Modern Strategies to Assess and Breed Forest Tree Adaptation to Changing Climate

编号 030026802

推送时间 20201207

研究领域 森林经理 

年份 2020 

类型 期刊 

语种 英语

标题 Modern Strategies to Assess and Breed Forest Tree Adaptation to Changing Climate

来源期刊 FRONTIERS IN PLANT SCIENCE

期 第268期

发表时间 20201021

关键词 genomics of adaptation;  genomic prediction;  genome-wide association studies;  genome-wide selection scans;  assisted gene flow;  machine learning;  big data; 

摘要 Studying the genetics of adaptation to new environments in ecologically and industrially important tree species is currently a major research line in the fields of plant science and genetic improvement for tolerance to abiotic stress. Specifically, exploring the genomic basis of local adaptation is imperative for assessing the conditions under which trees will successfully adapt in situ to global climate change. However, this knowledge has scarcely been used in conservation and forest tree improvement because woody perennials face major research limitations such as their outcrossing reproductive systems, long juvenile phase, and huge genome sizes. Therefore, in this review we discuss predictive genomic approaches that promise increasing adaptive selection accuracy and shortening generation intervals. They may also assist the detection of novel allelic variants from tree germplasm, and disclose the genomic potential of adaptation to different environments. For instance, natural populations of tree species invite using tools from the population genomics field to study the signatures of local adaptation. Conventional genetic markers and whole genome sequencing both help identifying genes and markers that diverge between local populations more than expected under neutrality, and that exhibit unique signatures of diversity indicative of "selective sweeps." Ultimately, these efforts inform the conservation and breeding status capable of pivoting forest health, ecosystem services, and sustainable production. Key long-term perspectives include understanding how trees' phylogeographic history may affect the adaptive relevant genetic variation available for adaptation to environmental change. Encouraging "big data" approaches (machine learning-ML) capable of comprehensively merging heterogeneous genomic and ecological datasets is becoming imperative, too.

服务人员 付贺龙

PDF文件 浏览全文