An integrative transethnic approach to identify novel functional genes in Parkinson's disease using in silico and in vivo experiments

到目前为止，对帕金森病的GWAS结果的解释一直是非常具有挑战性的。现在公认的是，在帕金森病和其他复杂疾病中起功能作用的绝大多数变体可能是非编码和调节的。因此，实际涉及的功能基因在很大程度上仍然是未知的。这项研究包括计算机实验和活体实验，目的是剖析帕金森病的遗传病因。在电子分析方面，我们的研究小组在这一领域取得了重要进展，使用遗传图谱有效地整合了疾病部位的高分辨率电子数据来推断功能。我们使用的多标记作图方法基于具有以加性连锁不平衡(LDU)单位(LDU)表示的距离的群体特定遗传图谱，获得精确因果位置的重复估计。这些地图是为收集全球气候变化数据的同一人群绘制的。因此，测绘位置分析直接考虑了该人群特有的LD模式。我们的工作证明，位于LDU图谱上的复制疾病相关基因座可以有效地与表达数据以及特定的调控注释相结合，以帮助定位潜在的功能遗传变异并识别这些基因座扰动的基因。我们的建议是将相同的计算方法应用于基因组和转录组数据的分析，以促进与PD相关的功能相关基因和途径的识别。最有希望的疾病转导基因和相应的相关功能基因将使用两个成熟的果蝇帕金森病模型进行筛选和验证。我们的建议提供了一个独特的机会，既可以用于电子作图，也可以用于体内功能实验，以获得对帕金森病遗传学的更深入了解。帕金森病易感基因的发现和功能的阐明为发现帕金森病新的治疗靶点和治疗策略提供了广阔的前景。

Interpretation of GWAS results for PD have to date been very challenging. It is now well established that the vast majority of variants with a functional role in PD and other complex diseases are likely to be non-coding and regulatory. As a result, the actual implicated functional genes remain largely undefined. This study encompasses both in silico and in vivo experiments with the aim of dissecting the genetic aetiology of PD. With regards to in silico analyses, our research group has made important progress in this field by using genetic maps to effectively integrate high-resolution in silico data at disease loci to infer function. The multi-marker mapping method we use obtains replicated estimates for precise causal locations based on population-specific genetic maps that have distances expressed in additive Linkage Disequilibrium (LD) Units (LDU maps). These maps are constructed for the same population from which the GWAS data was collected. Thus the mapping location analysis takes directly into account the patterns of LD that are specific to that population. Our work demonstrates that replicated disease-associated loci located on LDU maps can be effectively combined with expression data, as well as specific regulatory annotation to help localise the potential functional genetic variants and identify the genes that the loci perturb. Our proposal is to apply the same computational methods to the analysis of genomic and transcriptomic data to advance the identification of the functionally implicated genes and pathways related to PD. The most promising disease transethnic loci and corresponding implicated functional genes will be screened and validated using two well-established Drosophila models of PD. Our proposal provides the unique opportunity to use both in silico mapping and follow up with in vivo functional experiments in order to obtain greater insights into the genetics of PD. The identification and functional elucidation of PD susceptibility genes holds great promise for the discovery of new therapeutic targets and treatment strategies in PD.