Optimization of forest tree breeding through multiple-trait mixed models - Alves, Rodrigo Silva (2020)
The current techniques used for genetic evaluation involve the simultaneous prediction of genetic values and estimation of variance components. Best linear unbiased prediction (BLUP) is a widely used method worldwide to predict genetic values. Prediction using BLUP assumes that the variance components are known. However, these values are not known in practice and should thus be accurately estimated to obtain the empirical BLUP. At present, the standard method to estimate variance components is restricted maximum likelihood (REML). Therefore, genetic evaluation consists of using these methodologies together (REML/BLUP procedure). Nonetheless, when genetic selection is based on several traits, which may be genetically correlated due to pleiotropic genes and/or gametic phase imbalance, selection bias may occur if these traits are analyzed individually. Thus, the present work aimed to evaluate the applicability and efficiency of multiple-trait BLUP in forest-tree breeding. The multiple-trait models employed led to better results than the traditional univariate and repeatability models. On this basis, the multiple-trait BLUP can be advantageously used in forest breeding. Keywords: BLUP. REML. Mixed model methodology.