G. J. M. Rosa

Bayesian analysis of autoregressive panel data model: application in genetic evaluation of beef cattle

The animal breeding values forecasting at futures times is a relevant technological innovation in the field of Animal Science, since its enables a previous indication of animals that will be either kept by the producer for breeding purposes or discarded. This study discusses an MCMC Bayesian methodology applied to panel data in a time series context. We consider Bayesian analysis of an autoregressive, AR(p), panel data model of order p, using an exact likelihood function, comparative analysis of prior distributions and predictive distributions of future observations. The methodology was tested by a simulation study using three priors: hierarchical Multivariate Normal-Inverse Gamma (model 1), independent Multivariate Students t Inverse Gamma (model 2) and Jeffreys (model 3). Comparisons by Pseudo-Bayes Factor favored model 2. The proposed methodology was applied to longitudinal data relative to Expected Progeny Difference (EPD) of beef cattle sires. The forecast efficiency was around 80%. Regarding the mean width of the EPD interval estimation (95%) in a future time, a great advantage was observed for the proposed Bayesian methodology over usual asymptotic frequentist method.

Marker-assisted assessment of genotype by environment interaction: a case study of single nucleotide polymorphism-mortality association in broilers in two hygiene environments.

Interplay between genetic and environmental factors, genotype x environment interactions (G x E), affect phenotypes of complex traits. A methodology for assessing G x E was investigated by detecting hygiene (low and high) environment-specific SNP subsets associated with broiler chicken mortality, followed by an examination of consistency between SNP subsets selected from the 2 environments. The trait was mean progeny mortality rate in 253 sire families, after adjusting records for nuisance effects affecting mortality at the individual bird level. Over 5,000 whole-genome SNP were narrowed down via a machine-learning (filter-wrapper) feature selection procedure applied to mortality rates in each of the 2 environments. For both early and late mortality, it was found that the selected SNP subsets differed across hygiene environments, in terms of either across-environment predictive ability or extent of linkage disequilibrium between the subsets. Reduction in predictive ability due to G x E was assessed by the ratio of 2 predicted residual sum of squares statistics, one associated with SNP selected from the same hygiene environment and the other associated with the SNP subset from a different environment. Reduction was 30 and 20% for early and late mortality, respectively. An extremely low level of linkage disequilibrium between SNP subsets selected under low and high hygiene also indicated G x E. Findings suggest that there may not be a universally optimal SNP subset for predicting mortality and that interactions between genome and environmental factors need to be considered in association analysis of complex traits.

Características reprodutivas e suas associações com outras características de importância econômica na raça Nelore

Heritability (h 2 ) and genetic correlations (rg) were estimated between reproductive traits such as age at first calving (AFC), first calving interval (FCI) and other economically relevant traits, i.e., weight (W) at year (Y) and at 18 months of age (S), scrotal circumference (SC), and hip height (HH) in Nelore cattle. The genetic parameters were estimated in a multiple-trait analysis, with animal models using the Bayesian inference by Gibbs Sampling algorithm. The genetic parameters estimated in this work suggest the existence of genetic variability for AFC (h 2 = 0.26), where the selection for the reduction of Nelore females AFC should respond to mass selection, without causing genetic antagonism in the selection of W (rg = -0,22 (Y) and -0,44 (S)), and SC (rg = 0,02). The selection for the AFC in the long term could lead to an increase in the animals frame, although this association is relatively low (-0.35). The posteriori heritability estimate for FCI was low, 0.11±0.03. The rg between FCI and the other traits studied indicate that selection for these growth traits will not affect the FCI.

Estudos de expressão gênica utilizando-se microarrays: delineamento, análise, e aplicações na pesquisa zootécnica

Microarray technology allows monitoring thousands of genes simultaneously in a specific tissue of an organism, in different developmental stages or environmental conditions. Microarrays are very common in functional genomics experiments with both animals and plant species, and they have been increasingly used also in different areas of livestock research, such as growth and metabolism, reproduction, immune response to diseases and parasites, response to non-infectious stress factors (such as dietary restriction, exposure to toxic elements and other unfavorable environmental conditions) as well as animal breeding. Such experiments, however, are still considerably expensive and time consuming and, consequently, they are performed with relatively small sample sizes. Nonetheless, microarray experiments are extremely complex, as they involve a number of laboratorial procedures such as sample collection, RNA extraction, reverse transcription and labeling, and the final hybridization. Hence, microarray assays require careful experimental planning and statistical data analysis. In this manuscript, basic principles of experimental design for microarray studies are reviewed, as well as the most common statistical and computational tools used for their analysis. In addition, some examples of application of microarray technology in animal science are discussed, and some concluding remarks are presented afterward.

Modelos hierárquicos bayesianos para estimação robusta e análise de dados censurados em melhoramento animal

Data strongly influenced by factors not accounted for by the statistical model can bias estimates of genetic parameters and values. Moreover, several traits of economic importance do not follow a normal distribution or have censored data. The objective of this study is to describe and illustrate the application of hierarchical Bayesian models for the detection and muting of outliers and for the analysis of censored data. First, the traditional specification of the animal model in hierarchical stages is presented under the Bayesian approach for normally distributed uncensored data. Then, this model is extended by introducing an independent weighting variable, which allows for the specification of thick tail residual densities from the Normal/independent distribution family. Finally, to cover censored data analysis, the basic model is extended by the inclusion of a variable with truncated normal distribution based on the lower limit in the observed value of the trait at the evaluation time, for those animals that have not yet completed their reproductive life at the evaluation time.

Delineamento de experimentos em genética genômica

Genetica genomica e um termo utilizado para representar o estudo de processos geneticos controladores de caracteres fenotipicos de heranca complexa, a partir da analise conjunta de informacao relativa a fenotipos, estruturas de parentesco, marcadores moleculares e expressao genica. Estudos de genetica genomica sao utilizados, por exemplo, para a estimacao da herdabilidade de niveis de transcricao, para o mapeamento de locos controladores da expressao genica (eQTL, do ingles expression Quantitative Trait Loci), e para o estudo de redes regulatorias. Genetica genomica geralmente envolve experimentos com microarrays, os quais sao ainda bastante caros e trabalhosos, limitando o tamanho amostral e consequentemente o poder estatistico de tais estudos. Desta maneira, e essencial que tais experimentos sejam otimizados do ponto de vista do delineamento, a partir de criteriosa escolha das amostras (individuos) a serem utilizadas, e do controle rigoroso dos varios fatores que podem afetar as variaveis-resposta de interesse. Outro ponto fundamental na conducao de tais experimentos refere-se a marcacao das amostras de mRNA com os fluoroforos e ao pareamento das mesmas em cada lâmina de microarray, os quais devem ser cuidadosamente planejados para que nao haja confundimento entre estes efeitos e os fatores biologicos de interesse. Nesta apresentacao serao discutidas algumas estrategias para o planejamento de estudos de genetica genomica, incluindo a selecao de individuos objetivando-se a maximizacao da dissimilaridade genetica ou do numero de eventos de recombinacao, bem como a conducao eficiente dos ensaios com microarrays para diferentes objetivos experimentais.

Genome scan for postmortem carcass traits in Nellore cattle

Carcass traits measured after slaughter are economically relevant traits in beef cattle. In general, the slaughter house payment system is based on HCW. Ribeye area (REA) is associated with the amount of the meat in the carcass, and a minimum of backfat thickness (BFT) is necessary to protect the carcass during cooling. The aim of this study was to identify potential genomic regions harboring candidate genes affecting those traits in Nellore cattle. The data set used in the present study consisted of 1,756 Nellore males with phenotype records. A subset of 1,604 animals had both genotypic and phenotypic information. Genotypes were generated based on a panel with 777,962 SNPs from the Illumina Bovine HD chip. The SNP effects were calculated based on the genomic breeding values obtained by using the single-step GBLUP approach and a genomic matrix re-weighting procedure. The proportion of the variance explained by moving windows of 100 consecutive SNPs was used to assess potential genomic regions harboring genes with major effects on each trait. The top 10 non-overlapping SNP-windows explained 8.72%, 11.38%, and 9.31% of the genetic variance for REA, BFT, and HCW, respectively. These windows are located on chromosomes 5, 7, 8, 10, 12, 20, and 29 for REA; chromosomes 6, 8, 10, 13, 16, 17, 18, and 24 for BFT; and chromosomes 4, 6, 7, 8, 14, 16, 17, and 21 for HCW. For REA, there were identified genes ( and ) involved in the cell cycle biological process which affects many aspects of animal growth and development. The and genes, both from AA transporter family, was also associated with REA. The AA transporters are essential for cell growth and proliferation, acting as carriers of tissue nutrient supplies. Various genes identified for BFT (, , , , , and ) have been associated with lipid metabolism in different mammal species. One of the most promising genes identified for HCW was the . There is evidence, in the literature, that this gene is located in putative QTL affecting carcass weight in beef cattle. Our results showed several genomic regions containing plausible candidate genes that may be associated with carcass traits in Nellore cattle. Besides contributing to a better understanding of the genetic control of carcass traits, the identified genes can also be helpful for further functional genomic studies.

Inferring phenotypic causal structures among meat quality traits and the application of a structural equation model in Japanese Black cattle1

Meat quality is one of the most important traits determining carcass price in the Japanese beef market. Optimized breeding goals and management practices for the improvement of meat quality traits requires knowledge regarding any potential functional relationships between them. In this context, the objective of this research was to infer phenotypic causal networks involving beef marbling score (BMS), beef color score (BCL), firmness of beef (FIR), texture of beef (TEX), beef fat color score (BFS), and the ratio of MUFA to SFA (MUS) from 11,855 Japanese Black cattle. The inductive causation (IC) algorithm was implemented to search for causal links among these traits and was conditionally applied to their joint distribution on genetic effects. This information was obtained from the posterior distribution of the residual (co)variance matrix of a standard Bayesian multiple trait model (MTM). Apart from BFS, the IC algorithm implemented with 95% highest posterior density (HPD) intervals detected only undirected links among the traits. However, as a result of the application of 80% HPD intervals, more links were recovered and the undirected links were changed into directed ones, except between FIR and TEX. Therefore, 2 competing causal networks resulting from the IC algorithm, with either the arrow FIR → TEX or the arrow FIR ← TEX, were fitted using a structural equation model () to infer causal structure coefficients between the selected traits. Results indicated similar genetic and residual variances as well as genetic correlation estimates from both structural equation models. The genetic variances in BMS, FIR, and TEX from the structural equation models were smaller than those obtained from the MTM. In contrast, the variances in BCL, BFS, and MUS, which were not conditioned on any of the other traits in the causal structures, had no significant differences between the structural equation model and MTM. The structural coefficient for the path from MUS (BCL) to BMS showed that a 1-unit improvement in MUS (BCL) resulted in an increase of 0.85 or 1.45 (an decrease of 0.52 or 0.54) in BMS in the causal structures. The analysis revealed some interesting functional relationships, direct genetic effects, and the magnitude of the causal effects between these traits, for example, indicating that BMS would be affected by interventions on MUS and BCL. In addition, if interventions existed in this scenario, a breeding strategy based only on the MTM would lead to a mistaken selection for BMS.

Comparison of models for the genetic evaluation of reproductive traits with censored data in Nellore cattle.

In typical genetic evaluation, often some females have missing records due to reproductive failure and due to voluntary and involuntary culling before the breeding season. These partially or unobserved phenotypes are known as censored records and their inclusion into genetic evaluations might lead to better inferences and breeding value predictions. Then, the objective was to compare prediction ability of models in which the phenotypic expression of age at the first calving (AFC) and days to calving (DC) were considered to be censored and uncensored in a Nellore cattle population. Age at first calving and days to calving were analyzed as following: uncensored animals (LM); penalization of 21 d (PLM); censored records simulated from truncated normal distributions (CLM); threshold-linear model in which censored records were handled as missing (TLM) or coded as the upper AFC/DC value within contemporary group (PTLM); and Weibull frailty hazard model (WM). Pearson correlations (PC), the percentage of the 10% best bulls in common (pTOP10%), accuracy of estimated breeding values (), and a cross-validation scheme were performed. Heritability estimates for AFC were 0.18, 0.12, 0.12, 0.17, 0.14, and 0.07 for LM, PLM, CLM, TLM, PTLM, and WM, respectively. PC and pTOP10% were higher among linear models and smaller between these models and WM. The models provided similar r of sire breeding values. Heritability estimates for DC were 0.03, 0.08, 0.06, 0.02, 0.07, and 0.10 for LM, PLM, CLM, TLM, PTLM, and WM, respectively. Strongly associated predictions were observed in CLM, PLM, PTLM, and WM. The highest coincidence levels of sires in the TOP10% were between CLM, PLM, and PTLM. The r of sire breeding values obtained applying CLM, PLM, PTLM, and WM were similar and higher than those obtained with LM and TLM. In terms of prediction ability, WM, PLM, TLM, and PTLM showed similar prediction performance for AFC. On the other hand, CLM, PLM, PTLM, and WM showed the similar prediction ability for DC Therefore, these models would be recommended to perform genetic evaluation of age at first calving and days to calving in this Nellore population.

Mixed Effects Structural Equation Models and Phenotypic Causal Networks

Complex networks with causal relationships among variables are pervasive in biology. Their study, however, requires special modeling approaches. Structural equation models (SEM) allow the representation of causal mechanisms among phenotypic traits and inferring the magnitude of causal relationships. This information is important not only in understanding how variables relate to each other in a biological system, but also to predict how this system reacts under external interventions which are common in fields related to health and food production. Nevertheless, fitting a SEM requires defining a priori the causal structure among traits, which is the qualitative information that describes how traits are causally related to each other. Here, we present directions for the applications of SEM to investigate a system of phenotypic traits after searching for causal structures among them. The search may be performed under confounding effects exerted by genetic correlations.