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（MS）是一种常见且严重的中枢神经系统疾病，其特征为慢性炎症、髓鞘丢失、神经胶质增生、不同程度的轴突和少突胶质细胞病理学以及进行性神经功能障碍。MS发病机制包括复杂的遗传成分。尽管经过长期的努力，MS遗传学的知识仍然不完整，重要的遗传学问题仍然没有答案。特别是，我们计划解决易感性变体如何影响疾病易感性以及变体是否也影响疾病轨迹。这个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。通过整合来自中间水平的信息，将生物学复杂性的分层组织（即，在生物学复杂性的分层组织的底部）与更高的层（例如，蛋白质网络、成像内表型）相关联。我们提出这些目标：具体目标1。多发性硬化症的细胞特异性eQTL基于等位基因特异性RNA表达水平可以作为风险变体功能的替代的假设，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，以确定稳健的eQTL并缩小候选致病变体的范围。我们将通过RNAseq在从具有极端疾病轨迹的MS患者分离的4个相关细胞群（CD 4、CD 8、B细胞和单核细胞）中研究它们与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，以确定富含明确定义的极端表型的基因网络（例如侵袭性与良性疾病）。我们将遵循一种计算策略，考虑风险等位基因是否属于功能元件 并以组合方式将它们与可用的细胞特异性基因表达数据集整合。这一目标的最终目标是完善MS遗传图谱，并确定MS遗传异质性的来源。具体目标3。多发性硬化的基因型-MRI表型相关性我们将研究一个正在进行的前瞻性确定的纵向队列（n > 500）的深度表型患者的基因型-表型关联。将对这一独特的队列进行研究，以确定全基因组遗传变异是否与通过脑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。具体而言，将通过灰质体积变化的全局和区域测量来估计复发相关活动和脑神经元损失。我们还将使用OCT前瞻性地测量视网膜神经纤维层厚度和黄斑体积纵向作为疾病活动的替代物。

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.