Epistasis is a growing area of research in genome-wide studies, but the differences between alternative definitions of epistasis remain a source of confusion for many researchers. One problem is that models for epistasis are presented in a number of formats, some of which have difficult-to-interpret parameters. In addition, the relation between the different models is rarely explained. Existing software for testing epistatic interactions between single-nucleotide polymorphisms (SNPs) does not provide the flexibility to compare the available model parameterizations. For that reason we have developed an R package for investigating epistatic and penetrance models, Epi2Loc, to aid users who wish to easily compare, interpret, and utilize models for two-locus epistatic interactions. Epi2Loc facilitates research on SNP–SNP interactions by allowing the R user to easily convert between common parametric forms for two-locus interactions, generate data for simulation studies, and perform power analyses for the selected model with a continuous or dichotomous phenotype. The usefulness of the package for model interpretation and power analysis is illustrated using data on rheumatoid arthritis.

Epistasis is a growing area of research in genome-wide studies, but the differences between alternative definitions of epistasis remain a source of confusion for many researchers. One problem is that models for epistasis are presented in a number of formats, some of which have difficult-to-interpret parameters. In addition, the relation between the different models is rarely explained. Existing software for testing epistatic interactions between single-nucleotide polymorphisms (SNPs) does not provide the flexibility to compare the available model parameterizations. For that reason we have developed an R package for investigating epistatic and penetrance models, EpiPen, to aid users who wish to easily compare, interpret, and utilize models for two-locus epistatic interactions. EpiPen facilitates research on SNP-SNP interactions by allowing the R user to easily convert between common parametric forms for two-locus interactions, generate data for simulation studies, and perform power analyses for the selected model with a continuous or dichotomous phenotype. The usefulness of the package for model interpretation and power analysis is illustrated using data on rheumatoid arthritis.

Many important advances have been made in the past decade in understanding breast cancer at the molecular level, and two important high-penetrance breast cancer genes – BRCA1 and BRCA2 – have been identified. However, germline mutations in these two genes are responsible for only a minority (~5%) of all breast carcinomas, and the genes responsible for the majority of breast cancer cases remain to be identified. There is evidence that there are additional high-to-moderate-penetrance breast cancer susceptibility genes but, given the high degree of molecular heterogeneity in breast carcinomas, it is likely that each of these genes is responsible for only a subset of cases. There are also many candidate low-penetrance breast cancer genes and many more are likely to be identified. In addition to germline, and somatic, sequence alterations, epigenetic changes in many genes are likely to play an important role in the pathobiology of breast cancer. Recently developed genomic technologies and the completion of the human genome sequence provide us with powerful tools to identify novel candidate breast cancer genes that could play an important role in breast tumourigenesis.