Automated high-throughput phenotyping with sensors, imaging, and other on-farm technologies has resulted in a flood of data that are largely under-utilized. Drastic cost reductions in sequencing and other omics technology have also facilitated the ability for deep phenotyping of livestock at the molecular level. These advances have brought the animal sciences to a cross-roads in data science where increased training is needed to manage, record, and analyze data to generate knowledge and advances in agriscience related disciplines. This paper describes the opportunities and challenges in using high-throughput phenotyping, “big data,” analytics, and related technologies in the livestock industry based on discussions at the Livestock High-Throughput Phenotyping and Big Data Analytics meeting, held in November 2017 (see: https://www.animalgenome.org/bioinfo/community/workshops/2017/). Critical needs for investments in infrastructure for people (e.g. “big data” training), data (e.g. data transfer, management and analytics), and technology (e.g. development of low cost sensors) were defined by this group. Though some subgroups of animal science have extensive experience in predictive modeling, cross-training in computer science, statistics and related disciplines are needed to use big data for diverse applications in the field. Extensive opportunities exist for public and private entities to harness big data to develop valuable research knowledge and products to the benefit of society under the increased demands for food of a rapidly growing population.

Background: Glioblastoma is a complex multifactorial disorder that has swift and devastating consequences. Few genes have been consistently identified as prognostic biomarkers of glioblastoma survival. The goal of this study was to identify general and clinical-dependent biomarker genes and biological processes of three complementary events: lifetime, overall and progression-free glioblastoma survival. Methods: A novel analytical strategy was developed to identify general associations between the biomarkers and glioblastoma, and associations that depend on cohort groups, such as race, gender, and therapy. Gene network inference, cross-validation and functional analyses further supported the identified biomarkers. Results: A total of 61, 47 and 60 gene expression profiles were significantly associated with lifetime, overall, and progression-free survival, respectively. The vast majority of these genes have been previously reported to be associated with glioblastoma (35, 24, and 35 genes, respectively) or with other cancers (10, 19, and 15 genes, respectively) and the rest (16, 4, and 10 genes, respectively) are novel associations. Pik3r1, E2f3, Akr1c3, Csf1, Jag2, Plcg1, Rpl37a, Sod2, Topors, Hras, Mdm2, Camk2g, Fstl1, Il13ra1, Mtap and Tp53 were associated with multiple survival events. Most genes (from 90 to 96%) were associated with survival in a general or cohort-independent manner and thus the same trend is observed across all clinical levels studied. The most extreme associations between profiles and survival were observed for Syne1, Pdcd4, Ighg1, Tgfa, Pla2g7, and Paics. Several genes were found to have a cohort-dependent association with survival and these associations are the basis for individualized prognostic and gene-based therapies. C2, Egfr, Prkcb, Igf2bp3, and Gdf10 had gender-dependent associations; Sox10, Rps20, Rab31, and Vav3 had race-dependent associations; Chi3l1, Prkcb, Polr2d, and Apool had therapy-dependent associations. Biological processes associated glioblastoma survival included morphogenesis, cell cycle, aging, response to stimuli, and programmed cell death. Conclusions: Known biomarkers of glioblastoma survival were confirmed, and new general and clinical-dependent gene profiles were uncovered. The comparison of biomarkers across glioblastoma phases and functional analyses offered insights into the role of genes. These findings support the development of more accurate and personalized prognostic tools and gene-based therapies that improve the survival and quality of life of individuals afflicted by glioblastoma multiforme.

Intramuscular fat and collagen content are major factors affecting beef quality, but mechanisms regulating intramuscular adipose and connective tissue deposition are far from clear. Japanese Wagyu cattle are well known for their extremely high marbling. The objective of this study was to evaluate intramuscular fat (IMF) and collagen deposition in the muscle of Wagyu compared with Angus cattle. Animals were managed under the same condition and slaughtered at an averaging 585 ± 12.1 kg of BW. Samples of sternomandibularis muscle were collected from Wagyu (n = 3) and Angus (n = 3) for molecular and histological investigations of adipogenesis and fibrogenesis. With exception of C/EBPβ (P = 0.2864), the expression of the adipogenic markers C/EBPα (P = 0.008), PPARγ (P = 0.028), and zip finger protein 423 (Zfp423; P = 0.047) in Wagyu were greater than in Angus muscle, which was consistent with greater IMF deposition in Wagyu (P < 0.05). In addition, more adipocytes and preadipocytes were detected intramuscularly in Wagyu cattle. Similarly, fibrogenesis was also enhanced in Wagyu, with a greater expression of fibroblast growth factor (FGF)-2 (P = 0.028), FGF receptor 1 (P = 0.030), transforming growth factor (TGF)-β (P = 0.028), collagen I (P = 0.012), and collagen III (P = 0.025). Similarly, Wagyu muscle had greater collagen content (P = 0.002) and decreased collagen solubility (P = 0.005). In addition, muscle fiber diameter was larger (P < 0.0001) in Wagyu than in Angus cattle. These results clearly show that both IMF and collagen contents are enhanced in Wagyu cattle and more adipogenic cells are detected in Wagyu muscle, indicating intramuscular adipogenesis is enhanced in Wagyu compared with Angus muscle.

Previously, we identified a major quantitative trait locus (QTL) for host response to Porcine Respiratory and Reproductive Syndrome virus (PRRSV) infection in high linkage disequilibrium (LD) with SNP rs80800372 on Sus scrofa chromosome 4 (SSC4). Within this QTL, guanylate binding protein 5 (GBP5) was differentially expressed (DE) (p < 0.05) in blood from AA versus AB rs80800372 genotyped pigs at 7,11, and 14 days post PRRSV infection. All variants within the GBP5 transcript in LD with rs80800372 exhibited allele specific expression (ASE) in AB individuals (p < 0.0001). A transcript re-assembly revealed three alternatively spliced transcripts for GBP5. An intronic SNP in GBP5, rs340943904, introduces a splice acceptor site that inserts five nucleotides into the transcript. Individuals homozygous for the unfavorable AA genotype predominantly produced this transcript, with a shifted reading frame and early stop codon that truncates the 88 C-terminal amino acids of the protein. RNA-seq analysis confirmed this SNP was associated with differential splicing by QTL genotype (p < 0.0001) and this was validated by quantitative capillary electrophoresis (p < 0.0001). The wild-type transcript was expressed at a higher level in AB versus AA individuals, whereas the five-nucleotide insertion transcript was the dominant form in AA individuals. Splicing and ASE results are consistent with the observed dominant nature of the favorable QTL allele. The rs340943904 SNP was also 100 % concordant with rs80800372 in a validation population that possessed an alternate form of the favorable B QTL haplotype. GBP5 is known to play a role in inflammasome assembly during immune response. However, the role of GBP5 host genetic variation in viral immunity is novel. These findings demonstrate that rs340943904 is a strong candidate causal mutation for the SSC4 QTL that controls variation in host response to PRRSV.

Background: General, breed- and diet-dependent associations between feed efficiency in beef cattle and single nucleotide polymorphisms (SNPs) or haplotypes were identified on a population of 1321 steers using a 50 K SNP panel. Genomic associations with traditional two-step indicators of feed efficiency – residual feed intake (RFI), residual average daily gain (RADG), and residual intake gain (RIG) – were compared to associations with two complementary one-step indicators of feed efficiency: efficiency of intake (EI) and efficiency of gain (EG). Associations uncovered in a training data set were evaluated on independent validation data set. A multi-SNP model was developed to predict feed efficiency. Functional analysis of genes harboring SNPs significantly associated with feed efficiency and network visualization aided in the interpretation of the results. Results: For the five feed efficiency indicators, the numbers of general, breed-dependent, and diet-dependent associations with SNPs (P-value < 0.0001) were 31, 40, and 25, and with haplotypes were six, ten, and nine, respectively. Of these, 20 SNP and six haplotype associations overlapped between RFI and EI, and five SNP and one haplotype associations overlapped between RADG and EG. This result confirms the complementary value of the one and two-step indicators. The multi-SNP models included 89 SNPs and offered a precise prediction of the five feed efficiency indicators. The associations of 17 SNPs and 7 haplotypes with feed efficiency were confirmed on the validation data set. Nine clusters of Gene Ontology and KEGG pathway categories (mean P-value < 0.001) including, 9nucleotide binding; ion transport, phosphorous metabolic process, and the MAPK signaling pathway were overrepresented among the genes harboring the SNPs associated with feed efficiency. Conclusions: The general SNP associations suggest that a single panel of genomic variants can be used regardless of breed and diet. The breed- and diet-dependent associations between SNPs and feed efficiency suggest that further refinement of variant panels require the consideration of the breed and management practices. The unique genomic variants associated with the one- and two-step indicators suggest that both types of indicators offer complementary description of feed efficiency that can be exploited for genome-enabled selection purposes.

Studies have shown that intramuscular adipogenesis and fibrogenesis may concomitantly occur in skeletal muscle of beef cattle. Thus, we hypothesized that the discrepancy of intramuscular fat content in beef from Nellore and Angus was associated with differences in intramuscular adipogenesis and fibrogenesis during the finishing phase. To test our hypothesis, longissimus muscle samples of Nellore (n = 6; BW = 372.5 ± 37.3 kg) and Angus (n = 6; BW = 382.8 ± 23.9 kg) cattle were collected for analysis of gene and protein expression, and quantification of intramuscular fat and collagen. Least-squares means were estimated for the effect of Breed and differences were considered at P ≤ 0.05. A greater intramuscular fat content was observed in skeletal muscle of Angus compared to Nellore cattle (P≤0.05). No differences were observed for mRNA expression of lipogenic and lipolytic markers ACC, FAS, FABP4, SERBP–1, CPT–2, LPL, and ACOX (P > 0.05) in skeletal muscle of Nellore and Angus cattle. Similarly, no differences were observed in mRNA expression of adipogenic markers Zfp423, PPARγ, and C/EBPα (P>0.05) However, a greater PPARγ protein content was observed in skeletal muscle of Angus compared to Nellore cattle (P≤0.05). A greater abundance of adipo/fibrogenic cells, evaluated by the PDGFRα content, was observed in skeletal muscle of Angus than Nellore cattle (P≤0.05). No differences in fibrogenesis were observed in skeletal muscle of Angus and Nellore cattle, which is in accordance with the lack of differences in intramuscular collagen content in beef from both breeds (P>0.05). These findings demonstrate that difference in intramuscular fat content is associated with a slightly enhanced adipogenesis in skeletal muscle of Angus compared to Nellore cattle, while no difference in fibrogenesis.