FastNgsAdmixOld

This page contains information about the program called FastNGSadmixPCA, which is a very fast tool for finding admixture proportions from NGS data of a single individual to incorporate into PCA of NGS data. It is based on genotype likelihoods. The program is written in R.

Installation

wget http://popgen.dk/albrecht/kristian/tool_download.zip
unzip tool_download.zip
OR simply use SHINY:
http://popgen.dk:443/kristian/admixpca_human/

Run example

tool.zip contains all files needed to execute FASTNGSAdmixPCA. The sample is from the HAPMAP project. In need of more samples, one can find a couple more samples in http://popgen.dk/albrecht/kristian/ The Rscript below executes the tool. all output is directed to a output_folder that is created in the process. To see the preset: Rscript FastNGSAdmixPCA.r

Rscript FastNGSAdmixPCA.r infile=NA12763.mapped.ILLUMINA.bwa.CEU.low_coverage.20130502.bam.beagle.gz

All arguments can be altered. To alter the reference populations, one need to write comma separated populations to the refpops argument as shown below

Rscript FastNGSAdmixPCA.r infile=NA12763.mapped.ILLUMINA.bwa.CEU.low_coverage.20130502.bam.beagle.gz refpops=YRI,JPT,CHB,CEU

To get an overview of available reference populations, one can make a dry run

Rscript FastNGSAdmixPCA.r infile=TRUE dryrun=TRUE

Input Files

Input files are contains genotype likelihoods in genotype likelihood beagle input file format [1]. We recommend [ANGSD] for easy transformation of Next-generation sequencing data to beagle format.

The example below show how to make a beagle file of genotype likelihood using ANGSD.

HOME$ ./angsd0.594/angsd -i 'pathtoindi.bam' -GL 2 -sites 'SNP.sites' -doGlf 2 -doMajorMinor 3 -minMapQ 30 -minQ 20 -doDepth 1 -doCounts 1 -out indi_genotypelikelihood

Example of a beagle genotype likelihood input file for 3 individuals.

marker       allele1  allele2   Ind0      Ind0    Ind0
1_14000023      1       0       0.941    0.058    0.000
1_14000072      2       3       0.709    0.177    0.112
1_14000113      0       2       0.855    0.106    0.037
1_14000202      2       0       0.835    0.104    0.060
...

version 2

This page contains information about the program called fastNGSadmix which is a very fast tool for finding admixture proportions from NGS data of a single individual and a method for doing PCA of NGS data, using the estimated admixture proporitions. It is based on genotype likelihoods. The admixture estimation part is written in C++ and the PCA part is written in R.

Installation

SOMETHING SIMILAR I GUESS/PERHAPS IN PUBLIC

wget http://popgen.dk/albrecht/kristian/tool_download.zip
unzip tool_download.zip
OR simply use SHINY:
http://popgen.dk:443/kristian/admixpca_human/

AND THEN CLONE FROM GITHUB FOR C++ PROGRAM

Input Files

Input files are genotype likelihoods in the genotype likelihood beagle input file format [2]. Or called genotypes in the binary plink files (*.bed) format [3] We recommend [ANGSD] for easy transformation of Next-generation sequencing data to beagle format and plink2 for handling plink files.

The example below show how to make a beagle file of genotype likelihood using ANGSD.

HOME$ ./angsd0.594/angsd -i 'pathtoindi.bam' -GL 2 -sites 'SNP.sites' -doGlf 2 -doMajorMinor 3 -minMapQ 30 -minQ 20 -doDepth 1 -doCounts 1 -out indi_genotypelikelihood

Example of a beagle genotype likelihood input file for 1 individual.

marker       allele1  allele2   Ind0      Ind0    Ind0
1_14000023      1       0       0.941    0.058    0.000
1_14000072      2       3       0.709    0.177    0.112
1_14000113      0       2       0.855    0.106    0.037
1_14000202      2       0       0.835    0.104    0.060
...

Where the marker should be chr_pos, instead of rs ID.

A provided SNP.sites file has been included.

The program needs a reference panel of population specific frequencies, populations for which admixture proportions should be estimated, for instance from 1000 G or HGDP, or another custom made reference panel. The program also needs a file telling the size of each reference panel population. There is an R script plinkToRef.R, that can convert a plink file to a reference panel and size of reference panel populations. Example:

Rscript plinkToRef.R plinkFile

This generates 3 files, a reference panel named refPanel_plinkFile.txt, a number of individuals file called nInd_plinkFile.txt and a plinkFile.sites file with chr pos and minor major alleles. The .fam file must have the group or population specified as FID, meaning first column and then the individual ID as the second column:

French F1 0 0 1 1
French F2 0 0 1 1
Yoruba Y1 0 0 2 1
Yoruba Y2 0 0 1 1
Yoruba Y3 0 0 2 1
...

A second argument of MAF threshold can be supplied, meaning all sites where the MAF is below this in any population is removed.

Rscript plinkToRef.R plinkFile 0.05

The snpStats R package, is needed for running the plinkToRef.R script.

fastNGSadmix already comes with a premade reference panel, made from Lazaridis et al. (2014) where the curated dataset was selected.

I lifted the dataset hg19 using the program liftOver, I then translated snpNames to rs names, using 1000G data, generating a unique name for each site via "chr-pos-A1-A2" (where A1 and A2 are alphabetically sorted).

Furthermore I removed sites with more than 5 % missing and a MAF below 5 %, and kept only autosomal sites. I selected 7 populations French, Han, Chukchi, Karitiana, Papuan, Sindhi and Yoruba to have representation for most of the world. Furthermore I made sure that I only used unadmixed individuals within each population.

Run example

Admixture estimation

An example of a command running fastNGSadmix with a beagle file and a chosen K of 3:

./fastNGSadmix -likes indi_genotypelikelihood.beagle -fname refPanel.txt -Nname nInd.txt -outfiles indi_genotypelikelihood.beagle -K 3

If the reference panel has more than K populations, the program will just use the K first populations. You can pick which populations should be analyzed via the "-whichPops" option, where you write the names of the population comma seperated, K has to be same number as selected populations. It should also be noted that the program quits if there are duplicate sites (based on chr_pos ID) in the input or the reference panel.

It then produces two files indi_genotypelikelihood.beagle.qopt with the admixture proportions and indi_genotypelikelihood.beagle.log.

Or with a plink file:

./fastNGSadmix -plink plinkFile -fname refPanel.txt -Nname nInd.txt -outfiles plinkFile -K 3

A whole list of options can be explored by running fastNGSadmix without any input:

./fastNGSadmix

The option "-conv" specifies the number of convergence runs, with a new random starting point for each run. This is useful to test for convergence. The program will execute a maximum of 10 times. Another option is "-boot" which specifies the number of bootstrap runs, where random sites are sampled for each run. This is useful for generating a confidence interval of your estimate. The program will execute a maximum of 100 times. For faster inference of admixture proportions the "-Qconv" option can be set to 1, this bases the converge criteria on change in the admixture proportions values. The threshold of this can be set with "-Qtol". By default the "-Qtol" threshold is 0.0000001, it should not be put lower than this and generally this is only for when wanting a fast overview of the data, as it is less precise than the likelihood based convergence. By default the method adjusting the frequencies is used, to use the unadjusted approach set "-doAdjust 0". The accelerated EM algorithm is used by default, to use regular EM algorithm set "-method 0".

OPTIONS OPTIONS OPTIONS!??

PCA

After having run the admixture estimation, a PCA analysis can be run using the estimated admixture proportions to account for population structure in the PCA. The PCA method is implemented in R and is run using the script fastNGSAdmixPCA.R.

The method needs the estimated admixture proportions, the analyzed beagle or plink file as well as the genotypes of the reference panel used (as binary plink files). (refPanel is the binary plink files, refPanel.bim, refPanel.fam and refPanel.bed)

Rscript fastNGSAdmixPCA.R GLfile=indi_genotypelikelihood.beagle admixFile=indi_genotypelikelihood.beagle.qopt out=indi_genotypelikelihood.beagle genoFile=refPanel

Or using analyzed plink files.

Rscript fastNGSAdmixPCA.R plinkFile=plinkFile admixFile=plinkFile.qopt out=plinkFile genoFile=refPanel

By default the populations of the analyzed *.qopt file are used. It can be specified which PCs should be plotted (default PC1 and PC2).

The snpStats R package, is needed for running this script.

1. 1. 2. PCA part

Custom refpanel can be supplied, has to look like this, where the 5 first columns have to be, then populations frequencies:

chr,pos,name,A0,A1

The frequencies have to be of the A0 allele.

Basically the files have to look like this: bgl rs1 A B GL(AA) GL(AB) GL(BB) Then ref 1 1 rs1 B A f(B)

Then solution is this: bgl rs1 A B GL(AA) GL(AB) GL(BB) Then ref 1 1 rs1 B A 1-f(B)

Then prepFreqs.R will take care of preparing the files properly.

Example of running prepFreqs.R:

Rscript prepFreqs.R indi_genotypelikelihood.bgl

Can also specify other populations than the default 5 ones in the reference panel. Also a custom made reference panel can be supplied (has to be .Rdata file).

Rscript prepFreqs.R indi_genotypelikelihood.bgl Pop1,Pop2,Pop3,Pop4 customRefPanel

indi_genotypelikelihood

Then run prepFreqs.R to get the proper beagle, refpanel and nInd files for the analysis.

Then run fastNGSadmix.

All the awesome options with the program.

So bgl rs1 A B GL(AA) GL(AB) GL(BB) Then ref 1 1 rs1 A B f(A)

(So if the 3 columns with genotype likelihoods in the beagle file is coded like this AA AB BB, then the frequencies should be of the A allele.)

Furthermore a file with the number of individuals in each reference population should be supplied.

Then a lot of different options and filters can be specified:

(TO BE CONTINUED...)