Tag SNP selection for prediction of adaptation traits in Braford and Hereford cattle using Bayesian methods.

This study utilized Bayesian inference in a genome-wide association study (GWAS) to identify genetic markers associated with traits relevant to the adaptation of Hereford and Braford cattle breeds. We focused on eye pigmentation (EP), weaning hair coat (WHC), yearling hair coat (YHC), and breeding standard (BS). Our dataset comprised 126,290 animals in the pedigree. Out of these, 233 sires were genotyped using high-density (HD) chips, and 3750 animals with medium-density (50 K) single-nucleotide polymorphism (SNP) chips. Employing the Bayes B method with a prior probability of π = 0.99, we identified and tagged single nucleotide polymorphisms (Tag SNPs), ranging from 18 to 117 SNPs depending on the trait. These Tag SNPs facilitated the construction of reduced SNP panels. We then evaluated the predictive accuracy of these panels in comparison to traditional medium-density SNP chips. The accuracy of genomic predictions using these reduced panels varied significantly depending on the clustering method, ranging from 0.13 to 0.65. Additionally, we conducted functional enrichment analysis that found genes associated with the most informative SNP markers in the current study, thereby providing biological insights into the genomic basis of these traits.

Breeding objectives of Brangus cattle in Brazil.

Our goal was to define a breeding objective for Brangus beef cattle in Brazil. Bioeconomic models were produced and used to estimate economic values (EVs). The scenarios simulated were typical full-cycle beef production systems that are used in tropical and subtropical regions. The breeding objective contained pregnancy rate (PR), warm carcass weight (WCW), mature cow weight (MCW), number of nematode eggs per gram of faeces (EPG) and tick count (TICK). Two models were used in series to estimate the EV. A deterministic model was used to simulate effects of PR, WCW and MCW on profitability with a constant parasite load. Subsequently, stochastic models were used to estimate economic values for TICK and EPG as consequences of their environmental effects on weight gains, mortality and health costs. The EV of PR, WCW, MCW, EPG and TICK, was US$1.59, US$2.11, -US$0.24, -US$5.35 and -US$20.88, respectively. Results indicate positive emphasis should be placed on PR (12.49%) and WCW (65.07%) with negative emphasis on MCW (13.92%), EPG (2.77%) and TICK (5.75%). In comparison with the indexes usually used, these results suggest a reformulation in the selection indexes of the beef production system in tropical and subtropical regions in order to obtain greater profitability.

Tag SNP selection for prediction of tick resistance in Brazilian Braford and Hereford cattle breeds using Bayesian methods.

BACKGROUND: Cattle resistance to ticks is known to be under genetic control with a complex biological mechanism within and among breeds. Our aim was to identify genomic segments and tag single nucleotide polymorphisms (SNPs) associated with tick-resistance in Hereford and Braford cattle. The predictive performance of a very low-density tag SNP panel was estimated and compared with results obtained with a 50 K SNP dataset. RESULTS: BayesB (π = 0.99) was initially applied in a genome-wide association study (GWAS) for this complex trait by using deregressed estimated breeding values for tick counts and 41,045 SNP genotypes from 3455 animals raised in southern Brazil. To estimate the combined effect of a genomic region that is potentially associated with quantitative trait loci (QTL), 2519 non-overlapping 1-Mb windows that varied in SNP number were defined, with the top 48 windows including 914 SNPs and explaining more than 20% of the estimated genetic variance for tick resistance. Subsequently, the most informative SNPs were selected based on Bayesian parameters (model frequency and t-like statistics), linkage disequilibrium and minor allele frequency to propose a very low-density 58-SNP panel. Some of these tag SNPs mapped close to or within genes and pseudogenes that are functionally related to tick resistance. Prediction ability of this SNP panel was investigated by cross-validation using K-means and random clustering and a BayesA model to predict direct genomic values. Accuracies from these cross-validations were 0.27 ± 0.09 and 0.30 ± 0.09 for the K-means and random clustering groups, respectively, compared to respective values of 0.37 ± 0.08 and 0.43 ± 0.08 when using all 41,045 SNPs and BayesB with π = 0.99, or of 0.28 ± 0.07 and 0.40 ± 0.08 with π = 0.999. CONCLUSIONS: Bayesian GWAS model parameters can be used to select tag SNPs for a very low-density panel, which will include SNPs that are potentially linked to functional genes. It can be useful for cost-effective genomic selection tools, when one or a few key complex traits are of interest.