Methods for the analysis of population-genomic data

经济的全基因组测序方法的出现开创了人口基因组学领域。尽管全基因组测序有可能以前所未有的准确性产生种群遗传参数的估计，但对数据分析至关重要的方法已经大大落后。拟议的工作将为分析人口基因组数据制定一个一般统计框架。一般策略是推导和计算验证一组有效的估计群体遗传参数在三个层面：个体基因组；种群内的多个个体；还有多个种群。具体子项目包括测量核苷酸位点之间的变异和共变异模式，群体细分水平，以及开发促进基因组组装和遗传图谱改进的新方法。相当大的重点将集中在开发高效的估计算法，以供基因组学研究界使用。这些方法将在应用研究和基础研究中得到广泛应用。将特别关注确定最佳采样策略，包括每个个体的序列覆盖深度和个体数量之间的权衡，以及读取的长度、数量和质量之间的权衡。因此，由此产生的方法应该使研究人员能够从现有的数据集中获取最大可能的信息，同时也促进未来的设计策略，以最大限度地提高单位采样和测序工作的信息产量。待开发的软件将永久存放在国家基因组分析支持中心并免费提供，并将举办讲习班以协助用户社区实施这些工具。植物基因组研究计划共同资助了这项研究。

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.