Comparison of parametric, semiparametric and nonparametric methods in genomic evaluation.

Comparison of parametric, semiparametric and nonparametric methods in genomic evaluation. J Genet. 2019 Nov;98: Authors: Sahebalam H, Gholizadeh M, Hafezian H, Farhadi A Abstract Access to dense panels of molecular markers has facilitated genomic selection in animal breeding. The purpose of this study was to compare the nonparametric (random forest and support vector machine), semiparametric reproducing kernel Hilbert spaces (RKHS), and parametric methods (ridge regression and Bayes A) in prediction of genomic breeding values for traits with different genetic architecture. The predictive performance of different methods was compared in different combinations of distribution of QTL effects (normal and uniform), two levels of QTL numbers (50 and 200), three levels of heritability (0.1, 0.3 and 0.5), and two levels of training set individuals (1000 and 2000). To do this, a genome containing four chromosomes each 100-cM long was simulated on which 500, 1000 and 2000 evenly spaced single-nucleotide markers were distributed. With an increase in heritability and the number of markers, all the methods showed an increase in prediction accuracy (P<0.05). By increasing the number of QTLs from 50 to 200, we found a significant decrease in the prediction accuracy of breeding value in all methods (P<0.05). Also, with the increase in the number of training set individuals, the prediction accuracy increased significantly in all statistical met...
Source: Journal of Genetics - Category: Genetics & Stem Cells Authors: Tags: J Genet Source Type: research