RESUMEN

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.

RESUMEN

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.

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RESUMEN

A Markov chain Monte Carlo (MCMC) algorithm to sample an exchangeable covariance matrix, such as the one of the error terms (R0) in a multiple trait animal model with missing records under normal-inverted Wishart priors is presented. The algorithm (FCG) is based on a conjugate form of the inverted Wishart density that avoids sampling the missing error terms. Normal prior densities are assumed for the 'fixed' effects and breeding values, whereas the covariance matrices are assumed to follow inverted Wishart distributions. The inverted Wishart prior for the environmental covariance matrix is a product density of all patterns of missing data. The resulting MCMC scheme eliminates the correlation between the sampled missing residuals and the sampled R0, which in turn has the effect of decreasing the total amount of samples needed to reach convergence. The use of the FCG algorithm in a multiple trait data set with an extreme pattern of missing records produced a dramatic reduction in the size of the autocorrelations among samples for all lags from 1 to 50, and this increased the effective sample size from 2.5 to 7 times and reduced the number of samples needed to attain convergence, when compared with the 'data augmentation' algorithm.

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