PCAngsd

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.

Error creating thumbnail: File missing

Download

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

Latest release of PCAngsd: 0.3

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

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

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

# Estimate covariance matrix and 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. Usually all the desired analyses must be run in the same command, however PCAngsd can also be run in chunk-mode where per-site estimations are performed on a chunk of the data at a time using a pre-estimated covariance matrix. More information of chunk-mode estimations can be found here.

PCAngsd will always compute the covariance matrix (unless performing in chunk-mode estimations). It uses the computed principal components to estimate individual allele frequencies in an iterative procedure. This procedure is performed until the individual allele frequencies have converged.

-beagle [Beagle filename]

Path to file of the genotype likelihoods in Beagle format.

-beaglelist [filelist]

Parse a file with a list of 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 the modelling of individual allele frequencies. (Default: Automatically tested)

-reg [float]

Add regularization term in the modelling of individual allele frequencies to perform ridge regression. May help on convergence for individual allele frequencies. Must be used when scaling principal components prior to the modelling of individual allele frequencies.

-scaled

Scale significant principal components in relation to the top principal component: $(\lambda _{i}/\lambda _{0})^{2}$, for <math>\lambda_0<\math> being the top principal component.

-o [prefix]

Set the prefix for all output files created by PCAngsd (Default: "pcangsd").

Call genotypes

Genotypes can be called from posterior genotype probabilities incorporating the individual allele frequencies in prior.

-geno [float]

Call genotypes with defined threshold.

-genoInbreed [float]

Call genotypes with defined threshold also taking inbreeding into account. -inbreed is required.

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].

-inbreed 2

Simple estimator also computed by an EM algorithm. Based on ngsF.

-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 alongside likehood ratio tests for HWE can be computed as follows:

-inbreedSites

Selection

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

-selection 1

Using an extended model of FastPCA. Performs a genome selection scan along 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.

-LD [int]

(Not fully tested!) Select the window (in bases) of preceding sites to use in regression.

Relatedness

Work in progress...

Estimate kinship matrix:

-kinship

Automatically estimated if -inbreed 3 has been selected.

Example

# Estimate covariance matrix, inbreeding coefficients, kinship matrix and perform genome selection scan.
python pcangsd.py -beagle test.beagle.gz -inbreed 2 -kinship -selection 1 -o test