Spatial ecological genomics of free-ranging Great tits

自由放养大山雀的空间生态基因组学

• 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

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关于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，推动了许多进步，特别是在医学和进化生物学领域。基因组数据集的规模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，以处理与提出数据问题有关的分析挑战。直到最近几年，基因组数据仅可用于模式生物;然而，由于技术进步，这正在迅速改变，基因组数据的潜在力量可以改善我们对问题的理解，一旦超出基因组学的范围，现在已经得到了很好的认识，很明显，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转移才是。鉴于需要跨学科的专业知识，我们建议使用生物信息学的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。大山雀已经被证明在非常精细的空间尺度上对环境选择做出反应;然而，我们对人类活动如何影响自然种群进化的潜力知之甚少，如果它们确实可以进化的话。污染水平也可能影响种群结构，如果它们为大山雀创造了不合适的栖息地。人类密度通常与污染水平增加有关，而大山雀是常见的城市鸟类。如果低污染地区是个体的净生产者，而高污染地区是移民的净接收者，那么尽管城市中稳定的种群持续出现，但高污染地区可能更准确地说是由来自高质量栖息地的个体补充的内在不可持续的种群。最近才有关于非模式物种的数据和适当分析这些数据的专门知识。如果这些有价值的方法要与生态学和进化相结合，这种专业知识的专业性质需要与生物信息学家密切合作。在这里的四个月后将资助由博士后研究员将在世界领先的生物信息学研究小组之一主持交流。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，并启动一个长期的合作，将持续到未来。

项目成果

Evolutionary signals of selection on cognition from the great tit genome and methylome.

• DOI: 10.1038/ncomms10474
• 发表时间: 2016-01-25
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Laine VN;Gossmann TI;Schachtschneider KM;Garroway CJ;Madsen O;Verhoeven KJ;de Jager V;Megens HJ;Warren WC;Minx P;Crooijmans RP;Corcoran P;Great Tit HapMap Consortium;Sheldon BC;Slate J;Zeng K;van Oers K;Visser ME;Groenen MA
• 通讯作者: Groenen MA

Highly variable recombinational landscape modulates efficacy of natural selection in birds.

• DOI: 10.1093/gbe/evu157
• 发表时间: 2014-08
• 期刊: Genome biology and evolution
• 影响因子: 3.3
• 作者: Gossmann TI;Santure AW;Sheldon BC;Slate J;Zeng K
• 通讯作者: Zeng K