ArticleGenetics, selection, evolution : GSE2019
Accuracy of genomic BLUP when considering a genomic relationship matrix based on the number of the largest eigenvalues: a simulation study.
Article in Genetics, selection, evolution : GSE, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed.
- Leveraging AI and integrated genomic-enviromic prediction for intelligent sugarcane breeding.Plant communications · 2026Review
- Accounting for population structure in genomic prediction of strawberry sweetness at a global scale.Scientific reports · 2025Article
- Evaluation of genomic breeding values and accuracy for carcass traits in Korean Hanwoo cows using whole-genome SNP chip panels.Mammalian genome : official journal of the International Mammalian Genome Society · 2025Article
- Regenie.QRS: computationally efficient whole-genome quantile regression at biobank scale.bioRxiv : the preprint server for biology · 2025Article
- Article
- Optimizing multi-breed joint genomic prediction issues in numerically small breeds for sex-limited trait in a loosely structured dairy cattle breeding system.Tropical animal health and production · 2025Article
- Investigating the Therapeutic Property of Galium verum L. (GV) for MSG induced Audiogenic Epilepsy (AEs) and Neuroprotection throughCentral nervous system agents in medicinal chemistry · 2025Article
- Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.Frontiers in genetics · 2025Article
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- Preselecting Variants from Large-Scale Genome-Wide Association Study Meta-Analyses Increases the Genomic Prediction Accuracy of Growth and Carcass Traits in Large White Pigs.Animals : an open access journal from MDPI · 2023Article
- Dimensionality of genomic information and its impact on genome-wide associations and variant selection for genomic prediction: a simulation study.Genetics, selection, evolution : GSE · 2023Article
- Derivation of indirect predictions using genomic recursions across generations in a broiler population.Journal of animal science · 2023Article
- Theoretical accuracy for indirect predictions based on SNP effects from single-step GBLUP.Genetics, selection, evolution : GSE · 2022Article
- Genomic prediction with whole-genome sequence data in intensely selected pig lines.Genetics, selection, evolution : GSE · 2022Article
- A comprehensive study on size and definition of the core group in the proven and young algorithm for single-step GBLUP.Genetics, selection, evolution : GSE · 2022Article
- Genomic evaluation with multibreed and crossbred data.JDS communications · 2022Review
- Genomic prediction through machine learning and neural networks for traits with epistasis.Computational and structural biotechnology journal · 2022Article
- Improvement of Genomic Predictions in Small Breeds by Construction of Genomic Relationship Matrix Through Variable Selection.Frontiers in genetics · 2022Article
- Validation of single-step genomic predictions using the linear regression method for milk yield and heat tolerance in a Thai-Holstein population.Veterinary world · 2021Article
- Investigating the persistence of accuracy of genomic predictions over time in broilers.Journal of animal science · 2021Article
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4 authors.
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Abstract
backgroundThe dimensionality of genomic information is limited by the number of independent chromosome segments (M
resultsThe simulation included datasets with different population sizes and amounts of phenotypic information. Computations were done by genomic best linear unbiased prediction (GBLUP) with selected eigenvalues and corresponding eigenvectors of the GRM set to zero. About four eigenvalues in the GRM explained 10% of the genomic variation, and less than 2% of the total eigenvalues explained 50% of the genomic variation. With limited phenotypic information, the accuracy of GBLUP was close to the peak where most of the smallest eigenvalues were set to zero. With a large amount of phenotypic information, accuracy increased as smaller eigenvalues were added.
conclusionsA small amount of phenotypic data is sufficient to estimate only the effects of the largest eigenvalues and the associated eigenvectors that contain a large fraction of the genomic information, and a very large amount of data is required to estimate the remaining eigenvalues that account for a limited amount of genomic information. Core animals in the APY algorithm act as proxies of almost the same number of eigenvalues. By using an eigenvalues-based approach, it was possible to explain why the moderate accuracy of genomic selection based on small datasets only increases slowly as more data are added.
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