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Mutations in the DNA MMR genes MSH2, MLH1, MSH6 and PMS2 underlie a large subset of HNPCC cases, and a hallmark of the tumors is MSI. In many HNPCC families, however, a causative mutation has not been found. Therefore, the involvement of additional, thus far unknown, genes in MSI as well as MSS colorectal tumor predisposition is possible. The role of a relatively recently cloned MMR gene, MLH3, in familial CRC has been studied; but the results appear somewhat conflicting. To further evaluate the role of MLH3 in CRC predisposition, we analyzed 30 Finnish CRC cases for germline mutations by sequencing. These cases were selected from a large series of Finnish CRC patients, to match features previously proposed to associate with MLH3 germline defects. We found 5 missense variants, 4 of which were also found in Finnish cancer-free controls. The only remaining variant does not appear to be an attractive candidate for a disease-associated mutation because the amino acid change is located outside the conserved residues. We also screened for the previously reported variants, including a frameshift change, the most likely pathogenic MLH3 mutation observed so far. The frameshift was not present in the 30 CRC cases or in 700 cancer-free controls. While it is a difficult task to exclude a role of MLH3 in HNPCC, our study could not confirm a role for MLH3 in CRC predisposition. © 2003 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Motivation: Single nucleic polymorphisms (SNPs) are one of the most abundant genetic variations in the human genome. Recently, several platforms for high-throughput SNP analysis have become available, capable of measuring thousands of SNPs across the genome. Tools for analysing and visualizing these large genetic data sets in biologically relevant manner are rare. This hinders effective use of the SNP-array data in research on complex diseases, such as cancer. Results: We describe a computational framework to analyse and visualize SNP-array data, and link the results in relevant databases. Our major objective is to develop methods for identifying DNA regions that likely harbour recessive mutations. Thus, the algorithms are designed to have high sensitivity and the identified regions are ranked using a scoring algorithm. We have also developed annotation tools that automatically query gene IDs, exon counts, microarray probe IDs, etc. In our case study, we apply the methods for identifying candidate regions for recessively inherited colorectal cancer predisposition and suggest directions for wet-lab experiments. Availability: R-package implementation is available at http://www.ltdk.helsinki.fi/sysbio/csb/downloads/CohortComparator/ Contact: sampsa.hautaniemi@helsinki.fi Supplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]