Spatial ecological genomics of free-ranging Great tits

自由放养大山雀的空间生态基因组学

基本信息

批准号：
NE/K01126X/1
负责人：
Ben Sheldon
金额：
$ 3.99万
依托单位：
University of Oxford
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2013
资助国家：
英国
起止时间：
2013 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FK01126X%2F1
关键词：
Spatial ecological genomics free ranging

项目摘要

The genomics revolution now provides researchers with vast amounts of raw data about the genetic architecture of organisms. This information is fuelling numerous advances, particularly in medicine and evolutionary biology, by identifying genes and their function and by elucidating gene structures and their evolution. The size and scope of genomic data sets require specialised expertise to analyse. Indeed, due to the complexity and volume of genomics data, the new quantitative discipline of bionformatics has arisen to deal with the analytical challenges involved with asking questions of the data. Until very recent years, genomic data was only available for model organisms; however, due to technological advances, this is quickly changing and the potential power of genomic data to refine our understanding of questions once that to be outside the scope of genomics is now well recognised and it is clear that cross-disciplinary enterprise is needed. DNA sequencing is no longer the primary factor limiting genomic analyses outside of the more traditional genomic disciplines; rather the transfer of genomic expertise across disciplines is. Within light of the need for cross-disciplinary expertise we propose to use bioinformatic approaches to answers question about the population genetic structure of a free-ranging population of great tits. Particularly, we are interested in understanding how populations respond to intensity of pollution in terms of the potential for adaptation and its impact on population dynamics. Great tits have been shown to respond to environmental selection at very fine spatial scales; however, we know very little about how human activities affect the potential for natural populations to evolve, if indeed they can evolve at all. Pollution levels may also affect population structure if they create unsuitable habitat for great tits. Human density is often associated with increased pollution levels and great tits are common city birds. If low pollution sites are net producers of individuals and high pollution sites net recipients of immigrants then despite the on-going appearance of stable populations in cities high pollution areas may more accurately be intrinsically unsustainable populations supplemented by individuals from high quality habitat.To identify regions of the genome that are under selection and describe high resolution population structure requires high density genomic data, only recently available for non-model species and the expertise to appropriately analyse these data. The specialist nature of this expertise requires close collaboration with bioinformaticians if these valuable approaches are to be integrated with ecology and evolution. The four months post here will fund an exchange by a postdoctoral fellow to be hosted within one of the leading bioinformatics research groups in the world. By doing so we hope to begin to bridge the challenges associated with integrating ecology and genomics and initiate a long term collaboration that will continue into the future.

基因组学革命现在为研究人员提供了大量有关生物体遗传结构的原始数据。这些信息通过识别基因及其功能以及阐明基因结构及其进化，推动了众多进步，特别是在医学和进化生物学方面。基因组数据集的大小和范围需要专业知识来分析。事实上，由于基因组学数据的复杂性和数量，生物信息学的新定量学科已经出现，以应对与提出数据问题有关的分析挑战。直到最近几年，基因组数据仅适用于模式生物。然而，由于技术进步，这种情况正在迅速改变，并且基因组数据的潜在力量可以改善我们对超出基因组学范围的问题的理解，并且现在已经充分认识到需要跨学科的企业。 DNA 测序不再是限制传统基因组学科之外的基因组分析的主要因素；相反，跨学科的基因组专业知识的转移是。鉴于对跨学科专业知识的需求，我们建议使用生物信息学方法来回答有关大山雀自由放养种群的种群遗传结构的问题。特别是，我们有兴趣了解人口如何应对污染强度的适应潜力及其对人口动态的影响。研究表明，大山雀能够在非常精细的空间尺度上对环境选择作出反应。然而，我们对人类活动如何影响自然种群进化的潜力（如果它们确实能够进化的话）知之甚少。如果污染水平为大山雀创造了不合适的栖息地，那么污染水平也可能会影响种群结构。人口密度通常与污染水平的增加有关，而大山雀是常见的城市鸟类。如果低污染地点是个人的净生产者，而高污染地点是移民的净接收者，那么尽管城市中不断出现稳定的人口，高污染地区可能更准确地说是本质上不可持续的人口，并由来自高质量栖息地的个人补充。正在选择和描述高分辨率种群结构的基因组需要高密度基因组数据，这些数据最近才可用于非模式物种以及适当分析这些数据的专业知识。如果要将这些有价值的方法与生态学和进化论相结合，这种专业知识的专业性质需要与生物信息学家密切合作。在这里的四个月的职位将资助一名博士后研究员在世界领先的生物信息学研究小组之一内举办的交流活动。通过这样做，我们希望开始弥合与整合生态学和基因组学相关的挑战，并启动将持续到未来的长期合作。