Multi-Trait Genomic Prediction Improves Predictive Ability for Dry Matter Yield and Water-Soluble Carbohydrates in Perennial Ryegrass.

Sai Krishna Arojju,Mingshu Cao,Alan Stewart,Brent A Barrett,Marty J Faville,Colin Eady,Courtney Inch,Michael Trolove

doi:10.3389/fpls.2020.01197

Abstract

In perennial ryegrass (Lolium perenne L), annual and seasonal dry matter yield (DMY) and nutritive quality of herbage are high-priority traits targeted for improvement through selective breeding. Genomic prediction (GP) has proven to be a valuable tool for improving complex traits and may be further enhanced through the use of multi-trait (MT) prediction models. In this study, we evaluated the relative performance of MT prediction models to improve predictive ability for DMY and key nutritive quality traits, using two different training populations (TP1, n = 463 and TP2, n = 517) phenotyped at multiple locations. MT models outperformed single-trait (ST) models by 24% to 59% for DMY and 67% to 105% for nutritive quality traits, such as low, high, and total WSC, when a correlated secondary trait was included in both the training and test set (MT-CV2) or in the test set alone (MT-CV3) (trait-assisted genomic selection). However, when a secondary trait was included in training set and not the test set (MT-CV1), the predictive ability was not statistically significant (p > 0.05) compared to the ST model. We evaluated the impact of training set size when using a MT-CV2 model. Using a highly correlated trait (rg = 0.88) as the secondary trait in the MT-CV2 model, there was no loss in predictive ability for DMY even when the training set was reduced to 50% of its original size. In contrast, using a weakly correlated secondary trait (rg = 0.56) in the MT-CV2 model, predictive ability began to decline when the training set size was reduced by only 11% from its original size. Using a ST model, genomic predictive ability in a population unrelated to the training set was poor (rp = −0.06). However, when using an MT-CV2 model, the predictive ability was positive and high (rp = 0.76) for the same population. Our results demonstrate the first assessment of MT models in forage species and illustrate the prospects of using MT genomic selection in forages, and other outcrossing plant species, to accelerate genetic gains for complex agronomical traits, such as DMY and nutritive quality characteristics.

Highlights

Perennial ryegrass is one of the most valued forage species in temperate regions of the world, characterized by relatively high nutritional value, and optimum seasonal and annual dry matter yield (DMY) from herbage, providing a cost effective source of nutrition, for ruminant livestock (Wilkins and Humphreys, 2003; Baert and Muylle, 2016)
The family variance components measured in the Ruakura trial were larger than Darfield, and this was reflected in the genomic heritability for DMY and growth score (GS) traits, which were consistently higher at Ruakura (Supplementary Tables 2 and 3)
We have shown that applying MT genomic prediction can improve predictive ability for DMY or watersoluble carbohydrate (WSC) when compared to a single trait (ST) model

Summary

Introduction

Perennial ryegrass is one of the most valued forage species in temperate regions of the world, characterized by relatively high nutritional value, and optimum seasonal and annual dry matter yield (DMY) from herbage, providing a cost effective source of nutrition, for ruminant livestock (Wilkins and Humphreys, 2003; Baert and Muylle, 2016). Genomic prediction is considered a valuable tool for improving quantitative traits in both animal and plant breeding (Meuwissen et al, 2001; Habier et al, 2007; Heslot et al, 2015). Compared to traditional breeding approaches, genomic prediction provides opportunities to increase the rate of genetic gain, by reducing the time needed to complete a breeding cycle, increasing the selection intensity and by utilizing within-family variation that can be captured using molecular markers (Heslot et al, 2015; Faville et al, 2018). Indirect selection based on genomic prediction is considered a more appropriate and practical breeding tool over MAS (Jannink et al, 2010; Heslot et al, 2015). Improvements in next-generation sequencing technology, coupled with the development of reduced representation sequencing approaches such as genotyping-bysequencing (GBS) (Elshire et al, 2011) for single nucleotide polymorphism (SNP) DNA markers, has made genomic prediction adaptable to forages and other species which lack significant genomics resources such as SNP arrays

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