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在甘中使用机器学习进行基因组预测,甘是一种复杂的高度多倍体的克隆繁殖作物,主要特征具有实质性的非添加变异

Chensong Chen¹, Owen Powell¹, Eric Dinglasan¹

¹Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Queensland, Australia.

The plant genome

|September 20, 2023

概括

机器学习 (ML) 和深度学习 (DL) 模型显示的预测准确度与甘克隆性能的增材基因组最佳线性无偏预测 (GBLUP) 相当. 然而,包含遗传相互作用的扩展GBLUP模型更准确.

科学领域:

植物育种植物育种
基因组学就是基因组学.
生物信息学是一种生物信息学.

背景情况:

甘复杂的多倍体基因组对传统的基因组预测提出了挑战.
添加模型可能无法完全捕捉多倍体物种中的基因相互作用.

研究的目的:

评估机器学习 (ML) 和深度学习 (DL) 方法用于甘的基因组预测.
将ML/DL模型的预测精度与基因组最佳线性无偏预测 (GBLUP) 进行比较.

主要方法:

研究深度学习 (DL) 神经网络 (MLP,CNN) 和随机森林 (RF) 用于基因组预测.
利用了2912个含有26,086个SNP标记的甘克隆的数据.
将预测准确度与GBLUP模型进行比较,包括具有主导性和表现效应的模型.

主要成果:

ML模型 (RF,DL) 实现了0.35 (TCH),0.38 (CCS) 和0.48 (纤维) 的预测准确度.
在GBLUP模型中,准确度高达0.37 (TCH),0.43 (CCS) 和0.48 (光纤).
ML和DL模型的准确性与添加式GBLUP相比较,但不如扩展型GBLUP模型那么准确.

结论:

ML和DL模型提供了与甘添加剂GBLUP相比较的预测能力.
考虑到遗传相互作用的扩展GBLUP模型提供了卓越的预测准确性.
进一步的研究可能会探索复杂的甘基因组的混合方法.

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