Methods for the analysis of population-genomic data

经济地对整个基因组进行测序的方法的出现迎来了人口基因组学领域。虽然全基因组测序有可能以前所未有的准确度估计群体遗传参数，但数据分析所必需的方法却大大落后。拟议的工作将为分析人口基因组数据制定一个总体统计框架。一般的策略是在三个层次上推导和计算验证一组有效的群体遗传参数估计：个体基因组;群体内的多个个体;和多个群体。具体的子项目包括测量核苷酸位点之间的变异和共变异模式、种群细分水平，以及开发新方法以促进基因组组装和遗传图谱的完善。相当大的重点将集中在开发有效的估计算法，供基因组学研究界使用。这些方法将广泛用于应用和基础研究。特别注意将致力于确定最佳的采样策略，包括权衡每个人的序列覆盖深度和个体数量之间的关系，以及长度，数量和读取质量之间的关系。因此，由此产生的方法应使调查人员能够收获最大可能的信息，从他们现有的数据集，同时也促进未来的设计策略，以最大限度地提高每单位采样和测序工作的信息产量。将开发的软件将永久存放在国家基因组分析支助中心，并免费提供，将举办讲习班，协助用户使用这些工具。植物基因组研究项目共同资助这项研究。

The advent of methods for economically sequencing entire genomes has ushered in the field of population genomics. Although whole-genome sequencing harbors the potential to yield estimates of population-genetic parameters with unprecedented accuracy, the methods essential to the analysis of data have lagged behind greatly. The proposed work will develop a general statistical framework for the analysis of population-genomic data. The general strategy is to derive and computationally validate a set of efficient estimators for population-genetic parameters at three levels: individual genomes; multiple individuals within populations; and multiple populations. Specific subprojects include the measurement of patterns of variation and covariation among nucleotide sites, levels of population subdivision, and the development of novel methods to facilitate genome assembly and the refinement of genetic maps. Considerable emphasis will be focused on the development of efficient estimation algorithms for use by the genomics research community.These methods will be widely used in both applied and basic research. Special attention will be devoted to identifying optimal sampling strategies, including the tradeoffs between depth of sequence coverage per individual and numbers of individuals, and between length, number, and quality of reads. As a consequence, the resultant methods should enable investigators to harvest the maximum possible information from their existing data sets, while also promoting the future design strategies to maximize informational yield per unit sampling and sequencing effort. The software to be developed will be permanently housed and freely available at the National Center for Genome Analysis Support, and workshops will be held to assist the user community in the implementation of such tools. The Plant Genome Research Project is co-funding this research.