ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2013
Comparisons of single-stage and two-stage approaches to genomic selection.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genome-Enabled Prediction Methods Based on Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2022Pooled it
- Genotype-by-sequencing-enabled genome-wide association studies reveal genetic architecture of biomass and nitrogen modulation in tepary bean (Phaseolus acutifolius).G3 (Bethesda, Md.) · 2026Article
- Optimizing fully-efficient two-stage models for genomic selection using open-source software.Plant methods · 2025Article
- Complex traits and candidate genes: estimation of genetic variance components across multiple genetic architectures.G3 (Bethesda, Md.) · 2023Article
- Genomic prediction of switchgrass winter survivorship across diverse lowland populations.G3 (Bethesda, Md.) · 2023Article
- Genome-enabled prediction for sparse testing in multi-environmental wheat trials.The plant genome · 2021Article
- Two-stage Cox-nnet: biologically interpretable neural-network model for prognosis prediction and its application in liver cancer survival using histopathology and transcriptomic data.NAR genomics and bioinformatics · 2021Article
- Genomic Predictive Ability for Foliar Nutritive Traits in Perennial Ryegrass.G3 (Bethesda, Md.) · 2020Article
- Novel strategies for genomic prediction of untested single-cross maize hybrids using unbalanced historical data.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2020Article
- Robust estimation of heritability and predictive accuracy in plant breeding: evaluation using simulation and empirical data.BMC genomics · 2020Article
- Flexible modelling of spatial variation in agricultural field trials with the R package INLA.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Article
- Efficient Estimation of Marker Effects in Plant Breeding.G3 (Bethesda, Md.) · 2019Article
- Genomic Selection with Allele Dosage inG3 (Bethesda, Md.) · 2019Article
- Genomic Prediction Using Individual-Level Data and Summary Statistics from Multiple Populations.Genetics · 2018Article
- Genomic Selection for Ascochyta Blight Resistance in Pea.Frontiers in plant science · 2018Article
- A quantitative genetic framework highlights the role of epistatic effects for grain-yield heterosis in bread wheat.Nature genetics · 2017Article
- Needles: Toward Large-Scale Genomic Prediction with Marker-by-Environment Interaction.Genetics · 2016Article
- Genomic selection in a commercial winter wheat population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2016Article
- Potential and limits of whole genome prediction of resistance to Fusarium head blight and Septoria tritici blotch in a vast Central European elite winter wheat population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2015Article
- Influence of outliers on accuracy estimation in genomic prediction in plant breeding.G3 (Bethesda, Md.) · 2014Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic selection (GS) is a method for predicting breeding values of plants or animals using many molecular markers that is commonly implemented in two stages. In plant breeding the first stage usually involves computation of adjusted means for genotypes which are then used to predict genomic breeding values in the second stage. We compared two classical stage-wise approaches, which either ignore or approximate correlations among the means by a diagonal matrix, and a new method, to a single-stage analysis for GS using ridge regression best linear unbiased prediction (RR-BLUP). The new stage-wise method rotates (orthogonalizes) the adjusted means from the first stage before submitting them to the second stage. This makes the errors approximately independently and identically normally distributed, which is a prerequisite for many procedures that are potentially useful for GS such as machine learning methods (e.g. boosting) and regularized regression methods (e.g. lasso). This is illustrated in this paper using componentwise boosting. The componentwise boosting method minimizes squared error loss using least squares and iteratively and automatically selects markers that are most predictive of genomic breeding values. Results are compared with those of RR-BLUP using fivefold cross-validation. The new stage-wise approach with rotated means was slightly more similar to the single-stage analysis than the classical two-stage approaches based on non-rotated means for two unbalanced datasets. This suggests that rotation is a worthwhile pre-processing step in GS for the two-stage approaches for unbalanced datasets. Moreover, the predictive accuracy of stage-wise RR-BLUP was higher (5.0-6.1%) than that of componentwise boosting.
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