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

Abstract

Abstract Breeding programs require exhaustive phenotyping of germplasms, which is time-demanding and expensive. Genomic prediction based on next-generation sequencing techniques 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 26171 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. Most models gave close predictive ability values across traits for the whole marker set. Models covering non-additive effects yielded better predictive ability for low heritable traits, though no single model worked best across all traits. Marker subsets based on linkage disequilibrium decay distance gave similar predictive abilities to the whole marker set, but for randomly selected markers, it reached a plateau above 3000 markers. Markers having significant association with traits improved predictive abilities compared to the whole marker set, when marker selection was made on the whole population instead of the training set indicating a clear overfitting. The correction for population structure did not increase predictive abilities compared to the whole collection. However, stratified sampling by picking representative genotypes from each cluster improved predictive abilities. The indirect predictive ability for a trait was proportionate to its correlation with other traits. These results will help breeders to select the best models, optimum marker set, and suitable genotype set to perform an indirect selection for quantitative traits in this diverse flax germplasm collection.