Theoretical Studies of Linkage Disequilibrium

这个项目的长期目标是开发统计和计算方法 群体遗传学中的单倍型分析。随着人类基因组中大量遗传标记的获得和基因分型技术的进步，在群体遗传学研究中对来自多个群体的大量个体的数千个标记进行基因分型成为可能。对这类数据的分析提出了具有挑战性的统计和计算问题，需要进行理论和实证研究，以开发和评价能够最好地提取最相关信息的统计方法，以便对感兴趣的参数进行统计推断。该项目的具体目标是： (1)发展统计和计算方法，从观察到的多个群体的非阶段标记数据中推断单倍型频率；(2)制定标记选择的一般准则，以通过单倍型识别疾病易感性变异；(3)使用由单核苷酸多态和来自多个群体的微卫星组成的单倍型来推断群体参数和局部重组率；(4)调查统计方法的能力，以确定受自然选择影响的染色体区域；(5)在将分发给科学界的计算机程序中实施和验证所开发的方法；以及(6)合作 与其他调查人员一起，应用从这个项目中获得的方法和知识来分析来自其他项目的数据。我们的方法将利用要收集的数据中的两个独特特征：世界各地的大量种群和在扩展的染色体区域中对单倍型的详尽编目。这些新的统计方法和用户友好的计算机程序的发展将为群体遗传学研究提供有用的工具，而对从其他项目收集的数据的分析将有助于更好地理解不同群体之间的关系和导致人类基因组连锁不平衡模式的不同力量。

The long-term objective of this project is to develop statistical and computational methods for the analysis of haplotypes in population genetics. With the availability of large numbers of genetic markers in the human genome and the advances in genotyping technology, it is becoming feasible in population genetic studies to genotype thousands of markers in a large number of individuals from multiple populations. The analysis of such data poses challenging statistical and computational issues and both theoretical and empirical studies are needed to develop and evaluate statistical methods that can best extract the most relevant information for statistical inference of parameters of interest. The specific aims of this projects are: (1) Develop statistical and computational methods to infer haplotype frequencies from the observed unphased marker data in multiple populations; (2) Develop general guidelines for marker selection to identify disease susceptibility variants through haplotypes; (3) Use haplotypes consisting of single nucleotide polymorphisms as well as microsatellites from multiple populations for inference on population parameters as well as local recombination rates; (4) Investigate the power of statistical methods to identify chromosomal regions that have been subject to natural selections; (5) Implement and validate the developed methodologies in computer programs that will be distributed to the scientific community; and (6) Collaborate with other investigators to apply the methods and knowledge gained from this project to analyze data from other projects. Our methods will exploit two unique features in the data to be collected: the large number of populations around the world and the exhaustive cataloguing of haplotypes in extended chromosomal regions. The developments of these novel statistical methods and user-friendly computer programs will provide useful tools on population genetic studies and the analysis of data collected from other projects will lead to better understanding of relationships among various populations and different forces leading to linkage disequilibrium patterns in the human genome.