Mutant alleles of dominant, highly penetrant breast cancer genes, including BRCA1 and BRCA2, do not occur frequently, and hence account for only a small proportion of breast cancer cases. On the other hand, several studies have suggested an association between low-penetrant alleles and breast cancer risk. Although the contribution of low-penetrant alleles to the individual breast cancer risk is relatively small, they can contribute to a large proportion of breast cancer cases in the population because the risk-conferring alleles of these genes are common. Candidate gene approach is one of the most logical and practical strategies to identify these risk-enhancing, low-penetrant variants. Until now, a major obstacle in investigating the risk associated with multiple candidate genes has been a lack of technology for large-scale genotyping of large populations. Consequently, many studies have focused efforts on only one or two genetic polymorphisms, and even in these cases the analysis was only limited to relatively small sample sizes. Microarray technology is a solution to this obstacle. We plan to exploit the high-throughput power of microarrays to simultaneously genotype 32 different genetic polymorphisms derived from 26 genes in a well-defined, representative population-based sample containing a large number of subjects. We have selected genetic polymorphisms in genes functioning in biochemical/biological pathways frequently perturbed in cancers, those genetic polymorphisms in genes encoding components of the carcinogen metabolic pathways and the immune response pathways. We have access to the Ontario Familial Breast Cancer Registry (OFBCR), which is the largest population-based breast cancer registry in Canada. We also have support from the established microarray facility of the Ontario Cancer Institute in Toronto. The objective of the proposed study is to identify low-penetrant, yet commonly occurring, genetic polymorphisms which contribute to the risk of developing breast cancer. The establishment of this approach will prepare us for large-scale genotyping involving hundreds or even thousands of candidate genes in large defined populations. This will lead to a more complex analysis of gene-gene and gene-environment interactions than is currently possible. Advances in disease aetiology will significantly expand our abilities to design strategies for the prevention of breast cancer development and progression.