We performed a genome scan at an average resolution of 8 cM in 719 Finnish sib pairs with type 2 diabetes. Our strongest results are for chromosome 20, where we observe a weighted maximum LOD score (MLS) of 2.15 at map position 69.5 cM from pter and secondary weighted LOD-score peaks of 2.04 at 56.5 cM and 1.99 at 17.5 cM. Our next largest MLS is for chromosome 11 (MLS = 1.75 at 84.0 cM), followed by chromosomes 2 (MLS = 0.87 at 5.5 cM), 10 (MLS = 0.77 at 75.0 cM), and 6 (MLS = 0.61 at 112.5 cM), all under an additive model. When we condition on chromosome 2 at 8.5 cM, the MLS for chromosome 20 increases to 5.50 at 69.0 cM (P=.0014). An ordered-subsets analysis based on families with high or low diabetes-related quantitative traits yielded results that support the possible existence of disease-predisposing genes on chromosomes 6 and 10. Genomewide linkage-disequilibrium analysis using microsatellite marker data revealed strong evidence of association for D22S423 (P=.00007). Further analyses are being carried out to confirm and to refine the location of these putative diabetes-predisposing genes.

Cells deficient in a major DNA double-strand break repair pathway (nonhomologous DNA end joining [NHEJ]) have increased spontaneous chromosome breaks; however, the source of these chromosome breaks has remained undefined. Here, we show that the observed spontaneous chromosome breaks are partially suppressed by reducing the cellular oxygen tension. Conversely, elevating the level of reactive oxygen species by overexpressing the antioxidant enzyme superoxide dismutase 1 (SOD1), in a transgenic mouse, increases chromosome breakage. The effect of SOD1 can also be modulated by cellular oxygen tension. The elevated chromosome breakage correlates histologically with a significant increase in the amount of neuronal cell death in Ku86(-/-) SOD1 transgenic embryos over that seen in Ku86(-/-) embryos. Therefore, oxygen metabolism is a major source of the genomic instability observed in NHEJ-deficient cells and, presumably, in all cells.

Background-New markers to distinguish benign reactive glands from infiltrating ductal adenocarcinoma of the pancreas are needed.

Endonuclease G (endo G) is one of the most abundant nucleases in eukaryotic cells. It is encoded in the nucleus and imported to the mitochondrial intermembrane space. This nuclease is active on single-and double-stranded DNA. We genetically disrupted the endo G gene in mice without disturbing a conserved, overlapping gene of unknown function that is oriented tail to tail with the endo G gene. In these mice, the production of endo G protein is not detected, and the disruption abolishes the nuclease activity of endo G. The absence of endo G has no effect on mitochondrial DNA copy number, structure, or mutation rate over the first five generations. There is also no obvious effect on nuclear DNA degradation in standard apoptosis assays. The endo G null mice are viable and show no age-related or generational abnormalities anatomically or histologically. We infer that this highly conserved protein has no mitochondrial or apoptosis function that can discerned by the assays described here and that it may have a function yet to be determined. The early embryonic lethality of endo G null mice recently reported by others may be due to the disruption of the gene that overlaps the endo G gene.

Large-scale genotyping is required to generate dense identity-by-descent maps to map genes for human complex disease. In some studies the number of genotypes needed can approach or even exceed I million. Generally, linkage and linkage disequilibrium analyses depend on clear allele identification and subsequent allele frequency estimation. Accurate grouping or categorization of each allele in the sample (allele calling or binning) is therefore an absolute requirement. Hence, a genotyping system that can reliably achieve this is necessary. In the case of affected sib-pair analysis without parents, the need for accurate allele calling is even more critical. We describe methods that permit precise sizing of alleles across multiple gels using the fluorescence-based, Applied Biosystems (ABI) genotyping technology and discuss ways to reduce genotyping error rates. Using database utilities, we show how to minimize intergel allele size variation, to combine data effectively from different models of ABI sequencing machines, and automatically bin alleles. The final data can then be converted into a format ready for analysis by statistical genetic packages such as MENDEL.