Post GWAS approach to identify cell-specific genetic pathways underlying MS risk

描述（由申请人提供）：多发性硬化症（MS）是一种常见且严重的中枢神经系统疾病，其特征是慢性炎症、髓磷脂丢失、神经胶质瘤、不同程度的轴突和少突胶质细胞病理以及进行性神经功能障碍。多发性硬化症的发病机制包括一个复杂的遗传因素。尽管经过了长期的努力，但对MS遗传学的了解仍然不完整，重要的遗传问题仍然没有得到解答。特别是，我们计划解决易感性变异如何影响疾病易感性以及变异是否也影响疾病轨迹。该应用程序主要是假设和数据驱动的，并将采用先进的生物信息学和实验方法相结合，从而有效地连接干实验室和湿实验室，以提高我们对ms中起作用的途径的认识。该建议的统一原理是将DNA多态性（即生物复杂性层次组织的底部）与更高层次(例如蛋白质网络，影像内表型)通过整合来自中间水平的信息。我们提出以下目标：多发性硬化细胞特异性eqtl。基于等位基因特异性RNA表达水平可以作为风险变异功能的替代品的假设，我们将整合最新的基于snp的MS数据集与DNA（调控）元件百科全书（ENCODE）和其他数据库上的信息，以努力识别稳健的eqtl并缩小候选致病变异名册。我们将通过从具有极端疾病轨迹的MS患者中分离的4个相关细胞群（CD4, CD8， B细胞和单核细胞）中的RNAseq来研究它们与MS的功能相关性。具体目标2。基因x基因相互作用的计算机功能分析。在这里，我们建议扩展我们早期的工作，并开发有限的分层方案，以确定在明确定义的极端表型（例如侵袭性与良性疾病）中丰富的基因网络。我们将遵循一种计算策略，考虑到风险等位基因是否属于功能元素

DESCRIPTION (provided by applicant): Multiple sclerosis (MS) is a common and severe disorder of the central nervous system characterized by chronic inflammation, myelin loss, gliosis, varying degrees of axonal and oligodendrocyte pathology, and progressive neurological dysfunction. MS pathogenesis includes a complex genetic component. In spite of intensive long-standing efforts, the knowledge of MS genetics remains incomplete and significant genetic questions still remain unanswered. In particular, we plan to address how susceptibility variants influence disease susceptibility and whether variants also influence disease trajectory. This application is primarily hypothesis and data-driven, and will employ a combination of advanced bioinformatics and experimental approaches thus bridging efficiently the dry and wet laboratories to advance our knowledge on the pathways at play in MS. The unifying rationale of this proposal is to functionally link DNA polymorphisms (i.e. at the bottom of the hierarchical organization of biological complexity) with higher strata (e.g. protein networks, imaging endophenotypes) through the integration of information from intermediate levels. We propose these goals: Specific Aim 1. Cell specific eQTLs in multiple sclerosis. Building on the hypothesis that allele-specific RNA expression levels can act as surrogate of the functionality of the risk variants, we will integrate the most updated SNP-based MS dataset with information available on the encyclopedia of DNA (regulatory) elements (ENCODE) and additional databases in an effort to identify robust eQTLs and narrow the roster candidate causative variants. We will study their functional relevance for MS by RNAseq in 4 relevant cell populations (CD4, CD8, B cells and monocytes) isolated from MS patients with extreme disease trajectories. Specific Aim 2. In-silico functional analysis of gene x gene interactions. Here we propose to extend our earlier work and develop limited stratification schemes to identify gene networks enriched in well-defined extreme phenotypes (e.g. aggressive vs. benign disease). We will follow a computational strategy that takes into account whether risk alleles fall within functional elements and integrates them in a combinatorial fashion with available cell-specific gene expression datasets. The ultimate goal of this aim is to refine the MS genetics map and identify the source of genetic heterogeneity in MS. Specific Aim 3. Genotype-MRI phenotype associations in multiple sclerosis. We will examine an ongoing prospectively ascertained longitudinal cohort (n > 500) of deeply phenotyped patients for genotype-phenotype associations. This unique cohort will be studied to determine whether genetic variants genome-wide correlate with direct and indirect metrics of disease activity and progression as determined by brain MRI and optical coherence tomography (OCT). Specifically, relapse-related activity and brain neuronal loss will be estimated by global and regional measures of grey matter volume changes. We will also use OCT prospectively to measure thickness of the retinal nerve fiber layer and macular volume longitudinally as surrogates of disease activity.