Cortés Andrés J, López-Hernández Felipe, Osorio-Rodriguez Daniela
Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, Colombia.
Departamento de Ciencias Forestales, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia - Sede Medellín, Medellín, Colombia.
Front Genet. 2020 Sep 25;11:564515. doi: 10.3389/fgene.2020.564515. eCollection 2020.
Molecular evolution offers an insightful theory to interpret the genomic consequences of thermal adaptation to previous events of climate change beyond range shifts. However, disentangling often mixed footprints of selective and demographic processes from those due to lineage sorting, recombination rate variation, and genomic constrains is not trivial. Therefore, here we condense current and historical population genomic tools to study thermal adaptation and outline key developments (genomic prediction, machine learning) that might assist their utilization for improving forecasts of populations' responses to thermal variation. We start by summarizing how recent thermal-driven selective and demographic responses can be inferred by coalescent methods and in turn how quantitative genetic theory offers suitable multi-trait predictions over a few generations via the breeder's equation. We later assume that enough generations have passed as to display genomic signatures of divergent selection to thermal variation and describe how these footprints can be reconstructed using genome-wide association and selection scans or, alternatively, may be used for forward prediction over multiple generations under an infinitesimal genomic prediction model. Finally, we move deeper in time to comprehend the genomic consequences of thermal shifts at an evolutionary time scale by relying on phylogeographic approaches that allow for reticulate evolution and ecological parapatric speciation, and end by envisioning the potential of modern machine learning techniques to better inform long-term predictions. We conclude that foreseeing future thermal adaptive responses requires bridging the multiple spatial scales of historical and predictive environmental change research under modern cohesive approaches such as genomic prediction and machine learning frameworks.
分子进化提供了一个深刻的理论，用以解释热适应对气候变化先前事件的基因组后果，而不仅仅是范围转移。然而，将选择性和人口统计学过程中常常混杂的痕迹与由于谱系分选、重组率变化和基因组限制所导致的痕迹区分开来并非易事。因此，在这里我们整合了当前和历史的群体基因组工具来研究热适应，并概述了可能有助于利用这些工具改进对种群对热变化反应预测的关键进展（基因组预测、机器学习）。我们首先总结如何通过合并方法推断近期热驱动的选择性和人口统计学反应，以及定量遗传理论如何通过育种者方程在几代人时间内提供合适的多性状预测。随后，我们假设已经经过了足够多的世代，以显示出对热变化的分歧选择的基因组特征，并描述如何使用全基因组关联和选择扫描来重建这些痕迹，或者，在无穷小基因组预测模型下，这些痕迹可用于多代的向前预测。最后，我们更深入地探讨时间，通过依赖允许网状进化和生态邻域物种形成的系统地理学方法，来理解进化时间尺度上热变化的基因组后果，并以设想现代机器学习技术在更好地为长期预测提供信息方面的潜力作为结束。我们得出结论，预见未来的热适应反应需要在诸如基因组预测和机器学习框架等现代连贯方法下，弥合历史和预测性环境变化研究的多个空间尺度。
