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Systematic identification and modeling of rare and common genetic risk factors for sarcoidosis

结节病罕见和常见遗传危险因素的系统识别和建模

基本信息

批准号：
239536609
负责人：
Professor Dr. David Ellinghaus, Ph.D., since 11/2017
金额：
--
依托单位：
Institut für Physiologie
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2013
资助国家：
德国
起止时间：
2012-12-31 至 2019-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/239536609?language=en
关键词：
Systematic identification modeling rare common

项目摘要

In the past years, a reasonable number of common genetic risk variants for sarcoidosis have been discovered by genome-wide association studies, in its majority by the principle investigator of the applied project. However, few is known on the role of these variants in the disease process, and a large proportion of the heritability of sarcoidosis remains to be unexplained. Therefore, the applied project approaches the to-date most comprehensive investigation of the genetic basis of sarcoidosis, including the systemic identification of rare variants and a detailed genetic analysis of the HLA region. In order to translate genetic findings to pathogenic mechanism and to clinical application, a detailed investigation of the functional properties and modeling of associated variants will be applied.The applicant has acquired a multinational sample collection of unprecedented size, which is critical for the identification of rare risk variants. It comprises genome-wide genotype datasets of >4,400 sarcoidosis patients and >10,500 control individuals for variant discovery and DNA samples of >4,600 sarcoidosis patients and >8,800 controls for confirmation of the findings.A strength of this project is the availability of i) the largest genotype dataset of sarcoidosis patients and controls worldwide for screening ii) a unique multi-national collection of sarcoidosis patients and controls for replication and iii) all required technology on-site. This provides an excellent basis to reach the following project aims:-Detection of associations of HLA genotypes and -haplotypes with sarcoidosis -Identification of additional common risk variants for sarcoidosis-Identification of novel rare risk variants for sarcoidosis-Compilation of a sarcoidosis eQTL database -Identification of proteins potentially affected by risk variants-Protein network construction for a refined model of disease pathogenesis-Evaluation of the predictive value of the risk variantsTo achieve these objectives extensive genome-wide genotype datasets will be re-analyzed with a focus on HLA associations and rare variants. In addition, a meta-analysis with an US-American case-control dataset will be performed. Candidate risk variants from these analyses will be subjected to replication in six independent sarcoidosis populations of European origin. In order to bridge the gap between genetic studies and cellular processes, novel and known genetic risk factors will be subjected to detailed in silico analyses, using public available tools as well as a sarcoidosis specific eQTV database, which will be compiled by the applicant. The obtained functional hypotheses will be integrated in a refined model of sarcoidosis pathogenesis based on genetic interaction as well as protein-protein-interaction. These results are expected to provide important insights on the interaction of genetic factors in the disease processes and will further be used for patient classification and risk estimation.

在过去的几年中，通过全基因组关联研究发现了合理数量的结节病常见遗传风险变异，其中大多数是由应用项目的主要研究者发现的。然而，很少有人知道这些变异在疾病过程中的作用，结节病的遗传性的很大一部分仍然是无法解释的。因此，申请项目接近结节病遗传基础的最新最全面的调查，包括罕见变异的系统鉴定和HLA区域的详细遗传分析。为了将遗传学发现转化为致病机制和临床应用，将对相关变异体的功能特性和建模进行详细研究。申请人已获得前所未有规模的多国样本采集，这对于识别罕见风险变异体至关重要。它包括> 4，400名结节病患者和> 10，500名对照个体的全基因组基因型数据集，用于变体发现，以及> 4，600名结节病患者和>8，该项目的优势在于：i）全球范围内最大的结节病患者和对照基因型数据集，ii）独特的多基因型数据集，结节病患者和对照的国家收集用于复制和iii）现场所有需要的技术。这为实现以下项目目标提供了良好的基础：-检测HLA基因型和单体型与结节病的关联-鉴定结节病的其他常见风险变体-鉴定结节病的新型罕见风险变体-汇编结节病eQTL数据库-鉴定可能受风险变体影响的蛋白质-构建疾病发病机制的精细模型的蛋白质网络-评估风险变体的预测价值为了实现这些目标，将重新分析广泛的全基因组基因型数据集，重点关注HLA关联和罕见变体。此外，还将对美国-美国病例对照数据集进行荟萃分析。这些分析的候选风险变异将在6个独立的欧洲起源的结节病人群中进行复制。为了弥合遗传学研究和细胞过程之间的差距，将使用公共可用工具以及申请方编制的结节病特异性eQTV数据库，对新的和已知的遗传风险因素进行详细的计算机模拟分析。所获得的功能假说将整合在一个完善的模型结节病发病机制的基础上遗传相互作用以及蛋白质-蛋白质相互作用。这些结果有望为疾病过程中遗传因素的相互作用提供重要见解，并将进一步用于患者分类和风险评估。