No abstract

The determination of a list of differentially expressed genes is a basic objective in many cDNA microarray experiments. We present a statistical approach that allows direct control over the percentage of false positives in such a list and, under certain reasonable assumptions, improves on existing methods with respect to the percentage of false negatives. The method accommodates a wide variety of experimental designs and can simultaneously assess significant differences between multiple types of biological samples. Two interconnected mixed linear models are central to the method and provide a flexible means to properly account for variability both across and within genes. The mixed model also provides a convenient framework for evaluating the statistical power of any particular experimental design and thus enables a researcher to a priori select an appropriate number of replicates. We also suggest some basic graphics for visualizing lists of significant genes. Analyses of published experiments studying human cancer and yeast cells illustrate the results.

Loss of heterozygosity data from familial tumors suggest that BRCA1, a gene that confers susceptibility to ovarian and early-onset breast cancer, encodes a tumor suppressor. The BRCA1 region is also subject to allelic loss in sporadic breast and ovarian cancers, an indication that BRCA1 mutations may occur somatically in these tumors. The BRCA1 coding region was examined for mutations in primary breast and ovarian tumors that show allele loss at the BRCA1 locus. Mutations were detected in 3 of 32 breast and 1 of 12 ovarian carcinomas; all four mutations were germline alterations and occurred in early-onset cancers. These results suggest that mutation of BRCA1 may not be critical in the development of the majority of breast and ovarian cancers that arise in the absence of a mutant germline allele.