Title: Accuracy of Genomic Selection Methods in a Standard Dataset of Loblolly Pine

Genomic selection can increase genetic gain per generation through early selection. Genomic selection is expected to be particularly valuable for traits that are costly to phenotype, and expressed late in the life-cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Here the performance of four different original methods of genomic selection that differ with respect to assumptions regarding distribution of marker effects, including (i) Ridge Regression – Best Linear Unbiased Prediction (RRBLUP), (ii) Bayes A, (iii) Bayes Cπ, and (iv) Bayesian LASSO are presented. In addition, a modified RR-BLUP (RR-BLUP B) that utilizes a selected subset of markers was evaluated. The accuracy of these methods was compared across 17 traits with distinct heritabilities and genetic architectures, including growth, development and diseaseresistance properties, measured in a Pinus taeda (loblolly pine) training population of 951 individuals genotyped with 4,853 SNPs. The predictive ability of the methods was evaluated using a 10-fold, cross-validation approach, and differed only marginally for most method/trait combinations. Interestingly, for fusiform rust disease-resistance traits Bayes Cπ, Bayesian LASSO and RR-BLUB B had higher predictive ability than RRBLUP and Bayes A. Fusiform rust is controlled by few genes of large effect. A limitation of RR-BLUP is the assumption of equal contribution of all markers to the observed variation. The genotypic and phenotypic data used in this study is publically available for comparative analysis of genomic selection prediction models.