Fatty acid composition of muscle is an important trait contributing to meat quality. Recently, genome-wide association study (GWAS) has been extensively used to explore the molecular mechanism underlying important traits in cattle. In this study, we performed GWAS using high density SNP array to analyze the association between SNPs and fatty acids and evaluated the accuracy of genomic prediction for fatty acids in Chinese Simmental cattle. Using the BayesB method, we identified 35 and 7 regions in Chinese Simmental cattle that displayed significant associations with individual fatty acids and fatty acid groups, respectively. We further obtained several candidate genes which may be involved in fatty acid biosynthesis including elongation of very long chain fatty acids protein 5 (ELOVL5), fatty acid synthase (FASN), caspase 2 (CASP2) and thyroglobulin (TG). Specifically, we obtained strong evidence of association signals for one SNP located at 51.3 Mb for FASN using Genome-wide Rapid Association Mixed Model and Regression-Genomic Control (GRAMMAR-GC) approaches. Also, region-based association test identified multiple SNPs within FASN and ELOVL5 for C14:0. In addition, our result revealed that the effectiveness of genomic prediction for fatty acid composition using BayesB was slightly superior over GBLUP in Chinese Simmental cattle. We identified several significantly associated regions and loci which can be considered as potential candidate markers for genomics-assisted breeding programs. Using multiple methods, our results revealed that FASN and ELOVL5 are associated with fatty acids with strong evidence. Our finding also suggested that it is feasible to perform genomic selection for fatty acids in Chinese Simmental cattle.

Understanding the population structure and genetic bases of well-adapted cattle breeds to local environments is one of the most essential tasks to develop appropriate genetic improvement programs. We performed a comprehensive study to investigate the population structure, divergence and selection signatures at genome-wide level in diverse Chinese local cattle using Bovine HD SNPs array, including two breeds from North China, one breed from Northwest China, three breeds from Southwest China and two breeds from South China. Population genetic analyses revealed the genetic structures of these populations were mostly related to the geographic locations. Notably, we detected 294 and 1263 candidate regions under selection using the di and iHS approaches, respectively. A series of group-specific and breed-specific candidate genes were identified, which are involved in immune response, sexual maturation, stature related, birth and bone weight, embryonic development, coat colors and adaptation. Furthermore, haplotype diversity and network pattern for candidate genes, including LPGAT1, LCORL, PPP1R8, RXFP2 and FANCA, suggest that these genes have been under differential selection pressures in various environmental conditions. Our results shed insights into diverse selection during breed formation in Chinese local cattle. These findings may promote the application of genome-assisted breeding for well-adapted local breeds with economic and ecological importance.

Background: A detailed understanding of genetic variants that affect beef merit helps maximize the efficiency of breeding for improved production merit in beef cattle. To prioritize the putative variants and genes, we ran a comprehensive genome-wide association studies (GWAS) analysis for 21 agronomic traits using imputed whole-genome variants in Simmental beef cattle. Then, we applied expression quantitative trait loci (eQTL) mapping between the genotype variants and transcriptome of three tissues (longissimus dorsi muscle, backfat, and liver) in 120 cattle. Results: We identified 1,580 association signals for 21 beef agronomic traits using GWAS. We then illuminated 854,498 cis-eQTLs for 6,017 genes and 46,970 trans-eQTLs for 1,903 genes in three tissues and built a synergistic network by integrating transcriptomics with agronomic traits. These cis-eQTLs were preferentially close to the transcription start site and enriched in functional regulatory regions. We observed an average of 43.5% improvement in cis-eQTL discovery using multi-tissue eQTL mapping. Fine-mapping analysis revealed that 111, 192, and 194 variants were most likely to be causative to regulate gene expression in backfat, liver, and muscle, respectively. The transcriptome-wide association studies identified 722 genes significantly associated with 11 agronomic traits. Via the colocalization and Mendelian randomization analyses, we found that eQTLs of several genes were associated with the GWAS signals of agronomic traits in three tissues, which included genes, such as NADSYN1, NDUFS3, LTF and KIFC2 in liver, GRAMD1C, TMTC2 and ZNF613 in backfat, as well as TIGAR, NDUFS3 and L3HYPDH in muscle that could serve as the candidate genes for economic traits. Conclusions: The extensive atlas of GWAS, eQTL, fine-mapping, and transcriptome-wide association studies aid in the suggestion of potentially functional variants and genes in cattle agronomic traits and will be an invaluable source for genomics and breeding in beef cattle.

Principal component analysis (PCA) is a potential approach that can be applied in multiple-trait genome-wide association studies (GWAS) to explore pleiotropy, as well as increase the power of quantitative trait loci (QTL) detection. In this study, the relationship of test single nucleotide polymorphisms (SNPs) was determined between single-trait GWAS and PCA-based GWAS. We found that the estimated pleiotropic quantitative trait nucleotides (QTNs) $\widehat{{\beta }^{*}}$ were in most cases larger than the single-trait model estimations ( $\widehat{{\beta }_1}$ and $\widehat{{\beta }_2}$ ). Analysis using the simulated data showed that PCA-based multiple-trait GWAS has improved statistical power for detecting QTL compared to single-trait GWAS. For the minor allele frequency (MAF), when the MAF of QTNs was greater than 0.2, the PCA-based model had a significant advantage in detecting the pleiotropic QTNs, but when its MAF was reduced from 0.2 to 0, the advantage began to disappear. In addition, as the linkage disequilibrium (LD) of the pleiotropic QTNs decreased, its detection ability declined in the co-localization effect model. Furthermore, on the real data of 1141 Simmental cattle, we applied the PCA model to the multiple-trait GWAS analysis and identified a QTL that was consistent with a candidate gene, MCHR2, which was associated with presoma muscle development in cattle. In summary, PCA-based multiple-trait GWAS is an efficient model for exploring pleiotropic QTNs in quantitative traits.

Fatty acids are important traits that affect meat quality and nutritive values in beef cattle. Detection of genetic variants for fatty acid composition can help to elucidate the genetic mechanism underpinning these traits and promote the improvement of fatty acid profiles. In this study, we performed a genome-wide association study (GWAS) on fatty acid composition using high-density single nucleotide polymorphism (SNP) arrays in Chinese Wagyu cattle. In total, we detected 15 and 8 significant genome-wide SNPs for individual fatty acids and fatty acid groups in Chinese Wagyu cattle, respectively. Also, we identified nine candidate genes based on 100 kb regions around associated SNPs. Four SNPs significantly associated with C14:1 cis-9 were embedded with stearoyl-CoA desaturase (SCD), while three SNPs in total were identified for C22:6 n-3 within Phospholipid scramblase family member 5 (PLSCR5), Cytoplasmic linker associated protein 1 (CLASP1), and Chymosin (CYM). Notably, we found the top candidate SNP within SCD can explain ~ 7.37% of phenotypic variance for C14:1 cis-9. Moreover, we detected several blocks with high LD in the 100 kb region around SCD. In addition, we found three significant SNPs within a 100 kb region showing pleiotropic effects related to multiple FA groups (PUFA, n-6, and PUFA/SFA), which contains BAI1 associated protein 2 like 2 (BAIAP2L2), MAF bZIP transcription factor F (MAFF), and transmembrane protein 184B (TMEM184B). Our study identified several significant SNPs and candidate genes for individual fatty acids and fatty acid groups in Chinese Wagyu cattle, and these findings will further assist the design of breeding programs for meat quality in 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.