Faville2018_Article_PredictiveAbilityOfGenomicSele.pdf (1.34 MB)

Predictive ability of genomic selection models in a multi-population perennial ryegrass training set using genotyping-by-sequencing

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posted on 2023-05-10, 07:47 authored by Marty FavilleMarty Faville, Siva Ganesh, Mingshu CaoMingshu Cao, Zulfi JahuferZulfi Jahufer, Timothy BiltonTimothy Bilton, Sydney Easton, Douglas Ryan, Jason Trethewey, Philip Rolston, Andrew GriffithsAndrew Griffiths, Roger Moraga, Casey Flay, Jana SchmidtJana Schmidt, Rachel Tan, Brent BarrettBrent Barrett
Genomic prediction models for multi-year dry matter yield, via genotyping-by-sequencing in a composite training set, demonstrate potential for genetic gain improvement through within-half sibling family selection. Perennial ryegrass (Lolium perenne L.) is a key source of nutrition for ruminant livestock in temperate environments worldwide. Higher seasonal and annual yield of herbage dry matter (DMY) is a principal breeding objective but the historical realised rate of genetic gain for DMY is modest. Genomic selection was investigated as a tool to enhance the rate of genetic gain. Genotyping-by-sequencing (GBS) was undertaken in a multi-population (MP) training set of five populations, phenotyped as half-sibling (HS) families in five environments over 2 years for mean herbage accumulation (HA), a measure of DMY potential. GBS using the ApeKI enzyme yielded 1.02 million single-nucleotide polymorphism (SNP) markers from a training set of n = 517. MP-based genomic prediction models for HA were effective in all five populations, cross-validation-predictive ability (PA) ranging from 0.07 to 0.43, by trait and target population, and 0.40-0.52 for days-to-heading. Best linear unbiased predictor (BLUP)-based prediction methods, including GBLUP with either a standard or a recently developed (KGD) relatedness estimation, were marginally superior or equal to ridge regression and random forest computational approaches. PA was principally an outcome of SNP modelling genetic relationships between training and validation sets, which may limit application for long-term genomic selection, due to PA decay. However, simulation using data from the training experiment indicated a twofold increase in genetic gain for HA, when applying a prediction model with moderate PA in a single selection cycle, by combining among-HS family selection, based on phenotype, with within-HS family selection using genomic prediction.


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Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.


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Springer Nature

Journal title

Theoretical and Applied Genetics




Faville, M. J., Ganesh, S., Cao, M., Jahufer, M. Z. Z., Bilton, T. P., Easton, H. S., Ryan, D. L., Trethewey, J. A. K., Rolston, M. P., Griffiths, A. G., Moraga, R., Flay, C., Schmidt, J., Tan, R., & Barrett, B. A. (2018). Predictive ability of genomic selection models in a multi-population perennial ryegrass training set using genotyping-by-sequencing. Theoretical and Applied Genetics, 131(3), 703–720.

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