PDF(Pubmed)
Abstract：
Leveraging innovative tools to speed up prebreeding and discovery of genotypic sources of adaptation from landraces, crop wild relatives, and orphan crops is a key prerequisite to accelerate genetic gain of abiotic stress tolerance in annual crops such as legumes and cereals, many of which are still orphan species despite advances in major row crops. Here, we review a novel, interdisciplinary approach to combine ecological climate data with evolutionary genomics under the paradigm of a new field of study: genome-environment associations (GEAs). We first exemplify how GEA utilizes in situ georeferencing from genotypically characterized, gene bank accessions to pinpoint genomic signatures of natural selection. We later discuss the necessity to update the current GEA models to predict both regional- and local- or micro-habitat-based adaptation with mechanistic ecophysiological climate indices and cutting-edge GWAS-type genetic association models. Furthermore, to account for polygenic evolutionary adaptation, we encourage the community to start gathering genomic estimated adaptive values (GEAVs) for genomic prediction (GP) and multi-dimensional machine learning (ML) models. The latter two should ideally be weighted by de novo GWAS-based GEA estimates and optimized for a scalable marker subset. We end the review by envisioning avenues to make adaptation inferences more robust through the merging of high-resolution data sources, such as environmental remote sensing and summary statistics of the genomic site frequency spectrum, with the epigenetic molecular functionality responsible for plastic inheritance in the wild. Ultimately, we believe that coupling evolutionary adaptive predictions with innovations in ecological genomics such as GEA will help capture hidden genetic adaptations to abiotic stresses based on crop germplasm resources to assist responses to climate change. \"I shall endeavor to find out how nature\'s forces act upon one another, and in what manner the geographic environment exerts its influence on animals and plants. In short, I must find out about the harmony in nature\" Alexander von Humboldt-Letter to Karl Freiesleben, June 1799.
摘要：
利用创新工具加快繁殖速度，并从地方品种中发现适应的基因型来源，作物野生近亲，孤儿作物是促进豆类和谷物等一年生作物非生物胁迫耐受性遗传获得的关键先决条件，尽管主要作物取得了进展，但其中许多仍然是孤儿。这里,我们看了一部小说,在新研究领域的范式下，将生态气候数据与进化基因组学相结合的跨学科方法：基因组-环境关联（GEA）。我们首先举例说明GEA如何利用来自基因型表征的原位地理参考，基因库的加入，以查明自然选择的基因组特征。我们稍后讨论了更新当前GEA模型的必要性，以使用机械生态生理气候指数和前沿GWAS型遗传关联模型来预测基于区域和局部或微观栖息地的适应。此外，为了解释多基因进化适应，我们鼓励社区开始为基因组预测(GP)和多维机器学习(ML)模型收集基因组估计自适应值(GEAV).理想情况下，后两者应通过基于从头GWAS的GEA估计进行加权，并针对可扩展的标记子集进行优化。我们通过设想通过合并高分辨率数据源使适应推断更加稳健的途径来结束审查，例如环境遥感和基因组位点频谱的汇总统计，具有表观遗传分子功能，负责野外塑料遗传。最终,我们认为，将进化适应性预测与GEA等生态基因组学创新相结合，将有助于基于作物种质资源捕获对非生物胁迫的隐藏遗传适应性，以帮助应对气候变化。“我将努力找出大自然的力量是如何相互作用的，以及地理环境对动植物的影响。总之,我必须找出大自然中的和谐\“亚历山大·冯·洪堡-给卡尔·弗赖斯勒本的信，1799年6月
