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      Harnessing Crop Wild Diversity for Climate Change Adaptation

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          Abstract

          Warming and drought are reducing global crop production with a potential to substantially worsen global malnutrition. As with the green revolution in the last century, plant genetics may offer concrete opportunities to increase yield and crop adaptability. However, the rate at which the threat is happening requires powering new strategies in order to meet the global food demand. In this review, we highlight major recent ‘big data’ developments from both empirical and theoretical genomics that may speed up the identification, conservation, and breeding of exotic and elite crop varieties with the potential to feed humans. We first emphasize the major bottlenecks to capture and utilize novel sources of variation in abiotic stress (i.e., heat and drought) tolerance. We argue that adaptation of crop wild relatives to dry environments could be informative on how plant phenotypes may react to a drier climate because natural selection has already tested more options than humans ever will. Because isolated pockets of cryptic diversity may still persist in remote semi-arid regions, we encourage new habitat-based population-guided collections for genebanks. We continue discussing how to systematically study abiotic stress tolerance in these crop collections of wild and landraces using geo-referencing and extensive environmental data. By uncovering the genes that underlie the tolerance adaptive trait, natural variation has the potential to be introgressed into elite cultivars. However, unlocking adaptive genetic variation hidden in related wild species and early landraces remains a major challenge for complex traits that, as abiotic stress tolerance, are polygenic (i.e., regulated by many low-effect genes). Therefore, we finish prospecting modern analytical approaches that will serve to overcome this issue. Concretely, genomic prediction, machine learning, and multi-trait gene editing, all offer innovative alternatives to speed up more accurate pre- and breeding efforts toward the increase in crop adaptability and yield, while matching future global food demands in the face of increased heat and drought. In order for these ‘big data’ approaches to succeed, we advocate for a trans-disciplinary approach with open-source data and long-term funding. The recent developments and perspectives discussed throughout this review ultimately aim to contribute to increased crop adaptability and yield in the face of heat waves and drought events.

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          Biodiversity hotspots for conservation priorities.

          Conservationists are far from able to assist all species under threat, if only for lack of funding. This places a premium on priorities: how can we support the most species at the least cost? One way is to identify 'biodiversity hotspots' where exceptional concentrations of endemic species are undergoing exceptional loss of habitat. As many as 44% of all species of vascular plants and 35% of all species in four vertebrate groups are confined to 25 hotspots comprising only 1.4% of the land surface of the Earth. This opens the way for a 'silver bullet' strategy on the part of conservation planners, focusing on these hotspots in proportion to their share of the world's species at risk.
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            Maximum entropy modeling of species geographic distributions

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              Genome editing. The new frontier of genome engineering with CRISPR-Cas9.

              The advent of facile genome engineering using the bacterial RNA-guided CRISPR-Cas9 system in animals and plants is transforming biology. We review the history of CRISPR (clustered regularly interspaced palindromic repeat) biology from its initial discovery through the elucidation of the CRISPR-Cas9 enzyme mechanism, which has set the stage for remarkable developments using this technology to modify, regulate, or mark genomic loci in a wide variety of cells and organisms from all three domains of life. These results highlight a new era in which genomic manipulation is no longer a bottleneck to experiments, paving the way toward fundamental discoveries in biology, with applications in all branches of biotechnology, as well as strategies for human therapeutics. Copyright © 2014, American Association for the Advancement of Science.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Genes (Basel)
                Genes (Basel)
                genes
                Genes
                MDPI
                2073-4425
                20 May 2021
                May 2021
                : 12
                : 5
                : 783
                Affiliations
                [1 ]Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Km 7 Vía Rionegro, Las Palmas, Rionegro 054048, Colombia; llopez@ 123456agrosavia.co
                [2 ]Departamento de Ciencias Forestales, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia, Sede Medellín, Medellín 050034, Colombia
                Author notes
                [* ]Correspondence: acortes@ 123456agrosavia.co
                Author information
                https://orcid.org/0000-0003-4178-0675
                https://orcid.org/0000-0002-4967-6955
                Article
                genes-12-00783
                10.3390/genes12050783
                8161384
                34065368
                a1b79353-d952-46c7-ae82-c781361fffa8
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 29 March 2021
                : 19 May 2021
                Categories
                Review

                abiotic stress tolerance,genebanks,germplasm collections,ex situ conservation,genetic adaptation,genome-wide selection scans (gwss),genome–environment associations (gea),genomic prediction (gp),machine learning (ml)

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