The 4.(n) wheat breeding era: genomic-based predictions becoming part of the daily routine and beyond
Wheat (Triticum aestivum L.) is one of the most important crops worldwide, also as a role model in plant breeding and genomics. Along with gene edition, genomic selection (GS) is the most advancing area in plant breeding, and several developments are in curse in this field, which soon will result in the recently termed “breeding 4.0” stage. The aim of this review is to present and discuss the most recent and impacting advances in GS applied to wheat breeding. Wheat shows particular features, e.g. a crop with narrow genetic variability and a large and complex genome, which motivates especial discussions on the present theme. The advances in enviromics and phenomics are presented and also the way they literally enter the genomic prediction models. The most breeder-advantageous wheat genotyping platforms currently available are also presented. Regarding data analysis within the genomic selection scheme, machine learning and deep learning methods are the most advancing approaches for predicting phenotypes, and improvements on these algorithms are nowadays in the center of the debate. Several advances in course will move plant breeding from the current 3.0 to the 4.0 stage, so achieving the status of “rocket science” and requiring highly skilled breeders and multidisciplinary teams.