ArticleEvolutionary applications2026
Leveraging Knowledge of Traditional Italian Maize Landrace Diversity to Identify Signals of Local Adaptation.
Article in Evolutionary applications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Meeting Report on the Assisted Gene Flow and Climate Change Responses Workshop, Golden Gate National Recreation Area, CA, USA, 5-7 March 2025.Evolutionary applications · 2026Article
- Leveraging Knowledge of Traditional Italian Maize Landrace Diversity to Identify Signals of Local Adaptation.Evolutionary applications · 2026Article
- Genetic diversity of common beans (Frontiers in plant science · 2026Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Climate change is the greatest challenge to modern agriculture. It significantly impacts agricultural systems through an increased frequency and intensity of extreme environmental events. Maize, a vital crop for global food security, is particularly vulnerable to these changes, highlighting the urgent need to develop resilient varieties. This study aims to identify significant genes for adaptation to environmental conditions in 140 individuals derived from 28 Italian maize landraces using a landscape genomics approach to support the development of resilient maize genotypes. Landraces were genotyped using genotyping-by-sequencing, and the resulting genetic matrix was used to characterize the collection's diversity. Population genetic studies were conducted to investigate the genetic diversity and structure of the collection. Partial redundancy analysis (pRDA) was subsequently employed to analyze the relationship between climate variables and genetic variation of the materials. Among the 12 ancestral populations identified, both well-defined populations and highly admixed groups were observed. This degree of admixture was reflected in the clustering analysis and principal component analysis (PCA), although clear differentiation of individual populations was still apparent. pRDA revealed that 30% of the genetic variance in the collection was explained together by climate (45%), geography (11%), and genetic structure (31%). Three potential genomic signals of adaptation were identified in relation to the environmental variability across the sampling sites. The results highlight significant intra-landrace variability within the examined germplasm and reveal unique landraces tied to ancestral lineages. Notably, we identify distinct genetic markers strongly correlated with environmental factors. This discovery opens new avenues for potential genetic improvement in maize cultivation. Landraces preserve vital traits for the adaptation of maize to environmental stresses, thereby serving as key sources for breeding programs aimed at improving stress tolerance and yield stability under climate change.
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