This page contains information about the program PCAngsd, which estimates the covariance matrix for low depth NGS data in an iterative procedure based on genotype likelihoods. Based on the population structure inference PCAngsd is able to estimate individual allele frequencies. By incorporating these allele frequencies in Empirical Bayes approaches, PCAngsd can perform PCA (estimate covariance matrix), call genotypes, estimate inbreeding coefficients (per-individual and per-site) and perform a genome selection scan using principal components in structured populations. The entire program is written in Python 2.7.

Download

The program can be downloaded from Github: https://github.com/Rosemeis/pcangsd

git clone https://github.com/Rosemeis/pcangsd.git;
cd pcangsd/

The following Python packages are needed to run PCAngsd (found in all popular distributions): numpy and pandas.

PCAngsd should work on all platforms meeting the requirements but server-side usage is recommended.

Quick start

# See all options in PCAngsd
python pcangsd.py -h

# Estimate covariance matrix 
python pcangsd.py -beagle test.beagle.gz -o test

# Estimate inbreeding coefficients
python pcangsd.py -beagle test.beagle.gz -inbreed 1 -o test

# Perform selection scan
python pcangsd.py -beagle test.beagle.gz -selection 1 -o test

Input

The only input PCAngsd needs and accepts are genotype likelihoods in BEAGLE format. ANGSD can be easily be used to compute genotype likelihoods and output them in the required BEAGLE format.

./angsd -GL 1 -out genoLikes -nThreads 10 -doGlf 2 -doMajorMinor 1  -doMaf 2 -SNP_pval 1e-6 -bam bam.filelist

See ANGSD for more info on how to compute the genotype likelihoods and call SNPs.

Using PCAngsd

All the different options in PCAngsd are listed here. All required analyses must be run in the same command! (*For now...*)

PCAngsd will always compute the covariance matrix. It uses the computed principal components to estimate the individual allele frequencies in an iterative procedure. This procedure is performed until the individual allele frequencies have converged.

beagle [BEAGLE filename]

Positional argument for the path of the genotype likelihoods in BEAGLE format.

beaglelist [filelist]

Parse a filelist with multiple BEAGLE files, e.g. if the genotype likelihoods have been computed separately for each chromosome.

-M [int]

Maximum number of iterations for covariance estimation. Only needed in rare cases. (Default: 100)

-M_tole [float]

Tolerance value for the iterative covariance matrix estimation. (Default: 1e-4)

-EM [int]

Maximum number of EM iterations for computing the population allele frequencies. (Default: 200)

-EM_tole [float]

Tolerance value in EM algorithm for population allele frequencies estimation. (Default: 1e-4)

-e [int]

Manually select the number of eigenvalues to use in modelling of individual allele frequencies. (Default: Automatically selected)

-reg

(Not fully tested!) Toogle to use Tikhonov regularization in modelling of individual allele frequencies to penalize lesser important PCs. May also help on convergence.

-o [filename]

Set the prefix for all output files created by PCAngsd.

Call genotypes

Genotypes can be called very easily using the individual allele frequencies as prior.

-callGeno

Toggle to call genotypes.

Inbreeding

Per-individual inbreeding coefficients incorporating population structure can be computed using three different methods:

-inbreed 1

A maximum likelihood estimator computed by an EM algorithm. Only allows for F-values between 0 and 1. Based on [1],[2].

-inbreed 2

Simple estimator also computed by an EM algorithm. Based on [3].

-inbreed 3

(Not recommended for low depth NGS data!) Estimator using the kinship matrix. Based on PC-Relate.

-inbreed_iter [int]

Maximum number of iterations for the EM algorithm methods. (Default: 200)

-inbreed_tole [float]

Tolerance value for the EM algorithms for inbreeding coefficients estimation. (Default: 1e-4)

Per-site inbreeding coefficients incorporating population structure can be computed

Selection

A genome-wide selection scan can be computed using two different methods:

-selection 1

Using an extended model of FastPCA. Produces a genome selection scan for all significant PCs.

-selection 2

(Not fully tested!) Using an extended model of PCAdapt.

LD can also be taken into account when performing selection scans. LD regression has been implemented in PCAngsd but the functionality is not fully tested.

-LD [int]

Select the window (in bases) of preceding sites to use in regression.

Relatedness

Work in progress.

Estimate kinship matrix:

-kinship

PCAngsd

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