Genetic correlation estimates between beef fatty acid profile with meat and carcass traits in Nellore cattle finished in feedlot.

The objective of this study was to estimate the genetic-quantitative relationships between the beef fatty acid profile with the carcass and meat traits of Nellore cattle. A total of 1826 bulls finished in feedlot conditions and slaughtered at 24 months of age on average were used. The following carcass and meat traits were analysed: subcutaneous fat thickness (BF), shear force (SF) and total intramuscular fat (IMF). The fatty acid (FA) profile of the Longissimus thoracis samples was determined. Twenty-five FAs (18 individuals and seven groups of FAs) were selected due to their importance for human health. The animals were genotyped with the BovineHD BeadChip and, after quality control for single nucleotide polymorphisms (SNPs), only 470,007 SNPs from 1556 samples remained. The model included the random genetic additive direct effect, the fixed effect of the contemporary group and the animal's slaughter age as a covariable. The (co)variances and genetic parameters were estimated using the REML method, considering an animal model (single-step GBLUP). A total of 25 multi-trait analyses, with four traits, were performed considering SF, BF and IMF plus each individual FA. The heritability estimates for individual saturated fatty acids (SFA) varied from 0.06 to 0.65, for monounsaturated fatty acids (MUFA) it varied from 0.02 to 0.14 and for polyunsaturated fatty acids (PUFA) it ranged from 0.05 to 0.68. The heritability estimates for Omega 3, Omega 6, SFA, MUFA and PUFA sum were low to moderate, varying from 0.09 to 0.20. The carcass and meat traits, SF (0.06) and IMF (0.07), had low heritability estimates, while BF (0.17) was moderate. The genetic correlation estimates between SFA sum, MUFA sum and PUFA sum with BF were 0.04, 0.64 and -0.41, respectively. The genetic correlation estimates between SFA sum, MUFA sum and PUFA sum with SF were 0.29, -0.06 and -0.04, respectively. The genetic correlation estimates between SFA sum, MUFA sum and PUFA sum with IMF were 0.24, 0.90 and -0.67, respectively. The selection to improve meat tenderness in Nellore cattle should not change the fatty acid composition in beef, so it is possible to improve this attribute without affecting the nutritional beef quality in zebu breeds. However, selection for increased deposition of subcutaneous fat thickness and especially the percentage of intramuscular fat should lead to changes in the fat composition, highlighting a genetic antagonism between meat nutritional value and acceptability by the consumer.

Feitosa FL ，Olivieri BF ，Aboujaoude C ，Pereira AS ，de Lemos MV ，Chiaia HL ，Berton MP ，Peripolli E ，Ferrinho AM ，Mueller LF ，Mazalli MR ，de Albuquerque LG ，de Oliveira HN ，Tonhati H ，Espigolan R ，Tonussi RL ，de Oliveira Silva RM ，Gordo DG ，Magalhães AF ，Aguilar I ，Baldi F ... -  《-》

Association study between copy number variation and beef fatty acid profile of Nellore cattle.

The aim of this study was to analyze the association between the copy number variation regions (CNVRs) and fatty acid profile phenotypes for saturated (SFA), monosaturated (MUFA), polyunsaturated (PUFA), ω6 and ω3 fatty acids, PUFA/SFA and ω6/ω3 ratios, as well as for their sums, in Nellore cattle (Bos primigenius indicus). A total of 963 males were finished in feedlot and slaughtered with approximately 2 years of age. Animals were genotyped with the BovineHD BeadChip (Illumina Inc., San Diego, CA, USA). The copy number variation (CNV) detection was performed using the PennCNV algorithm. Log R ratio (LRR) and allele B frequency (BAF) were used to estimate the CNVs. The association analyses were done using the CNVRuler software and applying a logistic regression model. The phenotype was adjusted using a linear model considering the fixed effects of contemporary group and the animal age at slaughter. The fatty acid profile was analyzed on samples of longissimus thoracis muscle using gas chromatography with a 100-m capillary column. For the association analysis, the adjusted phenotypic values were considered for the traits, while the data was adjusted for the effects of the farm and year of birth, management groups at birth, weaning, and superannuation. A total of 186 CNVRs were significant for SFA (43), MUFA (42), PUFA (66), and omega fatty acid (35) groups, totaling 278 known genes. On the basis of the results, several genes were associated with several fatty acids of different saturations. Olfactory receptor genes were associated with C12:0, C14:0, and C18:0 fatty acids. The SAMD8 and BSCL2 genes, both related to lipid metabolic process, were associated with C12:0. The RAPGEF6 gene was found to be associated with C18:2 cis-9 cis-12 n-6, and its function is related to regulation of GTPase activity. Among the results, we highlighted the olfactory receptor activity (GO:0004984), G-protein-coupled receptor activity (GO:0004930), potassium:proton antiporter activity (GO:0015386), sodium:proton antiporter activity (GO:0015385), and odorant-binding (GO:0005549) molecular functions. A large number of genes associated with fatty acid profile within the CNVRs were identified in this study. These findings must contribute to better elucidate the genetic mechanism underlying the fatty acid profile of intramuscular fat in Nellore cattle.

de Lemos MVA ，Peripolli E ，Berton MP ，Feitosa FLB ，Olivieri BF ，Stafuzza NB ，Tonussi RL ，Kluska S ，Chiaia HLJ ，Mueller L ，Ferrinho AM ，Prereira ASC ，de Oliveira HN ，de Albuquerque LG ，Baldi F ... -  《-》

Genomic prediction ability for beef fatty acid profile in Nelore cattle using different pseudo-phenotypes.

The aim of the present study was to compare the predictive ability of SNP-BLUP model using different pseudo-phenotypes such as phenotype adjusted for fixed effects, estimated breeding value, and genomic estimated breeding value, using simulated and real data for beef FA profile of Nelore cattle finished in feedlot. A pedigree with phenotypes and genotypes of 10,000 animals were simulated, considering 50% of multiple sires in the pedigree. Regarding to phenotypes, two traits were simulated, one with high heritability (0.58), another with low heritability (0.13). Ten replicates were performed for each trait and results were averaged among replicates. A historical population was created from generation zero to 2020, with a constant size of 2000 animals (from generation zero to 1000) to produce different levels of linkage disequilibrium (LD). Therefore, there was a gradual reduction in the number of animals (from 2000 to 600), producing a "bottleneck effect" and consequently, genetic drift and LD starting in the generation 1001 to 2020. A total of 335,000 markers (with MAF greater or equal to 0.02) and 1000 QTL were randomly selected from the last generation of the historical population to generate genotypic data for the test population. The phenotypes were computed as the sum of the QTL effects and an error term sampled from a normal distribution with zero mean and variance equal to 0.88. For simulated data, 4000 animals of the generations 7, 8, and 9 (with genotype and phenotype) were used as training population, and 1000 animals of the last generation (10) were used as validation population. A total of 937 Nelore bulls with phenotype for fatty acid profiles (Sum of saturated, monounsaturated, omega 3, omega 6, ratio of polyunsaturated and saturated and polyunsaturated fatty acid profile) were genotyped using the Illumina BovineHD BeadChip (Illumina, San Diego, CA) with 777,962 SNP. To compare the accuracy and bias of direct genomic value (DGV) for different pseudo-phenotypes, the correlation between true breeding value (TBV) or DGV with pseudo-phenotypes and linear regression coefficient of the pseudo-phenotypes on TBV for simulated data or DGV for real data, respectively. For simulated data, the correlations between DGV and TBV for high heritability traits were higher than obtained with low heritability traits. For simulated and real data, the prediction ability was higher for GEBV than for Yc and EBV. For simulated data, the regression coefficient estimates (b), were on average lower than 1 for high and low heritability traits, being inflated. The results were more biased for Yc and EBV than for GEBV. For real data, the GEBV displayed less biased results compared to Yc and EBV for SFA, MUFA, n-3, n-6, and PUFA/SFA. Despite the less biased results for PUFA using the EBV as pseudo-phenotype, the b estimates obtained for the different pseudo-phenotypes (Yc, EBV and GEBV) were very close. Genomic information can assist in improving beef fatty acid profile in Zebu cattle, since the use of genomic information yielded genomic values for fatty acid profile with accuracies ranging from low to moderate. Considering both simulated and real data, the ssGBLUP model is an appropriate alternative to obtain more reliable and less biased GEBVs as pseudo-phenotype in situations of missing pedigree, due to high proportion of multiple sires, being more adequate than EBV and Yc to predict direct genomic value for beef fatty acid profile.

Chiaia HLJ ，Peripolli E ，de Oliveira Silva RM ，Feitosa FLB ，de Lemos MVA ，Berton MP ，Olivieri BF ，Espigolan R ，Tonussi RL ，Gordo DGM ，de Albuquerque LG ，de Oliveira HN ，Ferrinho AM ，Mueller LF ，Kluska S ，Tonhati H ，Pereira ASC ，Aguilar I ，Baldi F ... -  《-》

Genetic analysis of beef fatty acid composition predicted by near-infrared spectroscopy.

The aims of this study were 1) to investigate the potential application of near-infrared spectroscopy (NIRS) to predict intramuscular fat (IMF) and fatty acid (FA) composition of individual meat samples, 2) to estimate heritability of IMF and FA NIRS-based predictions, and 3) to assess the statistical relevance of the genetic background of such predictions by using the Bayes factor (BF) procedure. Young Piemontese bulls (n = 1,298) were raised and fattened on 124 farms, and slaughtered at the same commercial abattoir. Intramuscular fat content and FA composition were analyzed on a random subset of 148 samples of minced and homogenized longissimus thoracis muscle. Near-infrared spectroscopy spectra were collected on all samples (n = 1,298) in reflectance mode between 1,100 and 2,498 nm (every 2 nm) using fresh minced meat samples. Calibration models developed from the random subset of 148 samples were used to predict IMF and FA contents of the remaining 1,150 samples. Intramuscular fat content and FA predictions were analyzed under a Bayesian univariate animal linear models, and the statistical relevance of heritability estimates was assessed through BF; the model with polygenic additive effects was favored when BF > 1. In general, satisfactory results (R(2) > 0.60) were obtained for 6 out of the 8 major FA (C14:0, C:16:0, C16:1, C18:0, C18:1n-9 cis/trans, and C18:1n-11 trans), 6 out of the 19 minor FA (C10:0, C12:0, C17:0, C17:1, C18:2 cis-9,trans-11, and C20:2), and the total SFA, MUFA, and PUFA. Bayes factors between models with and without a genetic component provided values greater than 1 for IMF, C14:0, C16:0, C18:1n-9 cis/trans, C17:0, C17:1, C20:2, SFA, MUFA, and PUFA. The greatest BF was reached by C20:2 (BF >10), suggesting strong evidence of genetic determinism, whereas IMF, C18:1n-9 cis/trans, C17:0, C17:1, MUFA, and PUFA showed substantial evidence favoring the numerator model (3.16 < BF < 10). Point estimates of heritabilities for FA predicted by NIRS were low to moderate (0.07 to 0.21). Results support that NIRS is a useful technique to satisfactorily predict some FA of meat. The existence of an important genetic determinism affecting FA profile has been confirmed, suggesting that FA composition of meat can be genetically modified.

Cecchinato A ，De Marchi M ，Penasa M ，Casellas J ，Schiavon S ，Bittante G ... -  《-》

Estimates of genetic parameters for fatty acid compositions in the longissimus dorsi muscle of Hanwoo cattle.

We estimated the heritabilities (h 2) and genetic and phenotypic correlations among individual and groups of fatty acids, as well as their correlations with six important carcass and meat-quality traits in Korean Hanwoo cattle. Meat samples were collected from the longissimus dorsi muscles of 1000 Hanwoo steers that were 30-month-old (progeny of 85 proven Hanwoo bulls) to determine intramuscular fatty acid profiles. Phenotypic data on carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT), marbling score (MS), Warner-Bratzler shear force (WBSF) and intramuscular fat content (IMF) were also investigated using this half-sib population. Variance and covari.ance components were estimated using restricted maximum likelihood procedures under univariate and pairwise bivariate animal models. Oleic acid (C18:1n-9) was the most abundant fatty acid, accounting for 50.69% of all investigated fatty acids, followed by palmitic (C16:0; 27.33%) and stearic acid (C18:0; 10.96%). The contents of saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs) were 41.64%, 56.24% and 2.10%, respectively, and the MUFA/SFA ratio, PUFA/SFA ratio, desaturation index (DI) and elongation index (EI) were 1.36, 0.05, 0.59 and 0.66, respectively. The h 2 estimates for individual fatty acids ranged from very low to high (0.03±0.14 to 0.63±0.14). The h 2 estimates for SFAs, MUFAs, PUFAs, DI and EI were 0.53±0.14, 0.49±0.14, 0.23±0.10, 0.51±0.13 and 0.53±0.13, respectively. The genetic and phenotypic correlations among individual fatty acids and fatty acid classes varied widely (-0.99 to 0.99). Notably, C18:1n-9 had favourable (negative) genetic correlations with two detrimental fatty acids, C14:0 (-0.76) and C16:0 (-0.92). Genetic correlations of individual and group fatty acids with CWT, EMA, BFT, MS, WBSF and IMF ranged from low to moderate (both positive and negative) with the exception of low-concentration PUFAs. Low or near-zero phenotypic correlations reflected potential non-genetic contributions. This study provides insights on genetic variability and correlations among intramuscular fatty acids as well as correlations between fatty acids and carcass and meat-quality traits, which could be used in Hanwoo breeding programmes to improve fatty acid compositions in meat.

Bhuiyan MSA ，Lee DH ，Kim HJ ，Lee SH ，Cho SH ，Yang BS ，Kim SD ... -  《-》