Genomic prediction for beef fatty acid profile in Nellore cattle

Leveraging Functional Genomics for Understanding Beef Quality Complexities and Breeding Beef Cattle for Improved Meat Quality

Consumer perception of beef is heavily influenced by overall meat quality, a critical factor in the cattle industry. Genomics has the potential to improve important beef quality traits and identify genetic markers and causal variants associated with these traits through genomic selection (GS) and genome-wide association studies (GWAS) approaches. Transcriptomics, proteomics, and metabolomics provide insights into underlying genetic mechanisms by identifying differentially expressed genes, proteins, and metabolic pathways linked to quality traits, complementing GWAS data. Leveraging these functional genomics techniques can optimize beef cattle breeding for enhanced quality traits to meet high-quality beef demand. This paper provides a comprehensive overview of the current state of applications of omics technologies in uncovering functional variants underlying beef quality complexities. By highlighting the latest findings from GWAS, GS, transcriptomics, proteomics, and metabolomics studies, this work seeks to serve as a valuable resource for fostering a deeper understanding of the complex relationships between genetics, gene expression, protein dynamics, and metabolic pathways in shaping beef quality.

Genetic evaluations for endangered dual-purpose German Black Pied cattle using 50K SNPs, a breed-specific 200K chip, and whole-genome sequencing

Genetic evaluations of local cattle breeds are hampered due to small reference groups or biased due to the utilization of SNP effects estimated in other large populations. Against this background, there is a lack of studies addressing the possible advantage of whole-genome sequences (WGS) or consideration of specific variants from WGS data in genomic predictions for local breeds with small population size. Consequently, the aim of this study was to compare genetic parameters and accuracies of genomic estimated breeding values (GEBV) for 305-d production traits, fat-to protein ratio (FPR), and somatic cell score (SCS) at the first test date after calving and confirmation traits of the endangered German Black Pied cattle (DSN) breed using 4 different marker panels: (1) the commercial 50K Illumina BovineSNP50 BeadChip, (2) a customized 200K chip designed for DSN (DSN200K) which considers the most important variants for DSN from WGS, (3) randomly generated 200K chips based on WGS data, and (4) a WGS panel. The same number of animals was considered for all marker panel analyses (i.e., 1,811 genotyped or sequenced cows for conformation traits, 2,383 cows for lactation production traits, and 2,420 cows for FPR and SCS). Mixed models for the estimation of genetic parameters directly included the respective genomic relationship matrix from the different marker panels plus the trait-specific fixed effects. For the calculation of GEBV accuracies, we applied repeated random subsampling validation. In the process of separate cross-validations per trait, we created a validation set including 20% of cows with masked phenotypes, and a training set comprising 80% of the cows. The cows were selected randomly in a procedure with 10 replicates considering replacements in the different scenarios. The accuracy was defined as the correlation between the direct GEBV and the phenotypes with subtracted corresponding fixed effects for the cows in the validation set. For FPR and SCS, as well as for lactation production traits, heritabilities were largest based on WGS data, but the increase compared with the 50K or DSN200K applications was quite small in the range from 0.01 to 0.03. Also, for most of the conformation traits, heritabilities were largest based on WGS and DSN200K data, but the increase was in the range of the corresponding standard error. Accordingly, GEBV accuracies for most of the studied traits were highest based on WGS data or when utilizing the DSN200K chip, but the accuracy differences across the marker panels were quite small and nonsignificant. In conclusion, WGS data and the DSN200K chip only contributed to minor improvements in genomic predictions, still justifying the use of the commercial 50K chip. Nevertheless, WGS and the 200KDSN chip harbor breed-specific variants, which are valuable for studying causal genetic mechanisms in the endangered DSN population.

Lipogenic Gene Single Nucleotide Polymorphic DNA Markers Associated with Intramuscular Fat, Fat Melting Point, and Health-Beneficial Omega-3 Long-Chain Polyunsaturated Fatty Acids in Australian Pasture-Based Bowen Genetics Forest Pastoral Angus, Hereford, and Wagyu Beef Cattle

This study used targeted sequencing aimed at identifying single nucleotide polymorphisms (SNP) in lipogenic genes and their associations with health-beneficial omega-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFA), intramuscular fat (IMF), and fat melting point (FMP) of the M. longissimus dorsi muscle in Australian pasture-based Bowen Genetics Forest Pastoral Angus, Hereford, and Wagyu cattle. It was hypothesized that SNP encoding for the fatty acid-binding protein 4 (FABP4), stearoyl-CoA desaturase (SCD), and fatty acid synthase (FASN) genes will be significantly associated with health-beneficial n-3 LC-PUFA and the meat eating quality traits of IMF and FMP in an Australian pasture-based beef production system. Two SNP mutations, g.21267406 T>C and g.21271264 C>A, in the SCD gene were significantly (p < 0.05) associated with IMF, FMP, oleic acid (18:1n-9), linoleic acid (LA) 18:2n-6, alpha-linolenic acid (ALA) 18:3n-3, eicosapentaenoic acid (EPA) 20:5n-3, docosahexaenoic acid (DHA) 22:6-n-3, and docosapentaenoic acid (DPA) 22:5n-3. Significant positive correlations (p < 0.05) between FASN SNP g. 50787138 A>G and FMP, 18:1n-9, ALA, EPA, DHA, DPA, and total n-3 LC-PUFA were also detected. An SNP (g.44678794 G>A) in the FABP4 gene was associated with FMP. These results provide significant insights into the contributions of lipogenic genes to intramuscular fat deposition and the biosynthesis of health-beneficial n-3 LC-PUFA. The findings also unravel the potential use of lipogenic gene polymorphisms in marker-assisted selection to improve the content of health-promoting n-3 LC-PUFA and meat eating quality traits in Australian pasture-based Bowen Genetics Forest Pastoral Angus, Hereford, and Wagyu beef cattle.

Towards Sustainable Sources of Omega-3 Long-Chain Polyunsaturated Fatty Acids in Northern Australian Tropical Crossbred Beef Steers through Single Nucleotide Polymorphisms in Lipogenic Genes for Meat Eating Quality

This study aimed to identify single nucleotide polymorphisms (SNP) in lipogenic genes of northern Australian tropically adapted crossbred beef cattle and to evaluate associations with healthy lipid traits of the Longissimus dorsi (loin eye) muscle. The hypothesis tested was that there are significant associations between SNP loci encoding for the fatty acid binding protein 4 (FABP4), stearoyl-CoA desaturase (SCD) and fatty acid synthase (FASN) genes and human health beneficial omega-3 long-chain polyunsaturated fatty acids (ω3 LC-PUFA) within the loin eye muscle of northern Australian crossbred beef cattle. Brahman, Charbray, and Droughtmaster crossbred steers were fed on Rhodes grass hay augmented with desmanthus, lucerne, or both, for 140 days and the loin eye muscle sampled for intramuscular fat (IMF), fat melting point (FMP), and fatty acid composition. Polymorphisms in FABP4, SCD, and FASN genes with significant effects on lipid traits were identified with next-generation sequencing. The GG genotype at the FABP4 g.44677239C>G locus was associated with higher proportion of linoleic acid than the CC and CG genotypes (p < 0.05). Multiple comparisons of genotypes at the SCD g.21266629G>T locus indicated that the TT genotype had significantly higher eicosapentaenoic, docosapentaenoic, and docosahexaenoic acids than GG genotype (p < 0.05). Significant correlations (p < 0.05) between FASN SNP and IMF, saturated and monounsaturated fatty acids were observed. These results provide insights into the contribution of lipogenic genes to intramuscular fat deposition and SNP marker-assisted selection for improvement of meat-eating quality, with emphasis on alternate and sustainable sources of ω3 LC-PUFA, in northern Australian tropical crossbred beef cattle, hence an acceptance of the tested hypothesis.

Transcriptome Profile Reveals Genetic and Metabolic Mechanisms Related to Essential Fatty Acid Content of Intramuscular Longissimus thoracis in Nellore Cattle

Beef is a source of essential fatty acids (EFA), linoleic (LA) and alpha-linolenic (ALA) acids, which protect against inflammatory and cardiovascular diseases in humans. However, the intramuscular EFA profile in cattle is a complex and polygenic trait. Thus, this study aimed to identify potential regulatory genes of the essential fatty acid profile in Longissimus thoracis of Nellore cattle finished in feedlot. Forty-four young bulls clustered in four groups of fifteen animals with extreme values for each FA were evaluated through differentially expressed genes (DEG) analysis and two co-expression methodologies (WGCNA and PCIT). We highlight the ECHS1, IVD, ASB5, and ERLIN1 genes and the TF NFIA, indicated in both FA. Moreover, we associate the NFYA, NFYB, PPARG, FASN, and FADS2 genes with LA, and the RORA and ELOVL5 genes with ALA. Furthermore, the functional enrichment analysis points out several terms related to FA metabolism. These findings contribute to our understanding of the genetic mechanisms underlying the beef EFA profile in Nellore cattle finished in feedlot.

Genome-wide interaction study reveals epistatic interactions for beef lipid-related traits in Nellore cattle

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.

An assessment of genomic connectedness measures in Nellore cattle

An important criterion to consider in genetic evaluations is the extent of genetic connectedness across management units (MU), especially if they differ in their genetic mean. Reliable comparisons of genetic values across MU depend on the degree of connectedness: the higher the connectedness, the more reliable the comparison. Traditionally, genetic connectedness was calculated through pedigree-based methods; however, in the era of genomic selection, this can be better estimated utilizing new approaches based on genomics. Most procedures consider only additive genetic effects, which may not accurately reflect the underlying gene action of the evaluated trait, and little is known about the impact of non-additive gene action on connectedness measures. The objective of this study was to investigate the extent of genomic connectedness measures, for the first time, in Brazilian field data by applying additive and non-additive relationship matrices using a fatty acid profile data set from seven farms located in the three regions of Brazil, which are part of the three breeding programs. Myristic acid (C14:0) was used due to its importance for human health and reported presence of non-additive gene action. The pedigree included 427,740 animals and 925 of them were genotyped using the Bovine high-density genotyping chip. Six relationship matrices were constructed, parametrically and non-parametrically capturing additive and non-additive genetic effects from both pedigree and genomic data. We assessed genome-based connectedness across MU using the prediction error variance of difference (PEVD) and the coefficient of determination (CD). PEVD values ranged from 0.540 to 1.707, and CD from 0.146 to 0.456. Genomic information consistently enhanced the measures of connectedness compared to the numerator relationship matrix by at least 63%. Combining additive and non-additive genomic kernel relationship matrices or a non-parametric relationship matrix increased the capture of connectedness. Overall, the Gaussian kernel yielded the largest measure of connectedness. Our findings showed that connectedness metrics can be extended to incorporate genomic information and non-additive genetic variation using field data. We propose that different genomic relationship matrices can be designed to capture additive and non-additive genetic effects, increase the measures of connectedness, and to more accurately estimate the true state of connectedness in herds.

Differentially expressed genes identified through RNA-seq with extreme values of principal components for beef fatty acid in Nelore cattle

The aim of this study was to identify differentially expressed genes (DEG) in the Longissimus thoracis muscle of Nelore cattle related to fatty acid (FA) profile through RNA sequencing and principal component analysis (PCA). Two groups of 10 animals each were selected containing PC1 and PC2 extreme DEG values (HIGH × LOW) for each FA group. The intramuscular fat (IMF) was compared between cluster groups by ANOVA, and only the sum of monounsaturated FA (MUFA) and ω3 showed significant differences (p < .05). Interestingly, the highest percentage (95%) of phenotypic variation explained by the sum of the first two PC was observed for ω3, which also displayed the lowest number of DEG (n = 1). The lowest percentage (59%) was observed for MUFA, which also revealed the largest number of DEG (n = 66). Since only MUFA and ω3 exhibited significant differences between cluster groups, we can conclude that the differences observed for the remaining groups are not due to the percentage of IMF. Several genes that have been previously associated with meat quality and FA traits were identified as DEG in this study. The functional analysis revealed one KEGG pathway and eight GO terms as significant (p < .05), in which we highlighted the purine metabolism, glycolytic process, adenosine triphosphate binding and bone development. These results strongly contribute to the knowledge of the biological mechanisms involved in meat FA profile of Nelore cattle.

Comparison between haplotype-based and individual snp-based genomic predictions for beef fatty acid profile in Nelore cattle

The aim of this study was to evaluate the genomic predictions using the single-step genomic best linear unbiased predictor (ssGBLUP) method based on SNPs and haplotype markers associated with beef fatty acids (FAs) profile in Nelore cattle. The data set contained records from 963 Nelore bulls finished in feedlot (±90 days) and slaughtered with approximately 24 months of age. Meat samples from the Longissimus dorsi muscle were taken for FAs profile measurement. FAs were quantified by gas chromatography using a SP-2560 capillary column. Animals were genotyped with the high-density SNP panel (BovineHD BeadChip assay) containing 777,962 markers. SNPs with a minor allele frequency and a call rate lower than 0.05 and 0.90, respectively, monomorphic, located on sex chromosomes, and with unknown position were removed from the data set. After genomic quality control, a total of 469,981 SNPs and 892 samples were available for subsequent analyses. Missing genotypes were imputed and phased using the FImpute software. Haplotype blocks were defined based on linkage disequilibrium using the Haploview software. The model to estimate variance components and genetic parameters and to predict the genomic values included the random genetic additive effects, fixed effects of the contemporary group and the age at slaughter as a linear covariate. Accuracies using the haplotype-based approach ranged from 0.07 to 0.31, and those SNP-based ranged from 0.06 to 0.33. Regression coefficients ranged from 0.07 to 0.74 and from 0.08 to 1.45 using the haplotype- and SNP-based approaches, respectively. Despite the low to moderate accuracies for the genomic values, it is possible to obtain genetic progress trough selection using genomic information based either on SNPs or haplotype markers. The SNP-based approach allows less biased genomic evaluations, and it is more feasible when taking into account the computational and operational cost underlying the haplotypes inference.

Genome wide association study identifies SNPs associated with fatty acid composition in Chinese Wagyu cattle

Background

Fatty acids are important traits that affect meat quality and nutritive values in beef cattle. Detection of genetic variants for fatty acid composition can help to elucidate the genetic mechanism underpinning these traits and promote the improvement of fatty acid profiles. In this study, we performed a genome-wide association study (GWAS) on fatty acid composition using high-density single nucleotide polymorphism (SNP) arrays in Chinese Wagyu cattle.

Results

In total, we detected 15 and 8 significant genome-wide SNPs for individual fatty acids and fatty acid groups in Chinese Wagyu cattle, respectively. Also, we identified nine candidate genes based on 100 kb regions around associated SNPs. Four SNPs significantly associated with C14:1 cis-9 were embedded with stearoyl-CoA desaturase (SCD), while three SNPs in total were identified for C22:6 n-3 within Phospholipid scramblase family member 5 (PLSCR5), Cytoplasmic linker associated protein 1 (CLASP1), and Chymosin (CYM). Notably, we found the top candidate SNP within SCD can explain ~ 7.37% of phenotypic variance for C14:1 cis-9. Moreover, we detected several blocks with high LD in the 100 kb region around SCD. In addition, we found three significant SNPs within a 100 kb region showing pleiotropic effects related to multiple FA groups (PUFA, n-6, and PUFA/SFA), which contains BAI1 associated protein 2 like 2 (BAIAP2L2), MAF bZIP transcription factor F (MAFF), and transmembrane protein 184B (TMEM184B).

Conclusions

Our study identified several significant SNPs and candidate genes for individual fatty acids and fatty acid groups in Chinese Wagyu cattle, and these findings will further assist the design of breeding programs for meat quality in cattle.

Genome-wide association study and prediction of genomic breeding values for fatty-acid composition in Korean Hanwoo cattle using a high-density single-nucleotide polymorphism array

Genomic selection using high-density single-nucleotide polymorphism (SNP) markers is used in dairy and beef cattle breeds to accurately estimate genomic breeding values and accelerate genetic improvement by enabling selection of animals with high genetic merit. This genome-wide association study (GWAS) aimed to identify genetic variants associated with beef fatty-acid composition (FAC) traits and to evaluate the accuracy of genomic predictions (GPs) for those traits using genomic best linear unbiased prediction (GBLUP), pedigree BLUP (PBLUP), and BayesR models. Samples of the longissimus dorsi muscle of 965 thirty-month-old Hanwoo steers (progeny of 73 proven bulls) were used to investigate 14 FAC traits. Animals were genotyped or imputed using two bovine SNP platforms (50K and 777K), and after quality control, 38,715 (50K) and 633,448 (777K) SNPs were subjected to GWAS and GP study using a cross-validation scheme. SNP-based heritability estimates were moderate to high (0.25 to 0.47) for all studied traits, with some exceptions for polyunsaturated fatty acids. Association analysis revealed that 19 SNPs in BTA19 (98.7 kb) were significantly associated (P < 7.89 × 10-8) with C14:0 and C18:1n-9; these SNPs were in the fatty-acid synthase (FASN) and coiled-coil domain-containing 57 (CCDC57) genes. BayesR analysis revealed that 0.41 to 0.78% of the total SNPs (n = 2,571 to 4,904) explained almost all of the genetic variance; the majority of the SNPs (>99%) had negligible effects, suggesting that the FAC traits were polygenic. Genome partitioning analysis indicated mostly nonlinear and weak correlations between the variance explained by each chromosome and its length, which also reflected the considerable contributions of relatively few genes. The prediction accuracy of breeding values for FAC traits varied from low to high (0.25 to 0.57); the estimates using the GBLUP and BayesR methods were superior to those obtained by the PBLUP method. The BayesR method performed similarly to GBLUP for most of the studied traits but substantially better for those traits that were controlled by SNPs with large effects; this was supported by the GWAS results. In addition, the predictive abilities of the 50K and 777K SNP arrays were almost similar; thus, both are suitable for GP in Hanwoo cattle. In conclusion, this study provides important insight into the genetic architecture and predictive ability of FAC traits in Hanwoo cattle. Our findings could be used in selection and breeding programs to promote production of meat with enhanced nutritional value.