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

描述（由申请人提供）：明确已知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关键作用;然而，这一知识尚未对PD患者产生可行的翻译影响。除了已知的罕见因果突变外，最近还通过全基因组关联研究（GWAS）确定了与PD风险相关的常见变异。与罕见的编码突变不同，这些变异只显示出适度的影响，并且不太可能代表足够的因果因素，因为它们在一般人群中的频率很高。这表明这些SNCA风险变异需要额外的遗传或环境因素的存在才能启动疾病的因果机制。最近GWAS创建的丰富的可用SNP基因分型提供了必要的资源，以彻底检查SNCA和其他基因之间的潜在相互作用。然而，这些类型的相互作用研究都是时间和计算密集型的，以及容易受到不确定的结果，由于低功率。有证据表明，复杂的疾病，如PD，是由多个功能相关基因或分子网络的破坏引起的，这是一种有前途的新策略，以避免这些陷阱。将分析限制在具有功能连接性的基因上，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。我们建议使用一个全面的加权功能连锁网络（FLN），我们的相互作用研究的基础上，已知的物理或生化相互作用或功能相似性的基因优先级。使用这些已知的关系，我们将分析4个现有的PD GWAS，共计5396例病例和8796例对照，以确定PD风险SNP（rs356220和rs356198，最近PD meta-GWAS中发现的两个独立相关的SNCA SNP）与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组相比的发现的富集来评估FLN方法的有效性。我们还将测试将不同的截止值或过滤器应用于te FLN的实用性，以确定这些数据的最佳用途。最后，我们将通过对PD病例（N = 1163）和对照（N = 974）的独立样本中10个最强相关的相互作用SNP进行基因分型，完成最强相互作用结果的复制。与SNCA相互作用的SNP、基因和途径的鉴定具有直接的翻译意义，这是由于其作为治疗靶点的潜力，可以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PD提供了一个强有力的方法，以进一步了解PD的潜在疾病病理。该项目有可能确定重要的遗传关系，并立即解释功能连接，这可能是解决SNCA变体在特发性PD病例中的作用以及最终开发阻止疾病进程的治疗策略的关键。

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.