Genome‐based prediction of multiple wheat quality traits in multiple years

Abstract

Wheat quality improvement is an important objective in all wheat breeding programs. However, due to the cost, time and quantity of seed required, wheat quality is typically analyzed only in the last stages of the breeding cycle on a limited number of samples. The use of genomic prediction could greatly help to select for wheat quality more efficiently by reducing the cost and time required for this analysis. Here were evaluated the prediction performances of 13 wheat quality traits under two multi-trait models (Bayesian multi-trait multi-environment [BMTME] and multi-trait ridge regression [MTR]) using five data sets of wheat lines evaluated in the field during two consecutive years. Lines in the second year (testing) were predicted using the quality information obtained in the first year (training). For most quality traits were found moderate to high prediction accuracies, suggesting that the use of genomic selection could be feasible. The best predictions were obtained with the BMTME model in all traits and the worst with the MTR model. The best predictions with the BMTME model under the mean arctangent absolute percentage error (MAAPE) were for test weight across the five data sets, whereas the worst predictions were for the alveograph trait

An Overview of Key Factors Affecting Genomic Selection for Wheat Quality Traits

“…A significant gain of multitrait approach is expected only for low heritable traits that are incorporated with high heritable traits, between which high genetic correlation exists [64]. Data for traits incorporating together in a multitrait analysis must be already available or easy to obtain on a large number of samples in a short period of time [67].…”

The Modern Plant Breeding Triangle: Optimizing the Use of Genomics, Phenomics, and Enviromics Data

“…Because of this, the use of GS in MTME data is a promising approach to reduce field phenotyping efforts. For example, Ibba et al (2020) evaluated the prediction performance of 13 quality traits in wheat using two multi-trait models and five data sets based on field evaluations over two consecutive years. In the second year (testing), lines were predicted using the quality information obtained in the first year (training).…”

Quantitative dissection of color patterning in the foliar ornamental Coleus reveals underlying features driving aesthetic value

“…Advances in plant phenotyping have revolutionized how humans interact with botanical traits (Fahlgren et al, 2015;Gehan & Kellogg, 2017;Li et al, 2018b;Prunet & Duncan, 2020;Amézquita et al, 2020). High throughput data collection has enabled rapid agricultural trait selection (Singh et al, 2019;Shakoor et al, 2019;Ibba et al, 2020), early detection and management of disease (Mutka & Bart, 2014;Shakoor et al, 2017), and large-scale 2-dimensional morphological analyses (Li et al, 2018a). Penetrating high-resolution imaging technologies, such as X-ray CT and laser ablation tomography have also made complex, three-dimensional topologies accessible (Chitwood et al, 2019;Li et al, 2019bLi et al, , 2020aPrunet & Duncan, 2020;Amézquita et al, 2020;Vanhees et al, 2020).…”