Statistical Software for Genetic Association Studies

描述(申请人提供)：复杂疾病的风险，如癌症、高血压、糖尿病和精神分裂症，由遗传和环境因素决定。因此，人类基因组研究的进步不仅导致了对基因单独影响的流行病学调查，而且还导致了对它们与环境暴露的联合影响的流行病学调查。病例对照研究设计在经典的基于问卷的流行病学研究中被广泛使用，目前普遍用于研究基因和基因-环境相互作用在复杂疾病病因学中的作用。最近，一类广泛的半参数回溯似然方法被发展起来，用于分析存在环境因素的病例对照遗传数据。这些方法利用关于遗传变异分布的知识来建立估计，这些估计比其他方法在统计上更有效，并且在存在不完整的遗传数据时在统计上也是有效的，例如丢失标记等位基因和未知的单倍型。由于这种方法在任何商业软件中都不可用，研究人员求助于标准方法，这种方法缺乏统计效率，有时甚至缺乏有效性。其结果是，重要的基因-环境相互作用被掩盖，重要的主效应也是如此。本项目的目标是开发STATA软件来实现半参数回溯似然及相关方法。该软件将包括缺失的基因类型、阶段模糊、未分型的标记、具有基因-基因和基因-环境相互作用的灵活的疾病风险模型、全基因组关联研究、种群分层，以及考虑和不考虑Hardy-Weinberg平衡的模型。这一工具将对流行病学家和遗传学家在寻找复杂疾病的遗传和环境决定因素方面非常有用。 公共卫生相关性：癌症、高血压、糖尿病和精神分裂症等复杂疾病的风险由遗传和环境因素共同决定。因此，人类基因组研究的进步不仅导致了对基因单独影响的流行病学调查，而且还导致了对它们与环境暴露相结合的影响的流行病学调查。该项目将采用新颖有效的统计方法，在环境因素存在的情况下对病例对照遗传数据进行分析，从而将检测遗传效应和基因与环境相互作用的更好方法纳入主流。

DESCRIPTION (provided by applicant): Risks of complex diseases, such as cancers, hypertension, diabetes, and schizophrenia, are determined by both genetic and environmental factors. Advances in human genome research have thus led to epidemiologic investigations not only of the effects of genes alone, but also of their effects in combination with environmen- tal exposures. The case-control study design, which has been widely used in classical questionnaire-based epidemiologic studies, is now commonly employed to study the role of genes and gene-environment interac- tions in the etiology of complex diseases. Recently, a broad class of semiparametric retrospective-likelihood methods has been developed for the analysis of case-control genetic data in the presence of environmental factors. These methods exploit knowledge about the distribution of genetic variants in order to build esti- mators that are much more statistically efficient than other approaches, and are also statistically valid in the presence of incomplete genetic data, such as missing marker alleles and unknown haplotypes. Because this kind of methodology is not available in any commercial software, researchers have resorted to standard approaches, which lack statistical efficiency and sometimes validity. As a result, important gene-environment interactions are obscured, as are important main effects. The goal of this project is to develop Stata software to implement the semiparametric retrospective-likelihood and related methods. The software will accom- modate missing genotypes, phase ambiguity, untyped markers, flexible disease-risk models with gene-gene and gene-environment interactions, genomewide association studies, population stratification, and models both with and without Hardy-Weinberg equilibrium. This tool will be highly useful to epidemiologists and geneticists in their search for genetic and environmental determinants of complex diseases. PUBLIC HEALTH RELEVANCE: Risks of complex diseases, such as cancers, hypertension, diabetes, and schizophrenia, are determined by both genetic and environmental factors. Advances in human genome research have thus led to epidemiologic investigations not only of the effects of genes alone, but also of their effects in combination with environmental exposures. This project will implement novel and efficient statistical methods for the analysis of case-control genetic data in the presence of environmental factors, and thus bring into the mainstream better ways of detecting genetic effects and gene-environment interactions.