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-环境交互作用在复杂疾病病因学中的作用。最近，一类广泛的轮廓似然半参数方法被发展起来，用于在环境因素存在的情况下分析病例对照遗传数据。这些方法挖掘关于基本遗传信息分布的知识，以便建立比其他方法在统计上更有效的估计器，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分阶段的单倍型。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软件来实现轮廓似然半参数方法及相关方法。该软件将处理缺失的基因类型、非阶段性单倍型、具有单倍型-环境交互作用的灵活单倍型模型，以及有或没有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。患复杂疾病的风险，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。该项目将采用新的、更有效的统计方法，在环境因素存在的情况下对病例对照遗传数据进行分析，从而将检测主要遗传效应和基因-环境交互作用的更好方法引入主流。

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 ef- fects in combination with environmental 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 interactions in the etiology of complex diseases. Recently, a broad class of profile-likelihood semiparametric methods has been developed for the analysis of case-control genetic data in the presence of environmental factors. These methods ex- ploit knowledge about the distribution of the basic genetic information in order to build estimators 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 unphased 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 re- sult, important gene-environment interactions are obscured, as are important main effects. The goal of this project is to develop Stata software to implement the profile-likelihood semiparametric methods and related methods. The software will handle missing genotypes, unphased haplotypes, flexible haplotype models with haplotype-environment interactions, and models both with and with- out Hardy-Weinberg equilibrium. This tool will be highly useful to epidemiologists and geneticists in their search for genetic and environmental determinants of complex diseases. Risks of complex diseases, such as cancers, hypertension, diabetes, and schizophrenia, are deter- mined 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 new and more ef?cient statistical methods for the analysis of case-control genetic data in the presence of environmen- tal factors, and thus bring into the mainstream better ways of detecting main genetic effects and gene-environment interactions.