Genetic Association Analysis of Copy Number Variations for Meat Quality in Beef Cattle.

Meat quality is an economically important trait for global food production. Copy number variations (CNVs) have been previously implicated in elucidating the genetic basis of complex traits. In this article, we detected a total of 112,198 CNVs and 10,102 CNV regions (CNVRs) based on the Bovine HD SNP array. Next, we performed a CNV-based genome-wide association analysis (GWAS) of six meat quality traits and identified 12 significant CNV segments corresponding to eight candidate genes, including PCDH15, CSMD3, etc. Using region-based association analysis, we further identified six CNV segments relevant to meat quality in beef cattle. Among these, TRIM77 and TRIM64 within CNVR4 on BTA29 were detected as candidate genes for backfat thickness (BFT). Notably, we identified a 34 kb duplication for meat color (MC) which was supported by read-depth signals, and this duplication was embedded within the keratin gene family including KRT4, KRT78, and KRT79. Our findings will help to dissect the genetic architecture of meat quality traits from the aspects of CNVs, and subsequently improve the selection process in breeding programs.

Evaluation of Linear Programming and Optimal Contribution Selection Approaches for Long-Term Selection on Beef Cattle Breeding.

The optimized selection method can maximize the genetic gain in offspring under the premise of controlling the inbreeding level of the population. At present, genetic gain has been largely improved by using genomic selection in multiple farm animals. However, the design of the optimal selection method and assessment of its effects during long-term selection in beef cattle breeding are yet to be fully explored. In this study, a simulated beef cattle population was constructed, and 15 generations of simulated breeding were carried out using the linear programming breeding strategy (LP) and optimal contribution selection strategy (OCS), respectively. The truncation selection strategy (TS-I and TS-II) was used as the control. During the breeding process, genetic parameters including genetic gain, average kinship coefficient, QTL effect variance, and average observed heterozygosity were calculated and compared across generations. Our results showed that the LP method can significantly improve the genetic gain in the population, especially the genetic performance of the traits with high heritability and the traits with high weight in the breeding process, but the inbreeding level of the population is higher under LP strategy. Although the genetic gain in the population under the OCS strategy is lower than the TS-II strategy, this method can effectively control the inbreeding level of the population. Our findings also suggest that the LP and OCS method can be used as an effective means to improve genetic gain, while the OCS method is a more ideal method to obtain sustainable genetic gain during long-term selection.

Identifying Candidate Genes for Litter Size and Three Morphological Traits in Youzhou Dark Goats Based on Genome-Wide SNP Markers.

This study aimed to reveal the potential genetic basis for litter size, coat colour, black middorsal stripe and skin colour by combining genome-wide association analysis (GWAS) and selection signature analysis and ROH detection within the Youzhou dark (YZD) goat population (n = 206) using the Illumina GoatSNP54 BeadChip. In the GWAS, we identified one SNP (snp54094-scaffold824-899720) on chromosome 11 for litter size, two SNPs on chromosome 26 (snp11508-scaffold142-1990450, SORCS3) and chromosome 12 (snp55048-scaffold842-324525, LOC102187779) for coat colour and one SNP on chromosome 18 (snp56013-scaffold873-22716, TCF25) for the black middorsal stripe. In contrast, no SNPs were identified for skin colour. In selection signature analysis, 295 significant iHS genomic regions with a mean |iHS| score > 2.66, containing selection signatures encompassing 232 candidate genes were detected. In particular, 43 GO terms and one KEGG pathway were significantly enriched in the selected genes, which may contribute to the excellent environmental adaptability and characteristic trait formation during the domestication of YZD goats. In ROH detection, we identified 4446 ROH segments and 282 consensus ROH regions, among which nine common genes overlapped with those detected using the iHS method. Some known candidate genes for economic traits such as reproduction (TSHR, ANGPT4, CENPF, PIBF1, DACH1, DIS3, CHST1, COL4A1, PRKD1 and DNMT3B) and development and growth (TNPO2, IFT80, UCP2, UCP3, GHRHR, SIM1, CCM2L, CTNNA3 and CTNNA1) were revealed by iHS and ROH detection. Overall, this study is limited by the small population size, which affects the results of GWAS to a certain extent. Nevertheless, our findings could provide the first overview of the genetic mechanism underlying these important traits and provide novel insights into the future conservation and utilisation of Chinese goat germplasm resources.

Cis-eQTL Analysis and Functional Validation of Candidate Genes for Carcass Yield Traits in Beef Cattle.

Carcass yield traits are of considerable economic importance for farm animals, which act as a major contributor to the world's food supply. Genome-wide association studies (GWASs) have identified many genetic variants associated with carcass yield traits in beef cattle. However, their functions are not effectively illustrated. In this study, we performed an integrative analysis of gene-based GWAS with expression quantitative trait locus (eQTL) analysis to detect candidate genes for carcass yield traits and validate their effects on bovine skeletal muscle satellite cells (BSCs). The gene-based GWAS and cis-eQTL analysis revealed 1780 GWAS and 1538 cis-expression genes. Among them, we identified 153 shared genes that may play important roles in carcass yield traits. Notably, the identified cis-eQTLs of PON3 and PRIM2 were significantly (p < 0.001) enriched in previous GWAS loci for carcass traits. Furthermore, overexpression of PON3 and PRIM2 promoted the BSCs' proliferation, increased the expression of MYOD and downregulated the expression of MYOG, which indicated that these genes may inhibit myogenic differentiation. In contrast, PON3 and PRIM2 were significantly downregulated during the differentiation of BSCs. These findings suggested that PON3 and PRIM2 may promote the proliferation of BSCs and inhibit them in the pre-differentiation stage. Our results further contribute to the understanding of the molecular mechanisms of carcass yield traits in beef cattle.

Comparative transcriptomic analysis reveals region-specific expression patterns in different beef cuts.

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.

Transcriptional atlas analysis from multiple tissues reveals the expression specificity patterns in beef cattle.

BACKGROUND: A comprehensive analysis of gene expression profiling across tissues can provide necessary information for an in-depth understanding of their biological functions. We performed a large-scale gene expression analysis and generated a high-resolution atlas of the transcriptome in beef cattle. RESULTS: Our transcriptome atlas was generated from 135 bovine tissues in adult beef cattle, covering 51 tissue types of major organ systems (e.g., muscular system, digestive system, immune system, reproductive system). Approximately 94.76% of sequencing reads were successfully mapped to the reference genome assembly ARS-UCD1.2. We detected a total of 60,488 transcripts, and 32% of them were not reported before. We identified 2654 housekeeping genes (HKGs) and 477 tissue-specific genes (TSGs) across tissues. Using weighted gene co-expression network analysis, we obtained 24 modules with 237 hub genes (HUBGs). Functional enrichment analysis showed that HKGs mainly maintain the basic biological activities of cells, while TSGs were involved in tissue differentiation and specific physiological processes. HKGs in bovine tissues were more conserved in terms of expression pattern as compared to TSGs and HUBGs among multiple species. Finally, we obtained a subset of tissue-specific differentially expressed genes (DEGs) between beef and dairy cattle and several functional pathways, which may be involved in production and health traits. CONCLUSIONS: We generated a large-scale gene expression atlas across the major tissues in beef cattle, providing valuable information for enhancing genome assembly and annotation. HKGs, TSGs, and HUBGs further contribute to better understanding the biology and evolution of multiple tissues in cattle. DEGs between beef and dairy cattle also fill in the knowledge gaps about differential transcriptome regulation of bovine tissues underlying economically important traits.

Comparative Transcriptomic Analysis Reveals Diverse Expression Pattern Underlying Fatty Acid Composition among Different Beef Cuts.

Beef is an important dietary source of quality animal proteins and amino acids in human nutrition. The fatty acid composition is one of the indispensable indicators affecting nutritional value of beef. However, a comprehensive understanding of the expression changes underlying fatty acid composition in representative beef cuts is needed in cattle. This study aimed to characterize the dynamics of fatty acid composition using comparative transcriptomic analysis in five different type of beef cuts. We identified 7545 differentially expressed genes (DEGs) among 10 pair-wise comparisons. Co-expression gene network analysis identified two modules, which were significantly correlated with 2 and 20 fatty acid composition, respectively. We also identified 38 candidate genes, and functional enrichment showed that these genes were involved in fatty acid biosynthetic process and degradation, PPAR, and AMPK signaling pathway. Moreover, we observed a cluster of DEGs (e.g., SCD, LPL, FABP3, and PPARD) which were involved in the regulation of lipid metabolism and adipocyte differentiation. Our results provide some valuable insights into understanding the transcriptome regulation of candidate genes on fatty acid composition of beef cuts, and our findings may facilitate the designs of genetic selection program for beneficial fatty acid composition in beef cattle.

Identification of Candidate Variants Associated With Bone Weight Using Whole Genome Sequence in Beef Cattle.

Bone weight is critical to affect body conformation and stature in cattle. In this study, we conducted a genome-wide association study for bone weight in Chinese Simmental beef cattle based on the imputed sequence variants. We identified 364 variants associated with bone weight, while 350 of them were not included in the Illumina BovineHD SNP array, and several candidate genes and GO terms were captured to be associated with bone weight. Remarkably, we identified four potential variants in a candidate region on BTA6 using Bayesian fine-mapping. Several important candidate genes were captured, including LAP3, MED28, NCAPG, LCORL, SLIT2, and IBSP, which have been previously reported to be associated with carcass traits, body measurements, and growth traits. Notably, we found that the transcription factors related to MED28 and LCORL showed high conservation across multiple species. Our findings provide some valuable information for understanding the genetic basis of body stature in beef cattle.

Runs of homozygosity analysis reveals consensus homozygous regions affecting production traits in Chinese Simmental beef cattle.

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.

Validation of the Prediction Accuracy for 13 Traits in Chinese Simmental Beef Cattle Using a Preselected Low-Density SNP Panel.

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.

Integration of selection signatures and multi-trait GWAS reveals polygenic genetic architecture of carcass traits in beef cattle.

Carcass merits are widely considered as economically important traits affecting beef production in the beef cattle industry. However, the genetic basis of carcass traits remains to be well understood. Here, we applied multiple methods, including the Composite of Likelihood Ratio (CLR) and Genome-wide Association Study (GWAS), to explore the selection signatures and candidate variants affecting carcass traits. We identified 11,600 selected regions overlapping with 2214 candidate genes, and most of those were enriched in binding and gene regulation. Notably, we identified 66 and 110 potential variants significantly associated with carcass traits using single-trait and multi-traits analyses, respectively. By integrating selection signatures with single and multi-traits associations, we identified 12 and 27 putative genes, respectively. Several highly conserved missense variants were identified in OR5M13D, NCAPG, and TEX2. Our study supported polygenic genetic architecture of carcass traits and provided novel insights into the genetic basis of complex traits in beef cattle.

Genomic sequencing analysis reveals copy number variations and their associations with economically important traits in beef cattle.

Copy number variation (CNV) represents a major source of genetic variation, which may have potentially large effects, including alternating gene regulation and dosage, as well as contributing to gene expression and risk for normal phenotypic variability. We carried out a comprehensive analysis of CNV based on whole genome sequencing in Chinese Simmental beef cattle. Totally, we found 9313 deletion and 234 duplication events, covering 147.5 Mb autosomal regions. Within them, 257 deletion events of high frequency overlapped with 193 known RefGenes. Among these genes, we observed several genes were related to economically important traits, like residual feed intake, immune responding, pregnancy rate and muscle differentiation. Using a locus-based analysis, we identified 11 deletions and 1 duplication, which were significantly associated with three traits including carcass weight, tenderloin and longissimus muscle area. Our sequencing-based study provided important insights into investigating the association of CNVs with important traits in beef cattle.