Biometrical support for the priority program "Immune and Metabolic Modulation induced by severe Tissue Trauma" and data mining approaches for the identification of disease associated single nucleotide polymorphismus (SNPs)

对优先计划“严重组织创伤引起的免疫和代谢调节”的生物识别支持以及用于识别疾病相关单核苷酸多态性（SNP）的数据挖掘方法

• 批准号: 16852967
• 负责人: Professorin Dr. Inke Regina Koenig
• 金额: --
• 依托单位: Institut für Medizinische Biometrie und Statistik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2005
• 资助国家: 德国
• 起止时间: 2004-12-31 至 2006-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/16852967?language=en
• 关键词: Biometrical; support; priority; program; Immune

The first goal of this project is the biometrical support of genetic epidemiological projects within the priority program ¿Immune and Metabolic Modulation induced by severe Tissue Trauma¿ with a focus on the project ¿Genetic Predisposition for Adverse Outcomes in Trauma Patients¿. Here, the co-operation will comprise all biometrical aspects starting from quality control via statistical analyses up to the interpretation of results with regard to genomic controls, the group sequential study design, single marker and haplotype analysis, gene-gene and gene-environment interactions. The aim is to yield a validated model to predict the occurrence of adverse outcomes in trauma patients from a parsimonious number of genetic and environmental factors. The second goal is the methodological comparison and further development of different data mining algorithms for identifying genetic susceptibility SNPs and gene-gene interactions (epistasis). Emphasis will be put on two nonparametric approaches that have been developed recently, namely, the multifactor dimensionality reduction (MDR) method and random forests (RF). These will be compared with regard to accuracy, interpretability, and ease of application.

该项目的第一个目标是在优先计划<$免疫和代谢调节引起的严重组织创伤<$<$内的遗传流行病学项目的生物统计学支持，重点是创伤患者不良后果的遗传易感性项目<$。在这里，合作将包括所有生物统计学方面，从质量控制开始，通过统计分析，直到基因组控制，组序贯研究设计，单标记和单倍型分析，基因-基因和基因-环境相互作用的结果解释。其目的是产生一个经过验证的模型，以预测创伤患者因少量遗传和环境因素而发生的不良后果。第二个目标是方法学比较和进一步开发不同的数据挖掘算法，用于识别遗传易感性SNP和基因-基因相互作用（上位性）。重点将放在两个非参数的方法，最近开发的，即多因素降维（MDR）方法和随机森林（RF）。这些将比较方面的准确性，可解释性，和易于应用。