Computational identification of cancer susceptibility loci.

Abstract

The identification of novel cancer susceptibility syndromes and genes from very limited numbers of study individuals has become feasible through the use of high-throughput genotype microarrays. With such an approach, highly sensitive genome-wide computational methods are needed to identify the regions of interest. We have developed novel methods to identify and compare homozygous and compound heterozygous regions between cases and controls, to facilitate the identification of recessively inherited cancer susceptibility loci. As our approach is optimized for sensitivity, it creates many hits that may be unrelated to the phenotype of interest. We compensate for this compromised specificity by the automated use of additional sources of biological information along with a ranking function to focus on the most relevant regions. The methods are demonstrated here by comparing colorectal cancer patients to controls.