Quantitative Analysis Methods for Complex Trait Genetics

复杂性状遗传学的定量分析方法

• 批准号: 6745564
• 负责人: NANCY L SACCONE
• 金额: $ 10.38万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-04-01 至 2007-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6745564
• 关键词: artificial intelligence; behavioral /social science research tag; behavioral genetics; clinical research; computer simulation; data collection methodology /evaluation; drug addiction; family genetics; genetic susceptibility; human data; linkage mapping; mathematical model; nicotine; pharmacogenetics; smoking; tobacco abuse

DESCRIPTION: (Provided by Applicant): Cigarette smoking is a major risk factor for premature death and creates a substantial public health burden. While studies have shown that smoking and nicotine dependence are complex traits influenced by significant genetic components, further research is needed to identify genes that influence susceptibility and resistance. The research goal of this proposal is to develop and evaluate new methods for the genetic analysis of complex traits in the context of career development for the candidate, Dr. Nancy Saccone. Dr. Saccone will use artificial neural networks and logistic regression models as her primary methodological tools, and will develop and apply methods especially suited for studying the complex genetics of nicotine addiction and related phenotypes. Preliminary studies have been promising and indicate that further investigation of genetic methods based on these tools is warranted. The research plan will also extend the scope of these applications to include uses for genetic association analysis, as large-scale disequilibrium and association-based approaches are expected to become increasingly important for complex trait gene mapping. Dr. Saccone has chosen Dr. John Rice as mentor and will work with additional co-mentors and consultants. The training components of this proposal will expand Dr. Saccone?s experience to include: ongoing training in the ethical conduct of research, further training in the clinical and biological aspects of nicotine and other substance addiction, and the development of expertise in the use and analysis of other kinds of genetic data besides genotypic data, such as gene expression array data, which will play increasingly important roles in the study of human complex traits. The candidate has designed a program of coursework and mentoring to accomplish these research and training goals and thereby achieve research independence by the end of the award period.

描述：（申请人提供）： 吸烟是过早死亡的主要危险因素，并创造 实质性的公共卫生负担。研究表明吸烟和 尼古丁依赖性是受显着遗传影响的复杂性状 需要进一步的研究来识别影响的基因 敏感性和抵抗力。该提议的研究目标是发展 并评估新方法的复杂性状遗传分析 候选人Nancy Saccone博士的职业发展背景。 Saccone博士 将使用人工神经网络和逻辑回归模型作为她 主要的方法论工具，并将开发和应用方法尤其是 适合研究尼古丁成瘾和相关的复杂遗传学 表型。初步研究很有希望，并表明 有必要研究基于这些工具的遗传方法。这 研究计划还将将这些应用程序的范围扩展到包括用途 用于遗传关联分析，作为大规模的不平衡和 预计基于关联的方法将变得越来越重要 复杂的性状基因映射。 Saccone博士选择了John Rice博士为导师， 将与其他的委员和顾问合作。培训组件 该提议中将扩大Saccone博士的经验，包括： 研究道德行为的培训，临床的进一步培训 以及尼古丁和其他物质成瘾的生物学方面，以及 发展和分析其他类型的遗传数据的专业知识 除了基因型数据（例如基因表达阵列数据）之外，它将播放 在人类复杂性状的研究中越来越重要的作用。这 候选人设计了一个课程和指导计划来完成 这些研究和培训目标，从而通过 奖励期结束。