Functionally relevant mapping of human GWAS SNPs on model organisms

人类 GWAS SNP 在模式生物上的功能相关图谱

• 批准号: 10056966
• 负责人: Sunduz Keles
• 金额: $ 40.05万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-06 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10056966
• 关键词: Address; Animal Model; Binding Sites; Biological; CRISPR/Cas technology; Cells; ChIP-seq; Characteristics; Chromatin; Collaborations; Computer Analysis; Computer software; Data; Development; Disease; Engineering; Event; Exhibits; Genes; Genome; Genome engineering; Genomics; Hematology; Human; Human Genome; Joints; Lifting; Ligation; Link; Location; Maps; Mediating; Metabolic syndrome; Methodology; Methods; Modeling; Molecular; Mus; Neurologic; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Pattern; Process; Publications; Quantitative Trait Loci; Regulatory Element; Research Personnel; Resources; Single Nucleotide Polymorphism; Statistical Models; System; Time; Tissues; Untranslated RNA; Validation; Variant; analytical tool; autism spectrum disorder; base; cell type; chromosome conformation capture; clinical phenotype; comparative; comparative genomics; epigenome; epigenomics; experimental study; follow-up; genome editing; genome wide association study; genomic locus; human disease; human model; innovation; molecular phenotype; mouse genome; mouse model; novel; open source; promoter; risk variant; simulation; trait; transcription factor; transcriptome; Address; Animal Model; Binding Sites; Biological; CRISPR/Cas technology; Cells; ChIP-seq; Characteristics; Chromatin; Collaborations; Computer Analysis; Computer software; Data; Development; Disease; Engineering; Event; Exhibits; Genes; Genome; Genome engineering; Genomics; Hematology; Human; Human Genome; Joints; Lifting; Ligation; Link; Location; Maps; Mediating; Metabolic syndrome; Methodology; Methods; Modeling; Molecular; Mus; Neurologic; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Pattern; Process; Publications; Quantitative Trait Loci; Regulatory Element; Research Personnel; Resources; Single Nucleotide Polymorphism; Statistical Models; System; Time; Tissues; Untranslated RNA; Validation; Variant; analytical tool; autism spectrum disorder; base; cell type; chromosome conformation capture; clinical phenotype; comparative; comparative genomics; epigenome; epigenomics; experimental study; follow-up; genome editing; genome wide association study; genomic locus; human disease; human model; innovation; molecular phenotype; mouse genome; mouse model; novel; open source; promoter; risk variant; simulation; trait; transcription factor; transcriptome

Project Summary A large fraction of trait/disease-associated loci from genome-wide association studies (GWAS) is intronic or intergenic. A major barrier to elucidating the variants responsible for a given human trait/disease is the lack of understanding of the function of noncoding genome. While there have been major developments in analytical tools that exploit GWAS and large-scale epigenome resources to elucidate cell/tissue types and epigenomic events relevant for the GWAS loci, comparative genomics methods through mouse engineering approaches are critically lacking. This is a clear hindrance for leveraging large-scale and well-powered model organism eQTL and QTL studies such as the ones from diversity outbred mice to understand mechanisms underlying human diseases. Current practice of moving between human and model organism genomes solely pertains a sequence similarity-based mapping. However, this approach leads to 60-70% of the SNPs not mapping, and a significant fraction mapping to multiple locations. This project addresses key difficulties towards this end by developing a biologically relevant and statistically rigorous method, liftSNP, that goes beyond sequence similarity and incorporates epigenome and higher order regulatory grammar into mapping of human GWAS SNPs to model organism genomes. liftSNP will be developed and evaluated on GWAS SNPs from three diverse disease systems (hematologic/developmental; obesity, metabolic syndrome, T2D; neurological/autism). The results of these large-scale applications will be made available through atSNP Search and will enable researchers to lift over their GWAS SNP harboring genomic loci to mouse genome in a functionally relevant manner. The aims will be accomplished through a combination of methodological development, theoretical analysis, data-driven simulation, computational analysis, and experimental validation. Statistical resources generated from this project will be disseminated as open-source software. Collectively, these aims will significantly enhance our comparative genomics interpretation of GWAS results.

项目摘要 来自基因组关联研究的大量特质/疾病相关的基因座 （GWAS）是内含子或间基因的。阐明负责变体的主要障碍 对于给定的人类性状/疾病是对非编码功能的缺乏理解 基因组。虽然分析工具的主要发展是利用的 GWA和大规模的表观基因组资源，以阐明细胞/组织类型和 与GWAS基因座相关的表观基因组事件，通过比较基因组学方法 小鼠工程方法严重缺乏。这是一个明显的障碍 利用大规模且功能强大的模型有机体EQTL和QTL研究此类研究 作为来自多样性小鼠的人，以了解人类的机制 疾病。当前在人类和模型生物基因组之间移动的做法 仅与基于序列相似性的映射有关。但是，这种方法导致 60-70％的SNP未映射，并大量映射到多个 位置。该项目通过开发一个问题解决了这一目标的关键困难 生物学相关和统计上严格的方法，Liftsnp，超出了 序列相似性，并结合了表观基因组和高阶调节语法 将人类GWAS SNP映射到建模生物基因组中。 Liftsnp将是 对来自三种不同疾病系统的GWAS SNP进行了开发和评估 （血液学/发育；肥胖，代谢综合征，T2D；神经/自闭症）。 这些大规模应用的结果将通过ATSNP提供 搜索并将使研究人员能够抬起其GWAS SNP携带基因组基因局的SNP 以功能相关的方式到小鼠基因组。目标将实现 通过方法论发展，理论分析，数据驱动的结合 仿真，计算分析和实验验证。统计资源 该项目生成的将作为开源软件传播。共同 这些目标将大大增强我们对GWA的比较基因组学解释 结果。