Determining the Molecular Chain of Causality Through Which Genetic Variants Affect Physiology

确定遗传变异影响生理的因果关系分子链

• 批准号: 10558458
• 负责人: Stefan Zdraljevic
• 金额: $ 7.43万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10558458
• 关键词: Address; Affect; Autoimmune Diseases; Biological Assay; Biological Models; Biomedical Research; CRISPR/Cas technology; Caenorhabditis elegans; Cardiovascular Diseases; Cells; Chromosome Mapping; Communities; Complex; Data; Data Set; Diagnosis; Disease; Etiology; Event; Evolution; Exhibits; Gene Expression; Genes; Genetic; Genetic Risk; Genetic Variation; Genomic Segment; Goals; Growth; Health; Human; Individual; Intercistronic Region; Laboratory Organism; Life Cycle Stages; Link; Maps; Mediating; Methods; Molecular; Nematoda; Nucleic Acid Regulatory Sequences; Pathway interactions; Phenotype; Physiological; Physiology; Population; Procedures; Proteins; Quantitative Trait Loci; RNA; Recombinants; Research; Role; Sample Size; Source; Specificity; Surveys; System; Techniques; Tissue-Specific Gene Expression; Tissues; Transcript; Variant; Whole Organism; Work; Yeasts; candidate validation; causal variant; cell preparation; cohort; experimental study; fitness; gene expression variation; gene regulatory network; genetic variant; genome editing; genome wide association study; genome-wide analysis; human disease; improved; insight; molecular array; nervous system disorder; risk variant; segregation; single-cell RNA sequencing; tool; trait; whole genome

Project Summary A central goal of biomedical research is to decipher the genetic basis of complex traits. Though genome-wide association studies (GWAS) have successfully detected thousands of variants that are associated with complex cardiovascular, autoimmune, and neurological diseases, the molecular mechanisms are only known for a very limited subset of these risk-associated variants. A mechanistic understanding of risk-associated variants is difficult to ascertain because a vast majority of variants identified by GWAS are located in regulatory regions, which suggests that gene expression variation contributes a substantial portion of the genetic risk for complex human diseases. However, statistical power to map gene expression variation to genetic variants is often limited by the small sample sizes used in such mapping studies. Our ability to characterize the molecular chain of causality that links genetic variants to complex physiological traits is further limited because evidence is mounting that regulatory variants often manifest their disease-associated effects in specific cell and tissue types. Because of these limitations, I will leverage genetic diversity in the nematode Caenorhabditis elegans to achieve the statistical power necessary to precisely quantify the cell- and tissue-specific effects that genetic variants have on gene expression and physiology. We have recently developed a technique to identify genetic variants that affect cell- and tissue- specific gene expression (expression quantitative trait loci or eQTL) in experimental C. elegans crosses. This approach takes advantage of the short life cycle of C. elegans, the ability to easily generate hundreds of thousands of recombinant individuals, and well-established methods to prepare cells for single-cell RNA sequencing to associate single-cell transcript abundance with genetic variation segregating in experimental crosses. By combining this single-cell eQTL mapping approach with experimental evolution, I have identified several genomic regions that affect organismal fitness and tissue-specific gene expression variation of tens to hundreds of genes. In Aim 1, I will extend the scope of these initial experiments to survey the effects of a wide-range of C. elegans natural genetic variation on organismal fitness and cell- and tissue- specific gene expression. In Aim 2, I will use the vast molecular and genetic toolkit available in C. elegans to determine if the same underlying variants affect both tissue-specific gene expression and fitness. The completion of these aims will 1) characterize the cell- and tissue-specific phenotypic effects of hundreds of thousands of genetic variants; 2) determine if tissue-specific expression differences can affect organismal physiology; and 3) provide a mechanistic understanding of how genetic variation mediates its effect on organismal physiology. Together, these insights will facilitate the interpretation of how regulatory variation affects human health.

项目摘要 生物医学研究的一个中心目标是破译复杂性状的遗传基础。虽然全基因组 关联研究（GWAS）已经成功地检测到数千种与复杂的 心血管、自身免疫和神经系统疾病，分子机制仅为一个非常 这些风险相关变体的有限子集。对风险相关变异的机械理解是 由于GWAS鉴定的绝大多数变体位于调节区， 这表明基因表达变异是复杂性遗传风险的重要组成部分， 人类疾病。然而，将基因表达变异映射到遗传变异的统计能力通常是有限的 这是因为在这种绘图研究中使用的样本量很小。我们有能力描述 将遗传变异与复杂的生理特征联系起来的因果关系进一步受到限制，因为证据越来越多 调节变体通常在特定细胞和组织类型中表现出其疾病相关效应。因为 这些限制，我将利用线虫线虫的遗传多样性来实现 精确量化遗传变异对细胞和组织特异性影响所需的统计能力 基因表达和生理学的研究。 我们最近开发了一种技术来识别影响细胞和组织特异性的遗传变异， 基因表达（表达数量性状位点或eQTL）在实验C。优雅的十字架。这种方法 利用了C语言生命周期短的特点。elegans，能够轻易地产生数十万个 重组个体，以及制备用于单细胞RNA测序的细胞的成熟方法， 将单细胞转录本丰度与实验杂交中的遗传变异分离相关联。通过 将这种单细胞eQTL定位方法与实验进化相结合，我已经确定了几个基因组 影响生物体适应性和数十至数百个基因的组织特异性基因表达变异的区域。 在目标1中，我将扩展这些初始实验的范围，以调查各种C。elegans 生物体适应性和细胞及组织特异性基因表达的自然遗传变异。在目标2中，我将使用 在C.以确定是否相同的潜在变异影响 组织特异性基因表达和适应性。这些目标的完成将1）表征细胞-和 成千上万的遗传变异的组织特异性表型效应; 2）确定组织特异性表型效应是否与组织特异性表型效应相关。 表达差异可以影响生物体的生理学; 3）提供了一个机械的理解，如何 遗传变异介导其对生物体生理的影响。总之，这些见解将有助于 解释监管变化如何影响人类健康。