Genome-enabled predictions for binomial traits in sugar beet populations

Area of Science:

• Plant genetics
• Agricultural science
• Quantitative genetics

Background:

• Genomic information can predict continuous and categorical traits.
• Discrete phenotypes, such as disease status, are common in medicine and agriculture.
• Root vigor in sugar beet (B. vulgaris) is a binomial trait of significant agronomic importance.

Purpose of the Study:

• To genotype sugar beet individuals using single nucleotide polymorphisms (SNPs).
• To classify sugar beet plants based on root vigor (high or low).
• To assess the accuracy of genome-enabled prediction for a binomial trait.

Main Methods:

• A panel of 192 SNPs was used to genotype 124 sugar beet plants from 18 lines.
• A threshold model within a genomic BLUP (G-BLUP) framework was employed.
• A 5-fold cross-validation scheme with 500 testing subsets was utilized to evaluate prediction accuracy.

Main Results:

• The average cross-validation error rate was exceptionally low at 0.000731 (0.073%).
• Out of 12326 test observations, only 9 were misclassified across all replicates.
• High prediction accuracy was achieved despite a sparse SNP panel.

Conclusions:

• Genome-enabled prediction of sugar beet root vigor demonstrated high accuracy.
• The trait's high heritability (0.783) and the presence of few genes with large effects likely contributed to accurate predictions.
• Sufficient within-scaffold linkage disequilibrium (LD) enabled effective genome-enabled predictions.