Genomic approaches for dissecting regulatory variation

点击翻译按钮获取中文摘要

Project Summary / Abstract Much of the risk for common human disease arises from genetic variation among individuals. Research in my laboratory is focused on regulatory genetic variation. This type of variation causes differences in gene expression among individuals and contributes a substantial portion of the genetic risk for human diseases including cardiovascular, autoimmune, and neurological disease. In spite of its critical importance for human disease genetics, fundamental questions about regulatory variation remain unanswered. Because of low statistical power due to the small sample sizes commonly used when mapping regulatory variation in the genome, the source of most regulatory variation remains unknown. The regulatory loci that have been mapped typically span dozens of sequence variants, and we neither know the identity of the actual causal variants, nor the molecular mechanisms through which they alter expression. Nearly all work on regulatory variation is focused on mRNA levels, and it is unclear to what extent it translates to protein levels and cell biology. Finally, our knowledge of how the environment can modulate the effects of regulatory variation remains severely limited. This proposal outlines a research strategy to tackle these critical questions. We plan to combine methods from quantitative and statistical genetics with experimental approaches that leverage emerging techniques for reading, writing, and editing genomes. We will use powerful methods in the yeast Saccharomyces cerevisiae to reveal principles of regulatory variation that are shared among eukaryotes, and that are challenging to address in other species including humans. Our goals for the next five years are to dissect the influence of regulatory variation on the molecular cascade from DNA to mRNA, proteins and cellular fitness. We plan to comprehensively identify causal regulatory variants, understand genetic influences on mRNA and protein levels with high statistical power and across different environments, and evaluate the effects of regulatory variation on cellular fitness. Our long term vision is to turn yeast into the first eukaryotic species in which we can accurately predict the consequences of natural genetic variation. The lessons to emerge from this work are expected to be highly valuable for interpreting the role of regulatory variation in human health.