Combining genetic and functional genomic data to reveal underlying mechanisms of human disease

结合遗传和功能基因组数据揭示人类疾病的潜在机制

• 批准号: 2261544
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2261544
• 关键词: Combining; genetic; functional; genomic; data

The unprecedented access to genome-wide association (GWAS) signals for a wide variety of human diseases opens up new opportunities to improve understanding of the underlying mechanisms of disease and identify new therapies.Previous work in the Baillie lab has demonstrated that many biological pathways can be detected from expression patterns in high-resolution transcriptomic data. They interrogated shared activity patterns arising from regulatory regions containing variants associated with inflammatory bowel disease, where re-analysis of two large GWAS studies revealed two distinct groups of variants associated with both Crohn's disease and ulcerative colitis. This discovery may indicate two distinct mechanisms underlying each disease. Alternatively, it may indicate the existence of two distinct endotypes of each condition. It is at least plausible, that patients with a preponderance of 'immune' or 'epithelial' genetic variants will respond differently to immunomodulatory therapies. The project will develop the computational and statistical tools to detect and validate mechanistic relationships between diseases for which GWAS data are available (340 diseases at the time of writing). This will form several distinct stages which will overlap in time during the course of the project:1. Detection of mechanistic pathways. Optimisation of coexpression methodology for high-performance computing and incorporation of data from different sources including GTEx, Roadmap Epigenetics and ENCODE. 2. Development of methodology for evaluation of disease-disease interactions, including linkage disequilibrium score regression, genomic correlation and coexpression analysis.3. Application to existing and ongoing GWAS studies. Re-analyses of published and ongoing GWAS studies, including UK biobank, will be performed to detect distinct biological pathways underlying clinical phenotypes. Candidates will be chosen for further validation, in large population studies or clinical trials where genotyping data are available. Biological validation of specific mechanistic hypotheses will be performed in genome-editing experiments collaboration with wet-lab scientists in the Baillie lab (myeloid cells, endothelial cells) and others (hepatocytes, epithelial cells, iPSC-derived primary cells).

前所未有的全基因组关联（GWAS）信号对各种人类疾病的访问开辟了新的机会，以提高对疾病的潜在机制的理解，并确定新的治疗方法。Baillie实验室以前的工作已经证明，许多生物学途径可以从高分辨率转录组数据的表达模式中检测出来。他们询问了由含有与炎症性肠病相关的变体的调节区域产生的共享活动模式，其中对两项大型GWAS研究的重新分析揭示了与克罗恩病和溃疡性结肠炎相关的两组不同的变体。这一发现可能表明每种疾病的两种不同机制。或者，它可能表明存在两个不同的内型的每一个条件。这至少是合理的，具有优势的“免疫”或“上皮”遗传变异的患者将对免疫调节治疗产生不同的反应。该项目将开发计算和统计工具，以检测和验证GWAS数据可用的疾病之间的机制关系（在撰写本文时有340种疾病）。这将形成几个不同的阶段，这些阶段在项目过程中会在时间上重叠：1。检测机械路径。优化用于高性能计算的共表达方法，并整合来自不同来源的数据，包括GTEx，Roadmap Epigenetics和ENCODE。2.发展疾病间相互作用的评价方法，包括连锁不平衡评分回归、基因组相关和共表达分析.应用于现有和正在进行的GWAS研究。将对已发表和正在进行的GWAS研究（包括英国生物样本库）进行重新分析，以检测临床表型的不同生物学途径。将选择候选人进行进一步验证，在大人群研究或临床试验中，基因分型数据可用。特定机制假设的生物学验证将在与Baillie实验室（骨髓细胞，内皮细胞）和其他（肝细胞，上皮细胞，iPSC衍生的原代细胞）的湿实验室科学家合作的基因组编辑实验中进行。