Metabolomics has been applied to measure the dynamic metabolic responses, to understand the systematic biological networks, to reveal the potential genetic architecture, etc., for human diseases and livestock traits. For example, the current published results include the detected relevant candidate metabolites, identified metabolic pathways, potential systematic networks, etc., for different cattle traits that can be applied for further metabolomic and integrated omics studies. Therefore, summarizing the applications of metabolomics for economic traits is required in cattle. We here provide a comprehensive review about metabolomic analysis and its integration with other omics in five aspects: (1) characterization of the metabolomic profile of cattle; (2) metabolomic applications in cattle; (3) integrated metabolomic analysis with other omics; (4) methods and tools in metabolomic analysis; and (5) further potentialities. The review aims to investigate the existing metabolomic studies by highlighting the results in cattle, integrated with other omics studies, to understand the metabolic mechanisms underlying the economic traits and to provide useful information for further research and practical breeding programs in cattle.

Background: Genotyping by sequencing (GBS) still has problems with missing genotypes. Imputation is important for using GBS for genomic predictions, especially for low depths, due to the large number of missing genotypes. Minor allele frequency (MAF) is widely used as a marker data editing criteria for genomic predictions. In this study, three imputation methods (Beagle, IMPUTE2 and FImpute software) based on four MAF editing criteria were investigated with regard to imputation accuracy of missing genotypes and accuracy of genomic predictions, based on simulated data of livestock population.Results: Four MAFs (no MAF limit, MAF ≥ 0.001, MAF ≥ 0.01 and MAF ≥ 0.03) were used for editing marker data before imputation. Beagle, IMPUTE2 and FImpute software were applied to impute the original GBS. Additionally, IMPUTE2 also imputed the expected genotype dosage after genotype correction (GcIM). The reliability of genomic predictions was calculated using GBS and imputed GBS data. The results showed that imputation accuracies were the same for the three imputation methods, except for the data of sequencing read depth (depth) = 2, where FImpute had a slightly lower imputation accuracy than Beagle and IMPUTE2. GcIM was observed to be the best for all of the imputations at depth = 4, 5 and 10, but the worst for depth = 2. For genomic prediction, retaining more SNPs with no MAF limit resulted in higher reliability. As the depth increased to 10, the prediction reliabilities approached those using true genotypes in the GBS loci. Beagle and IMPUTE2 had the largest increases in prediction reliability of 5 percentage points, and FImpute gained 3 percentage points at depth = 2. The best prediction was observed at depth = 4, 5 and 10 using GcIM, but the worst prediction was also observed using GcIM at depth = 2.Conclusions: The current study showed that imputation accuracies were relatively low for GBS with low depths and high for GBS with high depths. Imputation resulted in larger gains in the reliability of genomic predictions for GBS with lower depths. These results suggest that the application of IMPUTE2, based on a corrected GBS (GcIM) to improve genomic predictions for higher depths, and FImpute software could be a good alternative for routine imputation.

Small non-coding microRNAs (miRNAs) are important modulators at post-transcriptional levels. Single-nucleotide polymorphisms (SNPs) located in miRNA genes can alter the secondary structure of pre-miRNA to either impair or promote the miRNA maturation processes. Furthermore, SNPs located in the miRNA seed regions can stabilize or disturb miRNA-target interactions, thereby, quantitatively influence the expression of target genes. Therefore, the main objective of this study was to detect SNPs in bovine miRNAs using the whole-genome re-sequencing datasets of 1632 cattle of five breeds from the 1000 bull genomes project. In total, our study identified 1109, 334, and 130 SNPs in the miRNA precursor, mature, and seed regions, respectively. The heterozygosity values were generally less than 0.3, and the minor allele frequencies (MAFs) were mainly less than 0.1. Most SNPs were in Hardy-Weinberg equilibrium (HWE) (HWE-P > 0.05). Furthermore, we found that the majority of SNPs (MAF > 0.1 and HWE-P > 0.05) in the miRNA seed regions altered the repertoire of target genes, which in turn were enriched in different KEGG pathways or GO terms. Thus target prediction for bta-miR-2888 revealed loss of 309 target genes and gain of 691 target genes. The 691 gained target genes were significantly enriched in 60 KEGG pathways and 21 GO terms. In summary, our study identified candidate SNPs in miRNA precursor, mature, and seed regions that are likely to affect target RNA interactions, thereby potentially influencing cattle phenotypic traits.

Max dimerization protein 3 (MXD3) belongs to the MYC superfamily of basic helix‐loop‐helix leucine zipper transcription factors, and MXD3‐MAX heterodimers can bind to promoters of target genes to modulate their expression. The aim of this study was to determine the MXD3 mRNA expression levels in various cattle tissues comprising heart, liver, spleen, lung, kidney, Longissimus dorsi muscle and subcutaneous fat in Chinese Qinchuan and Xianan cattle breeds. The RT‐qPCR data showed that the MXD3 gene was variably expressed between all tissues and at levels that were significantly different between two breeds (p < .05). We used the polymerase chain reaction‐restriction fragment length polymorphism (PCR‐RFLP) method to investigate the possible association between single‐nucleotide polymorphisms (SNP) within the MXD3 gene and five different growth traits in cattle. We found two intronic SNPs (g.2694 C>T and g.3801 T>C) and one SNP in 3′untranslated region (3′UTR) (g.6263 G>A) of MXD3 gene. Association analysis revealed strong associations between pairwise and triple SNP combinations and the growth traits. Based on these results, we suggest that MXD3 polymorphisms could be useful as molecular markers in the Chinese beef cattle breeding program.