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):
Show Content
Spoiler
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.