Open Access Highly Accessed Research article

Genome-enabled predictions for binomial traits in sugar beet populations

Filippo Biscarini1*, Piergiorgio Stevanato2, Chiara Broccanello2, Alessandra Stella13 and Massimo Saccomani2

Author Affiliations

1 Department of Bioinformatics, PTP, Via Einstein - Loc. Cascina Codazza, Lodi, Italy

2 DAFNE, Università di Padova, 24105 Padova, Italy

3 IBBA-CNR, Via Einstein, 26900 Lodi, Italy

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BMC Genetics 2014, 15:87  doi:10.1186/1471-2156-15-87

Published: 22 July 2014



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 of phenotypes (e.g. disease status). Root vigor in sugar beet (B. vulgaris) is an example of binomial trait 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.


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 estimated average 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.


The estimated prediction accuracy was quite high. Such accurate predictions may be related to the high estimated heritability for root vigor (0.783) and to the few genes with large effect underlying the trait. Despite the sparse SNP panel, there was sufficient within-scaffold LD where SNPs with large effect on root vigor were located to allow for genome-enabled predictions to work.

Genomic predictions; Binomial traits; Root vigor; Sugar beet