Improving Wheat Grain Yield Genomic Prediction Accuracy Using Historical Data.

Genomic selection (GS) is an essential tool to improve genetic gain in wheat breeding. This study aimed to enhance prediction accuracy (PA) for grain yield (GY) across various selection environments (SEs) using CIMMYT's (International Maize and Wheat Improvement Center) historical dataset. Ten years of GY data from six SEs were analyzed, with the populations of five years (2018-2023) as the validation population (VP) and earlier years (back to 2013-2014) as the training population (TP).

Stacking beneficial haplotypes from the Vavilov wheat collection to accelerate breeding for multiple disease resistance.

We revealed the neglected genetic relationships of resistance for six major wheat diseases and established a haploblock-based catalogue with novel forms of resistance by multi-trait haplotype characterisation. Genetic potential to improve multiple disease resistance was highlighted through haplotype stacking simulations. Wheat production is threatened by numerous fungal diseases, but the potential to breed for multiple disease resistance (MDR) mechanisms is yet to be explored.

Deep learning methods improve genomic prediction of wheat breeding.

In the field of plant breeding, various machine learning models have been developed and studied to evaluate the genomic prediction (GP) accuracy of unseen phenotypes. Deep learning has shown promise. However, most studies on deep learning in plant breeding have been limited to small datasets, and only a few have explored its application in moderate-sized datasets.