HaploNet is a framework for inferring fine-scale population structure using neural networks in an unsupervised approach for phased haplotypes of whole-genome sequencing (WGS) data. We utilize a variational autoencoder (VAE) framework to learn mappings to and from a low-dimensional latent space in which we will perform indirect clustering of haplotypes with a Gaussian mixture prior (Gaussian Mixture Variational Autoencoder).

Please cite our paper in Genome Research: Haplotype and Population Structure Inference using Neural Networks in Whole-Genome Sequencing Data.

The HaploNet framework relies on the following Python packages that you can install through conda (recommended, see next) or pip:

• pytorch
• numpy
• cython
• scipy
• cyvcf2

Follow the link to find more information on how to install PyTorch for your setup (GPU/CPU). You can create an environment through conda easily as follows:

# GPU setup
conda env create -f environment_gpu.yml

# CPU setup
conda env create -f environment_cpu.yml

git clone https://github.com/Rosemeis/HaploNet.git
cd HaploNet
pip3 install -e .

You can now run HaploNet with the haplonet command.

HaploNet can now be trained on a phased genotype file in VCF/BCF format as follows (using default parameters and on GPU):

haplonet train --bcf chr1.bcf --cuda --out haplonet.chr1
# Saves log-likelihoods in binary NumPy matrix (haplonet.chr1.loglike.npy)
# and a log-file with parameters used in the training (haplonet.chr1.log)

# Run for 22 chromosomes and save output path in a filelist (needed in downstream analyses)
for c in {1..22}
do
	haplonet train --bcf chr${c}.bcf --cuda --out haplonet.chr${c}
	realpath haplonet.chr${c}.loglike.npy >> haplonet.filelist
done

HaploNet outputs the neural network log-likelihoods by default which are used to infer global population structure (PCA and admixture). With the '--latent' argument, the parameters of the learnt latent spaces of the GMVAE can be saved as well. See all available options in HaploNet with the following command:

haplonet -h
haplonet train -h # training haplonet
haplonet admix -h # estimate ancestry
haplonet pca -h # perform pca

All the following analyses assume that HaploNet has been run for all chromosomes and a filelist has been created, which contains the log-likelihood output paths for each chromosome (e.g. haplonet.filelist). The argument "--like" can be used if you only have one chromosome or merged file.

The EM algorithm in HaploNet can be run with K=2 and 64 threads (CPU based).

haplonet admix --filelist haplonet.filelist --K 2 --threads 64 --seed 0 --out haplonet.admixture.k2

# Saves ancestry proportions in a text-file (haplonet.admixture.k2.q)
# and ancestral cluster frequencies in a binary NumPy matrix (haplonet.admixture.k2.f.npy)

And the admixture proportions can as an example be plotted in R as follows:

q <- read.table("haplonet.admixture.k2.q")
barplot(t(q), space=0, border=NA, col=c("dodgerblue3", "firebrick2"), xlab="Individuals", ylab="Proportions", main="HaploNet - Admixture")

Estimate eigenvectors directly using SVD (recommended for big datasets):

haplonet pca --filelist haplonet.filelist --threads 64 --out haplonet.pca

e <- as.matrix(read.table("haplonet.pca.eigenvecs"))
plot(e[,1:2], main="HaploNet - PCA", xlab="PC1", ylab="PC2")

Compute the covariance matrix followed by eigendecomposition in R:

haplonet pca --filelist haplonet.filelist --cov --threads 64 --out haplonet.pca

C <- as.matrix(read.table("haplonet.pca.cov"))
e <- eigen(C)
plot(e$vectors[,1:2], main="HaploNet - PCA", xlab="PC1", ylab="PC2")