cholotook / snprelate

R package: parallel computing toolset for relatedness and principal component analysis of SNP data (Development Version)

SNPRelate: Parallel computing toolset for relatedness and principal component analysis of SNP data

GNU General Public License, GPLv3

Features

Genome-wide association studies are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed SNPRelate (R package for multi-core symmetric multiprocessing computer architectures) to accelerate two key computations on SNP data: principal component analysis (PCA) and relatedness analysis using identity-by-descent measures. The kernels of our algorithms are written in C/C++ and highly optimized.

The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. The SNP GDS format in this package is also used by the GWASTools package with the support of S4 classes and generic functions. The extended GDS format is implemented in the SeqArray package to support the storage of single nucleotide variation (SNV), insertion/deletion polymorphism (indel) and structural variation calls. It is strongly suggested to use SeqArray for large-scale whole-exome and whole-genome sequencing variant data instead of SNPRelate.

Bioconductor

Release Version: v1.26.0

http://www.bioconductor.org/packages/SNPRelate

News

• v1.16.0: a new option 'useMatrix' for the packed symmetric matrix using Matrix to reduce memory usages;

• v1.4.0: supports the SeqArray GDS format, see the vignette;

• See package news.

Tutorials

http://bioconductor.org/packages/release/bioc/vignettes/SNPRelate/inst/doc/SNPRelateTutorial.html

Citations

Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS (2012). A High-performance Computing Toolset for Relatedness and Principal Component Analysis of SNP Data. Bioinformatics. DOI: 10.1093/bioinformatics/bts606.

Zheng X, Gogarten S, Lawrence M, Stilp A, Conomos M, Weir BS, Laurie C, Levine D (2017). SeqArray -- A storage-efficient high-performance data format for WGS variant calls. Bioinformatics. DOI: 10.1093/bioinformatics/btx145.

Installation

• Bioconductor repository:

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("SNPRelate")

• Development version from Github (for developers/testers only):

library("devtools")
install_github("zhengxwen/gdsfmt")
install_github("zhengxwen/SNPRelate")

The install_github() approach requires that you build from source, i.e. make and compilers must be installed on your system -- see the R FAQ for your operating system; you may also need to install dependencies manually.

Implementation with Intel Intrinsics

Functions	No SIMD	SSE2	AVX	AVX2	AVX-512
snpgdsDiss »	X
snpgdsEIGMIX »	X	X	X
snpgdsGRM »	X	X	X	.
snpgdsIBDKING »	X	X		X
snpgdsIBDMoM »	X
snpgdsIBS »	X	X
snpgdsIBSNum »	X	X
snpgdsIndivBeta »	X	X	P	X
snpgdsPCA »	X	X	X
snpgdsPCACorr »	X
snpgdsPCASampLoading »	X
snpgdsPCASNPLoading »	X
...

X: fully supported; .: partially supported; P: POPCNT instruction.

Install the package from the source code with the support of Intel SIMD Intrinsics:

You have to customize the package compilation, see: CRAN: Customizing-package-compilation

Change ~/.R/Makevars to, assuming GNU Compilers (gcc/g++) or Clang compiler (clang++) are installed:

## for C code
CFLAGS=-g -O3 -march=native -mtune=native
## for C++ code
CXXFLAGS=-g -O3 -march=native -mtune=native

Implementation with OpenCL

In progress ...