环境预测全球基因型竞赛揭示了多种模型策略可以提供令人满意的玉米产量估计
Jacob D Washburn1, José Ignacio Varela2,3, Alencar Xavier3,4
1USDA-ARS-MWA-PGRU, 302-A Curtis Hall, U. of MO., Columbia, MO, 65211, USA.
bioRxiv : the preprint server for biology
|September 30, 2024
概括
使用遗传学和环境预测表型是生物学的关键. "基因组到领域"竞赛探索了通过环境 (GxE) 预测基因型的多种方法,表明没有一种方法是卓越的.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 植物科学 植物科学
- 动物科学动物科学
背景情况:
- 从遗传和环境因素预测表型是现代生物学的一个主要挑战.
- 改进可以在健康,农业和环境管理方面取得重大进展.
- 基因组到领域 (G2F) 倡议在2022-2023年举办了一场公共基因型对环境 (GxE) 预测竞赛.
研究的目的:
- 通过环境相互作用评估基因型的各种预测建模策略.
- 通过汇集来自各个学科的全球参与者来促进创新.
- 利用基因组,表型,环境和管理数据的全面数据集.
主要方法:
- 利用了一个大型的多年数据集,包括基因组变异,表型,天气和现场管理数据.
- 参与者应用了广泛的建模方法,包括机器学习,深度学习,定量遗传学和机械模型.
- 获胜的策略结合了机器学习 (随机森林,回归,最小方形) 和传统的育种工具,并为环境和遗传强调提供了单独的模型.
主要成果:
- 该比赛吸引了来自学术界,工业界和政府的多元化全球观众.
- 采用了各种建模技术,突出了GxE预测的各种方法.
- 获胜的方法使用组合方法整合了环境和遗传因素,而其他高绩效的团队则使用了不同的策略.
结论:
- 没有一个单一的模型或策略证明了基因型对环境预测的普遍优越性.
- 成功方法的多样性强调了GxE相互作用的复杂性和跨学科方法的价值.
- 该竞赛为使用真实数据进行GxE预测的建模知识提供了有价值的见解.
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