A look under the hood of genomic-estimated breed compositions for brangus cattle: What have we learned?

The Brangus cattle were developed to utilize the superior traits of Angus and Brahman cattle. Their genetic compositions are expected to be stabilized at 3/8 Brahman and 5/8 Angus. Previous studies have shown more than expected Angus lineage with Brangus cattle, and the reasons are yet to be investigated.

A Causality Perspective of Genomic Breed Composition for Composite Animals.

Genomic breed composition (GBC) of an individual animal refers to the partition of its genome according to the inheritance from its ancestors or ancestral breeds. For crossbred or composite animals, knowing their GBC is useful to estimate heterosis, to characterize their actual inheritance from foundation breeds, and to make management decisions for crossbreeding programs. Various statistical approaches have been proposed to estimate GBC in animals, but the interpretations of estimates have varied with these methods.

Genomic mating as sustainable breeding for Chinese indigenous Ningxiang pigs.

An important economic reason for the loss of local breeds is that they tend to be less productive, and hence having less market value than commercial breeds. Nevertheless, local breeds often have irreplaceable values, genetically and sociologically. In the breeding programs with local breeds, it is crucial to balance the selection for genetic gain and the maintaining of genetic diversity.

Estimation of Genomic Breed Composition for Purebred and Crossbred Animals Using Sparsely Regularized Admixture Models.

A variety of statistical methods, such as admixture models, have been used to estimate genomic breed composition (GBC). These methods, however, tend to produce non-zero components to reference breeds that shared some genomic similarity with a test animal. These non-essential GBC components, in turn, offset the estimated GBC for the breed to which it belongs.

[Impacts of SNP genotyping call rate and SNP genotyping error rate on imputation accuracy inHolsteincattle].

Single nucleotide polymorphism (SNP) chips have been widely used in genetic studies and breeding applications in animal and plant species. The quality of SNP genotypes is of paramount importance. More often than not, there are situations in which a number of genotypes may fail, requiring them to be imputed.

Comparing SNP panels and statistical methods for estimating genomic breed composition of individual animals in ten cattle breeds.

Background: SNPs are informative to estimate genomic breed composition (GBC) of individual animals, but selected SNPs for this purpose were not made available in the commercial bovine SNP chips prior to the present study. The primary objective of the present study was to select five common SNP panels for estimating GBC of individual animals initially involving 10 cattle breeds (two dairy breeds and eight beef breeds). The performance of the five common SNP panels was evaluated based on admixture model and linear regression model, respectively.

[Estimating genomic breed composition of individual animals using selected SNPs].

Natural and artificial selection, geographical segregation and genetic drift can result in differentiation of allelic frequencies of single nucleotide polymorphism (SNP) at many loci in the animal genome. For individuals whose ancestors originated from different populations, their genetic compositions exhibit multiple components correlated with the genotypes or allele frequencies of these breeds or populations. Therefore, by using an appropriate statistical method, one can estimate the genomic contribution of each breed (ancestor) to the genome of each individual animal, which is referred to as the genomic breed composition (GBC).

Comparing strategies for selection of low-density SNPs for imputation-mediated genomic prediction in U. S. Holsteins.

SNP chips are commonly used for genotyping animals in genomic selection but strategies for selecting low-density (LD) SNPs for imputation-mediated genomic selection have not been addressed adequately. The main purpose of the present study was to compare the performance of eight LD (6K) SNP panels, each selected by a different strategy exploiting a combination of three major factors: evenly-spaced SNPs, increased minor allele frequencies, and SNP-trait associations either for single traits independently or for all the three traits jointly. The imputation accuracies from 6K to 80K SNP genotypes were between 96.

Optimal Design of Low-Density SNP Arrays for Genomic Prediction: Algorithm and Applications.

Low-density (LD) single nucleotide polymorphism (SNP) arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for the optimal design of LD SNP chips. A multiple-objective, local optimization (MOLO) algorithm was developed for design of optimal LD SNP chips that can be imputed accurately to medium-density (MD) or high-density (HD) SNP genotypes for genomic prediction. The objective function facilitates maximization of non-gap map length and system information for the SNP chip, and the latter is computed either as locus-averaged (LASE) or haplotype-averaged Shannon entropy (HASE) and adjusted for uniformity of the SNP distribution.

Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models.

Background: Artificial neural networks (ANN) mimic the function of the human brain and are capable of performing massively parallel computations for data processing and knowledge representation. ANN can capture nonlinear relationships between predictors and responses and can adaptively learn complex functional forms, in particular, for situations where conventional regression models are ineffective. In a previous study, ANN with Bayesian regularization outperformed a benchmark linear model when predicting milk yield in dairy cattle or grain yield of wheat.

An ensemble-based approach to imputation of moderate-density genotypes for genomic selection with application to Angus cattle.

Summary Imputation of moderate-density genotypes from low-density panels is of increasing interest in genomic selection, because it can dramatically reduce genotyping costs. Several imputation software packages have been developed, but they vary in imputation accuracy, and imputed genotypes may be inconsistent among methods. An AdaBoost-like approach is proposed to combine imputation results from several independent software packages, i.

A primer on high-throughput computing for genomic selection.

High-throughput computing (HTC) uses computer clusters to solve advanced computational problems, with the goal of accomplishing high-throughput over relatively long periods of time. In genomic selection, for example, a set of markers covering the entire genome is used to train a model based on known data, and the resulting model is used to predict the genetic merit of selection candidates. Sophisticated models are very computationally demanding and, with several traits to be evaluated sequentially, computing time is long, and output is low.

Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation.

Background: Genomic selection is a recently developed technology that is beginning to revolutionize animal breeding. The objective of this study was to estimate marker effects to derive prediction equations for direct genomic values for 16 routinely recorded traits of American Angus beef cattle and quantify corresponding accuracies of prediction.

Methods: Deregressed estimated breeding values were used as observations in a weighted analysis to derive direct genomic values for 3570 sires genotyped using the Illumina BovineSNP50 BeadChip.

An evaluation of the relative efficacy of a new formulation of oxytetracycline for the treatment of undifferentiated fever in feedlot calves in western Canada.

A field trial was performed under commercial feedlot conditions in western Canada to compare the efficacy of a new formulation of long-acting oxytetracycline (LA 30) to a standard long-acting oxytetracycline formulation (LA 20) and florfenicol (FLOR) for the treatment of undifferentiated fever (UF) in calves that received metaphylactic tilmicosin upon arrival at the feed-lot. Seven hundred and ninety-seven recently weaned, auction market derived, crossbred, beef calves suffering from UF were allocated to 1 of 3 experimental groups as follows: LA 30, which received intramuscular long-acting oxytetracycline (300 mg/mL formulation) at the rate of 30 mg/kg body weight (BW) at the time of allocation; LA 20, which received intramuscular long-acting oxytetracycline (200 mg/mL formulation) at the rate of 20 mg/kg BW at the time of allocation; or FLOR, which received intramuscular florfenicol administered at the rate of 20 mg/kg BW at the time of allocation and again 48 hours later. Two hundred and sixty-six animals were allocated to the LA 30 group, 265 animals were allocated to the LA 20 group, and 266 animals were allocated to the FLOR group.

A comparison of prophylactic efficacy of tilmicosin and a new formulation of oxytetracycline in feedlot calves.

Two replicated-pen field studies were performed under commercial feedlot conditions in western Canada to compare the administration of long-acting oxytetracycline at 30 mg/kg body weight (BW) versus tilmicosin at 10 mg/kg BW to feedlot calves upon arrival at the feedlot. Ten thousand nine hundred and eighty-nine, recently weaned, auction market derived, crossbred beef steer and bull calves were randomly allocated upon arrival at the feedlot to one of 2 experimental groups as follows: oxytetracycline, which received intramuscular long-acting oxytetracycline (300 mg/mL formulation) at a rate of 30 mg/kg BW; or tilmicosin, which received subcutaneous tilmicosin (300 mg/mL formulation) at a rate of 10 mg/kg BW. There were 20 pens in each experimental group.