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Abstract Background We have recently described a method for the construction of an informative gene expression correlation landscape for a single tissue, longissimus muscle (LM) of cattle, using a small number (less than a hundred) of diverse samples. Does this approach facilitate interspecies comparison of networks? Findings Using gene expression datasets from LM samples from a single postnatal time point for high and low muscling sheep, and from a developmental time course (prenatal to postnatal) for normal sheep and sheep exhibiting the Callipyge muscling phenotype gene expression correlations were calculated across subsets of the data comparable to the bovine analysis. An “Always Correlated” gene expression landscape was constructed by integrating the correlations from the subsets of data and was compared to the equivalent landscape for bovine LM muscle. Whilst at the high level apparently equivalent modules were identified in the two species, at the detailed level overlap between genes in the equivalent modules was limited and generally not significant. Indeed, only 395 genes and 18 edges were in common between the two landscapes. Conclusions Since it is unlikely that the equivalent muscles of two closely related species are as different as this analysis suggests, within tissue gene expression correlations appear to be very sensitive to the samples chosen for their construction, compounded by the different platforms used. Thus users need to be very cautious in interpretation of the differences. In future experiments, attention will be required to ensure equivalent experimental designs and use cross-species gene expression platform to enable the identification of true differences between different species.

Abstract High throughput gene expression technologies are a popular choice for researchers seeking molecular or systems-level explanations of biological phenomena. Nevertheless, there has been a groundswell of opinion that these approaches have not lived up to the hype because the interpretation of the data has lagged behind its generation. In our view a major problem has been an over-reliance on isolated lists of differentially expressed (DE) genes which – by simply comparing genes to themselves – have the pitfall of taking molecular information out of context. Numerous scientists have emphasised the need for better context. This can be achieved through holistic measurements of differential connectivity in addition to, or in replacement, of DE. However, many scientists continue to use isolated lists of DE genes as the major source of input data for common readily available analytical tools. Focussing this opinion article on our own research in skeletal muscle, we outline our resolutions to these problems – particularly a universally powerful way of quantifying differential connectivity. With a well designed experiment, it is now possible to use gene expression to identify causal mutations and the other major effector molecules with whom they cooperate, irrespective of whether they themselves are DE. We explain why, for various reasons, no other currently available experimental techniques or quantitative analyses are capable of reaching these conclusions.

Abstract Background Gene regulation by transcription factors (TF) is species, tissue and time specific. To better understand how the genetic code controls gene expression in bovine muscle we associated gene expression data from developing Longissimus thoracis et lumborum skeletal muscle with bovine promoter sequence information. Results We created a highly conserved genome-wide promoter landscape comprising 87,408 interactions relating 333 TFs with their 9,242 predicted target genes (TGs). We discovered that the complete set of predicted TGs share an average of 2.75 predicted TF binding sites (TFBSs) and that the average co-expression between a TF and its predicted TGs is higher than the average co-expression between the same TF and all genes. Conversely, pairs of TFs sharing predicted TGs showed a co-expression correlation higher that pairs of TFs not sharing TGs. Finally, we exploited the co-occurrence of predicted TFBS in the context of muscle-derived functionally-coherent modules including cell cycle, mitochondria, immune system, fat metabolism, muscle/glycolysis, and ribosome. Our findings enabled us to reverse engineer a regulatory network of core processes, and correctly identified the involvement of E2F1, GATA2 and NFKB1 in the regulation of cell cycle, fat, and muscle/glycolysis, respectively. Conclusion The pivotal implication of our research is two-fold: (1) there exists a robust genome-wide expression signal between TFs and their predicted TGs in cattle muscle consistent with the extent of promoter sharing; and (2) this signal can be exploited to recover the cellular mechanisms underpinning transcription regulation of muscle structure and development in bovine. Our study represents the first genome-wide report linking tissue specific co-expression to co-regulation in a non-model vertebrate.

Abstract Background Two types of horns are evident in cattle - fixed horns attached to the skull and a variation called scurs, which refers to small loosely attached horns. Cattle lacking horns are referred to as polled. Although both the Poll and Scurs loci have been mapped to BTA1 and 19 respectively, the underlying genetic basis of these phenotypes is unknown, and so far, no candidate genes regulating these developmental processes have been described. This study is the first reported attempt at transcript profiling to identify genes and pathways contributing to horn and scurs development in Brahman cattle, relative to polled counterparts. Results Expression patterns in polled, horned and scurs tissues were obtained using the Agilent 44 k bovine array. The most notable feature when comparing transcriptional profiles of developing horn tissues against polled was the down regulation of genes coding for elements of the cadherin junction as well as those involved in epidermal development. We hypothesize this as a key event involved in keratinocyte migration and subsequent horn development. In the polled-scurs comparison, the most prevalent differentially expressed transcripts code for genes involved in extracellular matrix remodelling, which were up regulated in scurs tissues relative to polled. Conclusion For this first time we describe networks of genes involved in horn and scurs development. Interestingly, we did not observe differential expression in any of the genes present on the fine mapped region of BTA1 known to contain the Poll locus.

Abstract Background Fleece rot (FR) and body-strike of Merino sheep by the sheep blowfly Lucilia cuprina are major problems for the Australian wool industry, causing significant losses as a result of increased management costs coupled with reduced wool productivity and quality. In addition to direct effects on fleece quality, fleece rot is a major predisposing factor to blowfly strike on the body of sheep. In order to investigate the genetic drivers of resistance to fleece rot, we constructed a combined ovine-bovine cDNA microarray of almost 12,000 probes including 6,125 skin expressed sequence tags and 5,760 anonymous clones obtained from skin subtracted libraries derived from fleece rot resistant and susceptible animals. This microarray platform was used to profile the gene expression changes between skin samples of six resistant and six susceptible animals taken immediately before, during and after FR induction. Mixed-model equations were employed to normalize the data and 155 genes were found to be differentially expressed (DE). Ten DE genes were selected for validation using real-time PCR on independent skin samples. The genomic regions of a further 5 DE genes were surveyed to identify single nucleotide polymorphisms (SNP) that were genotyped across three populations for their associations with fleece rot resistance. Results The majority of the DE genes originated from the fleece rot subtracted libraries and over-representing gene ontology terms included defense response to bacterium and epidermis development, indicating a role of these processes in modulating the sheep's response to fleece rot. We focused on genes that contribute to the physical barrier function of skin, including keratins, collagens, fibulin and lipid proteins, to identify SNPs that were associated to fleece rot scores. Conclusions We identified FBLN1 (fibulin) and FABP4 (fatty acid binding protein 4) as key factors in sheep's resistance to fleece rot. Validation of these markers in other populations could lead to vital tests for marker assisted selection that will ultimately increase the natural fleece rot resistance of Merino sheep.

Abstract Background The tissue specificity of gene expression has been linked to a number of significant outcomes including level of expression, and differential rates of polymorphism, evolution and disease association. Recent studies have also shown the importance of exploring differential gene connectivity and sequence conservation in the identification of disease-associated genes. However, no study relates gene interactions with tissue specificity and disease association. Methods We adopted an a priori approach making as few assumptions as possible to analyse the interplay among gene-gene interactions with tissue specificity and its subsequent likelihood of association with disease. We mined three large datasets comprising expression data drawn from massively parallel signature sequencing across 32 tissues, describing a set of 55,606 true positive interactions for 7,197 genes, and microarray expression results generated during the profiling of systemic inflammation, from which 126,543 interactions among 7,090 genes were reported. Results Amongst the myriad of complex relationships identified between expression, disease, connectivity and tissue specificity, some interesting patterns emerged. These include elevated rates of expression and network connectivity in housekeeping and disease-associated tissue-specific genes. We found that disease-associated genes are more likely to show tissue specific expression and most frequently interact with other disease genes. Using the thresholds defined in these observations, we develop a guilt-by-association algorithm and discover a group of 112 non-disease annotated genes that predominantly interact with disease-associated genes, impacting on disease outcomes. Conclusion We conclude that parameters such as tissue specificity and network connectivity can be used in combination to identify a group of genes, not previously confirmed as disease causing, that are involved in interactions with disease causing genes. Our guilt-by-association algorithm should be useful for the discovery of additional modifiers of genetic diseases, and more generally, for the ability to associate genes of unknown function to clusters of genes with defined functions allowing for novel biological inference that can be subsequently validated.

Abstract Background Mastitis in dairy cattle results from infection of mammary tissue by a range of micro-organisms but principally coliform bacteria and Gram positive bacteria such as Staphylococcus aureus. The former species are often acquired by environmental contamination while S. aureus is particularly problematic due to its resistance to antibiotic treatments and ability to reside within mammary tissue in a chronic, subclinical state. The transcriptional responses within bovine mammary epithelial tissue subjected to intramammary challenge with S. aureus are poorly characterised, particularly at the earliest stages of infection. Moreover, the effect of infection on the presence of bioactive innate immune proteins in milk is also unclear. The nature of these responses may determine the susceptibility of the tissue and its ability to resolve the infection. Results Transcriptional profiling was employed to measure changes in gene expression occurring in bovine mammary tissues sampled from three dairy cows after brief and graded intramammary challenges with S. aureus. These limited challenges had no significant effect on the expression pattern of the gene encoding β-casein but caused coordinated up-regulation of a number of cytokines and chemokines involved in pro-inflammatory responses. In addition, the enhanced expression of two genes, S100 calcium-binding protein A12 (S100A12) and Pentraxin-3 (PTX3) corresponded with significantly increased levels of their proteins in milk from infected udders. Both genes were shown to be expressed by mammary epithelial cells grown in culture after stimulation with lipopolysaccharide. There was also a strong correlation between somatic cell count, a widely used measure of mastitis, and the level of S100A12 in milk from a herd of dairy cows. Recombinant S100A12 inhibited growth of Escherichia coli in vitro and recombinant PTX3 bound to E. coli as well as C1q, a subunit of the first component of the complement cascade. Conclusion The transcriptional responses in infected bovine mammary tissue, even at low doses of bacteria and short periods of infection, probably reflect the combined contributions of gene expression changes resulting from the activation of mammary epithelial cells and infiltrating immune cells. The secretion of a number of proinflammatory cytokines and chemokines from mammary epithelial cells stimulated by the bacteria serves to trigger the recruitment and activation of neutrophils in mammary tissue. The presence of S100A12 and PTX3 in milk from infected udder quarters may increase the anti-bacterial properties of milk thereby helping to resolve the mammary tissue infection as well as potentially contributing to the maturation of the newborn calf epithelium and establishment of the newborn gut microbial population.

Abstract Background Microarray transcript profiling has the potential to illuminate the molecular processes that are involved in the responses of cattle to disease challenges. This knowledge may allow the development of strategies that exploit these genes to enhance resistance to disease in an individual or animal population. Results The Bovine Innate Immune Microarray developed in this study consists of 1480 characterised genes identified by literature searches, 31 positive and negative control elements and 5376 cDNAs derived from subtracted and normalised libraries. The cDNA libraries were produced from 'challenged' bovine epithelial and leukocyte cells. The microarray was found to have a limit of detection of 1 pg/μg of total RNA and a mean slide-to-slide correlation co-efficient of 0.88. The profiles of differentially expressed genes from Concanavalin A (ConA) stimulated bovine peripheral blood lymphocytes were determined. Three distinct profiles highlighted 19 genes that were rapidly up-regulated within 30 minutes and returned to basal levels by 24 h; 76 genes that were up-regulated between 2–8 hours and sustained high levels of expression until 24 h and 10 genes that were down-regulated. Quantitative real-time RT-PCR on selected genes was used to confirm the results from the microarray analysis. The results indicate that there is a dynamic process involving gene activation and regulatory mechanisms re-establishing homeostasis in the ConA activated lymphocytes. The Bovine Innate Immune Microarray was also used to determine the cross-species hybridisation capabilities of an ovine PBL sample. Conclusion The Bovine Innate Immune Microarray has been developed which contains a set of well-characterised genes and anonymous cDNAs from a number of different bovine cell types. The microarray can be used to determine the gene expression profiles underlying innate immune responses in cattle and sheep.

Cultivated cotton plants are the world's largest source of natural fibre, where yield and quality are key traits for this renewable and biodegradable commodity. The Gossypium hirsutum cotton genome contains ~80K protein-coding genes, making precision breeding of complex traits a challenge. This study tested approaches to improving the genomic prediction (GP) accuracy of valuable cotton fibre traits to help accelerate precision breeding. With a biology-informed basis, a novel approach was tested for improving GP for key cotton fibre traits with transcriptomics of key time points during fibre development, namely, fibre cells undergoing primary, transition, and secondary wall development. Three test approaches included weighting of SNPs in DE genes overall, in target DE gene lists informed by gene annotation, and in a novel approach of gene co-expression network (GCN) clusters created with partial correlation and information theory (PCIT) as the prior information in GP models. The GCN clusters were nucleated with known genes for fibre biomechanics, i.e., fasciclin-like arabinogalactan proteins, and cluster size effects were evaluated. The most promising improvements in GP accuracy were achieved by using GCN clusters for cotton fibre elongation by 4.6%, and strength by 4.7%, where cluster sizes of two and three neighbours proved most effective. Furthermore, the improvements in GP were due to only a small number of SNPs, in the order of 30 per trait using the GCN cluster approach. Non-trait-specific biological time points, and genes, were found to have neutral effects, or even reduced GP accuracy for certain traits. As the GCN clusters were generated based on known genes for fibre biomechanics, additional candidate genes were identified for fibre elongation and strength. These results demonstrate that GCN clusters make a specific and unique contribution in improving the GP of cotton fibre traits. The findings also indicate that there is room for incorporating biology-based GCNs into GP models of genomic selection pipelines for cotton breeding to help improve precision breeding of target traits. The PCIT-GCN cluster approach may also hold potential application in other crops and trees for enhancing breeding of complex traits. [ABSTRACT FROM AUTHOR]

The liver plays a central role in metabolism and produces important hormones. Hepatic estrogen receptors and the release of insulin-like growth factor 1 (IGF1) are critical links between liver function and the reproductive system. However, the role of liver in pubertal development is not fully understood. To explore this question, we applied transcriptomic analyses to liver samples of pre- and post-pubertal Brahman heifers and identified differentially expressed (DE) genes and genes encoding transcription factors (TFs). Differential expression of genes suggests potential biological mechanisms and pathways linking liver function to puberty. The analyses identified 452 DE genes and 82 TF with significant contribution to differential gene expression by using a regulatory impact factor metric. Brain-derived neurotrophic factor was observed as the most down-regulated gene (P = 0.003) in post-pubertal heifers and we propose this gene influences pubertal development in Brahman heifers. Additionally, co-expression network analysis provided evidence for three TF as key regulators of liver function during pubertal development: the signal transducer and activator of transcription 6, PBX homeobox 2, and polybromo 1. Pathway enrichment analysis identified transforming growth factor-beta and Wnt signaling pathways as significant annotation terms for the list of DE genes and TF in the co-expression network. Molecular information regarding genes and pathways described in this work are important to further our understanding of puberty onset in Brahman heifers.

The identification of biological processes related to the regulation of complex traits is a difficult task. Commonly, complex traits are regulated through a multitude of genes contributing each to a small part of the total genetic variance. Additionally, some loci can simultaneously regulate several complex traits, a phenomenon defined as pleiotropy. The lack of understanding on the biological processes responsible for the regulation of these traits results in the decrease of selection efficiency and the selection of undesirable hitchhiking effects. The identification of pleiotropic key-regulator genes can assist in developing important tools for investigating biological processes underlying complex traits. A multi-breed and multi-OMICs approach was applied to study the pleiotropic effects of key-regulator genes using three independent beef cattle populations evaluated for fertility traits. A pleiotropic map for 32 traits related to growth, feed efficiency, carcass and meat quality, and reproduction was used to identify genes shared among the different populations and breeds in pleiotropic regions. Furthermore, data-mining analyses were performed using the Cattle QTL database (CattleQTLdb) to identify the QTL category annotated in the regions around the genes shared among breeds. This approach allowed the identification of a main gene network (composed of 38 genes) shared among breeds. This gene network was significantly associated with thyroid activity, among other biological processes, and displayed a high regulatory potential. In addition, it was possible to identify genes with pleiotropic effects related to crucial biological processes that regulate economically relevant traits associated with fertility, production and health, such as MYC, PPARG, GSK3B, TG and IYD genes. These genes will be further investigated to better understand the biological processes involved in the expression of complex traits and assist in the identification of functional variants associated with undesirable phenotypes, such as decreased fertility, poor feed efficiency and negative energetic balance.

The identification of biological processes related to the regulation of complex traits is a difficult task. Commonly, complex traits are regulated through a multitude of genes contributing each to a small part of the total genetic variance. Additionally, some loci can simultaneously regulate several complex traits, a phenomenon defined as pleiotropy. The lack of understanding on the biological processes responsible for the regulation of these traits results in the decrease of selection efficiency and the selection of undesirable hitchhiking effects. The identification of pleiotropic key-regulator genes can assist in developing important tools for investigating biological processes underlying complex traits. A multi-breed and multi-OMICs approach was applied to study the pleiotropic effects of key-regulator genes using three independent beef cattle populations evaluated for fertility traits. A pleiotropic map for 32 traits related to growth, feed efficiency, carcass and meat quality, and reproduction was used to identify genes shared among the different populations and breeds in pleiotropic regions. Furthermore, data-mining analyses were performed using the Cattle QTL database (CattleQTLdb) to identify the QTL category annotated in the regions around the genes shared among breeds. This approach allowed the identification of a main gene network (composed of 38 genes) shared among breeds. This gene network was significantly associated with thyroid activity, among other biological processes, and displayed a high regulatory potential. In addition, it was possible to identify genes with pleiotropic effects related to crucial biological processes that regulate economically relevant traits associated with fertility, production and health, such as MYC, PPARG, GSK3B, TG and IYD genes. These genes will be further investigated to better understand the biological processes involved in the expression of complex traits and assist in the identification of functional variants associated with undesirable phenotypes, such as decreased fertility, poor feed efficiency and negative energetic balance.

There is debate over the functional basis of student evaluations of academics, and fresh potential for looking at the data in new ways. Student evaluation data was collated over a three year period (Semester 2 2015 to Semester 1 2018). We used a General Linear Model to estimate the variation in course scores explained by a number of coordinator and course attributes. Three significant factors collectively explain 49% of the School's variation in course scores--individual coordinator, student evaluation response rate, and mode of delivery. Next, we used hierarchical clustering to explore the inter-relationships among the eight course and teaching evaluation questions. Learning appears to be related to stimulation, whereas overall satisfaction appears to be related to quality of learning materials and course structure (i.e. aspects of course organisation). Student evaluation response rate is positively correlated to all eight course questions, but most positively to a question relating to receiving adequate feedback. This perhaps implies some reciprocity in the flow of information between student and coordinator. The overall teaching rating awarded to academics clusters most with approachability and encouragement of student input--aspects of temperament and style--and not with explanatory skill or organisational ability.

Using seven indicator traits, we investigated the genetic basis of bull fertility and predicted gene interactions from SNP associations. We used percent normal sperm as the key phenotype for the association weight matrix–partial correlation information theory (AWM‐PCIT) approach. Beyond a simple list of candidate genes, AWM‐PCIT predicts significant gene interactions and associations for the selected traits. These interactions formed a network of 537 genes: 38 genes were transcription cofactors, and 41 genes were transcription factors. The network displayed two distinct clusters, one with 294 genes and another with 243 genes. The network is enriched in fertility‐associated pathways: steroid biosynthesis, p53 signalling, and the pentose phosphate pathway. Enrichment analysis also highlighted gene ontology terms associated with 'regulation of neurotransmitter secretion' and 'chromatin formation'. Our network recapitulates some genes previously implicated in another network built with lower‐density genotypes. Sequence‐level data also highlights additional candidate genes relevant to bull fertility, such as FOXO4, FOXP3, GATA1, CYP27B1, and EBP. A trio of regulatory genes—KDM5C, LRRK2, and PME—was deemed core to the network because of their overarching connections. This trio probably influences bull fertility through their interaction with genes, both known and unknown as to their role in male fertility. Future studies may target the trio and their target genes to enrich our understanding of male fertility further. [ABSTRACT FROM AUTHOR]

Improvement in feed conversion efficiency can improve the sustainability of beef cattle production, but genomic selection for feed efficiency affects many underlying molecular networks and physiological traits. This study describes the differences between steer progeny of two influential Angus bulls with divergent genomic predictions for residual feed intake (RFI). Eight steer progeny of each sire were phenotyped for growth and feed intake from 8 mo. of age (average BW 254 kg, with a mean difference between sire groups of 4.8 kg) until slaughter at 14-16 mo. of age (average BW 534 kg, sire group difference of 28.8 kg). Terminal samples from pituitary gland, skeletal muscle, liver, adipose, and duodenum were collected from each steer for transcriptome sequencing. Gene expression networks were derived using partial correlation and information theory (PCIT), including differentially expressed (DE) genes, tissue specific (TS) genes, transcription factors (TF), and genes associated with RFI from a genome-wide association study (GWAS). Relative to progeny of the high RFI sire, progeny of the low RFI sire had -0.56 kg/d finishing period RFI (P = 0.05), -1.08 finishing period feed conversion ratio (P = 0.01), +3.3 kg^0.75 finishing period metabolic mid-weight (MMW; P = 0.04), +28.8 kg final body weight (P = 0.01), -12.9 feed bunk visits per day (P = 0.02) with +0.60 min/visit duration (P = 0.01), and +0.0045 carcass specific gravity (weight in air/weight in air-weight in water, a predictor of carcass fat content; P = 0.03). RNA-seq identified 633 DE genes between sire groups among 17,016 expressed genes. PCIT analysis identified >115,000 significant co-expression correlations between genes and 25 TF hubs, i.e. controllers of clusters of DE, TS, and GWAS SNP genes. Pathway analysis suggests low RFI bull progeny possess heightened gut inflammation and reduced fat deposition. This multi-omics analysis shows how differences in RFI genomic breeding values can impact other traits and gene co-expression networks.

Puberty is a complex physiological event by which animals mature into an adult capable of sexual reproduction. In order to enhance our understanding of the genes and regulatory pathways and networks involved in puberty, we characterized the transcriptome of five reproductive tissues (i.e. hypothalamus, pituitary gland, ovary, uterus, and endometrium) as well as tissues known to be relevant to growth and metabolism needed to achieve puberty (i.e., longissimus dorsi muscle, adipose, and liver). These tissues were collected from pre- and post-pubertal Brangus heifers (3/8 Brahman; Bos indicus x 5/8 Angus; Bos taurus) derived from a population of cattle used to identify quantitative trait loci associated with fertility traits (i.e., age of first observed corpus luteum (ACL), first service conception (FSC), and heifer pregnancy (HPG)). In order to exploit the power of complementary omics analyses, pre- and post-puberty co-expression gene networks were constructed by combining the results from genome-wide association studies (GWAS), RNA-Seq, and bovine transcription factors. Eight tissues among pre-pubertal and post-pubertal Brangus heifers revealed 1,515 differentially expressed and 943 tissue-specific genes within the 17,832 genes confirmed by RNA-Seq analysis. The hypothalamus experienced the most notable up-regulation of genes via puberty (i.e., 204 out of 275 genes). Combining the results of GWAS and RNA-Seq, we identified 25 loci containing a single nucleotide polymorphism (SNP) associated with ACL, FSC, and (or) HPG. Seventeen of these SNP were within a gene and 13 of the genes were expressed in uterus or endometrium. Multi-tissue omics analyses revealed 2,450 co-expressed genes relative to puberty. The pre-pubertal network had 372,861 connections whereas the post-pubertal network had 328,357 connections. A sub-network from this process revealed key transcriptional regulators (i.e., PITX2, FOXA1, DACH2, PROP1, SIX6, etc.). Results from these multi-tissue omics analyses improve understanding of the number of genes and their complex interactions for puberty in cattle.

Context. Wagyu Feeder Check is a genomic-based tool designed to provide genomic estimated breeding values (GEBV) for five feedlot growth and carcase traits. At present, Wagyu Feeder Check is based on a reference population of 8316 genotyped and phenotyped Australian fullblood (FB; N = 2120) Wagyu and Wagyu-crossed (XB; N = 6196) cattle, principally Wagyu × Angus F1 animals. Aims. We provide technical details behind the development of the Wagyu Feeder Check and validate the ability of its GEBV to predict differences in performance of Wagyu cattle in daily weight gain at feedlot, carcase weight, carcase eye muscle area, carcase marbling score and carcase rump fat at the P8 site. Methods. Data supplied from eight commercial supply chains across Australia was used to generate GEBV using mixed-model equations that incorporated a genomic relationship matrix build with 82 504 autosomal markers. The bias, dispersion, and accuracy of the GEBV were evaluated using a four-way cross-validation scheme where, in each turn, the phenotypes from a random 1549 (or 25%) XB cattle were set as missing. Key results. The genomic estimate of the Wagyu content in the FB and XB population averaged 99.12% and 59.55%, respectively, and with most of the non-Wagyu content associated with Angus. The estimates of heritability (± s.e.) were 0.497 ± 0.016, 0.474 ± 0.004, 0.347 ± 0.014, 0.429 ± 0.003 and 0.422 ± 0.003 for daily weight gain at feedlot, carcase weight, eye muscle area, marbling and rump fat, respectively. Averaged across the four XB validation populations, the accuracy of GEBV was 0.624, 0.634, 0.385, 0.620, and 0.526 for the same set of traits. Conclusions. Genomic predictions generated by Wagyu Feeder Check can predict differences in feedlot and carcase performance of Australian Wagyu cattle. Given the large content of Angus in the XB population, further research is required to determine the predictive ability of GEBV in Wagyu × Bos indicus and Wagyu × dairy animals. Implications. Commercial feedlot operators finishing animals with a strong Wagyu breed component will benefit from using Wagyu Feeder Check for decision making. [ABSTRACT FROM AUTHOR]

This study aimed to identify the target genes of IGFBP3（insulin growth factor binding protein）protein and to investigate its target genes effects on the proliferation and differentiation of Hu sheep skeletal muscle cells. IGFBP3 was an RNA-binding protein that regulates mRNA stability. Previous studies have reported that IGFBP3 promotes the proliferation of Hu sheep skeletal muscle cells and inhibits differentiation, but the downstream genes that bind to it have not been reported yet. We predicted the target genes of IGFBP3 through RNAct and sequencing data, and verified by qPCR and RIP（RNA Immunoprecipitation）experiments, and demonstrated GNAI2（G protein subunit alpha i2）as one of the target gene of IGFBP3. After interference with siRNA, we carried out qPCR, CCK8, EdU, and immunofluorescence experiments, and found that GNAI2 can promote the proliferation and inhibit differentiation of Hu sheep skeletal muscle cells. This study revealed the effects of GNAI2 and provided one of the regulatory mechanisms of IGFBP3 protein underlying sheep muscle development. [ABSTRACT FROM AUTHOR]