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Simple Summary: To reduce the breeding costs and promote the application of genomic selection (GS) in Chinese Simmental beef cattle, we developed a customized low-density single-nucleotide polymorphism (SNP) panel consisting of 30,684 SNPs. When comparing the predictive performance of the low-density SNP panel to that of the BovineHD Beadchip for 13 traits, we found that this ~30 K panel achieved moderate to high prediction accuracies for most traits, while reducing the prediction accuracies of six traits by 0.04–0.09 and decreasing the prediction accuracy of one trait by 0.2. For the remaining six traits, the usage of the low-density SNP panel was associated with a slight increase in prediction accuracy. Our studies suggested that the low-density SNP panel (~30 K) is a feasible and promising tool for cost-effective genomic prediction in Chinese Simmental beef cattle, which may provide breeding organizations with a cheaper option and greater returns on investment. Chinese Simmental beef cattle play a key role in the Chinese beef industry due to their great adaptability and marketability. To achieve efficient genetic gain at a low breeding cost, it is crucial to develop a customized cost-effective low-density SNP panel for this cattle population. Thirteen growth, carcass, and meat quality traits and a BovineHD Beadchip genotyping of 1346 individuals were used to select trait-associated variants and variants contributing to great genetic variance. In addition, highly informative SNPs with high MAF in each 500 kb sliding window and in each genic region were also included separately. A low-density SNP panel consisting of 30,684 SNPs was developed, with an imputation accuracy of 97.4% when imputed to the 770 K level. Among 13 traits, the average prediction accuracy levels evaluated by genomic best linear unbiased prediction (GBLUP) and BayesA/B/Cπ were 0.22–0.47 and 0.18–0.60 for the ~30 K array and BovineHD Beadchip, respectively. Generally, the predictive performance of the ~30 K array was trait-dependent, with reduced prediction accuracies for seven traits. While differences in terms of prediction accuracy were observed among the 13 traits, the low-density SNP panel achieved moderate to high accuracies for most of the traits and even improved the accuracies for some traits. [ABSTRACT FROM AUTHOR]

Background: Beef cuts in different regions of the carcass have different meat quality due to their distinct physiological function. The objective of this study was to characterize the region-specific expression differences using comparative transcriptomics analysis among five representative beef cuts (tenderloin, longissimus lumborum, rump, neck, chuck). Results: We obtained 15,701 expressed genes in 30 muscle samples across five regions from carcass meat. We identified a total of 80 region-specific genes (RSGs), ranging from three (identified in the rump cut) to thirty (identified in the longissimus lumborum cut), and detected 25 transcription factors (TFs) for RSGs. Using a co-expression network analysis, we detected seven region-specific modules, including three positively correlated modules and four negatively correlated modules. We finally obtained 91 candidate genes related to meat quality, and the functional enrichment analyses showed that these genes were mainly involved in muscle fiber structure (e.g., TNNI1, TNNT1), fatty acids (e.g., SCD, LPL), amino acids (ALDH2, IVD, ACADS), ion channel binding (PHPT1, SNTA1, SUMO1, CNBP), protein processing (e.g., CDC37, GAPDH, NRBP1), as well as energy production and conversion (e.g., ATP8, COX8B, NDUFB6). Moreover, four candidate genes (ALDH2, CANX, IVD, PHPT1) were validated using RT-qPCR analyses which further supported our RNA-seq results. Conclusions: Our results provide valuable insights into understanding the transcriptome regulation of meat quality in different beef cuts, and these findings may further help to improve the selection for health-beneficial meat in beef cattle. [ABSTRACT FROM AUTHOR]

Background: Genomic regions with a high frequency of runs of homozygosity (ROH) are related to important traits in farm animals. We carried out a comprehensive analysis of ROH and evaluated their association with production traits using the BovineHD (770 K) SNP array in Chinese Simmental beef cattle. Results: We detected a total of 116,953 homozygous segments with 2.47Gb across the genome in the studied population. The average number of ROH per individual was 99.03 and the average length was 117.29 Mb. Notably, we detected 42 regions with a frequency of more than 0.2. We obtained 17 candidate genes related to body size, meat quality, and reproductive traits. Furthermore, using Fisher's exact test, we found 101 regions were associated with production traits by comparing high groups with low groups in terms of production traits. Of those, we identified several significant regions for production traits (P < 0.05) by association analysis, within which candidate genes including ECT2, GABRA4, and GABRB1 have been previously reported for those traits in beef cattle. Conclusions: Our study explored ROH patterns and their potential associations with production traits in beef cattle. These results may help to better understand the association between production traits and genome homozygosity and offer valuable insights into managing inbreeding by designing reasonable breeding programs in farm animals. [ABSTRACT FROM AUTHOR]