GWAS TO GENE FUNCTION: NOS1AP AND OTHER QT INTERVAL GENES

描述（由申请人提供）：心电图QT间期（一种影响长QT综合征和心源性猝死风险的中间性状）的全基因组关联研究（GWAS）已在35个位点确定了68个独立变异，解释了8%的表型（20%的加性）变异。然而，潜在的DNA序列变体和基因的身份，功能和作用机制仍然未知，并且是理解这种复杂表型的分子结构和功能架构的主要障碍。我们假设，大多数功能性状变异是多态性的，非编码和干扰转录的特定基因通过改变其顺式调控元件的功能。我们提出了一个研究范式，系统地确定这些非编码性状变异，它们破坏的调控功能和特定的基因，其功能在每个数量性状位点发生改变。我们将利用整合的遗传学、计算、分子遗传学和细胞方法来阐明潜在的机制，使用QT间期作为模型“系统”。我们的具体目标是：（1）进行GWAS信号的高分辨率作图，以鉴定调节QT间期的基因座处的所有多态性（>1%）和罕见变体;（2）进行计算机模拟和体外分析，以预测和优先考虑所有心脏调节（增强子、沉默子、绝缘子）元件及其同源DNA结合蛋白;以及（3）鉴定性状变体、基因及其遗传作用机制。总体目标是提高分子遗传学和机制的理解，多因子性状的应用，以其他复杂的表型。

DESCRIPTION (provided by applicant): Genome-wide association studies (GWAS) of the electrographic QT-interval, an intermediate trait that impacts the risks of long QT syndrome and sudden cardiac death, have identified 68 independent variants at 35 loci, explaining 8% of the phenotypic (20% of the additive) variance. Nevertheless, the identity, function and mechanisms of action of the underlying DNA sequence variants and genes remain unknown, and are major impediments for understanding the molecular structure and functional architecture of this complex phenotype. We hypothesize that the majority of functional trait variants are polymorphic, non-coding and perturb transcription of a specific gene by altering the functions of their cis-regulatory elements. We propose a research paradigm for systematically identifying these non-coding trait variants, the regulatory functions they disrupt and the specific genes whose functions are altered at each quantitative trait locus. We will utilize integrative statisticl genetic, computational, molecular genetics and cellular approaches for elucidating the underlying mechanisms, using QT interval as a model 'system'. Our specific aims are: (1) to perform high- resolution mapping of GWAS signals to identify all polymorphic (>1%) and rare variants at loci that modulate the QT-interval; (2) to conduct in silico and in vitro analysis to predict and prioritize all cardiac regulatory (enhancer, silencer, insulator) elements and their cognate DNA-binding proteins; and, (3) to identify trait variants, genes and their mechanisms of genetic action. The overall goals are to improve the molecular genetic and mechanistic understanding of multifactorial traits for applications to other complex phenotypes.