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Summary: Gene–gene interactions cause hidden genetic variation in natural populations and could be responsible for the lack of replication that is typically observed in complex traits studies. This study aimed to identify gene–gene interactions using the empirical Hilbert–Schmidt Independence Criterion method to test for epistasis in beef fatty acid profile traits of Nellore cattle. The dataset contained records from 963 bulls, genotyped using a 777 962k SNP chip. Meat samples of Longissimus muscle, were taken to measure fatty acid composition, which was quantified by gas chromatography. We chose to work with the sums of saturated (SFA), monounsaturated (MUFA), polyunsaturated (PUFA), omega‐3 (OM3), omega‐6 (OM6), SFA:PUFA and OM3:OM6 fatty acid ratios. The SNPs in the interactions where P<10‐8 were mapped individually and used to search for candidate genes. Totals of 602, 3, 13, 23, 13, 215 and 169 candidate genes for SFAs, MUFAs, PUFAs, OM3s, OM6s and SFA:PUFA and OM3:OM6 ratios were identified respectively. The candidate genes found were associated with cholesterol, lipid regulation, low‐density lipoprotein receptors, feed efficiency and inflammatory response. Enrichment analysis revealed 57 significant GO and 18 KEGG terms (P < 0.05), most of them related to meat quality and complementary terms. Our results showed substantial genetic interactions associated with lipid profile, meat quality, carcass and feed efficiency traits for the first time in Nellore cattle. The knowledge of these SNP–SNP interactions could improve understanding of the genetic and physiological mechanisms that contribute to lipid‐related traits and improve human health by the selection of healthier meat products. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the association of single nucleotide polymorphisms (SNPs) with birth weight, weight gain from birth to weaning and from weaning to yearling, yearling height and cow weight in Nelore cattle. Data from 5064 animals participating in the DeltaGen and PAINT breeding programs were used. The animals were genotyped with a panel of 777 962 SNPs (Illumina BovineHD BeadChip) and 412 993 SNPs remained after quality control analysis of the genomic data. A genome-wide association study was performed using a single-step methodology. The analyses were processed with the BLUPF90 family of programs. When applied to a genome-wide association studies, the single-step GBLUP methodology is an iterative process that estimates weights for the SNPs. The weights of SNPs were included in all analyses by iteratively applying the single-step GBLUP methodology and repeated twice so that the effect of the SNP and the effect of the animal were recalculated in order to increase the weight of SNPs with large effects and to reduce the weight of those with small effects. The genome-wide association results are reported based on the proportion of variance explained by windows of 50 adjacent SNPs. Considering the two iterations, only windows with an additive genetic variance >1.5% were presented in the results. Associations were observed with birth weight on BTA 14, with weight gain from birth to weaning on BTA 5 and 29, with weight gain from weaning to yearling on BTA 11, and with yearling height on BTA 8, showing the genes TMEM68 (transmembrane protein 8B) associated with birth weight and yearling height, XKR4 (XK, Kell blood group complex subunit-related family, member 4) associated with birth weight, NPR2 (natriuretic peptide receptor B) associated with yearling height, and REG3G (regenerating islet-derived 3-gamma) associated with weight gain from weaning to yearling. These genes play an important role in feed intake, weight gain and the regulation of skeletal growth. [ABSTRACT FROM AUTHOR]

Carcass traits measured after slaughter are economically relevant traits in beef cattle. In general, the slaughter house payment system is based on HCW. Ribeye area (REA) is associated with the amount of the meat in the carcass, and a minimum of backfat thickness (BFT) is necessary to protect the carcass during cooling. The aim of this study was to identify potential genomic regions harboring candidate genes affecting those traits in Nellore cattle. The data set used in the present study consisted of 1,756 Nellore males with phenotype records. A subset of 1,604 animals had both genotypic and phenotypic information. Genotypes were generated based on a panel with 777,962 SNPs from the Illumina Bovine HD chip. The SNP effects were calculated based on the genomic breeding values obtained by using the single-step GBLUP approach and a genomic matrix re-weighting procedure. The proportion of the variance explained by moving windows of 100 consecutive SNPs was used to assess potential genomic regions harboring genes with major effects on each trait. The top 10 non-overlapping SNP-windows explained 8.72%, 11.38%, and 9.31% of the genetic variance for REA, BFT, and HCW, respectively. These windows are located on chromosomes 5, 7, 8, 10, 12, 20, and 29 for REA; chromosomes 6, 8, 10, 13, 16, 17, 18, and 24 for BFT; and chromosomes 4, 6, 7, 8, 14, 16, 17, and 21 for HCW. For REA, there were identified genes (CDKN2A and CDKN2B) involved in the cell cycle biological process which affects many aspects of animal growth and development. The SLC38A1 and SLC38A2 genes, both from SLC38 AA transporter family, was also associated with REA. The AA transporters are essential for cell growth and proliferation, acting as carriers of tissue nutrient supplies. Various genes identified for BFT (SORCS2, AQP3, AQP7, CDC42BPA, ASIP, and ACSS2) have been associated with lipid metabolism in different mammal species. One of the most promising genes identified for HCW was the PLAG1. There is evidence, in the literature, that this gene is located in putative QTL affecting carcass weight in beef cattle. Our results showed several genomic regions containing plausible candidate genes that may be associated with carcass traits in Nellore cattle. Besides contributing to a better understanding of the genetic control of carcass traits, the identified genes can also be helpful for further functional genomic studies. [ABSTRACT FROM AUTHOR]

In order to contribute to the genetic breeding programs of buffaloes, this study aimed to determine the influence of environmental effects on the stayability (ST) of dairy female Murrah buffalo in the herd. Data from 1016 buffaloes were used. ST was defined as the ability of the female to remain in the herd for 1, 2, 3, 4, 5 or 6 years after the first calving. Environmental effects were studied by survival analysis, adjusted to the fixed effects of farm, year and season of birth, class of first-lactation milk yield and age at first calving. The data were analyzed using the LIFEREG procedure of the SAS program that fits parametric models to failure time data (culling or ST50), and estimates parameters by maximum likelihood estimation. Breeding farm, year of birth and first-lactation milk yield significantly influenced (P<0.0001) the ST to the specific ages (1 to 6 years after the first calving). Buffaloes that were older at first calving presented higher probabilities of being culled 1 year after the first calving, without any effect on culling at older ages. Buffaloes with a higher milk yield at first calving presented a lower culling probability and remained for a longer period of time in the herd. The effects of breeding farm, year of birth and first-lactation milk yield should be included in models used for the analysis of ST in buffaloes. [ABSTRACT FROM AUTHOR]

The aim of this study was to identify genomic regions associated with the total amount of saturated (SFA), monounsaturated (MUFA), and polyunsaturated (PUFA) fatty acids in the beef of Nellore cattle. The investigated dataset contained records from 963 Nellore bulls, about two years old, finished in feedlot (90 days). Meat samples of Longissimus dorsi muscle, between the 12th and 13th ribs of the left half-carcasses, were taken to measure the fatty acids (FAs). FAs were quantified by gas chromatography (GC-2010 Plus - Shimadzu AOC 20i autoinjector) using SP-2560 capillary column (100 m × 0.25 mm diameter with 0.02 mm thickness; Supelco, Bellefonte, PA). The animals were genotyped using the high-density SNP panel (BovineHD BeadChip assay 777k, Illumina Inc., San Diego, CA). Those SNP markers with minor allele frequency less than 0.05, call rate less than 90%, monomorphic, located on sex chromosomes, and those with unknown position were removed from the analysis. After genomic quality control, 470,007 SNPs and 868 animals were available for the analyses. Missing genotypes were imputed using FImpute software. Genotypes were phased to haplotypes using fastPHASE software and then haplotype blocks were defined based on linkage disequilibrium using HaploView software. Genome-wide association analyses were performed considering one haplotype at a time. The model included fixed effects of contemporary group (92 levels), haplotype (linear regression on number of copies), and age at slaughter as a linear covariate. Bonferroni correction was applied at 5% significance to adjust for multiple tests. A total of 83,883 haplotypes were included in the genome-wide association analyses. From those haplotypes, 292, 17, and 31 were significantly associated (P < 0.05) with SFA, MUFA, and PUFA, respectively. Most associations were found on BTA17, BTA3, BTA1, BTA8, BTA25, and BTA2. These significant regions harbor genes such as GALNT12, SLC6A7, CAMK2A, SYTL3, EPAS1, PRKCB, EPHA6, MPZL1, UHMK1, KIRREL, FYN, LTBP1, PRKCE, SIK2, TOM1L1, NF1, DNAJA3, SH2B2, AFAP1L2, and AGAP1. These genes are involved in lipid metabolism, reproductive hormone receptors, transport and use of fatty acids and cholesterol, phospholipid and membrane hydrolysis and biosynthesis, energy metabolism, and protein kinase synthesis. Thus, the identification of these associated haplotypes may contribute to further studies to validate these regions and prospect candidate genes that would be useful for breeding programs to improve the beef quality of Nellore cattle. [ABSTRACT FROM AUTHOR]

The aim of this study was to estimate genetic and phenotypic associations of growth traits with carcass and meat traits in Nellore cattle. Data from male and female animals were used for weaning weight (WW; N = 241,416), yearling weight (YW, N = 126,596), weight gain from weaning to yearling (GWY, N = 78,687), and yearling hip height (YHH, N = 90,720), respectively; 877 male animals were used for hot carcass weight (HCW) and 884 for longissimus muscle area (LMA), backfat thickness (BT), marbling score (MS), and shear force (SF). The variance components were estimated by the restricted maximum likelihood method using three-trait animal models that included WW. The model for WW included direct and maternal additive genetic, maternal permanent environmental, and residual effects as random effects; contemporary group as fixed effects; and age of dam at calving and age of animal as covariates (linear and quadratic effects). For the other traits, maternal effects and the effect of age of dam at calving were excluded from the model. Heritability ranged from 0.10 ± 0.12 (LMA) to 0.44 ± 0.007 (YW). Genetic correlations ranged from -0.40 ± 0.38 (WW x LMA) to 0.55 ± 0.10 (HCW x YW). Growth, carcass, and meat traits have sufficient genetic variability to be included as selection criteria in animal breeding programs.

Buffalo milk has excellent physical and chemical qualities as a consequence of the high percentage of constituents. This milk property is desirable for the dairy industry because it facilitates manufacture of mozzarella cheese. We estimated genetic parameters for milk yield, milk fat and protein and their effects on mozzarella cheese production using Bayesian inference. Using information from 4907 lactation records of buffaloes, genetic and non-genetic parameters were estimated for accumulated 305-day milk yield (MY), milk fat (%F) and protein (%P) percentages and mozzarella production per lactation (MP). The (co)variance components were obtained by Bayesian inference using a multiple trait model, which included as fixed effects contemporary group, milking number and buffalo age at calving as covariables (linear and quadratic), along with the additive genetic, permanent environmental and residual random effects. Mean a posteriori heritability distributions for MY, %F, %P, and MP were 0.25, 0.30, 0.38, and 0.23, respectively. The genetic correlation estimates between MY with %P and %F were negative and moderate. Positive genetic correlation estimates varying from 0.19 (%P/MP) to 0.95 (MY/MP) were obtained among the traits. Milk yield, milk components, and mozzarella production in Murrah buffaloes have enough genetic variation for selection purposes. We conclude that selection to increase milk yield would be effective in improving mozzarella production.