Cortés Andrés J, López-Hernández Felipe
Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Km 7 Vía Rionegro, Las Palmas, Rionegro 054048, Colombia.
Departamento de Ciencias Forestales, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia, Sede Medellín, Medellín 050034, Colombia.
Genes (Basel). 2021 May 20;12(5):783. doi: 10.3390/genes12050783.
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.
气候变暖和干旱正在降低全球作物产量，有可能使全球营养不良状况大幅恶化。与上世纪的绿色革命一样，植物遗传学可能为提高产量和作物适应性提供切实机会。然而，威胁发生的速度需要推动新的策略，以满足全球粮食需求。在本综述中，我们重点介绍了实证基因组学和理论基因组学近期的主要“大数据”进展，这些进展可能加速具有养活人类潜力的外来和优良作物品种的鉴定、保护及育种。我们首先强调捕获和利用非生物胁迫（即高温和干旱）耐受性新变异来源的主要瓶颈。我们认为，作物野生近缘种对干旱环境的适应性可能有助于了解植物表型如何应对更干燥的气候，因为自然选择已经测试了比人类所能测试的更多的选择。由于偏远半干旱地区可能仍存在孤立的隐性多样性区域，我们鼓励为基因库开展基于新栖息地的种群导向型收集工作。我们继续讨论如何利用地理参考和广泛的环境数据，系统地研究这些野生和地方品种作物群体的非生物胁迫耐受性。通过揭示耐受性适应性状背后的基因，自然变异有可能渗入优良品种。然而，对于像非生物胁迫耐受性这样受多基因调控（即由许多低效应基因调控）的复杂性状而言，解锁隐藏在相关野生物种和早期地方品种中的适应性遗传变异仍然是一项重大挑战。因此，我们最后展望有助于克服这一问题的现代分析方法。具体而言，基因组预测、机器学习和多性状基因编辑都提供了创新的替代方法，以加速更准确的预育种和育种工作，提高作物适应性和产量，同时在面对日益增加的高温和干旱时满足未来全球粮食需求。为了使这些“大数据”方法取得成功，我们提倡采用跨学科方法，使用开源数据并提供长期资金。本综述中讨论的近期进展和观点最终旨在促进面对热浪和干旱事件时作物适应性和产量的提高。
