通过利用基因本体学先验和基于二进制的组合优化来优化SNP子集,增强Arabidopsis thaliana中的基因预测
Qingfang Ba1, Heng Zhou1, Zheming Yuan1
1Hunan Engineering and Technology Research Center for Agricultural Big Data Analysis & Decision-making, Hunan Agricultural University, Changsha, China.
Frontiers in bioinformatics
|July 3, 2025
概括
我们开发了binGO-GS,这是一种新的基因组选择方法,使用基因本体学途径来提高植物复杂特征的预测准确度. 这种方法提高了基因组预测性能,并有助于植物育种.
科学领域:
- 植物遗传学和基因组学
- 定量遗传学 是一种定量遗传学.
- 生物信息学是一种生物信息学.
背景情况:
- 基因组选择 (GS) 对于植物育种至关重要,利用高密度分子标记物和测序.
- 定量特征是由多个基因控制的,这些基因通常在特定的基因本体学 (GO) 途径中得到丰富.
- 在GO途径中优化基因组预测 (GP) 的标记子集仍然是一个挑战.
研究的目的:
- 引入binGO-GS,一个改进的基因组选择框架.
- 将基于GO的生物先验与基于bin的新型组合SNP子集选择策略集成在一起.
- 通过选择生物相关标记子集来提高GP的性能.
主要方法:
- 开发了binGO-GS框架,集成GO路径和组合SNP选择.
- 使用两个数据集,对Arabidopsis thaliana的9个定量特征进行了binGO-GS性能评估.
- 用基因组BLUP (GBLUP) 和其他六种回归模型将binGO-GS与全标记集和随机标记器进行比较.
主要成果:
- 与使用完整或随机标记集的GBLUP相比,binGO-GS显著提高了所有测试特征的预测准确性.
- 在另外六种回归模型中也观察到类似的准确性改善.
- 针对相似特征的选择标记物显示出一致的基因组分布,支持多基因遗传.
结论:
- binGO-GS提供了一种强大且可解释的方法来提高基因组选择性能.
- 该方法为加速植物育种和生殖质创新提供了一种方法论参考.
- 强调将生物途径信息整合到基因组预测模型中的实用性.
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