Defining the functional variation underlying atrial fibrillation risk

项目总结： 房颤(房颤)是最常见的心律失常，影响着300多万美国人。房颤 增加住院、中风、痴呆症、心力衰竭和死亡的风险，并将主要 医疗系统的负担。房颤，特别是在年轻人身上，是由体内的异位刺激引起的 肺静脉向左心房传播；然而，潜在的许多其他方面 房颤的发病机制仍未解决。虽然已经确定了许多临床风险因素，但近年来 我们已经认识到房颤是可遗传的。 这种遗传性的一部分是通过共同的遗传变异来实现的。使用，全基因组 联合研究(GWAS)、我们的团队和其他人已经确定了26个独立的房颤遗传位点。这些 基因座可以为了解疾病的机制提供一个独特的、经常是意想不到的窗口；然而，与 在其他GWAS研究中，主要的挑战是从一种关联转变为一种特定的疾病机制。 至少有三个主要挑战使人们很难将基因关联与特定的 疾病机制。首先，Gwas基因座通常位于基因密集的区域，因此很难识别哪些 基因与疾病有关。其次，绝大多数Gwas基因座都是非编码的 基因组的区域。疾病风险变异体被认为改变了调节元件的活性， 以细胞特有的方式调节附近的基因表达。最后，这些GWAS基因座可能很大，也可能 要从一个基因座的许多常见变异中识别出功能变异是一项挑战。 考虑到这些挑战，真正的致病变异和感兴趣的基因对大多数GWA来说都是未知的 精神错乱。这一知识差距极大地阻碍了利用Gwas数据实现翻译潜力的能力 对这些基因座上的基因功能进行建模将是一种推测。相反，这些问题并不是AF所独有的 不仅涉及其他心血管疾病和特征的类似研究，而且还涉及数以千计的 在过去十年中发现的全谱人类疾病和特征中的GWA基因座。 当前提案的重点是解决这些关于AF GWA型基因座的知识差距，最终 将新的见解应用于其他心血管疾病基因座的目标。鉴于MOST的基因组定位 信号，这一建议是在一个总体假设下进行的，即房颤的非编码变异- 关联基因座改变了成人左心房的基因表达，最终导致房颤易感性。我们 建议利用基于基因组测序的关联数据和我们的左房组织存储库 表观遗传学定义和功能变异发现。然后我们将使用相同的基因信息来 确定每个关联基因座的基因靶点。

Project Summary: Atrial fibrillation (AF) is the most common cardiac arrhythmia, which affects over 3 million Americans. AF confers an increased risk of hospitalization, stroke, dementia, heart failure and death and places a major burden on the healthcare system. AF, particularly in younger individuals, arises from the ectopic stimuli within the pulmonary veins with propagation to left atrium; however, many other aspects of the underlying mechanisms for AF remain unresolved. Although many clinical risk factors have been identified, in recent years we have come to appreciate that AF is heritable. A proportion of this heritability is conferred through common genetic variation. Using, genome-wide association studies (GWAS), our group and others have identified 26 independent genetic loci for AF. These loci can provide a unique and often unexpected window into the mechanisms of disease; however, as with other GWAS studies, a primary challenge is moving from an association to a specific disease mechanism. There are at least three major challenges that make it difficult to link a genetic association to a specific disease mechanism. First, GWAS loci often reside in gene-dense regions, making it difficult to identify which gene is associated with disease. Second, the overwhelming majority of GWAS loci reside in non-coding regions of the genome. The disease risk variants are presumed to alter the activity of regulatory elements that modulate nearby gene expression in a cell-specific manner. Finally, these GWAS loci can be large, and it can be challenge to identify the functional variant from among the many common variants at a locus. Given these challenges, the truly causative variants and the gene of interest are unknown for most GWAS loci. This knowledge gap greatly impedes the ability to leverage GWAS data for translational potential, as any modeling of gene function at these loci would be speculative. These issues are not exclusive to AF, instead pertaining not only to similar studies across other cardiovascular diseases and traits, but also the thousands of GWAS loci in the full spectrum of human diseases and traits uncovered in the past decade. The focus of the current proposal is to address these knowledge gaps for AF GWAS loci, with the ultimate goal of applying new insights to other cardiovascular disease loci. Given the genomic localization of most GWAS signals, this proposal operates under the overarching hypothesis that non-coding variation at AF- association loci alters gene expression in adult human left atrium, ultimately leading to AF susceptibility. We propose to leverage the genome sequencing based association data and our left atrial tissue repository for epigenetic definition and functional variant discovery. We will then use the same genotype information to identify the gene targets of each association locus.