Statistical Methods to Study the Genetic Basis and Mechanisms of Trans Gene Regulation

项目摘要 全基因组关联研究（GWAS）确定了数千个与各种疾病相关的遗传基因座。 复杂的性状和疾病。尽管如此，破译大多数GWAS变异与疾病的联系 仍然是非常具有挑战性的，因为大多数这些变异是非编码的。非编码变异可以 影响物理上邻近（顺式）或物理上远离（反式）的基因的调节。安装 有证据表明，反式遗传调节在基因表达的控制中起主导作用， 疾病风险。因此，迫切需要高质量的trans-eQTL和trans-regulatory网络，以充分 了解疾病相关变异如何通过基因网络影响致病基因和途径。 然而，迄今为止，大多数研究仅仅集中在研究顺式遗传调控， 难以检测trans-QTL。因此，缺乏高质量的trans-eQTL图谱代表了一个重要的问题。 我们对疾病机制的理解存在差距。在这项资助中，我建议通过开发 强大的统计方法，以产生高质量的，全面的反式QTL图谱在多个人类 组织和细胞类型。我们将利用这些图谱来揭示转基因的主要机制 调控最后，我们将开发新的方法来识别疾病基因，使用我们的高质量图谱， trans-eQTL。

Project Summary Genome wide association studies (GWAS) identified thousands of genetic loci associated with a variety of complex traits and diseases. Nonetheless, deciphering how most GWAS variants are linked to diseases remains exceptionally challenging, as the majority of these variants are noncoding. Noncoding variation can affect the regulation of genes that are either physically nearby (in cis), or physically distant (in trans). Mounting evidence suggest that genetic regulation in trans plays a dominant role in the control of gene expression and disease risk. Thus, high-quality trans-eQTL and trans regulatory networks are critically needed to fully understand how disease-associated variants flow through gene networks to affect causal genes and pathways. However, most studies to date have solely focused on studying genetic regulation in cis owing to the extreme difficulty in detecting trans-QTLs. Therefore, the lack of high-quality trans-eQTL maps represents a significant gap in our understanding of disease mechanisms. In this grant, I propose to address this gap by developing powerful statistical methods to produce high-quality, comprehensive maps of trans-QTLs in multiple human tissues and cell types. We will use these maps to uncover major mechanisms that underlie trans genetic regulation. Finally, we will develop novel methods to identify disease genes using our high-quality maps of trans-eQTLs.