ArticlePlant cell reports2023
A dimensionality-reduction genomic prediction method without direct inverse of the genomic relationship matrix for large genomic data.
Article in Plant cell reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Novel QTLs/genes affecting single stem elasticity, stem strength, and three lodging indices in bread wheat (Triticum aestivum L.) identified by genome-wide association analysis.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Efficient large-scale genomic prediction in approximate genome-based kernel model.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Genomic prediction of yield-related traits and genome-based establishment of heterotic pattern in maize hybrid breeding of Southwest China.Frontiers in plant science · 2024Article
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Authors and funding
2 authors.
Funding
Abstract
key messageA new genomic prediction method (RHPP) was developed via combining randomized Haseman-Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm. Computational efficiency is becoming a hot issue in the practical application of genomic prediction due to the large number of data generated by the high-throughput genotyping technology. In this study, we developed a fast genomic prediction method RHPP via combining randomized Haseman-Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm. The simulation results demonstrated similar prediction accuracy between RHPP and GBLUP, and significantly higher computational efficiency of the former with the increase of individuals. The results of real datasets of both bread wheat and loblolly pine demonstrated that RHPP had a similar or better predictive accuracy in most cases compared with GBLUP. In the future, RHPP may be an attractive choice for analyzing large-scale and high-dimensional data.
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