Genomic predictions and Genome-wide association studies on DNA pools to characterize traditional maize landraces and identify genomic regions associated with agronomic traits and environmental adaptation

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Ben-Sadoun, Sarah | Galaretto, Agustin Oscar | Roux, Antoine | Gouesnard, Brigitte | Charcosset, Alain | Moreau, Laurence | Madur, Delphine | Bourdin, V. | Nicolas, Stephane

Edité par CCSD -

International audience. Genetic resources are potential sources of diversity that can be mobilized to enrich plant breeding germplasm. They are therefore important to ensure long-term genetic gain and face new breeding objectives. Traditional maize landraces have a large genetic diversity and show various environmental adaptations. Using these landraces as a source of adaptative alleles for variety development could help to cope with climatic change and agroecological transition. However, traditional maize landraces remain relatively unexploited. To identify promising landraces and favorable alleles likely to enlarge the genetic diversity of modern varieties, we studied 262 traditional maize landraces from the French national collection. Each landrace was evaluated for different agro-morphological and seed composition traits, and was genotyped with a SNP array using pooled DNA samples. In addition, bioclimatic variables of the population collection sites were extracted from the WorldClim database. Genetic structure analysis showed that this collection is composed of 4 genetic groups. Genome-wide association studies (GWAS) identified several genomic regions associated with agronomic traits and environmental adaptation. Some of the identified genomic regions colocalize with known genes related to the studied traits. We also showed that genomic predictions (GP) can be used to accurately predict several agronomic traits of landraces, which could be a way to predict the potential of large collections of unphenotyped landraces maintained in genebanks. To conclude, our results suggest that pool genotyping of traditional landraces is highly efficient to conduct GWAS and GP.

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