Version 1.35
Framework for analyzing low-depth next-generation sequencing (NGS) data in heterogeneous/structured populations using principal component analysis (PCA). Population structure is inferred by estimating individual allele frequencies in an iterative approach using a truncated SVD model. The covariance matrix is estimated using the estimated individual allele frequencies as prior information for the unobserved genotypes in low-depth NGS data.
The estimated individual allele frequencies can further be used to account for population structure in other probabilistic methods. PCAngsd can perform the following analyses:
The PCAngsd software relies on the following Python packages that you can install through conda (recommended) or pip:
You can create an environment through conda easily or install dependencies through pip as follows:
# Conda environment
conda env create -f environment.yml
# pip
pip3 install --user -r requirements.txtgit clone https://github.com/Rosemeis/pcangsd.git
cd pcangsd
pip3 install .You can now run PCAngsd with the pcangsd command.
PCAngsd works directly on genotype likelihood files or PLINK files.
# See all options
pcangsd -h
# Genotype likelihood file in Beagle format with 2 eigenvectors using 64 threads
pcangsd -b input.beagle.gz -e 2 -t 64 -o pcangsd
# Outputs by default log-file (pcangsd.log) and covariance matrix (pcangsd.cov)
# PLINK files (using file-prefix, *.bed, *.bim, *.fam)
pcangsd -p input.plink -e 2 -t 64 -o pcangsd
# Perform PC-based selection scan and estimate admixture proportions
pcangsd -b input.beagle.gz -e 2 -t 64 -o pcangsd --selection --admix
# Outputs the following files:
# log-file (pcangsd.log)
# covariance matrix (pcangsd.cov)
# selection statistics (pcangsd.selection)
# admixture proportions (pcangsd.admix.3.Q)
# ancestral allele frequencies (pcangsd.admix.3.F)PCAngsd will output most files in text-format.
Quick example of reading output and creating PCA plot in R:
C <- as.matrix(read.table("pcangsd.cov")) # Reads estimated covariance matrix
D <- as.matrix(read.table("pcangsd.selection")) # Reads PC based selection statistics
# Plot PCA plot
e <- eigen(C)
plot(e$vectors[,1:2], xlab="PC1", ylab="PC2", main="PCAngsd")
# Obtain p-values from PC-based selection scan
p <- pchisq(D, 1, lower.tail=FALSE)Read files in python and create PCA plot using matplotlib:
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import chi2
C = np.loadtxt("pcangsd.cov") # Reads estimated covariance matrix
D = np.loadtxt("pcangsd.selection") # Reads PC based selection statistics
# Plot PCA plot
evals, evecs = np.linalg.eigh(C)
evecs = evecs[:,::-1]
plt.scatter(evecs[:,0], evecs[:,1])
plt.xlabel("PC1")
plt.ylabel("PC2")
plt.title("PCAngsd")
plt.show()
# Obtain p-values from PC-based selection scan
p = chi2.sf(D, 1)Beagle genotype likelihood files can be generated from BAM files using ANGSD. For inference of population structure in genotype data with non-random missigness, we recommend our EMU software that performs accelerated EM-PCA, however with fewer functionalities than PCAngsd.
Please cite our papers:
Population structure: Inferring Population Structure and Admixture Proportions in Low-Depth NGS Data.\ HWE test: Testing for Hardy‐Weinberg Equilibrium in Structured Populations using Genotype or Low‐Depth NGS Data.\ Selection: Detecting Selection in Low-Coverage High-Throughput Sequencing Data using Principal Component Analysis.