Metodologia para estimação do mérito genético de animais com paternidade incerta sob inferência bayesiana.

Modelo animal reduzido com efeito materno; Modelo hierárquico bayesiano para paternidade incerta; Validação por simulação; Análise de dados de desempenho de um rebanho Hereford; Inferência Bayesiana; Análises do estudo de simulação; Análises de dados do rebanho Hereford; Critérios para escolha do melhor modelo; Inferência sobre dados simulados; Inferência nos dados do rebanho Hereford; Ganho de peso pós-desmama; Peso à desmama.bitstream/item/55802/1/BP32.pd

Genomic wide-selection for tick resistance in Hereford and Braford cattle via reaction norm model.

The objective of this study was to compare a conventional genomic model (GBLUP) and its extension to a linear reaction norm model (GLRNM) specifying genotype by environment interaction (G*E) for tick resistance in Brazilian cattle. Tick counts (TC) from 4,363 Hereford and Braford cattle from 146 contemporary groups (CG) were available of which 3,591 animals had BovineSNP50 Illumina v2 BeadChip genotypes. The reaction norm covariate was based on CG estimates of TC from a first-step model. Analysis was conducted based on adapting the single step GBLUP/REML procedure. Fivefold cross validation based on K-means and random partitioning was used to compare the fit of the two models. Cross validation correlations were strong and not significantly different between models for either partitioning strategy. Nevertheless, it seems apparent that G*E for tick infestation exists and can captured by GLRNM models

Metodologia para avaliação genética de populações multirraciais usando modelos hierárquicos bayesianos.

bitstream/item/55803/1/DT75.pd

Genotype by environment interaction for tick resistance of Hereford and Braford beef cattle using reaction norm model.

The cattle tick is a parasite that adversely affects livestock performance in tropical areas. Although countries such as Australia and Brazil have developed genetic evaluations for tick resistance, these evaluations have not considered genotype by environment (G*E) interactions. Genetic gains could be adversely affected, since breed-stock comparisons are environmentally dependent on the presence of G*E interactions, particularly if residual vari-ability is also heterogeneous across environments. The objective of this study was to infer upon the existence of G*E interactions for tick resistance of cattle based on various models with different assumptions of genetic and residual variability.Article 3

Genomic Prediction Accounting for Residual Heteroskedasticity

Citation: Ou, Z. N., Tempelman, R. J., Steibel, J. P., Ernst, C. W., Bates, R. O., & Bello, N. M. (2016). Genomic Prediction Accounting for Residual Heteroskedasticity. G3-Genes Genomes Genetics, 6(1), 1-13. doi:10.1534/g3.115.022897Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroske-dasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit