Mechanisms of Action of Natural Genetic Variation

点击翻译按钮获取中文摘要

PROJECT SUMMARY Although we can readily determine a patient's genotype, we often cannot accurately predict their risk for disease or ascertain which of many variants of uncertain significance might underlie a pathology. Indeed, medically relevant phenotypes may emerge from the combination of thousands of polymorphisms. Complicating matters, the effects of genetic variants are not constant across individuals due to interactions with other variants in the genome and the environment. This project aims to build a fundamental understanding of which genetic variants give rise to complex traits and why. To do so, we will exploit a unique model system in the budding yeast Saccharomyces cerevisiae, in which we have already identified thousands of nucleotides that determine complex traits. These include regulatory variants that likely influence gene expression and many synonymous variants that, although often regarded as 'silent,' make substantial contributions to phenotype. Reversing typical functional genomics paradigms, we will examine the molecular consequences of known causal variants to identify the signatures that make them important to complex traits. We will focus on ascertaining the predictive power of functional measurements (such as nucleosome position, histone modification, gene expression level, and protein abundance) as a guide to the application of these technologies to patient- and tissue-specific genomics. In addition to examining these molecularly diverse linear contributors to phenotype, we will take advantage of a powerful genetic mapping panel (which contains more individuals than segregating polymorphisms) to begin dissecting the functional basis of gene ´ environment interactions and genetic background effects in complex traits. To chart this atlas of functionally important genetic variation, we will undertake the following specific aims: 1. Define the molecular impact of functional synonymous variants 2. Identify signatures of functional regulatory variants 3. Build integrative genotype-to-molecule-to-phenotype maps The inherent complexity of quantitative traits is a daunting problem that grows ever-more challenging with the growing catalog of variants of uncertain significance in the patient population. Using model systems in which the genotype-to-phenotype relationship can be comprehensively mapped is a powerful approach for understanding and building predictive models of which variants are likely to be causal. Indeed, linking changes in DNA both to their molecular consequences and their effects on cellular phenotypes is a central challenge in genetics that promises to allow the functional classification of never-before-seen mutations. Our approach will help to understand the fundamental structure of these relationships, with implications for genome reading and writing in medicine and biotechnology.