Genome-wide association studies (GWAS) are widely used to help determine the genetic basis of diseases and traits, but they pose many computational challenges. We developed gdsfmt and SNPRelate (high-performance computing R packages for multi-core symmetric multiprocessing computer architectures) to accelerate two key computations in GWAS: principal component analysis (PCA) and relatedness analysis using identity-by-descent (IBD) measures1. The kernels of our algorithms are written in C/C++ and have been highly optimized. The calculations of the genetic covariance matrix in PCA and pairwise IBD coefficients are split into non-overlapping parts and assigned to multiple cores for performance acceleration, as shown in Figure 1.

GDS is also used by the R/Bioconductor package GWASTools as one of its data storage formats2,3. GWASTools provides many functions for quality control and analysis of GWAS, including statistics by SNP or scan, batch quality, chromosome anomalies, association tests, etc. 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.