Functionally relevant mapping of human GWAS SNPs on model organisms

项目摘要 来自全基因组关联研究的大部分性状/疾病相关基因座 (Gwas)是内含子或基因间的。阐明致病变种的主要障碍 对于特定的人类特征/疾病来说，缺乏对非编码功能的理解 基因组。虽然在分析工具方面取得了重大进展，这些工具利用 Gwas和大规模表观基因组资源以阐明细胞/组织类型和 表观基因组学事件与Gwas基因座相关，比较基因组学方法通过 鼠标工程方法严重缺乏。这是一个明显的障碍 利用大规模和强大的模式生物eQTL和QTL研究 因为来自多样性的杂交小鼠了解人类潜在的机制 疾病。目前在人类和模式生物基因组之间移动的实践 仅涉及基于序列相似性的映射。然而，这种方法会导致 60%-70%的SNP没有映射，有相当一部分映射到多个 地点。该项目通过开发一种 生物学上相关的和统计上严格的方法，liftSNP，超越了 序列相似性，并结合了表观基因组和更高阶调节语法 用于绘制人类GWASSNPs的图谱，以模拟生物体基因组。LiftSNP将是 来自三个不同疾病系统的GWASSNPs的开发和评估 (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公布 搜索，并将使研究人员能够解除他们的GWASNP含有基因组基因座 以一种功能相关的方式与小鼠基因组进行比较。这些目标一定会实现的 通过方法论开发、理论分析、数据驱动相结合 模拟、计算分析和实验验证。统计资源 该项目产生的数据将作为开放源码软件分发。总而言之， 这些目标将大大增强我们对GWAs的比较基因组学解释 结果。

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.