RESEARCH-PGR: PanAnd - Harnessing convergence and constraint to predict adaptations to abiotic stress for maize and sorghum

玉米、高粱、甘蔗和芒草是世界上产量最高、用水效率最高的作物和生物燃料。这种生产力是由于在过去的1500万年中共享的生理和遗传祖先。虽然这四种作物将被广泛使用，但它们与另外800种植物密切相关，这些植物在世界各地的草原上占主导地位，能够适应多种环境压力，包括洪水、干旱、高温和霜冻。项目团队将使用现代基因组学和机器学习来调查和分析这些相关物种，确定它们共享的最重要的遗传特征，使它们能够适应高温和干旱。这项工作的成果将被商业和公共部门的植物育种者用来提高玉米和高粱的产量和对极端天气的适应能力。这种长期影响的关键是培养下一代计算生物学科学家来解决基本问题。这些技能将通过黑客马拉松和生物信息学培训研讨会来发展。该项目将通过巡回博物馆展览等方式向公众传播这一科学。Andropogoneae草族包含一千多种，总共代表了超过十亿年的进化史。它利用NADP-C4光合作用和广泛的适应性成为地球上的优势进化支。这个项目将利用这个部落的多样性和进化来理解植物基因组的适应性收敛和约束规则。该项目团队将取样并分析世界范围内的androgoneae遗传多样性，以开发详细的模型来测试(1)进化约束的定量估计是否能改善对适应性相关特征的预测，以及(2)在androgoneae中共享的趋同环境适应是否能解释总适应差异的很大一部分。这些假设将通过组装57个物种的基因和调控内容以及另外700个物种的全基因组测序来验证。对于8个物种，将在序列水平上调查其自然适应范围的多样性。进化和机器学习模型将用于量化每个祖先基因组元素突变的破坏性影响。特定物种之间和特定物种内部的调查也将有助于估计趋同进化的普遍程度。该项目解决了基因型到表型问题的两个关键要素——如何量化突变的破坏性影响，以及如何确定对环境压力的适应性解决方案是否在物种间趋同共享。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Maize, sorghum, sugarcane, and Miscanthus are the most productive and water efficient crops and biofuels in the world. This productivity is due to a shared physiology and genetic ancestry over the last 15 million years. While these four crops will be extensively used, they are closely related to another 800 species that dominate grasslands across the world and are adapted to numerous environmental stresses including flooding, drought, heat, and frost. The project team will use modern genomics and machine learning to survey and analyze these related species, determining the most important genetic features they share that allow them to adapt to heat and drought. The results of this work will be used by commercial and public sector plant breeders to make maize and sorghum more productive and resilient to extreme weather. Key to this long-term impact is training the next generation of scientists in computational biology to address fundamental questions. These skills will be developed through hackathons and bioinformatics training workshops. The project will communicate this science to the general public through venues such as a traveling museum exhibit.The Andropogoneae tribe of grasses contains a thousand species that collectively represent over a billion years of evolutionary history. It has used NADP-C4 photosynthesis and a wide range of adaptations to become a dominant clade on earth. This project will use the diversity and evolution across this tribe to understand the rules of adaptive convergence and constraint in plant genomes. The project team will sample and analyze the worldwide spectrum of genetic diversity in Andropogoneae to develop detailed models testing whether (1) quantitative estimates of evolutionary constraint improve predictions of fitness-related traits, and (2) convergent environmental adaptations shared across the Andropogoneae explain a substantial proportion of total adaptive variance. These hypotheses will be tested by assembling the gene and regulatory content of 57 species as well as whole genome sequencing of another 700 species. For eight species, diversity across their natural range of adaptation will be surveyed at the sequence level. Evolutionary and machine learning models will be used to quantify the disruptive impact of a mutation in every ancestral genomic element. The inter and intra-specific surveys will also permit an estimation of the prevalence of convergent evolution. This project addresses two key elements of the genotype to phenotype problem - how to quantify the disruptive impact of mutations and how to determine whether adaptive solutions to environmental stresses are convergently shared across species.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.