GbyE:一种集成的工具,用于基因组广泛的关联研究和基因组选择,基于基因与环境相互作用的基因
Xinrui Liu1,2, Mingxiu Wang1, Jie Qin1
1Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, 6110041, China.
BMC genomics
|April 19, 2024
概括
新的GbyE算法通过有效分析遗传环境相互作用来增强全基因组关联研究 (GWAS) 和基因组选择 (GS). 这种方法提高了育种计划中的预测准确性,特别是复杂的特征.
科学领域:
- 遗传学 遗传学 是一个
- 生物信息学是一种生物信息学.
- 定量遗传学 是一个量子遗传学.
背景情况:
- 生物体的生长和发育取决于遗传,环境和相互作用的影响.
- 全基因组关联研究 (GWAS) 已经确定了许多遗传标记,但由于计算局限性,交互效应仍未得到充分探索.
- 在繁殖和预测中利用这些互动标记,例如基因组选择 (GS),是具有挑战性的.
研究的目的:
- 开发一种新的基因型设计模型程序 (GbyE),用于分析GWAS和GS中的基因型与环境相互作用.
- 为了使附加性和相互作用性遗传效应的单独估计.
- 提高GWAS的统计能力和GS的预测准确性.
主要方法:
- 开发了使用Kronecker产品进行基因型设计的GbyE算法.
- 将GbyE应用于GWAS以检测显著的遗传位点,考虑到不同的遗传性和遗传相关性.
- 使用 rrBLUP 和 BGLR 模型,包括贝叶斯方法 (BRR,BayesA,BayesLASSO) 评估了 GbyE 在 GS 的表现.
主要成果:
- 与传统方法相比,GbyE可以检测出比传统方法更显著的遗传基因位点,特别是在遗传率低的情况下.
- 在各种遗传性和遗传关联水平上,GbyE显著提高了预测准确性,超过了平均方法.
- 在贝叶斯模型 (BRR,BayesA,BayesLASSO) 中,GbyE分别提高了9.4%,9.1%和11%的预测准确度.
- 在没有单一的环境数据和跨多种特征复杂性的情况下,GbyE表现出卓越的预测性能.
结论:
- GbyE程序有效地估计了添加和交互的遗传效应,提高了GWAS功率和GS预测准确度.
- 对于多个贝叶斯模型和各种环境条件,GbyE在预测准确度方面提供了实质性的改进.
- 这项研究促进了对复杂特征的基因型与环境相互作用的理解,并为GbyE算法提供了可访问的源代码.
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