Predicting Thermal Adaptation by Looking Into Populations’ Genomic Past

Abstract

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.

Allelic Diversity at Abiotic Stress Responsive Genes in Relationship to Ecological Drought Indices for Cultivated Tepary Bean, Phaseolus acutifolius A. Gray, and Its Wild Relatives

“…Furthermore, full sequencing of linked and well-understood genic regions, e.g., [ 79 ], as compared to “random” discovery of SNP markers in linkage equilibrium [ 80 , 81 ], allows for a more precise application of analytical tools, targeting adaptation in wild [ 82 ] and semi-domesticated materials [ 83 ]. Techniques such as gene-based species tree reconstruction [ 84 ] and inferences of the mutation/selection balance [ 85 , 86 ] presuppose physical linkage among markers.…”

Climate Vulnerability Assessment of the Espeletia Complex on Páramo Sky Islands in the Northern Andes

“…Last but not least, extending the modeling approach implemented in this work to other key pressures (e.g., fire, Wu and Porinchu, 2019;Rivadeneira et al, 2020;Zomer and Ramsay, 2020) and plant groups (Luteyn, 1999) in the Páramo, as well as to other sky islands around the mountains of the world (Hoorn et al, 2018;Pausas et al, 2018;Nürk et al, 2019;Testolin et al, 2020), and more broadly to other island-like systems (Papadopoulou and Knowles, 2015;Lamichhaney et al, 2017;Cámara-Leret et al, 2020;Flantua et al, 2020), will help understanding climate change effects on unrelated taxa experiencing similar evolutionary processes (Condamine et al, 2018;Cortés et al, 2020;Nürk et al, 2020). Such systems offer natural experiments to assess the role of colonization and adaptation (Ding et al, 2020;McGee et al, 2020;Tito et al, 2020) in the past and ongoing responses to climate change, which undeniably will complement ecological predictive modeling.…”

Inheritance of Rootstock Effects in Avocado (Persea americana Mill.) cv. Hass

“…A so far unexplored yet promising alternative would be to calibrate Genomic Prediction ( Crossa et al, 2017 ; Grattapaglia et al, 2018 ) and Machine Learning ( Gianola et al, 2011 ; Libbrecht and Noble, 2015 ; Schrider and Kern, 2018 ) models using high-throughput genotyping ( Cortés et al, 2020b ) of phenotyped ungrafted avocado trees spanning all three races, to predict rootstocks’ own unobserved phenotypes. Interpolating these predictions and quantitative genetic parameters across the rich ecological continuum of the northern Andean mountains ( Madriñán et al, 2013 ; Valencia et al, 2020 ), within a multi-climate ( Costa-Neto et al, 2020 ) “enviromic prediction” paradigm ( Resende et al, 2020 ), will be key to target optimum genotype x environment arrangements for yield ( Galeano et al, 2012 ; Blair et al, 2013 ) and quality ( Wu et al, 2020 ) components, as well as in the face of abiotic ( Cortés et al, 2020a ) and biotic ( Naidoo et al, 2019 ) stresses imposed by climate change.…”