LANDSCAPE GENOMICS AND BIG DATA ANALYSIS TO ENHANCE ADAPTABILITY AND SUSTAINABILITY IN MAIZE CULTIVATION

Modern agriculture's greatest challenge is climate change, significantly impacting agricultural systems through altered temperature and precipitation patterns, and increased frequency and intensity of extreme events. Maize, a crucial crop for food security worldwide, is known to be highly susceptible to these changes. Landraces represent germplasm of election where breeding may source favourable alleles for adaptation. This research aims to identify genetic markers that explain environmental variability to support the development of resilient maize genotypes. Selected landraces from Northern-Central Italy were genotyped using a double digest restriction-site associated DNA (ddRAD-seq) approach, followed by quality filtering for Phred Score, minor allele frequency and SNP missingness. The sequencing yielded 1,437,328 variants, and the dataset after filtering was reduced to 6,002 variants. Finally, an LD-pruned subset of 2,018 markers was created to represent the collection's diversity. Partial redundancy analysis was employed to investigate the relation existing between climate and genetic variation of the studied materials. The analysis used the LD-pruned SNPs as response variables and a set of noncollinear bioclimatic indicators as dependent variables while controlling for genetic structure and geographical origin. Outliers were identified with a Bonferroni correction at a 5% nominal p-value threshold. Partial redundancy analysis revealed that climate, geography, and genetic structure together explained 30% of the genetic variance in our maize landraces. Climate accounted for 45% of this variation, genetic structure for 31%, and geographic coordinates for 11%. Three significantly associated SNPs were identified: two of these are localized in two distinct genes, Zm00001eb068470 and Zm00001eb418760, respectively, not yet characterized. Performing analysis of chromosome-specific linkage disequilibrium decay, we estimated windows based on half-decay distance. In the 67 Kb LD-window of Zm00001eb068470, located on chromosome 2, we found Zm00001eb068520, a gene encoding the APETALA2 protein. AP2/ERFs are crucial transcription factors in maize, regulating hormone and stress responses, playing, moreover, significant roles in ethylene signalling pathways that affect ear length, flower number, fertility, and grain yield. They are therefore vital traits for maize's adaptation to environmental stresses like flooding and heat, making them essential targets for breeding programs to improve stress tolerance and ensure stable, high yields under climate change. This work is part of the project NODES, which has received funding from the MUR–M4C2 1.5 of PNRR with grant agreement no. ECS00000036.