Genomic selection strategies for clonally propagated crops Article Swipe

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Christian R. Werner , R. Chris Gaynor , Daniel James Sargent , Alessandra Lillo , Gregor Gorjanc , John M. Hickey ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.1007/s00122-023-04300-6

Key message For genomic selection in clonally propagated crops with diploid (-like) meiotic behavior to be effective, crossing parents should be selected based on genomic predicted cross-performance unless dominance is negligible. Abstract For genomic selection (GS) in clonal breeding programs to be effective, parents should be selected based on genomic predicted cross-performance unless dominance is negligible. Genomic prediction of cross-performance enables efficient exploitation of the additive and dominance value simultaneously. Here, we compared different GS strategies for clonally propagated crops with diploid (-like) meiotic behavior, using strawberry as an example. We used stochastic simulation to evaluate six combinations of three breeding programs and two parent selection methods. The three breeding programs included (1) a breeding program that introduced GS in the first clonal stage, and (2) two variations of a two-part breeding program with one and three crossing cycles per year, respectively. The two parent selection methods were (1) parent selection based on genomic estimated breeding values (GEBVs) and (2) parent selection based on genomic predicted cross-performance (GPCP). Selection of parents based on GPCP produced faster genetic gain than selection of parents based on GEBVs because it reduced inbreeding when the dominance degree increased. The two-part breeding programs with one and three crossing cycles per year using GPCP always produced the most genetic gain unless dominance was negligible. We conclude that (1) in clonal breeding programs with GS, parents should be selected based on GPCP, and (2) a two-part breeding program with parent selection based on GPCP to rapidly drive population improvement has great potential to improve breeding clonally propagated crops.

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Artificial Intelligence

Concepts

Biology Selection (genetic algorithm) Genetic gain Inbreeding Genomic selection Breeding program Dominance (genetics) Ploidy Plant breeding Genetics Population Plant biochemistry Genetic variation Agronomy Gene Cultivar Genotype Computer science Demography Sociology Single-nucleotide polymorphism Artificial intelligence

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Type: article
Language: en
Landing Page: https://doi.org/10.1007/s00122-023-04300-6
PDF: https://link.springer.com/content/pdf/10.1007/s00122-023-04300-6.pdf
OA Status: hybrid
Cited By: 39
References: 39
Related Works: 10
OpenAlex ID: https://openalex.org/W4360600125

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OpenAlex ID: https://openalex.org/W4360600125

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DOI: https://doi.org/10.1007/s00122-023-04300-6

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Title: Genomic selection strategies for clonally propagated crops

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Type: article

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Language: en

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Publication year: 2023

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Publication date: 2023-03-23

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Authors: Christian R. Werner, R. Chris Gaynor, Daniel James Sargent, Alessandra Lillo, Gregor Gorjanc, John M. Hickey

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Landing page: https://doi.org/10.1007/s00122-023-04300-6

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Concepts: Biology, Selection (genetic algorithm), Genetic gain, Inbreeding, Genomic selection, Breeding program, Dominance (genetics), Ploidy, Plant breeding, Genetics, Population, Plant biochemistry, Genetic variation, Agronomy, Gene, Cultivar, Genotype, Computer science, Demography, Sociology, Single-nucleotide polymorphism, Artificial intelligence

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Cited by: 39

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