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

Authors: HOQUE, A - North Dakota State University; Anderson, James; RAHMAN, M - North Dakota State University

Submitted to: Scientific Reports
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/31/2024
Publication Date: 2/8/2024
Citation: Hoque, A., Anderson, J.V., Rahman, M. 2024. Genomic prediction for agronomic traits in a diverse flax (Linum usitatissimum L.) germplasm collection. Scientific Reports. 14. Article 3196. https://doi.org/10.1038/s41598-024-53462-w.
DOI: https://doi.org/10.1038/s41598-024-53462-w

Interpretive Summary: Genomic prediction is a method that helps breeders evaluate crop plants for multiple traits at once, bypassing the time-demanding and expensive process of evaluating plants one trait at a time. In this study, genomic prediction models were used to determine the genetic potential for seed yield and other important agronomic traits in flax. A set of genetic markers was identified for evaluating genomic prediction models among a population of flax plants grown in multiple environments. Most models gave close predictive ability values across traits when using the whole or subsets of the markers. However, markers that were closely associated with traits improved predictive abilities compared to the whole marker set. Although correcting for the structural makeup of the flax population did not increase predictive abilities, picking closely related groups of flax improved predictive abilities. The results of this study will help breeders to select the best models, optimum marker sets, and suitable sets of plants to perform an indirect selection for agronomic traits in flax collections.

Technical 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 accessions, 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.