NgsAdmixv2: Difference between revisions

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The great thing about NGSadmix is that it is a new method that takes the uncertainty introduced in NGS sequencing data into account when inferring an individual's ancestry by using genotype likelihoods that considers the uncertainty caused by unobserved genotypes.  
The great thing about NGSadmix is that it is a new method that takes the uncertainty introduced in NGS sequencing data into account when inferring an individual's ancestry by using genotype likelihoods that considers the uncertainty caused by unobserved genotypes.  
As with the other existing software, ADMIXTURE and STRUCTURE, NGSadmix is only sensitive to admixture recent enough to cause structures in the population in terms of differing allele frequencies. Historical admixture events after which many generations has passed in the population, leaves no signature in terms of systematic differences in allele frequencies between individuals and are not a concern in association studies.


[[File:NgsAdmix.png|thumb]]
[[File:NgsAdmix.png|thumb]]
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The method was published in 2013 and can be found here: [http://www.ncbi.nlm.nih.gov/pubmed/24026093]
The method was published in 2013 and can be found here: [http://www.ncbi.nlm.nih.gov/pubmed/24026093]


==Citation==
http://www.genetics.org/content/early/2013/09/03/genetics.113.154138.full.pdf
Skotte, L., Korneliussen, T. S., & Albrechtsen, A. (2013). Estimating individual admixture proportions from next generation sequencing data. Genetics, 195(3), 693–702. doi:10.1534/genetics.113.154138
:<u>'''Bibtex'''</u>
:% 24026093
:@Article{pmid24026093,
:  Author="Skotte, L.  and Korneliussen, T. S.  and Albrechtsen, A. ",
:  Title="{{E}stimating {I}ndividual {A}dmixture {P}roportions from {N}ext {G}eneration {S}equencing {D}ata}",
:  Journal="Genetics",
:  Year="2013",
:  Pages=" ",
:  Month="Sep"
:}


==Latest Version==
==Software Download==
The latest version is 32 from June 25 2013 and can be downloaded here: [http://popgen.dk/software/download/NGSadmix/ngsadmix32.cpp].
The latest version of NGSadmix is ngsadmix32 from June 25, 2013 and can be downloaded here: [http://popgen.dk/software/download/NGSadmix/ngsadmix32.cpp].


:'''Older Versions'''
:'''Older Versions'''
:Previous versions of NGSadmix can be found here: [http://popgen.dk/software/download/NGSadmix/].
:The previous version of NGSadmix, ngsadmix31 can be found here: [http://popgen.dk/software/download/NGSadmix/ngsadmix31.cpp].
:Version Log:
:* v32 june 25-2013; modified code such that it now compiles on OSX
:* v32 june 25-2013; modified code such that it now compiles on OSX
:* v31 june 24-2013; First public version.
:* v31 june 24-2013; First public version.
Line 37: Line 24:
NGSadmix can be installed independently or as a part of ANGSD.
NGSadmix can be installed independently or as a part of ANGSD.


===NGSadmix Independent Installation===
====NGSadmix Independent Installation====
 
1. Login to your server using ssh on your terminal window.


# One Login to your server using ssh on your terminal window.
2. Create the directory where you will install your software and enter it, such as  
#Create the directory where you will install your software and enter it, such as  
:<code>mkdir ~/Software</code>
:<code>mkdir ~/Software</code>
:<code>cd ~/Software</code>
:<code>cd ~/Software</code>


#Download the source code:
3. Download the source code:
:<code>wget https://raw.githubusercontent.com/ANGSD/angsd/master/misc/ngsadmix32.cpp </code>
:<code>wget https://raw.githubusercontent.com/ANGSD/angsd/master/misc/ngsadmix32.cpp </code>


#Configure, Compile and Install:
4. Configure, Compile and Install:
:<code>g++ ngsadmix32.cpp -O3 -lpthread -lz -o NGSadmix</code>
:<code>g++ ngsadmix32.cpp -O3 -lpthread -lz -o NGSadmix</code>


#Delete source code to save space:
====NGSadmix Installation from ANGSD====
:<code>rm ~/Software/ngsadmix32.cpp</code>
 
:NGSadmix is part of the package ANGSD. To install ANGSD, please follow the instructions here [http://popgen.dk/angsd/index.php/Installation]


===NGSadmix Installation from ANGSD===
==Parameters==


<pre>
All parameters are set using '''-par value'''.
</pre>
For example, to get additional information, you would write '''-printInfo 1'''.


<pre>./NGSadmix  </pre>


==Run command example==
Arguments:


#Download the input file
::'''-likes''' .beagle format filename with genotype likelihoods
<code>wget popgen.dk/software/download/NGSadmix/data/input.gz</code>


#Execute NGSadmix
::'''-K''' Number of ancestral populations
<code>./NGSadmix -likes input.gz -K 3 -P 4 -o myoutfiles -minMaf 0.05</code>


==Parameters==
Optional:


<div class="toccolours mw-collapsible mw-collapsed">
::'''-fname''' Ancestral population frequencies


./NGSadmix
::'''-qname''' Admixture proportions


<pre class="mw-collapsible-content">
::'''-outfiles''' Prefix for output files


*** see doc for options/possible ranges/ and further explanation
::'''-printInfo''' print ID and mean maximum allele frequency (maf) for the SNPs that were analysed


Arguments:
-likes .beagle format filename with genotype likelihoods
-K Number of ancestral populations
Optional:
-fname Ancestral population frequencies
-qname Admixture proportions
-outfiles Prefix for output files
-printInfo print ID and mean maf for the SNPs that were analysed
Setup:
Setup:
-seed Seed for initial guess in EM
 
-P Number of threads
::'''-seed''' Seed for initial guess in EM algorithm (a number lower than 1M is preferred).
-method If 0 no acceleration of EM algorithm
::  The same seed can be used to reproduce the analysis, and 3 different seeds can be used to test convergence.
-misTol Tolerance for considering site as missing
 
::'''-P''' Number of threads
 
::'''-method''' 0 indicates no acceleration of EM algorithm. Please refer to the paper for more information.
 
::'''-misTol''' Tolerance for considering a site as missing. Default = 0.05.
::  To include high quality genotypes only, increase this value (for example, 0.9)
 
Stop criteria:
Stop criteria:
-tolLike50 Loglikelihood difference in 50 iterations
-tol Tolerance for convergence
-dymBound Use dymamic boundaries (1: yes (default) 0: no)
-maxiter Maximum number of EM iterations
Filtering
-minMaf Minimum minor allele frequency
-minLrt Minimum likelihood ratio value for maf>0
-minInd Minumum number of informative individuals


NB All parameters are set using '''-par value'''. So to get additional information you would write '''-printInfo 1'''.
::'''-tolLike50''' Loglikelihood difference in 50 iterations. Default= 0.1
 
::'''-tol''' Tolerance for convergence. Default = 1x10<sup>-5</sup>. Use maller values for higher accuracy.
::  It's the maximum squared difference of F and Q (please refer to the paper for formula).
 
::'''-dymBound''' Use dymamic boundaries (1: yes (default) 0: no).
 
 
::'''-maxiter''' Maximum number of EM iterations. Default = 2000 (high value).
::  In case it doesn't converge, this value needs to be higher.
 
Filtering:
 
::'''-minMaf''' Minimum minor allele frequency. Default = 5%
 
::'''-minLrt''' Minimum likelihood ratio value for maf>0. Default = 0


</pre>
::'''-minInd''' Minumum number of informative individuals. Default = 0
</div>
::  It only keeps sites where there is at least x # of individuals with NGS data.


==Input File==
==Input File==
Input files are contains genotype likelihoods in genotype likelihood beagle input file format [http://faculty.washington.edu/browning/beagle/beagle.html]. We recommend [[ANGSD]] for easy transformation of Next-generation sequencing data to beagle format. See [http://www.popgen.dk/angsd/index.php/Beagle_input Creation of Beagle files with ANGSD]


Example of a beagle genotype likelihood input file for 3 individuals.  
The input file contains genotype likelihoods in a .beagle file format [http://faculty.washington.edu/browning/beagle/beagle.html].
and can be compressed with gzip.
=== BAM files  ===
If you have BAM files you can use [[ANGSD]] to produce genotype likelihoods in .beagle format. Please
see [http://www.popgen.dk/angsd/index.php/Beagle_input Creation of Beagle files with ANGSD]
 
=== VCF files ===
If you already have made a VCF file that contains genotype likehood information then  it should be possible to convert .vcf files with genotype likelihoods to .beagle file via vcftools [https://vcftools.github.io/man_latest.html]
 
<pre>
<pre>
marker      allele1  allele2  Ind0      Ind0    Ind0    Ind1    Ind1    Ind1    Ind2    Ind2    Ind2
vcftools --vcf input.vcf --out test --BEAGLE-GL --chr 1,2
1_14000023      1       0      0.941    0.058    0.000    0.799    0.199    0.001    0.666    0.333    0.001
1_14000072      2      3      0.709    0.177    0.112    0.941    0.058    0.000    0.665    0.332    0.001
1_14000113      0      2      0.855    0.106    0.037    0.333    0.333    0.333    0.799    0.199    0.000
1_14000202      2       0      0.835    0.104    0.060    0.799    0.199    0.000    0.333    0.333    0.333
...
</pre>
</pre>
Column 1:The marker name (the information is not atually used)
Chromosome has to be specified.


Column 2 and 3: the major and minor allele (these two columns are not used within the program and can contain whatever dummy value).
You can also use bcftools' [https://samtools.github.io/bcftools/bcftools.html] 'query' option for generating a .beagle file from a .vcf file.


The rest of the colums are the genotypes likelihoods (not in log space). For each individual we have 3 columns.
==Output Files==
Note that the above values sum to one per sites for each individuals. This is just a normalization of the genotype likelihoods in order to avoid underflow problems in the beagle software it does not mean that they are genotype probabilities.
The analysis performed by NGSadmix produces 4 files:


The input file is allowed to be compressed with gzip.
* Log likelihood of the estimates: a .log file that summarizes the run. The Command line used for running the program, what the likelihood is every 50 iterations, and finally how long it took to do the run.


==Output Files==
* Estimated allele frequency: a zipped .fopt file, that contains an estimate of the allele frequency in each of the 3 assumed ancestral populations. There is a line for each locus.
Program outputs 3 files.


#  PREFIX.log
* Estimated admixture proportions: a .qopt file, that contains an estimate of the individual's ancestry proportion (admixture) from each of the three assumed ancestral populations for all individuals. There is a line for each individual.
#  PREFIX.fopt.gz
# PREFIX.qopt


* The .log file contains log information of the run. Commandline used for running the program, what the likelihood is every 50 iterations, and finally how long it took to do the run.
==Run command example==


* The .fopt.gz file is an compressed file, which contains an estimate of the frequency for each site for all populations.
Download the input file, for example
::<code>wget popgen.dk/software/download/NGSadmix/data/input.gz</code>


* The .qopt file contains the admixture proportions for all individuals.
Execute NGSadmix
::<code>./NGSadmix -likes input.gz -K 3 -P 4 -o myoutfiles -minMaf 0.05</code>


Examples of the output files are found below.
::* '''-likes''' Input .beagle file of genotype likelihoods = input.gz
::* '''-K''' Ancestral populations K=3
::* '''-P''' Number of threads used, as computer cores = 4
::* '''-o''' Output prefix, output file names = myoutfiles
::*'''-minMaf''' SNPs with MAF > 5% = 0.05


===Detailed Examples and Tutorial===


===Log file (.log)===
Please refer to the tutorial's page [http://www.popgen.dk/software/index.php/NgsAdmixTutorial]
<div class="toccolours mw-collapsible mw-collapsed">
Contents of the file log file
<pre class="mw-collapsible-content">
-> Dumping file: tskSim/tsk6GL.beagle.s1.log
-> Dumping file: tskSim/tsk6GL.beagle.s1.filter
Input: lname=tskSim/tsk6GL.beagle nPop=3, fname=(null) qname=(null) outfiles=tskSim/tsk6GL.beagle.s1
Setup: seed=1 nThreads=10 method=1
Convergence: maxIter=2000 tol=0.000000 tolLike50=0.010000 dymBound=0
Filters: misTol=0.050000 minMaf=0.000000 minLrt=0.000000 minInd=0
Input file has dim: nsites=100000 nind=75
Input file has dim (AFTER filtering): nsites=100000 nind=75
iter[start] like is=9299805.984931
iter[50] like is=-6531138.892608 thres=0.002800
iter[100] like is=-6528710.773349 thres=0.001289
iter[150] like is=-6528405.896951 thres=0.001211
iter[200] like is=-6528306.803820 thres=0.000420
iter[250] like is=-6528277.160993 thres=0.000546
iter[300] like is=-6528271.925055 thres=0.000033
iter[350] like is=-6528271.177692 thres=0.000008
iter[400] like is=-6528270.876315 thres=0.000005
iter[450] like is=-6528270.772894 thres=0.000140
iter[500] like is=-6528270.747721 thres=0.000002
iter[550] like is=-6528270.740654 thres=0.000002
Convergence achived because log likelihooditer difference for 50 iteraction is less than 0.010000
best like=-6528270.740654 after 550 iterations
-> Dumping file: tskSim/tsk6GL.beagle.s1.qopt
-> Dumping file: tskSim/tsk6GL.beagle.s1.fopt.gz
[ALL done] cpu-time used =  671.82 sec
[ALL done] walltime used =  114.00 sec
</pre>
</div>


===Allele frequency ouput (.fopt)===
==Citation==
Each column correponds to the estimated allele frequencies for each population and each line is a SNP
<div class="toccolours mw-collapsible mw-collapsed">
Example of a .fopt file for -K 3
<pre class="mw-collapsible-content">
...
0.75331646167520038837 0.51190946588401886608 0.50134051056701267601
0.99999999900000002828 0.80165850924934911603 0.97470665326916294813
0.99999999900000002828 0.89560828888972687789 0.88062641752218895341
0.99999999900000002828 0.99999999900000002828 0.86109994249930577048
0.70560445653074521655 0.78994686954000448154 0.93076614062025020413
0.99999999900000002828 0.88878537780630872955 0.92662857068149151463
0.05322676762098016434 0.22871739860812340117 0.17394852600322696645
0.00000000100000000000 0.27428885137150410545 0.19029599645013275944
0.57086006389212373691 0.42232596591112880891 0.74080063581586474974
0.77359733910003525281 0.47380864146016693494 0.72073560889718923939
0.49946404159405927148 0.21684946347150244050 0.15201985942558055021
0.41802171086717271331 0.55490556205954566504 0.85691127728452165524
0.77095213528720529794 0.60074618451005279418 0.70219544996184157792
0.26517850405564091787 0.48500265408436060710 0.85432254709914456914
0.80055081986260245852 0.74423201242010783574 0.87110476762969968334
0.30563054476851375663 0.05233529475348827620 0.25911912824038613179
0.51084997710733415222 0.62263692178557350498 0.50738250264097506381
0.64790272562679740442 0.91230541484222271720 0.73015721390331478347
0.07124629651164265942 0.37896482494356753534 0.29218012479334326548
0.00000000100000000000 0.26969100790961914038 0.28395781874856029781
0.97074775756045073027 0.79093498372643300520 0.64006920058897498471
0.64661948716978157048 0.84130009558421925409 0.76730057769159087933
0.86990900887920663553 0.79410745692063922085 0.69416721874359499367
0.34956069940263900797 0.27773038429396151860 0.25923476721423144298
0.77739744690560164120 0.51272232330145017798 0.53888718200036844763
0.35431569298041332150 0.20022780744715171219 0.43176580786072032980
0.91858160919413811563 0.99999999900000002828 0.93584179237779097082
0.90339823126358831384 0.94729687041528465308 0.84358671720630329371
0.87068129661127857677 0.65267891763324525911 0.59315740612546075106
0.24102496839012735319 0.42777100607917967201 0.39594098602469629533
0.99999999900000002828 0.99999999900000002828 0.78549330115836857313
0.15386277372522660922 0.18035502891341426146 0.26583557049163752950
0.22456748943597096280 0.25110807159057474403 0.17244618960511531869
0.74816053649164548922 0.54769319158907958656 0.44532166240679449398
0.76350303696805599252 0.86547244122202959815 0.94111974586621383043
0.40940400475566068872 0.67767095908245833513 0.40793761498610620064
0.85389765162910868934 0.78901563183853873351 0.93614065916219291186
0.54108661985898742763 0.61895909938546000983 0.88522763262549941654
0.99051495581855464323 0.78855843624128341141 0.77646441702623147929
0.51133721761171413434 0.74521610846562824637 0.32689774480116673416
0.66618479413060949224 0.67891474309775079465 0.80762116232856140385
0.81793598261160704865 0.77752326447671193943 0.95349025244041396565
0.82120324647844433752 0.99999999900000002828 0.89800731971059466474
...
</pre>
</div>
Use the "-printInfo 1" option to get the position of the lines in the fopt file if some sites have been flltered from the analysis (-minMaf, minInd, minLRT etc)


===Admixture proportion output file (.qopt)===
http://www.genetics.org/content/early/2013/09/03/genetics.113.154138.full.pdf
Infered admixture proporsions. Each line is an individual and each column is a population.
<div class="toccolours mw-collapsible mw-collapsed">
Contents of the qopt file # cat tsk48GL.beagle.gz.s1.qopt
<pre class="mw-collapsible-content">
0.00254460532103031574 0.00108987228478324210 0.99636552239418640919
0.00000015905647541105 0.00000000100000000000 0.99999983994352459327
0.00034770382567266174 0.02639209238328452459 0.97326020379104283275
0.00000000100000000000 0.00000000100000000000 0.99999999800000005656
0.00000467398081877176 0.00000000100000000000 0.99999532501918120264
0.00000000907496942853 0.00585150933779484805 0.99414848158723567728
0.00515826525767644137 0.01138897436535154552 0.98345276037697204607
0.03914841746468285949 0.00000000100000000000 0.96085158153531713410
0.00000000100000000000 0.00629199375758324100 0.99370800524241675866
0.00771173022930659625 0.00000154720357311662 0.99228672256712036059
0.00000000100000000000 0.00075135345721917719 0.99924864554278081119
0.00000000100000000000 0.00000000100000000000 0.99999999799999994554
0.00000005468413042120 0.00087279924180633879 0.99912714607406327705
0.00000000100000000000 0.00000000100000000000 0.99999999800000005656
0.00712941313019542066 0.00118955677574110528 0.99168103009406338710
0.00000000100000000000 0.00000000100000000000 0.99999999799999994554
0.00000000100000000000 0.00165385222968000606 0.99834614677032007535
0.00000000100000000000 0.00006297763597355473 0.99993702136402651259
0.00519087111391381209 0.00000000100000000000 0.99480912788608621966
0.00000000100000000000 0.00000000100000000000 0.99999999800000005656
0.00202872783596746379 0.00000000100000000000 0.99797127116403261393
0.00876424336999809782 0.00949457841911990376 0.98174117821088191516
0.00000000100000000000 0.00000000100000000000 0.99999999799999994554
0.00000000100000000000 0.00000000100000000000 0.99999999799999994554
0.00000000100000000000 0.00000000100000000000 0.99999999799999994554
0.00000000100000000000 0.00000000100000000000 0.99999999799999994554
0.00000000100000000000 0.00000000100000000000 0.99999999800000005656
0.01820430093358888640 0.00000694033297829119 0.98178875873343274261
0.00351013812443964728 0.00000020340562512923 0.99648965846993520223
0.00771897550085272680 0.00605259705033356268 0.98622842744881378252
0.00600595292580561029 0.00000000100000000000 0.99399404607419439284
0.01454910070242997067 0.00543457657939076105 0.98001632271817917808
0.02567862615486414535 0.00160921436783232220 0.97271215947730349516
0.00000000100000000000 0.00000000100000000000 0.99999999800000005656
0.00000000100000000000 0.00001041560507852223 0.99998958339492149960
0.00000000100000000000 0.01383432553657116572 0.98616567346342876021
0.00343840097404925389 0.00000000100000000000 0.99656159802595079000
0.00000000100000000000 0.00000000100000000000 0.99999999800000005656
0.00000000100000000000 0.00000000100000000000 0.99999999800000005656
0.00051244065751142103 0.00404846039501185508 0.99543909894747661937
0.02003953974792894652 0.00000004934009128878 0.97996041091197982897
0.00000000100000000000 0.00000000100000000000 0.99999999799999994554
0.00000000100000000000 0.00000000100000000000 0.99999999799999994554
0.00000000100000000000 0.00000000100000000000 0.99999999800000005656
0.00000000100000000000 0.00000000100000000000 0.99999999799999994554
0.02176809890633762956 0.00000000100000000000 0.97823190009366245423
0.00000000100000000000 0.00000000100000000000 0.99999999800000005656
0.01563096189267457192 0.00970868396771427770 0.97466035413961116252
0.00000000100000000000 0.00000000100000000000 0.99999999800000005656
0.00002540964943070735 0.00000000100000000000 0.99997458935056915408
0.99999999799999994554 0.00000000100000000000 0.00000000100000000000
0.99501476026684787524 0.00000000100000000000 0.00498523873315206718
0.99999999799999994554 0.00000000100000000000 0.00000000100000000000
0.99520671498720802983 0.00479241730266987201 0.00000086771012207898
0.95884374919730619435 0.00000000100000000000 0.04115624980269377842
0.99002104218586972628 0.00000000100000000000 0.00997895681413022567
0.99999999799999994554 0.00000000100000000000 0.00000000100000000000
0.99999999800000005656 0.00000000100000000000 0.00000000100000000000
0.99999999770925251941 0.00000000129074746013 0.00000000100000000000
0.99999999799999994554 0.00000000100000000000 0.00000000100000000000
0.99999999799999994554 0.00000000100000000000 0.00000000100000000000
0.99999999800000005656 0.00000000100000000000 0.00000000100000000000
0.99999999800000005656 0.00000000100000000000 0.00000000100000000000
0.98980053177767901573 0.00000005577971952226 0.01019941244260143612
0.99999999800000005656 0.00000000100000000000 0.00000000100000000000
0.99999785004878083416 0.00000000100000000000 0.00000214895121910354
0.99999999800000005656 0.00000000100000000000 0.00000000100000000000
0.99999999800000005656 0.00000000100000000000 0.00000000100000000000
0.99220030909132039820 0.00000000100000000000 0.00779968990867968733
0.99999996788621803301 0.00000000100000000000 0.00000003111378189772
0.99736783433174225344 0.00255940950853666971 0.00007275615972113173
0.99998096423035520708 0.00000000574461213317 0.00001903002503262207
0.99711097909957713270 0.00288887008493822353 0.00000015081548462101
0.99999999799999994554 0.00000000100000000000 0.00000000100000000000
0.99999999800000005656 0.00000000100000000000 0.00000000100000000000
0.99999999800000005656 0.00000000100000000000 0.00000000100000000000
0.99769262012085335734 0.00000000100000000000 0.00230737887914652393
0.99999820787375570674 0.00000000433914936351 0.00000178778709493472
0.98047422489554170166 0.00012980111977614777 0.01939597398468214523
0.99999999799999994554 0.00000000100000000000 0.00000000100000000000
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</pre>
</div>


Skotte, L., Korneliussen, T. S., & Albrechtsen, A. (2013). Estimating individual admixture proportions from next generation sequencing data. Genetics, 195(3), 693–702. doi:10.1534/genetics.113.154138


==Examples==
:<u>'''Bibtex'''</u>
 
:% 24026093
Please refer to the tutorial [http://www.popgen.dk/software/index.php/NgsAdmixTutorial]
:@Article{pmid24026093,
:  Author="Skotte, L.  and Korneliussen, T. S.  and Albrechtsen, A. ",
:  Title="{{E}stimating {I}ndividual {A}dmixture {P}roportions from {N}ext {G}eneration {S}equencing {D}ata}",
:  Journal="Genetics",
:  Year="2013",
:  Pages=" ",
:  Month="Sep"
:}

Latest revision as of 09:44, 12 July 2019

This page contains information about the program called NGSadmix, which is a very nice tool for estimating individual admixture proportions from NGS data. It is based on genotype likelihoods and works well for medium and low coverage NGS data. It is a fancy multithreaded c/c++ program which makes it useful for large datasets.

The great thing about NGSadmix is that it is a new method that takes the uncertainty introduced in NGS sequencing data into account when inferring an individual's ancestry by using genotype likelihoods that considers the uncertainty caused by unobserved genotypes.

As with the other existing software, ADMIXTURE and STRUCTURE, NGSadmix is only sensitive to admixture recent enough to cause structures in the population in terms of differing allele frequencies. Historical admixture events after which many generations has passed in the population, leaves no signature in terms of systematic differences in allele frequencies between individuals and are not a concern in association studies.

The method was published in 2013 and can be found here: [1]


Software Download

The latest version of NGSadmix is ngsadmix32 from June 25, 2013 and can be downloaded here: [2].

Older Versions
The previous version of NGSadmix, ngsadmix31 can be found here: [3].
Version Log:
  • v32 june 25-2013; modified code such that it now compiles on OSX
  • v31 june 24-2013; First public version.

Installation

NGSadmix can be installed independently or as a part of ANGSD.

NGSadmix Independent Installation

1. Login to your server using ssh on your terminal window.

2. Create the directory where you will install your software and enter it, such as

mkdir ~/Software
cd ~/Software

3. Download the source code:

wget https://raw.githubusercontent.com/ANGSD/angsd/master/misc/ngsadmix32.cpp

4. Configure, Compile and Install:

g++ ngsadmix32.cpp -O3 -lpthread -lz -o NGSadmix

NGSadmix Installation from ANGSD

NGSadmix is part of the package ANGSD. To install ANGSD, please follow the instructions here [4]

Parameters

All parameters are set using -par value. For example, to get additional information, you would write -printInfo 1.

./NGSadmix  

Arguments:

-likes .beagle format filename with genotype likelihoods
-K Number of ancestral populations

Optional:

-fname Ancestral population frequencies
-qname Admixture proportions
-outfiles Prefix for output files
-printInfo print ID and mean maximum allele frequency (maf) for the SNPs that were analysed

Setup:

-seed Seed for initial guess in EM algorithm (a number lower than 1M is preferred).
The same seed can be used to reproduce the analysis, and 3 different seeds can be used to test convergence.
-P Number of threads
-method 0 indicates no acceleration of EM algorithm. Please refer to the paper for more information.
-misTol Tolerance for considering a site as missing. Default = 0.05.
To include high quality genotypes only, increase this value (for example, 0.9)

Stop criteria:

-tolLike50 Loglikelihood difference in 50 iterations. Default= 0.1
-tol Tolerance for convergence. Default = 1x10-5. Use maller values for higher accuracy.
It's the maximum squared difference of F and Q (please refer to the paper for formula).
-dymBound Use dymamic boundaries (1: yes (default) 0: no).


-maxiter Maximum number of EM iterations. Default = 2000 (high value).
In case it doesn't converge, this value needs to be higher.

Filtering:

-minMaf Minimum minor allele frequency. Default = 5%
-minLrt Minimum likelihood ratio value for maf>0. Default = 0
-minInd Minumum number of informative individuals. Default = 0
It only keeps sites where there is at least x # of individuals with NGS data.

Input File

The input file contains genotype likelihoods in a .beagle file format [5]. and can be compressed with gzip.

BAM files

If you have BAM files you can use ANGSD to produce genotype likelihoods in .beagle format. Please see Creation of Beagle files with ANGSD

VCF files

If you already have made a VCF file that contains genotype likehood information then it should be possible to convert .vcf files with genotype likelihoods to .beagle file via vcftools [6]

vcftools --vcf input.vcf --out test --BEAGLE-GL --chr 1,2

Chromosome has to be specified.

You can also use bcftools' [7] 'query' option for generating a .beagle file from a .vcf file.

Output Files

The analysis performed by NGSadmix produces 4 files:

  • Log likelihood of the estimates: a .log file that summarizes the run. The Command line used for running the program, what the likelihood is every 50 iterations, and finally how long it took to do the run.
  • Estimated allele frequency: a zipped .fopt file, that contains an estimate of the allele frequency in each of the 3 assumed ancestral populations. There is a line for each locus.
  • Estimated admixture proportions: a .qopt file, that contains an estimate of the individual's ancestry proportion (admixture) from each of the three assumed ancestral populations for all individuals. There is a line for each individual.

Run command example

Download the input file, for example

wget popgen.dk/software/download/NGSadmix/data/input.gz

Execute NGSadmix

./NGSadmix -likes input.gz -K 3 -P 4 -o myoutfiles -minMaf 0.05
  • -likes Input .beagle file of genotype likelihoods = input.gz
  • -K Ancestral populations K=3
  • -P Number of threads used, as computer cores = 4
  • -o Output prefix, output file names = myoutfiles
  • -minMaf SNPs with MAF > 5% = 0.05

Detailed Examples and Tutorial

Please refer to the tutorial's page [8]

Citation

http://www.genetics.org/content/early/2013/09/03/genetics.113.154138.full.pdf

Skotte, L., Korneliussen, T. S., & Albrechtsen, A. (2013). Estimating individual admixture proportions from next generation sequencing data. Genetics, 195(3), 693–702. doi:10.1534/genetics.113.154138

Bibtex
% 24026093
@Article{pmid24026093,
Author="Skotte, L. and Korneliussen, T. S. and Albrechtsen, A. ",
Title="{{E}stimating {I}ndividual {A}dmixture {P}roportions from {N}ext {G}eneration {S}equencing {D}ata}",
Journal="Genetics",
Year="2013",
Pages=" ",
Month="Sep"
}