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 and is able to perform multiple population genetic analyses in heterogeneous populations. Based on population structure inference, PCAngsd is able to estimate individual allele frequencies. These individual allele frequencies can be used in various population genetic methods for heterogeneous populations, such that PCAngsd can perform PCA (estimate covariance matrix), call genotypes, estimate individual admixture proportions, estimate inbreeding coefficients (per-individual and per-site) and perform a genome selection scan using principal components. The entire program is written in Python 2.7 and is multithreaded to take advantage of several CPUs.

Download

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

Latest release of PCAngsd: 0.95

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

The following Python packages are needed to run PCAngsd: numpy[1], scipy[2], pandas[3], numba[4] and pysnptools[5].

The packages and their dependencies can easily be installed using the following command inside the pcangsd folder:

pip install --user -r python_packages.txt

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

Quick start

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

# Only estimate covariance matrix using 10 threads
python pcangsd.py -beagle data.beagle.gz -o test -threads 10

# Estimate covariance matrix and individual admixture proportions
python pcangsd.py -beagle data.beagle.gz -admix -o test -threads 10

# Estimate covariance matrix and inbreeding coefficients
python pcangsd.py -beagle data.beagle.gz -inbreed 2 -o test -threads 10

# Estimate covariance matrix and perform selection scan
python pcangsd.py -beagle data.beagle.gz -selection 1 -o test -threads 10

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 data -nThreads 10 -doGlf 2 -doMajorMinor 1 -doMaf 2 -SNP_pval 1e-6 -bam bam.filelist

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

Using PCAngsd

All the different options in PCAngsd are listed here. PCAngsd will always compute the covariance matrix, where it uses principal components to estimate individual allele frequencies in an iterative procedure. The estimated individual allele frequencies will then be used in any of the other specified options of PCAngsd.

Estimation of individual allele frequencies

-beagle [Beagle filename]

Input file of genotype likelihoods in Beagle format (.beagle.gz).

-indf [Individual allele frequencies filename]

Input file of individual allele frequencies (binary). Numpy format (.indf.npy).

-plink [Prefix for binary PLINK files]

Path to PLINK files using their prefix. (.bed, .bim, .fam)

-epsilon [float]

Include error assumption in PLINK genotypes. (Default: 0.00)

-minMaf [float]

Minimum minor allele frequency threshold. (Default: 0.05)

-threads [int]

Specify the number of thread(s) to use. (Default: 1)

-iter [int]

Maximum number of iterations for estimation of individual allele frequencies. (Default: 100)

-tole [float]

Tolerance value for update in estimation of individual allele frequencies. (Default: 1e-5)

-maf [int]

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

-maf_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 using MAP test)

-o [prefix]

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

-indf_save

Choose to save estimated individual allele frequencies (Binary). Numpy format (.npy).

-expg_save

Choose to save estimated genotype dosages (Binary). Numpy format (.npy).

-sites_save

Choose to save the marker IDs after performing filtering using population allele frequencies. Useful for especially selection scans and per-site inbreeding coefficients.

Call genotypes

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

-geno [float]

Call genotypes with defined threshold.

-genoInbreed [float]

Call genotypes with defined threshold also taking inbreeding into account. -inbreed [int] is required, since individual inbreeding coefficients must have been estimated prior to calling genotypes using that information.

Admixture

Individual admixture proportions and population-specific allele frequencies can be estimated based on assuming K ancestral populations using an accelerated mini-batch NMF method.

-admix

Toggles admixture estimations.

-admix_alpha [int-list]

Specify alpha (sparseness regularization parameter). Can be specified as a sequence to try several alpha's in a single run. Fully compatible with -admix_seed. (Default: 0)

-admix_seed [int-list]

Specify seed for random initializations of factor matrices in admixture estimations. Can be specified as a sequence to try several different seeds in a single run. Fully compatible with -admix_alpha.

-admix_K [int]

Not recommended. Manually specify the number of ancestral populations to use in admixture estimations (overrides number chosen from -e). Structure explained by individual allele frequencies may therefore not reflect the manually chosen K. It is recommended to adjust -e instead of -admix_K.

-admix_iter [int]

Maximum number of iterations for admixture estimations using NMF. (Default: 100)

-admix_tole [float]

Tolerance value for update in admixture estimations using NMF. (Default: 5e-5)

-admix_batch [int]

Specify the number of batches to use in NMF method. (Default: 5)

-admix_save

Choose to save the population-specific allele frequencies (Binary). Numpy format (.npy).

Inbreeding

Per-individual inbreeding coefficients incorporating population structure can be computed using three different methods. However, -inbreed 2 is recommended for low depth cases.

-inbreed 1

A maximum likelihood estimator computed by an EM algorithm. Only allows for F-values between 0 and 1. Based on [6].

-inbreed 2

Simple estimator also computed by an EM algorithm. Allows for F-values between -1 and 1. Based on ngsF.

-inbreed 3

Estimator using an estimated kinship matrix. Allows for F-values between -1 and 1. 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

Use likelihood ratio tests (.lrt.sites.gz) generated from -inbreedSites to filter out variable sites using a given threshold for HWE test p-value:

-HWE_filter [LRT filename]
-HWE_tole [float]

Tolerance value for HWE test. (Default: 1e-6)

Selection

A genome selection scan can be computed using two different methods based on posterior expectations of the genotypes (genotype dosages):

-selection 1

Using an extended model of FastPCA. Performs a genome selection scan along all significant PCs. Outputs the selection statistics and must be converted to p-values by user. Each column reflect the selection statistics along a tested PC and they are χ²-distributed with 1 degree of freedom.

-selection 2

Using an extended model of PCAdapt. Outputs the selection statistics and must be converted to p-values by user. Selection statistics are χ²-distributed with -e degrees of freedom (number of significant eigenvectors).

Relatedness

Work in progress...

Estimate kinship matrix based on method Based on PC-Relate:

-kinship

Automatically estimated if -inbreed 3 has been selected.

Citation

Our methods for inferring population structure have been published in GENETICS:

Inferring Population Structure and Admixture Proportions in Low Depth NGS Data

PCAngsd

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