Dataset Information

Genome-Enabled Prediction of Breeding Values for Feedlot Average Daily Weight Gain in Nelore Cattle.

ABSTRACT: Nelore is the most economically important cattle breed in Brazil, and the use of genetically improved animals has contributed to increased beef production efficiency. The Brazilian beef feedlot industry has grown considerably in the last decade, so the selection of animals with higher growth rates on feedlot has become quite important. Genomic selection (GS) could be used to reduce generation intervals and improve the rate of genetic gains. The aim of this study was to evaluate the prediction of genomic-estimated breeding values (GEBV) for average daily weight gain (ADG) in 718 feedlot-finished Nelore steers. Analyses of three Bayesian model specifications [Bayesian GBLUP (BGBLUP), BayesA, and BayesC?] were performed with four genotype panels [Illumina BovineHD BeadChip, TagSNPs, and GeneSeek High- and Low-density indicus (HDi and LDi, respectively)]. Estimates of Pearson correlations, regression coefficients, and mean squared errors were used to assess accuracy and bias of predictions. Overall, the BayesC? model resulted in less biased predictions. Accuracies ranged from 0.18 to 0.27, which are reasonable values given the heritability estimates (from 0.40 to 0.44) and sample size (568 animals in the training population). Furthermore, results from Bos taurus indicus panels were as informative as those from Illumina BovineHD, indicating that they could be used to implement GS at lower costs.

SUBMITTER: Somavilla AL

PROVIDER: S-EPMC5473763 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Genome-Enabled Prediction of Breeding Values for Feedlot Average Daily Weight Gain in Nelore Cattle.

Somavilla Adriana L AL Somavilla Adriana L AL Regitano Luciana C A LCA Rosa Guilherme J M GJM Mokry Fabiana B FB Mudadu Mauricio A MA Tizioto Polyana C PC Oliveira Priscila S N PSN Souza Marcela M MM Coutinho Luiz L LL Munari Danísio P DP

G3 (Bethesda, Md.) 20170607 6

Nelore is the most economically important cattle breed in Brazil, and the use of genetically improved animals has contributed to increased beef production efficiency. The Brazilian beef feedlot industry has grown considerably in the last decade, so the selection of animals with higher growth rates on feedlot has become quite important. Genomic selection (GS) could be used to reduce generation intervals and improve the rate of genetic gains. The aim of this study was to evaluate the prediction of ...[more]

PMID: 28391242

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Project description:Intensive production systems require high-yield genetics as obtained in Bos taurus × Bos indicus crossbreeding. Generally, high-producing taurine cattle are more susceptible to parasites and heat stress. This study evaluated animal performance, heat-stress measurement (infrared temperatures), and internal parasite infection with daily weight gain in heifers from 2 genetic groups (Nelore and F1-Nelore × Angus) reared in 2 forage production systems (with or without crop-livestock system) during 1 yr. The main objectives were to determine the relationship between infrared measures and animal performance and whether it differs between genetic groups and environments. Thirty-six heifers were randomly assigned to 2 forage production systems, one considered as high-input system with crop-livestock system and other exclusive livestock system considered as low input. At each 28 d, infrared thermography (IR) temperatures, weight, and internal parasite infection (fecal egg count) were measured. The temperatures of the eye, snout, forehead, dewlap, body, ground and squeeze chute were determined. F1 heifers had higher weight gain than Nelore (P < 0.05) and both did not differ in internal parasite infection (P > 0.05). F1 heifers had higher IR than Nelore (P < 0.05). The main body points that differentiate between genetic groups were dewlap, forehead, and eye. Higher dewlap IR temperature (DW) was associated with higher average daily gain (ADG) during dry season (independently of genetic groups) (ADG = -0.755 + 0.032 × DW; R2 = 0.44). Otherwise, the IR temperatures had a negative relationship with ADG during rainy season and low forehead IR temperature was related to higher average daily gain (ADG = 1.81 - 0.033 × forehead; R2 = 0.12 for F1 animals and ADG = 1.46 - 0.025 × forehead; R2 = 0.07 for Nelore). The infrared temperatures were more related to animal performance during the dry season, which had high temperature and low humidity. The infrared temperatures were able to identify the animal response to the environment challenge. Animals with higher temperatures (dewlap and forehead) had higher daily gain during the dry season.

Project description:BackgroundThe impact of additive-genetic relationships captured by single nucleotide polymorphisms (SNPs) on the accuracy of genomic breeding values (GEBVs) has been demonstrated, but recent studies on data obtained from Holstein populations have ignored this fact. However, this impact and the accuracy of GEBVs due to linkage disequilibrium (LD), which is fairly persistent over generations, must be known to implement future breeding programs.Materials and methodsThe data set used to investigate these questions consisted of 3,863 German Holstein bulls genotyped for 54,001 SNPs, their pedigree and daughter yield deviations for milk yield, fat yield, protein yield and somatic cell score. A cross-validation methodology was applied, where the maximum additive-genetic relationship (amax) between bulls in training and validation was controlled. GEBVs were estimated by a Bayesian model averaging approach (BayesB) and an animal model using the genomic relationship matrix (G-BLUP). The accuracy of GEBVs due to LD was estimated by a regression approach using accuracy of GEBVs and accuracy of pedigree-based BLUP-EBVs.ResultsAccuracy of GEBVs obtained by both BayesB and G-BLUP decreased with decreasing amax for all traits analyzed. The decay of accuracy tended to be larger for G-BLUP and with smaller training size. Differences between BayesB and G-BLUP became evident for the accuracy due to LD, where BayesB clearly outperformed G-BLUP with increasing training size.ConclusionsGEBV accuracy of current selection candidates varies due to different additive-genetic relationships relative to the training data. Accuracy of future candidates can be lower than reported in previous studies because information from close relatives will not be available when selection on GEBVs is applied. A Bayesian model averaging approach exploits LD information considerably better than G-BLUP and thus is the most promising method. Cross-validations should account for family structure in the data to allow for long-lasting genomic based breeding plans in animal and plant breeding.

Dataset Information

Genome-Enabled Prediction of Breeding Values for Feedlot Average Daily Weight Gain in Nelore Cattle.

Publications

Genome-Enabled Prediction of Breeding Values for Feedlot Average Daily Weight Gain in Nelore Cattle.

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