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Xiuwen Zheng edited this page Jul 18, 2014 · 7 revisions

Overview

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) measures (Zheng et al., 2012). 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. Benchmarks show the uniprocessor implementations of PCA and IBD are 8 to 50 times faster than the implementations provided in the popular EIGENSTRAT (v3.0) and PLINK (v1.07) programs respectively, and can be sped up to 30300 folds by utilizing multiple cores.

GDS is also used by an R/Bioconductor package GWASTools as one of its data storage formats (Zheng et al., 2012, Gogarten et al., 2012). GWASTools provides many functions for quality control and analysis of GWAS, including statistics by SNP or scan, batch quality, chromosome anomalies, association tests, etc.

Flowchart of parallel computing for principal component analysis and identity-by-descent analysis.

Figur 1: Flowchart of parallel computing for principal component analysis and identity-by-descent analysis.

R is the most popular statistical programming environment, but one not typically optimized for high performance or parallel computing which would ease the burden of large-scale GWAS calculations. To overcome these limitations we have developed a project named CoreArray (http://corearray.sourceforge.net/) that includes two R packages: gdsfmt to provide efficient, platform independent memory and file management for genome-wide numerical data, and SNPRelate to solve large-scale, numerically intensive GWAS calculations (i.e., PCA and IBD) on multi-core symmetric multiprocessing (SMP) computer architectures.

This vignette takes the user through the relatedness and principal component analysis used for genome wide association data. The methods in these vignettes have been introduced in the paper of Zheng et al. (2012). For replication purposes the data used here are taken from the HapMap Phase II project. These data were kindly provided by the Center for Inherited Disease Research (CIDR) at Johns Hopkins University and the Broad Institute of MIT and Harvard University (Broad). The data supplied here should not be used for any purpose other than this tutorial.

References:

  • Zheng, X., Levine, D., Shen, J., Gogarten, S.M., Laurie, C., and Weir, B.S. (2012). A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics (Oxford, England) 28, 3326-3328.
  • Gogarten, S.M., Bhangale, T., Conomos, M.P., Laurie, C.A., McHugh, C.P., Painter, I., Zheng, X., Crosslin, D.R., Levine, D., Lumley, T., et al. (2012). GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies. Bioinformatics (Oxford, England) 28, 3329-3331.
  • Laurie, C.C., Doheny, K.F., Mirel, D.B., Pugh, E.W., Bierut, L.J., Bhangale, T., Boehm, F., Caporaso, N.E., Cornelis, M.C., Edenberg, H.J., et al. (2010). Quality control and quality assurance in genotypic data for genome-wide association studies. Genetic Epidemiology 34, 591-602.
  1. Overview
  2. Preparing Data
  3. Data formats used in SNPRelate
  4. Create a GDS File of Your Own
  5. Format conversion from PLINK binary files
  6. Format conversion from VCF files
  7. Data Analysis
  8. LD-based SNP pruning
  9. Principal Component Analysis
  10. Relatedness Analysis
  11. Identity-by-State Analysis
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