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

描述(申请人提供)：多发性硬化症(MS)是一种常见和严重的中枢神经系统疾病，其特征是慢性炎症、髓鞘丢失、胶质增生、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。MS的发病机制包括复杂的遗传成分。尽管进行了长期密集的努力，但对多发性硬化遗传学的知识仍然不完整，重大的遗传学问题仍然没有得到回答。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。这一应用主要是假说和数据驱动的，并将使用先进的生物信息学和实验方法的组合，从而有效地在干燥和潮湿实验室之间架起桥梁，以促进我们对MS中所起作用的途径的了解。这一提议的统一的基本原理是通过整合来自中间水平的信息，在功能上将DNA多态(即，在生物复杂性的分层组织的底部)与更高的层次(例如，蛋白质网络、成像内表型)联系起来。我们提出了这些目标：特异性目标1.多发性硬化症中的细胞特异性eQTL。基于等位基因特异性RNA表达水平可以作为风险变量功能的替代这一假设，我们将整合最新的基于SNP的MS数据集与DNA(调节)元素百科全书(ENCODE)和其他数据库上的信息，以努力识别强大的eQTL并缩小候选致病变量名单。我们将通过RNAseq从具有极端疾病轨迹的MS患者分离的4个相关细胞群(CD4、CD8、B细胞和单核细胞)中研究它们与MS的功能相关性。具体目的2.基因x基因互作的电子计算机功能分析。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来识别富含明确定义的极端表型(例如，侵袭性疾病与良性疾病)的基因网络。我们将遵循一种计算策略，该策略考虑风险等位基因是否属于功能元素 并以组合的方式将它们与可用的细胞特异性基因表达数据集相结合。这一目标的最终目标是完善多发性硬化症的遗传学图谱，并确定多发性硬化症遗传异质性的来源。我们将检查正在进行的、前瞻性地确定的深度表型患者的纵向队列(n&gt；500)，以寻找基因-表型之间的关联。将对这一独特的队列进行研究，以确定全基因组的遗传变异是否与脑MRI和光学相干断层扫描(OCT)确定的疾病活动和进展的直接和间接指标相关。具体地说，与复发相关的活动和脑神经元丢失将通过全球和区域灰质体积变化的测量来估计。我们还将前瞻性地使用OCT测量视网膜神经纤维层的厚度和黄斑体积作为疾病活动性的替代指标。

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.