Functional characterization of regulatory sequence variants in complex diseases

复杂疾病中调控序列变异的功能表征

• 批准号: 8897418
• 负责人: MICHAEL OLIVIER
• 金额: $ 67.27万
• 依托单位: TEXAS BIOMEDICAL RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-24 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8897418
• 关键词: Affect; Alleles; Antibodies; Bayesian Analysis; Bayesian Modeling; Binding; Binding Proteins; Biological Assay; Cardiovascular Diseases; Cataloging; Catalogs; Cell Line; Cells; Chromatin; Cohort Analysis; Cohort Studies; Complex; Computer Simulation; DNA Resequencing; DNA-Protein Interaction; Data; Development; Diagnosis; Disease; Disease Progression; Disease susceptibility; Electrophoretic Mobility Shift Assay; Exons; Extended Family; Family; Gene Expression; Gene Frequency; Genes; Genetic; Genetic study; Genome; Genomics; Genotype; Health; Heart; Heritability; High Density Lipoprotein Cholesterol; Human; Human Genome; In Vitro; Individual; Lipids; Mass Spectrum Analysis; Mediating; Methodology; Methods; Modeling; Molecular Profiling; Nuclear Proteins; Nucleosomes; Obesity; Participant; Patients; Plasma; Promoter Regions; Protein Binding; Proteins; Quantitative Trait Loci; Resources; Sequence Analysis; Site; Technology; Transcript; Untranslated RNA; Up-Regulation; Validation; Variant; Work; base; clinically relevant; cohort; crosslink; ds-DNA; genetic linkage analysis; genome sequencing; genome wide association study; high throughput screening; high throughput technology; human disease; human study; improved; in vivo; next generation sequencing; novel; promoter; rare variant; tool; trait

DESCRIPTION (provided by applicant): This proposal focuses on the development and optimization of three complementary technologies that will improve our ability to efficiently characterize sequence variants uncovered in ongoing re-sequencing efforts of patients with both single gene and complex disorders. While the technologies are widely applicable to human disease studies and associated variants, our proof-of-principle analysis will focus on the analysis of putative regulatory variants located in non-coding regions of the genome. Specifically, we will analyze sequence variants that are associated with changes in expression of individual adjacent genes (cis-expression quantitative trait loci, cis-eQTL) and with lipid-related traits in families of the San Antonio Family Heart Study (SAFHS). It is clear that the identification and characterization of functional regulatory sequence variants affecting gene expression related to human diseases requires the development and application of novel analysis tools that allow the characterization of protein binding both in vitro and in vivo. We propose to use the resources of the SAFHS which include complete whole genome sequence (WGS) data, eQTL information and disease association, and cell lines available from all participants, to develop three independent complementary technologies for the functional characterization of regulatory variants influencing lipid variation: In vitro Technology: We will develop high-throughput dsDNA arrays for in vitro analysis of allele-specific protein-DNA interactions, and evaluate 1000 variants associated with plasma lipid traits and individual gene expression levels (cis-eQTL) with p<5x10-7 in the SAFHS cohort. Representative variants will be validated by EMSA, and binding proteins will be identified using mass spectrometry. In silico Technology: We will apply Bayesian analysis approaches incorporating empirically derived (i.e., cis- acting effect sizes, lipid-associated effect sizes, allele frequency, etc.) and bioinformaticaly derived (i.e., regulatory potential, nucleosome accessibility, etc.) features to develop computational prediction tools to statistically predict likely functional regulatory variants from WGS data. In vivo Technology: We will develop an analysis approach to validate allele-specific protein binding to regulatory variants directly in cells. We will use cell lines from the SAFHS cohort of known genotype for putative functional variants influencing gene expression to isolate target regions of cross linked chromatin using hybridization capture, and confirm allele-specific protein binding. In addition to the development of this novel in vitro, in silico, and in vivo technologies that can be utilized to characterize both common and rare variation in the human genome, our proposed work will identify and validate regulatory variants associated with lipid traits in humans. This will provide a unique resource for GWAS and other studies dissecting the genetic basis of human diseases.

描述（由申请人提供）：该提案侧重于开发和优化三种互补技术，这些技术将提高我们有效表征单基因和复杂疾病患者正在进行的重新测序工作中发现的序列变体的能力。虽然这些技术广泛适用于人类疾病研究和相关变体，但我们的原理验证分析将侧重于分析位于基因组非编码区的推定调控变体。具体来说，我们将分析序列变异，与个别相邻基因（顺式表达数量性状位点，顺式eQTL）的表达变化和与脂质相关的性状在家庭的圣安东尼奥家庭心脏研究（SAFHS）。很明显，影响与人类疾病相关的基因表达的功能性调控序列变体的鉴定和表征需要开发和应用新的分析工具，其允许在体外和体内表征蛋白质结合。我们建议使用SAFHS的资源，包括完整的全基因组序列（WGS）数据，eQTL信息和疾病关联，以及所有参与者提供的细胞系，开发三种独立的互补技术，用于影响脂质变异的调节变体的功能表征：体外技术：我们将开发高通量dsDNA阵列，用于体外分析等位基因特异性蛋白质-DNA相互作用，并在SAFHS组群中评估1000个与血浆脂质性状和个体基因表达水平相关的变异（cis-eQTL），p<5x 10 -7。将通过EMSA验证代表性变体，并使用质谱法鉴定结合蛋白。计算机技术：我们将应用贝叶斯分析方法，结合经验推导（即，顺式作用效应量、脂质相关效应量、等位基因频率等）和生物信息学衍生的（即，调节潜力、核小体可及性等）开发计算预测工具，从WGS数据中统计预测可能的功能调控变体。体内技术：我们将开发一种分析方法来验证等位基因特异性蛋白质直接在细胞中与调控变体的结合。我们将使用来自SAFHS队列的已知基因型的细胞系，用于影响基因表达的推定功能变体，以使用杂交捕获分离交联染色质的靶区域，并确认等位基因特异性蛋白结合。除了开发这种新的体外，计算机和体内技术，可用于表征人类基因组中常见和罕见的变异，我们提出的工作将确定和验证与人类脂质性状相关的调节变体。这将为GWAS和其他剖析人类疾病遗传基础的研究提供独特的资源。