Abstract
Key messageGenomic 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.
Highlights
IntroductionGenomic selection (GS), introduced by Meuwissen et al (2001), describes breeding strategies in which the effects of many DNA markers throughout the genome are used to predict the breeding value (genomic-estimated breeding value, GEBV) of selection candidates
Genomic selection (GS), introduced by Meuwissen et al (2001), describes breeding strategies in which the effects of many DNA markers throughout the genome are used to predict the breeding value of selection candidates
GS has recently emerged as a prospect for forage plant species largely due to the advent of flexible, low-costsingle nucleotide polymorphism (SNP) marker platforms, such as genotyping-by-sequencing (GBS) (Elshire et al 2011), that can provide the high-density marker information typically needed for GS (Annicchiarico et al 2015; Fè et al 2015a; Hayes et al 2013)
Summary
Genomic selection (GS), introduced by Meuwissen et al (2001), describes breeding strategies in which the effects of many DNA markers throughout the genome are used to predict the breeding value (genomic-estimated breeding value, GEBV) of selection candidates. GS methodology has become widely implemented in animal breeding,
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