Hungarian Discussion Thread

[Mythbuster General]

Source of humor from Hungary: according to recent 2022 census 2439 people declared themselves as Scythians and 345 as Huns, lol. Also 327 as Cumans and 233 as Jász, both long-gone populations, but at least in their case they have regional identity basis.

[kolompar]

During the years I kept collecting Y-DNA results from Hungarians from around the internet and I will publish below, to my knowledge, the largest dataset as of yet of Y-DNA distribution among modern Hungarians, numbering 980 individuals. Until the official results will be published by the Institute of Hungarian Research, I believe these are as representative as we can get to:

1. R-M420 (R1a): 28%
2. R-M343 (R1b): 18.5%
3. I-M438 (I2): 17%
4. E-M215 (E1b1b): 9%
5. I-M253 (I1): 7.5%
6. J-M172 (J2): 7.5%
7. G-M201: 4.5%
8. N-M231: 3.5%
9. Q-M242: 1.5%
10. J-M267 (J1): 0.5%
11. T-L206: 0.5%
12. L-M20: 0.5%
13. C-F3393 (C1): 0.5%

I don't know if you know this, maybe you even included it, but FTDNA's numbers are public, there's currently just over a thousand Hungarians there. You can get the country frequency at Discover or the absolute numbers at Haplotree. Jews may be a bit overrepresented, J is at 20% but it's mostly similar to your numbers.
N is at 2% (22/1064), in Serbia it's 7% but that's all from a recent founder effect from a weird Botai related line N-F14667 (TMRCA 900 and also includes a Hungarian so it could have come with Magyars), while Ukraine is 3% and Slovakia is 5%, both mostly the Baltic N-L550. So the conquerors did leave a visible impact. Q is 3% which is higher than any neighbouring countries.

[kolompar]

An attempt at a distal qpAdm model on the moderns in the 1240k AADR, right pops:
"Mbuti.DG", "Russia_Ust_Ishim_HG.DG", "China_Tianyuan", "Papuan.DG", "Russia_Kostenki14", "Turkey_N", "Italy_North_Villabruna_HG", "Georgia_Kotias.SG", "Russia_Karelia_HG", "Russia_Shamanka_Eneolithic.SG", "Yoruba.DG", "Ethiopia_4500BP", "Russia_Samara_EBA_Yamnaya"

Code:

$weights
# A tibble: 4 x 5
  target       left                     weight     se     z
  <chr>        <chr>                     <dbl>  <dbl> <dbl>
1 Hungarian.DG Luxembourg_Loschbour.DG  0.0723 0.0233  3.11
2 Hungarian.DG Serbia_IronGates_N       0.402  0.0202 19.9
3 Hungarian.DG Poland_CordedWare.SG     0.502  0.0315 16.0
4 Hungarian.DG Russia_Krasnoyarsk_BA.SG 0.0235 0.0119  1.97

$rankdrop
# A tibble: 4 x 7
  f4rank   dof   chisq         p dofdiff chisqdiff   p_nested
   <int> <int>   <dbl>     <dbl>   <int>     <dbl>      <dbl>
1      3     9    7.73 5.62e-  1      11      603.  3.97e-122
2      2    20  610.   1.96e-116      13     1308.  8.54e-272
3      1    33 1919.   0              15     3267.  0       
4      0    48 5185.   0              NA       NA  NA       

For comparison, Finnish, Estonian, Bulgarian, Polish, Czech, CEU (Utahns):

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Code:

$weights
# A tibble: 4 x 5
  target left                     weight      se     z
  <chr>  <chr>                     <dbl>   <dbl> <dbl>
1 FIN.SG Luxembourg_Loschbour.DG  0.111  0.0175   6.33
2 FIN.SG Serbia_IronGates_N       0.280  0.0159  17.6
3 FIN.SG Poland_CordedWare.SG     0.536  0.0238  22.6
4 FIN.SG Russia_Krasnoyarsk_BA.SG 0.0724 0.00862  8.40

$rankdrop
# A tibble: 4 x 7
  f4rank   dof  chisq         p dofdiff chisqdiff   p_nested
   <int> <int>  <dbl>     <dbl>   <int>     <dbl>      <dbl>
1      3     9   11.6 2.35e-  1      11      748.  2.35e-153
2      2    20  760.  4.46e-148      13     1255.  2.36e-260
3      1    33 2015.  0              15     3288.  0       
4      0    48 5303.  0              NA       NA  NA       

Code:

$weights
# A tibble: 4 x 5
  target     left                     weight     se     z
  <chr>      <chr>                     <dbl>  <dbl> <dbl>
1 Finnish.DG Luxembourg_Loschbour.DG  0.115  0.0240  4.79
2 Finnish.DG Serbia_IronGates_N       0.340  0.0217 15.7
3 Finnish.DG Poland_CordedWare.SG     0.488  0.0330 14.8
4 Finnish.DG Russia_Krasnoyarsk_BA.SG 0.0577 0.0117  4.92

$rankdrop
# A tibble: 4 x 7
  f4rank   dof  chisq         p dofdiff chisqdiff   p_nested
   <int> <int>  <dbl>     <dbl>   <int>     <dbl>      <dbl>
1      3     9   10.8 2.92e-  1      11      582.  1.12e-117
2      2    20  593.  1.09e-112      13     1290.  8.02e-268
3      1    33 1882.  0              15     3241.  0       
4      0    48 5123.  0              NA       NA  NA       

Code:

$weights
# A tibble: 4 x 5
  target      left                     weight     se     z
  <chr>       <chr>                     <dbl>  <dbl> <dbl>
1 Estonian.DG Luxembourg_Loschbour.DG  0.107  0.0235  4.56
2 Estonian.DG Serbia_IronGates_N       0.306  0.0236 12.9
3 Estonian.DG Poland_CordedWare.SG     0.566  0.0310 18.3
4 Estonian.DG Russia_Krasnoyarsk_BA.SG 0.0211 0.0112  1.88

$rankdrop
# A tibble: 4 x 7
  f4rank   dof   chisq         p dofdiff chisqdiff   p_nested
   <int> <int>   <dbl>     <dbl>   <int>     <dbl>      <dbl>
1      3     9    5.95 7.45e-  1      11      642.  1.42e-130
2      2    20  648.   2.20e-124      13     1273.  3.28e-264
3      1    33 1921.   0              15     3242.  0       
4      0    48 5163.   0              NA       NA  NA       

Code:

$weights
# A tibble: 4 x 5
  target       left                     weight     se      z
  <chr>        <chr>                     <dbl>  <dbl>  <dbl>
1 Bulgarian.DG Luxembourg_Loschbour.DG  0.0143 0.0231  0.619
2 Bulgarian.DG Serbia_IronGates_N       0.521  0.0215 24.2 
3 Bulgarian.DG Poland_CordedWare.SG     0.439  0.0329 13.3 
4 Bulgarian.DG Russia_Krasnoyarsk_BA.SG 0.0257 0.0118  2.17

$rankdrop
# A tibble: 4 x 7
  f4rank   dof   chisq         p dofdiff chisqdiff   p_nested
   <int> <int>   <dbl>     <dbl>   <int>     <dbl>      <dbl>
1      3     9    8.08 5.26e-  1      11      556.  4.15e-112
2      2    20  564.   1.21e-106      13     1352.  3.50e-281
3      1    33 1916.   0              15     3282.  0       
4      0    48 5198.   0              NA       NA  NA       

Code:

$weights
# A tibble: 4 x 5
  target    left                     weight     se      z
  <chr>     <chr>                     <dbl>  <dbl>  <dbl>
1 Polish.DG Luxembourg_Loschbour.DG  0.0904 0.0290  3.12
2 Polish.DG Serbia_IronGates_N       0.382  0.0261 14.6 
3 Polish.DG Poland_CordedWare.SG     0.515  0.0411 12.5 
4 Polish.DG Russia_Krasnoyarsk_BA.SG 0.0132 0.0154  0.855

$rankdrop
# A tibble: 4 x 7
  f4rank   dof  chisq         p dofdiff chisqdiff   p_nested
   <int> <int>  <dbl>     <dbl>   <int>     <dbl>      <dbl>
1      3     9   16.1 6.54e-  2      11      569.  5.32e-115
2      2    20  585.  3.59e-111      13     1291.  4.96e-268
3      1    33 1876.  0              15     3296.  0       
4      0    48 5172.  0              NA       NA  NA       

Code:

$weights
# A tibble: 4 x 5
  target   left                       weight     se       z
  <chr>    <chr>                       <dbl>  <dbl>   <dbl>
1 Czech.DG Luxembourg_Loschbour     0.0900   0.0442  2.03 
2 Czech.DG Serbia_IronGates_N       0.406    0.0357 11.4  
3 Czech.DG Poland_CordedWare.SG     0.503    0.0633  7.95 
4 Czech.DG Russia_Krasnoyarsk_BA.SG 0.000452 0.0194  0.0233

$rankdrop
# A tibble: 4 x 7
  f4rank   dof   chisq         p dofdiff chisqdiff   p_nested
   <int> <int>   <dbl>     <dbl>   <int>     <dbl>      <dbl>
1      3     9    9.41 4.01e-  1      11      216.  3.28e- 40
2      2    20  226.   9.30e- 37      13      684.  8.87e-138
3      1    33  910.   9.58e-170      15     1963.  0       
4      0    48 2872.   0              NA       NA  NA       

Code:

$weights
# A tibble: 4 x 5
  target left                      weight     se     z
  <chr>  <chr>                      <dbl>  <dbl> <dbl>
1 CEU.SG Luxembourg_Loschbour      0.0717 0.0288  2.49
2 CEU.SG Serbia_IronGates_N        0.394  0.0244 16.1
3 CEU.SG Poland_CordedWare.SG      0.551  0.0403 13.7
4 CEU.SG Russia_Krasnoyarsk_BA.SG -0.0162 0.0130 -1.24

$rankdrop
# A tibble: 4 x 7
  f4rank   dof   chisq         p dofdiff chisqdiff   p_nested
   <int> <int>   <dbl>     <dbl>   <int>     <dbl>      <dbl>
1      3     9    5.76 7.64e-  1      11      267.  6.69e- 51
2      2    20  273.   2.51e- 46      13      694.  8.04e-140
3      1    33  967.   1.01e-181      15     2054.  0       
4      0    48 3021.   0              NA       NA  NA       

Bulgarian makes me think the East Asian could also come from Iran/CHG, so adding a source for that, extra right pops were GanjDareh and Israel_PPNB IIRC.

Code:

$weights
# A tibble: 5 x 5
  target      left                      weight    se      z
  <chr>        <chr>                      <dbl>  <dbl>  <dbl>
1 Bulgarian.DG Luxembourg_Loschbour      0.0528  0.0658  0.802
2 Bulgarian.DG Serbia_IronGates_N        0.502  0.0874  5.75
3 Bulgarian.DG Poland_CordedWare.SG      0.406  0.118  3.43
4 Bulgarian.DG Russia_Krasnoyarsk_BA.SG -0.00949 0.0221 -0.430
5 Bulgarian.DG Iran_C_SehGabi            0.0486  0.141  0.345
$rankdrop
# A tibble: 5 x 7
  f4rank  dof  chisq        p dofdiff chisqdiff  p_nested
  <int> <int>  <dbl>    <dbl>  <int>    <dbl>      <dbl>
1      4    11  11.4 4.12e-  1      13      65.4  5.68e-  9
2      3    24  76.7 1.99e-  7      15    159.  3.55e- 26
3      2    39  236.  5.30e- 30      17    518.  3.49e- 99
4      1    56  754.  7.87e-123      19    1159.  6.92e-234
5      0    75 1912.  0              NA      NA  NA       

Code:

$weights
# A tibble: 5 x 5
  target      left                      weight    se      z
  <chr>        <chr>                      <dbl>  <dbl>  <dbl>
1 Hungarian.DG Luxembourg_Loschbour      0.0534 0.0640  0.835
2 Hungarian.DG Serbia_IronGates_N        0.440  0.0838  5.25
3 Hungarian.DG Poland_CordedWare.SG      0.590  0.118  5.01
4 Hungarian.DG Russia_Krasnoyarsk_BA.SG  0.0118 0.0239  0.492
5 Hungarian.DG Iran_C_SehGabi          -0.0948 0.135  -0.701

$rankdrop
# A tibble: 5 x 7
  f4rank  dof  chisq        p dofdiff chisqdiff  p_nested
  <int> <int>  <dbl>    <dbl>  <int>    <dbl>      <dbl>
1      4    11  10.7 4.66e-  1      13      77.0  4.09e- 11
2      3    24  87.7 3.45e-  9      15    153.  5.31e- 25
3      2    39  241.  6.21e- 31      17    519.  1.62e- 99
4      1    56  760.  3.60e-124      19    1205.  7.36e-244
5      0    75 1965.  0              NA      NA  NA       

Not 100% sure in this because it doesn't work on the Human Origins dataset or even the moderns in 1240k that only have 800k SNPs, and the two Hungarians (NA15202 and NA15203) don't score much if any Asian in G25, but the results are sensible on these populations, maybe you just need enough SNPs.

[kolompar]

My mistake, IronGates_N is terrible quality. Serbia_LepenskiVir_EN.SG is the high coverage version of the same thing. With that the model gives the same results and works well with the HO dataset too, gives a slightly lower 1.5% Kra for Hungarian but the pattern still holds.

Now for a more proximate model, G25. What I usually do in Vahaduo when something new comes out, is just put the whole ancient averages spreadsheet in source, and use the Reduce function. It's very good for finding divergent/non-European ancestry, not really for regular European because of overfitting I guess, but that's not interesting anyway.

So Hungarian (with an earlier datasheet I had saved... looks like the new one just has too much stuff and it didn't find this model)
Target: Hungarian
Distance: 0.6124% / 0.00612439 | R3P
65.0 SWE_Gotland_VA
19.4 DEU_MA_Baiuvaric_o
15.6 HUN_early_Arpadian_commoner

Then early Arpadian commoner with the rest of the ancients:
Target: HUN_early_Arpadian_commoner
Distance: 0.5094% / 0.00509383 | R3P
91.8 HUN_Conqueror_commoner
5.6 NLD_EIA_low_res
2.6 RUS_Chalmny-Varre

Then Conqueror commoner:
Target: HUN_Conqueror_commoner
Distance: 0.5907% / 0.00590695 | R3P
49.6 HUN_Avar_Late_Visonta
25.6 SWE_Oland_VA
24.8 HUN_Conqueror_elite

Had to click a few times to get a model with Conqueror elite but that's not because it's a worse fit (I think it's actually the best I found). I don't know how the algorithm actually works but it looks like it first chooses a few possible sources and from them makes the best 3-way mix model. And when there are just so many possible sources, like with the newer datasheet, it sometimes misses the best ones. But the fact is modern Hungarians can be modelled well with a source like Árpádian commoner, which can be modelled as one quarter Conqueror elite, which also gives around 2% East Asian like qpAdm.

[Mythbuster General]

The administrator of the Hungarian FTDNA project recently managed to come to the conclusion, that the highly likely ethnic origin of the Hunyadi family originates from a Vlach family that lived in Bulgaria in the 12th century. This is the strength of ancient DNA studies, that we can finally settle old historical disputes with the power of science.

https://www.youtube.com/watch?v=amZpglxNgXo&t=6s

[Riverman]

The administrator of the Hungarian FTDNA project recently managed to come to the conclusion, that the highly likely ethnic origin of the Hunyadi family originates from a Vlach family that lived in Bulgaria in the 12th century. This is the strength of ancient DNA studies, that we can finally settle old historical disputes with the power of science.

https://www.youtube.com/watch?v=amZpglxNgXo&t=6s

The whole distribution of the branch makes this conclusion at least pretty likely.

[szin]

Source of humor from Hungary: according to recent 2022 census 2439 people declared themselves as Scythians and 345 as Huns, lol. Also 327 as Cumans and 233 as Jász, both long-gone populations, but at least in their case they have regional identity basis.

I get Scythian on many ancient G25 runs as well as MTA, guess that counts for something...