.003,0.0095,0.0093,-0.0116,0.0097,0.0119,-0.0006,-0.0012,0.0022,-0.002,0.0016,0.0068,0.0029,-0.0039

Run them yourself if you want to

We are VERY similar on calc #3, unscaled, at .5x and 1x ADC:

https://i.postimg.cc/ZYs2BjQm/brad-andrei.jpg (https://postimg.cc/XXFQtK31)

[ScandinavianCelt]

This is a bit weird, it should have more Indian or Pakistani markers.

It's prob all lumped together into the India/Iran spot, but I added the two Iranian.

Some of the samples they list above are not found in the UNscaled individuals samples I have, so I've subbed in the averages for a couple of them. I will post a FINAL closest calc to this map tool in the original post now. Just a moment, por favor.

[unmoggable]

138112

[R1b-L51]

It's prob all lumped together into the India/Iran spot, but I added the two Iranian.

Some of the samples they list above are not found in the UNscaled individuals samples I have, so I've subbed in the averages for a couple of them. I will post a FINAL closest calc to this map tool in the original post now. Just a moment, por favor.

Calm down Professor, this is for fun, although your dedication to the purpose is appreciated.Thanks

[ScandinavianCelt]

According to Euroafricanid and some priests, we Iberians have the highest WHG of all Mediterraneans. We're creative like an Italian, but disciplined like a German, and apparently this WHG comes from the first Anatolian farmers.

I don't think anything special is happening in my case, in fact surely some Paleo-Atlantids like Gallop or some baskids as Gixajo could have even several points more.


ROBERTOFERN_scaled,0.100164,0.140143,0.035449,-0.000323,0.041854,-0.005578,0.00282,0.002538,0.020861,0.030616,-0.00406,0.006894,-0.013528,-0.009221,0.008007,-0.005967,-0.006258,-0.000253,-0.011816,-0.002626,0.004742,-0.012118,-0.010599,-0.003253,0.001437

ROBERTOFERN,0.0088,0.0138,0.0094,-0.0001,0.0136,-0.002,0.0012,0.0011,0.0102,0.0168,-0.0025,0.0046,-0.0091,-0.0067,0.0059,-0.0045,-0.0048,-0.0001997,-0.0094,-0.0021,0.0038,-0.0098,-0.0086,-0.0027,0.0012


AndreiKharchenko_scaled,0.129758,0.09546,0.084852, 0.062016,0.032621,0.025937,0.00564,0.010153,-0.006545,-0.024966,0.000487,-0.004496,0.014123,0.012799,-0.015744,0.012861,0.015516,-0.00076,-0.001508,0.002751,-0.002496,0.001978,0.008381,0.003494,-0.00467
AndreiKharchenko,0.0114,0.0094,0.0225,0.0192,0.010 6,0.0093,0.0024,0.0044,-0.0032,-0.0137,0.0003,-0.003,0.0095,0.0093,-0.0116,0.0097,0.0119,-0.0006,-0.0012,0.0022,-0.002,0.0016,0.0068,0.0029,-0.0039

Run them yourself if you want to


So this is how we three compare with SCALED specific/same sample set as the Map Tool:

https://i.postimg.cc/MKTrJ1H3/brad-rob-andrei2.jpg (https://postimages.org/)

[Gannicus]

138120

Eurasian Steppe Russia Progress 41.88%
Anatolia Barcin 36.69%
WHG Loschbour 9.57%
West-Siberia HG 6.79%
Levant Isr_C 3.87%
Iberomaurusian 1.2%


138121

Some don't like this model because of the high standard errors between Levant PPNB and Anatolia N. It could be caused by qpAdm having a hard time distinguishing between the two groups not because it's invalid.

Interpretation
This is one of your best-fitting models (P = 0.41), meaning it's statistically strong.
The Neolithic Anatolian (Turkey_N) + Jordan_PPNB suggests that a Near Eastern component is consistently present in your ancestry.
Jordan_PPNB has a high standard error, meaning its contribution is less certain—but it does not mean it's absent.
Steppe ancestry is still the largest component, making this model consistent with Northwestern European origins.
Comparing to Other Models
This model fits better than those with Greek, Phoenician, and Egyptian sources, but not by a huge margin.
Turkey_N consistently shows up in your qpAdm runs, reinforcing that Anatolian Neolithic ancestry is a fundamental part of your genetic makeup.
Jordan_PPNB could be picking up deeper Natufian-related ancestry that has been retained in European populations through indirect means.
Should Jordan_PPNB Be Dismissed?
No, but it should be taken cautiously because of its high standard error (10.6% ± 9.3%).
It likely represents very old Neolithic or early Bronze Age influences, rather than recent admixture.
If you replace it with later Levantine sources (e.g., Canaanite, Phoenician, or Egyptian), the fit might remain good while standard errors improve.
Final Verdict
✅ Good model (P = 0.41, Chi² = 6.12) ✅ Supports Steppe + Anatolian + Near Eastern admixture ⚠️ Jordan_PPNB may not be recent but reflects deep ancient input

Would you like to refine this model further by swapping Jordan_PPNB for a later Levantine population, or are you happy with this result?







You said:
does the high standard error mean it's having a hard time distinguishing between the 2 populations?
ChatGPT said:
Yes, a high standard error in qpAdm suggests that the model is having difficulty precisely distinguishing the contribution of that population. There are a few reasons why this might happen:

Why is the Standard Error High?
Overlap in Ancestry Between Sources

Jordan_PPNB and Turkey_N are both heavily Near Eastern-derived.
Since Turkey_N (Neolithic Anatolia) already carries Natufian-related ancestry, qpAdm struggles to separate them cleanly.
This can result in inflated standard errors because both populations share similar allele frequencies.
Low SNP Overlap Between Target and Sources

If Jordan_PPNB has fewer overlapping SNPs with your DNA than Turkey_N or Yamnaya, its contribution may become statistically uncertain, leading to a larger error margin.
Admixture Components Are Too Similar

qpAdm tries to create a best-fit combination, but if two source populations are genetically close, it struggles to assign the proportions accurately.
This often results in one population receiving an inflated standard error, indicating that qpAdm is uncertain whether the ancestry is coming from one source or a mixture of both.
Possible Overfitting of the Model

Adding too many similar reference populations (e.g., using both Jordan_PPNB and Natufian) can confuse the model.
The software struggles to pick one over the other, leading to large error margins.
What This Means for Your Model
Turkey_N is already carrying some of the same ancestry as Jordan_PPNB (both are Near Eastern Neolithic-derived).
Jordan_PPNB might not be an exact fit for your ancestry, or qpAdm is having difficulty distinguishing it from Turkey_N.
The model still fits well overall (P = 0.41), but the high standard error means the exact proportion of Jordan_PPNB is uncertain.
What Can You Do?
Try Removing Jordan_PPNB and see if the model still fits well.

If the fit remains good, the Jordan_PPNB signal may already be captured in Turkey_N.
Replace Jordan_PPNB with Later Levantines (e.g., Phoenician, Canaanite, or Egyptian).

If your model improves (lower standard error), this suggests your Near Eastern component is better captured by a later population.
Test Turkey_N Alone (Without Jordan_PPNB).

If the model still holds, then Jordan_PPNB was likely unnecessary.
Final Verdict
✔ Yes, the high standard error means qpAdm is struggling to distinguish between Turkey_N and Jordan_PPNB.
✔ Testing other Near Eastern populations or reducing redundancy can improve the accuracy.

[ScandinavianCelt]

138120

Eurasian Steppe Russia Progress 41.88%
Anatolia Barcin 36.69%
WHG Loschbour 9.57%
West-Siberia HG 6.79%
Levant Isr_C 3.87%
Iberomaurusian 1.2%


138121

You may have to add this one, based on the attached results:

UNscaled sample:

Jordan_PPNB,0.00665,0.01595,-0.007925,-0.041675,0.01025,-0.022,-0.0032,-0.003375,0.037325,0.02115,0.01255,-0.01005,0.0183,0.000925,-0.0201,0.00205,0.011925,0.000825,-0.005575,0.010425,-0.003075,0.0048,0.0031,-0.004,-0.002775

[ScandinavianCelt]

AndreiKharchenko_scaled,0.129758,0.09546,0.084852, 0.062016,0.032621,0.025937,0.00564,0.010153,-0.006545,-0.024966,0.000487,-0.004496,0.014123,0.012799,-0.015744,0.012861,0.015516,-0.00076,-0.001508,0.002751,-0.002496,0.001978,0.008381,0.003494,-0.00467
AndreiKharchenko,0.0114,0.0094,0.0225,0.0192,0.010 6,0.0093,0.0024,0.0044,-0.0032,-0.0137,0.0003,-0.003,0.0095,0.0093,-0.0116,0.0097,0.0119,-0.0006,-0.0012,0.0022,-0.002,0.0016,0.0068,0.0029,-0.0039

Run them yourself if you want to

I added Jordan_PPNB and though your farming is split a bit from me, overall our only real difference in groups is 4-5% I get Iberomaurasian HG while you get that same amount from Tyumen HG. It's strange how similar our results are:

https://i.postimg.cc/y8kLGRnh/brad-andrei-unscaled-jordan.jpg (https://postimg.cc/YvBNGh5j)

Using just one Turkey_N (.SG), we comp like this:

Target: AndreiKharchenko
Distance: 4.0436% / 0.04043576
49.2 Russia_Steppe_Eneolithic
21.4 Turkey_N.SG
16.2 Jordan_PPNB
6.8 Luxembourg_Loschbour.DG
4.6 Russia_Tyumen_HG
1.8 Russia_Shamanka_Eneolithic.SG

Target: SC_Official_Averaged_UNscaled
Distance: 3.7633% / 0.03763319
51.8 Russia_Steppe_Eneolithic
20.2 Turkey_N.SG
11.6 Israel_C
6.8 Luxembourg_Loschbour.DG
5.0 Russia_Shamanka_Eneolithic.SG
4.6 Morocco_Iberomaurusian

[Gannicus]

Gannicus_Mergedfile_officialDavidski_unscaled,0.01 14,0.0137,0.0152,0.011,0.0131,0.004,-0.0026,0.0017,0.0025,0.0047,-0.0056,0.0066,-0.0058,-0.0147,0.0171,0.0079,-0.0034,-0.0004,0.0033,0.0024,0.0094,0.0038,-0.0034,0.0129,0.0012

My Unscaled coordinates

[ScandinavianCelt]

This is probably my most Steppe-shifted chromosome, chromosome #21:

https://i.postimg.cc/2yq4ws44/chr21-unscaled-steppeshifted.jpg (https://postimg.cc/ctSg15kH)

map vs G25 result, unscaled:

Target: chr21
Distance: 2.3933% / 0.02393264
59.6 Russia_Steppe_Eneolithic
24.4 Turkey_N.SG
6.2 Israel_C
4.6 Russia_Shamanka_Eneolithic.SG
2.2 Russia_Tyumen_HG
1.6 Luxembourg_Loschbour.DG
1.4 Morocco_Iberomaurusian

[Gannicus]

Iberomaurasian is 2/3 Natufian, 1/3 SSA

Instead of SSA they can be also modeled with ~33% ANA. A 2018 study by Van de Loosdrecht showed that Iberomaurusians can be modeled as roughly 66% West Eurasian Natufian or Natufian like followed by 33% Ancient North African (ANA). And Ancient North Africans are a distinct North African group that diverged from other human populations around 50,000 years ago. Seems like it's still up for debate whether it's SSA or not.

[tk'es]

https://i.ibb.co/gFSMWFnW/0b3bc85b-49f2-4f66-895c-59056928f2a0.png

[gixajo]

AND NOW THE UNSCALED GOOD ONES!!! HAHHA

https://i.postimg.cc/rF1rvbSh/6e9e6a55-2aa0-4328-ac76-c6cbd1fe8556.png (https://postimg.cc/RN0htgpf)

Eso es más normal, con las otras coordenadas tenias solo un 10% de Yamnaya y casi un 20% de WHG, eso no puede ser.:D

So many compenents using unscaled coordinates should be used in a model adding penalization, this calculator can only gave us very distorted results with a lot of noise.

[gixajo]

Mine.

As I have said I think, using unscaled, and more using it without penalization, would give to us very distorted and noisy results, at least comparing these results with a similar scaled calculator.

It's not that some results are more "accurate" or valid than others, they simply represent a different standard, so we must change the paradigm we're used to and compare only scaled results with scaled results and unscaled results with unscaled results.

Something similar to using distances in miles or kilometers.

https://i.imgur.com/9bsFI8x.png

[EasternLusitanian]

https://i.imgur.com/HRFxezS.png

[gixajo]

Well, in this particular case, with penalization this calculator gives us even weird results.

Unscaled start to be confusing with just more than 3 components used as sources...;)


EEF:I0726,0.0106,0.0179,0.0009,-0.032,0.0175,-0.0194,-0.0034,-0.0013,0.0169,0.0472,0.0067,0.0112,-0.0183,0.0035,-0.0293,-0.0279,-0.0023,-0.0001,0.0181,-0.0146,-0.0114,0.0045,-0.0044,-0.0009,0.0054
WHG:Loschbour_snpAD.DG,0.0115,0.0108,0.054,0.0613, 0.0528,0.0212,0.0064,0.0165,0.049,0.0089,-0.0095,-0.0115,0.0134,-0.0009,0.0452,0.0533,0.002,0.0058,-0.0071,0.0523,0.0942,0.0084,-0.0401,-0.1441,0.0163
Caucasus-CHG:KK1_noUDG.SG,0.0079,0.0102,-0.0234,-0.0015,-0.0291,0.0073,0.0099,-0.0005,-0.0638,-0.0439,-0.0047,0.0166,-0.0354,0.0056,0.0195,-0.0224,0.0213,-0.0085,-0.0189,0.03,0.0248,-0.0054,0.0043,-0.0173,0.0013
Zagros:I1947,0.0038,0.0061,-0.041,0.0047,-0.0396,0.0085,0.0053,-0.0008,-0.0389,-0.0331,-0.0031,-0.0019,0.0006,-0.0096,0.0276,0.0482,-0.0028,0.0084,0.0085,-0.0284,0.0074,-0.0222,-0.0096,-0.0306,0.0223
Levant:I1182,0.0079,0.0158,-0.0137,-0.0447,0.0073,-0.0222,-0.0045,-0.0063,0.0276,0.0157,0.0081,-0.0075,0.0238,0.004,-0.0106,-0.001,0.0032,0.0003,0.0089,0.015,-0.0022,0.0015,0.0024,-0.0063,-0.003
Atlas:TAF010_noUDG,-0.0172,0.0083,-0.0069,-0.0269,0.0119,-0.0196,-0.0315,0.0082,0.0811,0.0044,0.0144,-0.0231,0.0533,-0.0297,0.0564,-0.0375,-0.0008,-0.0557,-0.1132,0.0299,-0.031,-0.1054,0.0594,-0.0114,0.0178
Eurasian-Steppe:PG2001,0.0098,0.0078,0.0026,0.0316,-0.0173,0.0163,0.0007,-0.0045,-0.0377,-0.0434,0.0032,0.0014,-0.0069,-0.018,0.0211,0.0013,-0.0049,-0.0029,-0.0048,0.006,-0.0079,0.0028,0.0026,0.0028,-0.0109
East-Asia:DA245_noUDG.SG,0.002,-0.0417,0.0251,-0.008,-0.0298,-0.0173,0.0041,0.006,0.0094,0.0108,-0.0121,0.0009,0.0048,-0.0036,-0.004,-0.0035,0.0027,-0.001,0.0036,0.0177,-0.0166,-0.0021,-0.0143,-0.0073,0.0031
West-Siberia:I1960,0.0092,-0.0059,0.0255,0.0623,-0.0287,0.0164,-0.0224,-0.0237,-0.0174,-0.0516,0.0195,-0.0085,0.0173,-0.0501,0.0206,0.0125,-0.0152,-0.0023,-0.0036,0.0055,-0.0243,0.0103,0.0192,0.0059,-0.01
India:I8728_enhanced,0.003,-0.008,-0.0477,0.038,-0.0315,0.0247,0.0004,0.003,0.0173,0.0111,-0.0031,0.0033,-0.0046,0.0051,0.0016,0.0033,0.0054,0.0058,-0.0011,0.0002,0.0035,-0.0071,-0.0007,-0.0013,-0.0059

Target: gixajo
Distance: 2.6538% / 0.02653810
49.2 EEF
30.6 Eurasian-Steppe
11.8 WHG
5.8 Atlas
2.6 India

Target: gixajo
Distance: 2.7137% / 0.02713697 | ADC: 0.25x
48.4 EEF
30.4 Eurasian-Steppe
10.4 WHG
4.0 Atlas
3.2 India
2.4 Levant
1.2 East-Asia

Target: gixajo
Distance: 3.2442% / 0.03244157 | ADC: 0.5x
43.2 EEF
30.0 Eurasian-Steppe
13.0 Levant
6.2 WHG
3.8 East-Asia
3.8 India

Target: gixajo
Distance: 4.2080% / 0.04207993 | ADC: 1x
40.2 EEF
27.2 Eurasian-Steppe
24.4 Levant
6.6 East-Asia
1.6 India

Target: gixajo
Distance: 5.9977% / 0.05997661 | ADC: 2x
49.0 Levant
39.6 EEF
6.8 East-Asia
4.6 Eurasian-Steppe

[gixajo]

138120


✔ Testing other Near Eastern populations or reducing redundancy can improve the accuracy.

I hope no one here confuses "statistically strong" with the results of this calculator better reflecting the reality of our ancestry, right?.;)

[R1b-L51]

I added Jordan_PPNB and though your farming is split a bit from me, overall our only real difference in groups is 4-5% I get Iberomaurasian HG while you get that same amount from Tyumen HG. It's strange how similar our results are:

https://i.postimg.cc/y8kLGRnh/brad-andrei-unscaled-jordan.jpg (https://postimg.cc/YvBNGh5j)

Using just one Turkey_N (.SG), we comp like this:

Target: AndreiKharchenko
Distance: 4.0436% / 0.04043576
49.2 Russia_Steppe_Eneolithic
21.4 Turkey_N.SG
16.2 Jordan_PPNB
6.8 Luxembourg_Loschbour.DG
4.6 Russia_Tyumen_HG
1.8 Russia_Shamanka_Eneolithic.SG

Target: SC_Official_Averaged_UNscaled
Distance: 3.7633% / 0.03763319
51.8 Russia_Steppe_Eneolithic
20.2 Turkey_N.SG
11.6 Israel_C
6.8 Luxembourg_Loschbour.DG
5.0 Russia_Shamanka_Eneolithic.SG
4.6 Morocco_Iberomaurusian

There are ancient Anatolian samples that have WHG, could be stealing from Luxembourg?

[R1b-L51]

Eso es más normal, con las otras coordenadas tenias solo un 10% de Yamnaya y casi un 20% de WHG, eso no puede ser.:D

So many compenents using unscaled coordinates should be used in a model adding penalization, this calculator can only gave us very distorted results with a lot of noise.

But as a joke it was okay ^^

[R1b-L51]

Mine.

As I have said I think, using unscaled, and more using it without penalization, would give to us very distorted and noisy results, at least comparing these results with a similar scaled calculator.

It's not that some results are more "accurate" or valid than others, they simply represent a different standard, so we must change the paradigm we're used to and compare only scaled results with scaled results and unscaled results with unscaled results.

Something similar to using distances in miles or kilometers.

https://i.imgur.com/9bsFI8x.png

That doesn't make much sense because the differences would still be the same, don't you think? In fact, in the model that SC created in another post, recreating this calculator in G25, he managed to give very similar results between scaled and unscaled.

In my opinion, it depends a lot on the samples... there are Anatolians that are full of WHG, and we (the iberians) score highly EEF due to genetic proximity, but if, on the other hand, if we find an Anatolian sample with less or little WHG, we suddenly score in Luxembourg as if we were Primarchs (excuse my lore, Sir).

[R1b-L51]

Well, in this particular case, with penalization this calculator gives us even weird results.

Unscaled start to be confusing with just more than 3 components used as sources...;)


EEF:I0726,0.0106,0.0179,0.0009,-0.032,0.0175,-0.0194,-0.0034,-0.0013,0.0169,0.0472,0.0067,0.0112,-0.0183,0.0035,-0.0293,-0.0279,-0.0023,-0.0001,0.0181,-0.0146,-0.0114,0.0045,-0.0044,-0.0009,0.0054
WHG:Loschbour_snpAD.DG,0.0115,0.0108,0.054,0.0613, 0.0528,0.0212,0.0064,0.0165,0.049,0.0089,-0.0095,-0.0115,0.0134,-0.0009,0.0452,0.0533,0.002,0.0058,-0.0071,0.0523,0.0942,0.0084,-0.0401,-0.1441,0.0163
Caucasus-CHG:KK1_noUDG.SG,0.0079,0.0102,-0.0234,-0.0015,-0.0291,0.0073,0.0099,-0.0005,-0.0638,-0.0439,-0.0047,0.0166,-0.0354,0.0056,0.0195,-0.0224,0.0213,-0.0085,-0.0189,0.03,0.0248,-0.0054,0.0043,-0.0173,0.0013
Zagros:I1947,0.0038,0.0061,-0.041,0.0047,-0.0396,0.0085,0.0053,-0.0008,-0.0389,-0.0331,-0.0031,-0.0019,0.0006,-0.0096,0.0276,0.0482,-0.0028,0.0084,0.0085,-0.0284,0.0074,-0.0222,-0.0096,-0.0306,0.0223
Levant:I1182,0.0079,0.0158,-0.0137,-0.0447,0.0073,-0.0222,-0.0045,-0.0063,0.0276,0.0157,0.0081,-0.0075,0.0238,0.004,-0.0106,-0.001,0.0032,0.0003,0.0089,0.015,-0.0022,0.0015,0.0024,-0.0063,-0.003
Atlas:TAF010_noUDG,-0.0172,0.0083,-0.0069,-0.0269,0.0119,-0.0196,-0.0315,0.0082,0.0811,0.0044,0.0144,-0.0231,0.0533,-0.0297,0.0564,-0.0375,-0.0008,-0.0557,-0.1132,0.0299,-0.031,-0.1054,0.0594,-0.0114,0.0178
Eurasian-Steppe:PG2001,0.0098,0.0078,0.0026,0.0316,-0.0173,0.0163,0.0007,-0.0045,-0.0377,-0.0434,0.0032,0.0014,-0.0069,-0.018,0.0211,0.0013,-0.0049,-0.0029,-0.0048,0.006,-0.0079,0.0028,0.0026,0.0028,-0.0109
East-Asia:DA245_noUDG.SG,0.002,-0.0417,0.0251,-0.008,-0.0298,-0.0173,0.0041,0.006,0.0094,0.0108,-0.0121,0.0009,0.0048,-0.0036,-0.004,-0.0035,0.0027,-0.001,0.0036,0.0177,-0.0166,-0.0021,-0.0143,-0.0073,0.0031
West-Siberia:I1960,0.0092,-0.0059,0.0255,0.0623,-0.0287,0.0164,-0.0224,-0.0237,-0.0174,-0.0516,0.0195,-0.0085,0.0173,-0.0501,0.0206,0.0125,-0.0152,-0.0023,-0.0036,0.0055,-0.0243,0.0103,0.0192,0.0059,-0.01
India:I8728_enhanced,0.003,-0.008,-0.0477,0.038,-0.0315,0.0247,0.0004,0.003,0.0173,0.0111,-0.0031,0.0033,-0.0046,0.0051,0.0016,0.0033,0.0054,0.0058,-0.0011,0.0002,0.0035,-0.0071,-0.0007,-0.0013,-0.0059

Target: gixajo
Distance: 2.6538% / 0.02653810
49.2 EEF
30.6 Eurasian-Steppe
11.8 WHG
5.8 Atlas
2.6 India

Target: gixajo
Distance: 2.7137% / 0.02713697 | ADC: 0.25x
48.4 EEF
30.4 Eurasian-Steppe
10.4 WHG
4.0 Atlas
3.2 India
2.4 Levant
1.2 East-Asia

Target: gixajo
Distance: 3.2442% / 0.03244157 | ADC: 0.5x
43.2 EEF
30.0 Eurasian-Steppe
13.0 Levant
6.2 WHG
3.8 East-Asia
3.8 India

Target: gixajo
Distance: 4.2080% / 0.04207993 | ADC: 1x
40.2 EEF
27.2 Eurasian-Steppe
24.4 Levant
6.6 East-Asia
1.6 India

Target: gixajo
Distance: 5.9977% / 0.05997661 | ADC: 2x
49.0 Levant
39.6 EEF
6.8 East-Asia
4.6 Eurasian-Steppe

EXTRAÑO ES DIOS NO CREES? The only strange thing is that the Gyvakarai1_10bp sample doesn't have any Indian characteristics, but I imagine that the West-Siberian eats it with potatoes.

Target: Gyvakarai1_10bp
Distance: 15.3606% / 0.15360619
55.0 Eurasian-Steppe
17.2 EEF
15.2 WHG
12.6 West-Siberia

Target: ROBERTOFERN
Distance: 2.4124% / 0.02412430
49.0 EEF
29.2 Eurasian-Steppe
10.4 WHG
8.0 Atlas
3.4 India

[noricum]

https://i.postimg.cc/KY3JtFNM/0123456789.jpg (https://postimages.org/)

[Gannicus]

I hope no one here confuses "statistically strong" with the results of this calculator better reflecting the reality of our ancestry, right?.;)

That ChatGPT response was from the results I showed it of my qpAdm results with the Jordan ppnb sample. In my case it’s probably from Roman/hellenistic period near east gene flow into northwest Europe. It’s part of my genetic makeup but it’s highly likely it’s not recent ( last 500 years)

[Gannicus]

Well, in this particular case, with penalization this calculator gives us even weird results.

Unscaled start to be confusing with just more than 3 components used as sources...;)


EEF:I0726,0.0106,0.0179,0.0009,-0.032,0.0175,-0.0194,-0.0034,-0.0013,0.0169,0.0472,0.0067,0.0112,-0.0183,0.0035,-0.0293,-0.0279,-0.0023,-0.0001,0.0181,-0.0146,-0.0114,0.0045,-0.0044,-0.0009,0.0054
WHG:Loschbour_snpAD.DG,0.0115,0.0108,0.054,0.0613, 0.0528,0.0212,0.0064,0.0165,0.049,0.0089,-0.0095,-0.0115,0.0134,-0.0009,0.0452,0.0533,0.002,0.0058,-0.0071,0.0523,0.0942,0.0084,-0.0401,-0.1441,0.0163
Caucasus-CHG:KK1_noUDG.SG,0.0079,0.0102,-0.0234,-0.0015,-0.0291,0.0073,0.0099,-0.0005,-0.0638,-0.0439,-0.0047,0.0166,-0.0354,0.0056,0.0195,-0.0224,0.0213,-0.0085,-0.0189,0.03,0.0248,-0.0054,0.0043,-0.0173,0.0013
Zagros:I1947,0.0038,0.0061,-0.041,0.0047,-0.0396,0.0085,0.0053,-0.0008,-0.0389,-0.0331,-0.0031,-0.0019,0.0006,-0.0096,0.0276,0.0482,-0.0028,0.0084,0.0085,-0.0284,0.0074,-0.0222,-0.0096,-0.0306,0.0223
Levant:I1182,0.0079,0.0158,-0.0137,-0.0447,0.0073,-0.0222,-0.0045,-0.0063,0.0276,0.0157,0.0081,-0.0075,0.0238,0.004,-0.0106,-0.001,0.0032,0.0003,0.0089,0.015,-0.0022,0.0015,0.0024,-0.0063,-0.003
Atlas:TAF010_noUDG,-0.0172,0.0083,-0.0069,-0.0269,0.0119,-0.0196,-0.0315,0.0082,0.0811,0.0044,0.0144,-0.0231,0.0533,-0.0297,0.0564,-0.0375,-0.0008,-0.0557,-0.1132,0.0299,-0.031,-0.1054,0.0594,-0.0114,0.0178
Eurasian-Steppe:PG2001,0.0098,0.0078,0.0026,0.0316,-0.0173,0.0163,0.0007,-0.0045,-0.0377,-0.0434,0.0032,0.0014,-0.0069,-0.018,0.0211,0.0013,-0.0049,-0.0029,-0.0048,0.006,-0.0079,0.0028,0.0026,0.0028,-0.0109
East-Asia:DA245_noUDG.SG,0.002,-0.0417,0.0251,-0.008,-0.0298,-0.0173,0.0041,0.006,0.0094,0.0108,-0.0121,0.0009,0.0048,-0.0036,-0.004,-0.0035,0.0027,-0.001,0.0036,0.0177,-0.0166,-0.0021,-0.0143,-0.0073,0.0031
West-Siberia:I1960,0.0092,-0.0059,0.0255,0.0623,-0.0287,0.0164,-0.0224,-0.0237,-0.0174,-0.0516,0.0195,-0.0085,0.0173,-0.0501,0.0206,0.0125,-0.0152,-0.0023,-0.0036,0.0055,-0.0243,0.0103,0.0192,0.0059,-0.01
India:I8728_enhanced,0.003,-0.008,-0.0477,0.038,-0.0315,0.0247,0.0004,0.003,0.0173,0.0111,-0.0031,0.0033,-0.0046,0.0051,0.0016,0.0033,0.0054,0.0058,-0.0011,0.0002,0.0035,-0.0071,-0.0007,-0.0013,-0.0059

Target: gixajo
Distance: 2.6538% / 0.02653810
49.2 EEF
30.6 Eurasian-Steppe
11.8 WHG
5.8 Atlas
2.6 India

Target: gixajo
Distance: 2.7137% / 0.02713697 | ADC: 0.25x
48.4 EEF
30.4 Eurasian-Steppe
10.4 WHG
4.0 Atlas
3.2 India
2.4 Levant
1.2 East-Asia

Target: gixajo
Distance: 3.2442% / 0.03244157 | ADC: 0.5x
43.2 EEF
30.0 Eurasian-Steppe
13.0 Levant
6.2 WHG
3.8 East-Asia
3.8 India

Target: gixajo
Distance: 4.2080% / 0.04207993 | ADC: 1x
40.2 EEF
27.2 Eurasian-Steppe
24.4 Levant
6.6 East-Asia
1.6 India

Target: gixajo
Distance: 5.9977% / 0.05997661 | ADC: 2x
49.0 Levant
39.6 EEF
6.8 East-Asia
4.6 Eurasian-Steppe

Distance to: Gannicus_Mergedfile_officialDavidski_unscaled
0.08076410 Eurasian-Steppe:PG2001
0.09196374 East-Asia:DA245_noUDG.SG
0.09351925 Levant:I1182
0.09560476 India:I8728_enhanced
0.09953909 EEF:I0726
0.11988365 West-Siberia:I1960
0.12374102 Zagros:I1947
0.12616469 Caucasus-CHG:KK1_noUDG.SG
0.21965962 WHG:Loschbour_snpAD.DG
0.23399026 Atlas:TAF010_noUDG

Target: Gannicus_Mergedfile_officialDavidski_unscaled
Distance: 0.0529% / 0.05287985 | ADC: 2x RC
55.8 Eurasian-Steppe
44.2 Levant



Target: Gannicus_Mergedfile_officialDavidski_unscaled
Distance: 0.0466% / 0.04663002 | ADC: 1x RC
52.7 Eurasian-Steppe
31.0 EEF
12.4 Levant
3.9 East-Asia


Target: Gannicus_Mergedfile_officialDavidski_unscaled
Distance: 0.0398% / 0.03979844 | ADC: 0.5x RC
49.0 Eurasian-Steppe
36.1 EEF
10.5 WHG
4.4 Levant

[ScandinavianCelt]

Well, in this particular case, with penalization this calculator gives us even weird results.

Unscaled start to be confusing with just more than 3 components used as sources...;)


EEF:I0726,0.0106,0.0179,0.0009,-0.032,0.0175,-0.0194,-0.0034,-0.0013,0.0169,0.0472,0.0067,0.0112,-0.0183,0.0035,-0.0293,-0.0279,-0.0023,-0.0001,0.0181,-0.0146,-0.0114,0.0045,-0.0044,-0.0009,0.0054
WHG:Loschbour_snpAD.DG,0.0115,0.0108,0.054,0.0613, 0.0528,0.0212,0.0064,0.0165,0.049,0.0089,-0.0095,-0.0115,0.0134,-0.0009,0.0452,0.0533,0.002,0.0058,-0.0071,0.0523,0.0942,0.0084,-0.0401,-0.1441,0.0163
Caucasus-CHG:KK1_noUDG.SG,0.0079,0.0102,-0.0234,-0.0015,-0.0291,0.0073,0.0099,-0.0005,-0.0638,-0.0439,-0.0047,0.0166,-0.0354,0.0056,0.0195,-0.0224,0.0213,-0.0085,-0.0189,0.03,0.0248,-0.0054,0.0043,-0.0173,0.0013
Zagros:I1947,0.0038,0.0061,-0.041,0.0047,-0.0396,0.0085,0.0053,-0.0008,-0.0389,-0.0331,-0.0031,-0.0019,0.0006,-0.0096,0.0276,0.0482,-0.0028,0.0084,0.0085,-0.0284,0.0074,-0.0222,-0.0096,-0.0306,0.0223
Levant:I1182,0.0079,0.0158,-0.0137,-0.0447,0.0073,-0.0222,-0.0045,-0.0063,0.0276,0.0157,0.0081,-0.0075,0.0238,0.004,-0.0106,-0.001,0.0032,0.0003,0.0089,0.015,-0.0022,0.0015,0.0024,-0.0063,-0.003
Atlas:TAF010_noUDG,-0.0172,0.0083,-0.0069,-0.0269,0.0119,-0.0196,-0.0315,0.0082,0.0811,0.0044,0.0144,-0.0231,0.0533,-0.0297,0.0564,-0.0375,-0.0008,-0.0557,-0.1132,0.0299,-0.031,-0.1054,0.0594,-0.0114,0.0178
Eurasian-Steppe:PG2001,0.0098,0.0078,0.0026,0.0316,-0.0173,0.0163,0.0007,-0.0045,-0.0377,-0.0434,0.0032,0.0014,-0.0069,-0.018,0.0211,0.0013,-0.0049,-0.0029,-0.0048,0.006,-0.0079,0.0028,0.0026,0.0028,-0.0109
East-Asia:DA245_noUDG.SG,0.002,-0.0417,0.0251,-0.008,-0.0298,-0.0173,0.0041,0.006,0.0094,0.0108,-0.0121,0.0009,0.0048,-0.0036,-0.004,-0.0035,0.0027,-0.001,0.0036,0.0177,-0.0166,-0.0021,-0.0143,-0.0073,0.0031
West-Siberia:I1960,0.0092,-0.0059,0.0255,0.0623,-0.0287,0.0164,-0.0224,-0.0237,-0.0174,-0.0516,0.0195,-0.0085,0.0173,-0.0501,0.0206,0.0125,-0.0152,-0.0023,-0.0036,0.0055,-0.0243,0.0103,0.0192,0.0059,-0.01
India:I8728_enhanced,0.003,-0.008,-0.0477,0.038,-0.0315,0.0247,0.0004,0.003,0.0173,0.0111,-0.0031,0.0033,-0.0046,0.0051,0.0016,0.0033,0.0054,0.0058,-0.0011,0.0002,0.0035,-0.0071,-0.0007,-0.0013,-0.0059

Target: gixajo
Distance: 2.6538% / 0.02653810
49.2 EEF
30.6 Eurasian-Steppe
11.8 WHG
5.8 Atlas
2.6 India

Target: gixajo
Distance: 2.7137% / 0.02713697 | ADC: 0.25x
48.4 EEF
30.4 Eurasian-Steppe
10.4 WHG
4.0 Atlas
3.2 India
2.4 Levant
1.2 East-Asia

Target: gixajo
Distance: 3.2442% / 0.03244157 | ADC: 0.5x
43.2 EEF
30.0 Eurasian-Steppe
13.0 Levant
6.2 WHG
3.8 East-Asia
3.8 India

Target: gixajo
Distance: 4.2080% / 0.04207993 | ADC: 1x
40.2 EEF
27.2 Eurasian-Steppe
24.4 Levant
6.6 East-Asia
1.6 India

Target: gixajo
Distance: 5.9977% / 0.05997661 | ADC: 2x
49.0 Levant
39.6 EEF
6.8 East-Asia
4.6 Eurasian-Steppe

mine: (which one/ADC is best?):

Target: SC_Official_Averaged_UNscaled
Distance: 3.5196% / 0.03519650
45.2 Eurasian-Steppe
34.0 EEF
8.8 WHG
5.0 India
4.2 Atlas
2.8 West-Siberia

Target: SC_Official_Averaged_UNscaled
Distance: 3.5256% / 0.03525587 | ADC: 0.25x RC
48.4 Eurasian-Steppe
33.4 EEF
8.8 WHG
4.8 India
4.4 Atlas
0.2 East-Asia

Target: SC_Official_Averaged_UNscaled
Distance: 3.6517% / 0.03651693 | ADC: 0.5x RC
48.2 Eurasian-Steppe
31.0 EEF
8.8 WHG
5.8 India
4.6 Levant
1.6 East-Asia

Target: SC_Official_Averaged_UNscaled
Distance: 4.1685% / 0.04168534 | ADC: 1x RC
49.8 Eurasian-Steppe
26.4 EEF
10.6 Levant
8.6 India
4.6 East-Asia

Target: SC_Official_Averaged_UNscaled
Distance: 4.8151% / 0.04815075 | ADC: 2x RC
58.2 Eurasian-Steppe
41.8 Levant

@cass -- my 2x plots me Kashubian for both Steppe and Levant scores. Kashubian scores 58% Steppe, 42% Levant.

[CeltiberoGermanico]

https://www.exploreyourdna.com/eurocentric.aspx

With official averaged UNscaled coordinates:

https://i.postimg.cc/hGCCq4QV/unscaled-g25-map.jpg (https://postimg.cc/B8K5T0gv)


Post your map from your best set of data!

My results:

Steppe: Eurasian-Steppe: 47.41%
Anatolia-Barcin: 33.76%
Loschbour: 7.95%
Iberomaurasian: 3.54%
Indian/Iranian: Shahr-I-Sokhta: 2.89%
Siberian: 2.62%
Levant: 1.82%

----------------------------------------

Centromeres & LCR, using K36 Gedmatch Superkit coordinates converted to UNscaled:

https://i.postimg.cc/mZMn65Gj/unscaled-steppe-k36centromeres-lcr-snip.jpg (https://postimg.cc/kDJw2wGR)

[ScandinavianCelt]

...

[ScandinavianCelt]

I meant to ask you I don't see a message there I just see you quoted my original post