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Admixtools My qpAdm Results Please Share Yours!

I grabbed these references from an X user:

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1776174608737.png

1776174728086.png


It's a little frustrating when I introduce a third NW Euro IA source. The standard errors are bad when I see others able to pull it off. This last 3-way to get the SEs ~10%, I had to use a southern Gaul instead of one from the north.

This is an example:
1776175117960.png


I'm thinking eventually there is a source and reference combination that will work for me. Considering I cluster in the British Isles and FST to English is 0.
 
IllustrativeDNA updated the dataset to AADR V.66. I'll have some different models to share later.
 
IllustrativeDNA is cool, I like how it can read your dna based on different time periods. It’s fun reading results from that service
 
1778760210081.png

Would anyone like to try to model this group as Steppe/ANF/WHG? I've tried a number of varying sources and references.
 
Captura de pantalla 2026-06-13 004432.png
 
Nice results! What references did you use?

View attachment 19707
View attachment 19708

It's very sensitive to the WHG outgroup. This failed with Italy Sicily Epigravettian.
Lefts: Latvia_LN_CordedWare.AG, Luxembourg_Mesolithic.DG, Greece_NeaNikomedeia_EN.SG

Rights: Mbuti.DG, Russia_UstIshim_IUP.DG, Russia_Kostenki14_UP.SG, Russia_Sunghir_UP.SG, Turkey_Central_Pinarbasi_Epipaleolithic.AG, Israel_Natufian.AG, Russia_Sidelkino_HG.SG, Georgia_Satsurblia_LateUP.SG, Switzerland_Bichon_Epipaleolithic.SG, Iran_Wezmeh_N.SG, Russia_MA1_UP.SG, China_AmurRiver_Paleolithic.AG


Hopefully this is fine
 
hey qh777 bro

these are my results

34.5% Anatolia Barcin Neolithic Farmer

29.1% Zagros Neolithic Farmer

12.9% Caucasus Hunter Gatherer . Satsurblia Georgia

11.1% East European Hunter Gatherer

8.8% Israel Natufian

3.7% East Asian



51088760yi.jpg
 
Last edited:
A random thought crossed my mind. "Can I model myself with Polynesians and other East Eurasians? Let's find out."

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What I think is going on is that qpAdm is borrowing from the steppe component and assigning it to the East Eurasian sources. It's picking up the Ancestral North Eurasian component I think and saying "This can be substituted for East Eurasian". The reason why I think this is because in many of these models I score ~42% Steppe(Yamnaya). Normally I score around 44%-45%.

While doing these models, I ran into a post on X that posted this map:

1784043541193.png


After seeing this map, it makes me wonder if it's a combination of this and ANE.
 
hey qh777 bro

these are my results

34.5% Anatolia Barcin Neolithic Farmer

29.1% Zagros Neolithic Farmer

12.9% Caucasus Hunter Gatherer . Satsurblia Georgia

11.1% East European Hunter Gatherer

8.8% Israel Natufian

3.7% East Asian



51088760yi.jpg

i have doubts about my qpAdm result (even though they are reasonable and similar to my G25 results ) because my forum friend who did it for me modelled other populations too and the results didnt make sense . for example campanian italians got 14% natufian which can not be true

we will try to modell me further and if we get a better model i will post it here

EDIT :

actually he ran more ethnicities and for many they seemed accurate imo and the p values and other parameters were good . so i think this qpAdm model of me is accurate :) if there will be some changes and we find a better run i will update you
 
Last edited:
qpAdm Raw R output. 10 examples of feasible models with my raw data:

I-Captura de tela 2026-07-15 000719.png

II-Captura de tela 2026-07-15 001059.png

III-Captura de tela 2026-07-15 001216.png

IV-Captura de tela 2026-07-15 001339.png

V-Captura de tela 2026-07-15 001834.png

VI-Captura de tela 2026-07-15 001948.png

VII-Captura de tela 2026-07-15 002104.png
VIII-Captura de tela 2026-07-15 002459.png
IX-Captura de tela 2026-07-15 002621.png

X-Captura de tela 2026-07-15 002906.png
 
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More 9 examples to finish:

XI-Captura de tela 2026-07-15 003041.png

XII-Captura de tela 2026-07-15 003232.png

XIII-Captura de tela 2026-07-15 003354.png

XIV-Captura de tela 2026-07-15 003600.png

XV-Captura de tela 2026-07-15 003746.png

XVI-Captura de tela 2026-07-15 004011.png

XVII-Captura de tela 2026-07-15 004311.png

XVII-Captura de tela 2026-07-15 004439.png

XIX-Captura de tela 2026-07-15 004611.png
 
I would like you to analyze the following qpAdm results, covering the 2-way, 3-way, and 4-way models. The individual is a Brazilian with a genetic background from the northwest of the Iberian Peninsula (Northern Portugal, Galicia, Asturias, and Cantabria). The outgroups (right sources) used were Mbuti.DG, Russia_Kostenki14_UP.SG, Georgia_Kotias_Mesolithic.SG, and Israel_Natufian.AG.
A) Model 1

── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 2 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Portugal_Conimbriga_Roman.SG 0.950 0.0120 79.3
2 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0498 0.0120 4.15

── RESULTS_POPDROP ──

A tibble: 3 × 13​

pat wt dof chisq p f4rank Portugal_Conimbriga_Roman.SG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 00 0 11 11.8 0.380 1 0.950 0.0498 TRUE NA NA NA NA
2 01 1 12 31.9 0.00145 0 1 NA TRUE TRUE 0 -6524. 1
3 10 1 12 6556. 0 0 NA 1 TRUE TRUE NA NA NA

B) Model 2
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 2 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Portugal_Miroico_LateRoman.SG 0.953 0.0108 88.4
2 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0471 0.0108 4.37

── RESULTS_POPDROP ──

A tibble: 3 × 13​

pat wt dof chisq p f4rank Portugal_Miroico_LateRoman.SG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 00 0 11 14.8 0.194 1 0.953 0.0471 TRUE NA NA NA NA
2 01 1 12 48.2 0.00000286 0 1 NA TRUE TRUE 0 -8250. 1
3 10 1 12 8298. 0 0 NA 1 TRUE TRUE NA NA NA

C) Model 3
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 2 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Portugal_MonteDaNora_LateRoman.SG 0.964 0.0112 85.8
2 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0358 0.0112 3.19

── RESULTS_POPDROP ──

A tibble: 3 × 13​

pat wt dof chisq p f4rank Portugal_MonteDaNora_LateRoman.SG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 00 0 11 19.0 0.0605 1 0.964 0.0358 TRUE NA NA NA NA
2 01 1 12 38.3 0.000139 0 1 NA TRUE TRUE 0 -7709. 1
3 10 1 12 7747. 0 0 NA 1 TRUE TRUE NA NA NA

D) Model 4
── RESULTS_WEIGHTS ──

A tibble: 3 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Portugal_Miroico_LateRoman.SG 0.569 0.219 2.60
2 Brazil_Belo-Horizonte_Portuguese Germany_AltInden_EarlyMedieval_Saxon.AG 0.367 0.211 1.73
3 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0647 0.0126 5.13

── RESULTS_POPDROP ──

A tibble: 7 × 14​

pat wt dof chisq p f4rank Portugal_Miroico_LateRoman.SG Germany_AltInden_EarlyMedieval_Saxon.AG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 000 0 9 7.96 5.38e- 1 2 0.569 0.367 0.0647 TRUE NA NA NA NA
2 001 1 10 27.4 2.26e- 3 1 2.09 -1.09 NA FALSE TRUE 0 9.07 0
3 010 1 10 18.3 4.99e- 2 1 0.948 NA 0.0525 TRUE TRUE 0 -10.0 1
4 100 1 10 28.3 1.60e- 3 1 NA 0.918 0.0823 TRUE TRUE NA NA NA
5 011 2 11 109. 3.52e-18 0 1 NA NA TRUE NA NA NA NA
6 101 2 11 310. 7.14e-60 0 NA 1 NA TRUE NA NA NA NA
7 110 2 11 8568. 0 0 NA NA 1 TRUE NA NA NA NA

E) Model 5
── RESULTS_WEIGHTS ──

A tibble: 3 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Portugal_Miroico_LateRoman.SG 0.510 0.180 2.83
2 Brazil_Belo-Horizonte_Portuguese Denmark_EarlyViking.SG 0.426 0.174 2.45
3 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0642 0.0121 5.29

── RESULTS_POPDROP ──

A tibble: 7 × 14​

pat wt dof chisq p f4rank Portugal_Miroico_LateRoman.SG Denmark_EarlyViking.SG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 000 0 9 7.73 5.62e- 1 2 0.510 0.426 0.0642 TRUE NA NA NA NA
2 001 1 10 35.3 1.12e- 4 1 1.74 -0.743 NA FALSE TRUE 0 15.0 0
3 010 1 10 20.3 2.64e- 2 1 0.952 NA 0.0483 TRUE TRUE 0 -4.16 1
4 100 1 10 24.5 6.41e- 3 1 NA 0.920 0.0802 TRUE TRUE NA NA NA
5 011 2 11 79.0 2.35e-12 0 1 NA NA TRUE NA NA NA NA
6 101 2 11 181. 7.61e-33 0 NA 1 NA TRUE NA NA NA NA
7 110 2 11 8589. 0 0 NA NA 1 TRUE NA NA NA NA

F) Model 6
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 3 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Portugal_Miroico_LateRoman.SG 0.662 0.331 2.00
2 Brazil_Belo-Horizonte_Portuguese Spain_Hellenistic_oLocal.AG 0.274 0.313 0.876
3 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0637 0.0210 3.03

── RESULTS_POPDROP ──

A tibble: 7 × 14​

pat wt dof chisq p f4rank Portugal_Miroico_LateRoman.SG Spain_Hellenistic_oLocal.AG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 000 0 9 10.8 2.91e- 1 2 0.662 0.274 0.0637 TRUE NA NA NA NA
2 001 1 10 18.6 4.53e- 2 1 1.80 -0.795 NA FALSE TRUE 0 4.79 0
3 010 1 10 13.8 1.81e- 1 1 0.951 NA 0.0493 TRUE TRUE 0 -5.35 1
4 100 1 10 19.2 3.79e- 2 1 NA 0.898 0.102 TRUE TRUE NA NA NA
5 011 2 11 61.1 5.89e- 9 0 1 NA NA TRUE NA NA NA NA
6 101 2 11 127. 8.75e-22 0 NA 1 NA TRUE NA NA NA NA
7 110 2 11 8434. 0 0 NA NA 1 TRUE NA NA NA NA

G) Model 7
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 3 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Portugal_Miroico_LateRoman.SG 0.649 0.294 2.20
2 Brazil_Belo-Horizonte_Portuguese Spain_Carolingian.AG 0.292 0.281 1.04
3 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0593 0.0173 3.43

── RESULTS_POPDROP ──

A tibble: 7 × 14​

pat wt dof chisq p f4rank Portugal_Miroico_LateRoman.SG Spain_Carolingian.AG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 000 0 9 11.0 0.277 2 0.649 0.292 0.0593 TRUE NA NA NA NA
2 001 1 10 18.8 0.0425 1 2.01 -1.01 NA FALSE TRUE 0 4.21 0
3 010 1 10 14.6 0.147 1 0.953 NA 0.0466 TRUE TRUE 0 -2.88 1
4 100 1 10 17.5 0.0639 1 NA 0.914 0.0859 TRUE TRUE NA NA NA
5 011 2 11 55.3 0.0000000681 0 1 NA NA TRUE NA NA NA NA
6 101 2 11 61.9 0.00000000414 0 NA 1 NA TRUE NA NA NA NA
7 110 2 11 8297. 0 0 NA NA 1 TRUE NA NA NA NA

H) Model 8
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 3 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Portugal_Conimbriga_Roman.SG 0.586 0.257 2.27
2 Brazil_Belo-Horizonte_Portuguese Germany_AltInden_EarlyMedieval_Saxon.AG 0.349 0.249 1.40
3 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0658 0.0135 4.89

── RESULTS_POPDROP ──

A tibble: 7 × 14​

pat wt dof chisq p f4rank Portugal_Conimbriga_Roman.SG Germany_AltInden_EarlyMedieval_Saxon.AG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 000 0 9 7.08 6.28e- 1 2 0.586 0.349 0.0658 TRUE NA NA NA NA
2 001 1 10 15.3 1.20e- 1 1 2.10 -1.10 NA FALSE TRUE 0 3.76 0
3 010 1 10 11.6 3.15e- 1 1 0.947 NA 0.0526 TRUE TRUE 0 -8.84 1
4 100 1 10 20.4 2.56e- 2 1 NA 0.918 0.0822 TRUE TRUE NA NA NA
5 011 2 11 45.7 3.67e- 6 0 1 NA NA TRUE NA NA NA NA
6 101 2 11 195. 8.10e-36 0 NA 1 NA TRUE NA NA NA NA
7 110 2 11 8745. 0 0 NA NA 1 TRUE NA NA NA NA

I) Model 9
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 3 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Portugal_Conimbriga_Roman.SG 0.538 0.245 2.20
2 Brazil_Belo-Horizonte_Portuguese Denmark_EarlyViking.SG 0.396 0.236 1.68
3 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0661 0.0134 4.94

── RESULTS_POPDROP ──

A tibble: 7 × 14​

pat wt dof chisq p f4rank Portugal_Conimbriga_Roman.SG Denmark_EarlyViking.SG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 000 0 9 7.31 6.05e- 1 2 0.538 0.396 0.0661 TRUE NA NA NA NA
2 001 1 10 15.6 1.13e- 1 1 2.13 -1.13 NA FALSE TRUE 0 4.60 0
3 010 1 10 11.0 3.60e- 1 1 0.949 NA 0.0514 TRUE TRUE 0 -6.53 1
4 100 1 10 17.5 6.41e- 2 1 NA 0.919 0.0809 TRUE TRUE NA NA NA
5 011 2 11 39.4 4.62e- 5 0 1 NA NA TRUE NA NA NA NA
6 101 2 11 137. 8.00e-24 0 NA 1 NA TRUE NA NA NA NA
7 110 2 11 8028. 0 0 NA NA 1 TRUE NA NA NA NA

J) Model 10
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 3 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Portugal_Conimbriga_Roman.SG 0.743 0.355 2.10
2 Brazil_Belo-Horizonte_Portuguese Spain_Hellenistic_oLocal.AG 0.198 0.337 0.588
3 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0584 0.0209 2.80

── RESULTS_POPDROP ──

A tibble: 7 × 14​

pat wt dof chisq p f4rank Portugal_Conimbriga_Roman.SG Spain_Hellenistic_oLocal.AG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 000 0 9 10.9 2.83e- 1 2 0.743 0.198 0.0584 TRUE NA NA NA NA
2 001 1 10 13.2 2.10e- 1 1 1.78 -0.775 NA FALSE TRUE 0 1.18 0
3 010 1 10 12.1 2.81e- 1 1 0.951 NA 0.0489 TRUE TRUE 0 -8.00 1
4 100 1 10 20.1 2.87e- 2 1 NA 0.901 0.0985 TRUE TRUE NA NA NA
5 011 2 11 36.5 1.39e- 4 0 1 NA NA TRUE NA NA NA NA
6 101 2 11 113. 5.29e-19 0 NA 1 NA TRUE NA NA NA NA
7 110 2 11 7352. 0 0 NA NA 1 TRUE NA NA NA NA

K) Model 11
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 3 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Portugal_MonteDaNora_LateRoman.SG 0.599 0.318 1.88
2 Brazil_Belo-Horizonte_Portuguese Germany_AltInden_EarlyMedieval_Saxon.AG 0.345 0.305 1.13
3 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0565 0.0164 3.44

── RESULTS_POPDROP ──

A tibble: 7 × 14​

pat wt dof chisq p f4rank Portugal_MonteDaNora_LateRoman.SG Germany_AltInden_EarlyMedieval_Saxon.AG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 000 0 9 10.7 2.93e- 1 2 0.599 0.345 0.0565 TRUE NA NA NA NA
2 001 1 10 20.3 2.63e- 2 1 1.73 -0.726 NA FALSE TRUE 0 0.825 0
3 010 1 10 19.5 3.43e- 2 1 0.960 NA 0.0400 TRUE TRUE 0 -7.80 1
4 100 1 10 27.3 2.34e- 3 1 NA 0.917 0.0834 TRUE TRUE NA NA NA
5 011 2 11 56.6 3.93e- 8 0 1 NA NA TRUE NA NA NA NA
6 101 2 11 255. 2.47e-48 0 NA 1 NA TRUE NA NA NA NA
7 110 2 11 8993. 0 0 NA NA 1 TRUE NA NA NA NA

L) Model 12
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 3 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Portugal_MonteDaNora_LateRoman.SG 0.451 0.174 2.59
2 Brazil_Belo-Horizonte_Portuguese Denmark_EarlyViking.SG 0.488 0.166 2.95
3 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0611 0.0131 4.68

── RESULTS_POPDROP ──

A tibble: 7 × 14​

pat wt dof chisq p f4rank Portugal_MonteDaNora_LateRoman.SG Denmark_EarlyViking.SG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 000 0 9 9.15 4.24e- 1 2 0.451 0.488 0.0611 TRUE NA NA NA NA
2 001 1 10 32.6 3.24e- 4 1 1.43 -0.435 NA FALSE TRUE 0 7.97 0
3 010 1 10 24.6 6.21e- 3 1 0.964 NA 0.0363 TRUE TRUE 0 3.38 0
4 100 1 10 21.2 1.98e- 2 1 NA 0.920 0.0799 TRUE TRUE NA NA NA
5 011 2 11 51.3 3.58e- 7 0 1 NA NA TRUE NA NA NA NA
6 101 2 11 159. 2.46e-28 0 NA 1 NA TRUE NA NA NA NA
7 110 2 11 8446. 0 0 NA NA 1 TRUE NA NA NA NA

M) Model 13
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 3 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Denmark_EarlyViking.SG 0.284 0.728 0.390
2 Brazil_Belo-Horizonte_Portuguese Italy_Sardinia_EarlyMedieval.AG 0.662 1.50 0.440
3 Brazil_Belo-Horizonte_Portuguese Morocco_KTG_EN.SG 0.0545 0.831 0.0656

── RESULTS_POPDROP ──

A tibble: 7 × 14​

pat wt dof chisq p f4rank Denmark_EarlyViking.SG Italy_Sardinia_EarlyMedieval.AG Morocco_KTG_EN.SG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 000 0 9 9.27 0.413 2 0.284 0.662 0.0545 TRUE NA NA NA NA
2 001 1 10 9.63 0.474 1 0.197 0.803 NA TRUE TRUE 0 -11.3 1
3 010 1 10 20.9 0.0216 1 0.592 NA 0.408 TRUE TRUE 0 11.2 0
4 100 1 10 9.72 0.465 1 NA 1.20 -0.201 FALSE TRUE NA NA NA
5 011 2 11 59.2 0.0000000131 0 1 NA NA TRUE NA NA NA NA
6 101 2 11 10.1 0.523 0 NA 1 NA TRUE NA NA NA NA
7 110 2 11 60.6 0.00000000731 0 NA NA 1 TRUE NA NA NA NA

N) Model 14
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 3 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Denmark_EarlyViking.SG 0.885 0.128 6.93
2 Brazil_Belo-Horizonte_Portuguese Italy_Sardinia_EarlyMedieval.AG 0.0557 0.140 0.398
3 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0597 0.0184 3.24

── RESULTS_POPDROP ──

A tibble: 7 × 14​

pat wt dof chisq p f4rank Denmark_EarlyViking.SG Italy_Sardinia_EarlyMedieval.AG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 000 0 9 9.70 0.376 2 0.885 0.0557 0.0597 TRUE NA NA NA NA
2 001 1 10 18.9 0.0414 1 0.318 0.682 NA TRUE TRUE 0 8.63 0
3 010 1 10 10.3 0.416 1 0.935 NA 0.0647 TRUE TRUE 0 -10.2 1
4 100 1 10 20.5 0.0249 1 NA 1.01 -0.00811 FALSE TRUE NA NA NA
5 011 2 11 51.9 0.000000287 0 1 NA NA TRUE NA NA NA NA
6 101 2 11 19.8 0.0477 0 NA 1 NA TRUE NA NA NA NA
7 110 2 11 4910. 0 0 NA NA 1 TRUE NA NA NA NA

O) Model 15
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 3 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Denmark_EarlyViking.SG 0.805 0.0981 8.20
2 Brazil_Belo-Horizonte_Portuguese Morocco_KTG_EN.SG 0.124 0.107 1.16
3 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0713 0.0141 5.07

── RESULTS_POPDROP ──

A tibble: 7 × 14​

pat wt dof chisq p f4rank Denmark_EarlyViking.SG Morocco_KTG_EN.SG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 000 0 9 8.98 4.39e- 1 2 0.805 0.124 0.0713 TRUE NA NA NA NA
2 001 1 10 47.5 7.56e- 7 1 0.479 0.521 NA TRUE TRUE 0 35.8 0
3 010 1 10 11.7 3.04e- 1 1 0.919 NA 0.0809 TRUE TRUE 0 -59.7 1
4 100 1 10 71.4 2.38e-11 1 NA 1.02 -0.0152 FALSE TRUE NA NA NA
5 011 2 11 133. 5.16e-23 0 1 NA NA TRUE NA NA NA NA
6 101 2 11 77.8 3.98e-12 0 NA 1 NA TRUE NA NA NA NA
7 110 2 11 8294. 0 0 NA NA 1 TRUE NA NA NA NA
P) Model 16
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 3 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Italy_Sardinia_EarlyMedieval.AG 0.0847 0.223 0.379
2 Brazil_Belo-Horizonte_Portuguese Spain_Carolingian.AG 0.844 0.205 4.11
3 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0711 0.0258 2.76

── RESULTS_POPDROP ──

A tibble: 7 × 14​

pat wt dof chisq p f4rank Italy_Sardinia_EarlyMedieval.AG Spain_Carolingian.AG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 000 0 9 11.4 0.249 2 0.0847 0.844 0.0711 TRUE NA NA NA NA
2 001 1 10 19.9 0.0305 1 0.590 0.410 NA TRUE TRUE 0 -2.16 1
3 010 1 10 22.0 0.0150 1 1.00 NA -0.00409 FALSE TRUE 0 10.2 0
4 100 1 10 11.9 0.294 1 NA 0.922 0.0777 TRUE TRUE NA NA NA
5 011 2 11 21.4 0.0292 0 1 NA NA TRUE NA NA NA NA
6 101 2 11 31.9 0.000780 0 NA 1 NA TRUE NA NA NA NA
7 110 2 11 3910. 0 0 NA NA 1 TRUE NA NA NA NA

Q) Model 17
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 3 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Italy_Sardinia_EarlyMedieval.AG 0.942 0.287 3.28
2 Brazil_Belo-Horizonte_Portuguese Hungary_Carolingian.SG 0.0302 0.270 0.112
3 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0281 0.0360 0.781

── RESULTS_POPDROP ──

A tibble: 7 × 14​

pat wt dof chisq p f4rank Italy_Sardinia_EarlyMedieval.AG Hungary_Carolingian.SG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 000 0 9 10.2 0.335 2 0.942 0.0302 0.0281 TRUE NA NA NA NA
2 001 1 10 8.86 0.545 1 1.06 -0.0560 NA FALSE TRUE 0 -1.27 1
3 010 1 10 10.1 0.429 1 0.973 NA 0.0267 TRUE TRUE 0 -16.3 1
4 100 1 10 26.4 0.00320 1 NA 0.928 0.0723 TRUE TRUE NA NA NA
5 011 2 11 9.12 0.611 0 1 NA NA TRUE NA NA NA NA
6 101 2 11 30.9 0.00113 0 NA 1 NA TRUE NA NA NA NA
7 110 2 11 5377. 0 0 NA NA 1 TRUE NA NA NA NA

R) Model 18
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 4 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Portugal_Miroico_LateRoman.SG 0.329 1.16 0.283
2 Brazil_Belo-Horizonte_Portuguese Portugal_Conimbriga_Roman.SG 0.229 1.32 0.173
3 Brazil_Belo-Horizonte_Portuguese Germany_AltInden_EarlyMedieval_Saxon.AG 0.375 0.261 1.44
4 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0667 0.0125 5.35

── RESULTS_POPDROP ──

A tibble: 15 × 15​

pat wt dof chisq p f4rank Portugal_Miroico_LateRoman.SG Portugal_Conimbriga_Roman.SG Germany_AltInden_EarlyMedieval_Saxon.AG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 0000 0 7 6.11 5.27e- 1 3 0.329 0.229 0.375 0.0667 TRUE NA NA NA NA
2 0001 1 8 7.15 5.21e- 1 2 -40.1 41.9 -0.773 NA FALSE TRUE 0 -1.18 1
3 0010 1 8 8.33 4.02e- 1 2 -11.1 12.0 NA 0.0464 FALSE TRUE 0 -0.0286 1
4 0100 1 8 8.36 3.99e- 1 2 0.569 NA 0.367 0.0645 TRUE TRUE 0 -1.13 1
5 1000 1 8 9.49 3.03e- 1 2 NA 0.666 0.273 0.0612 TRUE TRUE NA NA NA
6 0011 2 9 7.18 6.19e- 1 1 -54.7 55.7 NA NA FALSE NA NA NA NA
7 0101 2 9 27.3 1.24e- 3 1 2.16 NA -1.16 NA FALSE NA NA NA NA
8 0110 2 9 17.7 3.85e- 2 1 0.948 NA NA 0.0519 TRUE NA NA NA NA
9 1001 2 9 14.8 9.77e- 2 1 NA 2.13 -1.13 NA FALSE NA NA NA NA
10 1010 2 9 11.3 2.58e- 1 1 NA 0.948 NA 0.0515 TRUE NA NA NA NA
11 1100 2 9 26.2 1.86e- 3 1 NA NA 0.917 0.0829 TRUE NA NA NA NA
12 0111 3 10 109. 7.80e-19 0 1 NA NA NA TRUE NA NA NA NA
13 1011 3 10 44.8 2.36e- 6 0 NA 1 NA NA TRUE NA NA NA NA
14 1101 3 10 336. 3.08e-66 0 NA NA 1 NA TRUE NA NA NA NA
15 1110 3 10 8673. 0 0 NA NA NA 1 TRUE NA NA NA NA

S) Model 19
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 4 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Portugal_Miroico_LateRoman.SG 0.309 28.0 0.0110
2 Brazil_Belo-Horizonte_Portuguese Portugal_Conimbriga_Roman.SG 0.199 31.4 0.00633
3 Brazil_Belo-Horizonte_Portuguese Denmark_EarlyViking.SG 0.426 3.45 0.124
4 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0656 0.0299 2.20

── RESULTS_POPDROP ──

A tibble: 15 × 15​

pat wt dof chisq p f4rank Portugal_Miroico_LateRoman.SG Portugal_Conimbriga_Roman.SG Denmark_EarlyViking.SG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 0000 0 7 6.09 5.29e- 1 3 0.309 0.199 0.426 0.0656 TRUE NA NA NA NA
2 0001 1 8 4.92 7.66e- 1 2 -38.7 44.1 -4.41 NA FALSE TRUE 0 -1.48 1
3 0010 1 8 6.40 6.03e- 1 2 -3.06 4.00 NA 0.0589 FALSE TRUE 0 -1.58 1
4 0100 1 8 7.98 4.35e- 1 2 0.502 NA 0.433 0.0641 TRUE TRUE 0 -0.322 1
5 1000 1 8 8.30 4.04e- 1 2 NA 0.620 0.318 0.0617 TRUE TRUE NA NA NA
6 0011 2 9 5.22 8.15e- 1 1 111. -110. NA NA FALSE NA NA NA NA
7 0101 2 9 35.3 5.19e- 5 1 1.92 NA -0.916 NA FALSE NA NA NA NA
8 0110 2 9 20.6 1.48e- 2 1 0.952 NA NA 0.0478 TRUE NA NA NA NA
9 1001 2 9 15.4 7.95e- 2 1 NA 2.17 -1.17 NA FALSE NA NA NA NA
10 1010 2 9 10.4 3.20e- 1 1 NA 0.949 NA 0.0506 TRUE NA NA NA NA
11 1100 2 9 22.3 7.88e- 3 1 NA NA 0.919 0.0808 TRUE NA NA NA NA
12 0111 3 10 85.6 4.01e-14 0 1 NA NA NA TRUE NA NA NA NA
13 1011 3 10 42.2 6.89e- 6 0 NA 1 NA NA TRUE NA NA NA NA
14 1101 3 10 187. 8.22e-35 0 NA NA 1 NA TRUE NA NA NA NA
15 1110 3 10 8808. 0 0 NA NA NA 1 TRUE NA NA NA NA

T) Model 20
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 4 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Portugal_Miroico_LateRoman.SG 0.505 0.243 2.08
2 Brazil_Belo-Horizonte_Portuguese Germany_AltInden_EarlyMedieval_Saxon.AG 0.0644 0.564 0.114
3 Brazil_Belo-Horizonte_Portuguese Denmark_EarlyViking.SG 0.365 0.605 0.604
4 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0654 0.0124 5.28

── RESULTS_POPDROP ──

A tibble: 15 × 15​

pat wt dof chisq p f4rank Portugal_Miroico_LateRoman.SG Germany_AltInden_EarlyMedieval_Saxon.AG Denmark_EarlyViking.SG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 0000 0 7 6.47 4.86e- 1 3 0.505 0.0644 0.365 0.0654 TRUE NA NA NA NA
2 0001 1 8 13.8 8.59e- 2 2 1.25 -9.11 8.86 NA FALSE TRUE 0 1.78 0
3 0010 1 8 12.1 1.49e- 1 2 0.631 0.307 NA 0.0618 TRUE TRUE 0 -1.13 1
4 0100 1 8 13.2 1.05e- 1 2 0.586 NA 0.352 0.0623 TRUE TRUE 0 -1.47 1
5 1000 1 8 14.7 6.61e- 2 2 NA -3.76 4.70 0.0652 FALSE TRUE NA NA NA
6 0011 2 9 27.8 1.02e- 3 1 2.56 -1.56 NA NA FALSE NA NA NA NA
7 0101 2 9 26.6 1.65e- 3 1 3.02 NA -2.02 NA FALSE NA NA NA NA
8 0110 2 9 18.6 2.84e- 2 1 0.946 NA NA 0.0537 TRUE NA NA NA NA
9 1001 2 9 14.0 1.22e- 1 1 NA -18.1 19.1 NA FALSE NA NA NA NA
10 1010 2 9 31.6 2.36e- 4 1 NA 0.919 NA 0.0812 TRUE NA NA NA NA
11 1100 2 9 23.5 5.20e- 3 1 NA NA 0.924 0.0756 TRUE NA NA NA NA
12 0111 3 10 112. 1.93e-19 0 1 NA NA NA TRUE NA NA NA NA
13 1011 3 10 347. 1.48e-68 0 NA 1 NA NA TRUE NA NA NA NA
14 1101 3 10 180. 2.17e-33 0 NA NA 1 NA TRUE NA NA NA NA
15 1110 3 10 8510. 0 0 NA NA NA 1 TRUE NA NA NA NA

U) Model 21
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 4 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Portugal_Miroico_LateRoman.SG 0.510 0.195 2.61
2 Brazil_Belo-Horizonte_Portuguese Denmark_EarlyViking.SG 0.416 0.248 1.68
3 Brazil_Belo-Horizonte_Portuguese Spain_Carolingian.AG 0.0109 0.269 0.0407
4 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0629 0.0138 4.56

── RESULTS_POPDROP ──

A tibble: 15 × 15​

pat wt dof chisq p f4rank Portugal_Miroico_LateRoman.SG Denmark_EarlyViking.SG Spain_Carolingian.AG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 0000 0 7 6.80 4.50e- 1 3 0.510 0.416 0.0109 0.0629 TRUE NA NA NA NA
2 0001 1 8 12.9 1.16e- 1 2 1.46 1.71 -2.17 NA FALSE TRUE 0 5.38 0
3 0010 1 8 7.50 4.84e- 1 2 0.515 0.423 NA 0.0623 TRUE TRUE 0 -6.21 1
4 0100 1 8 13.7 8.97e- 2 2 0.489 NA 0.444 0.0666 TRUE TRUE 0 -1.42 1
5 1000 1 8 15.1 5.68e- 2 2 NA -0.591 1.50 0.0932 FALSE TRUE NA NA NA
6 0011 2 9 34.6 7.13e- 5 1 1.84 -0.838 NA NA FALSE NA NA NA NA
7 0101 2 9 19.4 2.21e- 2 1 2.07 NA -1.07 NA FALSE NA NA NA NA
8 0110 2 9 19.1 2.48e- 2 1 0.953 NA NA 0.0469 TRUE NA NA NA NA
9 1001 2 9 14.5 1.05e- 1 1 NA 10.7 -9.67 NA FALSE NA NA NA NA
10 1010 2 9 23.0 6.28e- 3 1 NA 0.921 NA 0.0793 TRUE NA NA NA NA
11 1100 2 9 15.7 7.44e- 2 1 NA NA 0.912 0.0882 TRUE NA NA NA NA
12 0111 3 10 78.1 1.19e-12 0 1 NA NA NA TRUE NA NA NA NA
13 1011 3 10 180. 2.28e-33 0 NA 1 NA NA TRUE NA NA NA NA
14 1101 3 10 63.8 6.98e-10 0 NA NA 1 NA TRUE NA NA NA NA
15 1110 3 10 8556. 0 0 NA NA NA 1 TRUE NA NA NA NA

V) Model 22
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 4 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Portugal_Conimbriga_Roman.SG 0.709 0.444 1.60
2 Brazil_Belo-Horizonte_Portuguese Germany_AltInden_EarlyMedieval_Saxon.AG 0.0867 0.469 0.185
3 Brazil_Belo-Horizonte_Portuguese Denmark_EarlyViking.SG 0.140 0.542 0.258
4 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0643 0.0158 4.07

── RESULTS_POPDROP ──

A tibble: 15 × 15​

pat wt dof chisq p f4rank Portugal_Conimbriga_Roman.SG Germany_AltInden_EarlyMedieval_Saxon.AG Denmark_EarlyViking.SG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 0000 0 7 5.00 6.60e- 1 3 0.709 0.0867 0.140 0.0643 TRUE NA NA NA NA
2 0001 1 8 7.54 4.80e- 1 2 3.02 -1.04 -0.979 NA FALSE TRUE 0 1.63 0
3 0010 1 8 5.90 6.58e- 1 2 0.936 0.00668 NA 0.0578 TRUE TRUE 0 -0.475 1
4 0100 1 8 6.38 6.05e- 1 2 0.995 NA -0.0505 0.0559 FALSE TRUE 0 -7.22 1
5 1000 1 8 13.6 9.29e- 2 2 NA 0.106 0.816 0.0777 TRUE TRUE NA NA NA
6 0011 2 9 8.19 5.16e- 1 1 2.82 -1.82 NA NA FALSE NA NA NA NA
7 0101 2 9 8.26 5.08e- 1 1 3.14 NA -2.14 NA FALSE NA NA NA NA
8 0110 2 9 6.28 7.11e- 1 1 0.943 NA NA 0.0569 TRUE NA NA NA NA
9 1001 2 9 15.5 7.74e- 2 1 NA -30.5 31.5 NA FALSE NA NA NA NA
10 1010 2 9 26.6 1.63e- 3 1 NA 0.919 NA 0.0812 TRUE NA NA NA NA
11 1100 2 9 21.5 1.05e- 2 1 NA NA 0.922 0.0781 TRUE NA NA NA NA
12 0111 3 10 46.1 1.37e- 6 0 1 NA NA NA TRUE NA NA NA NA
13 1011 3 10 290. 2.07e-56 0 NA 1 NA NA TRUE NA NA NA NA
14 1101 3 10 180. 2.25e-33 0 NA NA 1 NA TRUE NA NA NA NA
15 1110 3 10 8897. 0 0 NA NA NA 1 TRUE NA NA NA NA

W) Model 23
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 4 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Portugal_Conimbriga_Roman.SG 0.709 0.444 1.60
2 Brazil_Belo-Horizonte_Portuguese Germany_AltInden_EarlyMedieval_Saxon.AG 0.0867 0.469 0.185
3 Brazil_Belo-Horizonte_Portuguese Denmark_EarlyViking.SG 0.140 0.542 0.258
4 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0643 0.0158 4.07

── RESULTS_POPDROP ──

A tibble: 15 × 15​

pat wt dof chisq p f4rank Portugal_Conimbriga_Roman.SG Germany_AltInden_EarlyMedieval_Saxon.AG Denmark_EarlyViking.SG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 0000 0 7 5.00 6.60e- 1 3 0.709 0.0867 0.140 0.0643 TRUE NA NA NA NA
2 0001 1 8 7.54 4.80e- 1 2 3.02 -1.04 -0.979 NA FALSE TRUE 0 1.63 0
3 0010 1 8 5.90 6.58e- 1 2 0.936 0.00668 NA 0.0578 TRUE TRUE 0 -0.475 1
4 0100 1 8 6.38 6.05e- 1 2 0.995 NA -0.0505 0.0559 FALSE TRUE 0 -7.22 1
5 1000 1 8 13.6 9.29e- 2 2 NA 0.106 0.816 0.0777 TRUE TRUE NA NA NA
6 0011 2 9 8.19 5.16e- 1 1 2.82 -1.82 NA NA FALSE NA NA NA NA
7 0101 2 9 8.26 5.08e- 1 1 3.14 NA -2.14 NA FALSE NA NA NA NA
8 0110 2 9 6.28 7.11e- 1 1 0.943 NA NA 0.0569 TRUE NA NA NA NA
9 1001 2 9 15.5 7.74e- 2 1 NA -30.5 31.5 NA FALSE NA NA NA NA
10 1010 2 9 26.6 1.63e- 3 1 NA 0.919 NA 0.0812 TRUE NA NA NA NA
11 1100 2 9 21.5 1.05e- 2 1 NA NA 0.922 0.0781 TRUE NA NA NA NA
12 0111 3 10 46.1 1.37e- 6 0 1 NA NA NA TRUE NA NA NA NA
13 1011 3 10 290. 2.07e-56 0 NA 1 NA NA TRUE NA NA NA NA
14 1101 3 10 180. 2.25e-33 0 NA NA 1 NA TRUE NA NA NA NA
15 1110 3 10 8897. 0 0 NA NA NA 1 TRUE NA NA NA NA

X) Model 24
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 4 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Denmark_EarlyViking.SG 0.745 0.524 1.42
2 Brazil_Belo-Horizonte_Portuguese Italy_Sardinia_EarlyMedieval.AG 0.0367 0.188 0.195
3 Brazil_Belo-Horizonte_Portuguese Spain_Hellenistic_oLocal.AG 0.153 0.622 0.246
4 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0661 0.0367 1.80

── RESULTS_POPDROP ──

A tibble: 15 × 15​

pat wt dof chisq p f4rank Denmark_EarlyViking.SG Italy_Sardinia_EarlyMedieval.AG Spain_Hellenistic_oLocal.AG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 0000 0 7 9.48 2.20e- 1 3 0.745 0.0367 0.153 0.0661 TRUE NA NA NA NA
2 0001 1 8 10.6 2.28e- 1 2 2.01 0.317 -1.33 NA FALSE TRUE 0 -0.611 1
3 0010 1 8 11.2 1.92e- 1 2 0.876 0.0635 NA 0.0604 TRUE TRUE 0 1.19 0
4 0100 1 8 9.98 2.66e- 1 2 0.741 NA 0.188 0.0707 TRUE TRUE 0 -3.11 1
5 1000 1 8 13.1 1.09e- 1 2 NA -0.190 1.08 0.112 FALSE TRUE NA NA NA
6 0011 2 9 19.0 2.56e- 2 1 0.291 0.709 NA NA TRUE NA NA NA NA
7 0101 2 9 12.8 1.72e- 1 1 2.75 NA -1.75 NA FALSE NA NA NA NA
8 0110 2 9 11.8 2.24e- 1 1 0.935 NA NA 0.0650 TRUE NA NA NA NA
9 1001 2 9 19.6 2.05e- 2 1 NA 1.13 -0.126 NA FALSE NA NA NA NA
10 1010 2 9 20.4 1.55e- 2 1 NA 1.01 NA -0.00864 FALSE NA NA NA NA
11 1100 2 9 15.6 7.55e- 2 1 NA NA 0.906 0.0943 TRUE NA NA NA NA
12 0111 3 10 69.0 6.86e-11 0 1 NA NA NA TRUE NA NA NA NA
13 1011 3 10 20.0 2.94e- 2 0 NA 1 NA NA TRUE NA NA NA NA
14 1101 3 10 82.7 1.49e-13 0 NA NA 1 NA TRUE NA NA NA NA
15 1110 3 10 5046. 0 0 NA NA NA 1 TRUE NA NA NA NA

Y) Model 25
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 4 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Denmark_EarlyViking.SG 0.774 1.17 0.660
2 Brazil_Belo-Horizonte_Portuguese Italy_Sardinia_EarlyMedieval.AG 0.0463 0.130 0.355
3 Brazil_Belo-Horizonte_Portuguese Spain_Carolingian.AG 0.117 1.15 0.102
4 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0621 0.0229 2.71

── RESULTS_POPDROP ──

A tibble: 15 × 15​

pat wt dof chisq p f4rank Denmark_EarlyViking.SG Italy_Sardinia_EarlyMedieval.AG Spain_Carolingian.AG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 0000 0 7 9.30 0.232 3 0.774 0.0463 0.117 0.0621 TRUE NA NA NA NA
2 0001 1 8 11.9 0.154 2 9.79 -1.24 -7.56 NA FALSE TRUE 0 1.59 0
3 0010 1 8 10.3 0.242 2 0.884 0.0565 NA 0.0600 TRUE TRUE 0 0.516 0
4 0100 1 8 9.82 0.278 2 0.910 NA 0.0249 0.0651 TRUE TRUE 0 -3.42 1
5 1000 1 8 13.2 0.104 2 NA 0.137 0.795 0.0687 TRUE TRUE NA NA NA
6 0011 2 9 18.8 0.0270 1 0.310 0.690 NA NA TRUE NA NA NA NA
7 0101 2 9 12.3 0.198 1 4.36 NA -3.36 NA FALSE NA NA NA NA
8 0110 2 9 10.7 0.294 1 0.936 NA NA 0.0641 TRUE NA NA NA NA
9 1001 2 9 18.5 0.0300 1 NA 0.720 0.280 NA TRUE NA NA NA NA
10 1010 2 9 20.0 0.0177 1 NA 1.01 NA -0.00967 FALSE NA NA NA NA
11 1100 2 9 13.9 0.126 1 NA NA 0.921 0.0785 TRUE NA NA NA NA
12 0111 3 10 60.8 0.00000000261 0 1 NA NA NA TRUE NA NA NA NA
13 1011 3 10 19.7 0.0323 0 NA 1 NA NA TRUE NA NA NA NA
14 1101 3 10 38.3 0.0000339 0 NA NA 1 NA TRUE NA NA NA NA
15 1110 3 10 5049. 0 0 NA NA NA 1 TRUE NA NA NA NA

Z) Model 26
── RESULTS_SUMMARY ──
Target:
Blocks: 705 SNPs: 121,515

── RESULTS_WEIGHTS ──

A tibble: 4 × 5​

target left weight se z
<chr> <chr> <dbl> <dbl> <dbl>
1 Brazil_Belo-Horizonte_Portuguese Denmark_EarlyViking.SG 0.729 0.283 2.57
2 Brazil_Belo-Horizonte_Portuguese Morocco_KTG_EN.SG 0.103 0.131 0.785
3 Brazil_Belo-Horizonte_Portuguese Spain_Hellenistic_oLocal.AG 0.0932 0.335 0.278
4 Brazil_Belo-Horizonte_Portuguese Luhya.DG 0.0751 0.0195 3.86

── RESULTS_POPDROP ──

A tibble: 15 × 15​

pat wt dof chisq p f4rank Denmark_EarlyViking.SG Morocco_KTG_EN.SG Spain_Hellenistic_oLocal.AG Luhya.DG feasible best dofdiff chisqdiff p_nested
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl> <dbl> <dbl> <dbl>
1 0000 0 7 8.34 3.04e- 1 3 0.729 0.103 0.0932 0.0751 TRUE NA NA NA NA
2 0001 1 8 13.5 9.64e- 2 2 1.88 0.528 -1.41 NA FALSE TRUE 0 3.99 0
3 0010 1 8 9.49 3.02e- 1 2 0.798 0.132 NA 0.0707 TRUE TRUE 0 -0.624 1
4 0100 1 8 10.1 2.57e- 1 2 0.745 NA 0.170 0.0845 TRUE TRUE 0 -7.63 1
5 1000 1 8 17.7 2.32e- 2 2 NA -0.0919 0.981 0.110 FALSE TRUE NA NA NA
6 0011 2 9 52.5 3.63e- 8 1 0.408 0.592 NA NA TRUE NA NA NA NA
7 0101 2 9 18.0 3.57e- 2 1 4.98 NA -3.98 NA FALSE NA NA NA NA
8 0110 2 9 12.4 1.94e- 1 1 0.919 NA NA 0.0815 TRUE NA NA NA NA
9 1001 2 9 69.6 1.80e-11 1 NA 0.793 0.207 NA TRUE NA NA NA NA
10 1010 2 9 70.5 1.20e-11 1 NA 1.01 NA -0.0146 FALSE NA NA NA NA
11 1100 2 9 18.4 3.08e- 2 1 NA NA 0.901 0.0993 TRUE NA NA NA NA
12 0111 3 10 156. 2.06e-28 0 1 NA NA NA TRUE NA NA NA NA
13 1011 3 10 76.7 2.20e-12 0 NA 1 NA NA TRUE NA NA NA NA
14 1101 3 10 124. 8.42e-22 0 NA NA 1 NA TRUE NA NA NA NA
15 1110 3 10 8421. 0 0 NA NA NA 1 TRUE NA NA NA NA

You've uploaded a very large set of qpAdm models (23 models, including 2-way, 3-way, and 4-way fits). I can already see enough of the results to draw some robust conclusions, even though the very end of the file was truncated.

Here is my interpretation.


Overall assessment​

These models are remarkably consistent.

The picture that emerges is that your genome is essentially composed of:

  • ~93–96% Northwest Iberian (Roman/Late Roman Portugal)
  • ~4–7% Sub-Saharan African
  • no statistically necessary Germanic/Viking component
This is exactly what one would expect from a modern Brazilian whose ancestry comes almost entirely from:

  • Northern Portugal
  • Galicia
  • Asturias
  • Cantabria
plus a small colonial-era African contribution.


1. The 2-way models​

These are the cleanest models.

Model 1​

Conimbriga Roman

95.0%
Roman Portugal

4.98%
Luhya

p = 0.38

Excellent fit.


Model 2​

Miroiço Late Roman

95.3%

4.7%

p = 0.19

Again excellent.


Model 3​

Monte da Nora

96.4%

3.6%

p = 0.060

Still acceptable.

Slightly lower p-value but completely valid.


Interpretation​

All three Roman Portuguese populations explain almost the entire genome.

Nothing else is required.

This is a very strong result.


2. African ancestry​

This is one of the most stable parameters in every model.

It varies only between

3.5%

and

7%

regardless of:

Roman source

Germanic source

Viking source

Carolingian source

Hellenistic source

4-way model

That is exactly what we hope to see.

When qpAdm repeatedly estimates essentially the same proportion across dozens of models, that ancestry is almost certainly real.

Your African ancestry is therefore approximately

5 ± 1%

which matches many Brazilians of colonial Portuguese origin.


3. The Germanic/Viking models​

These are the interesting ones.

Examples:

Model 4

57%

Roman Portugal

37%

Saxon

6%

African


Model 5

51%

Roman Portugal

43%

Viking

6%

African


At first glance these look impressive.

But statistically they are not demonstrating real Germanic ancestry.

Why?

Because Roman Portuguese populations already contain substantial Iron Age and post-Roman northern European ancestry.

Late Roman Portuguese genomes are not "pure Mediterranean."

They already include:

Atlantic Bronze Age

Bell Beaker

Steppe

Celtic

Roman

early Germanic influences

Therefore qpAdm can split one ancestry into two correlated proxies.

This is a classic overfitting situation.


Notice what happens in the PopDrop table.

Removing the Germanic population usually gives

95%

Roman Portugal

5%

African

with essentially the same fit.

That tells us the Germanic source is not required.


4. Viking models​

Exactly the same phenomenon.

Models:

Denmark Early Viking

Germany Alt-Inden

Carolingian

Spain Carolingian

all behave similarly.

They absorb variation that already exists inside the Portuguese Roman genomes.

This is expected because

Roman Portuguese



already carry ancestry related to



Iron Age Celts



Bronze Age Bell Beaker



Steppe



Germanic populations


So qpAdm can trade one for another.


5. Spain Carolingian / Hellenistic Spain​

Same story.

These populations are genetically extremely close.

For example:

Model 7

65%

Roman Portugal

29%

Spain Carolingian

6%

African

looks reasonable,

but removing Spain Carolingian barely changes the fit.

That means it is not statistically required.


6. The Sardinian models​

These are probably the weakest biologically.

Example:

Model 14

89%

Viking

6%

Sardinian

6%

African

Clearly unrealistic.

No historian would argue that a person from northern Portugal is

90% Viking.

What is happening?

Sardinians are acting as a southern European pole,

while Vikings become a northern European pole.

Together they recreate Iberians.

This is a classic proxy effect.


Model 17

94%

Early Medieval Sardinia

3%

Hungary Carolingian

3%

African

is another example.

Again,

this is mathematically possible,

but biologically implausible.


7. The 4-way models​

These are the most informative.

For example

Model 20

Portugal

50%

Saxon

6%

Viking

36%

African

6%

Standard errors are enormous.

The z-scores collapse.

Notice:

Saxon

SE = 0.56

Viking

SE = 0.60

Those are huge.

That means qpAdm cannot distinguish those ancestries.

They are statistically interchangeable.

Whenever that happens,

the model is overparameterized.


The same occurs in Models 18–23.

The coefficients become unstable.

Large SE

Huge confidence intervals

Near-zero z-scores

Strong covariance

These are all signs that the added populations are collinear.


8. Which models should you trust?​

Highest confidence​

Models 1–3

These are simple.

Stable.

Biologically realistic.

Excellent p-values.


Moderate confidence​

Models 4–12

These tell us that

Northwest Iberian ancestry contains variation that can also be represented by northern European proxies.

Useful for exploring ancestry,

but not literal historical mixture.


Low confidence​

Models 13–23

These mostly reflect mathematical decomposition of similar populations.

Not actual historical admixture.


9. Historical interpretation​

Taken together, these results strongly support the following scenario:

  • ~94–96% ancestry from populations genetically very similar to Roman and Late Roman inhabitants of Portugal, who themselves already carried the Bronze Age, Iron Age Celtic, Roman, and limited early Germanic ancestry characteristic of northwest Iberia.
  • ~4–6% Sub-Saharan African ancestry, consistent with colonial-era admixture in Brazil.
  • No convincing statistical evidence that an additional, discrete Saxon, Viking, or Carolingian source is needed once an appropriate Northwest Iberian proxy is included.
This aligns very well with your documented family origins in northern Portugal, Galicia, Asturias, and Cantabria. Those regions were genetically quite homogeneous by the Late Roman and Early Medieval periods, and the Portuguese Roman samples you're using appear to be good proxies for that broader Northwest Iberian ancestry.

Final conclusion​

From a qpAdm perspective, the most parsimonious interpretation is:

  • Northwest Iberian (Roman/Late Roman Portuguese proxy): ~94–96%
  • Sub-Saharan African: ~4–6%
The additional Saxon, Viking, Carolingian, or Hellenistic components seen in more complex models are best interpreted as alternative statistical representations of ancestry already present within Northwest Iberian populations, rather than evidence of separate recent ancestral contributions.

Overall, this is a very coherent and statistically consistent set of qpAdm results. The simple two-way models are both the most stable and the most biologically plausible explanation of your ancestry.
 
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