qpAdm modelling, first attempt

[vbnetkhio]

qpAdm is a professional admixture tool, used in genetic studies, like that last one about Rome.
It isn't that complicated to use if you are a linux user.

qpAdm works directly with 100,000s of SNPs, not just with a dozen axes or admixture components like g25 or eurogenes k13.
in G25 Bell Beaker can be identical to a Norwegian, a half Jew, half Ukrainian turns out identical to to a South Slav, etc. it makes sense, but g25 is only good for models with 2 or 3 populations, or very distinct populations. qpAdm should be able to paint a more detailed picture.

i tried to model Serbs with 9 ancient populations in qpAdm, this is the model with the highest probability:

Serbs
Czech_EarlySlav 12.30%
Hungary_Slav 0.00%
Hungary_Langobard 0.00%
Hungary_Langobard_o1 15.00%
Hungary_Langobard_o2 0.00%
Italy_Imperial 0.00%
Moldova_Scythian 30.60%
Russia_Sunghir6 42.10%

Italy Imperial is completely ignored! In gedmatch calcs and g25 Italy_Imperial would work well. For our med shift, the Hungary Langobard outlier was picked instead, who is more likely related to us, given his geographic position. Also the Moldova_Scythians, who were Italian like. In usual calcualtors some Serbs get a lot of Moldova Scythian, some don't get it at all. But it makes sense that these Scythians were absorbed by Slavs on their way to the Balkans.

[JamesBond007]

Thanks for posting this I haven't used qpAdm , yet , because I was worried it was going to be like rocket science to use (not setup or install , though).

I use Debian Linux so I'm going to install it right now since I was somewhat dissapointed by G25 and GEDmatch is a clusterf*ck of trash.

[vbnetkhio]

Thanks for posting this I haven't used qpAdm , yet , because I was worried it was going to be like rocket science to use (not setup or install , though).

I use Debian Linux so I'm going to install it right now since I was somewhat dissapointed by G25 and GEDmatch is a clusterf*ck of trash.

you can work directly with these files (geno, snp, ind) to model different ancient and modern populations, no conversion needed:
https://reich.hms.harvard.edu/downloadable-genotypes-worlds-published-ancient-dna-data

adding your raw data to the model is a bit harder, you have to convert it to plink format, then also convert these geno+snp+ind files to plink, merge it with your raw data, then you can work with that plink file.

here is how to convert 23andme to plink, for example:
https://www.harappadna.org/2011/02/23andme-conversion-to-ped/

if you get stuck, feel free to ask here, i'll give tutorials.

[Zoro]

qpAdm is a professional admixture tool, used in genetic studies, like that last one about Rome.
It isn't that complicated to use if you are a linux user.

qpAdm works directly with 100,000s of SNPs, not just with a dozen axes or admixture components like g25 or eurogenes k13.
in G25 Bell Beaker can be identical to a Norwegian, a half Jew, half Ukrainian turns out identical to to a South Slav, etc. it makes sense, but g25 is only good for models with 2 or 3 populations, or very distinct populations. qpAdm should be able to paint a more detailed picture.

i tried to model Serbs with 9 ancient populations in qpAdm, this is the model with the highest probability:

Serbs
Czech_EarlySlav 12.30%
Hungary_Slav 0.00%
Hungary_Langobard 0.00%
Hungary_Langobard_o1 15.00%
Hungary_Langobard_o2 0.00%
Italy_Imperial 0.00%
Moldova_Scythian 30.60%
Russia_Sunghir6 42.10%

Italy Imperial is completely ignored! In gedmatch calcs and g25 Italy_Imperial would work well. For our med shift, the Hungary Langobard outlier was picked instead, who is more likely related to us, given his geographic position. Also the Moldova_Scythians, who were Italian like. In usual calcualtors some Serbs get a lot of Moldova Scythian, some don't get it at all. But it makes sense that these Scythians were absorbed by Slavs on their way to the Balkans.

Congrats on learning how to use it. I also learned a couple of months ago but ran into alot of issues. Fortunately i had an expert i was able to consult who helped me navigate the issues. Can you post the raw output from your run because i suspect the standard errors will be very high since you’re using a few closely related sources.

It’s also best to use sources more closer in time instead of a paleolithic source with Iron age source

[vbnetkhio]

Congrats on learning how to use it. I also learned a couple of months ago but ran into alot of issues. Fortunately i had an expert i was able to consult who helped me navigate the issues. Can you post the raw output from your run because i suspect the standard errors will be very since you’re using a few closely related sources.

It’s also best to use sources more closer in time instead of a paleolithic source with Iron age source

do you mean Sunghir6 by Paleolithic? Sunghir6 is a 12th century Kievan Russian, not the paleolithic sample from Sunghir. But He has no Finnic admixture like modern Russians from that area, he could be of pure proto-Slavic stock. Also, he's haplogroup i2a-din, so he's probably from a tribe closely related to the Slavic tribes which went south to the Balkans.

[Zoro]

do you mean Sunghir6 by Paleolithic? Sunghir6 is a 12th century Kievan Russian, not the paleolithic sample from Sunghir. But He has no Finnic admixture like modern Russians from that area, he could be of pure proto-Slavic stock. Also, he's haplogroup i2a-din, so he's probably from a tribe closely related to the Slavic tribes which went south to the Balkans.

ok thanks but can you post the full output so i can see how high your standard errors are and your outgroups

[JamesBond007]

you can work directly with these files (geno, snp, ind) to model different ancient and modern populations, no conversion needed:
https://reich.hms.harvard.edu/downloadable-genotypes-worlds-published-ancient-dna-data

adding your raw data to the model is a bit harder, you have to convert it to plink format, then also convert these geno+snp+ind files to plink, merge it with your raw data, then you can work with that plink file.

here is how to convert 23andme to plink, for example:
https://www.harappadna.org/2011/02/23andme-conversion-to-ped/

if you get stuck, feel free to ask here, i'll give tutorials.

No offense dude but that script sucks for my 23andme raw data format maybe because it is from 2011 and mine is from 2016.

For instance, obviously it won't work :


]fi
echo "Family ID: "
read fid
echo "Individual ID: "
read id
echo "Paternal ID: "
read pid
echo "Maternal ID: "
read mid
echo "Sex (m/f/u): "
read sexchr



# File generated by https://DNA.Land
#
# This is a 23andMe-compatible file using reference human assembly build 37
#
# Generated by DNA.Land server version 0.0.1.79-b530 on 2016-08-16
# from input file of type 'ancestry'
#
# rsid chromosome position genotype
rs369202065 MT 8838 GG
rs199476136 MT 8851 TT
rs190214723 1 693625 TT




I used this instead :


#!/usr/bin/env python

import argparse
import os

CHROMOSOME_TABLE = {'X': '23', 'Y': '24', 'XY': '25', 'MT': '26'}
GENDER_TABLE = {'male': '1', 'female': '2'}
PHENOTYPE = '-9'

# Determine the path and optionally the gender.
parser = argparse.ArgumentParser()
parser.add_argument('path', type=str)
parser.add_argument('--gender', type=str, nargs='?')
args = parser.parse_args()

gender = GENDER_TABLE.get(args.gender, '0')

# Open the genome file for reading.
with open(args.path, 'r') as genome_file:

# Create the PED file and write its header.
root, _ = os.path.splitext(args.path)
with open(root + '.ped', 'w') as ped_file:
ped_file.write(
'{0}_FAM {0} {0}_FATHER {0}_MOTHER {1} {2}'.
format(root, gender, PHENOTYPE))

# Create the map file.
with open(root + '.map', 'w') as map_file:

# Loop over every line of the genome file, skipping comments.
for line in genome_file:
if len(line) == 0 or line[0] == '#':
continue

# Decompose the line, and transform the fields if necessary.
rsid, chromosome, position, genotype = line.split()
chromosome = CHROMOSOME_TABLE.get(chromosome, chromosome)
genotype = genotype.ljust(2, genotype[0]).replace('-', '0')

# Write the map and ped file data.
map_file.write('{0}\t{1}\t0\t{2}\n'.format(chromos ome, rsid, position))
ped_file.write(' {0} {1}'.format(genotype[0], genotype[1]))

./python-script --gender male rawdata.txt

[gixajo]

qpAdm is a professional admixture tool, used in genetic studies, like that last one about Rome.
It isn't that complicated to use if you are a linux user.

qpAdm works directly with 100,000s of SNPs, not just with a dozen axes or admixture components like g25 or eurogenes k13.
in G25 Bell Beaker can be identical to a Norwegian, a half Jew, half Ukrainian turns out identical to to a South Slav, etc. it makes sense, but g25 is only good for models with 2 or 3 populations, or very distinct populations. qpAdm should be able to paint a more detailed picture.

i tried to model Serbs with 9 ancient populations in qpAdm, this is the model with the highest probability:

Serbs
Czech_EarlySlav 12.30%
Hungary_Slav 0.00%
Hungary_Langobard 0.00%
Hungary_Langobard_o1 15.00%
Hungary_Langobard_o2 0.00%
Italy_Imperial 0.00%
Moldova_Scythian 30.60%
Russia_Sunghir6 42.10%

Italy Imperial is completely ignored! In gedmatch calcs and g25 Italy_Imperial would work well. For our med shift, the Hungary Langobard outlier was picked instead, who is more likely related to us, given his geographic position. Also the Moldova_Scythians, who were Italian like. In usual calcualtors some Serbs get a lot of Moldova Scythian, some don't get it at all. But it makes sense that these Scythians were absorbed by Slavs on their way to the Balkans.

How does qpAdm improve compared to other statistical tools that are used in multivariate statistics?

Vahaduo or Nmonte works ok for this issue, why use other Monte Carlo or similar methods based in statistic package(or that support this type of "algorithmic process") for this issue if they are not of so intuitive use as these ones?

[Lucas]

qpAdm is a professional admixture tool, used in genetic studies, like that last one about Rome.
It isn't that complicated to use if you are a linux user.

qpAdm works directly with 100,000s of SNPs, not just with a dozen axes or admixture components like g25 or eurogenes k13.
in G25 Bell Beaker can be identical to a Norwegian, a half Jew, half Ukrainian turns out identical to to a South Slav, etc. it makes sense, but g25 is only good for models with 2 or 3 populations, or very distinct populations. qpAdm should be able to paint a more detailed picture.

i tried to model Serbs with 9 ancient populations in qpAdm, this is the model with the highest probability:

Serbs
Czech_EarlySlav 12.30%
Hungary_Slav 0.00%
Hungary_Langobard 0.00%
Hungary_Langobard_o1 15.00%
Hungary_Langobard_o2 0.00%
Italy_Imperial 0.00%
Moldova_Scythian 30.60%
Russia_Sunghir6 42.10%

Italy Imperial is completely ignored! In gedmatch calcs and g25 Italy_Imperial would work well. For our med shift, the Hungary Langobard outlier was picked instead, who is more likely related to us, given his geographic position. Also the Moldova_Scythians, who were Italian like. In usual calcualtors some Serbs get a lot of Moldova Scythian, some don't get it at all. But it makes sense that these Scythians were absorbed by Slavs on their way to the Balkans.

What you used as outgroup pops? Some say such choice is important.

[JamesBond007]

What you used as outgroup pops? Some say such choice is important.

Choosing the right population set is of the utmost importance in qpAdm.

[Lucas]

Choosing the right population set is of the utmost importance in qpAdm.

This is why I want to see his right pops.

Later I will model myself and post. But good if we all use the same outgroup list.

[JamesBond007]

[
*edited out*

[JamesBond007]

you can work directly with these files (geno, snp, ind) to model different ancient and modern populations, no conversion needed:
https://reich.hms.harvard.edu/downloadable-genotypes-worlds-published-ancient-dna-data

adding your raw data to the model is a bit harder, you have to convert it to plink format, then also convert these geno+snp+ind files to plink, merge it with your raw data, then you can work with that plink file.

here is how to convert 23andme to plink, for example:
https://www.harappadna.org/2011/02/23andme-conversion-to-ped/

if you get stuck, feel free to ask here, i'll give tutorials.

Screw this bullcrap man it's not worth the effort. For instance , in order to convert the geno+snp+ind files to plink format you need to use convertf from Eigensoft but the guy from Harvard was too mentally retarded or Lazy to create a Unix style man page for the program and he did even follow a GNUism by a --help switch functionality e.g. ./convertf -help.

This is one example of Linux people being stupid and f*cking up UNIX by not following standards. See here for standard documentation format on a Unix-like or Unix system :

https://man.openbsd.org/?query=man+man&apropos=0&sec=0&arch=default&manpath=OpenBSD-current

^^ The convertf documentation is convoluted at best compared to that.

[Zoro]

What you used as outgroup pops? Some say such choice is important.


Yes very important because it’s the outgroups that can differentiate 2 closely related pops. For example if you’re E European and you skip EHG as an outgroup qpAdm or qpWave outputs will show W and E Europeans as very similar because EHG is one ancient pop that differentiates E and W Europeans.

On the other hand you can’t use too many recent pops as outgroups because then you won’t get any passing p-values. I Learned these secrets from the best :)

[Zoro]

Screw this bullcrap man it's not worth the effort. For instance , in order to convert the geno+snp+ind files to plink format you need to use convertf from Eigensoft but the guy from Harvard was too mentally retarded or Lazy to create a Unix style man page for the program and he did even follow a GNUism by a --help switch functionality e.g. ./convertf -help.

This is one example of Linux people being stupid and f*cking up UNIX by not following standards. See here for standard documentation format on a Unix-like or Unix system :

https://man.openbsd.org/?query=man+man&apropos=0&sec=0&arch=default&manpath=OpenBSD-current

^^ The convertf documentation is convoluted at best compared to that.


Welcome to open source scripts. Definitely not written for amateurs but i have gotten this to work with help of course. I’ll post some commands that will help you in a few minutes

[JamesBond007]

Welcome to open source scripts. Definitely not written for amateurs but i have gotten this to work with help of course. I’ll post some commands that will help you in a few minutes

You are another Linux 'genius' of course. My point is Linux people are morons compared to OpenBSD people generally speaking the actual user base. For instance, you just called convertf a script when it's actually an elf binary. An elf binary program should have a man page :

I'm not amateur since I could figure it out if I had to but it's not worth the effort because mentally retarded Linux people don't know how to write proper documentation.

Man page format. This is what proper documentation looks like I chose elf semi-randomly as it is an elf binary :

https://man.openbsd.org/elf

[Zoro]

You are another Linux 'genius' of course. My point is Linux people are morons compared to OpenBSD people generally speaking the actual user base. For instance, you just called convertf a script when it's actually an elf binary. An elf binary program should have a man page :

I'm not amateur since I could figure it out if I had to but it's not worth the effort because mentally retarded Linux people don't know how to write proper documentation.

Man page format. This is what proper documentation looks like I chose elf semi-randomly as it is an elf binary :

https://man.openbsd.org/elf


Ok, 1st make sure you have Admixtools compiled properly with no error messages (make sure you install all dependencies for it to function properly on your machine).

Assuming you want to use one of Reich lab datasets which is in Eigenstrat format, its best to 1st convert it to plink format and add your own raw data in plink then convert it back to Eigenstrat. To do this create the following par file in your linux text editor and save it as par.PED.PACKEDPED:

genotypename: Data.geno
snpname: Data.snp
indivname: Data.ind
outputformat: PACKEDPED
genotypeoutname: Data.bed
snpoutname: Data.bim
indivoutname: Data.fam
familynames: YES


Then use the following command assuming your dataset is called Data
.....path-to-your-file-containing-Admixtools-Executibles../convertf -p par.PED.PACKEDPED


You'll then get plink output files if everything goes right.

Now you can add your genotype data in plink

For example to convert 23andme data to plink:

/..path to plink executible.../Plink/plink --23file Bond.txt Bond --out




Next add your plink format 23andme file to your dataset called Data:

/..path to plink executible../Plink/plink --bfile Data --bmerge Bond.bed Bond.bim Bond.fam --make-bed --out Mergeddata


Once you get this far let me know and I'll guide you further.

Good luck

[vbnetkhio]

ok thanks but can you post the full output so i can see how high your standard errors are and your outgroups

sorry, i wasn't on my pc. here it is


./qpAdm: parameter file: qp_par
### THE INPUT PARAMETERS
##PARAMETER NAME: VALUE
genotypename: qp.bed
snpname: qp.bim
indivname: qp.fam
popleft: left.pops
popright: right.pops
details: YES
allsnps: YES
## qpAdm version: 1000
seed: 1665725873
*** warning. genetic distances are in cM not Morgans
1 rs7418088 110.998 81208897 T G

genotype file processed

left pops:
Serb
Czech_EarlySlav
Hungary_Slav
Hungary_Langobard
Hungary_Langobard_o1
Hungary_Langobard_o2
Italy_Imperial
Moldova_Scythian
Russia_Sunghir6

right pops:
Mbuti
Natufian
Onge
Iran_N
Villabruna
Mixe
Ami
Nganasan
Itelmen

0 Serb 18
1 Czech_EarlySlav 2
2 Hungary_Slav 1
3 Hungary_Langobard 30
4 Hungary_Langobard_o1 1
5 Hungary_Langobard_o2 1
6 Italy_Imperial 45
7 Moldova_Scythian 7
8 Russia_Sunghir6 1
9 Mbuti 4
10 Natufian 6
11 Onge 1
12 Iran_N 10
13 Villabruna 1
14 Mixe 3
15 Ami 2
16 Nganasan 2
17 Itelmen 1
jackknife block size: 0.050
snps: 1177892 indivs: 136
number of blocks for block jackknife: 22
## ncols: 1177892
coverage: Serb 541868
coverage: Czech_EarlySlav 730657
coverage: Hungary_Slav 802391
coverage: Hungary_Langobard 1150370
coverage: Hungary_Langobard_o1 754025
coverage: Hungary_Langobard_o2 339475
coverage: Italy_Imperial 1142953
coverage: Moldova_Scythian 1087965
coverage: Russia_Sunghir6 1136668
coverage: Mbuti 1121438
coverage: Natufian 535079
coverage: Onge 1133184
coverage: Iran_N 1149892
coverage: Villabruna 883779
coverage: Mixe 1121394
coverage: Ami 1121313
coverage: Nganasan 311400
coverage: Itelmen 1119732
dof (jackknife): 18.421
numsnps used: 1177892
codimension 1
f4info:
f4rank: 7 dof: 1 chisq: 18.051 tail: 2.1508767e-05 dofdiff: 3 chisqdiff: -18.051 taildiff: 1
B:
scale 1.000 1.000 1.000 1.000 1.000 ...
1.000 1.000
Natufian 0.044 -0.852 0.429 -1.707 0.292 ...
-1.339 -1.814
Onge 0.441 0.034 -0.227 0.650 1.782 ...
1.519 0.629
Iran_N -0.000 -0.033 2.547 -0.476 -1.136 ...
-1.116 -0.264
Villabruna 1.672 -1.643 -0.672 1.760 1.433 ...
0.172 0.207
Mixe 1.239 0.968 0.781 -0.644 -0.888 ...
1.314 0.434
Ami 0.714 0.809 -0.406 0.564 0.025 ...
0.302 0.917
Nganasan 1.356 1.531 0.145 0.767 0.119 ...
0.788 1.639
Itelmen 1.060 0.799 -0.176 0.142 -0.770 ...
0.432 0.696
A:
scale 1873.469 7497.225 16173.322 7184.645 13651.051 ...
10643.035 7445.461
Czech_EarlySlav 0.357 -0.371 1.382 0.687 -1.676 ...
-0.050 0.366
Hungary_Slav 0.425 -0.934 0.531 1.143 0.980 ...
0.139 0.328
Hungary_Langobard 0.495 -1.199 1.201 -0.486 1.737 ...
-0.195 -0.984
Hungary_Langobard_o1 -0.727 -2.003 -0.464 -1.072 -0.958 ...
-1.679 -2.272
Hungary_Langobard_o2 0.316 -0.073 -0.428 0.087 -0.005 ...
-1.206 -0.051
Italy_Imperial -2.579 0.730 -0.096 -1.549 0.177 ...
-0.569 -1.172
Moldova_Scythian -0.069 0.829 1.648 -1.447 -0.017 ...
0.182 -0.457
Russia_Sunghir6 0.405 -0.561 -1.115 0.579 0.512 ...
1.819 0.208


full rank
f4info:
f4rank: 8 dof: 0 chisq: 0.000 tail: 1 dofdiff: 1 chisqdiff: 18.051 taildiff: 2.1508767e-05
B:
scale 2831.231 1232.765 3089.915 869.191 3522.564 ...
611.336 3136.280 1094.458
Natufian -0.203 0.126 1.200 0.982 1.486 ...
0.319 0.909 -0.416
Onge 0.005 0.642 0.511 -0.468 -0.475 ...
-0.505 0.167 0.660
Iran_N 0.921 0.561 0.779 0.299 0.159 ...
0.157 0.811 -0.002
Villabruna 1.600 1.995 2.312 -0.698 1.539 ...
-1.803 -2.143 1.871
Mixe 1.194 0.352 0.429 -0.892 -0.070 ...
-1.050 0.906 0.848
Ami -0.076 0.627 -0.275 -0.722 0.118 ...
-0.699 0.813 0.813
Nganasan 1.550 1.544 -0.286 -1.992 1.775 ...
-1.379 0.229 1.430
Itelmen 0.845 0.613 0.048 -0.978 -0.051 ...
-0.935 0.603 0.682
A:
scale 2.828 2.828 2.828 2.828 2.828 ...
2.828 2.828 2.828
Czech_EarlySlav 2.828 0.000 0.000 0.000 0.000 ...
0.000 0.000 0.000
Hungary_Slav 0.000 2.828 0.000 0.000 0.000 ...
0.000 0.000 0.000
Hungary_Langobard 0.000 0.000 2.828 0.000 0.000 ...
0.000 0.000 0.000
Hungary_Langobard_o1 0.000 0.000 0.000 2.828 0.000 ...
0.000 0.000 0.000
Hungary_Langobard_o2 0.000 0.000 0.000 0.000 2.828 ...
0.000 0.000 0.000
Italy_Imperial 0.000 0.000 0.000 0.000 0.000 ...
2.828 0.000 0.000
Moldova_Scythian 0.000 0.000 0.000 0.000 0.000 ...
0.000 2.828 0.000
Russia_Sunghir6 0.000 0.000 0.000 0.000 0.000 ...
0.000 0.000 2.828


best coefficients: -0.536 1.194 -0.956 0.412 0.070 -0.183 0.931 0.068
Jackknife mean: 0.486444190 -0.750369716 -0.200393501 -0.159037413 1.217376133 0.331475545 -0.639224207 0.713728968
std. errors: 0.777 0.880 0.754 0.467 0.358 ...
0.324 0.791 0.371

error covariance (* 1,000,000)
604080 -604106 486266 -243731 16145 ...
225815 -589412 104944
-604106 774803 -585887 284335 -73527 ...
-261454 652316 -186479
486266 -585887 569171 -322097 131425 ...
233813 -553743 41052
-243731 284335 -322097 217798 -122125 ...
-132027 292145 25703
16145 -73527 131425 -122125 127814 ...
47107 -73869 -52970
225815 -261454 233813 -132027 47107 ...
105290 -250685 32142
-589412 652316 -553743 292145 -73869 ...
-250685 625441 -102192
104944 -186479 41052 25703 -52970 ...
32142 -102192 137800


summ: Serb 8 0.000022 0.486 -0.750 -0.200 -0.159 1.217 0.331 -0.639 0.714 604080 -604106 486266 -243731 16145 ...
225815 -589412 104944 774803 -585887 ...
284335 -73527 -261454 652316 -186479 ...
569171 -322097 131425 233813 -553743 ...
41052 217798 -122125 -132027 292145 ...
25703 127814 47107 -73869 -52970 ...
105290 -250685 32142 625441 -102192 ...
137800

fixed pat wt dof chisq tail prob
00000000 0 1 18.051 2.15088e-05 -0.536 1.194 -0.956 0.412 0.070 -0.183 0.931 0.068 infeasible
00000001 1 2 8.779 0.0124087 -0.465 0.797 -0.388 0.179 0.119 -0.155 0.912 0.000 infeasible
00000010 1 2 9.917 0.00702473 0.329 -0.141 -0.038 0.006 0.276 0.163 0.000 0.405 infeasible
00000100 1 2 11.323 0.00347733 0.039 0.096 -0.247 0.159 0.254 0.000 0.345 0.354 infeasible
00001000 1 2 16.679 0.000238859 -0.303 0.708 -0.718 0.346 0.000 -0.016 0.630 0.353 infeasible
00010000 1 2 11.386 0.00336903 0.161 -0.047 -0.032 0.000 0.323 0.148 0.114 0.333 infeasible
00100000 1 2 8.954 0.011368 0.385 -0.338 0.000 0.150 0.063 0.197 -0.135 0.678 infeasible
01000000 1 2 12.523 0.00190849 0.210 0.000 -0.164 0.134 0.219 0.075 0.102 0.424 infeasible
10000000 1 2 9.843 0.00728879 0.000 0.209 -0.125 0.018 0.226 0.066 0.362 0.244 infeasible
00000011 2 3 13.598 0.00350594 -0.025 -0.138 0.754 -0.552 0.559 0.402 0.000 0.000 infeasible
00000101 2 3 13.446 0.00376414 -0.423 0.746 -0.241 -0.067 0.041 0.000 0.944 0.000 infeasible
00000110 2 3 13.961 0.00295846 0.259 -0.210 -0.440 0.315 0.546 0.000 0.000 0.529 infeasible
00001001 2 3 12.281 0.00648052 -0.748 1.185 -0.797 0.351 0.000 -0.268 1.277 0.000 infeasible
00001010 2 3 13.922 0.00301306 -1.163 -0.827 3.269 -2.291 0.000 1.571 0.000 0.443 infeasible
00001100 2 3 12.747 0.00521631 0.035 0.296 -0.505 0.319 0.000 0.000 0.369 0.485 infeasible
00010001 2 3 13.655 0.00341437 -0.631 0.875 -0.234 0.000 -0.094 -0.131 1.215 0.000 infeasible
00010010 2 3 11.331 0.0100665 0.376 -0.057 -0.009 0.000 0.126 0.188 0.000 0.376 infeasible
00010100 2 3 12.076 0.00712666 -0.422 0.635 -0.289 0.000 0.280 0.000 0.774 0.023 infeasible
00011000 2 3 12.833 0.00501239 -2.112 2.187 -0.681 0.000 0.000 -0.627 2.953 -0.720 infeasible
00100001 2 3 13.663 0.00340129 -0.067 0.137 0.000 -0.206 0.844 0.129 0.164 0.000 infeasible
00100010 2 3 15.256 0.00161046 0.218 -0.107 0.000 -0.039 0.393 0.186 0.000 0.349 infeasible
00100100 2 3 11.509 0.00926887 -1.179 1.149 0.000 -0.539 1.579 0.000 0.846 -0.856 infeasible
00101000 2 3 19.258 0.000241825 -0.247 0.650 0.000 -0.198 0.000 0.142 0.665 -0.010 infeasible
00110000 2 3 10.414 0.0153593 0.063 0.084 0.000 0.000 0.218 0.151 0.162 0.321
01000001 2 3 20.445 0.000137231 0.169 0.000 0.498 -0.504 0.419 0.395 0.023 0.000 infeasible
01000010 2 3 14.869 0.00193231 1.845 0.000 -0.688 1.522 -3.405 -0.123 0.000 1.848 infeasible
01000100 2 3 12.422 0.00606997 0.027 0.000 -0.177 0.157 0.387 0.000 0.233 0.374 infeasible
01001000 2 3 12.159 0.00685786 0.265 0.000 -0.550 0.438 0.000 -0.027 0.145 0.729 infeasible
01010000 2 3 28.370 3.03709e-06 0.350 0.000 0.047 0.000 0.118 0.151 0.063 0.272
01100000 2 3 12.443 0.00601112 0.315 0.000 0.000 -0.098 0.410 0.208 -0.050 0.216 infeasible
10000001 2 3 15.221 0.00163749 0.000 -0.106 0.638 -0.495 0.567 0.371 0.025 0.000 infeasible
10000010 2 3 10.636 0.0138674 0.000 -0.110 0.292 -0.380 0.946 0.252 0.000 0.000 infeasible
10000100 2 3 20.726 0.000119997 0.000 0.055 -0.586 0.328 0.491 0.000 0.174 0.537 infeasible
10001000 2 3 13.249 0.0041271 0.000 0.383 -0.686 0.453 0.000 -0.073 0.412 0.511 infeasible
10010000 2 3 12.822 0.00503734 0.000 0.176 -0.163 0.000 0.318 0.094 0.353 0.222 infeasible
10100000 2 3 10.061 0.0180575 0.000 0.098 0.000 -0.167 0.671 0.179 0.128 0.091 infeasible
11000000 2 3 13.074 0.00447841 0.000 0.000 -0.029 -0.185 0.832 0.156 0.110 0.116 infeasible
00000111 3 4 22.181 0.000184446 -0.385 -0.665 -0.532 -0.225 2.807 0.000 0.000 0.000 infeasible
00001011 3 4 23.649 9.39166e-05 0.710 0.154 0.114 -0.232 0.000 0.254 0.000 0.000 infeasible
00001101 3 4 17.799 0.00135062 -0.047 0.634 -0.275 0.016 0.000 0.000 0.672 0.000 infeasible
00001110 3 4 13.061 0.010981 0.601 0.013 -0.600 0.423 0.000 0.000 0.000 0.563 infeasible
00010011 3 4 17.144 0.00181205 -0.074 0.481 -0.203 0.000 0.619 0.178 0.000 0.000 infeasible
00010101 3 4 18.882 0.000828992 -0.410 1.132 -0.666 0.000 -0.236 0.000 1.179 0.000 infeasible
00010110 3 4 28.168 1.15302e-05 0.748 -0.647 0.311 0.000 0.042 0.000 0.000 0.545 infeasible
00011001 3 4 24.344 6.81328e-05 -0.279 0.963 -0.556 0.000 0.000 0.103 0.768 0.000 infeasible
00011010 3 4 19.274 0.000694328 0.399 0.115 -0.133 0.000 0.000 0.266 0.000 0.352 infeasible
00011100 3 4 14.472 0.00593018 -0.057 0.439 -0.033 0.000 0.000 0.000 0.587 0.064 infeasible
00100011 3 4 19.646 0.000586542 -0.368 -0.193 0.000 -0.279 1.718 0.122 0.000 0.000 infeasible
00100101 3 4 14.049 0.0071392 -3.161 -2.834 0.000 -1.655 8.122 0.000 0.528 0.000 infeasible
00100110 3 4 32.275 1.68108e-06 0.388 -0.270 0.000 0.249 0.179 0.000 0.000 0.453 infeasible
00101001 3 4 21.633 0.000237071 -0.941 0.954 0.000 -0.287 0.000 0.043 1.232 0.000 infeasible
00101010 3 4 17.543 0.00151523 0.421 -0.002 0.000 0.006 0.000 0.203 0.000 0.372 infeasible
00101100 3 4 15.104 0.00449016 -4.680 7.308 0.000 -2.345 0.000 0.000 4.958 -4.241 infeasible
00110001 3 4 23.077 0.000122203 4.806 -4.101 0.000 0.000 4.766 1.298 -5.769 0.000 infeasible
00110010 3 4 13.229 0.0102086 0.409 -0.083 0.000 0.000 0.097 0.202 0.000 0.374 infeasible
00110100 3 4 12.575 0.0135515 -0.463 0.786 0.000 0.000 -0.192 0.000 0.857 0.011 infeasible
00111000 3 4 25.713 3.6149e-05 0.573 -0.329 0.000 0.000 0.000 0.233 0.006 0.517 infeasible
01000011 3 4 16.262 0.00268716 0.267 0.000 0.087 -0.310 0.667 0.289 0.000 0.000 infeasible
01000101 3 4 18.628 0.000929964 -0.416 0.000 0.124 -0.239 0.941 0.000 0.591 0.000 infeasible
01000110 3 4 12.276 0.0154133 0.758 0.000 -0.873 0.619 -0.401 0.000 0.000 0.897 infeasible
01001001 3 4 16.578 0.0023342 -0.057 0.000 1.093 -0.768 0.000 0.479 0.252 0.000 infeasible
01001010 3 4 13.928 0.00752759 0.503 0.000 -0.267 0.160 0.000 0.139 0.000 0.464 infeasible
01001100 3 4 14.483 0.00590384 0.788 0.000 -1.391 0.875 0.000 0.000 -0.247 0.976 infeasible
01010001 3 4 20.972 0.000320742 -1.026 0.000 -0.669 0.000 2.287 -0.078 0.486 0.000 infeasible
01010010 3 4 23.331 0.00010875 -0.041 0.000 -0.277 0.000 0.962 0.095 0.000 0.262 infeasible
01010100 3 4 16.638 0.00227263 -0.162 0.000 -0.162 0.000 0.862 0.000 0.262 0.199 infeasible
01011000 3 4 18.176 0.00114021 0.428 0.000 0.097 0.000 0.000 0.168 0.004 0.303
01100001 3 4 14.661 0.00545886 5.872 0.000 0.000 -1.343 -1.494 2.901 -4.936 0.000 infeasible
01100010 3 4 17.653 0.00144249 0.278 0.000 0.000 -0.024 0.284 0.168 0.000 0.294 infeasible
01100100 3 4 14.402 0.00611698 -1.589 0.000 0.000 -2.398 7.951 0.000 0.571 -3.535 infeasible
01101000 3 4 13.336 0.00974597 0.439 0.000 0.000 -0.015 0.000 0.205 0.008 0.364 infeasible
01110000 3 4 15.214 0.00427797 -0.008 0.000 0.000 0.000 0.389 0.110 0.259 0.251 infeasible
10000011 3 4 24.302 6.94772e-05 0.000 -0.550 1.165 -0.719 0.791 0.313 0.000 0.000 infeasible
10000101 3 4 16.964 0.00196396 0.000 0.806 -0.358 -0.002 -0.258 0.000 0.813 0.000 infeasible
10000110 3 4 14.926 0.00485653 0.000 -0.492 -0.208 0.031 1.361 0.000 0.000 0.309 infeasible
10001001 3 4 20.783 0.000349694 0.000 0.503 0.125 -0.342 0.000 0.276 0.438 0.000 infeasible
10001010 3 4 18.324 0.00106654 0.000 -0.774 -2.005 1.914 0.000 -0.534 0.000 2.399 infeasible
10001100 3 4 14.989 0.00472479 0.000 0.387 -0.428 0.237 0.000 0.000 0.416 0.388 infeasible
10010001 3 4 15.670 0.00349495 0.000 0.794 -0.216 0.000 -0.210 0.115 0.518 0.000 infeasible
10010010 3 4 16.024 0.00298756 0.000 -0.390 -0.056 0.000 0.673 0.185 0.000 0.588 infeasible
10010100 3 4 20.627 0.000375462 0.000 0.457 0.112 0.000 0.109 0.000 0.436 -0.115 infeasible
10011000 3 4 11.322 0.0231758 0.000 0.377 -0.147 0.000 0.000 0.074 0.511 0.184 infeasible
10100001 3 4 23.505 0.000100363 0.000 0.327 0.000 -0.314 0.425 0.273 0.288 0.000 infeasible
10100010 3 4 26.969 2.01641e-05 0.000 1.225 0.000 -0.740 -0.008 0.735 0.000 -0.213 infeasible
10100100 3 4 18.450 0.00100777 0.000 -0.931 0.000 0.036 1.736 0.000 -0.322 0.479 infeasible
10101000 3 4 21.144 0.000296558 0.000 0.455 0.000 -0.241 0.000 0.217 0.479 0.090 infeasible
10110000 3 4 14.520 0.00580702 0.000 0.365 0.000 0.000 0.082 0.112 0.298 0.142
11000001 3 4 21.062 0.000307847 0.000 0.000 0.254 -0.365 0.785 0.231 0.095 0.000 infeasible
11000010 3 4 16.160 0.00281195 0.000 0.000 9.800 -10.312 0.257 5.410 0.000 -4.154 infeasible
11000100 3 4 15.591 0.00362037 0.000 0.000 -0.054 0.111 0.342 0.000 0.283 0.317 infeasible
11001000 3 4 16.015 0.0029994 0.000 0.000 10.854 -8.466 0.000 4.516 -0.867 -5.037 infeasible
11010000 3 4 21.271 0.000279798 0.000 0.000 -0.214 0.000 0.785 0.114 0.076 0.239 infeasible
11100000 3 4 11.406 0.0223614 0.000 0.000 0.000 -0.162 0.539 0.229 0.170 0.224 infeasible
00001111 4 5 27.658 4.24551e-05 -9.901 19.079 -16.797 8.619 0.000 0.000 0.000 0.000 infeasible
00010111 4 5 30.447 1.20419e-05 1.213 -0.368 -0.632 0.000 0.787 0.000 0.000 0.000 infeasible
00011011 4 5 35.086 1.44612e-06 0.812 0.167 -0.167 0.000 0.000 0.188 0.000 0.000 infeasible
00011101 4 5 29.454 1.88827e-05 0.063 0.718 -0.603 0.000 0.000 0.000 0.822 0.000 infeasible
00011110 4 5 31.057 9.12687e-06 -32.672 -13.922 22.402 0.000 0.000 0.000 0.000 25.191 infeasible
00100111 4 5 18.600 0.00228147 -0.019 -0.271 0.000 -0.143 1.433 0.000 0.000 0.000 infeasible
00101011 4 5 30.315 1.27875e-05 -0.957 1.409 0.000 0.279 0.000 0.270 0.000 0.000 infeasible
00101101 4 5 27.782 4.01534e-05 -1.076 0.911 0.000 -0.500 0.000 0.000 1.665 0.000 infeasible
00101110 4 5 32.408 4.9324e-06 0.627 -0.429 0.000 0.324 0.000 0.000 0.000 0.478 infeasible
00110011 4 5 25.452 0.000113941 0.053 0.840 0.000 0.000 -0.113 0.219 0.000 0.000 infeasible
00110101 4 5 23.871 0.000229823 0.164 0.423 0.000 0.000 -0.030 0.000 0.443 0.000 infeasible
00110110 4 5 17.558 0.00355527 1.782 -2.576 0.000 0.000 0.356 0.000 0.000 1.438 infeasible
00111001 4 5 21.594 0.000625229 -0.201 0.477 0.000 0.000 0.000 0.009 0.715 0.000 infeasible
00111010 4 5 37.805 4.12993e-07 0.362 0.173 0.000 0.000 0.000 0.218 0.000 0.247
00111100 4 5 15.860 0.00725681 -0.413 0.421 0.000 0.000 0.000 0.000 0.872 0.120 infeasible
01000111 4 5 26.715 6.48012e-05 -0.221 0.000 -0.828 0.155 1.895 0.000 0.000 0.000 infeasible
01001011 4 5 22.209 0.000477737 0.262 0.000 0.956 -0.779 0.000 0.562 0.000 0.000 infeasible
01001101 4 5 40.307 1.29508e-07 1.948 0.000 -0.968 0.741 0.000 0.000 -0.721 0.000 infeasible
01001110 4 5 20.739 0.000907347 0.738 0.000 -3.485 1.348 0.000 0.000 0.000 2.399 infeasible
01010011 4 5 22.329 0.000453371 -0.552 0.000 -0.496 0.000 1.949 0.099 0.000 0.000 infeasible
01010101 4 5 24.367 0.000184553 -0.158 0.000 -0.257 0.000 1.043 0.000 0.372 0.000 infeasible
01010110 4 5 16.420 0.00574275 -0.570 0.000 -0.554 0.000 1.986 0.000 0.000 0.139 infeasible
01011001 4 5 21.643 0.00061213 7.571 0.000 -3.674 0.000 0.000 1.962 -4.859 0.000 infeasible
01011010 4 5 32.867 4.00003e-06 -0.760 0.000 0.740 0.000 0.000 0.205 0.000 0.815 infeasible
01011100 4 5 16.886 0.00472024 -0.298 0.000 0.355 0.000 0.000 0.000 0.576 0.368 infeasible
01100011 4 5 28.108 3.46791e-05 0.169 0.000 0.000 -0.339 0.885 0.286 0.000 0.000 infeasible
01100101 4 5 33.920 2.47005e-06 -0.608 0.000 0.000 -0.315 0.962 0.000 0.961 0.000 infeasible
01100110 4 5 23.599 0.000259246 -0.658 0.000 0.000 -0.811 3.723 0.000 0.000 -1.254 infeasible
01101001 4 5 18.306 0.00258582 45.445 0.000 0.000 -23.061 0.000 32.348 -53.732 0.000 infeasible
01101010 4 5 25.309 0.000121458 0.471 0.000 0.000 0.090 0.000 0.119 0.000 0.320
01101100 4 5 13.919 0.0161342 0.123 0.000 0.000 0.150 0.000 0.000 0.306 0.421
01110001 4 5 24.473 0.000176099 -0.239 0.000 0.000 0.000 0.832 -0.050 0.457 0.000 infeasible
01110010 4 5 21.036 0.000797387 0.045 0.000 0.000 0.000 0.768 0.095 0.000 0.093
01110100 4 5 22.438 0.000432147 0.620 0.000 0.000 0.000 0.740 0.000 -0.142 -0.217 infeasible
01111000 4 5 17.030 0.00444244 0.469 0.000 0.000 0.000 0.000 0.185 -0.013 0.359 infeasible
10000111 4 5 26.796 6.24949e-05 0.000 27.533 -32.449 11.075 -5.159 0.000 0.000 0.000 infeasible
10001011 4 5 23.944 0.000222601 0.000 0.822 -0.004 -0.256 0.000 0.438 0.000 0.000 infeasible
10001101 4 5 19.165 0.00179047 0.000 0.499 -0.102 -0.060 0.000 0.000 0.663 0.000 infeasible
10001110 4 5 26.293 7.82975e-05 0.000 0.885 -0.954 0.651 0.000 0.000 0.000 0.418 infeasible
10010011 4 5 27.873 3.8541e-05 0.000 1.400 0.035 0.000 -0.907 0.472 0.000 0.000 infeasible
10010101 4 5 23.390 0.000284263 0.000 0.477 -0.119 0.000 0.199 0.000 0.443 0.000 infeasible
10010110 4 5 21.305 0.000709362 0.000 0.489 0.285 0.000 0.753 0.000 0.000 -0.526 infeasible
10011001 4 5 18.753 0.00213685 0.000 0.614 -0.126 0.000 0.000 0.051 0.460 0.000 infeasible
10011010 4 5 12.655 0.0268316 0.000 -0.237 0.107 0.000 0.000 0.307 0.000 0.823 infeasible
10011100 4 5 29.544 1.813e-05 0.000 0.508 -0.266 0.000 0.000 0.000 0.783 -0.025 infeasible
10100011 4 5 34.390 1.99074e-06 0.000 -0.765 0.000 2.890 0.653 -1.778 0.000 0.000 infeasible
10100101 4 5 22.653 0.000393175 0.000 1.553 0.000 0.098 -2.041 0.000 1.390 0.000 infeasible
10100110 4 5 26.612 6.78681e-05 0.000 -1.217 0.000 -0.166 1.876 0.000 0.000 0.506 infeasible
10101001 4 5 41.558 7.23778e-08 0.000 0.583 0.000 0.095 0.000 -0.020 0.343 0.000 infeasible
10101010 4 5 24.197 0.000198942 0.000 1.313 0.000 -0.856 0.000 0.761 0.000 -0.218 infeasible
10101100 4 5 15.796 0.00745173 0.000 0.587 0.000 -0.106 0.000 0.000 0.784 -0.265 infeasible
10110001 4 5 21.736 0.0005878 0.000 1.308 0.000 0.000 -1.502 0.041 1.153 0.000 infeasible
10110010 4 5 20.143 0.00117488 0.000 -0.568 0.000 0.000 1.288 0.036 0.000 0.243 infeasible
10110100 4 5 18.939 0.00197267 0.000 19.731 0.000 0.000 -16.855 0.000 6.168 -8.044 infeasible
10111000 4 5 18.719 0.00216842 0.000 0.433 0.000 0.000 0.000 0.046 0.460 0.061
11000011 4 5 19.310 0.00168239 0.000 0.000 0.717 -0.677 0.426 0.533 0.000 0.000 infeasible
11000101 4 5 15.899 0.00713973 0.000 0.000 -0.186 -0.068 1.091 0.000 0.164 0.000 infeasible
11000110 4 5 28.134 3.42645e-05 0.000 0.000 -1.184 0.432 1.462 0.000 0.000 0.290 infeasible
11001001 4 5 20.695 0.000925009 0.000 0.000 1.323 -1.032 0.000 0.763 -0.055 0.000 infeasible
11001010 4 5 23.286 0.000297618 0.000 0.000 5.620 -4.830 0.000 2.337 0.000 -2.127 infeasible
11001100 4 5 18.344 0.00254496 0.000 0.000 -0.473 0.357 0.000 0.000 0.440 0.676 infeasible
11010001 4 5 18.141 0.0027746 0.000 0.000 -0.598 0.000 1.478 0.089 0.031 0.000 infeasible
11010010 4 5 24.372 0.000184128 0.000 0.000 -0.429 0.000 0.893 0.221 0.000 0.316 infeasible
11010100 4 5 19.096 0.00184446 0.000 0.000 -2.738 0.000 4.482 0.000 -0.485 -0.258 infeasible
11011000 4 5 14.458 0.0129479 0.000 0.000 0.029 0.000 0.000 0.259 0.160 0.552
11100001 4 5 23.927 0.000224254 0.000 0.000 0.000 -0.297 0.931 0.225 0.141 0.000 infeasible
11100010 4 5 17.012 0.00447672 0.000 0.000 0.000 -0.483 1.503 0.267 0.000 -0.287 infeasible
11100100 4 5 21.798 0.000571965 0.000 0.000 0.000 0.076 0.247 0.000 0.353 0.324
11101000 4 5 16.429 0.00572058 0.000 0.000 0.000 0.133 0.000 0.111 0.299 0.458
11110000 4 5 15.690 0.00778862 0.000 0.000 0.000 0.000 0.392 0.151 0.118 0.338
00011111 5 6 32.091 1.56759e-05 -3.710 1.549 3.162 0.000 0.000 0.000 0.000 0.000 infeasible
00101111 5 6 41.851 1.96794e-07 0.705 0.098 0.000 0.197 0.000 0.000 0.000 0.000
00110111 5 6 48.207 1.07444e-08 0.556 -0.068 0.000 0.000 0.513 0.000 0.000 0.000 infeasible
00111011 5 6 27.039 0.000142361 0.080 0.748 0.000 0.000 0.000 0.173 0.000 0.000
00111101 5 6 20.997 0.00183684 -0.428 0.777 0.000 0.000 0.000 0.000 0.651 0.000 infeasible
00111110 5 6 22.439 0.00100794 -7.299 18.205 0.000 0.000 0.000 0.000 0.000 -9.906 infeasible
01001111 5 6 33.147 9.82472e-06 6.194 0.000 -5.626 0.432 0.000 0.000 0.000 0.000 infeasible
01010111 5 6 45.639 3.49319e-08 1.313 0.000 -0.348 0.000 0.036 0.000 0.000 0.000 infeasible
01011011 5 6 32.683 1.20662e-05 1.378 0.000 -0.478 0.000 0.000 0.100 0.000 0.000 infeasible
01011101 5 6 56.297 2.53536e-10 -249.878 0.000 349.967 0.000 0.000 0.000 -99.089 0.000 infeasible
01011110 5 6 28.950 6.21786e-05 1.612 0.000 -0.052 0.000 0.000 0.000 0.000 -0.560 infeasible
01100111 5 6 34.104 6.42309e-06 0.645 0.000 0.000 0.066 0.289 0.000 0.000 0.000
01101011 5 6 35.433 3.55253e-06 1.684 0.000 0.000 -1.633 0.000 0.949 0.000 0.000 infeasible
01101101 5 6 70.841 2.74863e-13 0.446 0.000 0.000 -1.306 0.000 0.000 1.860 0.000 infeasible
01101110 5 6 34.060 6.55135e-06 -1.761 0.000 0.000 1.135 0.000 0.000 0.000 1.626 infeasible
01110011 5 6 31.415 2.112e-05 -0.451 0.000 0.000 0.000 1.360 0.091 0.000 0.000 infeasible
01110101 5 6 36.826 1.90388e-06 -0.030 0.000 0.000 0.000 0.777 0.000 0.253 0.000 infeasible
01110110 5 6 23.577 0.000624586 -1.311 0.000 0.000 0.000 2.570 0.000 0.000 -0.259 infeasible
01111001 5 6 51.455 2.39944e-09 0.839 0.000 0.000 0.000 0.000 -0.142 0.303 0.000 infeasible
01111010 5 6 16.472 0.0114333 0.480 0.000 0.000 0.000 0.000 0.182 0.000 0.338
01111100 5 6 35.788 3.03025e-06 0.655 0.000 0.000 0.000 0.000 0.000 0.161 0.184
10001111 5 6 22.439 0.00100788 0.000 1.368 -0.889 0.521 0.000 0.000 0.000 0.000 infeasible
10010111 5 6 29.639 4.60311e-05 0.000 -0.222 -0.677 0.000 1.898 0.000 0.000 0.000 infeasible
10011011 5 6 16.563 0.011033 0.000 0.966 -0.238 0.000 0.000 0.272 0.000 0.000 infeasible
10011101 5 6 41.491 2.31715e-07 0.000 1.536 -1.362 0.000 0.000 0.000 0.826 0.000 infeasible
10011110 5 6 45.209 4.25278e-08 0.000 -3.088 2.604 0.000 0.000 0.000 0.000 1.484 infeasible
10100111 5 6 28.074 9.10035e-05 0.000 -1.535 0.000 -0.687 3.222 0.000 0.000 0.000 infeasible
10101011 5 6 19.285 0.00370907 0.000 0.780 0.000 0.076 0.000 0.143 0.000 0.000
10101101 5 6 45.366 3.95816e-08 0.000 0.226 0.000 -0.244 0.000 0.000 1.018 0.000 infeasible
10101110 5 6 37.938 1.1554e-06 0.000 0.537 0.000 0.373 0.000 0.000 0.000 0.090
10110011 5 6 32.006 1.62716e-05 0.000 0.561 0.000 0.000 0.205 0.234 0.000 0.000
10110101 5 6 25.682 0.000255129 0.000 0.835 0.000 0.000 -0.556 0.000 0.721 0.000 infeasible
10110110 5 6 18.381 0.005348 0.000 1.180 0.000 0.000 0.760 0.000 0.000 -0.940 infeasible
10111001 5 6 36.126 2.60547e-06 0.000 0.450 0.000 0.000 0.000 -0.073 0.623 0.000 infeasible
10111010 5 6 21.054 0.00179415 0.000 0.551 0.000 0.000 0.000 0.246 0.000 0.203
10111100 5 6 17.541 0.0074863 0.000 0.528 0.000 0.000 0.000 0.000 0.491 -0.019 infeasible
11000111 5 6 28.354 8.0581e-05 0.000 0.000 -0.399 0.011 1.388 0.000 0.000 0.000 infeasible
11001011 5 6 22.830 0.00085547 0.000 0.000 1.444 -1.729 0.000 1.284 0.000 0.000 infeasible
11001101 5 6 33.847 7.20073e-06 0.000 0.000 0.342 -0.222 0.000 0.000 0.880 0.000 infeasible
11001110 5 6 19.393 0.0035495 0.000 0.000 -1.981 1.244 0.000 0.000 0.000 1.738 infeasible
11010011 5 6 27.029 0.000143018 0.000 0.000 -0.581 0.000 1.466 0.116 0.000 0.000 infeasible
11010101 5 6 37.094 1.68808e-06 0.000 0.000 -0.339 0.000 0.985 0.000 0.354 0.000 infeasible
11010110 5 6 37.954 1.14694e-06 0.000 0.000 -3.130 0.000 4.890 0.000 0.000 -0.760 infeasible
11011001 5 6 33.013 1.04267e-05 0.000 0.000 0.284 0.000 0.000 -0.088 0.804 0.000 infeasible
11011010 5 6 29.763 4.36088e-05 0.000 0.000 0.209 0.000 0.000 0.276 0.000 0.515
11011100 5 6 37.156 1.64162e-06 0.000 0.000 0.283 0.000 0.000 0.000 0.631 0.085
11100011 5 6 17.415 0.00787425 0.000 0.000 0.000 -0.383 1.089 0.294 0.000 0.000 infeasible
11100101 5 6 21.146 0.00172694 0.000 0.000 0.000 -0.051 0.780 0.000 0.270 0.000 infeasible
11100110 5 6 58.535 8.92537e-11 0.000 0.000 0.000 0.384 0.273 0.000 0.000 0.342
11101001 5 6 67.339 1.43439e-12 0.000 0.000 0.000 -2.398 0.000 0.344 3.054 0.000 infeasible
11101010 5 6 16.309 0.0121883 0.000 0.000 0.000 0.072 0.000 0.292 0.000 0.636
11101100 5 6 53.436 9.58345e-10 0.000 0.000 0.000 -0.041 0.000 0.000 0.668 0.373 infeasible
11110001 5 6 26.960 0.000147336 0.000 0.000 0.000 0.000 0.901 0.046 0.053 0.000
11110010 5 6 17.260 0.00837438 0.000 0.000 0.000 0.000 0.296 0.274 0.000 0.430
11110100 5 6 33.672 7.78274e-06 0.000 0.000 0.000 0.000 0.789 0.000 0.273 -0.062 infeasible
11111000 5 6 28.984 6.12612e-05 0.000 0.000 0.000 0.000 0.000 0.831 -1.214 1.383 infeasible
00111111 6 7 47.612 4.24012e-08 1.081 -0.081 0.000 0.000 0.000 0.000 0.000 0.000 infeasible
01011111 6 7 53.397 3.09434e-09 -2.093 0.000 3.093 0.000 0.000 0.000 0.000 0.000 infeasible
01101111 6 7 69.117 2.22821e-12 1.205 0.000 0.000 -0.205 0.000 0.000 0.000 0.000 infeasible
01110111 6 7 28.416 0.000184828 0.536 0.000 0.000 0.000 0.464 0.000 0.000 0.000
01111011 6 7 29.439 0.000120293 0.897 0.000 0.000 0.000 0.000 0.103 0.000 0.000
01111101 6 7 72.051 5.68225e-13 0.463 0.000 0.000 0.000 0.000 0.000 0.537 0.000
01111110 6 7 40.974 8.18814e-07 0.936 0.000 0.000 0.000 0.000 0.000 0.000 0.064
10011111 6 7 57.459 4.84365e-10 0.000 1.977 -0.977 0.000 0.000 0.000 0.000 0.000 infeasible
10101111 6 7 28.690 0.000164792 0.000 0.755 0.000 0.245 0.000 0.000 0.000 0.000
10110111 6 7 26.464 0.00041605 0.000 -0.194 0.000 0.000 1.194 0.000 0.000 0.000 infeasible
10111011 6 7 19.560 0.00660389 0.000 0.780 0.000 0.000 0.000 0.220 0.000 0.000
10111101 6 7 48.603 2.7137e-08 0.000 0.418 0.000 0.000 0.000 0.000 0.582 0.000
10111110 6 7 35.941 7.4367e-06 0.000 1.691 0.000 0.000 0.000 0.000 0.000 -0.691 infeasible
11001111 6 7 235.204 0 0.000 0.000 0.702 0.298 0.000 0.000 0.000 0.000
11010111 6 7 200.231 0 0.000 0.000 1.912 0.000 -0.912 0.000 0.000 0.000 infeasible
11011011 6 7 158.608 0 0.000 0.000 0.914 0.000 0.000 0.086 0.000 0.000
11011101 6 7 59.828 1.63371e-10 0.000 0.000 0.459 0.000 0.000 0.000 0.541 0.000
11011110 6 7 77.433 4.59315e-14 0.000 0.000 0.582 0.000 0.000 0.000 0.000 0.418
11100111 6 7 41.430 6.69446e-07 0.000 0.000 0.000 0.042 0.958 0.000 0.000 0.000
11101011 6 7 51.643 6.8663e-09 0.000 0.000 0.000 2.555 0.000 -1.555 0.000 0.000 infeasible
11101101 6 7 62.084 5.78433e-11 0.000 0.000 0.000 -1.253 0.000 0.000 2.253 0.000 infeasible
11101110 6 7 57.001 5.97541e-10 0.000 0.000 0.000 0.413 0.000 0.000 0.000 0.587
11110011 6 7 25.480 0.000623485 0.000 0.000 0.000 0.000 0.934 0.066 0.000 0.000
11110101 6 7 33.359 2.2694e-05 0.000 0.000 0.000 0.000 0.854 0.000 0.146 0.000
11110110 6 7 31.205 5.69779e-05 0.000 0.000 0.000 0.000 0.743 0.000 0.000 0.257
11111001 6 7 48.900 2.37311e-08 0.000 0.000 0.000 0.000 0.000 -0.249 1.249 0.000 infeasible
11111010 6 7 20.328 0.00490363 0.000 0.000 0.000 0.000 0.000 0.313 0.000 0.687
11111100 6 7 39.780 1.38688e-06 0.000 0.000 0.000 0.000 0.000 0.000 0.851 0.149
01111111 7 8 36.166 1.63762e-05 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
10111111 7 8 58.754 8.17644e-10 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000
11011111 7 8 153.891 3.02368e-29 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000
11101111 7 8 145.780 1.48622e-27 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000
11110111 7 8 28.155 0.000445744 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000
11111011 7 8 1448.612 0 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000
11111101 7 8 47.131 1.44796e-07 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000
11111110 7 8 55.836 3.03671e-09 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000
best pat: 00000000 2.15088e-05 - -
best pat: 00000001 0.0124087 chi(nested): -9.272 p-value for nested model: -nan infeasible
best pat: 01010000 3.03709e-06 not nested
best pat: 10110000 0.00580702 not nested
best pat: 01101100 0.0161342 not nested
best pat: 11101010 0.0121883 not nested
best pat: 10111011 0.00660389 not nested
best pat: 11110111 0.000445744 not nested

coeffs: -0.536 1.194 -0.956 0.412 0.070 -0.183 0.931 0.068

## dscore:: f_4(Base, Fit, Rbase, right2)
## genstat:: f_4(Base, Fit, right1, right2)

details: Czech_EarlySlav Natufian -0.000072 -0.250481
details: Hungary_Slav Natufian 0.000102 0.304828
details: Hungary_Langobard Natufian 0.000388 3.180591
details: Hungary_Langobard_o1 Natufian 0.001130 3.113443
details: Hungary_Langobard_o2 Natufian 0.000422 2.054134
details: Italy_Imperial Natufian 0.000522 5.088506
details: Moldova_Scythian Natufian 0.000290 1.126055
details: Russia_Sunghir6 Natufian -0.000380 -1.133395
dscore: Natufian f4: 0.000432 Z: 0.738738

details: Czech_EarlySlav Onge 0.000002 0.006286
details: Hungary_Slav Onge 0.000521 1.544997
details: Hungary_Langobard Onge 0.000166 1.614390
details: Hungary_Langobard_o1 Onge -0.000538 -1.748170
details: Hungary_Langobard_o2 Onge -0.000135 -0.706880
details: Italy_Imperial Onge -0.000827 -9.723909
details: Moldova_Scythian Onge 0.000053 0.312221
details: Russia_Sunghir6 Onge 0.000603 2.175346
dscore: Onge f4: 0.000474 Z: 0.970603

details: Czech_EarlySlav Iran_N 0.000325 2.168814
details: Hungary_Slav Iran_N 0.000455 1.779585
details: Hungary_Langobard Iran_N 0.000252 2.396625
details: Hungary_Langobard_o1 Iran_N 0.000344 1.685504
details: Hungary_Langobard_o2 Iran_N 0.000045 0.215040
details: Italy_Imperial Iran_N 0.000257 2.845024
details: Moldova_Scythian Iran_N 0.000258 1.600666
details: Russia_Sunghir6 Iran_N -0.000001 -0.005656
dscore: Iran_N f4: 0.000466 Z: 1.310817

details: Czech_EarlySlav Villabruna 0.000565 1.571130
details: Hungary_Slav Villabruna 0.001618 3.916689
details: Hungary_Langobard Villabruna 0.000748 7.369016
details: Hungary_Langobard_o1 Villabruna -0.000803 -2.208170
details: Hungary_Langobard_o2 Villabruna 0.000437 1.547861
details: Italy_Imperial Villabruna -0.002949 -24.463715
details: Moldova_Scythian Villabruna -0.000683 -2.768401
details: Russia_Sunghir6 Villabruna 0.001710 3.595984
dscore: Villabruna f4: 0.000633 Z: 0.977743

details: Czech_EarlySlav Mixe 0.000422 1.570869
details: Hungary_Slav Mixe 0.000286 0.907110
details: Hungary_Langobard Mixe 0.000139 1.118774
details: Hungary_Langobard_o1 Mixe -0.001026 -4.933370
details: Hungary_Langobard_o2 Mixe -0.000020 -0.095771
details: Italy_Imperial Mixe -0.001718 -18.550810
details: Moldova_Scythian Mixe 0.000289 1.646807
details: Russia_Sunghir6 Mixe 0.000775 2.859979
dscore: Mixe f4: 0.000195 Z: 0.396624

details: Czech_EarlySlav Ami -0.000027 -0.097120
details: Hungary_Slav Ami 0.000508 1.716000
details: Hungary_Langobard Ami -0.000089 -0.721565
details: Hungary_Langobard_o1 Ami -0.000830 -3.234701
details: Hungary_Langobard_o2 Ami 0.000033 0.134640
details: Italy_Imperial Ami -0.001143 -11.715003
details: Moldova_Scythian Ami 0.000259 1.261952
details: Russia_Sunghir6 Ami 0.000742 2.660776
dscore: Ami f4: 0.000868 Z: 1.703686

details: Czech_EarlySlav Nganasan 0.000548 1.603660
details: Hungary_Slav Nganasan 0.001253 2.195466
details: Hungary_Langobard Nganasan -0.000093 -0.658560
details: Hungary_Langobard_o1 Nganasan -0.002292 -6.013191
details: Hungary_Langobard_o2 Nganasan 0.000504 1.017198
details: Italy_Imperial Nganasan -0.002256 -18.668418
details: Moldova_Scythian Nganasan 0.000073 0.284060
details: Russia_Sunghir6 Nganasan 0.001307 2.673449
dscore: Nganasan f4: 0.000951 Z: 1.154746

details: Czech_EarlySlav Itelmen 0.000299 0.960198
details: Hungary_Slav Itelmen 0.000497 1.752273
details: Hungary_Langobard Itelmen 0.000015 0.121217
details: Hungary_Langobard_o1 Itelmen -0.001125 -3.443461
details: Hungary_Langobard_o2 Itelmen -0.000014 -0.055376
details: Italy_Imperial Itelmen -0.001529 -16.260003
details: Moldova_Scythian Itelmen 0.000192 0.867014
details: Russia_Sunghir6 Itelmen 0.000623 1.833027
dscore: Itelmen f4: 0.000456 Z: 0.898694

gendstat: Mbuti Natufian 0.739
gendstat: Mbuti Onge 0.971
gendstat: Mbuti Iran_N 1.311
gendstat: Mbuti Villabruna 0.978
gendstat: Mbuti Mixe 0.397
gendstat: Mbuti Ami 1.704
gendstat: Mbuti Nganasan 1.155
gendstat: Mbuti Itelmen 0.899
gendstat: Natufian Onge 0.076
gendstat: Natufian Iran_N 0.073
gendstat: Natufian Villabruna 0.363
gendstat: Natufian Mixe -0.379
gendstat: Natufian Ami 0.778
gendstat: Natufian Nganasan 0.779
gendstat: Natufian Itelmen 0.043
gendstat: Onge Iran_N -0.023
gendstat: Onge Villabruna 0.239
gendstat: Onge Mixe -0.526
gendstat: Onge Ami 0.936
gendstat: Onge Nganasan 0.680
gendstat: Onge Itelmen -0.041
gendstat: Iran_N Villabruna 0.330
gendstat: Iran_N Mixe -0.747
gendstat: Iran_N Ami 1.126
gendstat: Iran_N Nganasan 0.786
gendstat: Iran_N Itelmen -0.030
gendstat: Villabruna Mixe -0.750
gendstat: Villabruna Ami 0.387
gendstat: Villabruna Nganasan 0.431
gendstat: Villabruna Itelmen -0.298
gendstat: Mixe Ami 1.811
gendstat: Mixe Nganasan 1.392
gendstat: Mixe Itelmen 0.790
gendstat: Ami Nganasan 0.170
gendstat: Ami Itelmen -1.343
gendstat: Nganasan Itelmen -0.889

##end of qpAdm: 305.462 seconds cpu 4164.452 Mbytes in use

[Zoro]

qpAdm is a professional admixture tool, used in genetic studies, like that last one about Rome.
It isn't that complicated to use if you are a linux user.

qpAdm works directly with 100,000s of SNPs, not just with a dozen axes or admixture components like g25 or eurogenes k13.
in G25 Bell Beaker can be identical to a Norwegian, a half Jew, half Ukrainian turns out identical to to a South Slav, etc. it makes sense, but g25 is only good for models with 2 or 3 populations, or very distinct populations. qpAdm should be able to paint a more detailed picture.

i tried to model Serbs with 9 ancient populations in qpAdm, this is the model with the highest probability:

Serbs
Czech_EarlySlav 12.30%
Hungary_Slav 0.00%
Hungary_Langobard 0.00%
Hungary_Langobard_o1 15.00%
Hungary_Langobard_o2 0.00%
Italy_Imperial 0.00%
Moldova_Scythian 30.60%
Russia_Sunghir6 42.10%

Italy Imperial is completely ignored! In gedmatch calcs and g25 Italy_Imperial would work well. For our med shift, the Hungary Langobard outlier was picked instead, who is more likely related to us, given his geographic position. Also the Moldova_Scythians, who were Italian like. In usual calcualtors some Serbs get a lot of Moldova Scythian, some don't get it at all. But it makes sense that these Scythians were absorbed by Slavs on their way to the Balkans.


If you look at your raw output from this run you'll see that your standard errors are unacceptably high. My guess is your errors are between 50% and 100% for some of those. This is because your sources are too closely related. To get your errors down to 10% or less drop a bunch of those closely related pops.

Also qpAdm is not really designed for more than 4 source pops. If you read the papers you'll notice that they usually use 2 or 3 or at the most 4 source pops. You'll need to limit your sources to 4 to get meaningful results.


Edit:
Just saw your posted output. Like i guessed these standard errors are unacceptable. Follow what i wrote and they'll come way down

std. errors: 0.777 0.880 0.754 0.467 0.358 ...
0.324 0.791 0.371

[vbnetkhio]

What you used as outgroup pops? Some say such choice is important.

i used these:
https://comppopgenworkshop2019.readthedocs.io/en/latest/contents/05_qpwave_qpadm/qpwave_qpadm.html#preparing-left-and-right-populations
"Mbuti Natufian Onge Iran_N Villabruna Mixe Ami Nganasan Itelmen"

Nganasan from the Turkic file from evolbio.ut.ee, the rest from Reich

[Zoro]

i used these:
https://comppopgenworkshop2019.readthedocs.io/en/latest/contents/05_qpwave_qpadm/qpwave_qpadm.html#preparing-left-and-right-populations
"Mbuti Natufian Onge Iran_N Villabruna Mixe Ami Nganasan Itelmen"

Nganasan from the Turkic file from evolbio.ut.ee, the rest from Reich


Try not to use too many moderns in outgroups and also you don't need to use all these Siberian/Amerindian ones

Mixe Ami Nganasan Itelmen


This is one of the best outgroup sets for modeling predominantly West Eurasians. You'll see most of these in the papers and you'll get good results

Mbuti.DG
Russia_Ust_Ishim.DG
China_Tianyuan
Goyet_Neanderthal.SG
Russia_Sunghir3.SG
Russia_Kostenki14.SG
Onge_1000G
Morocco_Iberomaurusian
Israel_Natufian
Iberia_ElMiron
Russia_MA1_HG.SG
Georgia_Satsurblia.SG
DevilsCave_N.SG
Papuan.DG
Anatolia_N
Iran_GanjDareh_N
Switzerland_Bichon.SG

[vbnetkhio]

Screw this bullcrap man it's not worth the effort. For instance , in order to convert the geno+snp+ind files to plink format you need to use convertf from Eigensoft but the guy from Harvard was too mentally retarded or Lazy to create a Unix style man page for the program and he did even follow a GNUism by a --help switch functionality e.g. ./convertf -help.

This is one example of Linux people being stupid and f*cking up UNIX by not following standards. See here for standard documentation format on a Unix-like or Unix system :

https://man.openbsd.org/?query=man+man&apropos=0&sec=0&arch=default&manpath=OpenBSD-current

^^ The convertf documentation is convoluted at best compared to that.

on Ubuntu you can install convertf with "apt-get install EIGENSTRAT" idk if that works on Debian.

then make a parfile like this:

genotypename: v42.4.1240K.geno
snpname: v42.4.1240K.snp
indivname: v42.4.1240K.ind
outputformat: PACKEDPED
genotypeoutname: reich.bed
snpoutname: reich.bim
indivoutname: reich.fam

then run "convertf -p parfile"

[vbnetkhio]

Try not to use too many moderns in outgroups and also you don't need to use all these Siberian/Amerindian ones

Mixe Ami Nganasan Itelmen


This is one of the best outgroup sets for modeling predominantly West Eurasians. You'll see most of these in the papers and you'll get good results

Mbuti.DG
Russia_Ust_Ishim.DG
China_Tianyuan
Goyet_Neanderthal.SG
Russia_Sunghir3.SG
Russia_Kostenki14.SG
Onge_1000G
Morocco_Iberomaurusian
Israel_Natufian
Iberia_ElMiron
Russia_MA1_HG.SG
Georgia_Satsurblia.SG
DevilsCave_N.SG
Papuan.DG
Anatolia_N
Iran_GanjDareh_N
Switzerland_Bichon.SG

is this better or worse? what are the optimal std error values?


./qpAdm: parameter file: qp_par
### THE INPUT PARAMETERS
##PARAMETER NAME: VALUE
genotypename: qp.bed
snpname: qp.bim
indivname: qp.fam
popleft: left.pops
popright: right.pops
details: YES
allsnps: YES
## qpAdm version: 1000
seed: 1661942465
*** warning. genetic distances are in cM not Morgans
1 rs7418088 110.998 81208897 T G

genotype file processed

left pops:
Serb
Hungary_Slav
Hungary_Langobard_o1
Hungary_Langobard_o2
Hungary_AvarPeriod

right pops:
Mbuti
Papuan
Ust_ishim
Anatolia_N
Iran_N
Mal_ta
Kostenki14
Natufian
El_miron
Bichon
Satsurblia
Tianyuan
Sung3
Iberomaurusian
Onge
Goyet_nean
Devils_cave

0 Serb 18
1 Hungary_Slav 1
2 Hungary_Langobard_o1 1
3 Hungary_Langobard_o2 1
4 Hungary_AvarPeriod 1
5 Mbuti 13
6 Papuan 17
7 Ust_ishim 1
8 Anatolia_N 32
9 Iran_N 8
10 Mal_ta 1
11 Kostenki14 1
12 Natufian 6
13 El_miron 1
14 Bichon 1
15 Satsurblia 1
16 Tianyuan 1
17 Sung3 1
18 Iberomaurusian 6
19 Onge 1
20 Goyet_nean 1
21 Devils_cave 4
jackknife block size: 0.050
snps: 1181274 indivs: 118
number of blocks for block jackknife: 128
## ncols: 1181274
coverage: Serb 541868
coverage: Hungary_Slav 802391
coverage: Hungary_Langobard_o1 754025
coverage: Hungary_Langobard_o2 339475
coverage: Hungary_AvarPeriod 1149395
coverage: Mbuti 641215
coverage: Papuan 641215
coverage: Ust_ishim 1141960
coverage: Anatolia_N 1144441
coverage: Iran_N 1052099
coverage: Mal_ta 801884
coverage: Kostenki14 1043686
coverage: Natufian 532350
coverage: El_miron 624136
coverage: Bichon 1139750
coverage: Satsurblia 797305
coverage: Tianyuan 881011
coverage: Sung3 1143847
coverage: Iberomaurusian 1095879
coverage: Onge 1127465
coverage: Goyet_nean 883239
coverage: Devils_cave 1143816
dof (jackknife): 48.274
numsnps used: 1181274
codimension 1
f4info:
f4rank: 3 dof: 13 chisq: 13.246 tail: 0.428997207 dofdiff: 15 chisqdiff: -13.246 taildiff: 1
B:
scale 1.000 1.000 1.000
Papuan 0.765 -0.788 0.463
Ust_ishim 0.527 -0.377 0.157
Anatolia_N -1.155 1.869 -1.127
Iran_N 0.163 1.603 -0.093
Mal_ta 1.273 -0.656 -0.986
Kostenki14 0.961 -0.055 0.062
Natufian -0.736 1.627 0.996
El_miron 1.445 0.392 1.346
Bichon 2.320 1.206 -1.686
Satsurblia 0.103 0.301 -0.338
Tianyuan 0.380 1.628 2.185
Sung3 1.200 0.166 0.452
Iberomaurusian -0.128 1.100 -0.449
Onge 0.651 -0.017 -0.269
Goyet_nean -0.295 -0.089 0.883
Devils_cave 1.047 -0.707 1.307
A:
scale 1270.348 2231.401 3769.558
Hungary_Slav 0.970 1.362 0.401
Hungary_Langobard_o1 -1.579 0.979 0.328
Hungary_Langobard_o2 0.614 0.806 0.935
Hungary_AvarPeriod -0.433 -0.733 1.690


full rank
f4info:
f4rank: 4 dof: 0 chisq: 0.000 tail: 1 dofdiff: 13 chisqdiff: 13.246 taildiff: 0.428997207
B:
scale 1054.554 739.955 1485.272 1536.350
Papuan 0.070 -0.902 0.455 0.307
Ust_ishim 0.443 -0.532 0.065 -0.051
Anatolia_N 0.151 1.613 -0.236 -1.094
Iran_N 1.164 0.331 1.112 -0.992
Mal_ta 0.615 -1.442 0.671 -0.643
Kostenki14 0.914 -0.952 0.370 -0.097
Natufian 0.126 1.235 1.580 -0.030
El_miron 1.932 -1.074 1.622 -0.443
Bichon 2.078 -1.951 2.096 -3.149
Satsurblia -0.026 0.008 0.739 -0.619
Tianyuan 1.547 0.168 1.320 0.681
Sung3 1.016 -1.011 0.891 -0.169
Iberomaurusian 0.380 0.560 0.575 -0.916
Onge 0.938 -0.675 -0.304 -0.001
Goyet_nean -0.631 0.258 0.314 0.662
Devils_cave 0.542 -0.914 0.886 1.017
A:
scale 2.000 2.000 2.000 2.000
Hungary_Slav 2.000 0.000 0.000 0.000
Hungary_Langobard_o1 0.000 2.000 0.000 0.000
Hungary_Langobard_o2 0.000 0.000 2.000 0.000
Hungary_AvarPeriod 0.000 0.000 0.000 2.000


best coefficients: 5.135 -0.020 -6.491 2.377
Jackknife mean: 209.500018656 -3.127914113 -281.604136149 76.232031605
std. errors: 118.840 2.216 159.678 42.941

error covariance (* 1,000,000)
14122904053 -247336741 -18974973277 5099405965
-247336741 4912422 331867122 -89442803
-18974973277 331867122 25497013707 -6853907552
5099405965 -89442803 -6853907552 1843944390


summ: Serb 4 0.428997 209.500 -3.128 -281.604 76.232 14122904053 -247336741 -18974973277 5099405965 4912422 ...
331867122 -89442803 25497013707 -6853907552 1843944390

fixed pat wt dof chisq tail prob
0000 0 13 13.246 0.428997 5.135 -0.020 -6.491 2.377 infeasible
0001 1 14 14.449 0.416837 -1.262 0.073 2.189 0.000 infeasible
0010 1 14 24.850 0.0360784 0.323 0.074 0.000 0.603
0100 1 14 13.147 0.514945 8.540 0.000 -11.138 3.597 infeasible
1000 1 14 22.524 0.0684764 0.000 0.100 0.613 0.286
0011 2 15 47.491 3.07116e-05 0.902 0.098 0.000 0.000
0101 2 15 14.883 0.459886 -1.472 0.000 2.472 0.000 infeasible
0110 2 15 26.638 0.0318278 0.297 0.000 0.000 0.703
1001 2 15 30.241 0.0110806 0.000 0.139 0.861 0.000
1010 2 15 33.665 0.00379407 0.000 0.032 0.000 0.968
1100 2 15 33.114 0.00452667 0.000 0.000 -0.308 1.308 infeasible
0111 3 16 49.566 2.68754e-05 1.000 0.000 0.000 0.000
1011 3 16 306.882 0 0.000 1.000 0.000 0.000
1101 3 16 36.005 0.00288904 0.000 0.000 1.000 0.000
1110 3 16 33.747 0.00587633 0.000 0.000 0.000 1.000
best pat: 0000 0.428997 - -
best pat: 1000 0.0684764 chi(nested): 9.278 p-value for nested model: 0.00231966
best pat: 0110 0.0318278 not nested
best pat: 1110 0.00587633 chi(nested): 7.109 p-value for nested model: 0.00767152

coeffs: 5.135 -0.020 -6.491 2.377

## dscore:: f_4(Base, Fit, Rbase, right2)
## genstat:: f_4(Base, Fit, right1, right2)

details: Hungary_Slav Papuan 0.000066 0.167813
details: Hungary_Langobard_o1 Papuan -0.001219 -3.129702
details: Hungary_Langobard_o2 Papuan 0.000306 0.822497
details: Hungary_AvarPeriod Papuan 0.000200 0.505385
dscore: Papuan f4: -0.001147 Z: -0.338578

details: Hungary_Slav Ust_ishim 0.000420 0.982201
details: Hungary_Langobard_o1 Ust_ishim -0.000720 -1.620261
details: Hungary_Langobard_o2 Ust_ishim 0.000044 0.103071
details: Hungary_AvarPeriod Ust_ishim -0.000033 -0.080978
dscore: Ust_ishim f4: 0.001809 Z: 0.500275

details: Hungary_Slav Anatolia_N 0.000143 0.452933
details: Hungary_Langobard_o1 Anatolia_N 0.002180 6.570480
details: Hungary_Langobard_o2 Anatolia_N -0.000159 -0.443134
details: Hungary_AvarPeriod Anatolia_N -0.000712 -1.907312
dscore: Anatolia_N f4: 0.000029 Z: 0.010071

details: Hungary_Slav Iran_N 0.001104 3.054394
details: Hungary_Langobard_o1 Iran_N 0.000448 1.268130
details: Hungary_Langobard_o2 Iran_N 0.000749 2.090825
details: Hungary_AvarPeriod Iran_N -0.000646 -1.608997
dscore: Iran_N f4: -0.000733 Z: -0.247497

details: Hungary_Slav Mal_ta 0.000583 1.022840
details: Hungary_Langobard_o1 Mal_ta -0.001948 -3.733778
details: Hungary_Langobard_o2 Mal_ta 0.000452 0.897982
details: Hungary_AvarPeriod Mal_ta -0.000418 -0.784342
dscore: Mal_ta f4: -0.000893 Z: -0.203521

details: Hungary_Slav Kostenki14 0.000867 1.837575
details: Hungary_Langobard_o1 Kostenki14 -0.001287 -2.553888
details: Hungary_Langobard_o2 Kostenki14 0.000249 0.565830
details: Hungary_AvarPeriod Kostenki14 -0.000063 -0.117730
dscore: Kostenki14 f4: 0.002712 Z: 0.696601

details: Hungary_Slav Natufian 0.000119 0.235659
details: Hungary_Langobard_o1 Natufian 0.001669 3.538118
details: Hungary_Langobard_o2 Natufian 0.001064 1.841509
details: Hungary_AvarPeriod Natufian -0.000019 -0.034617
dscore: Natufian f4: -0.006373 Z: -1.454016

details: Hungary_Slav El_miron 0.001832 2.978769
details: Hungary_Langobard_o1 El_miron -0.001451 -2.496976
details: Hungary_Langobard_o2 El_miron 0.001092 1.576967
details: Hungary_AvarPeriod El_miron -0.000288 -0.473481
dscore: El_miron f4: 0.001663 Z: 0.297021

details: Hungary_Slav Bichon 0.001970 3.551116
details: Hungary_Langobard_o1 Bichon -0.002637 -5.241503
details: Hungary_Langobard_o2 Bichon 0.001411 3.045039
details: Hungary_AvarPeriod Bichon -0.002050 -3.670204
dscore: Bichon f4: -0.003863 Z: -0.875480

details: Hungary_Slav Satsurblia -0.000025 -0.049837
details: Hungary_Langobard_o1 Satsurblia 0.000011 0.022097
details: Hungary_Langobard_o2 Satsurblia 0.000498 0.962133
details: Hungary_AvarPeriod Satsurblia -0.000403 -0.777071
dscore: Satsurblia f4: -0.004317 Z: -0.960165

details: Hungary_Slav Tianyuan 0.001467 2.988347
details: Hungary_Langobard_o1 Tianyuan 0.000227 0.412851
details: Hungary_Langobard_o2 Tianyuan 0.000889 1.754543
details: Hungary_AvarPeriod Tianyuan 0.000443 0.925819
dscore: Tianyuan f4: 0.002809 Z: 0.654228

details: Hungary_Slav Sung3 0.000964 1.950397
details: Hungary_Langobard_o1 Sung3 -0.001366 -2.472620
details: Hungary_Langobard_o2 Sung3 0.000600 1.370709
details: Hungary_AvarPeriod Sung3 -0.000110 -0.212290
dscore: Sung3 f4: 0.000820 Z: 0.215177

details: Hungary_Slav Iberomaurusian 0.000361 1.066144
details: Hungary_Langobard_o1 Iberomaurusian 0.000757 2.305732
details: Hungary_Langobard_o2 Iberomaurusian 0.000387 1.185692
details: Hungary_AvarPeriod Iberomaurusian -0.000596 -1.573851
dscore: Iberomaurusian f4: -0.002090 Z: -0.737928

details: Hungary_Slav Onge 0.000889 1.687005
details: Hungary_Langobard_o1 Onge -0.000913 -1.879391
details: Hungary_Langobard_o2 Onge -0.000205 -0.412358
details: Hungary_AvarPeriod Onge -0.000000 -0.000808
dscore: Onge f4: 0.005915 Z: 1.262184

details: Hungary_Slav Goyet_nean -0.000598 -1.763944
details: Hungary_Langobard_o1 Goyet_nean 0.000349 1.162036
details: Hungary_Langobard_o2 Goyet_nean 0.000211 0.654021
details: Hungary_AvarPeriod Goyet_nean 0.000431 1.290624
dscore: Goyet_nean f4: -0.003424 Z: -1.244822

details: Hungary_Slav Devils_cave 0.000514 1.181943
details: Hungary_Langobard_o1 Devils_cave -0.001235 -2.917863
details: Hungary_Langobard_o2 Devils_cave 0.000596 1.536829
details: Hungary_AvarPeriod Devils_cave 0.000662 1.416889
dscore: Devils_cave f4: 0.000367 Z: 0.103691

gendstat: Mbuti Papuan -0.339
gendstat: Mbuti Ust_ishim 0.500
gendstat: Mbuti Anatolia_N 0.010
gendstat: Mbuti Iran_N -0.247
gendstat: Mbuti Mal_ta -0.204
gendstat: Mbuti Kostenki14 0.697
gendstat: Mbuti Natufian -1.454
gendstat: Mbuti El_miron 0.297
gendstat: Mbuti Bichon -0.875
gendstat: Mbuti Satsurblia -0.960
gendstat: Mbuti Tianyuan 0.654
gendstat: Mbuti Sung3 0.215
gendstat: Mbuti Iberomaurusian -0.738
gendstat: Mbuti Onge 1.262
gendstat: Mbuti Goyet_nean -1.245
gendstat: Mbuti Devils_cave 0.104
gendstat: Papuan Ust_ishim 0.756
gendstat: Papuan Anatolia_N 0.359
gendstat: Papuan Iran_N 0.130
gendstat: Papuan Mal_ta 0.057
gendstat: Papuan Kostenki14 0.926
gendstat: Papuan Natufian -1.082
gendstat: Papuan El_miron 0.522
gendstat: Papuan Bichon -0.641
gendstat: Papuan Satsurblia -0.666
gendstat: Papuan Tianyuan 0.994
gendstat: Papuan Sung3 0.504
gendstat: Papuan Iberomaurusian -0.279
gendstat: Papuan Onge 1.689
gendstat: Papuan Goyet_nean -0.511
gendstat: Papuan Devils_cave 0.458
gendstat: Ust_ishim Anatolia_N -0.495
gendstat: Ust_ishim Iran_N -0.676
gendstat: Ust_ishim Mal_ta -0.547
gendstat: Ust_ishim Kostenki14 0.202
gendstat: Ust_ishim Natufian -1.472
gendstat: Ust_ishim El_miron -0.026
gendstat: Ust_ishim Bichon -1.173
gendstat: Ust_ishim Satsurblia -1.223
gendstat: Ust_ishim Tianyuan 0.210
gendstat: Ust_ishim Sung3 -0.226
gendstat: Ust_ishim Iberomaurusian -0.955
gendstat: Ust_ishim Onge 0.949
gendstat: Ust_ishim Goyet_nean -1.086
gendstat: Ust_ishim Devils_cave -0.356
gendstat: Anatolia_N Iran_N -0.285
gendstat: Anatolia_N Mal_ta -0.217
gendstat: Anatolia_N Kostenki14 0.747
gendstat: Anatolia_N Natufian -1.425
gendstat: Anatolia_N El_miron 0.330
gendstat: Anatolia_N Bichon -1.061
gendstat: Anatolia_N Satsurblia -1.055
gendstat: Anatolia_N Tianyuan 0.649
gendstat: Anatolia_N Sung3 0.216
gendstat: Anatolia_N Iberomaurusian -0.687
gendstat: Anatolia_N Onge 1.338
gendstat: Anatolia_N Goyet_nean -0.893
gendstat: Anatolia_N Devils_cave 0.100
gendstat: Iran_N Mal_ta -0.037
gendstat: Iran_N Kostenki14 0.894
gendstat: Iran_N Natufian -1.131
gendstat: Iran_N El_miron 0.429
gendstat: Iran_N Bichon -0.730
gendstat: Iran_N Satsurblia -0.824
gendstat: Iran_N Tianyuan 0.805
gendstat: Iran_N Sung3 0.391
gendstat: Iran_N Iberomaurusian -0.387
gendstat: Iran_N Onge 1.426
gendstat: Iran_N Goyet_nean -0.646
gendstat: Iran_N Devils_cave 0.318
gendstat: Mal_ta Kostenki14 0.675
gendstat: Mal_ta Natufian -0.967
gendstat: Mal_ta El_miron 0.405
gendstat: Mal_ta Bichon -0.602
gendstat: Mal_ta Satsurblia -0.611
gendstat: Mal_ta Tianyuan 0.735
gendstat: Mal_ta Sung3 0.385
gendstat: Mal_ta Iberomaurusian -0.256
gendstat: Mal_ta Onge 1.224
gendstat: Mal_ta Goyet_nean -0.462
gendstat: Mal_ta Devils_cave 0.273
gendstat: Kostenki14 Natufian -1.811
gendstat: Kostenki14 El_miron -0.186
gendstat: Kostenki14 Bichon -1.394
gendstat: Kostenki14 Satsurblia -1.500
gendstat: Kostenki14 Tianyuan 0.019
gendstat: Kostenki14 Sung3 -0.431
gendstat: Kostenki14 Iberomaurusian -1.114
gendstat: Kostenki14 Onge 0.633
gendstat: Kostenki14 Goyet_nean -1.274
gendstat: Kostenki14 Devils_cave -0.530
gendstat: Natufian El_miron 1.336
gendstat: Natufian Bichon 0.511
gendstat: Natufian Satsurblia 0.369
gendstat: Natufian Tianyuan 1.690
gendstat: Natufian Sung3 1.435
gendstat: Natufian Iberomaurusian 0.942
gendstat: Natufian Onge 2.264
gendstat: Natufian Goyet_nean 0.558
gendstat: Natufian Devils_cave 1.335
gendstat: El_miron Bichon -1.050
gendstat: El_miron Satsurblia -1.078
gendstat: El_miron Tianyuan 0.194
gendstat: El_miron Sung3 -0.145
gendstat: El_miron Iberomaurusian -0.683
gendstat: El_miron Onge 0.748
gendstat: El_miron Goyet_nean -0.816
gendstat: El_miron Devils_cave -0.230
gendstat: Bichon Satsurblia -0.088
gendstat: Bichon Tianyuan 1.299
gendstat: Bichon Sung3 1.048
gendstat: Bichon Iberomaurusian 0.388
gendstat: Bichon Onge 1.795
gendstat: Bichon Goyet_nean 0.081
gendstat: Bichon Devils_cave 0.992
gendstat: Satsurblia Tianyuan 1.392
gendstat: Satsurblia Sung3 1.001
gendstat: Satsurblia Iberomaurusian 0.461
gendstat: Satsurblia Onge 1.787
gendstat: Satsurblia Goyet_nean 0.175
gendstat: Satsurblia Devils_cave 0.917
gendstat: Tianyuan Sung3 -0.416
gendstat: Tianyuan Iberomaurusian -1.064
gendstat: Tianyuan Onge 0.588
gendstat: Tianyuan Goyet_nean -1.206
gendstat: Tianyuan Devils_cave -0.612
gendstat: Sung3 Iberomaurusian -0.713
gendstat: Sung3 Onge 1.022
gendstat: Sung3 Goyet_nean -0.889
gendstat: Sung3 Devils_cave -0.106
gendstat: Iberomaurusian Onge 1.764
gendstat: Iberomaurusian Goyet_nean -0.331
gendstat: Iberomaurusian Devils_cave 0.674
gendstat: Onge Goyet_nean -1.697
gendstat: Onge Devils_cave -1.262
gendstat: Goyet_nean Devils_cave 0.805

##end of qpAdm: 171.779 seconds cpu 2712.752 Mbytes in use

[vbnetkhio]

another one


./qpAdm: parameter file: qp_par
### THE INPUT PARAMETERS
##PARAMETER NAME: VALUE
genotypename: qp.bed
snpname: qp.bim
indivname: qp.fam
popleft: left.pops
popright: right.pops
details: YES
allsnps: YES
## qpAdm version: 1000
seed: 1675975310
*** warning. genetic distances are in cM not Morgans
1 rs7418088 110.998 81208897 T G

genotype file processed

left pops:
Serb
Russia_MA
Ancient_Greek

right pops:
Mbuti
Papuan
Ust_ishim
Anatolia_N
Iran_N
Mal_ta
Kostenki14
Natufian
El_miron
Bichon
Satsurblia
Tianyuan
Sung3
Iberomaurusian
Onge
Goyet_nean
Devils_cave

0 Serb 18
1 Russia_MA 1
2 Ancient_Greek 5
3 Mbuti 13
4 Papuan 17
5 Ust_ishim 1
6 Anatolia_N 32
7 Iran_N 8
8 Mal_ta 1
9 Kostenki14 1
10 Natufian 6
11 El_miron 1
12 Bichon 1
13 Satsurblia 1
14 Tianyuan 1
15 Sung3 1
16 Iberomaurusian 6
17 Onge 1
18 Goyet_nean 1
19 Devils_cave 4
jackknife block size: 0.050
snps: 1181202 indivs: 120
number of blocks for block jackknife: 128
## ncols: 1181202
coverage: Serb 541868
coverage: Russia_MA 1136668
coverage: Ancient_Greek 470478
coverage: Mbuti 641215
coverage: Papuan 641215
coverage: Ust_ishim 1141960
coverage: Anatolia_N 1144441
coverage: Iran_N 1052099
coverage: Mal_ta 801884
coverage: Kostenki14 1043686
coverage: Natufian 532350
coverage: El_miron 624136
coverage: Bichon 1139750
coverage: Satsurblia 797305
coverage: Tianyuan 881011
coverage: Sung3 1143847
coverage: Iberomaurusian 1095879
coverage: Onge 1127465
coverage: Goyet_nean 883239
coverage: Devils_cave 1143816
dof (jackknife): 47.967
numsnps used: 1181202
codimension 1
f4info:
f4rank: 1 dof: 15 chisq: 19.669 tail: 0.184988309 dofdiff: 17 chisqdiff: -19.669 taildiff: 1
B:
scale 1.000
Papuan 0.738
Ust_ishim 0.660
Anatolia_N -1.135
Iran_N -0.071
Mal_ta 1.259
Kostenki14 0.910
Natufian -1.262
El_miron 0.982
Bichon 2.369
Satsurblia 0.481
Tianyuan 0.518
Sung3 1.074
Iberomaurusian -0.465
Onge 0.666
Goyet_nean 0.163
Devils_cave 0.896
A:
scale 929.396
Russia_MA 0.754
Ancient_Greek -1.196


full rank
f4info:
f4rank: 2 dof: 0 chisq: 0.000 tail: 1 dofdiff: 15 chisqdiff: 19.669 taildiff: 0.184988309
B:
scale 1056.960 832.932
Papuan 0.557 -0.772
Ust_ishim 0.813 -0.644
Anatolia_N -0.786 1.330
Iran_N 0.212 0.158
Mal_ta 0.515 -1.355
Kostenki14 0.751 -0.929
Natufian -0.563 1.407
El_miron 1.190 -0.827
Bichon 2.282 -2.298
Satsurblia 0.499 -0.391
Tianyuan 1.209 -0.333
Sung3 1.162 -0.973
Iberomaurusian -0.021 0.686
Onge 0.958 -0.542
Goyet_nean -0.055 0.015
Devils_cave 1.617 -0.759
A:
scale 1.414 1.414
Russia_MA 1.414 0.000
Ancient_Greek 0.000 1.414


best coefficients: 0.613 0.387
Jackknife mean: 0.609828960 0.390171040
std. errors: 0.040 0.040

error covariance (* 1,000,000)
1595 -1595
-1595 1595


summ: Serb 2 0.184988 0.610 0.390 1595 -1595 1595

fixed pat wt dof chisq tail prob
00 0 15 19.669 0.184988 0.613 0.387
01 1 16 71.514 5.41138e-09 1.000 0.000
10 1 16 379.787 0 0.000 1.000
best pat: 00 0.184988 - -
best pat: 01 5.41138e-09 chi(nested): 51.844 p-value for nested model: 6.0077e-13

coeffs: 0.613 0.387

## dscore:: f_4(Base, Fit, Rbase, right2)
## genstat:: f_4(Base, Fit, right1, right2)

details: Russia_MA Papuan 0.000527 1.533743
details: Ancient_Greek Papuan -0.000927 -4.019914
dscore: Papuan f4: -0.000035 Z: -0.148045

details: Russia_MA Ust_ishim 0.000769 1.842421
details: Ancient_Greek Ust_ishim -0.000773 -2.832406
dscore: Ust_ishim f4: 0.000172 Z: 0.614096

details: Russia_MA Anatolia_N -0.000743 -2.468675
details: Ancient_Greek Anatolia_N 0.001597 6.437180
dscore: Anatolia_N f4: 0.000162 Z: 0.804715

details: Russia_MA Iran_N 0.000200 0.623523
details: Ancient_Greek Iran_N 0.000189 0.760089
dscore: Iran_N f4: 0.000196 Z: 0.934409

details: Russia_MA Mal_ta 0.000488 1.013975
details: Ancient_Greek Mal_ta -0.001626 -4.359025
dscore: Mal_ta f4: -0.000330 Z: -1.001261

details: Russia_MA Kostenki14 0.000711 1.510140
details: Ancient_Greek Kostenki14 -0.001116 -3.784293
dscore: Kostenki14 f4: 0.000004 Z: 0.013153

details: Russia_MA Natufian -0.000533 -1.152628
details: Ancient_Greek Natufian 0.001689 4.790938
dscore: Natufian f4: 0.000326 Z: 1.008738

details: Russia_MA El_miron 0.001126 2.071589
details: Ancient_Greek El_miron -0.000992 -2.592493
dscore: El_miron f4: 0.000307 Z: 0.809100

details: Russia_MA Bichon 0.002159 4.019681
details: Ancient_Greek Bichon -0.002759 -9.449809
dscore: Bichon f4: 0.000257 Z: 0.725199

details: Russia_MA Satsurblia 0.000472 0.903017
details: Ancient_Greek Satsurblia -0.000469 -1.241370
dscore: Satsurblia f4: 0.000108 Z: 0.315011

details: Russia_MA Tianyuan 0.001143 2.747270
details: Ancient_Greek Tianyuan -0.000400 -1.289156
dscore: Tianyuan f4: 0.000547 Z: 1.846707

details: Russia_MA Sung3 0.001100 2.314079
details: Ancient_Greek Sung3 -0.001168 -3.713277
dscore: Sung3 f4: 0.000223 Z: 0.706227

details: Russia_MA Iberomaurusian -0.000020 -0.065498
details: Ancient_Greek Iberomaurusian 0.000823 3.362776
dscore: Iberomaurusian f4: 0.000306 Z: 1.345291

details: Russia_MA Onge 0.000907 2.080652
details: Ancient_Greek Onge -0.000651 -1.934576
dscore: Onge f4: 0.000304 Z: 1.048371

details: Russia_MA Goyet_nean -0.000052 -0.166970
details: Ancient_Greek Goyet_nean 0.000018 0.072215
dscore: Goyet_nean f4: -0.000025 Z: -0.119749

details: Russia_MA Devils_cave 0.001530 4.135096
details: Ancient_Greek Devils_cave -0.000912 -3.419521
dscore: Devils_cave f4: 0.000586 Z: 2.380268

gendstat: Mbuti Papuan -0.148
gendstat: Mbuti Ust_ishim 0.614
gendstat: Mbuti Anatolia_N 0.805
gendstat: Mbuti Iran_N 0.934
gendstat: Mbuti Mal_ta -1.001
gendstat: Mbuti Kostenki14 0.013
gendstat: Mbuti Natufian 1.009
gendstat: Mbuti El_miron 0.809
gendstat: Mbuti Bichon 0.725
gendstat: Mbuti Satsurblia 0.315
gendstat: Mbuti Tianyuan 1.847
gendstat: Mbuti Sung3 0.706
gendstat: Mbuti Iberomaurusian 1.345
gendstat: Mbuti Onge 1.048
gendstat: Mbuti Goyet_nean -0.120
gendstat: Mbuti Devils_cave 2.380
gendstat: Papuan Ust_ishim 0.806
gendstat: Papuan Anatolia_N 0.761
gendstat: Papuan Iran_N 0.895
gendstat: Papuan Mal_ta -0.837
gendstat: Papuan Kostenki14 0.117
gendstat: Papuan Natufian 1.022
gendstat: Papuan El_miron 0.912
gendstat: Papuan Bichon 0.893
gendstat: Papuan Satsurblia 0.390
gendstat: Papuan Tianyuan 1.725
gendstat: Papuan Sung3 0.800
gendstat: Papuan Iberomaurusian 1.295
gendstat: Papuan Onge 1.227
gendstat: Papuan Goyet_nean 0.035
gendstat: Papuan Devils_cave 2.625
gendstat: Ust_ishim Anatolia_N -0.040
gendstat: Ust_ishim Iran_N 0.082
gendstat: Ust_ishim Mal_ta -1.375
gendstat: Ust_ishim Kostenki14 -0.459
gendstat: Ust_ishim Natufian 0.400
gendstat: Ust_ishim El_miron 0.339
gendstat: Ust_ishim Bichon 0.231
gendstat: Ust_ishim Satsurblia -0.170
gendstat: Ust_ishim Tianyuan 1.059
gendstat: Ust_ishim Sung3 0.149
gendstat: Ust_ishim Iberomaurusian 0.450
gendstat: Ust_ishim Onge 0.439
gendstat: Ust_ishim Goyet_nean -0.585
gendstat: Ust_ishim Devils_cave 1.364
gendstat: Anatolia_N Iran_N 0.154
gendstat: Anatolia_N Mal_ta -1.412
gendstat: Anatolia_N Kostenki14 -0.478
gendstat: Anatolia_N Natufian 0.501
gendstat: Anatolia_N El_miron 0.399
gendstat: Anatolia_N Bichon 0.278
gendstat: Anatolia_N Satsurblia -0.157
gendstat: Anatolia_N Tianyuan 1.280
gendstat: Anatolia_N Sung3 0.210
gendstat: Anatolia_N Iberomaurusian 0.699
gendstat: Anatolia_N Onge 0.505
gendstat: Anatolia_N Goyet_nean -0.665
gendstat: Anatolia_N Devils_cave 1.611
gendstat: Iran_N Mal_ta -1.763
gendstat: Iran_N Kostenki14 -0.583
gendstat: Iran_N Natufian 0.359
gendstat: Iran_N El_miron 0.291
gendstat: Iran_N Bichon 0.175
gendstat: Iran_N Satsurblia -0.255
gendstat: Iran_N Tianyuan 1.169
gendstat: Iran_N Sung3 0.084
gendstat: Iran_N Iberomaurusian 0.434
gendstat: Iran_N Onge 0.341
gendstat: Iran_N Goyet_nean -0.737
gendstat: Iran_N Devils_cave 1.415
gendstat: Mal_ta Kostenki14 0.879
gendstat: Mal_ta Natufian 1.549
gendstat: Mal_ta El_miron 1.447
gendstat: Mal_ta Bichon 1.322
gendstat: Mal_ta Satsurblia 1.068
gendstat: Mal_ta Tianyuan 2.399
gendstat: Mal_ta Sung3 1.374
gendstat: Mal_ta Iberomaurusian 1.817
gendstat: Mal_ta Onge 1.654
gendstat: Mal_ta Goyet_nean 0.810
gendstat: Mal_ta Devils_cave 2.588
gendstat: Kostenki14 Natufian 0.796
gendstat: Kostenki14 El_miron 0.689
gendstat: Kostenki14 Bichon 0.594
gendstat: Kostenki14 Satsurblia 0.247
gendstat: Kostenki14 Tianyuan 1.426
gendstat: Kostenki14 Sung3 0.643
gendstat: Kostenki14 Iberomaurusian 0.926
gendstat: Kostenki14 Onge 0.774
gendstat: Kostenki14 Goyet_nean -0.076
gendstat: Kostenki14 Devils_cave 1.667
gendstat: Natufian El_miron -0.043
gendstat: Natufian Bichon -0.165
gendstat: Natufian Satsurblia -0.538
gendstat: Natufian Tianyuan 0.537
gendstat: Natufian Sung3 -0.260
gendstat: Natufian Iberomaurusian -0.064
gendstat: Natufian Onge -0.056
gendstat: Natufian Goyet_nean -0.955
gendstat: Natufian Devils_cave 0.682
gendstat: El_miron Bichon -0.122
gendstat: El_miron Satsurblia -0.451
gendstat: El_miron Tianyuan 0.550
gendstat: El_miron Sung3 -0.214
gendstat: El_miron Iberomaurusian -0.002
gendstat: El_miron Onge -0.006
gendstat: El_miron Goyet_nean -0.803
gendstat: El_miron Devils_cave 0.667
gendstat: Bichon Satsurblia -0.348
gendstat: Bichon Tianyuan 0.702
gendstat: Bichon Sung3 -0.090
gendstat: Bichon Iberomaurusian 0.137
gendstat: Bichon Onge 0.120
gendstat: Bichon Goyet_nean -0.735
gendstat: Bichon Devils_cave 0.897
gendstat: Satsurblia Tianyuan 1.120
gendstat: Satsurblia Sung3 0.280
gendstat: Satsurblia Iberomaurusian 0.540
gendstat: Satsurblia Onge 0.485
gendstat: Satsurblia Goyet_nean -0.354
gendstat: Satsurblia Devils_cave 1.183
gendstat: Tianyuan Sung3 -0.896
gendstat: Tianyuan Iberomaurusian -0.743
gendstat: Tianyuan Onge -0.668
gendstat: Tianyuan Goyet_nean -1.600
gendstat: Tianyuan Devils_cave 0.123
gendstat: Sung3 Iberomaurusian 0.260
gendstat: Sung3 Onge 0.224
gendstat: Sung3 Goyet_nean -0.680
gendstat: Sung3 Devils_cave 1.141
gendstat: Iberomaurusian Onge -0.005
gendstat: Iberomaurusian Goyet_nean -1.109
gendstat: Iberomaurusian Devils_cave 0.987
gendstat: Onge Goyet_nean -1.015
gendstat: Onge Devils_cave 0.915
gendstat: Goyet_nean Devils_cave 2.052

##end of qpAdm: 173.874 seconds cpu 2673.267 Mbytes in use

[Zoro]

another one


./qpAdm: parameter file: qp_par
### THE INPUT PARAMETERS
##PARAMETER NAME: VALUE
genotypename: qp.bed
snpname: qp.bim
indivname: qp.fam
popleft: left.pops
popright: right.pops
details: YES
allsnps: YES
## qpAdm version: 1000
seed: 1675975310
*** warning. genetic distances are in cM not Morgans
1 rs7418088 110.998 81208897 T G

genotype file processed

left pops:
Serb
Russia_MA
Ancient_Greek

right pops:
Mbuti
Papuan
Ust_ishim
Anatolia_N
Iran_N
Mal_ta
Kostenki14
Natufian
El_miron
Bichon
Satsurblia
Tianyuan
Sung3
Iberomaurusian
Onge
Goyet_nean
Devils_cave

0 Serb 18
1 Russia_MA 1
2 Ancient_Greek 5
3 Mbuti 13
4 Papuan 17
5 Ust_ishim 1
6 Anatolia_N 32
7 Iran_N 8
8 Mal_ta 1
9 Kostenki14 1
10 Natufian 6
11 El_miron 1
12 Bichon 1
13 Satsurblia 1
14 Tianyuan 1
15 Sung3 1
16 Iberomaurusian 6
17 Onge 1
18 Goyet_nean 1
19 Devils_cave 4
jackknife block size: 0.050
snps: 1181202 indivs: 120
number of blocks for block jackknife: 128
## ncols: 1181202
coverage: Serb 541868
coverage: Russia_MA 1136668
coverage: Ancient_Greek 470478
coverage: Mbuti 641215
coverage: Papuan 641215
coverage: Ust_ishim 1141960
coverage: Anatolia_N 1144441
coverage: Iran_N 1052099
coverage: Mal_ta 801884
coverage: Kostenki14 1043686
coverage: Natufian 532350
coverage: El_miron 624136
coverage: Bichon 1139750
coverage: Satsurblia 797305
coverage: Tianyuan 881011
coverage: Sung3 1143847
coverage: Iberomaurusian 1095879
coverage: Onge 1127465
coverage: Goyet_nean 883239
coverage: Devils_cave 1143816
dof (jackknife): 47.967
numsnps used: 1181202
codimension 1
f4info:
f4rank: 1 dof: 15 chisq: 19.669 tail: 0.184988309 dofdiff: 17 chisqdiff: -19.669 taildiff: 1
B:
scale 1.000
Papuan 0.738
Ust_ishim 0.660
Anatolia_N -1.135
Iran_N -0.071
Mal_ta 1.259
Kostenki14 0.910
Natufian -1.262
El_miron 0.982
Bichon 2.369
Satsurblia 0.481
Tianyuan 0.518
Sung3 1.074
Iberomaurusian -0.465
Onge 0.666
Goyet_nean 0.163
Devils_cave 0.896
A:
scale 929.396
Russia_MA 0.754
Ancient_Greek -1.196


full rank
f4info:
f4rank: 2 dof: 0 chisq: 0.000 tail: 1 dofdiff: 15 chisqdiff: 19.669 taildiff: 0.184988309
B:
scale 1056.960 832.932
Papuan 0.557 -0.772
Ust_ishim 0.813 -0.644
Anatolia_N -0.786 1.330
Iran_N 0.212 0.158
Mal_ta 0.515 -1.355
Kostenki14 0.751 -0.929
Natufian -0.563 1.407
El_miron 1.190 -0.827
Bichon 2.282 -2.298
Satsurblia 0.499 -0.391
Tianyuan 1.209 -0.333
Sung3 1.162 -0.973
Iberomaurusian -0.021 0.686
Onge 0.958 -0.542
Goyet_nean -0.055 0.015
Devils_cave 1.617 -0.759
A:
scale 1.414 1.414
Russia_MA 1.414 0.000
Ancient_Greek 0.000 1.414


best coefficients: 0.613 0.387
Jackknife mean: 0.609828960 0.390171040
std. errors: 0.040 0.040

error covariance (* 1,000,000)
1595 -1595
-1595 1595


summ: Serb 2 0.184988 0.610 0.390 1595 -1595 1595

fixed pat wt dof chisq tail prob
00 0 15 19.669 0.184988 0.613 0.387
01 1 16 71.514 5.41138e-09 1.000 0.000
10 1 16 379.787 0 0.000 1.000
best pat: 00 0.184988 - -
best pat: 01 5.41138e-09 chi(nested): 51.844 p-value for nested model: 6.0077e-13

coeffs: 0.613 0.387

## dscore:: f_4(Base, Fit, Rbase, right2)
## genstat:: f_4(Base, Fit, right1, right2)

details: Russia_MA Papuan 0.000527 1.533743
details: Ancient_Greek Papuan -0.000927 -4.019914
dscore: Papuan f4: -0.000035 Z: -0.148045

details: Russia_MA Ust_ishim 0.000769 1.842421
details: Ancient_Greek Ust_ishim -0.000773 -2.832406
dscore: Ust_ishim f4: 0.000172 Z: 0.614096

details: Russia_MA Anatolia_N -0.000743 -2.468675
details: Ancient_Greek Anatolia_N 0.001597 6.437180
dscore: Anatolia_N f4: 0.000162 Z: 0.804715

details: Russia_MA Iran_N 0.000200 0.623523
details: Ancient_Greek Iran_N 0.000189 0.760089
dscore: Iran_N f4: 0.000196 Z: 0.934409

details: Russia_MA Mal_ta 0.000488 1.013975
details: Ancient_Greek Mal_ta -0.001626 -4.359025
dscore: Mal_ta f4: -0.000330 Z: -1.001261

details: Russia_MA Kostenki14 0.000711 1.510140
details: Ancient_Greek Kostenki14 -0.001116 -3.784293
dscore: Kostenki14 f4: 0.000004 Z: 0.013153

details: Russia_MA Natufian -0.000533 -1.152628
details: Ancient_Greek Natufian 0.001689 4.790938
dscore: Natufian f4: 0.000326 Z: 1.008738

details: Russia_MA El_miron 0.001126 2.071589
details: Ancient_Greek El_miron -0.000992 -2.592493
dscore: El_miron f4: 0.000307 Z: 0.809100

details: Russia_MA Bichon 0.002159 4.019681
details: Ancient_Greek Bichon -0.002759 -9.449809
dscore: Bichon f4: 0.000257 Z: 0.725199

details: Russia_MA Satsurblia 0.000472 0.903017
details: Ancient_Greek Satsurblia -0.000469 -1.241370
dscore: Satsurblia f4: 0.000108 Z: 0.315011

details: Russia_MA Tianyuan 0.001143 2.747270
details: Ancient_Greek Tianyuan -0.000400 -1.289156
dscore: Tianyuan f4: 0.000547 Z: 1.846707

details: Russia_MA Sung3 0.001100 2.314079
details: Ancient_Greek Sung3 -0.001168 -3.713277
dscore: Sung3 f4: 0.000223 Z: 0.706227

details: Russia_MA Iberomaurusian -0.000020 -0.065498
details: Ancient_Greek Iberomaurusian 0.000823 3.362776
dscore: Iberomaurusian f4: 0.000306 Z: 1.345291

details: Russia_MA Onge 0.000907 2.080652
details: Ancient_Greek Onge -0.000651 -1.934576
dscore: Onge f4: 0.000304 Z: 1.048371

details: Russia_MA Goyet_nean -0.000052 -0.166970
details: Ancient_Greek Goyet_nean 0.000018 0.072215
dscore: Goyet_nean f4: -0.000025 Z: -0.119749

details: Russia_MA Devils_cave 0.001530 4.135096
details: Ancient_Greek Devils_cave -0.000912 -3.419521
dscore: Devils_cave f4: 0.000586 Z: 2.380268

gendstat: Mbuti Papuan -0.148
gendstat: Mbuti Ust_ishim 0.614
gendstat: Mbuti Anatolia_N 0.805
gendstat: Mbuti Iran_N 0.934
gendstat: Mbuti Mal_ta -1.001
gendstat: Mbuti Kostenki14 0.013
gendstat: Mbuti Natufian 1.009
gendstat: Mbuti El_miron 0.809
gendstat: Mbuti Bichon 0.725
gendstat: Mbuti Satsurblia 0.315
gendstat: Mbuti Tianyuan 1.847
gendstat: Mbuti Sung3 0.706
gendstat: Mbuti Iberomaurusian 1.345
gendstat: Mbuti Onge 1.048
gendstat: Mbuti Goyet_nean -0.120
gendstat: Mbuti Devils_cave 2.380
gendstat: Papuan Ust_ishim 0.806
gendstat: Papuan Anatolia_N 0.761
gendstat: Papuan Iran_N 0.895
gendstat: Papuan Mal_ta -0.837
gendstat: Papuan Kostenki14 0.117
gendstat: Papuan Natufian 1.022
gendstat: Papuan El_miron 0.912
gendstat: Papuan Bichon 0.893
gendstat: Papuan Satsurblia 0.390
gendstat: Papuan Tianyuan 1.725
gendstat: Papuan Sung3 0.800
gendstat: Papuan Iberomaurusian 1.295
gendstat: Papuan Onge 1.227
gendstat: Papuan Goyet_nean 0.035
gendstat: Papuan Devils_cave 2.625
gendstat: Ust_ishim Anatolia_N -0.040
gendstat: Ust_ishim Iran_N 0.082
gendstat: Ust_ishim Mal_ta -1.375
gendstat: Ust_ishim Kostenki14 -0.459
gendstat: Ust_ishim Natufian 0.400
gendstat: Ust_ishim El_miron 0.339
gendstat: Ust_ishim Bichon 0.231
gendstat: Ust_ishim Satsurblia -0.170
gendstat: Ust_ishim Tianyuan 1.059
gendstat: Ust_ishim Sung3 0.149
gendstat: Ust_ishim Iberomaurusian 0.450
gendstat: Ust_ishim Onge 0.439
gendstat: Ust_ishim Goyet_nean -0.585
gendstat: Ust_ishim Devils_cave 1.364
gendstat: Anatolia_N Iran_N 0.154
gendstat: Anatolia_N Mal_ta -1.412
gendstat: Anatolia_N Kostenki14 -0.478
gendstat: Anatolia_N Natufian 0.501
gendstat: Anatolia_N El_miron 0.399
gendstat: Anatolia_N Bichon 0.278
gendstat: Anatolia_N Satsurblia -0.157
gendstat: Anatolia_N Tianyuan 1.280
gendstat: Anatolia_N Sung3 0.210
gendstat: Anatolia_N Iberomaurusian 0.699
gendstat: Anatolia_N Onge 0.505
gendstat: Anatolia_N Goyet_nean -0.665
gendstat: Anatolia_N Devils_cave 1.611
gendstat: Iran_N Mal_ta -1.763
gendstat: Iran_N Kostenki14 -0.583
gendstat: Iran_N Natufian 0.359
gendstat: Iran_N El_miron 0.291
gendstat: Iran_N Bichon 0.175
gendstat: Iran_N Satsurblia -0.255
gendstat: Iran_N Tianyuan 1.169
gendstat: Iran_N Sung3 0.084
gendstat: Iran_N Iberomaurusian 0.434
gendstat: Iran_N Onge 0.341
gendstat: Iran_N Goyet_nean -0.737
gendstat: Iran_N Devils_cave 1.415
gendstat: Mal_ta Kostenki14 0.879
gendstat: Mal_ta Natufian 1.549
gendstat: Mal_ta El_miron 1.447
gendstat: Mal_ta Bichon 1.322
gendstat: Mal_ta Satsurblia 1.068
gendstat: Mal_ta Tianyuan 2.399
gendstat: Mal_ta Sung3 1.374
gendstat: Mal_ta Iberomaurusian 1.817
gendstat: Mal_ta Onge 1.654
gendstat: Mal_ta Goyet_nean 0.810
gendstat: Mal_ta Devils_cave 2.588
gendstat: Kostenki14 Natufian 0.796
gendstat: Kostenki14 El_miron 0.689
gendstat: Kostenki14 Bichon 0.594
gendstat: Kostenki14 Satsurblia 0.247
gendstat: Kostenki14 Tianyuan 1.426
gendstat: Kostenki14 Sung3 0.643
gendstat: Kostenki14 Iberomaurusian 0.926
gendstat: Kostenki14 Onge 0.774
gendstat: Kostenki14 Goyet_nean -0.076
gendstat: Kostenki14 Devils_cave 1.667
gendstat: Natufian El_miron -0.043
gendstat: Natufian Bichon -0.165
gendstat: Natufian Satsurblia -0.538
gendstat: Natufian Tianyuan 0.537
gendstat: Natufian Sung3 -0.260
gendstat: Natufian Iberomaurusian -0.064
gendstat: Natufian Onge -0.056
gendstat: Natufian Goyet_nean -0.955
gendstat: Natufian Devils_cave 0.682
gendstat: El_miron Bichon -0.122
gendstat: El_miron Satsurblia -0.451
gendstat: El_miron Tianyuan 0.550
gendstat: El_miron Sung3 -0.214
gendstat: El_miron Iberomaurusian -0.002
gendstat: El_miron Onge -0.006
gendstat: El_miron Goyet_nean -0.803
gendstat: El_miron Devils_cave 0.667
gendstat: Bichon Satsurblia -0.348
gendstat: Bichon Tianyuan 0.702
gendstat: Bichon Sung3 -0.090
gendstat: Bichon Iberomaurusian 0.137
gendstat: Bichon Onge 0.120
gendstat: Bichon Goyet_nean -0.735
gendstat: Bichon Devils_cave 0.897
gendstat: Satsurblia Tianyuan 1.120
gendstat: Satsurblia Sung3 0.280
gendstat: Satsurblia Iberomaurusian 0.540
gendstat: Satsurblia Onge 0.485
gendstat: Satsurblia Goyet_nean -0.354
gendstat: Satsurblia Devils_cave 1.183
gendstat: Tianyuan Sung3 -0.896
gendstat: Tianyuan Iberomaurusian -0.743
gendstat: Tianyuan Onge -0.668
gendstat: Tianyuan Goyet_nean -1.600
gendstat: Tianyuan Devils_cave 0.123
gendstat: Sung3 Iberomaurusian 0.260
gendstat: Sung3 Onge 0.224
gendstat: Sung3 Goyet_nean -0.680
gendstat: Sung3 Devils_cave 1.141
gendstat: Iberomaurusian Onge -0.005
gendstat: Iberomaurusian Goyet_nean -1.109
gendstat: Iberomaurusian Devils_cave 0.987
gendstat: Onge Goyet_nean -1.015
gendstat: Onge Devils_cave 0.915
gendstat: Goyet_nean Devils_cave 2.052

##end of qpAdm: 173.874 seconds cpu 2673.267 Mbytes in use


The 1st one was a disaster because those ancient outgroups were not able to differentiate your very related more recent populations Meaning all those Langboard dources are almost equally related to All the outgroups.

The 2nd one is much much better. Your std errors are only 4%! AND you have a passing p-value of 0.18 for your model of Serbs=61% +/-4% Russia MA + 39% +/-4% Greek_Anc

You’ll also notice that Serbs share alot more genetic drift with Russia-MA because the fixed path 01 (Serb=100% Russia-MA) has a chi-sq of 71 whereas the 10 fixed path (Serb=100% GreeK-Anc) has a much higher chi sq of 379..

Based on what i’m seeing in the 2nd run you can use 3 sources to model Serbs. Try perhaps a EEF source + WHG source + steppe source which has a little E Eurasian

Looks like you’re slowly getting the hang of it. Keep at it and you’ll get better.

Try to keep your std errors under 15% and your p-values above 0.05

[vbnetkhio]

The 1st one was a disaster because those ancient outgroups were not able to differentiate your very related more recent populations Meaning all those Langboard dources are almost equally related to All the outgroups.

The 2nd one is much much better. Your std errors are only 4%! AND you have a passing p-value of 0.18 for your model of Serbs=61% +/-4% Russia MA + 39% +/-4% Greek_Anc

You’ll also notice that Serbs share alot more genetic drift with Russia-MA because the fixed path 01 (Serb=100% Russia-MA) has a chi-sq of 71 whereas the 10 fixed path (Serb=100% GreeK-Anc) has a much higher chi sq of 379..

Based on what i’m seeing in the 2nd run you can use 3 sources to model Serbs. Try perhaps a EEF source + WHG source + steppe source which has a little E Eurasian

Looks like you’re slowly getting the hang of it. Keep at it and you’ll get better.

Try to keep your std errors under 15% and your p-values above 0.05

in the first model i used exclusively early medieval Hungarian samples to try to get the closest to the real source of our ancestry. The Avar and the Langobard outliers are some weird steppe/Roman/Germanic mixes and they usually (in amateur calcs) don't work as a pre-Slavic proxy for South Slavs, so i didn't expect much from them.

The Hungarian Slav usually works as a proto-Slavic proxy, and is quite similiar to the medieval Russian, but he seems to be completely unrelated to the wave of Slavs which Serbs come from.

[michal3141]

Does it make sense to use qpAdm for modelling modern admixture?
What is the best tutorial to learn this qpAdm from scratch? Looks like a pretty sophisticated tool...

[Zoro]

in the first model i used exclusively early medieval Hungarian samples to try to get the closest to the real source of our ancestry. The Avar and the Langobard outliers are some weird steppe/Roman/Germanic mixes and they usually (in amateur calcs) don't work as a pre-Slavic proxy for South Slavs, so i didn't expect much from them.

The Hungarian Slav usually works as a proto-Slavic proxy, and is quite similiar to the medieval Russian, but he seems to be completely unrelated to the wave of Slavs which Serbs come from.

You could still use one of the Langboards but don’t use all in the same run because it’s hard to differentiate them that’s why your se is so high. Use 1 at a time and see which gives you the best result.

You may also introduce EHG to outgroups to help differentiate but if you do that drop 1 of the outgroups

[Zoro]

Does it make sense to use qpAdm for modelling modern admixture?
What is the best tutorial to learn this qpAdm from scratch? Looks like a pretty sophisticated tool...

No issues with using it to model modern admixture but the outgroups have to be chosen wisely

[Lucas]

This looks promising, should be more easy to use https://rdrr.io/github/bodkan/admixr/

Tutorial https://bodkan.net/admixr/articles/tutorial.html

Official paper https://academic.oup.com/bioinformatics/article/35/17/3194/5298728

[Lucas]

This looks promising, should be more easy to use https://rdrr.io/github/bodkan/admixr/

Tutorial https://bodkan.net/admixr/articles/tutorial.html


Official paper https://academic.oup.com/bioinformatics/article/35/17/3194/5298728

Fuckers... I was stucked with R problems only to install it but finally works:) Now trying demo content.

[Lucas]

It's very cool stuff. We can try many models very fast, and finally this which is good enough we can replicate in raw admixtools to see full output.

[michal3141]

It's very cool stuff. We can try many models very fast, and finally this which is good enough we can replicate in raw admixtools to see full output.

Is it better than ADMIXTURE and G25 coordinates ? :)

[Lucas]

Is it better than ADMIXTURE and G25 coordinates ? :)

You don't see plethora of exotic references in result but you can test crucial hypotheses for your ancestry. In your case if you really have Finnic admixture I know you always wondered:) And those results are most solid comparing to mentioned by you.

[Ion Basescul]

It would have been more feasible if it didn't require so much space for data. Those geno packs can easily bleed into dozens of GB when unpacked.

[JamesBond007]

It would have been more feasible if it didn't require so much space for data. Those geno packs can easily bleed into dozens of GB when unpacked.

It would be more feasible if Linux was not an amateur shitshow unlike the traditional Unix it replaced. Sure, you can get it to work but you have to waste a significant amount your time figuring out how because Linux is poorly documentated. I don't have time to waste of this shit especially when my country is going through a cultural civil war. I don't have time for this because some retard Linux user , from Harvard, did not write a man page for his binary (source code is available ,though) to convert files to the format I need to work with the program :



Why documentation on GNU/Linux sucks

This is based on a post on reddit, published on 2012-09-12.

The documentation situation on GNU/Linux based operating systems is right now a mess. In the world of documentation, there are basically 3 camps, the “UNIX” camp, the “GNU” camp, and the “GNU/Linux” camp.

The UNIX camp is the man page camp, they have quality, terse but informative man pages, on everything, including the system’s design and all system files. If it was up to the UNIX camp, man grub.cfg, man grub.d, and man grub-mkconfig_lib would exist and actually be helpful. The man page would either include inline examples, or point you to a directory. If I were to print off all of the man pages, it would actually be a useful manual for the system.

Then GNU camp is the info camp. They basically thought that each piece of software was more complex than a man page could handle. They essentially think that some individual pieces software warrant a book. So, they developed the info system. The info pages are usually quite high quality, but are very long, and a pain if you just want a quick look. The info system can generate good HTML (and PDF, etc.) documentation. But the standard info is awkward as hell to use for non-Emacs users.

Then we have the “GNU/Linux” camp, they use GNU software, but want to use man pages. This means that we get low-quality man pages for GNU software, and then we don’t have a good baseline for documentation, developers each try to create their own. The documentation that gets written is frequently either low-quality, or non-standard. A lot of man pages are auto-generated from --help output or info pages, meaning they are either not helpful, or overly verbose with low information density. This camp gets the worst of both worlds, and a few problems of its own.

https://www.lukeshu.com/blog/poor-system-documentation.html





Linux Bug #1: Bad Documentation


The Internet and Google enable laziness in FOSS development because they make it too easy to abdicate the job of proper documentation to "The community." Telling users and potential contributors to use Google, mailing lists, and forums is not documentation. It's a way to guarantee having fewer users, unhappy users, and fewer contributors.

...




https://www.linuxtoday.com/blog/2009/11/linux-bug-1-bad.html

[Lucas]

It would have been more feasible if it didn't require so much space for data. Those geno packs can easily bleed into dozens of GB when unpacked.

You can extract in Plink set of relevants refs and then convert it to Geno again. Don't need whole Reich dataset (with Amerindian, African samples etc) to test yourself.

[Schwop]

I want to do this but im a casual windows peasant :D

[Zoro]

It would have been more feasible if it didn't require so much space for data. Those geno packs can easily bleed into dozens of GB when unpacked.


Hard disk space is not an issue. The latest Reich Lab 4700 sample set geno file is only 3.5 Gb. You just need decent RAM to do the each run in under 5 min. The main thing here is learning UNIX commands and how to compile software.

I'll help out if anyone runs into snags or needs help interpreting and refining outputs. Just post your error codes and a brief description of the issue you're having

[Zoro]

Is it better than ADMIXTURE and G25 coordinates ? :)

Yes it's better but it does require a deeper understanding of genetic concepts to understand how to run. Also unlike Admixture and G25 it's not a one shoe fits all because each individual may require different sources and this regard each run has to be tailor made for an individual but it's worth the effort because this is what scientific papers use for fine analysis.

[vbnetkhio]

I want to do this but im a casual windows peasant :D

you can run a virtual Ubuntu on windows with https://www.virtualbox.org/
i do it succesfully this way.


It would have been more feasible if it didn't require so much space for data. Those geno packs can easily bleed into dozens of GB when unpacked.

you can remove samples you don't need in plink. i always do this because i don't have much ram.

[michal3141]

Good I was able to run some model :)

> result <- qpAdm(
+ target = c("Polish"),
+ sources = c("Lithuanian", "Sardinian"),
+ outgroups = c("Yoruba", "Han", "Papuan"),
+ data = snps
+ )

>
> result$proportions
# A tibble: 1 x 6
target Lithuanian Sardinian stderr_Lithuanian stderr_Sardinian nsnps
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Polish 0.823 0.177 0.051 0.051 607247

I am wondering:
1. if I can add my raw data to the already existing EIGENSTRAT dataset.
2. what is the idea behind using outgroups? Do they change anything? Sorry for newbie question.

[Lucas]

1. if I can add my raw data to the already existing EIGENSTRAT dataset.
.

1. Yes, of course. Just convert raw file to bim/bed/fam merge with existing plink dataset and then convert to eigenstrat.

[Zoro]

Good I was able to run some model :)

> result <- qpAdm(
+ target = c("Polish"),
+ sources = c("Lithuanian", "Sardinian"),
+ outgroups = c("Yoruba", "Han", "Papuan"),
+ data = snps
+ )

>
> result$proportions
# A tibble: 1 x 6
target Lithuanian Sardinian stderr_Lithuanian stderr_Sardinian nsnps
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Polish 0.823 0.177 0.051 0.051 607247

I am wondering:
1. if I can add my raw data to the already existing EIGENSTRAT dataset.
2. what is the idea behind using outgroups? Do they change anything? Sorry for newbie question.

1- Best to 1st convert Eigenstrat to plink format using procedure I outlined in a prior page. Next merge your data (make sure its 23andme format) with dataset you just converted to plink (procedure i gave on a previous page) . Once everything is merged in plink and positions with over 80% missingness rate removed and samples with too many missing positions removed your ready to convert Everything back to EigenStrat format

Once you’re there ill give instructions on how to

2- concept of outgroups is to filter out from analysis old ancestral alleles that most pops share. Do sources are checked with outgroups to see how many derived Alleles EACH source shares with EACH outgroup

Let’s say you are trying yo model Poles with Lithuanian + some other pop. If poles and lithuanians share same number of derived alleles with EACH Outgroup then they must be sister pops and can be modelled as 100% of each other

[michal3141]

1- Best to 1st convert Eigenstrat to plink format using procedure I outlined in a prior page. Next merge your data (make sure its 23andme format) with dataset you just converted to plink (procedure i gave on a previous page) . Once everything is merged in plink and positions with over 80% missingness rate removed and samples with too many missing positions removed your ready to convert Everything back to EigenStrat format

Once you’re there ill give instructions on how to

2- concept of outgroups is to filter out from analysis old ancestral alleles that most pops share. Do sources are checked with outgroups to see how many derived Alleles EACH source shares with EACH outgroup

Let’s say you are trying yo model Poles with Lithuanian + some other pop. If poles and lithuanians share same number of derived alleles with EACH Outgroup then they must be sister pops and can be modelled as 100% of each other

Thanks! This explains a lot!
One more question:
Could some of the outgroup populations be similar to source populations (or even the same?).
I am trying to figure out a good set of outgroup populations for modelling my ancestry.

[Lucas]

Thanks! This explains a lot!
One more question:
Could some of the outgroup populations be similar to source populations (or even the same?).
I am trying to figure out a good set of outgroup populations for modelling my ancestry.

I also want to know. First I want to choose the same model as michal to see what would be a difference between us, Poles.

I guess EHG is needed as one of the outgroup for Poles.

[Zoro]

Thanks! This explains a lot!
One more question:
Could some of the outgroup populations be similar to source populations (or even the same?).
I am trying to figure out a good set of outgroup populations for modelling my ancestry.

Definitely not. In fact the outgroups have to be further distance from target than ANY source you use from target and there shouldn’t be any direct geneflow outgroup —> target outside of source—> target

Let me know what sources you’re trying to model yourself and I’ll suggest some outgroups

[Zoro]

I also want to know. First I want to choose the same model as michal to see what would be a difference between us, Poles.

I guess EHG is needed as one of the outgroup for Poles.

Outgroups are dictated by which sources you choose. For ex if you try to model yourself as steppe + whg you’ll need EHG as outgroup to differentiate steppe from WHG

[vbnetkhio]

Outgroups are dictated by which sources you choose. For ex if you try to model yourself as steppe + whg you’ll need EHG as outgroup to differentiate steppe from WHG

i think samples from the previous period should always be used, right?
i'm modelling with bronze, iron and medieval age samples so i should use neolithic and chalcolithic samples as outgroups?

and Michal should use bronze/iron/medieval as outgroups

[vbnetkhio]

Good I was able to run some model :)

> result <- qpAdm(
+ target = c("Polish"),
+ sources = c("Lithuanian", "Sardinian"),
+ outgroups = c("Yoruba", "Han", "Papuan"),
+ data = snps
+ )

>
> result$proportions
# A tibble: 1 x 6
target Lithuanian Sardinian stderr_Lithuanian stderr_Sardinian nsnps
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Polish 0.823 0.177 0.051 0.051 607247

I am wondering:
1. if I can add my raw data to the already existing EIGENSTRAT dataset.
2. what is the idea behind using outgroups? Do they change anything? Sorry for newbie question.

I'm trying to figure that out. I think the idea is to filter out the distant ancestries you aren't interested in. For example, let's say the program now modelled Poles as Lithuanian + Sardinian just because Lithuanians have high Yamnaya and Sardinians high EEF.

Now you can include Yamnaya and EEF as outgroups, to tell the program not to do that, to ignore Yamnaya and EEF ancestry, and look at more recent drift which makes Lithuanians and Sardinians special. Zoro can correct me if i'm worng.

[michal3141]

Definitely not. In fact the outgroups have to be further distance from target than ANY source you use from target and there shouldn’t be any direct geneflow outgroup —> target outside of source—> target

Let me know what sources you’re trying to model yourself and I’ll suggest some outgroups

I would like to model myself as a combination of Polish and some more northern and eastern populations e.g. Balts, Finns. What would be good outgroups? I would guess some Baltic_HG and Siberian. Still need to merge some datasets.

[Zoro]

i think samples from the previous period should always be used, right?
i'm modelling with bronze, iron and medieval age samples so i should use neolithic and chalcolithic samples as outgroups?

and Michal should use bronze/iron/medieval as outgroups


Neolithic and Paleolithic would be preferable. You can also add Papuans and Karitiana


I would try to stay away from W Eurasian Iron Age and Medieval as they are too recent.

[Zoro]

I would like to model myself as a combination of Polish and some more northern and eastern populations e.g. Balts, Finns. What would be good outgroups? I would guess some Baltic_HG and Siberian. Still need to merge some datasets.

The list of outgroups I previously gave plus EHG and the following higher coverage WHG (I removed the lower coverage Iron Gates samples from this list) but remove 2 outgroups from my previous list which are less relevant to W Eurasians

Serbia_Mesolithic_IronGates I5407
Serbia_Mesolithic_IronGates I5242
Serbia_Mesolithic_IronGates I5238
Serbia_Mesolithic_IronGates I5233
Serbia_Mesolithic_IronGates I5237
Serbia_Mesolithic_IronGates I4881_pub
Serbia_Mesolithic_IronGates I5236
Serbia_Mesolithic_IronGates I5235
Serbia_Mesolithic_IronGates I4877
Serbia_Mesolithic_IronGates I4915
Serbia_Mesolithic_IronGates I5239
Serbia_Mesolithic_IronGates I4875
Serbia_Mesolithic_IronGates I5244
Serbia_Mesolithic_IronGates I5240
Serbia_Mesolithic_IronGates I5402
Serbia_Mesolithic_IronGates I4880
Serbia_Mesolithic_IronGates I4914_pub
Serbia_Mesolithic_IronGates I4873
Serbia_Mesolithic_IronGates I4876
Serbia_Mesolithic_IronGates I4874
Serbia_Mesolithic_IronGates I4878
Serbia_Mesolithic_IronGates I4917
Serbia_Mesolithic_IronGates I4916

[Lucas]

The list of outgroups I previously gave plus EHG and the following higher coverage WHG (I removed the lower coverage Iron Gates samples from this list) but remove 2 outgroups from my previous list which are less relevant to W Eurasians

Serbia_Mesolithic_IronGates I5407
Serbia_Mesolithic_IronGates I5242
Serbia_Mesolithic_IronGates I5238
Serbia_Mesolithic_IronGates I5233
Serbia_Mesolithic_IronGates I5237
Serbia_Mesolithic_IronGates I4881_pub
Serbia_Mesolithic_IronGates I5236
Serbia_Mesolithic_IronGates I5235
Serbia_Mesolithic_IronGates I4877
Serbia_Mesolithic_IronGates I4915
Serbia_Mesolithic_IronGates I5239
Serbia_Mesolithic_IronGates I4875
Serbia_Mesolithic_IronGates I5244
Serbia_Mesolithic_IronGates I5240
Serbia_Mesolithic_IronGates I5402
Serbia_Mesolithic_IronGates I4880
Serbia_Mesolithic_IronGates I4914_pub
Serbia_Mesolithic_IronGates I4873
Serbia_Mesolithic_IronGates I4876
Serbia_Mesolithic_IronGates I4874
Serbia_Mesolithic_IronGates I4878
Serbia_Mesolithic_IronGates I4917
Serbia_Mesolithic_IronGates I4916

For EHG it could be all three?

EHG I0061
EHG I0124
EHG I0211

And those for WHG?

WHG I0585
WHG I1507
WHG Loschbour

[Zoro]

For EHG it could be all three?

EHG I0061
EHG I0124
EHG I0211

And those for WHG?

WHG I0585
WHG I1507
WHG Loschbour

It's always better to use more samples to get a better estimate of allele frequencies in a population but in the case of EHG I0124 I0211 are quite a bit lower coverage and have far fewer overlapping SNPs to be of much use. They also appear to be diverged from I0061 I would just use I0061.

By the same logic I would just use Loschbour or the above Iron Gates samples

[vbnetkhio]

here is an unexpected model with a very good fit:


$proportions
# A tibble: 1 x 6
target Russia_MA Croatia_EIA stderr_Russia_MA stderr_Croatia_EIA nsnps
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Serb 0.611 0.389 0.065 0.065 541478

$ranks
# A tibble: 2 x 8
target rank df chisq tail dfdiff chisqdiff taildiff
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Serb 1 14 4.11 0.995 16 -4.11 1
2 Serb 2 0 0 1 14 4.11 0.995

$subsets
# A tibble: 3 x 8
target pattern wt dof chisq tail Russia_MA Croatia_EIA
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Serb 00 0 14 4.11 0.995 0.611 0.389
2 Serb 01 1 15 25.0 0.0493 1 0
3 Serb 10 1 15 55.8 0.00000133 0 1

or to summarize:

Serbs
Russia_MA 61.1%
Croatia_EIA 38.9%
tail prob 0.995

in the Rome study, tail prob had to be higher than 0.05 to be accepted and they are always below 1, so this model is as good as it gets.
in gedmatch calcs this model doesn't work, because Croatia_EIA is too Celtic shifted, and something more southern is needed for Serbs. It seems the amateur tools can be really misleading.

[Lucas]

here is an unexpected model with a very good fit:


$proportions
# A tibble: 1 x 6
target Russia_MA Croatia_EIA stderr_Russia_MA stderr_Croatia_EIA nsnps
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Serb 0.611 0.389 0.065 0.065 541478

$ranks
# A tibble: 2 x 8
target rank df chisq tail dfdiff chisqdiff taildiff
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Serb 1 14 4.11 0.995 16 -4.11 1
2 Serb 2 0 0 1 14 4.11 0.995

$subsets
# A tibble: 3 x 8
target pattern wt dof chisq tail Russia_MA Croatia_EIA
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Serb 00 0 14 4.11 0.995 0.611 0.389
2 Serb 01 1 15 25.0 0.0493 1 0
3 Serb 10 1 15 55.8 0.00000133 0 1

or to summarize:

Serbs
Russia_MA 61.1%
Croatia_EIA 38.9%
tail prob 0.995

in the Rome study, tail prob had to be higher than 0.05 to be accepted and they are always below 1, so this model is as good as it gets.
in gedmatch calcs this model doesn't work, because Croatia_EIA is too Celtic shifted, and something more southern is needed for Serbs. It seems the amateur tools can be really misleading.

Serb was modern average or some selection of samples?
What is exact sample number for Croatia EIA?

[vbnetkhio]

Serb was modern average or some selection of samples?
What is exact sample number for Croatia EIA?

Serbs are 18 academic samples, Russia_MA and Croatia EIA are one sample each

[vbnetkhio]

outgroups used in the Roman study:

set 1: Anatolia_N (25), CHG (2), EHG (4), ElMiron (1), Iran_Ganj_Dareh_N (3),
Jordan_PPNB (1), MA1 (1), Mbuti (10), Natufian (6), Ust_Ishim (1), Vestonice16 (1), WHG (6),
Russia_Yamnaya_Samara (9)

set 2: Anatolia_N (25), CHG (2), EHG (4), ElMiron (1), GoyetQ116-1 (1), Iran_Ganj_Dareh_N (3),
Jordan_PPNB (1), Kostenki14 (1), MA1 (1), Morocco_Iberomaurusian (6), Mota (1), Natufian (6),
Ust_Ishim (1), Vestonice16 (1), Italy_Villabruna (1), WHG (6), Russia_Yamnaya_Samara (9).

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7093155/bin/NIHMS1551077-supplement-Supplement.pdf
there is more info on page 20 and some models on page 76 onwards

[michal3141]

Is there a good tutorial how to convert between formats, handle these issues with missing SNPs?
Also which datasets do you use?

[vbnetkhio]

Is there a good tutorial how to convert between formats, handle these issues with missing SNPs?
Also which datasets do you use?

which issues do you have? i use the datasets from here:

https://reich.hms.harvard.edu/downloadable-genotypes-worlds-published-ancient-dna-data
https://evolbio.ut.ee/

[michal3141]

which issues do you have? i use the datasets from here:

https://reich.hms.harvard.edu/downloadable-genotypes-worlds-published-ancient-dna-data
https://evolbio.ut.ee/

How do you merge yourself with these datasets? I can convert them to BED, BIM, FAM format and also convert my raw data to BED, BIM, FAM. I have some issues with merging because of missing SNPs.
Then I can remove these SNPs and merge.
Finally I am trying to convert to EIGENSTRAT (geno) but these population labels are not getting combined correctly...
Another issues is merging multiple datasets takes GBs of disk space and I have very little disk space. Guess I need to buy some disks.

[vbnetkhio]

How do you merge yourself with these datasets? I can convert them to BED, BIM, FAM format and also convert my raw data to BED, BIM, FAM. I have some issues with merging because of missing SNPs.
Then I can remove these SNPs and merge.
Finally I am trying to convert to EIGENSTRAT (geno) but these population labels are not getting combined correctly...
Another issues is merging multiple datasets takes GBs of disk space and I have very little disk space. Guess I need to buy some disks.

qpadm can usually work directly with bed files. the R version works only with geno, but i currently use that, because it's more practical.

i think you have problems with strand inconsisetncy when merging, right?

so you try to merge 2 files like this:

plink --allow-no-sex --bfile file1 --bmerge file2 --make-bed --out files_merged

then you get a strand inconsistency error (meaning that for example one of the files has GA where the other has AG )
then you should flip the iconsistent snps:

plink --allow-no-sex --bfile file2 --flip files_merged-merge.missnp --make-bed --out file2_flipped

theny try to merge again:

plink --allow-no-sex --bfile file1 --bmerge file2_flipped --make-bed --out files_merged

then if you get an error again, because some variants are multiallelic,(e.g. one of the files has GA and the other GC )

then you should delete these:

plink --allow-no-sex --bfile file2 --exclude files_merged-merge.missnp --make-bed --out file2_filtered

then finally try to merge them again:

plink --allow-no-sex --bfile file1 --bmerge file2_filtered --make-bed --out files_merged

this way you lose the least SNPs when merging.
also, you can remove samples you don't need with the --remove command. this way the files will be smaller.

when you have your final bed/bim/fam file, open the fam file in excel or libreoffice calc and add the population name for each sample in the last (6th) column, and save the changes. be careful not to reorder the samples, each sample is tied to its line.
then convert that file to geno format.

[Lucas]

which issues do you have? i use the datasets from here:

https://reich.hms.harvard.edu/downloadable-genotypes-worlds-published-ancient-dna-data
https://evolbio.ut.ee/

Best, to merge all datasets from evolbio.ut.ee to one big package.

[Zoro]

here is an unexpected model with a very good fit:


$proportions
# A tibble: 1 x 6
target Russia_MA Croatia_EIA stderr_Russia_MA stderr_Croatia_EIA nsnps
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Serb 0.611 0.389 0.065 0.065 541478

$ranks
# A tibble: 2 x 8
target rank df chisq tail dfdiff chisqdiff taildiff
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Serb 1 14 4.11 0.995 16 -4.11 1
2 Serb 2 0 0 1 14 4.11 0.995

$subsets
# A tibble: 3 x 8
target pattern wt dof chisq tail Russia_MA Croatia_EIA
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Serb 00 0 14 4.11 0.995 0.611 0.389
2 Serb 01 1 15 25.0 0.0493 1 0
3 Serb 10 1 15 55.8 0.00000133 0 1

or to summarize:

Serbs
Russia_MA 61.1%
Croatia_EIA 38.9%
tail prob 0.995

in the Rome study, tail prob had to be higher than 0.05 to be accepted and they are always below 1, so this model is as good as it gets.
in gedmatch calcs this model doesn't work, because Croatia_EIA is too Celtic shifted, and something more southern is needed for Serbs. It seems the amateur tools can be really misleading.

It's good to see you're making progress

[Zoro]

How do you merge yourself with these datasets? I can convert them to BED, BIM, FAM format and also convert my raw data to BED, BIM, FAM. I have some issues with merging because of missing SNPs.
Then I can remove these SNPs and merge.
Finally I am trying to convert to EIGENSTRAT (geno) but these population labels are not getting combined correctly...
Another issues is merging multiple datasets takes GBs of disk space and I have very little disk space. Guess I need to buy some disks.

Looks like you were given some good advise above about navigating some of the issues you're having. A good practice is to consistently remove positions which are not genotyped in most samples after merging. Use this command frequently

/path to your plink executible /plink --bfile Merged --geno 0.95 --make-bed --out Merged1

What this command does it removes positions that have a missing genotype in over 95% of the samples.

All you really need is just that one Reich Lab dataset (latest one) because it has about 6500 samples.

After merging yourself or any of the raw files you manage in plink simply convert back to Eigenstrat so that you can do dstats and qpAdm using the following procedure:

1- Convert your bed bim fam to ped using this : ......./plink --bfile Merged1 --recode --Merged1. This will output 2 files a ped and a map file.

2- Create a pedind file in excel by taking your fam file and copying column 1 into column 6. Save this as a text file such as Merged1.pedind.

3- Put the 3 files you just created into the folder containing convertf and create the following par file and save as a text file called par.PED.EIGENSTRAT :

genotypename: Merged1.ped
snpname: Merged1.map
indivname: Merged1.pedind
outputformat: EIGENSTRAT
genooutfilename: Merged1.geno
snpoutfilename: Merged1.snp
indoutfilename: Merged1.ind


and run the following command;

/path to your covertf file /convertf -p par.PED.EIGENSTRAT


Now you'll get back geno snp and ind files which you can process with qpAdm

[vbnetkhio]

How do you merge yourself with these datasets? I can convert them to BED, BIM, FAM format and also convert my raw data to BED, BIM, FAM. I have some issues with merging because of missing SNPs.
Then I can remove these SNPs and merge.
Finally I am trying to convert to EIGENSTRAT (geno) but these population labels are not getting combined correctly...
Another issues is merging multiple datasets takes GBs of disk space and I have very little disk space. Guess I need to buy some disks.

i also forgot to add, sample IDs and population names shouldn't be longer than 40 characters. if you have some that are longer you'll get an error.
you can just edit them manually. but don't edit the .ind file, they shouldn't be edited manually. instead edit the .fam file, and then convert the set into geno/snp/ind again.

[Lucas]

Looks like you were given some good advise above about navigating some of the issues you're having. A good practice is to consistently remove positions which are not genotyped in most samples after merging. Use this command frequently

/path to your plink executible /plink --bfile Merged --geno 0.95 --make-bed --out Merged1

What this command does it removes positions that have a missing genotype in over 95% of the samples.



Could be add minor allelels frequency pruning too (--maf 0.05) or instead --geno using --mind 0.95 to prune not snps but samples with missing genotype data.
Generally check it https://zzz.bwh.harvard.edu/plink/thresh.shtml

And to prune even more... Read more under specific topic (Linkage disequilibrium based SNP pruning ) here http://zzz.bwh.harvard.edu/plink/summary.shtml
plink --file data --indep-pairwise 50 5 0.5


It is also good practice when creating dataset for admixture calculator:)

[vbnetkhio]

Could be add minor allelels frequency pruning too (--maf 0.05) or instead --geno using --mind 0.95 to prune not snps but samples with missing genotype data.
Generally check it https://zzz.bwh.harvard.edu/plink/thresh.shtml

And to prune even more... Read more under specific topic (Linkage disequilibrium based SNP pruning ) here http://zzz.bwh.harvard.edu/plink/summary.shtml
plink --file data --indep-pairwise 50 5 0.5


It is also good practice when creating dataset for admixture calculator:)

i think i read somewhere that this shouldn't be done for qpadm, that here every SNP is important.

[Lucas]

i think i read somewhere that this shouldn't be done for qpadm, that here every SNP is important.

Maybe for qpadm not. Ok Zoro tells us:)

[Zoro]

Maybe for qpadm not. Ok Zoro tells us:)

Using --geno 0.95 flag is beneficial because it's only removing positions that are missing in 95% of the samples and since those genotypes are already missing in 95% of the dataset it does more good than bad. Often there will be a couple of samples genotyped on 1240K positons but most of the dataset that is being used is only genotyped on 600K positons. In this case it doesn't make sense to keep 1240K positions if they are missing in 95% of the samples not to mention that convertf will probably not work if that many samples are missing those positons.

[vbnetkhio]

For EHG it could be all three?

EHG I0061
EHG I0124
EHG I0211

And those for WHG?

WHG I0585
WHG I1507
WHG Loschbour

there is a new EHG in the Reich dataset now
UzOO77 Russia EHG

[vbnetkhio]

This looks promising, should be more easy to use https://rdrr.io/github/bodkan/admixr/

Tutorial https://bodkan.net/admixr/articles/tutorial.html

Official paper https://academic.oup.com/bioinformatics/article/35/17/3194/5298728

can you do a one population comparison in admixr? (only 1 target and 1 source)
i get an error when i try it. it accepts only 2 or more pops.

edit: it doesn't work in qpadm either. but how did they do one-way models in The Rome study then?

[Lucas]

can you do a one population comparison in admixr? (only 1 target and 1 source)
i get an error when i try it. it accepts only 2 or more pops.

edit: it doesn't work in qpadm either. but how did they do one-way models in The Rome study then?

I asked few days ago author on twitter and he added now function of full log of qpadm. Maybe see what errors are there?

Check last tutorial section about logs
https://bodkan.net/admixr/articles/tutorial.html

[vbnetkhio]

I asked few days ago author on twitter and he added now function of full log of qpadm. Maybe see what errors are there?

Check last tutorial section about logs
https://bodkan.net/admixr/articles/tutorial.html

i figured it out. it can be done in qpWave

[vbnetkhio]

my one-way comparisons with some ancient samples, sorted by chisq (lower chisq=more shared genetic drift):

high quality samples:


populationsnp countchisqP-value
HUN_scy105204223.820.0935185239
Czech_EarlySlav72713431.7450.0107912726
Hun_Avar114354531.9080.0102790055
Hun_Slav179861536.6990.00231142156
HUN_LBA_K67125337.6790.00168099673
HUN_LBA114442440.4990.000658080287
MDA_Scythian108245647.7375.22366916E-05
Hun_Slav276789350.0892.21886965E-05
HUN_EBA_bb107400750.6441.8092044E-05
HUN_IA_scy77477951.9661.10923218E-05
HUN_Langobard114447252.3199.7249898E-06
HUN_EBA_P79314353.1247.20170209E-06
HUN_Vatya_MBA93928054.5694.18510884E-06
UKR_scy107652955.972.46123996E-06
Cro_EIA79183965.1127.0480884E-08
MDA_Scythian_o278396469.5891.17744135E-08
HUN_EBA_Mako97241096.4081.62531795E-13
Cro_MBA84790398.3367.09509764E-14
HUN_Langobard_o1144311114.2846.84478936E-17
BGR_IA700543195.297.08647609E-33
UKR_wScythian_IA767338206.6263.61787304E-35
BGR_MLBA804354253.0611.21885816E-44
UKR_EBA_GAC902191275.9952.33103458E-49
HUN_EBA_bb_o849663282.1081.2769829E-50
UKR_EBA_Yamnaya848355293.1456.69009951E-53
BGR_EBA981587313.1044.90152601E-57
HUN_LC_EBA864375513.9153.83088055E-99
MDA_Cimmerian748426682.2077.89419449E-135

low quality samples:


UKR_Chernyakhiv_o188989.7610.878811068
HUN_MA35032117.1290.377289049
Montenegro_IA1515221.4250.162739265
UKR_Cimmerian22536732.8340.00777380277
MDA_Scythian_o13575037.0670.00205193941
UKR_wScythian_IA_o30226538.2220.00140664454
HUN_Maros_EBA44820738.5580.00125928234
UKR_Chernyakhiv28301641.7330.000432492631
BGR_EBA_yamnaya36384347.9584.82188894E-05
Cro_Vuc_LC_EBA43932252.0361.08082561E-05
UKR_Lsrub11358456.5641.96103909E-06
Montenegro_LBA2556457.851.19876608E-06
BGR_EBA_e6590270.6077.81084251E-09
UKR_Cimmerian_o118264483.8893.28871556E-11
AFG_MBA3591191.4671.34186922E-12
SRB_GEP384764103.7670.678384809
UKR_BA_Catacomb167911120.4344.53183454E-18
BGR_EBA_bb367625130.3335.53015885E-20
UKR_EBA_yam_oz598982155.297.04786043E-25
UKR_Cimmerian_o2248358185.9225.45547856E-31
UKR_EBA582858223.3211.46972661E-38
MDA_Scythian_o314698239.3947.69643396E-42
Cro_Vucedol187376321.4249.17965781E-59

i think this is the tool you get the most out of your autosomal data with.

[Ion Basescul]

my one-way comparisons with some ancient samples, sorted by chisq (lower chisq=more shared genetic drift):

high quality samples:


<tbody>
population
snp count
chisq
P-value


HUN_scy
1052042
23.82
0.0935185239


Czech_EarlySlav
727134
31.745
0.0107912726


Hun_Avar
1143545
31.908
0.0102790055


Hun_Slav1
798615
36.699
0.00231142156


HUN_LBA_K
671253
37.679
0.00168099673


HUN_LBA
1144424
40.499
0.000658080287


MDA_Scythian
1082456
47.737
5.22366916E-05


Hun_Slav2
767893
50.089
2.21886965E-05


HUN_EBA_bb
1074007
50.644
1.8092044E-05


HUN_IA_scy
774779
51.966
1.10923218E-05


HUN_Langobard
1144472
52.319
9.7249898E-06


HUN_EBA_P
793143
53.124
7.20170209E-06


HUN_Vatya_MBA
939280
54.569
4.18510884E-06


Cro_EIA
791839
65.112
7.0480884E-08


MDA_Scythian_o2
783964
69.589
1.17744135E-08


HUN_EBA_Mako
972410
96.408
1.62531795E-13


Cro_MBA
847903
98.336
7.09509764E-14


HUN_Langobard_o
1144311
114.284
6.84478936E-17


BGR_IA
700543
195.29
7.08647609E-33


BGR_MLBA
804354
253.061
1.21885816E-44


UKR_EBA_GAC
902191
275.995
2.33103458E-49


HUN_EBA_bb_o
849663
282.108
1.2769829E-50


BGR_EBA
981587
313.104
4.90152601E-57


HUN_LC_EBA
864375
513.915
3.83088055E-99


MDA_Cimmerian
748426
682.207
7.89419449E-135

</tbody>


low quality samples:


<tbody>
population
snp count
chisq
P-value


UKR_Chernyakhiv_o
18898
9.761
0.878811068


HUN_MA
350321
17.129
0.377289049


Montenegro_IA
15152
21.425
0.162739265


UKR_Cimmerian
225367
32.834
0.00777380277


MDA_Scythian_o1
35750
37.067
0.00205193941


HUN_Maros_EBA
448207
38.558
0.00125928234


UKR_Chernyakhiv
283016
41.733
0.000432492631


BGR_EBA_yamnaya
363843
47.958
4.82188894E-05


Cro_Vuc_LC_EBA
439322
52.036
1.08082561E-05


Montenegro_LBA
25564
57.85
1.19876608E-06


BGR_EBA_e
65902
70.607
7.81084251E-09


UKR_Cimmerian_o1
182644
83.889
3.28871556E-11


AFG_MBA
35911
91.467
1.34186922E-12


SRB_GEP
384764
103.767
0.678384809


UKR_BA_Catacomb
167911
120.434
4.53183454E-18


BGR_EBA_bb
367625
130.333
5.53015885E-20


UKR_EBA_yam_oz
598982
155.29
7.04786043E-25


UKR_Cimmerian_o2
248358
185.922
5.45547856E-31


UKR_EBA
582858
223.321
1.46972661E-38


MDA_Scythian_o3
14698
239.394
7.69643396E-42


Cro_Vucedol
187376
321.424
9.17965781E-59

</tbody>


i think this is the tool you get the most out of your autosomal data with.

How does this compare to your Global 25 single distances?

[vbnetkhio]

How does this compare to your Global 25 single distances?

i didn't buy g25 yet, but k13 gives very similar results to g25, so here you go:

https://i.imgur.com/h5l2FB3.png

now i noticed k13 results are very similar to the qpAdm output. however qpAdm's method reveals much more details.

for example, in k13 and g25 the Hungarian Langobards and Moldovan Scythian o2 (probably a Germanic Bastarnae) aren't in any way close to Serbs, because they are Scandinavian-like. But qpAdm detects i share quite a lot genetic drift with them, which makes sense given their time period and geographic position.

[Zoro]

my one-way comparisons with some ancient samples, sorted by chisq (lower chisq=more shared genetic drift):

high quality samples:


populationsnp countchisqP-value
HUN_scy105204223.820.0935185239
Czech_EarlySlav72713431.7450.0107912726
Hun_Avar114354531.9080.0102790055
Hun_Slav179861536.6990.00231142156
HUN_LBA_K67125337.6790.00168099673
HUN_LBA114442440.4990.000658080287
MDA_Scythian108245647.7375.22366916E-05
Hun_Slav276789350.0892.21886965E-05
HUN_EBA_bb107400750.6441.8092044E-05
HUN_IA_scy77477951.9661.10923218E-05
HUN_Langobard114447252.3199.7249898E-06
HUN_EBA_P79314353.1247.20170209E-06
HUN_Vatya_MBA93928054.5694.18510884E-06
UKR_scy107652955.972.46123996E-06
Cro_EIA79183965.1127.0480884E-08
MDA_Scythian_o278396469.5891.17744135E-08
HUN_EBA_Mako97241096.4081.62531795E-13
Cro_MBA84790398.3367.09509764E-14
HUN_Langobard_o1144311114.2846.84478936E-17
BGR_IA700543195.297.08647609E-33
UKR_wScythian_IA767338206.6263.61787304E-35
BGR_MLBA804354253.0611.21885816E-44
UKR_EBA_GAC902191275.9952.33103458E-49
HUN_EBA_bb_o849663282.1081.2769829E-50
UKR_EBA_Yamnaya848355293.1456.69009951E-53
BGR_EBA981587313.1044.90152601E-57
HUN_LC_EBA864375513.9153.83088055E-99
MDA_Cimmerian748426682.2077.89419449E-135

low quality samples:


UKR_Chernyakhiv_o188989.7610.878811068
HUN_MA35032117.1290.377289049
Montenegro_IA1515221.4250.162739265
UKR_Cimmerian22536732.8340.00777380277
MDA_Scythian_o13575037.0670.00205193941
UKR_wScythian_IA_o30226538.2220.00140664454
HUN_Maros_EBA44820738.5580.00125928234
UKR_Chernyakhiv28301641.7330.000432492631
BGR_EBA_yamnaya36384347.9584.82188894E-05
Cro_Vuc_LC_EBA43932252.0361.08082561E-05
UKR_Lsrub11358456.5641.96103909E-06
Montenegro_LBA2556457.851.19876608E-06
BGR_EBA_e6590270.6077.81084251E-09
UKR_Cimmerian_o118264483.8893.28871556E-11
AFG_MBA3591191.4671.34186922E-12
SRB_GEP384764103.7670.678384809
UKR_BA_Catacomb167911120.4344.53183454E-18
BGR_EBA_bb367625130.3335.53015885E-20
UKR_EBA_yam_oz598982155.297.04786043E-25
UKR_Cimmerian_o2248358185.9225.45547856E-31
UKR_EBA582858223.3211.46972661E-38
MDA_Scythian_o314698239.3947.69643396E-42
Cro_Vucedol187376321.4249.17965781E-59

i think this is the tool you get the most out of your autosomal data with.


Looks like you’re very related to Hungarian Scythians since p-value is above 0.05. Can you post the full output for that run so i can see the outgroups and fixed path chis

You may also want to run other modern samples from your area against Hungarian Scythians to see how related to them they are compared to you.

[vbnetkhio]

Looks like you’re very related to Hungarian Scythians since p-value is above 0.05. Can you post the full output for that run.

You may also want to run other modern samples from your area against Hungarian Scythians to see how related to them they are compared to you.

here. maybe i should also try with a different outgroup set? or is this ok?


./qpadm: parameter file: parfile
### THE INPUT PARAMETERS
##PARAMETER NAME: VALUE
genotypename: qp.geno
snpname: qp.snp
indivname: qp.ind
popleft: left.txt
popright: right.txt
details: YES
allsnps: YES
## qpAdm version: 1000
seed: 1978976272
packed geno read OK
end of inpack

left pops:
me
HUN_scy

right pops:
EHG
Mbuti
Ust
Anatolia_N
Iran_N
Mal_ta
Kostenki
Natufian
El_miron
Bichon
Satsurblia
Tianyuan
Sunghir3
Iberomaurusian
Onge
Goyet
Devils_cave

0 me 1
1 HUN_scy 5
2 EHG 5
3 Mbuti 30
4 Ust 1
5 Anatolia_N 32
6 Iran_N 8
7 Mal_ta 1
8 Kostenki 1
9 Natufian 6
10 El_miron 1
11 Bichon 1
12 Satsurblia 1
13 Tianyuan 1
14 Sunghir3 1
15 Iberomaurusian 6
16 Onge 1
17 Goyet 1
18 Devils_cave 4
jackknife block size: 0.050
snps: 1826952 indivs: 107
number of blocks for block jackknife: 83
## ncols: 1826952
coverage: me 1089131
coverage: HUN_scy 1052042
coverage: EHG 1116406
coverage: Mbuti 640509
coverage: Ust 1139533
coverage: Anatolia_N 1141989
coverage: Iran_N 1049898
coverage: Mal_ta 800186
coverage: Kostenki 1041488
coverage: Natufian 531280
coverage: El_miron 622872
coverage: Bichon 1137333
coverage: Satsurblia 795644
coverage: Tianyuan 879205
coverage: Sunghir3 1141409
coverage: Iberomaurusian 1093556
coverage: Onge 1125078
coverage: Goyet 881461
coverage: Devils_cave 1141373
dof (jackknife): 36.880
numsnps used: 1826952
codimension 1
f4info:
f4rank: 0 dof: 16 chisq: 23.820 tail: 0.0935185239 dofdiff: 0 chisqdiff: 0.000 taildiff: 1


full rank
f4info:
f4rank: 1 dof: 0 chisq: 0.000 tail: 1 dofdiff: 16 chisqdiff: 23.820 taildiff: 0.0935185239
B:
scale 3912.428
Mbuti -2.421
Ust 1.190
Anatolia_N 0.695
Iran_N 0.109
Mal_ta 0.314
Kostenki 0.251
Natufian -0.061
El_miron 1.053
Bichon 0.296
Satsurblia -0.975
Tianyuan -0.103
Sunghir3 0.221
Iberomaurusian 1.713
Onge -0.799
Goyet 0.939
Devils_cave -1.184
A:
scale 1.000
HUN_scy 1.000


best coefficients: 0.000
Jackknife mean: 1.000000000
std. errors: 0.000

error covariance (* 1,000,000)
0


summ: me 1 0.093519 1.000 0

single source. terminating
run qpDstat if you want Z scores for f4 stats
## end of run

[Zoro]

i didn't buy g25 yet, but k13 gives very similar results to g25, so here you go:

https://i.imgur.com/h5l2FB3.png

now i noticed k13 results are very similar to the qpAdm output. however qpAdm's method reveals much more details.

for example, in k13 and g25 the Hungarian Langobards and Moldovan Scythian o2 (probably a Germanic Bastarnae) aren't in any way close to Serbs, because they are Scandinavian-like. But qpAdm detects i share quite a lot genetic drift with them, which makes sense given their time period and geographic position.

Yes because admixture grabs hold of a very very few SNPs to show differnces between populations and 23andme exaggerates this effect. So basically similarity between you and some other pop based on the majority of snps is ignored especially when it tries to assign a drifted pop it’s own comp like Baloch or Kalash. In this case it makes those pops seem they’re from a different planet when they’re in fact quite related to other iranics

[Zoro]

here. maybe i should also try with a different outgroup set? or is this ok?


./qpadm: parameter file: parfile
### THE INPUT PARAMETERS
##PARAMETER NAME: VALUE
genotypename: qp.geno
snpname: qp.snp
indivname: qp.ind
popleft: left.txt
popright: right.txt
details: YES
allsnps: YES
## qpAdm version: 1000
seed: 1978976272
packed geno read OK
end of inpack

left pops:
me
HUN_scy

right pops:
EHG
Mbuti
Ust
Anatolia_N
Iran_N
Mal_ta
Kostenki
Natufian
El_miron
Bichon
Satsurblia
Tianyuan
Sunghir3
Iberomaurusian
Onge
Goyet
Devils_cave

0 me 1
1 HUN_scy 5
2 EHG 5
3 Mbuti 30
4 Ust 1
5 Anatolia_N 32
6 Iran_N 8
7 Mal_ta 1
8 Kostenki 1
9 Natufian 6
10 El_miron 1
11 Bichon 1
12 Satsurblia 1
13 Tianyuan 1
14 Sunghir3 1
15 Iberomaurusian 6
16 Onge 1
17 Goyet 1
18 Devils_cave 4
jackknife block size: 0.050
snps: 1826952 indivs: 107
number of blocks for block jackknife: 83
## ncols: 1826952
coverage: me 1089131
coverage: HUN_scy 1052042
coverage: EHG 1116406
coverage: Mbuti 640509
coverage: Ust 1139533
coverage: Anatolia_N 1141989
coverage: Iran_N 1049898
coverage: Mal_ta 800186
coverage: Kostenki 1041488
coverage: Natufian 531280
coverage: El_miron 622872
coverage: Bichon 1137333
coverage: Satsurblia 795644
coverage: Tianyuan 879205
coverage: Sunghir3 1141409
coverage: Iberomaurusian 1093556
coverage: Onge 1125078
coverage: Goyet 881461
coverage: Devils_cave 1141373
dof (jackknife): 36.880
numsnps used: 1826952
codimension 1
f4info:
f4rank: 0 dof: 16 chisq: 23.820 tail: 0.0935185239 dofdiff: 0 chisqdiff: 0.000 taildiff: 1


full rank
f4info:
f4rank: 1 dof: 0 chisq: 0.000 tail: 1 dofdiff: 16 chisqdiff: 23.820 taildiff: 0.0935185239
B:
scale 3912.428
Mbuti -2.421
Ust 1.190
Anatolia_N 0.695
Iran_N 0.109
Mal_ta 0.314
Kostenki 0.251
Natufian -0.061
El_miron 1.053
Bichon 0.296
Satsurblia -0.975
Tianyuan -0.103
Sunghir3 0.221
Iberomaurusian 1.713
Onge -0.799
Goyet 0.939
Devils_cave -1.184
A:
scale 1.000
HUN_scy 1.000


best coefficients: 0.000
Jackknife mean: 1.000000000
std. errors: 0.000

error covariance (* 1,000,000)
0


summ: me 1 0.093519 1.000 0

single source. terminating
run qpDstat if you want Z scores for f4 stats
## end of run

They’re ok, but I would replace Bichon with the Iron Gates WHG ( the higher quality 10 or 15 samples i posted earlier) and also replace Ust with a more contemporary source such as Shamanka-EN and Goyet with a S Eurasian outgroup like Onge or Papuan. Don’t worry about chi sq rising for you because these outgroups will help differentiate you from other Europeans with regards to your relatedness to Hun-Scythians vs theirs since the outgroups I suggest help differentiate europeans amongst themselves as well as against steppe ia

[vbnetkhio]

They’re ok, but I would replace Bichon with the Iron Gates WHG ( the higher quality 10 or 15 samples i posted earlier) and also replace Ust with a more contemporary source such as Shamanka-EN and Goyet with a S Eurasian outgroup like Onge or Papuan. Don’t worry about chi sq rising for you because these outgroups will help differentiate you from other Europeans with regards to your relatedness to Hun-Scythians vs theirs since the outgroups I suggest help differentiate europeans amongst themselves as well as against steppe ia

Onge is already in there, sould i still add Papuan?

[Ion Basescul]

i didn't buy g25 yet, but k13 gives very similar results to g25, so here you go:

https://i.imgur.com/h5l2FB3.png

now i noticed k13 results are very similar to the qpAdm output. however qpAdm's method reveals much more details.

for example, in k13 and g25 the Hungarian Langobards and Moldovan Scythian o2 (probably a Germanic Bastarnae) aren't in any way close to Serbs, because they are Scandinavian-like. But qpAdm detects i share quite a lot genetic drift with them, which makes sense given their time period and geographic position.

If you are interested in East Germanics from this area, you might also consider adding Iberia_Northeast_c.6CE_PL:I12163 and Iberia_Northeast_c.6CE_PL:I12031.Those were identified as Visigoths. MJ19 and MJ37 are also partially Germanic, as they belong to the mixed Santana de Mures/Chernaykhov culture.

https://en.wikipedia.org/wiki/Chernyakhov_culture

With regards to Global 25, it has more samples than G25 right now and it's a bit more reliable when it comes to ancients, as the PCA coords that we grab from K13 are obviously modelled from moderns.
Your results are pretty normal for the region.
Here's how the compare to mine.

Global 25 first and K13 second


<tbody>
0.03713259
UKR_Chernyakhiv_Shyshaky:MJ37


0.04037977
HUN_MA_Szolad:SZ5


0.04234164
Bell_Beaker_CZE:I4945


0.04272543
Scythian_HUN:DA195


0.04330064
HUN_MA:DA199


0.04384687
Iberia_Northeast_c.6CE_PL:I12163


0.04547043
Iberia_Northeast_c.6CE_PL:I12031


0.04798289
DEU_MA_ACD:STR_228


0.04840529
ITA_Rome_MA:RMPR62


0.04915139
HUN_MA_Szolad:SZ18


0.04983864
DEU_MA:AED_1135


0.05107296
Bell_Beaker_CZE:I4896


0.05110078
ITA_Rome_MA:RMPR1288


0.05111728
CZE_Early_Slav:RISE569


0.05129607
Scythian_UKR:scy010


0.05214690
Bell_Beaker_CZE:I4886


0.05261331
ITA_Collegno_MA:CL53


0.05285255
HUN_MA_Szolad:SZ45


0.05312334
Bell_Beaker_Bavaria:I6591


0.05341262
Scythian_HUN:DA197


0.05347483
ITA_Rome_Renaissance:RMPR1219


0.05430416
HUN_MA_Szolad:SZ27


0.05490905
HUN_MA_Szolad_o2:SZ25


0.05510494
HUN_MA_Szolad:SZ23


0.05526064
Bell_Beaker_Bavaria:I3600

</tbody>


<tbody>
8.72745667
MJ37_Chernyakhov_2430386_AD_


10.52948242
scy010_Ukraine_Scythian.SG_2619_ybp


10.71107838
DA195_Hungary_Scythian.SG_2553_ybp


10.91116401
STR228_Germany_Early_Medieval.SG_1483_ybp


10.95320045
AED513_Germany_Early_Medieval.SG_1528_ybp


11.88403130
I2165_Bulgaria_EBA_4908_ybp


12.00044583
scy301_Moldova_Scythian.SG_2248_ybp


12.16778534
DA197_Hungary_Scythian.SG_2510_ybp


12.28962571
SZ5_Hungary_Langobard.SG_1442_ybp


12.77648621
MJ34_West_Scythian_R1a0Z93_


12.79413538
ANI163_Bulgaria_Varna_EN3_6577_ybp


12.92410152
scy301_scythian_Moldova_390_205bce_


13.08319533
I4137_Czech_Early_Slav_dup.I4137.SG_1235_ybp


13.28931149
R62_Lazio_Rome_Medieval_Italy


13.29131671
Bul4_Bulgaria_Yamnaya_o_4906_ybp


13.63486340
SZ45_Hungary_Langobard.SG_1442_ybp


13.85520841
AV1_Hungary_Avar_1361_ybp


14.00694828
STR310_Germany_Early_Medieval.SG_1430_ybp


14.08216958
SZ18_Hungary_Langobard_1442_ybp


14.31358446
R61_Lazio_Rome_Late_Medieval_Italy


14.65151528
I3529_Hungary_Bell_Beaker_EBA_4300_ybp


14.71642959
I4124_Germany_Bell_Beaker_4284_ybp


14.96552705
I5666_Czech_Bell_Beaker_4250_ybp


15.07714164
CL53_longobard_north_italy


15.24100390
I4886_Czech_Bell_Beaker_4074_ybp

</tbody>

[Zoro]

Onge is already in there, sould i still add Papuan?
Not necessary unless you’re going to compare her self with ancients that have south Asian or you are comparing yourself with moderns that have south Asian

[vbnetkhio]

If you are interested in East Germanics from this area, you might also consider adding Iberia_Northeast_c.6CE_PL:I12163 and Iberia_Northeast_c.6CE_PL:I12031.Those were identified as Visigoths. MJ19 and MJ37 are also partially Germanic, as they belong to the mixed Santana de Mures/Chernaykhov culture.

Chernyakhiv is also very close too me, check the lower quality samples list. i plan to compare myself to all the Iberian sampels too.




With regards to Global 25, it has more samples than G25 right now and it's a bit more reliable when it comes to ancients, as the PCA coords that we grab from K13 are obviously modelled from moderns.


G25 axes are based mostly on moderns too, plus maybe some older ancient samples like WHGs or Yamnaya.

for example, these are the sampels in which "PC1" peaks:

ROU_Iron_Gates_N 0.138864
Baltic_LTU_Late_Antiquity_low_res 0.138864
HUN_Koros_N_HG 0.137726
Lithuanian_VZ 0.1372991
FRA_Lingolsheim_FN_steppe 0.136588
French_Seine-Maritime 0.1360185
Lithuanian_PZ 0.1356773
Latvian 0.1354494
UKR_Cimmerian_o 0.135449
FIN_Levanluhta_IA_o 0.135449
Cossack_Ukrainian 0.135449

it was probably extracted from modern Lithuanian samples.

[vbnetkhio]

Looks like you’re very related to Hungarian Scythians since p-value is above 0.05. Can you post the full output for that run so i can see the outgroups and fixed path chis

You may also want to run other modern samples from your area against Hungarian Scythians to see how related to them they are compared to you.

here it is with a different outgroup set:

left pops:
me
HUN_scy

right pops:
EHG
Mbuti
Anatolia_N
Iran_N
Mal_ta
Kostenki
Natufian
El_miron
Satsurblia
Tianyuan
Sunghir3
Iberomaurusian
Onge
Devils_cave
Shamanka
Iron_Gates
Papuan

0 me 1
1 HUN_scy 5
2 EHG 5
3 Mbuti 30
4 Anatolia_N 32
5 Iran_N 8
6 Mal_ta 1
7 Kostenki 1
8 Natufian 6
9 El_miron 1
10 Satsurblia 1
11 Tianyuan 1
12 Sunghir3 1
13 Iberomaurusian 6
14 Onge 1
15 Devils_cave 4
16 Shamanka 10
17 Iron_Gates 44
18 Papuan 15
jackknife block size: 0.050
snps: 1826956 indivs: 173
number of blocks for block jackknife: 83
## ncols: 1826956
coverage: me 1089131
coverage: HUN_scy 1052042
coverage: EHG 1116406
coverage: Mbuti 640509
coverage: Anatolia_N 1141989
coverage: Iran_N 1049898
coverage: Mal_ta 800186
coverage: Kostenki 1041488
coverage: Natufian 531280
coverage: El_miron 622872
coverage: Satsurblia 795644
coverage: Tianyuan 879205
coverage: Sunghir3 1141409
coverage: Iberomaurusian 1093556
coverage: Onge 1125078
coverage: Devils_cave 1141373
coverage: Shamanka 1144117
coverage: Iron_Gates 1143958
coverage: Papuan 1116200
dof (jackknife): 36.906
numsnps used: 1826956
codimension 1
f4info:
f4rank: 0 dof: 16 chisq: 27.251 tail: 0.0387751705 dofdiff: 0 chisqdiff: 0.000 taildiff: 1


full rank
f4info:
f4rank: 1 dof: 0 chisq: 0.000 tail: 1 dofdiff: 16 chisqdiff: 27.251 taildiff: 0.0387751705
B:
scale 3389.760
Mbuti -2.097
Anatolia_N 0.602
Iran_N 0.094
Mal_ta 0.272
Kostenki 0.217
Natufian -0.052
El_miron 0.912
Satsurblia -0.845
Tianyuan -0.089
Sunghir3 0.192
Iberomaurusian 1.484
Onge -0.692
Devils_cave -1.025
Shamanka -0.856
Iron_Gates 1.275
Papuan -1.850
A:
scale 1.000
HUN_scy 1.000


best coefficients: 0.000
Jackknife mean: 1.000000000
std. errors: 0.000

error covariance (* 1,000,000)
0


summ: me 1 0.038775 1.000 0

and me as a mix of the Czech early Slavs and the Hungarian Scythians. is the std error too high maybe?


### THE INPUT PARAMETERS
##PARAMETER NAME: VALUE
genotypename: qp.geno
snpname: qp.snp
indivname: qp.ind
popleft: left.txt
popright: right.txt
details: YES
allsnps: YES
## qpAdm version: 1000
seed: 1623245354

left pops:
me
HUN_scy
Czech_EarlySlav

right pops:
EHG
Mbuti
Anatolia_N
Iran_N
Mal_ta
Kostenki
Natufian
El_miron
Satsurblia
Tianyuan
Sunghir3
Iberomaurusian
Onge
Devils_cave
Shamanka
Iron_Gates
Papuan

0 me 1
1 HUN_scy 5
2 Czech_EarlySlav 2
3 EHG 5
4 Mbuti 30
5 Anatolia_N 32
6 Iran_N 8
7 Mal_ta 1
8 Kostenki 1
9 Natufian 6
10 El_miron 1
11 Satsurblia 1
12 Tianyuan 1
13 Sunghir3 1
14 Iberomaurusian 6
15 Onge 1
16 Devils_cave 4
17 Shamanka 10
18 Iron_Gates 44
19 Papuan 15
jackknife block size: 0.050
snps: 1826956 indivs: 175
number of blocks for block jackknife: 83
## ncols: 1826956
coverage: me 1089131
coverage: HUN_scy 1052042
coverage: Czech_EarlySlav 727134
coverage: EHG 1116406
coverage: Mbuti 640509
coverage: Anatolia_N 1141989
coverage: Iran_N 1049898
coverage: Mal_ta 800186
coverage: Kostenki 1041488
coverage: Natufian 531280
coverage: El_miron 622872
coverage: Satsurblia 795644
coverage: Tianyuan 879205
coverage: Sunghir3 1141409
coverage: Iberomaurusian 1093556
coverage: Onge 1125078
coverage: Devils_cave 1141373
coverage: Shamanka 1144117
coverage: Iron_Gates 1143958
coverage: Papuan 1116200
dof (jackknife): 36.912
numsnps used: 1826956
codimension 1
f4info:
f4rank: 1 dof: 15 chisq: 19.563 tail: 0.189328278 dofdiff: 17 chisqdiff: -19.563 taildiff: 1
B:
scale 1.000
Mbuti 1.070
Anatolia_N 1.986
Iran_N 0.753
Mal_ta -0.598
Kostenki 0.414
Natufian 0.693
El_miron 1.070
Satsurblia 0.380
Tianyuan 1.120
Sunghir3 0.954
Iberomaurusian 1.627
Onge 0.902
Devils_cave 0.553
Shamanka 0.620
Iron_Gates 1.214
Papuan 0.509
A:
scale 3416.023
HUN_scy 0.852
Czech_EarlySlav -1.128


full rank
f4info:
f4rank: 2 dof: 0 chisq: 0.000 tail: 1 dofdiff: 15 chisqdiff: 19.563 taildiff: 0.189328278
B:
scale 3389.722 1690.255
Mbuti -2.098 -2.179
Anatolia_N 0.602 -1.527
Iran_N 0.094 -0.429
Mal_ta 0.272 0.377
Kostenki 0.217 -0.265
Natufian -0.052 -0.733
El_miron 0.912 -0.261
Satsurblia -0.845 -0.900
Tianyuan -0.089 -1.092
Sunghir3 0.192 -0.757
Iberomaurusian 1.484 -0.690
Onge -0.692 -0.946
Devils_cave -1.025 -1.028
Shamanka -0.856 -0.974
Iron_Gates 1.274 -0.427
Papuan -1.850 -1.335
A:
scale 1.414 1.414
HUN_scy 1.414 0.000
Czech_EarlySlav 0.000 1.414


best coefficients: 0.570 0.430
Jackknife mean: 0.568321593 0.431678407
std. errors: 0.158 0.158

error covariance (* 1,000,000)
25078 -25078
-25078 25078


summ: me 2 0.189328 0.568 0.432 25078 -25078 25078

fixed pat wt dof chisq tail prob
00 0 15 19.563 0.189328 0.570 0.430
01 1 16 26.859 0.0430779 1.000 0.000
10 1 16 28.681 0.0261788 0.000 1.000
best pat: 00 0.189328 - -
best pat: 01 0.0430779 chi(nested): 7.296 p-value for nested model: 0.00691262

coeffs: 0.570 0.430

## dscore:: f_4(Base, Fit, Rbase, right2)
## genstat:: f_4(Base, Fit, right1, right2)

details: HUN_scy Mbuti -0.000619 -1.551975
details: Czech_EarlySlav Mbuti -0.001289 -2.614379
dscore: Mbuti f4: -0.000907 Z: -2.360978

details: HUN_scy Anatolia_N 0.000178 0.573488
details: Czech_EarlySlav Anatolia_N -0.000903 -3.241578
dscore: Anatolia_N f4: -0.000288 Z: -1.107712

details: HUN_scy Iran_N 0.000028 0.107076
details: Czech_EarlySlav Iran_N -0.000254 -1.008977
dscore: Iran_N f4: -0.000093 Z: -0.413253

details: HUN_scy Mal_ta 0.000080 0.300620
details: Czech_EarlySlav Mal_ta 0.000223 0.920180
dscore: Mal_ta f4: 0.000142 Z: 0.625797

details: HUN_scy Kostenki 0.000064 0.211329
details: Czech_EarlySlav Kostenki -0.000157 -0.615110
dscore: Kostenki f4: -0.000031 Z: -0.124529

details: HUN_scy Natufian -0.000015 -0.060838
details: Czech_EarlySlav Natufian -0.000434 -1.541639
dscore: Natufian f4: -0.000196 Z: -0.863408

details: HUN_scy El_miron 0.000269 1.057927
details: Czech_EarlySlav El_miron -0.000155 -0.538480
dscore: El_miron f4: 0.000087 Z: 0.375358

details: HUN_scy Satsurblia -0.000249 -1.032728
details: Czech_EarlySlav Satsurblia -0.000532 -2.101660
dscore: Satsurblia f4: -0.000371 Z: -1.775880

details: HUN_scy Tianyuan -0.000026 -0.096920
details: Czech_EarlySlav Tianyuan -0.000646 -2.769749
dscore: Tianyuan f4: -0.000293 Z: -1.362747

details: HUN_scy Sunghir3 0.000057 0.226771
details: Czech_EarlySlav Sunghir3 -0.000448 -1.729071
dscore: Sunghir3 f4: -0.000161 Z: -0.707881

details: HUN_scy Iberomaurusian 0.000438 1.352521
details: Czech_EarlySlav Iberomaurusian -0.000408 -1.522786
dscore: Iberomaurusian f4: 0.000074 Z: 0.271535

details: HUN_scy Onge -0.000204 -0.561381
details: Czech_EarlySlav Onge -0.000560 -2.076233
dscore: Onge f4: -0.000357 Z: -1.244859

details: HUN_scy Devils_cave -0.000303 -1.248394
details: Czech_EarlySlav Devils_cave -0.000608 -2.448168
dscore: Devils_cave f4: -0.000434 Z: -1.927594

details: HUN_scy Shamanka -0.000253 -0.996857
details: Czech_EarlySlav Shamanka -0.000576 -2.383811
dscore: Shamanka f4: -0.000392 Z: -1.816650

details: HUN_scy Iron_Gates 0.000376 1.638003
details: Czech_EarlySlav Iron_Gates -0.000253 -1.003935
dscore: Iron_Gates f4: 0.000105 Z: 0.503404

details: HUN_scy Papuan -0.000546 -1.767798
details: Czech_EarlySlav Papuan -0.000790 -2.751542
dscore: Papuan f4: -0.000651 Z: -2.443331

gendstat: EHG Mbuti -2.361
gendstat: EHG Anatolia_N -1.108
gendstat: EHG Iran_N -0.413
gendstat: EHG Mal_ta 0.626
gendstat: EHG Kostenki -0.125
gendstat: EHG Natufian -0.863
gendstat: EHG El_miron 0.375
gendstat: EHG Satsurblia -1.776
gendstat: EHG Tianyuan -1.363
gendstat: EHG Sunghir3 -0.708
gendstat: EHG Iberomaurusian 0.272
gendstat: EHG Onge -1.245
gendstat: EHG Devils_cave -1.928
gendstat: EHG Shamanka -1.817
gendstat: EHG Iron_Gates 0.503
gendstat: EHG Papuan -2.443
gendstat: Mbuti Anatolia_N 2.427
gendstat: Mbuti Iran_N 2.993
gendstat: Mbuti Mal_ta 2.736
gendstat: Mbuti Kostenki 2.687
gendstat: Mbuti Natufian 2.059
gendstat: Mbuti El_miron 2.599
gendstat: Mbuti Satsurblia 1.515
gendstat: Mbuti Tianyuan 1.844
gendstat: Mbuti Sunghir3 2.712
gendstat: Mbuti Iberomaurusian 3.256
gendstat: Mbuti Onge 1.991
gendstat: Mbuti Devils_cave 1.741
gendstat: Mbuti Shamanka 2.026
gendstat: Mbuti Iron_Gates 3.039
gendstat: Mbuti Papuan 1.473
gendstat: Anatolia_N Iran_N 1.283
gendstat: Anatolia_N Mal_ta 1.591
gendstat: Anatolia_N Kostenki 1.082
gendstat: Anatolia_N Natufian 0.449
gendstat: Anatolia_N El_miron 1.461
gendstat: Anatolia_N Satsurblia -0.325
gendstat: Anatolia_N Tianyuan -0.023
gendstat: Anatolia_N Sunghir3 0.594
gendstat: Anatolia_N Iberomaurusian 1.932
gendstat: Anatolia_N Onge -0.306
gendstat: Anatolia_N Devils_cave -0.644
gendstat: Anatolia_N Shamanka -0.526
gendstat: Anatolia_N Iron_Gates 1.871
gendstat: Anatolia_N Papuan -1.736
gendstat: Iran_N Mal_ta 0.950
gendstat: Iran_N Kostenki 0.270
gendstat: Iran_N Natufian -0.460
gendstat: Iran_N El_miron 0.722
gendstat: Iran_N Satsurblia -1.175
gendstat: Iran_N Tianyuan -0.868
gendstat: Iran_N Sunghir3 -0.341
gendstat: Iran_N Iberomaurusian 0.850
gendstat: Iran_N Onge -1.202
gendstat: Iran_N Devils_cave -1.646
gendstat: Iran_N Shamanka -1.613
gendstat: Iran_N Iron_Gates 0.902
gendstat: Iran_N Papuan -2.757
gendstat: Mal_ta Kostenki -0.647
gendstat: Mal_ta Natufian -1.138
gendstat: Mal_ta El_miron -0.208
gendstat: Mal_ta Satsurblia -1.979
gendstat: Mal_ta Tianyuan -1.574
gendstat: Mal_ta Sunghir3 -1.158
gendstat: Mal_ta Iberomaurusian -0.260
gendstat: Mal_ta Onge -1.820
gendstat: Mal_ta Devils_cave -2.072
gendstat: Mal_ta Shamanka -2.006
gendstat: Mal_ta Iron_Gates -0.132
gendstat: Mal_ta Papuan -2.663
gendstat: Kostenki Natufian -0.634
gendstat: Kostenki El_miron 0.458
gendstat: Kostenki Satsurblia -1.079
gendstat: Kostenki Tianyuan -0.981
gendstat: Kostenki Sunghir3 -0.597
gendstat: Kostenki Iberomaurusian 0.441
gendstat: Kostenki Onge -1.357
gendstat: Kostenki Devils_cave -1.525
gendstat: Kostenki Shamanka -1.515
gendstat: Kostenki Iron_Gates 0.528
gendstat: Kostenki Papuan -2.596
gendstat: Natufian El_miron 1.020
gendstat: Natufian Satsurblia -0.636
gendstat: Natufian Tianyuan -0.390
gendstat: Natufian Sunghir3 0.132
gendstat: Natufian Iberomaurusian 1.068
gendstat: Natufian Onge -0.527
gendstat: Natufian Devils_cave -0.888
gendstat: Natufian Shamanka -0.816
gendstat: Natufian Iron_Gates 1.277
gendstat: Natufian Papuan -1.633
gendstat: El_miron Satsurblia -1.654
gendstat: El_miron Tianyuan -1.382
gendstat: El_miron Sunghir3 -0.981
gendstat: El_miron Iberomaurusian -0.049
gendstat: El_miron Onge -1.705
gendstat: El_miron Devils_cave -1.837
gendstat: El_miron Shamanka -1.885
gendstat: El_miron Iron_Gates 0.082
gendstat: El_miron Papuan -2.535
gendstat: Satsurblia Tianyuan 0.304
gendstat: Satsurblia Sunghir3 0.779
gendstat: Satsurblia Iberomaurusian 1.550
gendstat: Satsurblia Onge 0.048
gendstat: Satsurblia Devils_cave -0.287
gendstat: Satsurblia Shamanka -0.089
gendstat: Satsurblia Iron_Gates 1.845
gendstat: Satsurblia Papuan -1.036
gendstat: Tianyuan Sunghir3 0.563
gendstat: Tianyuan Iberomaurusian 1.361
gendstat: Tianyuan Onge -0.235
gendstat: Tianyuan Devils_cave -0.691
gendstat: Tianyuan Shamanka -0.465
gendstat: Tianyuan Iron_Gates 1.711
gendstat: Tianyuan Papuan -1.369
gendstat: Sunghir3 Iberomaurusian 1.026
gendstat: Sunghir3 Onge -0.828
gendstat: Sunghir3 Devils_cave -1.160
gendstat: Sunghir3 Shamanka -1.183
gendstat: Sunghir3 Iron_Gates 1.177
gendstat: Sunghir3 Papuan -2.466
gendstat: Iberomaurusian Onge -1.855
gendstat: Iberomaurusian Devils_cave -2.001
gendstat: Iberomaurusian Shamanka -2.023
gendstat: Iberomaurusian Iron_Gates 0.122
gendstat: Iberomaurusian Papuan -3.153
gendstat: Onge Devils_cave -0.300
gendstat: Onge Shamanka -0.165
gendstat: Onge Iron_Gates 1.703
gendstat: Onge Papuan -1.425
gendstat: Devils_cave Shamanka 0.313
gendstat: Devils_cave Iron_Gates 2.425
gendstat: Devils_cave Papuan -1.049
gendstat: Shamanka Iron_Gates 2.368
gendstat: Shamanka Papuan -1.356
gendstat: Iron_Gates Papuan -2.827

##end of qpAdm: 921.515 seconds cpu 2607.956 Mbytes in use

[Zoro]

here it is with a different outgroup set:

left pops:
me
HUN_scy

right pops:
EHG
Mbuti
Anatolia_N
Iran_N
Mal_ta
Kostenki
Natufian
El_miron
Satsurblia
Tianyuan
Sunghir3
Iberomaurusian
Onge
Devils_cave
Shamanka
Iron_Gates
Papuan

0 me 1
1 HUN_scy 5
2 EHG 5
3 Mbuti 30
4 Anatolia_N 32
5 Iran_N 8
6 Mal_ta 1
7 Kostenki 1
8 Natufian 6
9 El_miron 1
10 Satsurblia 1
11 Tianyuan 1
12 Sunghir3 1
13 Iberomaurusian 6
14 Onge 1
15 Devils_cave 4
16 Shamanka 10
17 Iron_Gates 44
18 Papuan 15
jackknife block size: 0.050
snps: 1826956 indivs: 173
number of blocks for block jackknife: 83
## ncols: 1826956
coverage: me 1089131
coverage: HUN_scy 1052042
coverage: EHG 1116406
coverage: Mbuti 640509
coverage: Anatolia_N 1141989
coverage: Iran_N 1049898
coverage: Mal_ta 800186
coverage: Kostenki 1041488
coverage: Natufian 531280
coverage: El_miron 622872
coverage: Satsurblia 795644
coverage: Tianyuan 879205
coverage: Sunghir3 1141409
coverage: Iberomaurusian 1093556
coverage: Onge 1125078
coverage: Devils_cave 1141373
coverage: Shamanka 1144117
coverage: Iron_Gates 1143958
coverage: Papuan 1116200
dof (jackknife): 36.906
numsnps used: 1826956
codimension 1
f4info:
f4rank: 0 dof: 16 chisq: 27.251 tail: 0.0387751705 dofdiff: 0 chisqdiff: 0.000 taildiff: 1


full rank
f4info:
f4rank: 1 dof: 0 chisq: 0.000 tail: 1 dofdiff: 16 chisqdiff: 27.251 taildiff: 0.0387751705
B:
scale 3389.760
Mbuti -2.097
Anatolia_N 0.602
Iran_N 0.094
Mal_ta 0.272
Kostenki 0.217
Natufian -0.052
El_miron 0.912
Satsurblia -0.845
Tianyuan -0.089
Sunghir3 0.192
Iberomaurusian 1.484
Onge -0.692
Devils_cave -1.025
Shamanka -0.856
Iron_Gates 1.275
Papuan -1.850
A:
scale 1.000
HUN_scy 1.000


best coefficients: 0.000
Jackknife mean: 1.000000000
std. errors: 0.000

error covariance (* 1,000,000)
0


summ: me 1 0.038775 1.000 0

and me as a mix of the Czech early Slavs and the Hungarian Scythians. is the std error too high maybe?


### THE INPUT PARAMETERS
##PARAMETER NAME: VALUE
genotypename: qp.geno
snpname: qp.snp
indivname: qp.ind
popleft: left.txt
popright: right.txt
details: YES
allsnps: YES
## qpAdm version: 1000
seed: 1623245354

left pops:
me
HUN_scy
Czech_EarlySlav

right pops:
EHG
Mbuti
Anatolia_N
Iran_N
Mal_ta
Kostenki
Natufian
El_miron
Satsurblia
Tianyuan
Sunghir3
Iberomaurusian
Onge
Devils_cave
Shamanka
Iron_Gates
Papuan

0 me 1
1 HUN_scy 5
2 Czech_EarlySlav 2
3 EHG 5
4 Mbuti 30
5 Anatolia_N 32
6 Iran_N 8
7 Mal_ta 1
8 Kostenki 1
9 Natufian 6
10 El_miron 1
11 Satsurblia 1
12 Tianyuan 1
13 Sunghir3 1
14 Iberomaurusian 6
15 Onge 1
16 Devils_cave 4
17 Shamanka 10
18 Iron_Gates 44
19 Papuan 15
jackknife block size: 0.050
snps: 1826956 indivs: 175
number of blocks for block jackknife: 83
## ncols: 1826956
coverage: me 1089131
coverage: HUN_scy 1052042
coverage: Czech_EarlySlav 727134
coverage: EHG 1116406
coverage: Mbuti 640509
coverage: Anatolia_N 1141989
coverage: Iran_N 1049898
coverage: Mal_ta 800186
coverage: Kostenki 1041488
coverage: Natufian 531280
coverage: El_miron 622872
coverage: Satsurblia 795644
coverage: Tianyuan 879205
coverage: Sunghir3 1141409
coverage: Iberomaurusian 1093556
coverage: Onge 1125078
coverage: Devils_cave 1141373
coverage: Shamanka 1144117
coverage: Iron_Gates 1143958
coverage: Papuan 1116200
dof (jackknife): 36.912
numsnps used: 1826956
codimension 1
f4info:
f4rank: 1 dof: 15 chisq: 19.563 tail: 0.189328278 dofdiff: 17 chisqdiff: -19.563 taildiff: 1
B:
scale 1.000
Mbuti 1.070
Anatolia_N 1.986
Iran_N 0.753
Mal_ta -0.598
Kostenki 0.414
Natufian 0.693
El_miron 1.070
Satsurblia 0.380
Tianyuan 1.120
Sunghir3 0.954
Iberomaurusian 1.627
Onge 0.902
Devils_cave 0.553
Shamanka 0.620
Iron_Gates 1.214
Papuan 0.509
A:
scale 3416.023
HUN_scy 0.852
Czech_EarlySlav -1.128


full rank
f4info:
f4rank: 2 dof: 0 chisq: 0.000 tail: 1 dofdiff: 15 chisqdiff: 19.563 taildiff: 0.189328278
B:
scale 3389.722 1690.255
Mbuti -2.098 -2.179
Anatolia_N 0.602 -1.527
Iran_N 0.094 -0.429
Mal_ta 0.272 0.377
Kostenki 0.217 -0.265
Natufian -0.052 -0.733
El_miron 0.912 -0.261
Satsurblia -0.845 -0.900
Tianyuan -0.089 -1.092
Sunghir3 0.192 -0.757
Iberomaurusian 1.484 -0.690
Onge -0.692 -0.946
Devils_cave -1.025 -1.028
Shamanka -0.856 -0.974
Iron_Gates 1.274 -0.427
Papuan -1.850 -1.335
A:
scale 1.414 1.414
HUN_scy 1.414 0.000
Czech_EarlySlav 0.000 1.414


best coefficients: 0.570 0.430
Jackknife mean: 0.568321593 0.431678407
std. errors: 0.158 0.158

error covariance (* 1,000,000)
25078 -25078
-25078 25078


summ: me 2 0.189328 0.568 0.432 25078 -25078 25078

fixed pat wt dof chisq tail prob
00 0 15 19.563 0.189328 0.570 0.430
01 1 16 26.859 0.0430779 1.000 0.000
10 1 16 28.681 0.0261788 0.000 1.000
best pat: 00 0.189328 - -
best pat: 01 0.0430779 chi(nested): 7.296 p-value for nested model: 0.00691262

coeffs: 0.570 0.430

## dscore:: f_4(Base, Fit, Rbase, right2)
## genstat:: f_4(Base, Fit, right1, right2)

details: HUN_scy Mbuti -0.000619 -1.551975
details: Czech_EarlySlav Mbuti -0.001289 -2.614379
dscore: Mbuti f4: -0.000907 Z: -2.360978

details: HUN_scy Anatolia_N 0.000178 0.573488
details: Czech_EarlySlav Anatolia_N -0.000903 -3.241578
dscore: Anatolia_N f4: -0.000288 Z: -1.107712

details: HUN_scy Iran_N 0.000028 0.107076
details: Czech_EarlySlav Iran_N -0.000254 -1.008977
dscore: Iran_N f4: -0.000093 Z: -0.413253

details: HUN_scy Mal_ta 0.000080 0.300620
details: Czech_EarlySlav Mal_ta 0.000223 0.920180
dscore: Mal_ta f4: 0.000142 Z: 0.625797

details: HUN_scy Kostenki 0.000064 0.211329
details: Czech_EarlySlav Kostenki -0.000157 -0.615110
dscore: Kostenki f4: -0.000031 Z: -0.124529

details: HUN_scy Natufian -0.000015 -0.060838
details: Czech_EarlySlav Natufian -0.000434 -1.541639
dscore: Natufian f4: -0.000196 Z: -0.863408

details: HUN_scy El_miron 0.000269 1.057927
details: Czech_EarlySlav El_miron -0.000155 -0.538480
dscore: El_miron f4: 0.000087 Z: 0.375358

details: HUN_scy Satsurblia -0.000249 -1.032728
details: Czech_EarlySlav Satsurblia -0.000532 -2.101660
dscore: Satsurblia f4: -0.000371 Z: -1.775880

details: HUN_scy Tianyuan -0.000026 -0.096920
details: Czech_EarlySlav Tianyuan -0.000646 -2.769749
dscore: Tianyuan f4: -0.000293 Z: -1.362747

details: HUN_scy Sunghir3 0.000057 0.226771
details: Czech_EarlySlav Sunghir3 -0.000448 -1.729071
dscore: Sunghir3 f4: -0.000161 Z: -0.707881

details: HUN_scy Iberomaurusian 0.000438 1.352521
details: Czech_EarlySlav Iberomaurusian -0.000408 -1.522786
dscore: Iberomaurusian f4: 0.000074 Z: 0.271535

details: HUN_scy Onge -0.000204 -0.561381
details: Czech_EarlySlav Onge -0.000560 -2.076233
dscore: Onge f4: -0.000357 Z: -1.244859

details: HUN_scy Devils_cave -0.000303 -1.248394
details: Czech_EarlySlav Devils_cave -0.000608 -2.448168
dscore: Devils_cave f4: -0.000434 Z: -1.927594

details: HUN_scy Shamanka -0.000253 -0.996857
details: Czech_EarlySlav Shamanka -0.000576 -2.383811
dscore: Shamanka f4: -0.000392 Z: -1.816650

details: HUN_scy Iron_Gates 0.000376 1.638003
details: Czech_EarlySlav Iron_Gates -0.000253 -1.003935
dscore: Iron_Gates f4: 0.000105 Z: 0.503404

details: HUN_scy Papuan -0.000546 -1.767798
details: Czech_EarlySlav Papuan -0.000790 -2.751542
dscore: Papuan f4: -0.000651 Z: -2.443331

gendstat: EHG Mbuti -2.361
gendstat: EHG Anatolia_N -1.108
gendstat: EHG Iran_N -0.413
gendstat: EHG Mal_ta 0.626
gendstat: EHG Kostenki -0.125
gendstat: EHG Natufian -0.863
gendstat: EHG El_miron 0.375
gendstat: EHG Satsurblia -1.776
gendstat: EHG Tianyuan -1.363
gendstat: EHG Sunghir3 -0.708
gendstat: EHG Iberomaurusian 0.272
gendstat: EHG Onge -1.245
gendstat: EHG Devils_cave -1.928
gendstat: EHG Shamanka -1.817
gendstat: EHG Iron_Gates 0.503
gendstat: EHG Papuan -2.443
gendstat: Mbuti Anatolia_N 2.427
gendstat: Mbuti Iran_N 2.993
gendstat: Mbuti Mal_ta 2.736
gendstat: Mbuti Kostenki 2.687
gendstat: Mbuti Natufian 2.059
gendstat: Mbuti El_miron 2.599
gendstat: Mbuti Satsurblia 1.515
gendstat: Mbuti Tianyuan 1.844
gendstat: Mbuti Sunghir3 2.712
gendstat: Mbuti Iberomaurusian 3.256
gendstat: Mbuti Onge 1.991
gendstat: Mbuti Devils_cave 1.741
gendstat: Mbuti Shamanka 2.026
gendstat: Mbuti Iron_Gates 3.039
gendstat: Mbuti Papuan 1.473
gendstat: Anatolia_N Iran_N 1.283
gendstat: Anatolia_N Mal_ta 1.591
gendstat: Anatolia_N Kostenki 1.082
gendstat: Anatolia_N Natufian 0.449
gendstat: Anatolia_N El_miron 1.461
gendstat: Anatolia_N Satsurblia -0.325
gendstat: Anatolia_N Tianyuan -0.023
gendstat: Anatolia_N Sunghir3 0.594
gendstat: Anatolia_N Iberomaurusian 1.932
gendstat: Anatolia_N Onge -0.306
gendstat: Anatolia_N Devils_cave -0.644
gendstat: Anatolia_N Shamanka -0.526
gendstat: Anatolia_N Iron_Gates 1.871
gendstat: Anatolia_N Papuan -1.736
gendstat: Iran_N Mal_ta 0.950
gendstat: Iran_N Kostenki 0.270
gendstat: Iran_N Natufian -0.460
gendstat: Iran_N El_miron 0.722
gendstat: Iran_N Satsurblia -1.175
gendstat: Iran_N Tianyuan -0.868
gendstat: Iran_N Sunghir3 -0.341
gendstat: Iran_N Iberomaurusian 0.850
gendstat: Iran_N Onge -1.202
gendstat: Iran_N Devils_cave -1.646
gendstat: Iran_N Shamanka -1.613
gendstat: Iran_N Iron_Gates 0.902
gendstat: Iran_N Papuan -2.757
gendstat: Mal_ta Kostenki -0.647
gendstat: Mal_ta Natufian -1.138
gendstat: Mal_ta El_miron -0.208
gendstat: Mal_ta Satsurblia -1.979
gendstat: Mal_ta Tianyuan -1.574
gendstat: Mal_ta Sunghir3 -1.158
gendstat: Mal_ta Iberomaurusian -0.260
gendstat: Mal_ta Onge -1.820
gendstat: Mal_ta Devils_cave -2.072
gendstat: Mal_ta Shamanka -2.006
gendstat: Mal_ta Iron_Gates -0.132
gendstat: Mal_ta Papuan -2.663
gendstat: Kostenki Natufian -0.634
gendstat: Kostenki El_miron 0.458
gendstat: Kostenki Satsurblia -1.079
gendstat: Kostenki Tianyuan -0.981
gendstat: Kostenki Sunghir3 -0.597
gendstat: Kostenki Iberomaurusian 0.441
gendstat: Kostenki Onge -1.357
gendstat: Kostenki Devils_cave -1.525
gendstat: Kostenki Shamanka -1.515
gendstat: Kostenki Iron_Gates 0.528
gendstat: Kostenki Papuan -2.596
gendstat: Natufian El_miron 1.020
gendstat: Natufian Satsurblia -0.636
gendstat: Natufian Tianyuan -0.390
gendstat: Natufian Sunghir3 0.132
gendstat: Natufian Iberomaurusian 1.068
gendstat: Natufian Onge -0.527
gendstat: Natufian Devils_cave -0.888
gendstat: Natufian Shamanka -0.816
gendstat: Natufian Iron_Gates 1.277
gendstat: Natufian Papuan -1.633
gendstat: El_miron Satsurblia -1.654
gendstat: El_miron Tianyuan -1.382
gendstat: El_miron Sunghir3 -0.981
gendstat: El_miron Iberomaurusian -0.049
gendstat: El_miron Onge -1.705
gendstat: El_miron Devils_cave -1.837
gendstat: El_miron Shamanka -1.885
gendstat: El_miron Iron_Gates 0.082
gendstat: El_miron Papuan -2.535
gendstat: Satsurblia Tianyuan 0.304
gendstat: Satsurblia Sunghir3 0.779
gendstat: Satsurblia Iberomaurusian 1.550
gendstat: Satsurblia Onge 0.048
gendstat: Satsurblia Devils_cave -0.287
gendstat: Satsurblia Shamanka -0.089
gendstat: Satsurblia Iron_Gates 1.845
gendstat: Satsurblia Papuan -1.036
gendstat: Tianyuan Sunghir3 0.563
gendstat: Tianyuan Iberomaurusian 1.361
gendstat: Tianyuan Onge -0.235
gendstat: Tianyuan Devils_cave -0.691
gendstat: Tianyuan Shamanka -0.465
gendstat: Tianyuan Iron_Gates 1.711
gendstat: Tianyuan Papuan -1.369
gendstat: Sunghir3 Iberomaurusian 1.026
gendstat: Sunghir3 Onge -0.828
gendstat: Sunghir3 Devils_cave -1.160
gendstat: Sunghir3 Shamanka -1.183
gendstat: Sunghir3 Iron_Gates 1.177
gendstat: Sunghir3 Papuan -2.466
gendstat: Iberomaurusian Onge -1.855
gendstat: Iberomaurusian Devils_cave -2.001
gendstat: Iberomaurusian Shamanka -2.023
gendstat: Iberomaurusian Iron_Gates 0.122
gendstat: Iberomaurusian Papuan -3.153
gendstat: Onge Devils_cave -0.300
gendstat: Onge Shamanka -0.165
gendstat: Onge Iron_Gates 1.703
gendstat: Onge Papuan -1.425
gendstat: Devils_cave Shamanka 0.313
gendstat: Devils_cave Iron_Gates 2.425
gendstat: Devils_cave Papuan -1.049
gendstat: Shamanka Iron_Gates 2.368
gendstat: Shamanka Papuan -1.356
gendstat: Iron_Gates Papuan -2.827

##end of qpAdm: 921.515 seconds cpu 2607.956 Mbytes in use



Looks good. Std errors are still acceptable. Now it’s probably useful to run some other Europeans against Hun-Scythians in qpWave and make a table by chi sq value from most to least related