使用神经网络将遗传标记和作物模型参数联系起来,以增强整合性特征的基因组预测
Florian Larue1,2, Lauriane Rouan1,2, David Pot1,2
1Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche, Institut Amélioration Génétique et Adaptation des Plantes méditerranéennes et Tropicales (UMR AGAP), Montpellier, France.
Frontiers in plant science
|August 14, 2024
概括
卷积神经网络 (CNN) 通过捕捉复杂的遗传相互作用,准确地预测的产量. 将CNN与作物生长模型 (CGM) 结合起来,进一步提高了植物性能预测的准确性.
科学领域:
- 植物育种和遗传学
- 机器学习在农业中的应用
- 基因组预测 基因组预测
背景情况:
- 预测作物表现是复杂的,涉及遗传,环境和表观相互作用.
- 经典的基因组预测 (GP) 模型主要捕捉附加效应,限制它们模拟非附加相互作用的能力,如epistasis.
- 机器学习和深度学习为模拟这些复杂的非线性效应提供了潜力.
研究的目的:
- 评估卷积神经网络 (CNN) 与传统的GP方法来预测的地面新鲜重量.
- 评估将作物生长模型 (CGM) 与GP集成的实用性,以提高整合性特征的预测.
主要方法:
- 开发了一个卷积神经网络 (CNN),并与LASSO和贝叶斯C回归方法进行了比较.
- 土上空新鲜重量被用作预测的整合性质.
- 研究了一种混合方法,将CGM与GP结合起来.
主要成果:
- 与LASSO和Bayes C.相比,CNN显示出更高的预测准确度.
- 整合CGM与GP显著提高了所有测试的回归方法的预测能力.
- CNN更适合预测复杂的,整合性的植物特征.
结论:
- 非线性预测方法,特别是CNN,对于模拟植物中复杂的遗传结构是有效的.
- 将作物生长模型与基因组预测相结合,为改善作物性能预测提供了一个有希望的策略.
- 这些发现支持开发先进的基因组到表型模型.
相关概念视频
02:16Light Acquisition
8.4K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.4K
01:59Plant Breeding and Biotechnology
18.9K
Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.
18.9K
01:25Multiple Regression
3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K