Genetic parameters for visual scores, growth and carcass traits in Nellore Cattle.

Growth and carcass traits are essential selection criteria for beef cattle breeding programs. However, it is necessary to combine these measurements with body composition traits to meet the demand of the consumer market. This study aimed to estimate the genetic parameters for visual scores, growth (pre and post-weaning weights), and carcass (rib eye area (REA), back and rump fat thickness) traits in Nellore cattle using Bayesian inference. Data from 12,060 animals belonging to the HoRa Hofig Ramos herd were used. Morphological traits were evaluated by the MERCOS methodology. The heritability estimates obtained ranged from low to high magnitude, from 0.15 to 0.28 for visual scores, 0.13 to 0.44 for growth, and from 0.42 to 0.46 for carcass traits. Genetic correlations between visual scores and growth traits were generally of moderate to high magnitudes, however, visual scores showed low correlations with carcass traits, except between sacral bone and structure and REA. Selection for visual score traits can lead to favorable responses in body weight and vice versa, but the same is not true for carcass traits. Morphological categorical traits can be used as complementary tools that add value to selection.

Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks.

The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non-autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10-6 ), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree-based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relatively simple architecture can provide superior genomic predictions for meat tenderness in Nellore cattle.

SNP discovery in common bean by restriction-associated DNA (RAD) sequencing for genetic diversity and population structure analysis.

Researchers have made great advances into the development and application of genomic approaches for common beans, creating opportunities to driving more real and applicable strategies for sustainable management of the genetic resource towards plant breeding. This work provides useful polymorphic single-nucleotide polymorphisms (SNPs) for high-throughput common bean genotyping developed by RAD (restriction site-associated DNA) sequencing. The RAD tags were generated from DNA pooled from 12 common bean genotypes, including breeding lines of different gene pools and market classes. The aligned sequences identified 23,748 putative RAD-SNPs, of which 3357 were adequate for genotyping; 1032 RAD-SNPs with the highest ADT (assay design tool) score are presented in this article. The RAD-SNPs were structurally annotated in different coding (47.00 %) and non-coding (53.00 %) sequence components of genes. A subset of 384 RAD-SNPs with broad genome distribution was used to genotype a diverse panel of 95 common bean germplasms and revealed a successful amplification rate of 96.6 %, showing 73 % of polymorphic SNPs within the Andean group and 83 % in the Mesoamerican group. A slightly increased He (0.161, n = 21) value was estimated for the Andean gene pool, compared to the Mesoamerican group (0.156, n = 74). For the linkage disequilibrium (LD) analysis, from a group of 580 SNPs (289 RAD-SNPs and 291 BARC-SNPs) genotyped for the same set of genotypes, 70.2 % were in LD, decreasing to 0.10 %in the Andean group and 0.77 % in the Mesoamerican group. Haplotype patterns spanning 310 Mb of the genome (60 %) were characterized in samples from different origins. However, the haplotype frameworks were under-represented for the Andean (7.85 %) and Mesoamerican (5.55 %) gene pools separately. In conclusion, RAD sequencing allowed the discovery of hundreds of useful SNPs for broad genetic analysis of common bean germplasm. From now, this approach provides an excellent panel of molecular tools for whole genome analysis, allowing integrating and better exploring the common bean breeding practices.