Inferring the recent ancestry ofmyostatinalleles affecting muscle mass in cattle

Quantitative proteomic analysis of beef tenderness of Piemontese young bulls by SWATH-MS

Quantitative proteomic approach is a suitable way to tackle the beef tenderness. Ten aged-beef samples from Longissimus thoracis of Piemontese breed classified as tender (n = 5) and tough (n = 5) meat were evaluated using SWATH-MS and bioinformatic tools for the identification of the proteins and pathways most influencing tenderness variability. Between the two textural groups, proteomic changes were mainly caused by 43 differentially abundant proteins (DAPs) arranged in reference patterns as displayed by the heat map analysis. Most of these DAPs were associated with energy metabolism. From the functional proteomic analysis, two clusters of proteins, including ACO2, MDH1, MDH2 and CS in one cluster and FBP2, PFKL, LDHA, TPI1 and GAPDH/S in the other cluster, suggest gluconeogenesis, glycolysis and citrate cycle as key pathways for Piemontese breed beef tenderness. These findings contribute to a deeper insight into molecular pathways related to beef tenderness.

A genome-wide association study for body weight in Japanese Thoroughbred racehorses clarifies candidate regions on chromosomes 3, 9, 15, and 18

Body weight is an important trait to confirm growth and development in humans and animals. In Thoroughbred racehorses, it is measured in the postnatal, training, and racing periods to evaluate growth and training degrees. The body weight of mature Thoroughbred racehorses generally ranges from 400 to 600 kg, and this broad range is likely influenced by environmental and genetic factors. Therefore, a genome-wide association study (GWAS) using the Equine SNP70 BeadChip was performed to identify the genomic regions associated with body weight in Japanese Thoroughbred racehorses using 851 individuals. The average body weight of these horses was 473.9 kg (standard deviation: 28.0) at the age of 3, and GWAS identified statistically significant SNPs on chromosomes 3 (BIEC2_808466, P=2.32E-14), 9 (BIEC2_1105503, P=1.03E-7), 15 (BIEC2_322669, P=9.50E-6), and 18 (BIEC2_417274, P=1.44E-14), which were associated with body weight as a quantitative trait. The genomic regions on chromosomes 3, 9, 15, and 18 included ligand-dependent nuclear receptor compressor-like protein (LCORL), zinc finger and AT hook domain containing (ZFAT), tribbles pseudokinase 2 (TRIB2), and myostatin (MSTN), respectively, as candidate genes. LCORL and ZFAT are associated with withers height in horses, whereas MSTN affects muscle mass. Thus, the genomic regions identified in this study seem to affect the body weight of Thoroughbred racehorses. Although this information is useful for breeding and growth management of the horses, the production of genetically modified animals and gene doping (abuse/misuse of gene therapy) should be prohibited to maintain horse racing integrity.

Crossbred young bulls and heifers sired by double-muscled Piemontese or Belgian Blue bulls exhibit different effects of sexual dimorphism on fattening performance and muscularity but not on meat quality traits

As double-muscled Belgian Blue (BB) and Piemontese (PIEM) breeds are heavily selected for different objectives (the former mainly for muscularity, the latter for ease of calving), the aim of this study was to compare sexual dimorphism in 56 crossbred young bulls and heifers obtained from dairy cows mated to bulls of the two beef breeds. Young PIEM- and BB-sired bulls had similar fattening performances and beef traits, although the BB crossbreds were slightly more muscular. Otherwise, the BB-sired heifers exceeded the PIEM-sired heifers in growth rate (1.12 vs. 0.98kg/d), feed efficiency (0.129 vs. 0.121kg/kg DM), increases in muscle scores (1.45 vs. 0.98 SEUROP scores) and carcass yield (0.612 vs. 0.605), but not in fatness, retail cut proportions and meat quality traits. Sexual dimorphism is, therefore, less distinct in BB than in PIEM crossbreds. In conclusion, BB sires are to be preferred for "product quality", and PIEM sires for "process quality", on account of welfare and ethical issues.

INVITED REVIEW: Inhibitors of myostatin as methods of enhancing muscle growth and development

With the increasing demand for affordable, high-quality meat, livestock and poultry producers must continually find ways to maximize muscle growth in their animals without compromising palatability of the meat products. Muscle mass relies on myoblast proliferation during prenatal or prehatch stages and fiber hypertrophy through protein synthesis and nuclei donation by satellite cells after birth or hatch. Therefore, understanding the cellular and molecular mechanisms of myogenesis and muscle development is of great interest. Myostatin is a well-known negative regulator of muscle growth and development that inhibits proliferation and differentiation in myogenic cells as well as protein synthesis in existing muscle fibers. In this review, various inhibitors of myostatin activity or signaling are examined that may be used in animal agriculture for enhancing muscle growth. Myostatin inhibitors are relevant as potential therapies for muscle-wasting diseases and muscle weakness in humans and animals. Currently, there are no commercial myostatin inhibitors for agriculture or biomedical purposes because the safest and most effective option has yet to be identified. Further investigation of myostatin inhibitors and administration strategies may revolutionize animal production and the medical field.

Genetic disorders in beef cattle: a review

The main purpose of present review is to describe and organize autosomal recessive disorders (arachnomelia, syndactylism, osteopetrosis, dwarfism, crooked tail syndrome, muscular hyperplasia, glycogen storage disease, protoporphyria), which occur among beef cattle, and methods that can be applied to detect these defects. Prevalence of adverse alleles in beef breeds happens due to human activity—selections of favorable features, e.g. developed muscle tissue. Unfortunately, carriers of autosomal recessive diseases are often characterized by these attributes. Fast and effective identification of individuals, that may carry faulty genes, can prevent economical losses.

Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits

BACKGROUND

Dense SNP genotypes are often combined with complex trait phenotypes to map causal variants, study genetic architecture and provide genomic predictions for individuals with genotypes but no phenotype. A single method of analysis that jointly fits all genotypes in a Bayesian mixture model (BayesR) has been shown to competitively address all 3 purposes simultaneously. However, BayesR and other similar methods ignore prior biological knowledge and assume all genotypes are equally likely to affect the trait. While this assumption is reasonable for SNP array genotypes, it is less sensible if genotypes are whole-genome sequence variants which should include causal variants.

RESULTS

We introduce a new method (BayesRC) based on BayesR that incorporates prior biological information in the analysis by defining classes of variants likely to be enriched for causal mutations. The information can be derived from a range of sources, including variant annotation, candidate gene lists and known causal variants. This information is then incorporated objectively in the analysis based on evidence of enrichment in the data. We demonstrate the increased power of BayesRC compared to BayesR using real dairy cattle genotypes with simulated phenotypes. The genotypes were imputed whole-genome sequence variants in coding regions combined with dense SNP markers. BayesRC increased the power to detect causal variants and increased the accuracy of genomic prediction. The relative improvement for genomic prediction was most apparent in validation populations that were not closely related to the reference population. We also applied BayesRC to real milk production phenotypes in dairy cattle using independent biological priors from gene expression analyses. Although current biological knowledge of which genes and variants affect milk production is still very incomplete, our results suggest that the new BayesRC method was equal to or more powerful than BayesR for detecting candidate causal variants and for genomic prediction of milk traits.

CONCLUSIONS

BayesRC provides a novel and flexible approach to simultaneously improving the accuracy of QTL discovery and genomic prediction by taking advantage of prior biological knowledge. Approaches such as BayesRC will become increasing useful as biological knowledge accumulates regarding functional regions of the genome for a range of traits and species.

On the History of Cattle Genetic Resources

Cattle are our most important livestock species because of their production and role in human culture. Many breeds that differ in appearance, performance and environmental adaptation are kept on all inhabited continents, but the historic origin of the diverse phenotypes is not always clear. We give an account of the history of cattle by integrating archaeological record and pictorial or written sources, scarce until 300 years ago, with the recent contributions of DNA analysis. We describe the domestication of their wild ancestor, migrations to eventually all inhabited continents, the developments during prehistory, the antiquity and the Middle Ages, the relatively recent breed formation, the industrial cattle husbandry in the Old and New World and the current efforts to preserve the cattle genetic resources. Surveying the available information, we propose three main and overlapping phases during the development of the present genetic diversity: (i) domestication and subsequent wild introgression; (ii) natural adaptation to a diverse agricultural habitat; and (iii) breed development.

Selection for complex traits leaves little or no classic signatures of selection

Background

Selection signatures aim to identify genomic regions underlying recent adaptations in populations. However, the effects of selection in the genome are difficult to distinguish from random processes, such as genetic drift. Often associations between selection signatures and selected variants for complex traits is assumed even though this is rarely (if ever) tested. In this paper, we use 8 breeds of domestic cattle under strong artificial selection to investigate if selection signatures are co-located in genomic regions which are likely to be under selection.

Results

Our approaches to identify selection signatures (haplotype heterozygosity, integrated haplotype score and F_ST) identified strong and recent selection near many loci with mutations affecting simple traits under strong selection, such as coat colour. However, there was little evidence for a genome-wide association between strong selection signatures and regions affecting complex traits under selection, such as milk yield in dairy cattle. Even identifying selection signatures near some major loci was hindered by factors including allelic heterogeneity, selection for ancestral alleles and interactions with nearby selected loci.

Conclusions

Selection signatures detect loci with large effects under strong selection. However, the methodology is often assumed to also detect loci affecting complex traits where the selection pressure at an individual locus is weak. We present empirical evidence to suggests little discernible ‘selection signature’ for complex traits in the genome of dairy cattle despite very strong and recent artificial selection.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-246) contains supplementary material, which is available to authorized users.

A systems biology approach using metabolomic data reveals genes and pathways interacting to modulate divergent growth in cattle

BACKGROUND

Systems biology enables the identification of gene networks that modulate complex traits. Comprehensive metabolomic analyses provide innovative phenotypes that are intermediate between the initiator of genetic variability, the genome, and raw phenotypes that are influenced by a large number of environmental effects. The present study combines two concepts, systems biology and metabolic analyses, in an approach without prior functional hypothesis in order to dissect genes and molecular pathways that modulate differential growth at the onset of puberty in male cattle. Furthermore, this integrative strategy was applied to specifically explore distinctive gene interactions of non-SMC condensin I complex, subunit G (NCAPG) and myostatin (GDF8), known modulators of pre- and postnatal growth that are only partially understood for their molecular pathways affecting differential body weight.

RESULTS

Our study successfully established gene networks and interacting partners affecting growth at the onset of puberty in cattle. We demonstrated the biological relevance of the created networks by comparison to randomly created networks. Our data showed that GnRH (Gonadotropin-releasing hormone) signaling is associated with divergent growth at the onset of puberty and revealed two highly connected hubs, BTC and DGKH, within the network. Both genes are known to directly interact with the GnRH signaling pathway. Furthermore, a gene interaction network for NCAPG containing 14 densely connected genes revealed novel information concerning the functional role of NCAPG in divergent growth.

CONCLUSIONS

Merging both concepts, systems biology and metabolomic analyses, successfully yielded new insights into gene networks and interacting partners affecting growth at the onset of puberty in cattle. Genetic modulation in GnRH signaling was identified as key modifier of differential cattle growth at the onset of puberty. In addition, the benefit of our innovative concept without prior functional hypothesis was demonstrated by data suggesting that NCAPG might contribute to vascular smooth muscle contraction by indirect effects on the NO pathway via modulation of arginine metabolism. Our study shows for the first time in cattle that integration of genetic, physiological and metabolomics data in a systems biology approach will enable (or contribute to) an improved understanding of metabolic and gene networks and genotype-phenotype relationships.

Detection of quantitative trait loci in Bos indicus and Bos taurus cattle using genome-wide association studies

BACKGROUND

The apparent effect of a single nucleotide polymorphism (SNP) on phenotype depends on the linkage disequilibrium (LD) between the SNP and a quantitative trait locus (QTL). However, the phase of LD between a SNP and a QTL may differ between Bos indicus and Bos taurus because they diverged at least one hundred thousand years ago. Here, we test the hypothesis that the apparent effect of a SNP on a quantitative trait depends on whether the SNP allele is inherited from a Bos taurus or Bos indicus ancestor.

METHODS

Phenotype data on one or more traits and SNP genotype data for 10 181 cattle from Bos taurus, Bos indicus and composite breeds were used. All animals had genotypes for 729 068 SNPs (real or imputed). Chromosome segments were classified as originating from B. indicus or B. taurus on the basis of the haplotype of SNP alleles they contained. Consequently, SNP alleles were classified according to their sub-species origin. Three models were used for the association study: (1) conventional GWAS (genome-wide association study), fitting a single SNP effect regardless of subspecies origin, (2) interaction GWAS, fitting an interaction between SNP and subspecies-origin, and (3) best variable GWAS, fitting the most significant combination of SNP and sub-species origin.

RESULTS

Fitting an interaction between SNP and subspecies origin resulted in more significant SNPs (i.e. more power) than a conventional GWAS. Thus, the effect of a SNP depends on the subspecies that the allele originates from. Also, most QTL segregated in only one subspecies, suggesting that many mutations that affect the traits studied occurred after divergence of the subspecies or the mutation became fixed or was lost in one of the subspecies.

CONCLUSIONS

The results imply that GWAS and genomic selection could gain power by distinguishing SNP alleles based on their subspecies origin, and that only few QTL segregate in both B. indicus and B. taurus cattle. Thus, the QTL that segregate in current populations likely resulted from mutations that occurred in one of the subspecies and can have both positive and negative effects on the traits. There was no evidence that selection has increased the frequency of alleles that increase body weight.

Evidence for pleiotropism and recent selection in the PLAG1 region in Australian Beef cattle

A putative functional mutation (rs109231213) near PLAG1 (BTA14) associated with stature was studied in beef cattle. Data from 8199 Bos taurus, Bos indicus and Tropical Composite cattle were used to test the associations between rs109231213 and various phenotypes. Further, 23 496 SNPs located on BTA14 were tested for association with these phenotypes, both independently and fitted together with rs109231213. The C allele of rs109231213 significantly increased hip height, weight, net food intake, age at puberty in males and females and decreased IGF-I concentration in blood and fat depth. When rs109231213 was fitted as a fixed effect in the model, there was an overall reduction in associations between other SNPs and these traits but some SNPs remained associated (P < 10(-4) ). Frequency of the mutant C allele of rs109231213 differed among B. indicus (0.52), B. taurus (0.96) and Tropical Composite (0.68). Most chromosomes carrying the C allele had the same surrounding 10 SNP haplotype, probably because the C allele was introgressed into Brahman from B. taurus cattle. A region of reduced heterozygosity surrounds the C allele; this is small in B. taurus but 20 Mb long in Brahmans, indicating recent and strong selection for the mutant allele. Thus, the C allele appears to mark a mutation that has been selected almost to fixation in the B. taurus breeds studied here and introduced into Brahman cattle during grading up and selected to a frequency of 0.52 despite its negative effects on fertility.

Genes contributing to genetic variation of muscling in sheep

Selective breeding programs aiming to increase the productivity and profitability of the sheep meat industry use elite, progeny tested sires. The broad genetic traits of primary interest in the progeny of these sires include skeletal muscle yield, fat content, eating quality, and reproductive efficiency. Natural mutations in sheep that enhance muscling have been identified, while a number of genome scans have identified and confirmed quantitative trait loci (QTL) for skeletal muscle traits. The detailed phenotypic characteristics of sheep carrying these mutations or QTL affecting skeletal muscle show a number of common biological themes, particularly changes in developmental growth trajectories, alterations of whole animal morphology, and a shift toward fast twitch glycolytic fibers. The genetic, developmental, and biochemical mechanisms underpinning the actions of some of these genetic variants are described. This review critically assesses this research area, identifies gaps in knowledge, and highlights mechanistic linkages between genetic polymorphisms and skeletal muscle phenotypic changes. This knowledge may aid the discovery of new causal genetic variants and in some cases lead to the development of biochemical and immunological strategies aimed at enhancing skeletal muscle.