1. Multi-model genome-wide association study on key organic naked barley agronomic, phenological, diseases, and grain quality traits.
- Author
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Paire, Laura, McCabe, Cathal, and McCabe, Tomás
- Subjects
- *
GENOME-wide association studies , *PLANT breeding , *SINGLE nucleotide polymorphisms , *AGRONOMY , *BARLEY , *INDEX numbers (Economics) - Abstract
The study objective was to assess the potential benefits of using genomic tools in organic plant breeding programs to enhance selection efficiency. A diversity panel of 247 spring naked barley accessions was characterized under Irish organic conditions over 3 years. Genome-wide association studies (GWAS) were performed on 19 traits related to agronomy, phenology, diseases, and grain quality, using the information on 50 K Single Nucleotide Polymorphisms (SNP). Four models (EMMA, G model, BLINK, 3VMrMLM) were applied to 5 types of Best Linear Unbiased Predictors (BLUP): within-year, mean, aggregated within-year). 1653 Marker-Trait-Associations (MTA) were identified, with 259 discovered in at least two analyses. 3VMrMLM was the best-performing model with significant MTA together explaining the largest proportion of the additive variance for most traits and BLUP types (from 1.4 to 50%). This study proposed a methodology to prioritize main effect MTA from different models' outputs, using multi-marker regression analyses with markers fitted as fixed or random factors. 36 QTL, considered major, explained more than 5% of the trait variance on each BLUP type. A candidate gene or known QTL was found for 18 of them, with 13 discovered with 3VMrMLM. Multi-model GWAS was useful for validating additional QTL, including 8 only discovered with BLINK or G model, thus allowing a broader understanding of the traits' genetic architecture. In addition, results highlighted a correlation between the trait value and the number of favorable major QTL exhibited by accessions. We suggest inputting this number in a multi-trait index for a more efficient Marker-Assisted Selection (MAS) of accessions best balancing multiple quantitative traits. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
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