Genome-wide scans identify biological and metabolic pathways regulating carcass and meat quality traits in beef cattle.

Genome association studies (GWAS) provides knowledge about the genetic architecture of beef-related traits that allow linking the target phenotype to genomic information aiding breeding decision. Thus, the present study aims to uncover the genetic mechanism involved in carcass (REA: rib eye area, BF: backfat thickness, and HCW: hot carcass weight) and meat quality traits (SF: shear-force, MARB: marbling score, and IMF: intramuscular fat content) in Nellore cattle. For this, 6910 young bulls with phenotypic information and 23,859 animals genotyped with 435 k markers were used to perform the weighted single-step GBLUP (WssGBLUP) approach, considering two iterations. The top 10 genomic regions explained 8.13, 11.81, and 9.58% of the additive genetic variance, harboring a total of 119, 143, and 95 positional candidate genes for REA, BF, and HCW, respectively. For meat quality traits, the top 10 windows explained a large proportion of the total genetic variance for SF (14.95%), MARB (17.56%), and IMF (21.41%) surrounding 92, 155, and 111 candidate genes, respectively. Relevant candidate genes (CAST, PLAG1, XKR4, PLAGL2, AQP3/AQP7, MYLK2, WWOX, CARTPT, and PLA2G16) are related to physiological aspects affecting growth, carcass, meat quality, feed intake, and reproductive traits by signaling pathways controlling muscle control, key signal metabolic molecules INS / IGF-1 pathway, lipid metabolism, and adipose tissue development. The GWAS results provided insights into the genetic control of the traits studied and the genes found are potential candidates to be used in the improvement of carcass and meat quality traits.

Identification of candidate lethal haplotypes and genomic association with post-natal mortality and reproductive traits in Nellore cattle.

The wide use of genomic information has enabled the identification of lethal recessive alleles that are the major genetic causes of reduced conception rates, longer calving intervals, or lower survival for live-born animals. This study was carried out to screen the Nellore cattle genome for lethal recessive haplotypes based on deviation from the expected population homozygosity, and to test SNP markers surrounding the lethal haplotypes region for association with heifer rebreeding (HR), post-natal mortality (PNM) and stayability (STAY). This approach requires genotypes only from apparently normal individuals and not from affected embryos. A total of 62,022 animals were genotyped and imputed to a high-density panel (777,962 SNP markers). Expected numbers of homozygous individuals were calculated, and the probabilities of observing 0 homozygotes was obtained. Deregressed genomic breeding values [(G)EBVs] were used in a GWAS to identify candidate genes and biological mechanisms affecting HR, STAY and PNM. In the functional analyses, genes within 100 kb down and upstream of each significant SNP marker, were researched. Thirty haplotypes had high expected frequency, while no homozygotes were observed. Most of the alleles present in these haplotypes had a negative mean effect for PNM, HR and STAY. The GWAS revealed significant SNP markers involved in different physiological mechanisms, leading to harmful effect on the three traits. The functional analysis revealed 26 genes enriched for 19 GO terms. Most of the GO terms found for biological processes, molecular functions and pathways were related to tissue development and the immune system. More phenotypes underlying these putative regions in this population could be the subject of future investigation. Tests to find putative lethal haplotype carriers could help breeders to eliminate them from the population or manage matings in order to avoid homozygous.

Genetic analysis of growth, visual scores, height, and carcass traits in Nelore cattle.

Covariance components were estimated for growth traits (BW, birth weight; WW, weaning weight; YW, yearling weight), visual scores (BQ, breed quality; CS, conformation; MS, muscling; NS, navel; PS, finishing precocity), hip height (HH), and carcass traits (BF, backfat thickness; LMA, longissimus muscle area) measured at yearling. Genetic gains were obtained and validation models on direct and maternal effects for BW and WW were fitted. Genetic correlations of growth traits with CS, PS, MS, and HH ranged from 0.20 ± 0.01 to 0.94 ± 0.01 and were positive and low with NS (0.11 ± 0.01 to 0.20 ± 0.01) and favorable with BQ (0.14 ± 0.02 to 0.37 ± 0.02). Null to moderate genetic correlations were obtained between growth and carcass traits. Genetic gains were positive and significant, except for BW. An increase of 0.76 and 0.72 kg is expected for BW and WW, respectively, per unit increase in estimated breeding value (EBV) for direct effect and an additional 0.74 and 1.43, respectively, kg per unit increase in EBV for the maternal effect. Monitoring genetic gains for HH and NS is relevant to maintain an adequate body size and a navel morphological correction, if necessary. Simultaneous selection for growth, morphological, and carcass traits in line with improve maternal performance is a feasible strategy to increase herd productivity.

Genomic prediction using different estimation methodology, blending and cross-validation techniques for growth traits and visual scores in Hereford and Braford cattle.

The objective of the present study was to evaluate the accuracy and bias of direct and blended genomic predictions using different methods and cross-validation techniques for growth traits (weight and weight gains) and visual scores (conformation, precocity, muscling, and size) obtained at weaning and at yearling in Hereford and Braford breeds. Phenotypic data contained 126,290 animals belonging to the Delta G Connection genetic improvement program, and a set of 3,545 animals genotyped with the 50K chip and 131 sires with the 777K. After quality control, 41,045 markers remained for all animals. An animal model was used to estimate (co)variance components and to predict breeding values, which were later used to calculate the deregressed estimated breeding values (DEBV). Animals with genotype and phenotype for the traits studied were divided into 4 or 5 groups by random and k-means clustering cross-validation strategies. The values of accuracy of the direct genomic values (DGV) were moderate to high magnitude for at weaning and at yearling traits, ranging from 0.19 to 0.45 for the k-means and 0.23 to 0.78 for random clustering among all traits. The greatest gain in relation to the pedigree BLUP (PBLUP) was 9.5% with the BayesB method with both the k-means and the random clustering. Blended genomic value accuracies ranged from 0.19 to 0.56 for k-means and from 0.21 to 0.82 for random clustering. The analyses using the historical pedigree and phenotypes contributed additional information to calculate the GEBV, and in general, the largest gains were for the single-step (ssGBLUP) method in bivariate analyses with a mean increase of 43.00% among all traits measured at weaning and of 46.27% for those evaluated at yearling. The accuracy values for the marker effects estimation methods were lower for k-means clustering, indicating that the training set relationship to the selection candidates is a major factor affecting accuracy of genomic predictions. The gains in accuracy obtained with genomic blending methods, mainly ssGBLUP in bivariate analyses, indicate that genomic predictions should be used as a tool to improve genetic gains in relation to the traditional PBLUP selection.