Predicting Thermal Adaptation by Looking Into Populations’ Genomic Past
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-09-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2020.564515/full |
| _version_ | 1819118680725782528 |
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| author | Andrés J. Cortés Andrés J. Cortés Felipe López-Hernández Daniela Osorio-Rodriguez |
| author_facet | Andrés J. Cortés Andrés J. Cortés Felipe López-Hernández Daniela Osorio-Rodriguez |
| author_sort | Andrés J. Cortés |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T05:52:44Z |
| format | Article |
| id | doaj.art-5a808e02bafa4181898429784b374679 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-22T05:52:44Z |
| publishDate | 2020-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-5a808e02bafa4181898429784b3746792022-12-21T18:36:49ZengFrontiers Media S.A.Frontiers in Genetics1664-80212020-09-011110.3389/fgene.2020.564515564515Predicting Thermal Adaptation by Looking Into Populations’ Genomic PastAndrés J. Cortés0Andrés J. Cortés1Felipe López-Hernández2Daniela Osorio-Rodriguez3Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, ColombiaDepartamento de Ciencias Forestales, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia – Sede Medellín, Medellín, ColombiaCorporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, ColombiaDivision of Geological and Planetary Sciences, California Institute of Technology (Caltech), Pasadena, CA, United StatesMolecular 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.https://www.frontiersin.org/article/10.3389/fgene.2020.564515/fullcoalescent theorygenome-wide association studiesgenome-wide selection scansgenome–environment associationsphylogeographybreeder’s equation |
| spellingShingle | Andrés J. Cortés Andrés J. Cortés Felipe López-Hernández Daniela Osorio-Rodriguez Predicting Thermal Adaptation by Looking Into Populations’ Genomic Past Frontiers in Genetics coalescent theory genome-wide association studies genome-wide selection scans genome–environment associations phylogeography breeder’s equation |
| title | Predicting Thermal Adaptation by Looking Into Populations’ Genomic Past |
| title_full | Predicting Thermal Adaptation by Looking Into Populations’ Genomic Past |
| title_fullStr | Predicting Thermal Adaptation by Looking Into Populations’ Genomic Past |
| title_full_unstemmed | Predicting Thermal Adaptation by Looking Into Populations’ Genomic Past |
| title_short | Predicting Thermal Adaptation by Looking Into Populations’ Genomic Past |
| title_sort | predicting thermal adaptation by looking into populations genomic past |
| topic | coalescent theory genome-wide association studies genome-wide selection scans genome–environment associations phylogeography breeder’s equation |
| url | https://www.frontiersin.org/article/10.3389/fgene.2020.564515/full |
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