Mechanisms of Action of Natural Genetic Variation

自然遗传变异的作用机制

• 批准号: 10587460
• 负责人: Daniel Jarosz
• 金额: $ 38.32万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-09 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10587460
• 关键词: Affinity; Alleles; Amino Acid Sequence; Atlases; Biological; Biological Models; Biology; Biotechnology; Catalogs; Cell model; Cellular biology; Chromosome Mapping; Classification; Collection; Complex; DNA; DNA-Directed RNA Polymerase; Data; Diagnosis; Disease; Environment; Etiology; Foundations; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Models; Genetic Polymorphism; Genetic Variation; Genome; Genomic approach; Genomics; Genotype; Goals; Haplotypes; Human Genome; Individual; Intelligence; Intuition; Knowledge; Link; Maps; Measurement; Medical; Medicine; Messenger RNA; Missense Mutation; Modeling; Molecular; Mutation; Nucleosomes; Nucleotides; Open Reading Frames; Pathology; Patients; Phenotype; Play; Positioning Attribute; Property; Proteins; Proteomics; RNA; RNA-Binding Proteins; Reading; Resolution; Resources; Role; Saccharomyces cerevisiae; Saccharomycetales; Shapes; Structure; System; Technology; Testing; Time; Tissues; Training; Transcript; Transcription Initiation; Translations; Validation; Variant; Writing; causal variant; cell type; disorder risk; experimental study; functional genomics; gene environment interaction; genetic architecture; genetic manipulation; genetic variant; genomic data; genotyped patients; histone modification; human disease; insight; model organism; patient population; personalized medicine; precision medicine; predictive modeling; recruit; segregation; tool; trait; transcriptomics; ultra high resolution; variant of unknown significance

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.

项目摘要 虽然我们可以很容易地确定患者的基因型，但我们往往不能准确地预测他们的风险， 疾病或确定许多不确定意义的变体中的哪一个可能是病理学的基础。的确， 医学上相关的表型可以从数千种多态性的组合中出现。复杂化 重要的是，由于与其他变体的相互作用，遗传变体的影响在个体之间并不恒定 在基因组和环境中。该项目旨在建立一个基本的理解， 变异导致了复杂的性状以及原因。 为此，我们将在芽殖酵母酿酒酵母中开发一种独特的模型系统，其中 我们已经鉴定出数千种决定复杂性状的核苷酸。其中包括监管 可能影响基因表达的变异和许多同义变异，尽管通常被认为是 “沉默"对表型有实质性的贡献。逆转典型的功能基因组学范式，我们将 检查已知致病变异的分子后果，以确定产生这些变异的特征。 对复杂性状的重要性。我们将重点关注确定功能测量的预测能力（例如 如核小体位置、组蛋白修饰、基因表达水平和蛋白质丰度）作为指导。 将这些技术应用于患者和组织特异性基因组学。除了检查这些 分子多样性的线性贡献者表型，我们将利用一个强大的遗传作图面板 （其中包含更多的个人比分离多态性），开始解剖的功能基础， 复杂性状的基因环境互作和遗传背景效应。 为了绘制这一功能重要的遗传变异图谱，我们将实现以下具体目标： 1.定义功能性同义变体的分子影响 2.识别功能性调节变体的特征 3.构建基因型-分子-表型整合图谱 数量性状的内在复杂性是一个令人生畏的问题，随着 患者群体中不确定重要性的变异的不断增长的目录。使用模型系统， 基因型与表型的关系可以被全面地定位，这是一种强有力的方法， 理解和建立预测模型，哪些变体可能是因果关系。事实上， 在DNA中，它们的分子后果和它们对细胞表型的影响是一个核心挑战， 遗传学有望对以前从未见过的突变进行功能分类。我们的方法将 有助于理解这些关系的基本结构，对基因组阅读和 撰写医学和生物技术方面的文章。