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| =Brief description= | | = NEW VERSION = |
| This page contains information about the program called NgsRelate, which can be used to infer relatedness coefficients for pairs of individuals for low coverage Next Generation Sequencing (NGS) data by using genotype likelihoods. To be able to infer the relatedness you will need to know the population frequencies and have genotype likelihoods. This can be obtained e.g. using the program ANGSD as shown in the example below.
| | For the NEW version of ngsRelate that coestimates relatedness and inbreeding go to this link https://github.com/ANGSD/NgsRelate |
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| =Download and Installation=
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| Primary repository is github. https://github.com/ANGSD/NgsRelate. On a linux system with curl and g++ installed you can download NgsRelate as follows:
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| <pre>
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| curl https://raw.githubusercontent.com/ANGSD/NgsRelate/master/NgsRelate.cpp >NgsRelate.cpp
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| g++ NgsRelate.cpp -O3 -lz -o ngsrelate
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| </pre>
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| =Intro to run examples= | | = OLD VERSION = |
| Below is a few example of different ways NgsRelate can be used to estimate relatedness from NGS data. To be able to run all steps of the run examples below you also need to have the program ANGSD installed since we use this for generating input files for NgsRelate (for information about this program see http://popgen.dk/angsd/index.php/Quick_Start). If you just want to try out NgsRelate then you can download example NgsRelate input data for the last example here: . And you can find the expected output from the same example here: .
| | For the old version please use this link: http://www.popgen.dk/software/index.php?title=NgsRelate&oldid=694 |
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| = Run example using only NGS data= | |
| Assume we have file containing paths to 100 BAM/CRAM files. Then we can use ANGSD to estimate frequencies and calculate genotype likelihoods while doing SNP calling and in the end produce the the input files needed for the NgsRelate program as follows:
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| <pre>
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| ### First we generate a file with allele frequencies (angsdput.mafs.gz) and a file with genotpe likelihoods (angsdput.glf.gz).
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| angsd -b filelist -gl 1 -domajorminor 1 -snp_pval 1e-6 - domaf 1 -minmaf 0.05 -doGlf 3
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| ### The we extract the frequency column from the allele frequency file and remove the header (to make it in the format NgsRelate needs)
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| cut -f5 angsdput.mafs.gz |sed 1d >freq
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| </pre>
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| Once we have these files we can use NgsRelate to estimate relatedness between any pairs of individuals. E.g. if we want to estimate relatedness between the first two individuals (0 and 1) we can do it using the following command:
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| <pre>
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| ngsrelate -g angsdput.glf.gz -n 100 -f freq -a 0 -b 1 >gl.res
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| </pre>
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| Here we specify the name of our file with genotype likelihoods after the option "-g", the number of individuals in the file after the option "-n", the name of the file with allele frequencies after the option "-f" and the number of the two individuals after the options "-a" and "-b" . If -a and -b are not specified NgsRelate will loop through all pairs of individuals in the input file.
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| = Run example using NGS data with population frequencies estimated from genetic data from PLINK files =
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| In this example we show how you can estimate relatedness between a number of individuals which you have NGS data from (in bam files) using genetic data from PLINK files for frequency estimation.
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| Assume the individuals we want to estimate relatedness from are from the population called LWK and assume we have files with genetic data from individuals from LWK as well as other populations in binary PLINK format (e.g. hapmap3_r2_b36_fwd.consensus.qc.polyHg19.*) and a file, LWK.fam, with the IDs of the LWK individuals in this dataset. Then using PLINK we can produce allele frequency information in a format that NgsRelate can use as follows:
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| <pre>
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| ### extract individuals from LWK from huge binary plink file
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| plink --bfile hapmap3_r2_b36_fwd.consensus.qc.polyHg19 --keep LWK.fam --make-bed --out hapmap3Hg19LWK --noweb
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| ### calculate frequencies for this population
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| plink --bfile hapmap3Hg19LWK --freq --noweb --out LWKsub
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| </pre>
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| Afterwards we can use ANGSD to calculate genotype likelihoods for the sites for which we have frequency in
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| <pre>
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| ### extract the chr,pos,major,minor information about the sites we have frequency info from into a file
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| ### (so we can extract data from these sites from the NGS data files)
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| cut -f1,4-6 hapmap3Hg19LWK.bim >forAngsd.txt
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| ### index this file for angsd
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| angsd sites index forAngsd.txt
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| ### calculate genotype likelihoods for the six individuals for the sites we have frequency info on based on the bam files
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| ### (assuming the paths to the bam files are listed in the file 'list'):
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| angsd -gl 1 -doglf 3 -sites forAngsd.txt -b list -domajorminor 3 -P 2 -minMapQ 30 -minQ 20
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| ### this generates the output files angsdput.glf.gz and a angsdput.glf.pos.gz.
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| </pre>
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| Finally we can use NgsRelate to estimate relatedness for the six individuals from which we have NGS data in bam files:
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| <pre>
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| ### extract the frequencies and sync it to the angsd output
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| ngsrelate extract_freq angsdput.glf.pos.gz files/hapmap3Hg19LWK.bim files/LWKsub.frq >freq
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| ### run ngsrelate
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| ngsrelate -g angsdput.glf.gz -n 6 -f freq >res
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| </pre>
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| The final relatedness estimates will then be available in the file called "res".
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| =Output= | |
| Example of output of with two samples
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| <pre>
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| Pair k0 k1 k2 loglh nIter coverage
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| (0,1) 0.673213 0.326774 0.000013 -1710940.769941 19 0.814658
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| </pre>
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| Example of output with 6 samples:
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| <pre>
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| cat res
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| Pair k0 k1 k2 loglh nIter coverage
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| (0,1) 0.675337 0.322079 0.002584 -1710946.832375 10 0.813930
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| (0,2) 0.458841 0.526377 0.014782 -1666215.528333 10 0.808822
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| (0,3) 1.000000 0.000000 0.000000 -1743992.363193 -1 0.816266
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| (0,4) 1.000000 0.000000 0.000000 -1759202.971213 -1 0.818856
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| (0,5) 1.000000 0.000000 0.000000 -1550475.615322 -1 0.752663
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| (1,2) 0.007111 0.991020 0.001868 -1580995.130867 10 0.806912
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| (1,3) 1.000000 0.000000 0.000000 -1728859.988212 -1 0.814272
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| (1,4) 1.000001 -0.000001 0.000000 -1744055.203870 9 0.816887
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| (1,5) 1.000000 0.000000 0.000000 -1536858.187440 -1 0.750917
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| (2,3) 1.000000 0.000000 0.000000 -1705157.832621 -1 0.809297
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| (2,4) 1.000000 0.000000 0.000000 -1719681.338365 -1 0.811804
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| (2,5) 1.000000 0.000000 0.000000 -1517388.260612 -1 0.746903
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| (3,4) 0.547602 0.439423 0.012975 -1743899.789842 10 0.819276
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| (3,5) 0.265819 0.482953 0.251228 -1467343.087647 10 0.754637
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| (4,5) 0.004655 0.995345 -0.000000 -1473415.049411 8 0.755734
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| </pre>
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| The first column contain the individuals that was used for the analysis . The next three columns are the estimated relatedness coefficient. The fifth column is the log of the likelihood of the MLE. The sixth column is the number of iterations required to find the MLE, and finally the seventh column is fraction of non-missing sites, i.e. the fraction of sites where data was available for both individuals, and the minor allele frequency (MAF) above the threshold (default is 0.05 but may also user specified).
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| = Input file format =
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| The input files are binary gz compressed, log like ratios encoded as double. 3 values per sample.
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| The freq file is allowed to be gz compressed.
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| = Citing and references =
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| = Changelog =
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| See github for log
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