Saturday, July 12, 2014

snpStats references

snpStats published here:
Hum Hered. 2007;64(1):45-51. Epub 2007 Apr 27.

An R package for analysis of whole-genome association studies.


To provide data classes and methods to facilitate the analysis of whole genome association studies in the R language for statistical computing.


We have implemented data classes in which each genotype call is stored as a single byte. At this density, data for single chromosomes derived from large studies and new high-throughput gene chip platforms can be handled in memory. We use the object-oriented programming model introduced with version 4 of the S-plus package, usually termed 'S4 methods'.


At the current state of development the package only supports population-based studies, although we would hope to provide support for family-based studies soon. Both quantitative and qualitative phenotypes may be analysed. Flexible association testing functions are provided which can carry out single SNP tests which control for potential confounding by quantitative and qualitative covariates. Tests involving several SNPs taken together as 'tags' are also supported. Efficient calculation of pair-wise linkage disequilibrium measures is implemented and data input functions include a function which can download data directly from the international HapMap project website.
(c) 2007 S. Karger AG, Basel

Another R package similar to snpStats: 

GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies

Summary: GWASTools is an R/Bioconductor package for quality control and analysis of genome-wide association studies (GWAS). GWASTools brings the interactive capability and extensive statistical libraries of R to GWAS. Data are stored in NetCDF format to accommodate extremely large datasets that cannot fit within R’s memory limits. The documentation includes instructions for converting data from multiple formats, including variants called from sequencing. GWASTools provides a convenient interface for linking genotypes and intensity data with sample and single nucleotide polymorphism annotation.
Availability and implementation: GWASTools is implemented in R and is available from Bioconductor ( An extensive vignette detailing a recommended work flow is included.