复杂疾病是一种复杂的生物系统，受到多个基因、环境因素及其它们交互作用的影响，每个基因与疾病之间可能只存在弱关联而不存在主基因效应，必须研究多个基因（环境）联合作用才有可能寻找到真正的易感基因；GWAS就是针对复杂疾病的基因定位而进行的全基因组关联分析，全球第一轮GWAS高潮已初战告捷，但其成果与预期结果差距甚远，这与GWAS试验设计和数据分析中某些关键问题有关。二代生物测序技术的兴起，把GWAS研究再次推向深入。二代测序技术产生数据的最大特点就是"低频富集"，这种"低频富集"区域能解释第一轮GWAS所不能解释的"遗传丢失"现象；本项目针对病例对照设计，从整体基因的角度，通过构造多种新的统计推断方法，研究低频位点、高频位点和环境因素是如何通过交互作用对复杂疾病产生的影响。旨在构建"基于二代测序技术的全基因组基因-环境交互作用的统计推断方法"体系，为充分挖掘GWAS数据信息提供高效的新方法。

Complex diseases that are the sort of complex biological systems are caused by multiple genes, environments and their interactions. In general, genetic variation that is the existence of weak effects alone cannot account for the development of complex diseases. Rather, combinations of genetic and environmental factors cause disease. Over the past several years, genome-wide association studies (GWAS) have succeeded in identifying thousands of genetic markers associated with complex diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. One reason is that the statistical strategies and methods don't efficiently identify genetic and environmental factors contributing to the risk of complex diseases. The next generation sequencing technologies to produce data that the biggest characteristic of "lower frequency enrichment ",this kind of "lower frequency enrichment" area, can explain the first round GWAS couldn't explain the "missing heritability"; This project, from the point of view of the whole genes, will construct a variety of new statistical inference method to explore that the lower frequency causal alleles(rare variants), the higher frequency causal alleles(common variants) and environmental factor is how to affect complex diseases through gene-gene and gene-environment interaction based on case-control design. The our aim: Develop novel statistical inference methods for detecting gene-gene, gene- environment interaction in order to fully mining data information of GWAS based on the next generation sequence.

复杂疾病是一种复杂的生物系统，受到多个基因、环境因素及其它们交互作用的影响，每个基因与疾病之间可能只存在弱关联而不存在主基因效应，必须研究多个基因（环境）联合作用才有可能寻找到真正的易感基因。本项目构造了3种新的统计量来检测影响复杂疾病基因、环境间可能存在的交互作用。通过大量的统计模拟、比较，并利用实际数据验证，构建的基因-基因，基因-环境交互作用理论框架，可以检测单个SNP、整体基因以及它们与环境变量间存在的交互作用，检验效能优于常用的方法。为验证构建的基因-环境交互用模型，本项目从约30万人口的蚌埠市龙子湖区随机抽样调查了4000人，用于量化环境变量（如抽烟、饮酒及生理指标），取得了一些成果。最终，把本项目的方法软件化，以利于更好地推广应用。通过本项目的研究，构建了检测基因-基因，基因-环境交互作的理论框架，共发表学术论文9篇，待发表论文6篇；培养了本研究领域的人才梯队；并开发一套医学统计学软件，取得了预期效果。