Combining Genomic and Phenomic Information for Predicting Grain Protein Content and Grain Yield in Spring Wheat

Abstract

Genomics and high throughput phenomics have the potential to revolutionize the field of wheat (Triticum aestivum L.) breeding. Genomic selection (GS) has been used for predicting various quantitative traits in wheat, especially grain yield. However, there are few GS studies for grain protein content (GPC), which is a crucial quality determinant. Incorporation of secondary correlated traits in GS models has been demonstrated to improve accuracy. The objectives of this research were to compare performance of single and multi-trait GS models for predicting GPC and grain yield in wheat and to identify optimal growth stages for collecting secondary traits. We used 650 recombinant inbred lines from a spring wheat nested association mapping (NAM) population. The population was phenotyped over 3 years (2014–2016), and spectral information was collected at heading and grain filling stages. The ability to predict GPC and grain yield was assessed using secondary traits, univariate, covariate, and multivariate GS models for within and across cycle predictions. Our results indicate that GS accuracy increased by an average of 12% for GPC and 20% for grain yield by including secondary traits in the models. Spectral information collected at heading was superior for predicting GPC, whereas grain yield was more accurately predicted during the grain filling stage. Green normalized difference vegetation index had the largest effect on the prediction of GPC either used individually or with multiple indices in the GS models. An increased prediction ability for GPC and grain yield with the inclusion of secondary traits demonstrates the potential to improve the genetic gain per unit time and cost in wheat breeding.

Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models

“…Residuals from the model were used to calculate the adjusted means (line effect). Adjusted means across the environments were calculated following the method implemented in Sandhu et al [18,32] and is as follows…”

“…These trained models are subsequently used to predict the genomic estimated breeding values (GEBVs) of genotyped lines [15]. GS has shown the potential to enhance genetic gain by reducing the breeding cycle time and improving selection accuracy [16][17][18]. This is especially important for winter wheat end-use quality traits, as phenotyping requires more than three months and data from the quality lab is often not available between harvest and planting.…”

Genome-Wide Association Studies and Genomic Selection for Grain Protein Content Stability in a Nested Association Mapping Population of Spring Wheat

“…Twenty-six families with genotype data provided by Kansas State University (Jordan et al 2018) were then selected, and 25 random RILs from each family (650 total RIL; named NAM650) were planted between 2014 and 2016 at Spillman Agronomy Farm in Pullman, WA, under rainfed conditions (Sandhu et al 2021b). A modified augmented design was used in each trial with three check cultivars (Berkut, 'Thatcher', and 'McNeal' (Lanning et al 1994)).…”

Omics in Major Cereals: Applications, Challenges, and Prospects

“…Various aerial or ground based HTP platforms have been developed for measuring different plant traits at different growth stages with more precision, throughput, and accuracy [180]. As shown in Table 8, these platforms demonstrated their superiority in rice, wheat, maize, barley, and sorghum (Tables 9, 10).…”