Analysis of Genetic-Environment Networks in Spondyloarthritis

点击翻译按钮获取中文摘要

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.