Phylogenetic relationships and genetic diversity of Tunisian maize landraces.

Based on history, maize was first introduced into Tunisia and northern Africa, at large, from the south of Spain. Several subsequent introductions were made from diverse origins, generating new landraces by recombination and selection for adaptation to arid environments. This study aimed to investigate the phylogenetic relationships among Tunisian maize landraces with possible sources of introduction from neighboring countries.

metaGE: Investigating genotype x environment interactions through GWAS meta-analysis.

Elucidating the genetic components of plant genotype-by-environment interactions is of key importance in the context of increasing climatic instability, diversification of agricultural practices and pest pressure due to phytosanitary treatment limitations. The genotypic response to environmental stresses can be investigated through multi-environment trials (METs). However, genome-wide association studies (GWAS) of MET data are significantly more complex than that of single environments.

Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions.

Classical genomic prediction approaches rely on statistical associations between traits and markers rather than their biological significance. Biologically informed selection of genomic regions can help prioritize polymorphisms by considering underlying biological processes, making prediction models robust and accurate. Gene ontology (GO) terms can be used for this purpose, and the information can be integrated into genomic prediction models through marker categorization.

High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions.

Transcriptomics and proteomics information collected on a platform can predict additive and non-additive effects for platform traits and additive effects for field traits. The effects of climate change in the form of drought, heat stress, and irregular seasonal changes threaten global crop production. The ability of multi-omics data, such as transcripts and proteins, to reflect a plant's response to such climatic factors can be capitalized in prediction models to maximize crop improvement.

Genetic and Phenotypic Evaluation of European Maize Landraces as a Tool for Conservation and Valorization of Agrobiodiversity.

The ECPGR European Evaluation Network (EVA) for Maize involves genebanks, research institutions, and private breeding companies from nine countries focusing on the valorization of maize genetic resources across Europe. This study describes a diverse collection of 626 local landraces and traditional varieties of maize ( L.) from nine European genebanks, including criteria for selection of the collection and its genetic and phenotypic diversity.