Background:
Genomic information can be used to predict not only continuous but also categorical (e.g. binomial)traits. Several traits of interest in human medicine and agriculture present a discrete distribution ofphenotypes (e.g. disease status). Root vigor in sugar beet (B. vulgaris) is an example of binomialtrait of agronomic importance. In this paper, a panel of 192 SNPs (single nucleotide polymorphisms)was used to genotype 124 sugar beet individual plants from 18 lines, and to classify them as showing"high" or "low" root vigor.
Results:
A threshold model was used to fit the relationship between binomial root vigor and SNP genotypes,through the matrix of genomic relationships between individuals in a genomic BLUP (G-BLUP)approach. From a 5-fold cross-validation scheme, 500 testing subsets were generated. The estimatedaverage cross-validation error rate was 0.000731 (0.073%). Only 9 out of 12326 test observations(500 replicates for an average test set size of 24.65) were misclassified.
Conclusions:
The estimated prediction accuracy was quite high. Such accurate predictions may be related to thehigh estimated heritability for root vigor (0.783) and to the few genes with large effect underlying thetrait. Despite the sparse SNP panel, there was sufficient within-scaffold LD where SNPs with largeeffect on root vigor were located to allow for genome-enabled predictions to work.
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