Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection.

ABSTRACT: Breeding programs require exhaustive phenotyping of germplasms, which is time-demanding and expensive. Genomic prediction helps breeders harness the diversity of any collection to bypass phenotyping. Here, we examined the genomic prediction's potential for seed yield and nine agronomic traits using 26,171 single nucleotide polymorphism (SNP) markers in a set of 337 flax (Linum usitatissimum L.) germplasm, phenotyped in five environments. We evaluated 14 prediction models and several factors affecting predictive ability based on cross-validation schemes. Models yielded significant variation among predictive ability values across traits for the whole marker set. The ridge regression (RR) model covering additive gene action yielded better predictive ability for most of the traits, whereas it

SUBMITTER: Hoque A 

PROVIDER: S-EPMC10850546 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature