Empirical bayes methods in statistical genetics

统计遗传学中的经验贝叶斯方法

• 批准号: 327067-2006
• 负责人: Labbe, Aurélie
• 金额: $ 0.95万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=364873
• 关键词: Empirical; bayes; methods; statistical; genetics

The goal of this program is the development of statistical tools in the field of statistical genetics. With the recent development of molecular biotechnologies and instruments, genetic and genomic data can now be generated from any organism almost with no limits. Statistical genetics, aimed to provide effective and efficient analyses and utilization of these data, has thus become a very active and promising area of statistics. The overall objective of my research is to develop a collection of theoretical models and statistical methods for genetic and genomic research in the context of microarray data and pedigree data. Microarray technology is one of the most recent and important experimental breakthroughs in molecular biology, allowing the measurement and comparison of expression level of thousands of genes simultaneously. I am interested in the development of methods leading to the detection of differentially expressed genes between one or more cell conditions. The applications of such methods are very wide, and can be used in the field of cancer research or drug development, for example. The second type of genetic data I consider comes from gene mapping studies involving large families. Co-inheritance patterns between the affection status of a disease and genetic markers are studied through linkage analysis. I am interested in modeling the dependence framework between individuals of such studies.

该计划的目标是开发统计遗传学领域的统计工具。随着分子生物技术和仪器的最新发展，现在可以从任何生物体中几乎没有限制地生成遗传和基因组数据。统计遗传学旨在对这些数据进行有效且高效的分析和利用，因此已成为一个非常活跃和有前途的统计学领域。我研究的总体目标是在微阵列数据和谱系数据的背景下开发一系列用于遗传和基因组研究的理论模型和统计方法。微阵列技术是分子生物学中最新、最重要的实验突破之一，可以同时测量和比较数千个基因的表达水平。我对开发能够检测一种或多种细胞条件之间差异表达基因的方法感兴趣。此类方法的应用非常广泛，例如可以用于癌症研究或药物开发领域。我考虑的第二种遗传数据来自涉及大家庭的基因图谱研究。通过连锁分析来研究疾病的影响状态和遗传标记之间的共遗传模式。我有兴趣对此类研究的个体之间的依赖框架进行建模。