» Articles » PMID: 27898764

Genetic Gain and Inbreeding from Genomic Selection in a Simulated Commercial Breeding Program for Perennial Ryegrass

Overview
Journal Plant Genome
Specialties Biology
Genetics
Date 2016 Nov 30
PMID 27898764
Citations 32
Authors
Affiliations
Soon will be listed here.
Abstract

Genomic selection (GS) provides an attractive option for accelerating genetic gain in perennial ryegrass () improvement given the long cycle times of most current breeding programs. The present study used simulation to investigate the level of genetic gain and inbreeding obtained from GS breeding strategies compared with traditional breeding strategies for key traits (persistency, yield, and flowering time). Base population genomes were simulated through random mating for 60,000 generations at an effective population size of 10,000. The degree of linkage disequilibrium (LD) in the resulting population was compared with that obtained from empirical studies. Initial parental varieties were simulated to match diversity of current commercial cultivars. Genomic selection was designed to fit into a company breeding program at two selection points in the breeding cycle (spaced plants and miniplot). Genomic estimated breeding values (GEBVs) for productivity traits were trained with phenotypes and genotypes from plots. Accuracy of GEBVs was 0.24 for persistency and 0.36 for yield for single plants, while for plots it was lower (0.17 and 0.19, respectively). Higher accuracy of GEBVs was obtained for flowering time (up to 0.7), partially as a result of the larger reference population size that was available from the clonal row stage. The availability of GEBVs permit a 4-yr reduction in cycle time, which led to at least a doubling and trebling genetic gain for persistency and yield, respectively, than the traditional program. However, a higher rate of inbreeding per cycle among varieties was also observed for the GS strategy.

Citing Articles

Genomic selection shows improved expected genetic gain over phenotypic selection of agronomic traits in allotetraploid white clover.

Ehoche O, Arojju S, Jahufer M, Jauregui R, Larking A, Cousins G Theor Appl Genet. 2025; 138(1):34.

PMID: 39847157 PMC: 11757872. DOI: 10.1007/s00122-025-04819-w.


Genomic selection strategies to overcome genotype by environment interactions in biosecurity-based aquaculture breeding programs.

Kang Z, Kong J, Li Q, Sui J, Dai P, Luo K Genet Sel Evol. 2025; 57(1):2.

PMID: 39844028 PMC: 11752716. DOI: 10.1186/s12711-025-00949-3.


Genomic-inferred cross-selection methods for multi-trait improvement in a recurrent selection breeding program.

Atanda S, Bandillo N Plant Methods. 2024; 20(1):133.

PMID: 39218896 PMC: 11367796. DOI: 10.1186/s13007-024-01258-4.


Including marker x environment interactions improves genomic prediction in red clover ( L.).

Skot L, Nay M, Grieder C, Frey L, Pegard M, Ohlund L Front Plant Sci. 2024; 15:1407609.

PMID: 38916032 PMC: 11194335. DOI: 10.3389/fpls.2024.1407609.


Genetic Gain and Inbreeding in Different Simulated Genomic Selection Schemes for Grain Yield and Oil Content in Safflower.

Zhao H, Khansefid M, Lin Z, Hayden M Plants (Basel). 2024; 13(11).

PMID: 38891385 PMC: 11174797. DOI: 10.3390/plants13111577.