I made recalibration of qpAdm references to improve accuracy. Please read my previous post to find my opinion and to see the problematic with qpAdm.
New references:
Kostenki14 813405
MA1 625746
WHG 703903
EHG 975726
CHG 889688
Ganj_Dareh_N 794892
West_Siberia_N 670626
Anatolia_Neolithic 889986
Mbuti1M 971767
Wichi1M 971774
At first I tried to find out the admixture of present-day Estonians and it is really challenging without proper Iron Age samples. So I had to use best available modern samples.
Latvian1M FI12 AncFinn
best coefficients: 0.408 0.451 0.141
Jackknife mean: 0.404965620 0.441429933 0.153604447
std. errors: 0.248 0.159 0.203
fixed pat wt dof chisq tail prob
000 0 7 2.724 0.90932 0.408 0.451 0.141
001 1 8 3.392 0.907406 0.531 0.469 0.000
010 1 8 10.133 0.255821 2.398 -0.000 -1.398 infeasible
100 1 8 5.005 0.757038 0.000 0.626 0.374
Admixtures:
Latvian1M - Latvian samples covering 1 million SNP's
FI12 - it is me, because I am one of my individual samples covering 1 million SNP's and in this case giving best fit. So I represent here present-day Finns.
AncFinn - an ancient Finnish sample from Damgaard et al. 2018.
Reasonable fit for Basques was even more challenging. My test shows that the Basques are averagely two thirds ancient people from the Iberian peninsula and one third from Steppe origin.
SE_Iberia_CA Yamnaya_Samara
best coefficients: 0.671 0.329
Jackknife mean: 0.670091689 0.329908311
std. errors: 0.022 0.022
fixed pat wt dof chisq tail prob
00 0 8 5.956 0.652145 0.671 0.329
edit 25.12.2018 12:55
A new Southwest Finnish result using recalibrated references. Recalibration here means better results of Asian and African admixtures. There was also a inconsistency between Iron Gate and WHG - Iron Gate removed.
Scania_IA Baltic_IA Levaluhta
best coefficients: 0.483 0.358 0.159
Jackknife mean: 0.450272111 0.373492239 0.176235650
std. errors: 0.182 0.183 0.111
fixed pat wt dof chisq tail prob
000 0 7 0.657 0.998647 0.483 0.358 0.159
001 1 8 2.229 0.97317 0.596 0.404 0.000
010 1 8 3.018 0.93322 0.803 0.000 0.197
100 1 8 5.884 0.660177 -0.000 0.849 0.151
011 2 9 4.219 0.896439 1.000 0.000 0.000
101 2 9 6.568 0.681978 0.000 1.000 0.000
110 2 9 21.991 0.00890644 0.000 0.000 1.000
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