NSF Postdoctoral Fellowship in Biology FY 2010

本行动资助美国国家科学基金会2010年度博士后研究奖学金。该奖学金支持Thomas McGreevy的一项名为“用景观基因组学方法识别地理多样性蜥蜴的遗传适应机制”的研究和培训计划。这项研究的主办机构是波士顿大学，赞助科学家是克里斯托弗·施耐德。识别适应的遗传基础是进化生物学的一个主要目标，对生态学和保护生物学具有广泛的意义。种群基因组学和景观遗传学学科的最新进展极大地促进了适应性位点的鉴定，并允许使用景观基因组学方法整合环境变量。本研究利用地理信息系统（GIS）框架，合并了加勒比地区蜥蜥模型系统的空间参考遗传、形态和环境数据。蜥蜴是研究适应遗传机制的理想群体，因为它们已经分化成400多个物种，是适应性辐射和物种形成的经典例子。该项目的目的是：1)发展与环境变量相关的适应性遗传变异的空间分析，以确定适应性多样化的可能驱动因素；2)确定是否存在一组环境变量可以作为热带山地物种适应多样性的替代预测因子。所建立的基于地理信息系统的分析方法将作为一种模型框架，为进一步的调查提供参考。培训目标包括获得和提高基因组分析、生物信息学技术、大规模数据库管理、空间分析和地理信息系统建模方面的技能。更广泛的影响包括开发工具和方法，这些工具和方法对于识别和保存产生适应性遗传变异的重要进化过程至关重要。鉴于气候变化迫在眉睫的影响，绘制和了解适应性遗传变异的分布是保护的优先事项。

This action funds an NSF Postdoctoral Research Fellowship for FY 2010. The fellowship supports a research and training plan entitled "A Landscape Genomics Approach to Identify the Genetic Mechanism of Adaptation in a Geographically Diverse Lizard" for Thomas McGreevy. The host institution for this research is Boston University, and the sponsoring scientist is Christopher Schneider.The identification of the genetic basis of adaptation is a major objective of evolutionary biology and has broad implications for ecology and conservation biology. Recent advances in the population genomics and landscape genetics disciplines have greatly facilitated the identification of adaptive loci and allowed for the integration of environmental variables using a landscape genomics approach. This research merges spatially referenced genetic, morphological, and environmental data from a model system of Anolis lizards in the Caribbean using a Geographic Information Systems (GIS) framework. Anolis lizards are an ideal group to investigate the genetic mechanism of adaptation because they have diverged into over 400 species and are a classic example of adaptive radiation and speciation. The aims of this project are to: 1) develop spatial analyses of adaptive genetic variation in relation to environmental variables to identify possible drivers of adaptive diversification; and 2) determine if there are sets of environmental variables that can serve as surrogate predictors of adaptive diversity within species in tropical montane regions. The GIS based analytical approach created will serve as a model framework for additional investigations of Anolis and other taxa.Training objectives include acquiring and advancing skills in genomic analyses, bioinformatics techniques, large-scale database management, spatial analyses, and GIS modeling. Broader impacts include development of tools and approaches that will be essential for identifying and conserving evolutionary processes important in producing adaptive genetic variation. Given the impending impact of climate change, mapping and understanding the distribution of adaptive genetic variation is a priority for conservation.