Genome-wide association study of the white matter lesion volume in patients with multiple sclerosis

多发性硬化症患者白质病变体积的全基因组关联研究

• 批准号: 513308106
• 负责人: Dr. Christiane Gasperi
• 金额: --
• 依托单位: Klinikum rechts der Isar der Technischen Universität München (MRI)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/513308106?language=en
• 关键词: Genome; wide; association; study; white

More than 230 genetic risk factors for multiple sclerosis (MS) have been identified over the last decades. At the same time, knowledge on how genetics shape the disease course and severity of MS is lacking. The disease course of MS is highly variable. While some patients experience benign, almost subclinical courses, others suffer from a highly active disease with frequent relapses and rapid disability progression. A number of different immunomodulatory therapies have been approved for the treatment of MS with different modes of action, efficacy and side effects. Currently, no reliable biomarker for the prediction of the individual disease course of patients with MS is available, which prevents an early stratification of patients regarding the risk for an aggressive disease course and thereby the identification of patients in need of highly effective treatments early in the disease course. A well-established marker of MS severity and progression is the number and volume of white-matter lesions. While genetic MS risk factors might influence the presentation of the disease in the central nervous system, the extent of demyelination and neurodegeneration in the individual patient is likely also shaped by genetic variants not influencing disease susceptibility. Identifying genetic variants influencing the development and persistence of these lesions can contribute to the development of prediction models for the patients’ disease course and stimulate the development of new therapeutic options. To date, no genome-wide association study (GWAS) on the white-matter lesion volume has been published. Likely, a reason for this lack of studies is that large samples of MS patients with available magnetic resonance imaging (MRI) and genotype data are required for such a project. Therefore, we propose to generate the most extensive existing dataset for genetic studies of white-matter lesions in patients with MS by genotyping patients from several European MS centers, leading to a dataset consisting of more than 6600 patients. Using these data, we plan to conduct multicentric GWAS meta-analyses with the following aims: First, identify common genetic variants associated with the white-matter lesion volume in patients with MS. Second, functionally annotate these variants and identify the genes influencing white-matter lesions. Third, analyze whether genetic variants associated with the white-matter lesion volume also affect other disease severity phenotypes like brain atrophy and disability scores. Fourth, generating a large, harmonized, multi-centric dataset that will stimulate and facilitate future research projects. Thereby, the planned project holds strong potential for the discovery of genes influencing MS disease severity. Knowledge of these genes will be the first step towards personalized prediction models of the MS disease course and novel MS-specific drugs. These innovations would ultimately improve the quality of life of patients with MS.

在过去的几十年中，已经确定了230多个多发性硬化症（MS）的遗传风险因素。与此同时，缺乏关于遗传学如何塑造MS的病程和严重程度的知识。MS的病程变化很大。虽然有些患者经历良性的，几乎亚临床的过程，其他人患有高度活跃的疾病，经常复发和快速残疾进展。许多不同的免疫调节疗法已被批准用于治疗MS，具有不同的作用模式、疗效和副作用。目前，没有可靠的生物标志物可用于预测MS患者的个体病程，这阻止了患者关于侵袭性病程风险的早期分层，从而阻止了在病程早期识别需要高效治疗的患者。 MS严重程度和进展的公认标志物是白质病变的数量和体积。虽然遗传性MS风险因素可能会影响中枢神经系统疾病的表现，但个体患者的脱髓鞘和神经变性程度也可能由不影响疾病易感性的遗传变异形成。识别影响这些病变的发展和持续性的遗传变异有助于开发患者病程的预测模型，并刺激新治疗方案的开发。迄今为止，尚未发表关于白质病变体积的全基因组关联研究（GWAS）。很可能，缺乏研究的一个原因是这样的项目需要大量具有可用磁共振成像（MRI）和基因型数据的MS患者样本。因此，我们建议通过对来自几个欧洲MS中心的患者进行基因分型来生成用于MS患者白质病变遗传研究的最广泛的现有数据集，从而产生由超过6600名患者组成的数据集。利用这些数据，我们计划进行多中心GWAS荟萃分析，其目的如下：首先，确定与MS患者白质病变体积相关的常见遗传变异。其次，对这些变异进行功能注释，并确定影响白质病变的基因。第三，分析与白质病变体积相关的遗传变异是否也影响其他疾病严重程度表型，如脑萎缩和残疾评分。第四，生成一个大型的、协调的、多中心的数据集，以激励和促进未来的研究项目。因此，计划中的项目具有发现影响MS疾病严重程度的基因的强大潜力。这些基因的知识将是迈向MS病程和新型MS特异性药物的个性化预测模型的第一步。这些创新将最终改善MS患者的生活质量。