Abstract
Traditionally, a pedigree-based individual-tree mixed model (ABLUP) has been used in forest genetic evaluations to identify individuals with the highest breeding values (BVs). ABLUP is a Markovian causal model, as any individual BV can be expressed as a linear regression on its parental BVs. The regression coefficients are based on the genealogical parent-offspring relationship and are equal to one-half. This study aimed to develop and apply two new causal models that replace these fixed coefficients with ones calculated using genomic information, specifically derived from the genomic-based relationship matrix. We compared the performance of these genomic-based causal models with ABLUP and non-causal GBLUP models. To do so, we evaluated a four-generation population of Eucalyptus grandis, consisting of 3082 genotyped trees with 14,033 single nucleotide polymorphism markers. Six traits were assessed in 1219 trees across the first three breeding cycles. The heritability and genetic means estimates were higher in the causal pedigree- and genomic-based models compared to GBLUP. Realized genetic gains were similar across all models, but the causal models more closely matched the predicted gains than GBLUP. In turn, GBLUP demonstrated better predictive performance, albeit with lower precision. The causal models developed in this study enable discerning intra-familial variations in the predictions of BVs at a lower computational burden and offer a potential alternative to the GBLUP model.