Expression quantitative trait loci (eQTL) mapping studies have become a widely-used tool for identifying genetic variants that affect gene regulation. In these studies, gene expression levels are viewed as quantitative traits, and expression phenotypes are mapped to particular genomic loci by combining estimates of gene expression levels across individuals with genome-wide genotyping data. Recent eQTL studies in humans, as well as in other species, have revealed substantial variation in gene expression levels within and between populations, and identified a large number of genetic factors that influence gene regulation. However, to date, all eQTL mapping studies, regardless of species, have considered variation in steady-state gene expression levels and thus could not determine the exact regulatory mechanism underlying the eQTL association. In particular, typical eQTL studies, as well as most other genome-wide studies of gene expression phenotypes, do not collect data that will allow one to distinguish between variation in transcriptional regulation and variation in RNA decay rates. In general, perhaps because transcription initiation rates are commonly assumed to be the major determinants of overall gene expression levels, RNA decay mechanisms are under-studied compared with the regulation of transcription. As a result, we know relatively little about variation in RNA decay rates across genes or between individuals, and the potential importance of such variation in determining ultimate physiological phenotypes such as human disease remains unclear. To address this issue, we propose to perform a genome-wide eQTL mapping study of RNA decay rates in HapMap lymphoblastoid cell lines (LCLs) for which genome-wide steady- state gene-expression data and genotype data are also available. The data we propose to collect will allow us to identify genetic factors underlying variation in RNA decay rates as well as to study the relative importance of variation in RNA decay rates to overall regulatory variation in gene expression levels.
We propose to use a combination of genomic approaches to map QTLs that affect variation in RNA decay rates in order to increase our understanding of regulatory mechanisms underlying variation in overall gene expression levels.
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