Identification of genetic markers for fat deposition and meat tenderness on bovine chromosome 5: development of a low-density single nucleotide polymorphism map

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner-Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.

Bibliometric Analysis of Research on the Main Genes Involved in Meat Tenderness

Simple Summary

A bibliometric analysis was carried out to know the evolution of research on genes associated with meat tenderness, of interest for the development of selection programs. Since 1993, studies have been limited to a few researchers in high-income countries due to the costs associated with the techniques. The main findings showed that the scientific production had a discontinuous growth because science experienced a significant change since approximately 2010. Marker-assisted selection was widely used, evaluating mainly CAPN (calpain) and CAST (calpastatin) genes for their contribution to meat tenderness, especially in cattle. However, the effects are small; therefore, genomic selection was implemented by genotyping thousands of single nucleotide polymorphisms (SNPs) for further explanation of genetic variation. The results shown are important for scholars to identify emerging methodologies and gaps in the literature and to know who the prolific authors and institutions in the field for possible collaborations, etc., are.

Abstract

Tenderness is one of the main characteristics of meat because it determines its price and acceptability. This is the first bibliometric study on the trend of research on the role of genes in meat tenderness. A total of 175 original and English-language articles published up to 2021 were retrieved from Scopus. The bibliometric analysis was carried out with VOSviewer (version 1.6.18, Eck and Waltman, Leiden, Netherlands) and complemented with the Analyze search results service from Scopus. Erroneous and duplicate data were eliminated, and incomplete information was added to standardize the results. Scientific production was evaluated by means of quantity, quality and structure indicators. As a first glance, 8.816% of authors have published more than 50% of papers mainly related to genes encoding the calpain (CAPN)-calpastatin (CAST) system and single nucleotide polymorphisms (SNPs). Among other findings, a strong link was found between the contribution of the main countries (led by the United States with) and their institutions (led by the USDA Agricultural Research Service with) to their gross domestic product. Most studies on the topic are published in the Journal of Animal Science, and other journals with high impact according to the number of citations and different metrics. Finally, when evaluating the most cited articles, the occurrence and association of the main keywords, it was confirmed that research is focused on the role of CAPN and CAST genes and of SNPs in beef tenderness. The change in science was emphasized; although marker-assisted selection is still used, genes have an infinitesimal effect on complex traits. Therefore, since about 2010, new research groups adopted genomic selection to evaluate dense panels of SNPs and better explain genetic variation in meat tenderness.

Whole Genome Sequencing Provides New Insights Into the Genetic Diversity and Coat Color of Asiatic Wild Ass and Its Hybrids

The diversity of livestock coat color results from human positive selection and is an indispensable part of breed registration. As an important biodiversity resource, Asiatic wild ass has many special characteristics, including the most visualized feature, its yellowish-brown coat color, and excellent adaptation. To explore the genetic mechanisms of phenotypic characteristics in Asiatic wild ass and its hybrids, we resequenced the whole genome of one Mongolian Kulan (a subspecies of Asiatic wild ass) and 29 Kulan hybrids (Mongolian Kulan ♂×Xinjiang♀), and the ancestor composition indicated the true lineage of the hybrids. XP-EHH (Cross Population Extended Haplotype Homozygosity), θπ-ratio (Nucleotide Diversity Ratio), CLR (Composite Likelihood Ratio) and θπ (Nucleotide Diversity) methods were used to detect the candidate regions of positive selection in Asiatic wild ass and its hybrids. Several immune genes (DEFA1, DEFA5, DEFA7, GIMAP4, GIMAP1, IGLC1, IGLL5, GZMB and HLA) were observed by the CLR and θπ methods. XP-EHH and θπ-ratio revealed that these genes are potentially responsible for coat color (KITLG) and meat quality traits (PDE1B and MYLK2). Furthermore, the heatmap was able to show the clear difference in the haplotype of the KITLG gene between the Kulan hybrids and Asiatic wild ass group and the Guanzhong black donkey group, which is a powerful demonstration of the key role of KITLG in donkey color. Therefore, our study may provide new insights into the genetic basis of coat color, meat quality traits and immunity of Asiatic wild ass and its hybrids.

Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle

Several methods have been used for genome-enabled prediction (or genomic selection) of complex traits, for example, multiple regression models describing a target trait with a linear function of a set of genetic markers. Genomic selection studies have been focused mostly on single-trait analyses. However, most profitability traits are genetically correlated, and an increase in prediction accuracy of genomic breeding values for genetically correlated traits is expected when using multiple-trait models. Thus, this study was carried out to assess the accuracy of genomic prediction for carcass and meat quality traits in Nelore cattle, using single- and multiple-trait approaches. The study considered 15 780, 15 784, 15 742 and 526 records of rib eye area (REA, cm²), back fat thickness (BF, mm), rump fat (RF, mm) and Warner-Bratzler shear force (WBSF, kg), respectively, in Nelore cattle, from the Nelore Brazil Breeding Program. Animals were genotyped with a low-density single nucleotide polymorphism (SNP) panel and subsequently imputed to arrays with 54 and 777 k SNPs. Four Bayesian specifications of genomic regression models, namely, Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression; blending methods, BLUP; and single-step genomic best linear unbiased prediction (ssGBLUP) methods were compared in terms of prediction accuracy using a fivefold cross-validation. Estimates of heritability ranged from 0.20 to 0.35 and from 0.21 to 0.46 for RF and WBSF on single- and multiple-trait analyses, respectively. Prediction accuracies for REA, BF, RF and WBSF were all similar using the different specifications of regression models. In addition, this study has shown the impact of genomic information upon genetic evaluations in beef cattle using the multiple-trait model, which was also advantageous compared to the single-trait model because it accounted for the selection process using multiple traits at the same time. The advantage of multi-trait analyses is attributed to the consideration of correlations and genetic influences between the traits, in addition to the non-random association of alleles.

Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks

The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non-autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10^-6 ), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree-based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relatively simple architecture can provide superior genomic predictions for meat tenderness in Nellore cattle.

Associations between Bovine β-Defensin 4 Genotypes and Production Traits of Polish Holstein-Friesian Dairy Cattle

Simple Summary

Mastitis negatively affects dairy cattle, causing inferior milk quality and premature animal culling, which leads to economic losses. Therefore, selection based on genetic markers (i.g., marker-assisted selection) should also include functional traits with low heritability, such as resistance to udder inflammation. Single nucleotide polymorphisms (SNPs) identified in genes involved in the immune system, such as defensins with antibacterial properties, could be valuable markers. We chose two SNPs within the bovine neutrophil beta-defensin 4 (BNBD4) gene analyzed in a previous study related to milk production and udder health. Since these SNPs are located very close to each other in the gene intron, it is useful to analyze their association with production traits as a combined genotype. The results showed that these genotypes are indeed associated with productivity, as well as functional traits (milk, fat, and protein yields, fat, protein, lactose, and dry matter contents, and somatic cell count). The differences between the results based on the phenotypic data and the breeding values of studied traits may confirm the results of simulation studies that indicate a high rate of false-positives in genome-wide association study (GWAS) based on classically calculated estimated breeding values (EBVs) using best linear unbiased prediction (BLUP) methodology.

Abstract

This study analyzed the associations between two single-nucleotide polymorphisms (C2239T and A1674C), used together as a genotype located in BNBD4, and milk traits and breeding values of productivity traits of Polish Holstein-Friesian dairy cows. The research was carried out on 322 cows, with 7070 milk parameter and somatic cell count records in daily milking, as well as 897 records covering data on whole lactations, and 2209 breeding value records for productivity traits. The DMU statistical package with a one-trait repeatability test-day animal model was used to estimate the associations. The differences between the genotype effects were analyzed using Duncan’s post-hoc tests. The CC/AA and CT/AC genotypes had the highest frequencies (0.62 and 0.23, respectively). For use in marker-assisted selection, the CC/AC genotype is the most promising as an indicator of high-yielding cows potentially resistant to mastitis, because it was associated with the lowest somatic cell count (SCC), highest milk, fat, and protein yields in daily milking, as well as with milk yield in the whole lactation. The studied genotypes were also related to the breeding values of all the investigated production traits. However, some simulation studies have indicated a high rate of false-positives in GWAS based on classically calculated EBVs.

MicroRNA-378 controls classical brown fat expansion to counteract obesity

Both classical brown adipocytes and brown-like beige adipocytes are considered as promising therapeutic targets for obesity; however, their development, relative importance, and functional coordination are not well understood. Here we show that a modest expression of miR-378/378* in adipose tissue specifically increases classical brown fat (BAT) mass, but not white fat (WAT) mass. Remarkably, BAT expansion, rather than miR-378 per se, suppresses formation of beige adipocytes in subcutaneous WAT. Despite this negative feedback, the expanded BAT depot is sufficient to prevent both genetic and high fat diet-induced obesity. At the molecular level, we find that miR-378 targets phosphodiesterase Pde1b in BAT, but not in WAT. Indeed, miR-378 and Pde1b inversely regulate brown adipogenesis in vitro in the absence of phosphodiesterase inhibitor IBMX. Our work identifies miR-378 as a key regulatory component underlying classical BAT-specific expansion and obesity resistance, and adds novel insights into the physiological cross-talk between BAT and WAT.

Adaptive admixture in the West African bovine hybrid zone: insight from the Borgou population

Understanding the adaptive response to environmental fluctuations represents a central issue in evolutionary biology. Population admixture between divergent ancestries has often been considered as an efficient short-term adaptation strategy. Cattle populations from the West African Bos taurus × Bos indicus hybrid zone represent a valuable resource to characterize the effect of such adaptive admixture at the genome level. We here provide a detailed assessment of the global and local genome ancestries of the Borgou breed, one of the most representative cattle of this hybrid zone. We analysed a large data set consisting of 38,100 SNPs genotyped on 203 Borgou and 591 individuals representative of all the different cattle ancestries. At the global genomic level, we show that Borgou is a stabilized admixed breed whose origin (c. 130 years ago) traces back to the great African rinderpest pandemic, several centuries after the last admixture events, the West African zebus originate from (c. 500 years ago). To identify footprints of adaptive admixture, we combined the identification of signatures of selection and the functional annotation of the underlying genes using systems biology tools. The detection of the SILV coat coloration gene likely under artificial selection may be viewed as a validation of our approach. Overall, our results suggest that the long-term presence of pathogens and the intermediate environmental conditions are the main acting selective pressures. Our analytical framework can be extended to other model or nonmodel species to understand the process that shapes the patterns of genetic variability in hybrid zones.

Genome scan for meat quality traits in Nelore beef cattle

Meat quality traits are economically important because they affect consumers' acceptance, which, in turn, influences the demand for beef. However, selection to improve meat quality is limited by the small numbers of animals on which meat tenderness can be evaluated due to the cost of performing shear force analysis and the resultant damage to the carcass. Genome wide-association studies for Warner-Bratzler shear force measured at different times of meat aging, backfat thickness, ribeye muscle area, scanning parameters [lightness, redness (a*), and yellowness] to ascertain color characteristics of meat and fat, water-holding capacity, cooking loss (CL), and muscle pH were conducted using genotype data from the Illumina BovineHD BeadChip array to identify quantitative trait loci (QTL) in all phenotyped Nelore cattle. Phenotype count for these animals ranged from 430 to 536 across traits. Meat quality traits in Nelore are controlled by numerous QTL of small effect, except for a small number of large-effect QTL identified for a*fat, CL, and pH. Genomic regions harboring these QTL and the pathways in which the genes from these regions act appear to differ from those identified in taurine cattle for meat quality traits. These results will guide future QTL mapping studies and the development of models for the prediction of genetic merit to implement genomic selection for meat quality in Nelore cattle.

Leptin in farm animals: where are we and where can we go?

Fat affects meat quality, value and production efficiency as well as providing energy reserves for pregnancy and lactation in farm livestock. Leptin, the adipocyte product of the obese (ob) gene, was quickly seen as a predictor of body fat content in animals approaching slaughter and an aid to assessing reproductive readiness in females. Its participation in inflammation and immune responses that help animals survive infection and trauma has clear additional relevance to meat and milk production. Furthermore, almost a decade of discoveries of nucleotide polymorphisms in the leptin and leptin receptor genes has suggested useful applications relating to feed intake regulation, the efficiency of feed use, the composition of growth, the timing of puberty, mammogenesis and mammary gland function and fertility in cattle, pigs and poultry. The current review attempts to summarise where research has taken us in each of these aspects and speculates on where future research might lead.

Genome-wide association analysis for quantitative trait loci influencing Warner-Bratzler shear force in five taurine cattle breeds

We performed a genome-wide association study for Warner–Bratzler shear force (WBSF), a measure of meat tenderness, by genotyping 3360 animals from five breeds with 54 790 BovineSNP50 and 96 putative single-nucleotide polymorphisms (SNPs) within μ-calpain [HUGO nomenclature calpain 1, (mu/I) large subunit; CAPN1] and calpastatin (CAST). Within- and across-breed analyses estimated SNP allele substitution effects (ASEs) by genomic best linear unbiased prediction (GBLUP) and variance components by restricted maximum likelihood under an animal model incorporating a genomic relationship matrix. GBLUP estimates of ASEs from the across-breed analysis were moderately correlated (0.31–0.66) with those from the individual within-breed analyses, indicating that prediction equations for molecular estimates of breeding value developed from across-breed analyses should be effective for genomic selection within breeds. We identified 79 genomic regions associated with WBSF in at least three breeds, but only eight were detected in all five breeds, suggesting that the within-breed analyses were underpowered, that different quantitative trait loci (QTL) underlie variation between breeds or that the BovineSNP50 SNP density is insufficient to detect common QTL among breeds. In the across-breed analysis, CAPN1 was followed by CAST as the most strongly associated WBSF QTL genome-wide, and associations with both were detected in all five breeds. We show that none of the four commercialized CAST and CAPN1SNP diagnostics are causal for associations with WBSF, and we putatively fine-map the CAPN1 causal mutation to a 4581-bp region. We estimate that variation in CAST and CAPN1 explains 1.02 and 1.85% of the phenotypic variation in WBSF respectively.

Genetic association of phosphodiesterase 1B (PDE1B) with carcass traits in Korean cattle

Quantitative trait loci for fat deposition and carcass traits have been identified in the vicinity of the gene encoding phosphodiesterase 1B (PDE1B) on bovine chromosome 5. Therefore, the PDE1B gene can be considered as a positional and functional candidate gene for carcass traits in beef cattle. This study aimed to identify single nucleotide polymorphisms (SNPs) in the PDE1B gene and to evaluate their associations with carcass traits in Korean cattle. Eight SNPs, g.440T>G, g.17122A>G, g.17507A>C, g.17575A>G, g.17607T>C, g.17609C>A, g.17692C>T, and g.17707C>G, were identified in the region ranged from exon 1 to intron 6. Five of them were used for association analysis because of their availability of restriction fragment length polymorphisms. As a result, g.17122A>G in intron 3 was significantly associated with backfat thickness (BFT), and g.17507A>C in exon 5 was associated with longissimus dorsi muscle area (LMA, P < 0.05). Animals with the AG genotype of g.17122 had thicker BFT than those with the AA genotype. Animals with the AA or AC genotype of g.17507A>C had larger LMA than those with the CC genotype. We suggested the PDE1B gene as a candidate gene for carcass traits of beef cattle. Fine mapping would be required for application to marker-assisted selection.

Footprints of selection in the ancestral admixture of a New World Creole cattle breed

Admixed populations represent attractive biological models to study adaptive selection. Originating from several waves of recent introduction from European (EUT), African (AFT) and zebus (ZEB) cattle, New World Creole cattle allow investigating the response to tropical environmental challenges of these three ancestries. We here provide a detailed assessment of their genetic contributions to the Creole breed from Guadeloupe (CGU). We subsequently look for footprints of selection by combining results from tests based on the extent of haplotype homozygosity and the identification of excess/deficiency of local ancestry. To tackle these issues, 140 CGU individuals and 25 Brahman zebus from Martinique were genotyped at 44 057 SNPs. These data were combined to those available on 23 populations representative of EUT, AFT or ZEB. We found average proportions of 26.1%, 36.0% and 37.9% of EUT, AFT and ZEB ancestries in the CGU genome indicating a higher level of African and zebu ancestries than suggested by historical records. We further identified 23 genomic regions displaying strong signal of selection, most of them being characterized by an excess of ZEB local ancestry. Among the candidate gene underlying these regions, several are associated with reproductive functions (RXFP2, PMEPA1, IGFBP3, KDR, PPP1R8, TBXA2R and SLC7A5) and metabolism (PDE1B and CYP46A1). Finally, two genes (CENTD3 and SAMD12) are involved in cellular signalization of immune response. This study illustrates the relevance of admixed populations to identify footprints of selection by combining several tests straightforward to implement on large data sets.

Polymorphisms in the IGF1 gene and their effect on growth traits in Mexican beef cattle

The IGF1 gene (insulin-like growth factor 1) is a candidate gene for marker-assisted selection strategies. A single nucleotide polymorphism in the promoter region (IGF1/SnaBI) has been reported to be associated with production traits in several cattle breeds. Here, we report its allelic frequencies in Charolais and Beefmaster breeds; we confirm its association with three growth traits: weaning weight, weaning weight adjusted to 210 days and preweaning weight gain in the Charolais breed. In addition, we designed a strategy to search these breeds for new polymorphisms in four coding regions of the gene. A C/A transversion was detected in intron 4, but it was not associated with the growth traits. A single nucleotide polymorphism (IGF1/SnaBI) is proposed as a selection marker for Mexican Charolais cattle; validation of its association with weaning weight, weaning weight adjusted to 210 days and preweaning weight gain, could complement the genetic evaluations of this breed through marker-assisted management strategies.

Molecular cloning of the HGD gene and association of SNPs with meat quality traits in Chinese red cattle

Homogentisate 1, 2 dioxygenase (HGD) is one of six enzymes required for the catabolism of the aromatic amino acids phenylalanine and tyrosine. Here we present the nucleotide sequence of transcripts of the bovine HGD gene. The full length cDNA of bovine HGD has been identified, encoding a deduced protein of 445 amino acids (Accession No. FJ515744). The bovine HGD gene comprises 14 exons and 13 introns. This is the first published cDNA bovine sequences that share high sequence similarity with other species. Semi-quantitative RT-PCR analysis demonstrated that the bovine HGD transcript was mainly expressed in liver and kidney tissues. Nine single nucleotide polymorphisms (SNPs) were identified, five in the coding region and four intronic. Four of the SNPs change an amino acid in the HGD protein sequence. Genotype and allelic frequencies were determined in Chinese red cattle breeds. Ten haplotypes were determined based upon the genotype of 9 SNPs. Moreover, for the first time an association was reported between HGD gene polymorphism and meat quality traits in Chinese red cattle (n = 224). Marker-trait association analyses showed that the HGD/PvuII genotypes showed a significant effect on meat cooking rate, muscle fiber diameter, and shear force (P < 0.05). The HGD DraIII genotypes showed a significant effect on muscle fiber diameter, shear force, and drip loss (P < 0.05). The HGD/AluI genotypes showed a significant effect on meat cooking rate, shear force, and drip loss (P < 0.05). The HGD/DraI genotypes showed a significant effect on meat cooking rate and shear force (P < 0.05). The HGD/EcoRV genotypes showed a significant effect on meat cooking rate, muscle fiber diameter, and shear force (P < 0.05). In all loci, no statistically significant differences were observed for pHu (P > 0.05). This is the first incidence where polymorphisms of a bovine HGD gene have demonstrated a correlation with meat traits in Chinese red cattle.

Linkage mapping bovine EST-based SNP

Background

Existing linkage maps of the bovine genome primarily contain anonymous microsatellite markers. These maps have proved valuable for mapping quantitative trait loci (QTL) to broad regions of the genome, but more closely spaced markers are needed to fine-map QTL, and markers associated with genes and annotated sequence are needed to identify genes and sequence variation that may explain QTL.

Results

Bovine expressed sequence tag (EST) and bacterial artificial chromosome (BAC)sequence data were used to develop 918 single nucleotide polymorphism (SNP) markers to map genes on the bovine linkage map. DNA of sires from the MARC reference population was used to detect SNPs, and progeny and mates of heterozygous sires were genotyped. Chromosome assignments for 861 SNPs were determined by twopoint analysis, and positions for 735 SNPs were established by multipoint analyses. Linkage maps of bovine autosomes with these SNPs represent 4585 markers in 2475 positions spanning 3058 cM . Markers include 3612 microsatellites, 913 SNPs and 60 other markers. Mean separation between marker positions is 1.2 cM. New SNP markers appear in 511 positions, with mean separation of 4.7 cM. Multi-allelic markers, mostly microsatellites, had a mean (maximum) of 216 (366) informative meioses, and a mean 3-lod confidence interval of 3.6 cM Bi-allelic markers, including SNP and other marker types, had a mean (maximum) of 55 (191) informative meioses, and were placed within a mean 8.5 cM 3-lod confidence interval. Homologous human sequences were identified for 1159 markers, including 582 newly developed and mapped SNP.

Conclusion

Addition of these EST- and BAC-based SNPs to the bovine linkage map not only increases marker density, but provides connections to gene-rich physical maps, including annotated human sequence. The map provides a resource for fine-mapping quantitative trait loci and identification of positional candidate genes, and can be integrated with other data to guide and refine assembly of bovine genome sequence. Even after the bovine genome is completely sequenced, the map will continue to be a useful tool to link observable phenotypes and animal genotypes to underlying genes and molecular mechanisms influencing economically important beef and dairy traits.