Efficient large-scale genomic prediction in approximate genome-based kernel model.

IF 4.4 1区 农林科学 Q1 AGRONOMY
Hailan Liu, Jinqing Xu, Xuesong Wang, Handong Wang, Lei Wang, Yuhu Shen
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引用次数: 0

Abstract

Key message: Three computationally efficient algorithms of GP including RHBK, RHDK, and RHPK were developed in approximate genome-based kernel model. The drastically growing amount of genomic information contributes to increasing computational burden of genomic prediction (GP). In this study, we developed three computationally efficient algorithms of GP including RHBK, RHDK, and RHPK in approximate genome-based kernel model, which reduces dimension of genomic data via Nyström approximation and decreases the computational cost significantly thereby. According to the simulation study and real datasets, our three methods demonstrated predictive accuracy similar to or better than RHAPY, GBLUP, and rrBLUP in most cases. They also demonstrated a substantial reduction in computational time compared to GBLUP and rrBLUP in simulation. Due to their advanced computing efficiency, our three methods can be used in a wide range of application scenarios in the future.

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来源期刊
CiteScore
9.60
自引率
7.40%
发文量
241
审稿时长
2.3 months
期刊介绍: Theoretical and Applied Genetics publishes original research and review articles in all key areas of modern plant genetics, plant genomics and plant biotechnology. All work needs to have a clear genetic component and significant impact on plant breeding. Theoretical considerations are only accepted in combination with new experimental data and/or if they indicate a relevant application in plant genetics or breeding. Emphasizing the practical, the journal focuses on research into leading crop plants and articles presenting innovative approaches.
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