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Z is directly proportional to p-value. The higher the p the lower the Z
Your passing models are generally >0.05. The higher ones such as >0.40 are a little more accurate
Agree with regards to SE Adygei due to Georgian. If you really need to use both you have to add another pright that's shares more genetic drift with one vs the other.
Your SE is still acceptable. Your p of 0.68 is very good.
How many SNPs were used in the f2 run?






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So I did this for every population I use, exluded those with very high missing ratios. But some populations like Ganj_Dareh only has a few samples and decided to stick to SNPs in the trade-off you mentioned. (I think I only eliminated those with 0.85+ missing)
Removed Tepecik and went on with only Turkey_N. Since my right pops cannot distinguish Greek vs Bulgarian, I wanted to use Polish for slavic proxy. (Not sure if this is a good idea). After going with Turkey_N, Adygei never fell below 0.6. This tells me that I am missing something. I´ll read this next: https://www.biorxiv.org/content/10.1...664v1.full.pdf
Perhaps I should use earlier, let´s say medieval, pops as left references. As I am mostly interested in getting a rough estimation of my admixture.
Code:======================================= target left weight se z --------------------------------------- 1 my_geno Adygei.DG 0.624 0.152 4.103 2 my_geno Turkmen.SG 0.012 0.044 0.282 3 my_geno Greek_1.DG 0.069 0.147 0.47 4 my_geno Polish.DG 0.294 0.094 3.138 --------------------------------------- ====================================================== f4rank dof chisq p dofdiff chisqdiff p_nested ------------------------------------------------------ 1 3 5 0.57 0.989 7 64.579 0 2 2 12 65.149 0 9 164.988 0 3 1 21 230.138 0 11 893.415 0 4 0 32 1123.552 0 NA NA NA ------------------------------------------------------
Those f2 statistics came from the run with 93k~ snps.
New one with 100k~ SNPs: (you can see the populations I use, moderns left ancients right.)
bonus:Code:=============================================== pop1 pop2 est se ----------------------------------------------- 1 me Bulgarian.DG 0.00071 0.00108 2 me Russia_Abkhasian.DG 0.00094 0.00111 3 me Georgian.DG 0.00097 0.00111 4 me Armenian.DG 0.00109 0.00109 5 me Adygei.DG 0.00114 0.00116 6 me Turkish.DG 0.00143 0.00113 7 me Greek_1.DG 0.00157 0.00112 8 me Russia_NorthOssetian.DG 0.00182 0.00114 9 me Iranian.DG 0.00219 0.00112 10 me Polish.DG 0.0022 0.00146 11 me Turkmen.SG 0.00557 0.00136 12 me Turkey_N 0.00895 0.00101 13 me Iran_GanjDareh_N 0.0279 0.00127 14 me Ukraine_N 0.0285 0.00113 15 me Russia_Yana_UP.SG 0.03287 0.00161 16 me Russia_DevilsCave_N.SG 0.06759 0.00159 17 me Georgia_Kotias.SG 0.18128 0.00152 18 me Russia_HG_Karelia 0.18468 0.00151 19 me Switzerland_Bichon.SG 0.18638 0.00168 20 me Russia_Kolyma_M.SG 0.20019 0.00163 -----------------------------------------------
possibly historically not accurate, but just felt curious to see what I´d get.
Thank you a lot again, Zoro.Code:========================================== target left weight se z ------------------------------------------ 1 Turkish.DG Greek_1.DG 0.504 0.09 5.619 2 Turkish.DG Turkmen.SG 0.113 0.037 3.066 3 Turkish.DG Iranian.DG 0.383 0.115 3.33 ------------------------------------------ ====================================================== f4rank dof chisq p dofdiff chisqdiff p_nested ------------------------------------------------------ 1 2 6 10.737 0.097 8 98.977 0 2 1 14 109.714 0 10 911.517 0 3 0 24 1021.231 0 NA NA NA ------------------------------------------------------
50% Turkish_Deliorman + 50% Adygei @ 4,879





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I'm having hard time while interpreting these tbh:
F2s
Fst:Code:pop1 pop2 est se1 Kaspias Adygei.DG 0.361 0.00114 2 Kaspias Albanian.DG 0.359 0.00128 3 Kaspias Bashkir.SG 0.363 0.00114 4 Kaspias Bulgarian.DG 0.360 0.00114 5 Kaspias Cretan.DG 0.360 0.00118 6 Kaspias French.DG 0.360 0.00113 7 Kaspias Greek_1.DG 0.360 0.00121 8 Kaspias Greek_2.DG 0.359 0.00122 9 Kaspias Iranian.DG 0.362 0.00115 10 Kaspias Italian_North.DG 0.360 0.00129 11 Kaspias Jordanian.DG 0.365 0.00107 12 Kaspias Mansi.DG 0.365 0.00111 13 Kaspias Polish.DG 0.360 0.00121 14 Kaspias Russian.DG 0.361 0.00113 15 Kaspias Tatar_Tomsk.SG 0.366 0.00108 16 Kaspias Turkish.DG 0.362 0.00112 17 Kaspias Turkmen.SG 0.364 0.00113 18 Kaspias Tuscan_1.DG 0.360 0.00115 19 Kaspias Uzbek.SG 0.366 0.00109
But I should add, the overlapping SNP number is ~610K now when used the 1240K dataset.Code:pop1 pop2 est se1 Kaspias Adygei.DG 0.134 0.00284 2 Kaspias Albanian.DG 0.137 0.00395 3 Kaspias Bashkir.SG 0.137 0.00296 4 Kaspias Bulgarian.DG 0.130 0.00288 5 Kaspias Cretan.DG 0.131 0.00294 6 Kaspias French.DG 0.134 0.00272 7 Kaspias Greek_1.DG 0.131 0.00337 8 Kaspias Greek_2.DG 0.129 0.00364 9 Kaspias Iranian.DG 0.133 0.00293 10 Kaspias Italian_North.DG 0.132 0.00346 11 Kaspias Jordanian.DG 0.141 0.00278 12 Kaspias Mansi.DG 0.156 0.00281 13 Kaspias Polish.DG 0.135 0.00352 14 Kaspias Russian.DG 0.134 0.00292 15 Kaspias Tatar_Tomsk.SG 0.142 0.00293 16 Kaspias Turkish.DG 0.133 0.00275 17 Kaspias Turkmen.SG 0.135 0.00298 18 Kaspias Tuscan_1.DG 0.134 0.00295 19 Kaspias Uzbek.SG 0.140 0.00270
Code:target left weight se z 1 Kaspias Bulgarian.DG 0.75 0.041 18.11 2 Kaspias Uzbek.SG 0.25 0.041 6.05 f4rank dof chisq p.value dofdiff chisqdiff p_nested 1 1 5 5.81 0.32551 7 735.49 1.5e-154 2 0 12 741.3 < 2e-16
Last edited by Kaspias; 03-05-2021 at 10:16 AM.





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I'm glad your getting the hang of it. Keep at it and you'll slowly get better !
Yes, it's best to use ancients to model yourself and others since they are the ones ancestral and not moderns allthough moderns can be used to see how one is shifted compared to neighboring moderns.
It's always good to get a few passing p-value models with decent SE and then pick the one you think is most accurate based on historical.
The model you show for the Kayseri Turks is close to what I had but I didn't use Iranians. I used Armenians, Bulgarians and Turkmen and got about 16% Turkmen with equal portions of Armenian and Bulgarian





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I'm glad your getting the hang of it. Keep at it and you'll slowly get better ! Good to see that you were able to use the 1240K set.
I like your FST results better than F2s and I think you should use that more than F2s in the future for distances.
I see that Greek-2 and Bulgarians are closest to you but it looks like you split Greeks into individual samples whereas with Bulgarians it was the average of both Simons samples.
Can you split the Bulgarians and others into individual samples and repost for kicks
Your qpAdm model looks good ! but I'm interested to know how many SNPs in the extract and which pright you used
You're showing 25% Usbek -/+4% which is higher than what I got for the Iraqi Kurds and Kayseri Turks. The Iraqi Kurds were something like ~80% Armenian + ~20% Bashkir or Turkmen.
I like to model with moderns sometimes because it gives me an idea how shifted compared to their neighbors a population is
For ex Iraqi Kurds consistently get Iranian or Georgian or Armenian + 10% Estonian or some percentage Tatar or Bashkir. It doesn't mean that they have Estonian ancestry bu this tells me that a significant proportion of their ancestors came from NE of Armenia or Iran
How many SNPs did you end up with when you did your extract ?






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btw, I read on the supplement to the performance assessment paper that the choice of the top right population matters. Is that still the case? As far as I know, it was necessary to put the target population on top of the left populations before. It seems to have changed on R.
I also played around with the allsnps = TRUE feature, and I can say that the results change A LOT.Right Population File
This is alist of reference populations to be included in the qpAdm model. The number of reference populations must be greater than the number of left (i.e. target and source) populations. One population should be listed per line. The first population in the list will be used as a base for all f4-statistics calculated. Population order after the first population does not matter.
Last edited by Korialstrasz; 03-05-2021 at 06:09 PM.
50% Turkish_Deliorman + 50% Adygei @ 4,879





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Fst, Bulgarian and Turkish also separated:
I do not get, how can I be closer to French and Adygei than Albanians? The rest seems ok though.Code:pop1 pop2 est se1 Kaspias Adygei.DG 0.134 0.00285 2 Kaspias Albanian.DG 0.137 0.00395 3 Kaspias Bashkir.SG 0.136 0.00300 4 Kaspias Bulgarian_1.DG 0.126 0.00347 5 Kaspias Bulgarian_2.DG 0.130 0.00358 6 Kaspias Cretan.DG 0.131 0.00294 7 Kaspias French.DG 0.134 0.00274 8 Kaspias Greek_1.DG 0.130 0.00339 9 Kaspias Greek_2.DG 0.129 0.00364 10 Kaspias Iranian.DG 0.133 0.00292 11 Kaspias Italian_North.DG 0.132 0.00351 12 Kaspias Jordanian.DG 0.141 0.00279 13 Kaspias Mansi.DG 0.156 0.00283 14 Kaspias Polish.DG 0.135 0.00354 15 Kaspias Russian.DG 0.134 0.00293 16 Kaspias Tatar_Tomsk.SG 0.142 0.00294 17 Kaspias Turkish_1.DG 0.145 0.00381 18 Kaspias Turkish_2.DG 0.132 0.00348 19 Kaspias Turkmen.SG 0.135 0.00297 20 Kaspias Tuscan_1.DG 0.134 0.00296 21 Kaspias Uzbek.SG 0.140 0.00272
Your qpAdm model looks good ! but I'm interested to know how many SNPs in the extract and which pright you used
You're showing 25% Usbek -/+4% which is higher than what I got for the Iraqi Kurds and Kayseri Turks. The Iraqi Kurds were something like ~80% Armenian + ~20% Bashkir or Turkmen.
I like to model with moderns sometimes because it gives me an idea how shifted compared to their neighbors a population is
For ex Iraqi Kurds consistently get Iranian or Georgian or Armenian + 10% Estonian or some percentage Tatar or Bashkir. It doesn't mean that they have Estonian ancestry bu this tells me that a significant proportion of their ancestors came from NE of Armenia or Iran
How many SNPs did you end up with when you did your extractLet me show you all the models:Code:√ 1578508 SNPs read in total ! 565741 SNPs remain after filtering. 540537 are polymorphic.
Right(Adygei, Cretan, Iranian, Mansi, Polish, Jordanian, Tatar_Tomsk, Italian_North, Albanian, French) -all the of Simeon's-
Code:target left weight se z 1 Kaspias Bulgarian_1.DG 0.78 0.044 17.51 2 Kaspias Turkmen.SG 0.22 0.044 4.98 f4rank dof chisq p.value dofdiff chisqdiff p_nested 1 1 8 5.96 0.65119 10 553.51 1.6e-112 2 0 18 559.48 < 2e-16
Code:target left weight se z 1 Kaspias Bulgarian_1.DG 0.76 0.048 15.89 2 Kaspias Bashkir.SG 0.24 0.048 4.89 f4rank dof chisq p.value dofdiff chisqdiff p_nested 1 1 8 6.44 0.59806 10 478.01 2.2e-96 2 0 18 484.45 < 2e-16Code:target left weight se z 1 Kaspias Bulgarian_1.DG 0.8 0.039 20.51 2 Kaspias Uzbek.SG 0.2 0.039 5.13 f4rank dof chisq p.value dofdiff chisqdiff p_nested 1 1 8 4.4 0.81917 10 796.21 1.3e-164 2 0 18 800.62 < 2e-16I get negative scores when used Turkish samples to model my Turkic part, which might mean Turkic ancestors of Balkan Turks were not Anatolian Turkish-like.Code:target left weight se z 1 Kaspias Bulgarian_1.DG 0.82 0.044 18.83 2 Kaspias Tatar_Tomsk.SG 0.18 0.044 4.15 f4rank dof chisq p.value dofdiff chisqdiff p_nested 1 1 8 6.16 0.62951 10 628.79 1.2e-128 2 0 18 634.94 < 2e-16
Code:target left weight se z 1 Kaspias Bulgarian_1.DG 1.04 5.3 0.2 2 Kaspias Turkish_1.DG -0.04 5.3 -0.01 f4rank dof chisq p.value dofdiff chisqdiff p_nested 1 1 8 28.18 0.00044187 10 36.1 0.000081 2 0 18 64.28 4.09e-07Code:target left weight se z 1 Kaspias Bulgarian_1.DG 11.44 364.26 0.031 2 Kaspias Turkish_2.DG -10.44 364.26 -0.029 f4rank dof chisq p.value dofdiff chisqdiff p_nested 1 1 8 29.87 0.00022259 10 30.66 0.00067 2 0 18 60.54 1.6747e-06





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Problem with modern people who identify as French is they may have recent N. African or other European ancestors. That aside since the default in Admixtools is to use common and less common alleles in calculations ENF in Europeans and W. Asians confounds results because it was so successful and Europeans and W. Asians have alot of it.
I have found that when I exclude very common alleles from analysis you have less of those sorts of problems due to ENF in Italians and French. Default in Admixtools is to use all alleles subject to maxmiss meaning that if an allele has a MAF of 0.50 it is still used. MAF 0.50 means that minor or ALT allele is found in all your pops in the analysis whether African, Asian or European meaning if "T" is the minor allele at that position then all Africans and Eurasians in your analysis are "T" at that position.
If you want to exclude these very ancient common alleles from your analysis set maxmaf=0.4 or 0.3 and then rerun. I'm pretty sure you wont get French or British or whatever having a small distance to you because of shared ENF or EHG.





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