A dimensionality-reduction genomic prediction method without direct inverse of the genomic relationship matrix for large genomic data

Key message A 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...

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Vydané v:Plant cell reports Ročník 42; číslo 11; s. 1825 - 1832
Hlavní autori: Liu, Hailan, Yu, Shizhou
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023
Springer Nature B.V
Predmet:
Accuracy
Algorithms
Biomedical and Life Sciences
Biotechnology
Cell Biology
Computational efficiency
Computer applications
Computing time
Conjugate gradient method
data collection
Efficiency
Genomics
Genotyping
Life Sciences
Machine learning
Methods
Original Article
Pine trees
Pinus taeda
Plant Biochemistry
Plant Sciences
prediction
Predictions
wheat
Genomic prediction
Principal component regression
GBLUP
Preconditioned conjugate gradient algorithm
Randomized Haseman–Elston regression
ISSN:0721-7714, 1432-203X, 1432-203X
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Shrnutí:Key message A 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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0721-7714
1432-203X
1432-203X
DOI:10.1007/s00299-023-03069-8