BackgroundSNP (single nucleotide polymorphisms) genotype data are increasingly available in cattle populations and, among other things, can be used to predict carriers of specific mutations. It is therefore convenient to have a practical statistical method for the accurate classification of individuals into carriers and non-carriers. In this paper, we compared – through cross-validation– five classification models (Lasso-penalized logistic regression –Lasso, Support Vector Machines with either linear or radial kernel –SVML and SVMR, k-nearest neighbors –KNN, and multi-allelic gene prediction –MAG), for the identification of carriers of the TUBD1 recessive mutation on BTA19 (Bos taurus autosome 19), known to be associated with high calf mortality. A population of 3116 Fleckvieh and 392 Brown Swiss animals genotyped with the 54K SNP-chip was available for the analysis.
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