Genomic Prediction Accuracies for Growth and Carcass Traits in a Brangus Heifer Population.

The predictive abilities and accuracies of genomic best linear unbiased prediction (GBLUP) and the Bayesian (BayesA, BayesB, BayesC and Lasso) genomic selection (GS) methods for economically important growth (birth, weaning, and yearling weights) and carcass (depth of rib fat, apercent intramuscular fat and longissimus muscle area) traits were characterized by estimating the linkage disequilibrium (LD) structure in Brangus heifers using single nucleotide polymorphisms (SNP) markers. Sharp declines in LD were observed as distance among SNP markers increased. The application of the GBLUP and the Bayesian methods to obtain the GEBV for growth and carcass traits within k-means and random clusters showed that k-means and random clustering had quite similar heritability estimates, but the Bayesian methods resulted in the lower estimates of heritability between 0.

Multi-trait Bayesian analysis and genetic parameter estimates in production characters of Mecheri sheep of India.

The present study was carried out to estimate the genetic parameters for direct and maternal influences on Mecheri sheep (Ovis aries) growth traits using Bayesian multi-trait animal model. The genetic parameters were calculated using data from 2825 Mecheri lambs born between 2010 and 2020 that were kept in semi-arid tropical climate. Mecheri sheep body weight (mean ± SE) at various stages, viz.

Comparative accuracies of genetic values predicted for economically important milk traits, genome-wide association, and linkage disequilibrium patterns of Canadian Holstein cows.

Genomic selection methodologies and genome-wide association studies use powerful statistical procedures that correlate large amounts of high-density SNP genotypes and phenotypic data. Actual 305-d milk (MY), fat (FY), and protein (PY) yield data on 695 cows and 76,355 genotyping-by-sequencing-generated SNP marker genotypes from Canadian Holstein dairy cows were used to characterize linkage disequilibrium (LD) structure of Canadian Holstein cows. Also, the comparison of pedigree-based BLUP, genomic BLUP (GBLUP), and Bayesian (BayesB) statistical methods in the genomic selection methodologies and the comparison of Bayesian ridge regression and BayesB statistical methods in the genome-wide association studies were carried out for MY, FY, and PY.

Comparison of linear model and artificial neural network using antler beam diameter and length of white-tailed deer (Odocoileus virginianus) dataset.

Evaluation of harvest data remains one of the most important sources of information in the development of strategies to manage regional populations of white-tailed deer. While descriptive statistics and simple linear models are utilized extensively, the use of artificial neural networks for this type of data analyses is unexplored. Linear model was compared to Artificial Neural Networks (ANN) models with Levenberg-Marquardt (L-M), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) learning algorithms, to evaluate the relative accuracy in predicting antler beam diameter and length using age and dressed body weight in white-tailed deer.

Genomic Prediction from Multiple-Trait Bayesian Regression Methods Using Mixture Priors.

Bayesian multiple-regression methods incorporating different mixture priors for marker effects are used widely in genomic prediction. Improvement in prediction accuracies from using those methods, such as BayesB, BayesC, and BayesC, have been shown in single-trait analyses with both simulated and real data. These methods have been extended to multi-trait analyses, but only under the restrictive assumption that a locus simultaneously affects all the traits or none of them.

Reduction in accuracy of genomic prediction for ordered categorical data compared to continuous observations.

Background: Accuracy of genomic prediction depends on number of records in the training population, heritability, effective population size, genetic architecture, and relatedness of training and validation populations. Many traits have ordered categories including reproductive performance and susceptibility or resistance to disease. Categorical scores are often recorded because they are easier to obtain than continuous observations.

Genome-wide association study of infectious bovine keratoconjunctivitis in Angus cattle.

Background: Infectious Bovine Keratoconjunctivitis (IBK) in beef cattle, commonly known as pinkeye, is a bacterial disease caused by Moraxellabovis. IBK is characterized by excessive tearing and ulceration of the cornea. Perforation of the cornea may also occur in severe cases.

Morphological and microsatellite DNA diversity of Nigerian indigenous sheep.

Background: Sheep is important in the socio-economic lives of people around the world. It is estimated that more than half of our once common livestock breeds are now endangered. Since genetic characterization of Nigerian sheep is still lacking, we analyzed ten morphological traits on 402 animals and 15 microsatellite DNA markers in 384 animals of the 4 Nigerian sheep breeds to better understand genetic diversity for breeding management and germplasm conservation.

Whole genome analysis of infectious bovine keratoconjunctivitis in Angus cattle using Bayesian threshold models.

Infectious bovine keratoconjunctivitis (IBK), also known as pinkeye, is characterized by damage to the cornea and is an economically important, lowly heritable, categorical disease trait in beef cattle. Scores of eye damage were collected at weaning on 858 Angus cattle. SNP genotypes for each animal were obtained from BovineSNP50 Infinium-beadchips.

Extension of the bayesian alphabet for genomic selection.

Background: Two bayesian methods, BayesCπ and BayesDπ, were developed for genomic prediction to address the drawback of BayesA and BayesB regarding the impact of prior hyperparameters and treat the prior probability π that a SNP has zero effect as unknown. The methods were compared in terms of inference of the number of QTL and accuracy of genomic estimated breeding values (GEBVs), using simulated scenarios and real data from North American Holstein bulls.

Results: Estimates of π from BayesCπ, in contrast to BayesDπ, were sensitive to the number of simulated QTL and training data size, and provide information about genetic architecture.

Use of linear mixed models for genetic evaluation of gestation length and birth weight allowing for heavy-tailed residual effects.

Background: The distribution of residual effects in linear mixed models in animal breeding applications is typically assumed normal, which makes inferences vulnerable to outlier observations. In order to mute the impact of outliers, one option is to fit models with residuals having a heavy-tailed distribution. Here, a Student's-t model was considered for the distribution of the residuals with the degrees of freedom treated as unknown.

Effects of postmenopausal hormone replacement therapy on body fat composition.

Aim: To evaluate the effects of different types, regimens and administration routes of hormone replacement therapy (HRT) on body fat composition indices in postmenopausal women at increased risk of anthropometry-related cardiovascular disease (CVD).

Methods: Fifty-nine postmenopausal women (aged 41-57 years, mean +/- standard deviation: 49.9 +/- 3.

Effects of oral continuous 17beta-estradiol plus norethisterone acetate replacement therapy on abdominal subcutaneous fat, serum leptin levels and body composition.

Aim: To evaluate the effects of oral continuous 17beta-estradiol plus norethisterone acetate (E2/NETA) replacement therapy on abdominal subcutaneous fat, serum leptin level (SLL) and body composition in postmenopausal women.

Materials And Methods: A 6-month, prospective, randomized, double-blind and placebo-controlled study was conducted. Forty-three healthy naturally postmenopausal women aged 43-65 years were randomly assigned to receive E2/NETA (2 mg E2 plus 1 mg NETA, n = 22) or placebo (n = 21).

A general approach to mixed effects modeling of residual variances in generalized linear mixed models.

We propose a general Bayesian approach to heteroskedastic error modeling for generalized linear mixed models (GLMM) in which linked functions of conditional means and residual variances are specified as separate linear combinations of fixed and random effects. We focus on the linear mixed model (LMM) analysis of birth weight (BW) and the cumulative probit mixed model (CPMM) analysis of calving ease (CE). The deviance information criterion (DIC) was demonstrated to be useful in correctly choosing between homoskedastic and heteroskedastic error GLMM for both traits when data was generated according to a mixed model specification for both location parameters and residual variances.

Cumulative t-link threshold models for the genetic analysis of calving ease scores.

In this study, a hierarchical threshold mixed model based on a cumulative t-link specification for the analysis of ordinal data or more, specifically, calving ease scores, was developed. The validation of this model and the Markov chain Monte Carlo (MCMC) algorithm was carried out on simulated data from normally and t4 (i.e.