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

对多种人类疾病的全基因组关联（GWAS）信号的前所未有的获取，为提高对疾病潜在机制的理解和确定新的治疗方法开辟了新的机会。Baillie实验室之前的工作已经证明，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到。他们询问了包含与炎症性肠病相关变异的调节区域产生的共同活动模式，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异。这一发现可能表明每种疾病背后的两种不同机制。或者，它可能表明每种疾病存在两种不同的内型。至少，具有“免疫”或“上皮”遗传变异优势的患者对免疫调节疗法的反应不同是合理的。该项目将开发计算和统计工具，以检测和验证可获得全球疾病系统数据的疾病之间的机制关系（撰写本文时共有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：机制途径的检测。优化共表达方法，用于高性能计算和整合来自不同来源的数据，包括GTEx， Roadmap表观遗传学和ENCODE。2. 2 .发展疾病-疾病相互作用的评估方法，包括连锁不平衡评分回归、基因组相关性和共表达分析。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UK biobank）进行重新分析，以检测临床表型背后的不同生物学途径。在有基因分型数据的大规模人群研究或临床试验中，将选择候选药物进行进一步验证。特定机制假设的生物学验证将在基因组编辑实验中与Baillie实验室的湿实验室科学家合作进行（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）。

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).