Background: High density single nucleotide polymorphism microarrays (SNP chips) provide information on a subject's genome, such as the chromosomal copy numbers and the genotype (heterozygosity/homozygosity). In contrast to fluorescence in situ hybridization and karyotyping, SNP chips provide a high resolution map of the human genome that can be used to detect, for example, aneuploidies, microdeletions, microduplications, and loss of heterozygosity. As a variety of diseases are linked to such chromosomal abnormalities, SNP chips promise new insights for these diseases by aiding in the discovery of such regions, and may suggest targets for intervention. The R package SNPchip contains classes and methods useful storing, visualizing, and analyzing high density SNP data. Originally developed from the SNPscan web-tool, SNPchip utilizes S4 classes and extends other open source R tools available at Bioconductor, including the R packages Biobase and oligo. This has numerous advantages, including the ability to build statistical models for SNP-level data that operate on instances of the class, and to communicate with other R packages that add additional functionality.

SNPchip was developed in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, and the Pevsner Laboratory in the Kennedy Krieger Institute. The R package SNPchip is freely available from the Bioconductor webpage.

Acknowledgments: We thank Seth Falcon, Rafael Irizarry, Benilton Carvalho, and Giovanni Parmigiani for their help on software issues and the manuscript. RBS was supported by the training grant 5T32ES012871 from the U.S. National Institute of Environmental Health Sciences (P.I. Thomas Louis) and grant DMS034211 from the National Science Foundation (P.I. Giovanni Parmigiani). JP was supported by NIH grants R01 HD046598 and MRDDRC HD24061. IR was supported by NIH grant CA074841.