Data from: Forest tree breeding using genomic Markov causal models: A new approach to genomic tree breeding improvement

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 3,082 genotyped trees with 14,033 single nucleotide
polymorphism markers. Six traits were assessed in 1,219 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.