Methods for the analysis of population-genomic data

群体基因组数据分析方法

• 批准号: 1257806
• 负责人: Michael Lynch
• 金额: $ 104.99万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-15 至 2018-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1257806&HistoricalAwards=false
• 关键词: Methods; analysis; population; genomic; data

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.

经济测序整个基因组的方法的出现已经引入了人群基因组学领域。尽管整个基因组测序具有前所未有的准确性估算人口遗传参数的潜力，但对数据分析必不可少的方法却极大地落后了。拟议的工作将开发一个一般的统计框架，用于分析人口基因组数据。一般策略是在三个级别的人口基因参数中得出并在计算上验证一组有效的估计值：个体基因组；人口中的多个人；和多个人口。特定的次要注射包括核苷酸位点之间的变异模式和协方差的测量，种群细分水平以及促进基因组组装和遗传图的细化的新方法的发展。基因组学研究界使用的有效估计算法的开发将重点放在开发上，这些方法将在应用和基础研究中广泛使用。特别关注的是确定最佳抽样策略，包括每个个人和个人数量的序列覆盖深度之间的权衡以及读取的长度，数量和质量之间的权衡。结果，最终的方法应使研究人员能够从其现有数据集中收集最大可能的信息，同时还促进未来的设计策略，以最大程度地提高单位抽样和测序工作的信息收益率。要开发的软件将在国家基因组分析支持中心永久容纳并免费提供，并将举行研讨会，以帮助用户社区实施此类工具。植物基因组研究项目正在共同资助这项研究。