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

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 (r g = 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 (r g = 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 (r p = −0.06). However, when using an MT-CV2 model, the predictive ability was positive and high (r p = 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.

Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat

“…MT-CV1 and ST-CV1 models showed similar predictive ability in most cases, consistent with many other studies [ 47 , 48 , 51 ]. This illustrates that MT models are not always better than the ST model.…”

“…MT-GS methods have recently been applied due to increased prediction accuracies when the correlated traits are incorporated into the model [ 36 , 37 , 40 – 42 ] and showed the improved predictive ability of GY in wheat by including physiological traits [ 42 – 45 ]. In addition to yield, MT-GS has also been used to improve the predictive ability of other traits such as grain end-use quality [ 46 ], dry matter yield and water-soluble carbohydrates [ 47 ], and baking quality [ 48 ]. The main objectives of this study were to compare the relative performance of ST and MT-GS models and determine whether incorporating in-season physiological traits (NDVI and CT) in prediction models can improve the predictive ability of primary traits including HI, GN, GY, FE, and SPI.…”

Multi-Trait Multi-Environment Genomic Prediction of Agronomic Traits in Advanced Breeding Lines of Winter Wheat

“…In contrast to MT-CV1, the MT-CV2 model significantly improved the PA for all agronomic traits in all the environments, suggesting that the inclusion of secondary traits in the training and testing sets improves the predictive performance of complex traits (Supplementary Tables 4, 5). Several studies have reported a similar improvement in prediction using the MT-CV2 model for agronomic and end-use quality traits in wheat (Rutkoski et al, 2016;Sun et al, 2017;Lado et al, 2018), rice (Wang et al, 2017), barley (Bhatta et al, 2020), sorghum (Fernandes et al, 2018), and ryegrass (Arojju et al, 2020). The MT-CV2 model outperformed the single-trait model for YLD prediction in all environments.…”

“…In this study, the PA of the MT-CV1 model was found similar to that of the ST-CV1 model for most of the trait-environment combinations in both growing seasons (Supplementary Tables 4, 5). Several studies have reported marginal or no improvement with MT-CV1, where information from secondary traits is limited to the training set (Calus and Veerkamp, 2011;Lado et al, 2018;Schulthess et al, 2018;Arojju et al, 2020;Bhatta et al, 2020). However, other studies reported an improvement in GP when the MT-CV1 model included secondary traits with moderate-high heritability (Jia and Jannink, 2012;Rutkoski et al, 2012;Guo et al, 2014).…”

The Impact of Alkaloid-Producing Epichloë Endophyte on Forage Ryegrass Breeding: A New Zealand Perspective

“…As mentioned above, ryegrass breeding methods are improving, new phenotyping [ 64 ] and genotyping techniques [ 57 ] are starting to deliver cost effective genomic selection [ 58 , 60 , 100 ] and these are being applied to improve endophyte stability and transmission [ 56 ]. Addition of hybrid breeding [ 61 ], self-fertility [ 101 ], along with gene editing technologies [ 102 ] will likely reduce variation between genotypes within a host germplasm (cultivar or F1 hybrid).…”