Statistical Software for Genetic Association Studies

用于遗传关联研究的统计软件

• 批准号: 7843725
• 负责人: Yulia Marchenko
• 金额: $ 37.49万
• 依托单位: STATACORP, LP
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2012-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7843725
• 关键词: Algorithms; Alleles; Area; Candidate Disease Gene; Case Study; Case-Control Studies; Cataloging; Catalogs; Code; Companions; Complex; Computer software; Computers; Data; Development; Diabetes Mellitus; Disease; Disease Association; Documentation; Environment; Environmental Exposure; Environmental Risk Factor; Epidemiologic Studies; Epidemiologist; Equation; Equilibrium; Etiology; Flavoring; Genes; Genetic; Genetic Markers; Genetic Models; Genotype; Goals; Hand; Haplotypes; Human Genome; Hypertension; Journals; Knowledge; Language; Literature; Mainstreaming; Malignant Neoplasms; Manuals; Memory; Methodology; Methods; Metric; Modeling; Output; Paper; Phase; Population; Process; Publishing; Questionnaires; Rare Diseases; Research; Research Design; Research Personnel; Resort; Risk; Role; Running; Schizophrenia; Series; Single Nucleotide Polymorphism; Speed; Statistical Methods; Stratification; Text; Work; Writing; base; case control; disorder risk; flexibility; gene environment interaction; gene interaction; genetic analysis; genetic association; genetic variant; genome wide association study; graphical user interface; novel; professor; programs; prospective; prototype; public health relevance; research and development; success; tool; user-friendly

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.

描述（由申请人提供）：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展不仅导致了对基因单独影响的流行病学调查，而且还导致了对基因与人体暴露相结合的影响的流行病学调查。病例-对照研究设计在经典的以药物为基础的流行病学研究中被广泛应用，现在被广泛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，一个广泛的类的半参数回顾性似然方法已被开发用于分析病例对照遗传数据中存在的环境因素。这些方法利用关于遗传变体的分布的知识，以便建立比其他方法统计学上有效得多的估计器，并且在不完整的遗传数据（例如缺失的标记等位基因和未知的单倍型）存在下也是统计学上有效的。由于这种方法在任何商业软件中都不可用，研究人员不得不求助于标准方法，这些方法缺乏统计效率，有时甚至是有效性。因此，重要的基因-环境相互作用被掩盖了，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法和相关方法。该软件将包含缺失基因型、相位模糊性、未分型标记、具有基因-基因和基因-环境相互作用的灵活疾病风险模型、全基因组关联研究、人群分层以及具有和不具有Hardy-Weinberg平衡的模型。这个工具将是非常有用的流行病学家和遗传学家在他们的搜索遗传和环境决定因素的复杂疾病。 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决定。因此，人类基因组研究的进展不仅导致了对基因本身的影响的流行病学调查，而且还导致了对基因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，在存在环境因素的情况下分析病例对照遗传数据，从而使检测遗传效应和基因-环境相互作用的更好方法成为主流。