Natural variation in gene expression is extensive in humans and other organisms, and variation in the baseline expression level of many genes has a heritable component. To localize the genetic determinants of these quantitative traits (expression phenotypes) in humans, we used microarrays to measure gene expression levels and performed genome-wide linkage analysis for expression levels of 3,554 genes in 14 large families. For approximately 1,000 expression phenotypes, there was significant evidence of linkage to specific chromosomal regions. Both cis- and trans-acting loci regulate variation in the expression levels of genes, although most act in trans. Many gene expression phenotypes are influenced by several genetic determinants. Furthermore, we found hotspots of transcriptional regulation where significant evidence of linkage for several expression phenotypes (up to 31) coincides, and expression levels of many genes that share the same regulatory region are significantly correlated. The combination of microarray techniques for phenotyping and linkage analysis for quantitative traits allows the genetic mapping of determinants that contribute to variation in human gene expression.

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The results suggest that knowledge of mutations in BAP1 and EIF1AX can enhance prognostication of UM beyond that determined by chromosome 3 and tumor characteristics. Tumors with chromosome 3 disomy/BAP1-WT/EIF1AX-WT have a 10-fold increased risk of metastasis at 48 months compared with disomy-3/BAP1-WT/EIF1AX mutant tumors.

The present analysis suggests that a PCOS susceptibility locus maps very close to D19S884. Additional studies that systematically characterize DNA sequence variation in the immediate area of D19S884 are required to identify the PCOS susceptibility variant.

In UM, tumor size and location, tissue source, and sex were all significantly associated with increased metastasis. In addition, chromosome 3-loss and 8p-loss were found to be independent predictors of poor metastatic outcome and CNA signatures were identified that can add a specific HR value for classification of risk categories.