人工智能,机械模型和快速育种方面的最新进展为精确和加速的基因组学辅助育种提供了令人兴奋的机会
Javaid Akhter Bhat1, Xianzhong Feng1,2, Zahoor A Mir3
1Zhejiang Lab, Hangzhou, China.
Physiologia plantarum
|July 4, 2023
概括
开发高产,耐压作物至关重要. 将人工智能 (AI) 与基因组辅助育种 (GAB) 和快速育种相结合,可以加速作物改进,以实现全球粮食安全.
科学领域:
- 农业科学 农业科学
- 植物育种 植物育种
- 计算生物学 计算生物学
背景情况:
- 由于人口增长和气候变化,传统的作物育种方法难以满足对高产,耐压力品种的需求.
- 传统育种的局限性包括效率低下,缺乏精度和劳动密集度.
- 高通量现象学和基因组学辅助育种 (GAB) 的进步提供了更高的效率,但面临着诸如复杂数据分析和线性模型无法捕捉复杂的特征相互作用等挑战.
研究的目的:
- 探索整合人工智能 (AI) 与基因组辅助育种 (GAB) 和加速育种以加速作物改进的潜力.
- 解决传统育种和当前GAB方法在开发抗气候作物的局限性.
- 要突出AI在捕捉GAB的非线性和表观相互作用中的作用.
主要方法:
- 使用高通量现象学和基因组学辅助育种 (GAB).
- 在全基因组关联研究 (GWAS) 和基因组选择 (GS) 中应用人工智能 (AI) 技术进行非线性建模.
- 采用快速育种来缩短作物发育时间表.
主要成果:
- 人工智能技术可以捕捉非线性和表观相互作用,提高GAB的适用性.
- 快速繁殖可以将作物发育时间缩短3-5倍.
- 人工智能,GAB和快速育种的整合有望提高作物品种开发的准确性和效率.
结论:
- 整合AI,GAB和速度育种为作物育种提供了一种革命性的方法.
- 这一综合战略可以显著加快高产,耐压作物品种的发展.
- 这种方法对于保护全球粮食生产免受人口增长和气候变化等挑战至关重要.
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