NgsRelate

Brief description

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.

Download and Installation

Primary repository is github. https://github.com/ANGSD/NgsRelate. On a linux system with curl and g++ installed you can download NgsRelate as follows:

curl https://raw.githubusercontent.com/ANGSD/NgsRelate/master/NgsRelate.cpp >NgsRelate.cpp
g++ NgsRelate.cpp -O3 -lz -o ngsrelate

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 calculate genotype likelihoods while doing SNP calling and dumping the input files needed for the NgsRelate program

./angsd -b filelist -gl 1 -domajorminor 1 -snp_pval 1e-6 - domaf 1 -minmaf 0.05 -doGlf 3
#this generates an angsdput.mafs.gz and a angsdput.glf.gz.
#we will need to extract the frequency column from the mafs file and remove the header
cut -f5 angsdput.mafs.gz |sed 1d >freq
./ngsrelate -g angsdput.glf.gz -n 100 -f freq -a 0 -b 1 >gl.res

Here we specify that our binary genotype likelihood file contains 100 samples, and that we want to run the analysis for the first two samples -a 0 -b 1. If no -a and -b are specified it will loop through all pairs

Run example using NGS data with population frequencies estimated from PLINK data

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.

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:

### extract individuals from LWK from huge binary plink file
plink --bfile hapmap3_r2_b36_fwd.consensus.qc.polyHg19 --keep LWK.fam  --make-bed --out hapmap3Hg19LWK --noweb

### calculate frequencies for this population
plink --bfile  hapmap3Hg19LWK --freq --noweb --out LWKsub

Afterwards we can use ANGSD to calculate genotype likelihoods for the sites for which we have frequency in

### extract the chr,pos,major,minor information about the sites we have frequency info from into a file 
### (so we can extract data from these sites from the NGS data files) 
cut -f1,4-6  hapmap3Hg19LWK.bim >forAngsd.txt

### index this file for angsd
angsd sites index forAngsd.txt

### calculate genotype likelihoods for the six individuals for the sites we have frequency info on based on the bam files 
### (assuming the paths to the bam files are listed in the file 'list'):
angsd -gl 1 -doglf 3 -sites forAngsd.txt -b list -domajorminor 3 -P 2 -minMapQ 30 -minQ 20
### this generates the output files angsdput.glf.gz and a angsdput.glf.pos.gz.

Finally we can use NgsRelate to estimate relatedness for the six individuals from which we have NGS data in bam files:

### extract the frequencies and sync it to the angsd output
ngsrelate extract_freq angsdput.glf.pos.gz files/hapmap3Hg19LWK.bim files/LWKsub.frq >freq

### run ngsrelate 
ngsrelate  -g angsdput.glf.gz -n 6 -f freq >res

The final relatedness estimates will then be available in the file called "res".

Output

Example of output of with two samples

Pair	k0	k1	k2	loglh	nIter	coverage
(0,1)	0.673213	0.326774	0.000013	-1710940.769941	19	0.814658

Example of output with 6 samples:

cat res
Pair	k0	k1	k2	loglh	nIter	coverage
(0,1)	0.675337	0.322079	0.002584	-1710946.832375	10	0.813930
(0,2)	0.458841	0.526377	0.014782	-1666215.528333	10	0.808822
(0,3)	1.000000	0.000000	0.000000	-1743992.363193	-1	0.816266
(0,4)	1.000000	0.000000	0.000000	-1759202.971213	-1	0.818856
(0,5)	1.000000	0.000000	0.000000	-1550475.615322	-1	0.752663
(1,2)	0.007111	0.991020	0.001868	-1580995.130867	10	0.806912
(1,3)	1.000000	0.000000	0.000000	-1728859.988212	-1	0.814272
(1,4)	1.000001	-0.000001	0.000000	-1744055.203870	9	0.816887
(1,5)	1.000000	0.000000	0.000000	-1536858.187440	-1	0.750917
(2,3)	1.000000	0.000000	0.000000	-1705157.832621	-1	0.809297
(2,4)	1.000000	0.000000	0.000000	-1719681.338365	-1	0.811804
(2,5)	1.000000	0.000000	0.000000	-1517388.260612	-1	0.746903
(3,4)	0.547602	0.439423	0.012975	-1743899.789842	10	0.819276
(3,5)	0.265819	0.482953	0.251228	-1467343.087647	10	0.754637
(4,5)	0.004655	0.995345	-0.000000	-1473415.049411	8	0.755734

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).

Input file format

The input files are binary gz compressed, log like ratios encoded as double. 3 values per sample. The freq file is allowed to be gz compressed.

Changelog

See github for log