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Optimizing selection based on BLUPs or BLUEs in multiple sets of genotypes differing in their population parameters
by
Melchinger, Albrecht E.
, Melchinger, Andreas J.
, Schön, Chris-Carolin
, Fernando, Rohan
in
Accuracy
/ Agriculture
/ Analysis
/ Biochemistry
/ Biomedical and Life Sciences
/ Biotechnology
/ equations
/ Evaluation
/ Genetic diversity
/ genetic variation
/ Genomics
/ Genomics - methods
/ Genotype
/ Genotypes
/ Humans
/ Hybrids
/ Life Sciences
/ marker-assisted selection
/ Mathematical optimization
/ Models, Genetic
/ Original
/ Original Article
/ Phenotype
/ Plant Biochemistry
/ Plant breeding
/ Plant Breeding - methods
/ Plant Breeding/Biotechnology
/ Plant Genetics and Genomics
/ Plant selection
/ Plants - genetics
/ Population genetics
/ prediction
/ Predictions
/ Quantitative genetics
/ Resource management
/ selection response
/ Selection, Genetic
/ truncation selection
/ variance
2024
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Optimizing selection based on BLUPs or BLUEs in multiple sets of genotypes differing in their population parameters
by
Melchinger, Albrecht E.
, Melchinger, Andreas J.
, Schön, Chris-Carolin
, Fernando, Rohan
in
Accuracy
/ Agriculture
/ Analysis
/ Biochemistry
/ Biomedical and Life Sciences
/ Biotechnology
/ equations
/ Evaluation
/ Genetic diversity
/ genetic variation
/ Genomics
/ Genomics - methods
/ Genotype
/ Genotypes
/ Humans
/ Hybrids
/ Life Sciences
/ marker-assisted selection
/ Mathematical optimization
/ Models, Genetic
/ Original
/ Original Article
/ Phenotype
/ Plant Biochemistry
/ Plant breeding
/ Plant Breeding - methods
/ Plant Breeding/Biotechnology
/ Plant Genetics and Genomics
/ Plant selection
/ Plants - genetics
/ Population genetics
/ prediction
/ Predictions
/ Quantitative genetics
/ Resource management
/ selection response
/ Selection, Genetic
/ truncation selection
/ variance
2024
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Optimizing selection based on BLUPs or BLUEs in multiple sets of genotypes differing in their population parameters
by
Melchinger, Albrecht E.
, Melchinger, Andreas J.
, Schön, Chris-Carolin
, Fernando, Rohan
in
Accuracy
/ Agriculture
/ Analysis
/ Biochemistry
/ Biomedical and Life Sciences
/ Biotechnology
/ equations
/ Evaluation
/ Genetic diversity
/ genetic variation
/ Genomics
/ Genomics - methods
/ Genotype
/ Genotypes
/ Humans
/ Hybrids
/ Life Sciences
/ marker-assisted selection
/ Mathematical optimization
/ Models, Genetic
/ Original
/ Original Article
/ Phenotype
/ Plant Biochemistry
/ Plant breeding
/ Plant Breeding - methods
/ Plant Breeding/Biotechnology
/ Plant Genetics and Genomics
/ Plant selection
/ Plants - genetics
/ Population genetics
/ prediction
/ Predictions
/ Quantitative genetics
/ Resource management
/ selection response
/ Selection, Genetic
/ truncation selection
/ variance
2024
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Optimizing selection based on BLUPs or BLUEs in multiple sets of genotypes differing in their population parameters
Journal Article
Optimizing selection based on BLUPs or BLUEs in multiple sets of genotypes differing in their population parameters
2024
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Overview
Key message
Selection response in truncation selection across multiple sets of candidates hinges on their post-selection proportions, which can deviate grossly from their initial proportions. For BLUPs, using a uniform threshold for all candidates maximizes the selection response, irrespective of differences in population parameters.
Plant breeding programs typically involve multiple families from either the same or different populations, varying in means, genetic variances and prediction accuracy of BLUPs or BLUEs for true genetic values (TGVs) of candidates. We extend the classical breeder's equation for truncation selection from single to multiple sets of genotypes, indicating that the expected overall selection response
(
Δ
G
Tot
)
for TGVs depends on the selection response within individual sets and their post-selection proportions. For BLUEs, we show that maximizing
Δ
G
Tot
requires thresholds optimally tailored for each set, contingent on their population parameters. For BLUPs, we prove that
Δ
G
Tot
is maximized by applying a uniform threshold across all candidates from all sets. We provide explicit formulas for the origin of the selected candidates from different sets and show that their proportions before and after selection can differ substantially, especially for sets with inferior properties and low proportion. We discuss implications of these results for (a) optimum allocation of resources to training and prediction sets and (b) the need to counteract narrowing the genetic variation under genomic selection. For genomic selection of hybrids based on BLUPs of GCA of their parent lines, selecting distinct proportions in the two parent populations can be advantageous, if these differ substantially in the variance and/or prediction accuracy of GCA. Our study sheds light on the complex interplay of selection thresholds and population parameters for the selection response in plant breeding programs, offering insights into the effective resource management and prudent application of genomic selection for improved crop development.
Publisher
Springer Berlin Heidelberg,Springer,Springer Nature B.V
Subject
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