Network-based genetic interaction studies of SNCA in Parkinson's disease

描述（由申请人提供）：α -突触核蛋白基因SNCA在帕金森病（PD）的发病机制和遗传病因学中具有明确的关键作用；然而，这些知识尚未对PD患者产生可行的转化影响。除了已知的罕见的因果突变外，最近通过全基因组关联研究（GWAS）发现了与帕金森病风险相关的常见变异。与罕见的编码突变不同，这些变异仅显示出适度的影响，并且不太可能单独代表足够的因果因素，因为它们在一般人群中发现的频率很高。这表明这些SNCA风险变异需要存在额外的遗传或环境因素来启动疾病的因果机制。最近的GWAS创造了丰富的可用SNP基因分型，为详尽地检查SNCA和其他基因之间的潜在相互作用提供了必要的资源。然而，这些类型的相互作用研究都是时间和计算密集型的，并且由于低功率而容易产生不确定的结果。有证据表明，复杂的疾病，如帕金森病，是由多个功能相关基因或分子网络的破坏引起的，这是避免这些陷阱的一种有希望的新策略。限制对具有功能连接的基因的分析使我们能够有效地限制分析的数量，同时仍然考虑最有希望的潜在相互作用。我们建议基于已知的物理或生化相互作用或功能相似性，使用综合加权功能链接网络（FLN）来优先考虑基因的相互作用研究。利用这些已知的关系，我们将分析4个现有的PD GWAS，总计5396例病例和8796个对照，以确定PD风险snp （rs356220和rs356198，两个独立相关的SNCA snp，在最近的PD meta-GWAS中发现）和SNCA功能相关基因附近的snp （N=104个来自FLN的常染色体基因）之间的相互作用。我们将通过测试与随机选择的基因集相比发现的丰富性来评估FLN方法的有效性。我们还将测试对FLN应用不同截止值或过滤器的效用，以确定这些数据的最佳用途。最后，我们将在PD病例（N=1163）和对照组（N= 974）的独立样本中，通过对10个最强烈相关的相互作用snp进行基因分型，完成最强相互作用结果的复制。鉴定与SNCA相互作用的SNPs、基因和途径具有直接的翻译意义，因为它有可能作为治疗靶点，最大限度地减少该基因在PD中的毒性作用。解决为什么一些携带SNCA风险等位基因的个体避免帕金森病，为进一步了解帕金森病的潜在疾病病理学提供了一个有力的方法。该项目有潜力确定重要的遗传关系，具有立即可解释的功能联系，这可能是解决SNCA变异在特发性PD病例中的作用的关键，并最终开发出阻止疾病进程的治疗策略。

DESCRIPTION (provided by applicant): The alpha-synuclein gene, SNCA, is unequivocally known to have a key role in the pathogenesis and genetic etiology of Parkinson's disease (PD); however, this knowledge has yet to have a viable translational impact on PD patients. In addition to the known rare causal mutations, common variants related to PD risk have been more recently identified by genome-wide association studies (GWAS). Unlike the rare coding mutations, these variants show only modest effects and are unlikely to represent sufficient causal factors on their own, as they are found at a high frequency in the general population. This suggests that these SNCA risk variants require the presence of additional genetic or environmental factors to initiate the causal mechanism to disease. The abundance of available SNP genotyping created by recent GWAS provides the necessary resource to exhaustively examine potential interactions between SNCA and other genes. However, these types of interaction studies are both time- and computationally-intensive, as well as susceptible to inconclusive findings due to low power. A promising and novel strategy to avoid these pitfalls arises from evidence suggesting that complex diseases, such as PD, result from the disruption of multiple functionally-related genes, or molecular networks. Restricting analyses to genes with functional connectivity allows us to effectively limit the number of analyses, while still considering the most promising potential interactions. We propose to use a comprehensive weighted functional linkage network (FLN) to prioritize genes for our interaction study based on known physical or biochemical interactions or functional similarities. Using these known relationships, we will analyze 4 existing PD GWAS totaling 5396 cases and 8796 controls to identify interactions between PD risk SNPs (rs356220 and rs356198, two independently associated SNCA SNPs identified in recent PD meta-GWAS), and SNPs near genes with functional relationships to SNCA (N=104 autosomal genes from FLN). We will evaluate the effectiveness of the FLN method by testing for enrichment of findings compared to randomly selected sets of genes. We will also test the utility of applying different cutoffs or filters to te FLN to identify optimal uses for these data. Finally, we will complete a replication of the strongest interaction results by genotyping the 10 most strongly associated interacting SNPs in an independent sample of PD cases (N=1163) and controls (N= 974). The identification of SNPs, genes and pathways that interact with SNCA has immediate translational significance due to the potential as therapeutic targets to minimize the toxic effects of this gene in PD. Resolving why some individuals carrying SNCA risk alleles avoid PD provides a powerful approach to furthering our understanding of the underlying disease pathology in PD. This project has the potential to identify important genetic relationships, with immediately interpretable functional connections, which may be key to resolving the role of SNCA variants in idiopathic PD cases and ultimately in the development of therapeutic strategies for halting the disease process.