Genomic prediction vs. gene-based crop models: a case study on rice trait prediction

Optimizing phenotypic data analysis strategies to enhance genomic prediction in winter wheat breeding

Proper analysis of phenotypic data is essential for reliable genomic prediction (GP) and sustained genetic gain in breeding programs. In this study, we used phenotypic and genotypic data generated from the winter wheat breeding program of Deutsche Saatveredelung AG (DSV), Lippstadt, Germany, comprising 1,941 genotypes...

Integrating predictive models and GWAS to identify candidate loci and genes for agronomic traits in rice

Rice is a staple crop whose improvement relies on breeding advances; precision agriculture demands predictive loci/genes for agronomic traits to innovate rice production. To cut experimental costs and boost efficiency, this study built predictive models using seedling leaf metabolomes to forecast rice agronomic traits...

Advances in predictive breeding for wheat: concepts, methods, and applications

Wheat is one of the world’s main crops. Its improvement is pivotal given the threat of climate change and the growing population. However, enhancing breeding efficiency and improving wheat are challenging due to strong genotype-by-environment (G×E) interactions and the biological complexity underlying the wheat genome...

A Public Mid-Density Genotyping Platform for Genomic Selection in Wheat

The results suggest that, in elite wheat germplasm characterized by long-range linkage disequilibrium and strong realized genomic relationships, medium-density targeted genotyping platforms can retain most of the predictability achieved by higher-density systems.

Targeted Multi-Omics Machine Learning Framework for Robust Prediction of Maize Yield and Disease Resistance

Genomic Selection Integrated with High-Throughput Phenotyping and Speed Breeding for Smart and Greener Rice (Oryza sativa) Improvement

This narrative review critically examines recent advances in genomic selection for rice and its integration with high-throughput genotyping, high-throughput phenotyping, machine learning, multi-environment prediction, and speed breeding.