Efficient arabinoxylan assay for wheat: Exploring variability and molecular marker associations in Wholemeal and refined flour.

In this study, we present a modified high throughput phloroglucinol colorimetric assay for the quantification of arabinoxylans (AX) in wheat named PentoQuant. The method was downscaled from a 10 ml glass tube to 2 ml microcentrifuge tube format, resulting in a fivefold increase in throughput while concurrently reducing the overall cost and manual labor required for the analysis. Comparison with established colorimetric assays and gas chromatography validates the modified protocol, demonstrating its superior repeatability, rapidity, and simplicity.

Bayesian multitrait kernel methods improve multienvironment genome-based prediction.

When multitrait data are available, the preferred models are those that are able to account for correlations between phenotypic traits because when the degree of correlation is moderate or large, this increases the genomic prediction accuracy. For this reason, in this article, we explore Bayesian multitrait kernel methods for genomic prediction and we illustrate the power of these models with three-real datasets. The kernels under study were the linear, Gaussian, polynomial, and sigmoid kernels; they were compared with the conventional Ridge regression and GBLUP multitrait models.

Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials.

Implementing genomic-based prediction models in genomic selection requires an understanding of the measures for evaluating prediction accuracy from different models and methods using multi-trait data. In this study, we compared prediction accuracy using six large multi-trait wheat data sets (quality and grain yield). The data were used to predict 1 year (testing) from the previous year (training) to assess prediction accuracy using four different prediction models.