Genomic and phenomic predictions help capture low-effect alleles promoting seed germination in oilseed rape in addition to QTL analyses

• Published in: Theoretical and applied genetics Vol. 137; no. 7; p. 156
• Main Authors: Laurençon, Marianne, Legrix, Julie, Wagner, Marie-Hélène, Demilly, Didier, Baron, Cécile, Rolland, Sophie, Ducournau, Sylvie, Laperche, Anne, Nesi, Nathalie
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-07-2024; Springer Nature B.V; Springer Verlag
• Subjects: Agriculture; Alleles; Biochemistry; Biomedical and Life Sciences; Biotechnology; Brassica napus - genetics; Brassica napus - growth & development; Developmental stages; Genetic resources; Genetic structure; Genomics; Genomics - methods; Genotype; Germination - genetics; Life Sciences; Oilseeds; Original; Original Article; Phenomics - methods; Phenotype; Plant Biochemistry; Plant Breeding - methods; Plant Breeding/Biotechnology; Plant Genetics and Genomics; Polygenic inheritance; Predictions; Quantitative Trait Loci; Rape plants; Seed germination; Seeds - genetics; Seeds - growth & development
• ISSN: 0040-5752; 1432-2242; 1432-2242
• DOI: 10.1007/s00122-024-04659-0

Key message
Phenomic prediction implemented on a large diversity set can efficiently predict seed germination, capture low-effect favorable alleles that are not revealed by GWAS and identify promising genetic resources.
Oilseed rape faces many challenges, especially at the beginning of its developmental cycle. Achieving rapid and uniform seed germination could help to ensure a successful establishment and therefore enabling the crop to compete with weeds and tolerate stresses during the earliest developmental stages. The polygenic nature of seed germination was highlighted in several studies, and more knowledge is needed about low- to moderate-effect underlying loci in order to enhance seed germination effectively by improving the genetic background and incorporating favorable alleles. A total of 17 QTL were detected for seed germination-related traits, for which the favorable alleles often corresponded to the most frequent alleles in the panel. Genomic and phenomic predictions methods provided moderate-to-high predictive abilities, demonstrating the ability to capture small additive and non-additive effects for seed germination. This study also showed that phenomic prediction estimated phenotypic values closer to phenotypic values than GEBV. Finally, as the predictive ability of phenomic prediction was less influenced by the genetic structure of the panel, it is worth using this prediction method to characterize genetic resources, particularly with a view to design prebreeding populations.