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Sun PW, Chang JT, Luo MX, Liao PC. Genomic insights into local adaptation and vulnerability of Quercus longinux to climate change. BMC PLANT BIOLOGY 2024; 24:279. [PMID: 38609850 PMCID: PMC11015620 DOI: 10.1186/s12870-024-04942-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 03/22/2024] [Indexed: 04/14/2024]
Abstract
BACKGROUND Climate change is expected to alter the factors that drive changes in adaptive variation. This is especially true for species with long life spans and limited dispersal capabilities. Rapid climate changes may disrupt the migration of beneficial genetic variations, making it challenging for them to keep up with changing environments. Understanding adaptive genetic variations in tree species is crucial for conservation and effective forest management. Our study used landscape genomic analyses and phenotypic traits from a thorough sampling across the entire range of Quercus longinux, an oak species native to Taiwan, to investigate the signals of adaptation within this species. RESULTS Using ecological data, phenotypic traits, and 1,933 single-nucleotide polymorphisms (SNPs) from 205 individuals, we classified three genetic groups, which were also phenotypically and ecologically divergent. Thirty-five genes related to drought and freeze resistance displayed signatures of natural selection. The adaptive variation was driven by diverse environmental pressures such as low spring precipitation, low annual temperature, and soil grid sizes. Using linear-regression-based methods, we identified isolation by environment (IBE) as the optimal model for adaptive SNPs. Redundancy analysis (RDA) further revealed a substantial joint influence of demography, geology, and environments, suggesting a covariation between environmental gradients and colonization history. Lastly, we utilized adaptive signals to estimate the genetic offset for each individual under diverse climate change scenarios. The required genetic changes and migration distance are larger in severe climates. Our prediction also reveals potential threats to edge populations in northern and southeastern Taiwan due to escalating temperatures and precipitation reallocation. CONCLUSIONS We demonstrate the intricate influence of ecological heterogeneity on genetic and phenotypic adaptation of an oak species. The adaptation is also driven by some rarely studied environmental factors, including wind speed and soil features. Furthermore, the genetic offset analysis predicted that the edge populations of Q. longinux in lower elevations might face higher risks of local extinctions under climate change.
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Affiliation(s)
- Pei-Wei Sun
- School of Life Science, National Taiwan Normal University, No. 88 Ting-Chow Rd., Sec. 4, Taipei, 116, Taiwan
| | - Jui-Tse Chang
- School of Life Science, National Taiwan Normal University, No. 88 Ting-Chow Rd., Sec. 4, Taipei, 116, Taiwan
| | - Min-Xin Luo
- School of Life Science, National Taiwan Normal University, No. 88 Ting-Chow Rd., Sec. 4, Taipei, 116, Taiwan
| | - Pei-Chun Liao
- School of Life Science, National Taiwan Normal University, No. 88 Ting-Chow Rd., Sec. 4, Taipei, 116, Taiwan.
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2
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Maduna SN, Jónsdóttir ÓDB, Imsland AKD, Gíslason D, Reynolds P, Kapari L, Hangstad TA, Meier K, Hagen SB. Genomic Signatures of Local Adaptation under High Gene Flow in Lumpfish-Implications for Broodstock Provenance Sourcing and Larval Production. Genes (Basel) 2023; 14:1870. [PMID: 37895225 PMCID: PMC10606024 DOI: 10.3390/genes14101870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/20/2023] [Accepted: 09/23/2023] [Indexed: 10/29/2023] Open
Abstract
Aquaculture of the lumpfish (Cyclopterus lumpus L.) has become a large, lucrative industry owing to the escalating demand for "cleaner fish" to minimise sea lice infestations in Atlantic salmon mariculture farms. We used over 10K genome-wide single nucleotide polymorphisms (SNPs) to investigate the spatial patterns of genomic variation in the lumpfish along the coast of Norway and across the North Atlantic. Moreover, we applied three genome scans for outliers and two genotype-environment association tests to assess the signatures and patterns of local adaptation under extensive gene flow. With our 'global' sampling regime, we found two major genetic groups of lumpfish, i.e., the western and eastern Atlantic. Regionally in Norway, we found marginal evidence of population structure, where the population genomic analysis revealed a small portion of individuals with a different genetic ancestry. Nevertheless, we found strong support for local adaption under high gene flow in the Norwegian lumpfish and identified over 380 high-confidence environment-associated loci linked to gene sets with a key role in biological processes associated with environmental pressures and embryonic development. Our results bridge population genetic/genomics studies with seascape genomics studies and will facilitate genome-enabled monitoring of the genetic impacts of escapees and allow for genetic-informed broodstock selection and management in Norway.
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Affiliation(s)
- Simo Njabulo Maduna
- Department of Ecosystems in the Barents Region, Svanhovd Research Station, Norwegian Institute of Bioeconomy Research, 9925 Svanvik, Norway;
| | | | - Albert Kjartan Dagbjartarson Imsland
- Akvaplan-Niva Iceland Office, Akralind 6, 201 Kópavogur, Iceland; (Ó.D.B.J.); (A.K.D.I.)
- Department of Biological Sciences, High Technology Centre, University of Bergen, 5020 Bergen, Norway
| | | | | | - Lauri Kapari
- Akvaplan-Niva, Framsenteret, 9296 Tromsø, Norway;
| | | | | | - Snorre B. Hagen
- Department of Ecosystems in the Barents Region, Svanhovd Research Station, Norwegian Institute of Bioeconomy Research, 9925 Svanvik, Norway;
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3
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Campbell AM, Hauton C, Baker-Austin C, van Aerle R, Martinez-Urtaza J. An integrated eco-evolutionary framework to predict population-level responses of climate-sensitive pathogens. Curr Opin Biotechnol 2023; 80:102898. [PMID: 36739640 DOI: 10.1016/j.copbio.2023.102898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 12/22/2022] [Accepted: 01/02/2023] [Indexed: 02/05/2023]
Abstract
It is critical to gain insight into how climate change impacts evolutionary responses within climate-sensitive pathogen populations, such as increased resilience, opportunistic responses and the emergence of dominant variants from highly variable genomic backgrounds and subsequent global dispersal. This review proposes a framework to support such analysis, by combining genomic evolutionary analysis with climate time-series data in a novel spatiotemporal dataframe for use within machine learning applications, to understand past and future evolutionary pathogen responses to climate change. Recommendations are presented to increase the feasibility of interdisciplinary applications, including the importance of robust spatiotemporal metadata accompanying genome submission to databases. Such workflows will inform accessible public health tools and early-warning systems, to aid decision-making and mitigate future human health threats.
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Affiliation(s)
- Amy M Campbell
- School of Ocean and Earth Science, University of Southampton, National Oceanography Centre, Southampton, UK; Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK
| | - Chris Hauton
- School of Ocean and Earth Science, University of Southampton, National Oceanography Centre, Southampton, UK
| | - Craig Baker-Austin
- Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK
| | - Ronny van Aerle
- Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK
| | - Jaime Martinez-Urtaza
- Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK; Department of Genetics and Microbiology, Autonomous University of Barcelona, Barcelona, Spain.
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Gates K, Sandoval-Castillo J, Brauer CJ, Unmack PJ, Laporte M, Bernatchez L, Beheregaray LB. Environmental selection, rather than neutral processes, best explain regional patterns of diversity in a tropical rainforest fish. Heredity (Edinb) 2023:10.1038/s41437-023-00612-x. [PMID: 36997655 DOI: 10.1038/s41437-023-00612-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 03/14/2023] [Accepted: 03/14/2023] [Indexed: 03/31/2023] Open
Abstract
AbstractTo conserve the high functional and genetic variation in hotspots such as tropical rainforests, it is essential to understand the forces driving and maintaining biodiversity. We asked to what extent environmental gradients and terrain structure affect morphological and genomic variation across the wet tropical distribution of an Australian rainbowfish, Melanotaenia splendida splendida. We used an integrative riverscape genomics and morphometrics framework to assess the influence of these factors on both putative adaptive and non-adaptive spatial divergence. We found that neutral genetic population structure was largely explainable by restricted gene flow among drainages. However, environmental associations revealed that ecological variables had a similar power to explain overall genetic variation, and greater power to explain body shape variation, than the included neutral covariables. Hydrological and thermal variables were the strongest environmental predictors and were correlated with traits previously linked to heritable habitat-associated dimorphism in rainbowfishes. In addition, climate-associated genetic variation was significantly associated with morphology, supporting heritability of shape variation. These results support the inference of evolved functional differences among localities, and the importance of hydroclimate in early stages of diversification. We expect that substantial evolutionary responses will be required in tropical rainforest endemics to mitigate local fitness losses due to changing climates.
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Cortés AJ, Barnaby JY. Editorial: Harnessing genebanks: High-throughput phenotyping and genotyping of crop wild relatives and landraces. FRONTIERS IN PLANT SCIENCE 2023; 14:1149469. [PMID: 36968416 PMCID: PMC10036837 DOI: 10.3389/fpls.2023.1149469] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 01/26/2023] [Indexed: 06/18/2023]
Affiliation(s)
- Andrés J. Cortés
- Corporación Colombiana de Investigación Agropecuaria – AGROSAVIA, C.I. La Selva, Rionegro, Colombia
| | - Jinyoung Y. Barnaby
- U.S. Department of Agriculture, U.S. National Arboretum, Floral and Nursery Plants Research Unit, Beltsville, MD, United States
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Kramer IM, Pfenninger M, Feldmeyer B, Dhimal M, Gautam I, Shreshta P, Baral S, Phuyal P, Hartke J, Magdeburg A, Groneberg DA, Ahrens B, Müller R, Waldvogel AM. Genomic profiling of climate adaptation in Aedes aegypti along an altitudinal gradient in Nepal indicates nongradual expansion of the disease vector. Mol Ecol 2023; 32:350-368. [PMID: 36305220 DOI: 10.1111/mec.16752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 10/21/2022] [Accepted: 10/25/2022] [Indexed: 01/11/2023]
Abstract
Driven by globalization, urbanization and climate change, the distribution range of invasive vector species has expanded to previously colder ecoregions. To reduce health-threatening impacts on humans, insect vectors are extensively studied. Population genomics can reveal the genomic basis of adaptation and help to identify emerging trends of vector expansion. By applying whole genome analyses and genotype-environment associations to populations of the main dengue vector Aedes aegypti, sampled along an altitudinal gradient in Nepal (200-1300 m), we identify putatively adaptive traits and describe the species' genomic footprint of climate adaptation to colder ecoregions. We found two differentiated clusters with significantly different allele frequencies in genes associated to climate adaptation between the highland population (1300 m) and all other lowland populations (≤800 m). We revealed nonsynonymous mutations in 13 of the candidate genes associated to either altitude, precipitation or cold tolerance and identified an isolation-by-environment differentiation pattern. Other than the expected gradual differentiation along the altitudinal gradient, our results reveal a distinct genomic differentiation of the highland population. Local high-altitude adaptation could be one explanation of the population's phenotypic cold tolerance. Carrying alleles relevant for survival under colder climate increases the likelihood of this highland population to a worldwide expansion into other colder ecoregions.
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Affiliation(s)
- Isabelle Marie Kramer
- Institute of Occupational, Social and Environmental Medicine, Goethe University, Frankfurt am Main, Germany.,Senckenberg Biodiversity and Climate Research Centre, Frankfurt am Main, Germany
| | - Markus Pfenninger
- Senckenberg Biodiversity and Climate Research Centre, Frankfurt am Main, Germany.,Institute of Organismic and Molecular Evolution, Johannes Gutenberg University, Mainz, Germany
| | - Barbara Feldmeyer
- Senckenberg Biodiversity and Climate Research Centre, Frankfurt am Main, Germany
| | | | - Ishan Gautam
- Natural History Museum, Tribhuvan University, Kathmandu, Nepal
| | | | | | - Parbati Phuyal
- Institute of Occupational, Social and Environmental Medicine, Goethe University, Frankfurt am Main, Germany
| | - Juliane Hartke
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University, Mainz, Germany
| | - Axel Magdeburg
- Institute of Occupational, Social and Environmental Medicine, Goethe University, Frankfurt am Main, Germany
| | - David A Groneberg
- Institute of Occupational, Social and Environmental Medicine, Goethe University, Frankfurt am Main, Germany
| | - Bodo Ahrens
- Institute for Atmospheric and Environmental Sciences, Goethe University, Frankfurt am Main, Germany
| | - Ruth Müller
- Institute of Occupational, Social and Environmental Medicine, Goethe University, Frankfurt am Main, Germany.,Unit Entomology, Institute of Tropical Medicine, Antwerp, Belgium
| | - Ann-Marie Waldvogel
- Senckenberg Biodiversity and Climate Research Centre, Frankfurt am Main, Germany.,Institute of Zoology, University of Cologne, Cologne, Germany
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Cortés AJ, López-Hernández F, Blair MW. Genome–Environment Associations, an Innovative Tool for Studying Heritable Evolutionary Adaptation in Orphan Crops and Wild Relatives. Front Genet 2022; 13:910386. [PMID: 35991553 PMCID: PMC9389289 DOI: 10.3389/fgene.2022.910386] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 05/30/2022] [Indexed: 11/23/2022] Open
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.
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Affiliation(s)
- Andrés J. Cortés
- Corporacion Colombiana de Investigacion Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, Colombia
- *Correspondence: Andrés J. Cortés, ; Matthew W. Blair,
| | - Felipe López-Hernández
- Corporacion Colombiana de Investigacion Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, Colombia
| | - Matthew W. Blair
- Department of Agricultural & Environmental Sciences, Tennessee State University, Nashville, TN, United States
- *Correspondence: Andrés J. Cortés, ; Matthew W. Blair,
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Láruson ÁJ, Fitzpatrick MC, Keller SR, Haller BC, Lotterhos KE. Seeing the Forest for the trees: Assessing genetic offset predictions from Gradient Forest. Evol Appl 2022; 15:403-416. [PMID: 35386401 PMCID: PMC8965365 DOI: 10.1111/eva.13354] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 01/22/2022] [Accepted: 01/30/2022] [Indexed: 12/02/2022] Open
Abstract
Gradient Forest (GF) is a machine learning algorithm designed to analyze spatial patterns of biodiversity as a function of environmental gradients. An offset measure between the GF‐predicted environmental association of adapted alleles and a new environment (GF Offset) is increasingly being used to predict the loss of environmentally adapted alleles under rapid environmental change, but remains mostly untested for this purpose. Here, we explore the robustness of GF Offset to assumption violations, and its relationship to measures of fitness, using SLiM simulations with explicit genome architecture and a spatial metapopulation. We evaluate measures of GF Offset in: (1) a neutral model with no environmental adaptation; (2) a monogenic “population genetic” model with a single environmentally adapted locus; and (3) a polygenic “quantitative genetic” model with two adaptive traits, each adapting to a different environment. We found GF Offset to be broadly correlated with fitness offsets under both single locus and polygenic architectures. However, neutral demography, genomic architecture, and the nature of the adaptive environment can all confound relationships between GF Offset and fitness. GF Offset is a promising tool, but it is important to understand its limitations and underlying assumptions, especially when used in the context of predicting maladaptation.
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Affiliation(s)
- Áki Jarl Láruson
- Department of Natural Resources Cornell University Ithaca NY 14853 USA
| | - Matthew C. Fitzpatrick
- Appalachian Laboratory University of Maryland Center for Environmental Science Frostburg Maryland 21532 USA
| | - Stephen R. Keller
- Department of Plant Biology University of Vermont Burlington Vermont 05405 USA
| | - Benjamin C. Haller
- Department of Computational Biology Cornell University Ithaca NY 14853 USA
| | - Katie E. Lotterhos
- Department of Marine and Environmental Sciences Northeastern University Marine Science Center Nahant MA 01908 USA
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Yévenes M, Núñez-Acuña G, Gallardo-Escárate C, Gajardo G. Adaptive Differences in Gene Expression in Farm-Impacted Seedbeds of the Native Blue Mussel Mytilus chilensis. Front Genet 2021; 12:666539. [PMID: 34093658 PMCID: PMC8174845 DOI: 10.3389/fgene.2021.666539] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 04/23/2021] [Indexed: 01/02/2023] Open
Abstract
The study of adaptive population differences is relevant for evolutionary biology, as it evidences the power of selective local forces relative to gene flow in maintaining adaptive phenotypes and their underlying genetic determinants. However, human-mediated hybridization through habitat translocations, a common and recurrent aquaculture practice where hybrids could eventually replace local genotypes, risk populations' ability to cope with perturbations. The endemic marine mussel Mytilus chilensis supports a booming farming industry in the inner sea of Chiloé Island, southern Chile, which entirely relies on artificially collected seeds from natural beds that are translocated to ecologically different fattening centers. A matter of concern is how farm-impacted seedbeds will potentially cope with environmental shifts and anthropogenic perturbations. This study provides the first de novo transcriptome of M. chilensis; assembled from tissue samples of mantles and gills of individuals collected in ecologically different farm-impacted seedbeds, Cochamó (41°S) and Yaldad (43°S). Both locations and tissue samples differentially expressed transcripts (DETs) in candidate adaptive genes controlling multiple fitness traits, involved with metabolism, genetic and environmental information processing, and cellular processes. From 189,743 consensus contigs assembled: 1,716 (Bonferroni p value ≤ 0.05) were DETs detected in different tissues of samples from different locations, 210 of them (fold change ≥ | 100|) in the same tissue of samples from a different location, and 665 (fold change ≥ | 4|) regardless of the tissue in samples from a different location. Site-specific DETs in Cochamó (169) and Yaldad (150) in candidate genes controlling tolerance to temperature and salinity shifts, and biomineralization exhibit a high number of nucleotide genetic variants with regular occurrence (frequency > 99%). This novel M. chilensis transcriptome should help assessing and monitoring the impact of translocations in wild and farm-impacted mussel beds in Chiloé Island. At the same time, it would help designing effective managing practices for conservation, and translocation traceability.
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Affiliation(s)
- Marco Yévenes
- Programa de Doctorado en Ciencias, Mención Conservación y Manejo de Recursos Naturales, Universidad de Los Lagos, Osorno, Chile
- Laboratorio de Genética, Acuicultura & Biodiversidad, Departamento de Ciencias Biológicas y Biodiversidad, Universidad de Los Lagos, Osorno, Chile
| | - Gustavo Núñez-Acuña
- Laboratorio de Biotecnología y Genómica Acuícola, Centro Interdisciplinario para la Investigación en Acuicultura, Universidad de Concepción, Concepción, Chile
| | - Cristian Gallardo-Escárate
- Laboratorio de Biotecnología y Genómica Acuícola, Centro Interdisciplinario para la Investigación en Acuicultura, Universidad de Concepción, Concepción, Chile
| | - Gonzalo Gajardo
- Laboratorio de Genética, Acuicultura & Biodiversidad, Departamento de Ciencias Biológicas y Biodiversidad, Universidad de Los Lagos, Osorno, Chile
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Cortés AJ, López-Hernández F, Osorio-Rodriguez D. Predicting Thermal Adaptation by Looking Into Populations' Genomic Past. Front Genet 2020; 11:564515. [PMID: 33101385 PMCID: PMC7545011 DOI: 10.3389/fgene.2020.564515] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 08/24/2020] [Indexed: 12/18/2022] Open
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.
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Affiliation(s)
- Andrés J Cortés
- 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
| | - Felipe López-Hernández
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, Colombia
| | - Daniela Osorio-Rodriguez
- Division of Geological and Planetary Sciences, California Institute of Technology (Caltech), Pasadena, CA, United States
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