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

GWAS 后方法可识别 MS 风险背后的细胞特异性遗传途径

基本信息

批准号：
9116321
负责人：
SERGIO E BARANZINI
金额：
$ 41.05万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-15 至 2019-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9116321
关键词：
6p21.3 Accounting Address Affect Alleles Anterior B-Lymphocytes Benign Bioinformatics Biological Brain CD8B1 gene Candidate Disease Gene Cells Central Nervous System Diseases Chromosome Mapping Chronic Clinical Code Communities Complex Computer Simulation Custom DNA Data Data Analyses Data Set Databases Development Disabled Persons Disease Disease susceptibility Elements Enrollment Future Gene Expression Genes Genetic Genetic Epistasis Genetic Heterogeneity Genetic Polymorphism Genetic Predisposition to Disease Genetic Transcription Genets Genotype Gliosis Goals HLA-DRB1 Health Heritability Image Individual Inflammation Inflammatory International Interruption Knowledge Laboratories Link Linkage Disequilibrium Magnetic Resonance Imaging Major Histocompatibility Complex Maps Measures Meta-Analysis Multiple Sclerosis Myelin Nature Neuraxis Neurologic Neurologic Dysfunctions Oligodendroglia Optical Coherence Tomography Participant Pathogenesis Pathology Pathway interactions Phenotype Play Population Predisposition Proteins Regulatory Element Relapse Reporting Resolution Retina Risk Scheme Signal Transduction Source Stratification Susceptibility Gene Thick Trauma Update Variant Visual Pathways Work base cohort combinatorial disability disorder risk disorder subtype endophenotype exhaust exome falls genetic variant genome wide association study genome-wide genotyped patients gray matter handicapping condition imaging genetics innovation interest macula monocyte multiple sclerosis patient neuron loss novel therapeutics personalized therapeutic quantitative imaging research clinical testing retinal nerve fiber layer risk variant trait transcriptome sequencing young adult

项目摘要

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.

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，其特征为慢性炎症、髓磷脂缺失、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因素。尽管经过长期的努力，多发性硬化症遗传学知识仍然不完整，重要的遗传学问题仍未得到解答。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会影响疾病轨迹。该应用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的结合，从而有效地连接干实验室和湿实验室，以增进我们对 MS 中发挥作用的途径的了解。该提案的统一基本原理是通过整合来自中间水平的信息，在功能上将 DNA 多态性（即位于生物复杂性层次结构的底部）与更高层（例如蛋白质网络、成像内表型）联系起来。我们提出以下目标：具体目标 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异功能替代的假设，我们将把最新的基于 SNP 的 MS 数据集与 DNA（调控）元件 (ENCODE) 百科全书上的可用信息整合起来，额外的数据库，以努力识别强大的 eQTL 并缩小候选致病变异名册的范围。我们将通过 RNAseq 在从具有极端疾病轨迹的 MS 患者中分离出的 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，以识别富含明确定义的极端表型（例如侵袭性与良性疾病）的基因网络。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素并将它们以组合方式与可用的细胞特异性基因表达数据集集成。该目标的最终目标是完善 MS 遗传学图谱并确定 MS 遗传异质性的来源。具体目标 3. 多发性硬化症中的基因型-MRI 表型关联。我们将检查正在进行的前瞻性确定的纵向队列（n > 500）深度表型患者的基因型-表型关联。我们将研究这个独特的队列，以确定全基因组的遗传变异是否与通过脑部 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。具体来说，将通过灰质体积变化的全局和区域测量来估计与复发相关的活动和脑神经元损失。我们还将前瞻性地使用 OCT 纵向测量视网膜神经纤维层的厚度和黄斑体积，作为疾病活动的替代指标。