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/

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 also needs frequencies of a reference panel with the 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 called plinkToRefV2.R, that can convert a plink file to a reference panel and size of reference panel populations. Example:

Rscript plinkToRefV2.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.

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 only autosomal sites. I selected 5 populations French, Han, Karitiana, Papuan 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

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!??

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...)