EAGER: Population Genomics to Discern How Global Change Affects Species

EAGER：通过群体基因组学来了解全球变化如何影响物种

• 批准号: 1008542
• 负责人: Marjorie Oleksiak
• 金额: $ 12.36万
• 依托单位: University of Miami
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-02-01 至 2012-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1008542&HistoricalAwards=false
• 关键词: EAGER; Population; Genomics; Discern; How

This research will apply new expertise and a novel approach to fundamental questions in Biological Oceanography: how is population structure affected by connectivity and transport and what traits drive spatial patterns in survival of larvae or adults. Many key species in the marine environment inhabit and need to deal with spatially and temporally variable environments. Yet, in Biological Oceanography and for the study of ecology in non-model species (those lacking genomes, or strong genetic analyses), investigators have largely relied on one or a few loci to investigate spatial and temporal patterns. This shortcoming provides little resolution of population structure and how oceanographic parameters affect populations.In this EAGER project, the PIs will use high throughput genomic approaches to identify and analyze genetic markers in hundreds of individuals without a need for prior genome information. The approach will use a modification of methods used with model species to sequence a reduced representation cDNA library made from many individuals in the species of choice in order to identify, at minimum, 300-500 single nucleotide polymorphisms (SNPs). SNP identification will be followed by MassARRAY genotyping of 100s of individuals within and among populations and, depending on genetic distance, species. Analyses will reveal population structure and importantly, SNPs associated with particular traits in the genotyped individuals. Because these SNPs are from coding sequences (expressed genes), the associations will be between specific genes and traits of interest (e.g., survivorship, fitness, growth). This approach is novel and innovative and contains some risk, making it highly suitable as an EAGER project. It does not rely on existing genome sequences, yet provides many SNPs per chromosome. This depth of information provides two important results: very high resolution of population structure and association between SNP and biologically important traits. The interdisciplinary aspects of the work combine bioinformatics, statistics, biological oceanography, molecular biology, marine conservation, and population genetics. By applying the most recent high-throughput technology with methods to identify SNPs in outbred natural populations, the PIs will resolve species distributions and the effects of global warming and habitat change on populations and better assess conservation practices. Additionally, because so many (300-500) markers are used for coding regions, it will be possible to ascertain selective differences among genes that affect biologically important traits. Thus, by identifying and utilizing 100s of SNPs from the coding sequences of any organism, the PIs will both measure population structure and connectivity and identify genes important for particular life-history traits. Notably, a sequenced genome is not necessary for this work, making it broadly applicable across species.The Broader Impacts consist of introducing new technologies to important, basic problems in population structure of marine organisms, with broad applicability in biological oceanography.

这项研究将应用新的专业知识和新的方法来解决生物海洋学中的基本问题：种群结构如何受到连通性和运输的影响，以及是什么特征决定了幼虫或成虫生存的空间格局。海洋环境中的许多关键物种栖息并需要应对空间和时间变化的环境。然而，在生物海洋学和非模式物种(缺乏基因组或强大的遗传分析)的生态学研究中，研究人员主要依赖一个或几个基因座来调查空间和时间模式。在这个迫切的项目中，PI将使用高通量的基因组方法来识别和分析数百个个体的遗传标记，而不需要事先的基因组信息。该方法将使用对模式物种使用的方法的修改来对从所选择的物种中的许多个体产生的降低代表性的cDNA文库进行测序，以便至少识别300-500个单核苷酸多态(SNPs)。在SNP鉴定之后，将对种群内和种群之间的100个个体以及物种(取决于遗传距离)进行大规模基因分型。分析将揭示群体结构，更重要的是，SNPs与基因分型个体的特定特征相关。因为这些SNP来自编码序列(表达的基因)，所以特定基因和感兴趣的特征(例如，生存、健康、生长)之间将存在关联。这种方法新颖、创新，而且包含一些风险，非常适合作为一个迫切的项目。它不依赖于现有的基因组序列，但每条染色体提供许多SNPs。这种信息的深度提供了两个重要的结果：非常高的种群结构分辨率以及SNP与生物重要性状之间的关联。这项工作的跨学科方面结合了生物信息学、统计学、生物海洋学、分子生物学、海洋保护和种群遗传学。通过应用最新的高通量技术和方法在近亲繁殖的自然种群中识别SNPs，PI将解决物种分布以及全球变暖和栖息地变化对种群的影响，并更好地评估保护做法。此外，由于如此多的(300-500)标记用于编码区，因此有可能确定影响生物重要性状的基因之间的选择差异。因此，通过从任何生物体的编码序列中识别和利用100个SNP，PI将既测量种群结构和连接性，又识别对特定生活史特征重要的基因。值得注意的是，这项工作不需要测序的基因组，使其广泛适用于各种物种。更广泛的影响包括引入新技术来解决海洋生物种群结构中的重要、基本问题，在生物海洋学中具有广泛的适用性。