Accelerating wheat breeding for end-use quality with multi-trait genomic predictions incorporating near infrared and nuclear magnetic resonance-derived phenotypes

Abstract

Using NIR and NMR predictions of quality traits overcomes a major barrier for the application of genomic selection to accelerate improvement in grain end-use quality traits of wheat. Grain end-use quality traits are among the most important in wheat breeding. These traits are difficult to breed for, as their assays require flour quantities only obtainable late in the breeding cycle, and are expensive. These traits are therefore an ideal target for genomic selection. However, large reference populations are required for accurate genomic predictions, which are challenging to assemble for these traits for the same reasons they are challenging to breed for. Here, we use predictions of end-use quality derived from near infrared (NIR) or nuclear magnetic resonance (NMR), that require very small amounts of flour, as well as end-use quality measured by industry standard assay in a subset of accessions, in a multi-trait approach for genomic prediction. The NIR and NMR predictions were derived for 19 end-use quality traits in 398 accessions, and were then assayed in 2420 diverse wheat accessions. The accessions were grown out in multiple locations and multiple years, and were genotyped for 51208 SNP. Incorporating NIR and NMR phenotypes in the multi-trait approach increased the accuracy of genomic prediction for most quality traits. The accuracy ranged from 0 to 0.47 before the addition of the NIR/NMR data, while after these data were added, it ranged from 0 to 0.69. Genomic predictions were reasonably robust across locations and years for most traits. Using NIR and NMR predictions of quality traits overcomes a major barrier for the application of genomic selection for grain end-use quality traits in wheat breeding.

Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality

“…Model predictive ability using single-trait prediction through CV1 with 40% of randomly masked individuals (198 individuals) was lower (between 0.24 and 0.43) than previously found in Battenfield et al 2016 (between 0.45 and 0.60) predicted using 20% randomly masked individuals. In addition, they were higher than those found in Hayes et al (2017) although the strategy used to predict performance was different; here, we used cross-validation approaches and Hayes et al (2017) predicted traits across years and locations.…”

“…On the other hand, simulated and empirical studies show that multi-trait models were useful for predicting traits when individuals were partially phenotyped (Rutkoski et al 2012; Jia and Jannink 2012; Guo et al 2014; Rutkoski et al 2016; Hayes et al 2017; Sun et al 2017). Both Rutkoski et al 2012 and Sun et al (2017) found advantages of multi-trait models using correlated traits from high-throughput phenotyping (i.e., NDVI and canopy temperature) in wheat.…”

“…Jia and Jannink (2012) also found an improvement in predicting rust gall volume and the presence or absence of rust in pine. Finally, Hayes et al 2017 found that end-use quality traits could be better predicted using near-infrared (NIR) or nuclear magnetic resonance (NMR). The results show that multi-trait models could be used to decide the optimal depth of phenotyping for each trait, mainly for expensive or difficult-to-measure traits (Guo et al 2014).…”

Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat

“…GS has indeed shifted the paradigm in plant and animal breeding and has the potential to deliver more accurate predictions, reduce cycle time, reduce the cost of phenotyping, and facilitate rapid gains from selection (Muir 2007; Heffner et al 2009; van der Werf 2009; Jannink et al 2010; Hayes et al 2013). While several studies demonstrate the usefulness of GS for complex traits in wheat (Crossa et al 2014; Battenfield et al 2016; Hayes et al 2017; Juliana et al 2017a, b; Pérez-Rodríguez et al 2017), one of our key objectives was to evaluate both forward and backward genomic predictions for GY across different nurseries and years, and also explore the potential of utilizing GS in breeding programs.…”

Multi-trait regressor stacking increased genomic prediction accuracy of sorghum grain composition

“…al. [14] introduced a reaction norm model 28 which introduces the main and interaction effects of markers and environmental covariates by using 29 high-dimensional random variance-covariance structures of markers and environmental covariates. 30 While most of the genomic prediction studies have been on individual traits, breeding programs use 31 selection indices based on several traits to make breeding decisions.…”