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.

Transcriptome Profiling of the Liver in Nellore Cattle Phenotypically Divergent for RFI in Two Genetic Groups.

The identification and selection of genetically superior animals for residual feed intake (RFI) could enhance productivity and minimize environmental impacts. The aim of this study was to use RNA-seq data to identify the differentially expressed genes (DEGs), known non-coding RNAs (ncRNAs), specific biomarkers and enriched biological processes associated with RFI of the liver in Nellore cattle in two genetic groups. In genetic group 1 (G1), 24 extreme RFI animals (12 low RFI (LRFI) versus 12 high RFI (HRFI)) were selected from a population of 60 Nellore bulls. The RNA-seq of the samples from their liver tissues was performed using an Illumina HiSeq 2000. In genetic group 2 (G2), 20 samples of liver tissue of Nellore bulls divergent for RFI (LRFI, n = 10 versus HRFI, n = 10) were selected from 83 animals. The raw data of the G2 were chosen from the ENA repository. A total of 1811 DEGs were found for the G1 and 2054 for the G2 (p-value ≤ 0.05). We detected 88 common genes in both genetic groups, of which 33 were involved in the immune response and in blocking oxidative stress. In addition, seven (B2M, ADSS, SNX2, TUBA4A, ARHGAP18, MECR, and ABCF3) possible gene biomarkers were identified through a receiver operating characteristic analysis (ROC) considering an AUC > 0.70. The B2M gene was overexpressed in the LRFI group. This gene regulates the lipid metabolism protein turnover and inhibits cell death. We also found non-coding RNAs in both groups. MIR25 was up-regulated and SNORD16 was down-regulated in the LRFI for G1. For G2, up-regulated RNase_MRP and SCARNA10 were found. We highlight MIR25 as being able to act by blocking cytotoxicity and oxidative stress and RMRP as a blocker of mitochondrial damage. The biological pathways associated with RFI of the liver in Nellore cattle in the two genetic groups were for energy metabolism, protein turnover, redox homeostasis and the immune response. The common transcripts, biomarkers and metabolic pathways found in the two genetic groups make this unprecedented work even more relevant, since the results are valid for different herds raised in different ways. The results reinforce the biological importance of these known processes but also reveal new insights into the complexity of the liver tissue transcriptome of Nellore cattle.