Analysis of Genetic-Environment Networks in Spondyloarthritis

大多数表型变异，包括与强直性脊柱炎等复杂疾病有关的变异 (AS)和药物反应的差异，是由多个基因和 环境因素。现有的方法可能会提供分析工具，但随着即将完成的 HapMap项目提供了来自不同领域的数百万个SNP和单倍型的综合目录 种群和高通量基因分型技术的快速发展，基因的范式转变 从个体标记分析到全基因组关联和系统水平的复杂性状研究 分析是不可或缺的。复合体的全基因组关联研究和系统水平分析 疾病在三个方面提出了巨大的挑战。首先，几乎不可能确保全基因组的 用传统统计方法检测数百万个SNPs的显著性水平为0.05。第二，最多 表型变异是多种遗传和环境因素综合作用的结果 通过基因之间以及基因和环境之间的复杂相互作用。检测交互 在基因或SNP标记之间进行选择是一项艰巨的任务。第三，现有的大多数分析方法对每个标记进行分析 (或单倍型)和表型，并且不考虑多个 表型和多个标记。因此，需要提出新的技术来解决这些问题 具有挑战性的任务。该项目的总体目标是(1)开发全基因组的非线性统计 确保全基因组显著水平的关联研究，(2)开发新的统计方法 基因互作检测及构建遗传互作的高效计算算法 网络，(3)开发用于多种表型的网络建模的概念框架，以及(4) 开发或采用新的统计方法，用于多表型和多标记的联合分析。AS 是一种复杂的疾病。解剖复杂基因的项目1-3中的基因和表型数据 AS的架构将用于方法的开发和评估，以及实际数据分析。

Most phenotypic variations, including those involved in complex diseases such as ankylosing spondylitis (AS) and differences in drug response, are generated by integrated actions of multiple genetic and environmental factors. Existing methods may provide tools for analysis, but with the imminent completion of the HapMap Project providing a comprehensive catalogue of millions of SNPs and haplotypes across diverse populations and rapid development of high throughput genotyping technologies, a paradigm shift for genetic studies of complex traits from individual marker analysis to genome-wide association studies and systemslevel analysis is indispensable. Genome-wide association studies and systems-level analysis for complex diseases raise great challenges in three aspects. First, it is practically impossible to ensure a genome-wide significance level of 0.05 for testing millions of SNPs using traditional statistic methods. Second, most phenotypic variations are generated by integrated actions of multiple genetic and environmental factors through complex interactions between genes, and between gene and environments. Detecting interactions among genes or SNP markers is a daunting task. Third, most existing analytic methods analyze each marker (or haplotype) and phenotype individually, and do not consider network structures among multiple phenotypes and multiple markers. Therefore, new techniques need to be proposed to address these challenging tasks. The overall goal of this project is (1) to develop nonlinear statistics for genome-wide association studies for ensuring genome-wide significance levels, (2) to develop novel statistical methods for detection of gene interaction and efficient computational algorithms for construction of genetic interaction networks, (3) to develop a conceptual framework for network modeling of multiple phenotypes, and (4) to develop or adopt novel statistical methods for joint analysis of multiple phenotypes and multiple markers. AS is a complex disease. Genotype and phenotype data from projects 1-3 for dissecting complex genetic architecture of AS will be used for development and evaluation of methodology, and real data analysis.