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/疾病相关位点 （GWAS）是内含子或基因间的。一个主要的障碍，以阐明变异负责 对于一个给定的人类特征/疾病是缺乏对非编码的功能的理解， 基因组虽然分析工具有了重大发展， GWAS和大规模表观基因组资源，以阐明细胞/组织类型， 与GWAS基因座相关的表观基因组事件，比较基因组学方法， 严重缺乏小鼠工程方法。这是一个明显的障碍， 利用大规模和高效能的模式生物eQTL和QTL研究， 就像那些来自不同品种的远交小鼠， 疾病目前在人类和模式生物基因组之间转移的做法 仅涉及基于序列相似性的映射。然而，这种方法导致 60-70%的SNP没有映射，并且显著部分映射到多个SNP。 地点该项目通过开发一个 生物学相关和统计学上严格的方法，liftSNP，超越了 序列相似性，并结合表观基因组和高阶调控语法 将人类GWAS SNP映射到模型生物基因组中。liftSNP将是 开发并评估了来自三种不同疾病系统的GWAS SNP （血液学/发育;肥胖症、代谢综合征、T2 D;神经学/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解除他们的GWAS SNP携带基因组位点 以功能相关的方式与小鼠基因组相关联。目标一定会实现 通过方法学发展、理论分析、数据驱动 仿真、计算分析和实验验证。统计资源 这一项目产生的信息将作为开放源码软件传播。总的来说， 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.