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.

项目概要 虽然我们可以很容易地确定患者的基因型，但我们通常无法准确预测他们的风险 疾病或确定许多具有不确定意义的变体中的哪一个可能是病理学的基础。 医学相关的表型可能是由数千个复杂的多态性组合产生的。 重要的是，由于与其他变异的相互作用，遗传变异的影响在个体之间并不恒定 该项目旨在建立对基因组和环境的基本了解。 变异会产生复杂的特征及其原因。 为此，我们将在芽殖酵母酿酒酵母中开发一个独特的模型系统，其中 我们已经鉴定出数千种决定复杂性状的核苷酸。 可能影响基因表达的变体，尽管许多同义变体通常被认为是 “沉默”，对表型做出重大贡献，扭转典型的功能基因组学范式。 检查已知因果变异的分子后果，以确定造成它们的特征 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。 表型的分子多样性线性贡献者，我们将利用强大的遗传图谱面板 （其中包含比分离多态性更多的个体）开始剖析功能基础 复杂性状中的基因与环境相互作用和遗传背景效应。 为了绘制这个具有重要功能的遗传变异图谱，我们将实现以下具体目标： 1. 定义功能同义变异的分子影响 2. 识别功能性监管变体的特征 3. 构建综合基因型到分子到表型图谱 数量性状固有的复杂性是一个令人畏惧的问题，随着 使用模型系统在患者群体中不断增加具有不确定意义的变异。 可以全面绘制基因型与表型关系，这是一种强有力的方法 理解并建立哪些变异可能是因果关系的预测模型，确实将变化联系起来。 DNA 中的分子后果及其对细胞表型的影响是一个核心挑战 我们的方法将有望对从未见过的突变进行功能分类。 有助于理解这些关系的基本结构，对基因组阅读和 医学和生物技术方面的写作。