Rodolfo Juan Carlos Cantet

Heterosis was documented in the 18th century, but its biological basis has been debated since. The theoretical framework proposed by Hill, and adapted by Lynch, is based on two central parameters: admixed composition ( S ), and the heterozygosity ( H ). Using genomic information, it is now possible to estimate independently the individual realized S _i and H _i . In this research, a methodology for estimating the contribution of dominance and additive × additive effects to heterosis is proposed. This approach would be especially relevant in cases where there is insufficient phenotypic information available, or an adequate genetic group experimental design, common in humans, wild species and other admixed populations. We also provide theoretical arguments highlighting the enhanced precision of the estimations of heterosis parameters through this method. Furthermore, we exemplify this procedure by analysing data from an experimental F ₂ pig population, which was initially designed for QTL mapping. Notably, all animals in this population were genotyped (including F ₁ and parental breeds), but phenotypic information was only available for F ₂ individuals and included 13 traits related to growth, fat deposition, carcass characteristics and meat quality. Significant additive effects ( p < 0.05) were detected for longissimus muscle area and carcass temperature, suggesting complementary additive effects for these traits. Significant dominance and additive × additive effects were also detected for birth weight and carcass length, respectively ( p < 0.05), indicating that heterosis for these traits is primarily attributable to dominance and additive × additive interactions. These results demonstrate that the proposed methodology can successfully estimate the genetic components underlying heterosis and underscores the utility of this approach in situations where we possess genomic data but limited phenotypic data.

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.

Although Genome Wide Analysis (GWAS) have been widely used to understand the genetic architecture of complex quantitative traits, interpreting their results in terms of the biological processes that determine those traits has been difficult or even lacking, because of the variability in responses to the tests of hypotheses within a trait, species, and breed or cross, and the lack of follow‐up studies. It is then essential employing appropriate statistical tests that point out to the causal genes responsible of the relevant fraction of the genetic variability observed. We briefly review the main theoretical aspects of the two schools of causal inference (Rubin's Causal Model, RCM, and Pearl's causal inference, PCI). RCM approachs the hypothesis testing from a randomization perspective by considering a wider space of the observation, i.e. the “potential outcomes”, rather than the narrower space that results from defining “treatment” effects after observing the data. Next, we discuss the assumptions involved to meet the requirements of randomization for RCM with observational data (non‐designed experiments) with special emphasis on the Stable Unit Treatment Analysis (SUTVA). Due to the presence of “confounders” (i.e. systematic fixed effects, environmental permanent effects, interaction among genes, etc.), causal average treatment effects are viewed through the familiar lens of normal linear (or mixed) models. To overcome the difficulties of association analyses, a tests of causal effects is introduced using independent predicted residual breeding values from animal models of genetic evaluation that avoids the effects of population structure and confounder effects. An independent section discusses the issue of whether the additive effects defined at the “gene” level by R. A. Fisher and popularized in D. S. Falconer's textbook of quantitative genetics can be termed causal from either RCM or PCI.

Traditionally, in forest genetic evaluations, the pedigree-based individual-tree mixed model (ABLUP) has been utilized to identify individuals with the highest breeding value (BV). The ABLUP is considered a Markov causal model as the BV of an individual is independent of all its non-descendants, conditional to including the BV of its parents. Under this model, an individual's BV is expressed as a linear regression on parental BVs, along with a Mendelian sampling term. The availability of genome-wide marker panels has led to the development of a technique known as genomic selection (GS). In forest tree breeding, the most widespread GS method is the GBLUP model, which replaces the pedigree-based genetic covariance matrix with a marker-based relatedness matrix (G-matrix) derived from genotyped individuals. The GBLUP model has demonstrated improved BV accuracy compared to the ABLUP model. However, the use of Markov causal models incorporating genomic information remains unexplored in forest tree breeding. Therefore, the objective of this study was to develop and apply two new Markov causal models that substitute the “path coefficient” of the ABLUP model (which is 0.5) with an alternative coefficient, calculated using genomic information. Subsequently, we compared the performance of these new two causal models with the classical causal ABLUP and non-causal GBLUP models in terms of their predictive ability (PA) for BVs. Our investigation focused on a fourth-generation population of Eucalyptus grandis (Hill ex Maiden), comprising 3,082 genotyped trees with 14,033 single-nucleotide polymorphism markers. The study assessed six growth and wood quality traits in 1,219 trees spanning the first three generations. Progeny validation was conducted to evaluate PA, where individuals from generations 1 and 2 comprised the training set, and individuals from generation 3 constituted the validation set. Overall, the GBLUP model exhibited slightly superior predictive performance compared to the Markov causal models for most analyzed traits (with an average PA of 0.24 for causal models compared to 0.27 for GBLUP). However, when analyzing BVs of individuals from generation 4 in the greenhouse, without phenotypic data, the GBLUP model displayed greater dispersion in predicted BVs compared to those obtained through the genomic Markov causal models. The Markov causal models developed in this study appear to be more “stable”, and, due to their lower computational burden, represent a potential alternative to classical non-causal GBLUP GS model employed in forest breeding programs.

The proceedings of the 12th World Congress on Genetics Applied to Livestock Production provide you with 816 papers representing the leading research in livestock genetics around the globe. This boo...

Brangus is a composite cattle breed developed with the objective of combining the advantages of Angus and Zebuine breeds (Brahman, mainly) in tropical climates. The aim of this work was to estimate breed composition both genome‐wide and locally, at the chromosome level, and to uncover genomic regions evidencing positive selection in the Argentinean Brangus population/nucleus. To do so, we analysed marker data from 478 animals, including Brangus, Angus and Brahman. Average breed composition was 35.0% ± 9.6% of Brahman, lower than expected according to the theoretical fractions deduced by the usual cross‐breeding practice in this breed. Local ancestry analysis evidenced that breed composition varies between chromosomes, ranging from 19.6% for BTA26 to 56.1% for BTA5. Using approaches based on allelic frequencies and linkage disequilibrium, genomic regions with putative selection signatures were identified in several chromosomes (BTA1, BTA5, BTA6 and BTA14). These regions harbour genes involved in horn development, growth, lipid metabolism, reproduction and immune response. We argue that the overlapping of a chromosome segment originated in one of the parental breeds and over‐represented in the sample with the location of a signature of selection constitutes evidence of a selection process that has occurred in the breed since its take off in the 1950s. In this regard, our results could contribute to the understanding of the genetic mechanisms involved in cross‐bred cattle adaptation and productivity in tropical environments.