Numerous studies have shown that combining populations from similar or closely related genetic breeds improves the accuracy of genomic predictions (GP). Extensive experimentation with diverse Bayesian and genomic best linear unbiased prediction (GBLUP) models have been developed to explore multi-breed genomic selection (GS) in livestock, ultimately establishing them as successful approaches for predicting genomic estimated breeding value (GEBV). This study aimed to assess the effectiveness of using BayesR and GBLUP models with linkage disequilibrium (LD)-weighted genomic relationship matrices (GRMs) for genomic prediction in three different beef cattle breeds to identify the best approach for enhancing the accuracy of multi-breed genomic selection in beef cattle. Additionally, a comparison was conducted to evaluate the predictive precision of different marker densities and genetic correlations among the three breeds of beef cattle. The GRM between Yunling cattle (YL) and other breeds demonstrated modest affinity and highlighted a notable genetic concordance of 0.87 between Chinese Wagyu (WG) and Huaxi (HX) cattle. In the within-breed GS, BayesR demonstrated an advantage over GBLUP. The prediction accuracies for HX cattle using the BayesR model were 0.52 with BovineHD BeadChip data (HD) and 0.46 with whole-genome sequencing data (WGS). In comparison to the GBLUP model, the accuracy increased by 26.8% for HD data and 9.5% for WGS data. For WG and YL, BayesR doubled the within-breed prediction accuracy to 14.3% from 7.1%, outperforming GBLUP across both HD and WGS datasets. Moreover, analyzing multiple breeds using genomic selection showed that BayesR consistently outperformed GBLUP in terms of predictive accuracy, especially when using WGS. For instance, in a mixed reference population of HX and WG, BayesR achieved a significant accuracy of 0.53 using WGS for HX, which was a substantial enhancement over the accuracies obtained with GBLUP models. The research further highlights the benefit of including various breeds in the reference group, leading to enhanced accuracy in predictions and emphasizing the importance of comprehensive genomic selection methods. Our research findings indicate that BayesR exhibits superior performance compared to GBLUP in multi-breed genomic prediction accuracy, achieving a maximum improvement of 33.3%, especially in genetically diverse breeds. The improvement can be attributed to the effective utilization of higher single nucleotide polymorphism (SNP) marker density by BayesR, resulting in enhanced prediction accuracy. This evidence conclusively demonstrates the significant impact of BayesR on enhancing genomic predictions in diverse cattle populations, underscoring the crucial role of genetic relatedness in selection methodologies. In parallel, subsequent studies should focus on refining GRM and exploring alternative models for GP.

(1) Background: Myostatin (MSTN) is a protein that regulates skeletal muscle development and plays a crucial role in maintaining animal body composition and muscle structure. The loss-of-function mutation of MSTN gene can induce the muscle hypertrophic phenotype. (2) Methods: Growth indexes and blood parameters of the cattle of different months were analyzed via multiple linear regression. (3) Results: Compared with the control group, the body shape parameters of F2 cattle were improved, especially the body weight, cross height, and hip height, representing significant development of hindquarters, and the coat color of the F2 generation returned to the yellow of Luxi cattle. As adults, MSTN gene-edited bulls have a tall, wide acromion and a deep, wide chest. Both the forequarters and hindquarters are double-muscled with clear muscle masses. The multiple linear regression demonstrates that MSTN gene-edited hybrid beef cattle gained weight due to the higher height of the hindquarters. Significant differences in blood glucose, calcium, and low-density lipoprotein. Serum insulin levels decreased significantly at 24 months of age. MSTN gene editing improves the adaptability of cattle. (4) Conclusions: Our findings suggest that breeding with MSTN gene-edited Luxi bulls can improve the growth and performance of hybrid cattle, with potential benefits for both farmers and consumers.

Background: Numerous studies have demonstrated that the amalgamation of populations belonging to the same breed or closely related breeds leads to enhanced accuracies in genomic predictions (GP). Extensive experimentation with diverse Bayesian and Genome-enabled best linear unbiased prediction (GBLUP) models has been conducted to explore multi-breed genomic selection (GS) in livestock, ultimately establishing they as successful approaches for predicting genomic estimated breeding value (GEBV). This study aimed to examine the efficacy of BayesR and GBLUP model with different weighted genomic relationship matrices (GRM) in making genomic predictions for three distinct beef cattle breeds. Subsequently, we conducted a comparative analysis of the predictive accuracy pertaining to various marker densities and genetic correlations across three distinct beef cattle breeds. This investigation aimed to identify the optimal approach for enhancing the predictive accuracy of multi-breed genomic selection in beef cattle. Results：Genetic relationship matrices revealed moderate similarities between YL and the other breeds, with a striking genetic similarity of 0.87 between WG and HX. In HX cattle, BayesR demonstrated an enhancement in prediction accuracy, achieving 0.52 with HD and 0.46 with WGS, a marked improvement over 0.41 with HD and 0.42 with WGS in GBLUP. In WG and YL breeds, both methods showed comparable accuracies with HD, but BayesR slightly outperformed GBLUP with WGS. Further, multi-breed GS analysis indicated that BayesR consistently surpassed GBLUP in prediction accuracy, particularly with WGS data. For instance, in a combined HX and WG reference population, BayesR achieved a superior accuracy of 0.53 with WGS in HX cattle, a significant enhancement over GBLUP models. The study also underscores the advantage of incorporating multiple breeds in the reference population, which improved prediction accuracy, underscoring the value of broad-based genomic selection strategies. Conclusion: The results show that accuracy of multi-breed genomic predictions was higher with BayesR than with GBLUP, especially for the distantly genetic relationship between reference and validation breeds. Further improvements of multi-breed accuracy of genomic predictions could be achieved by increasing the density of the SNP marker. These findings underscore that BayesR providing a substantial improvement in genomic prediction and the importance of considering genetic relationships in the development of GS strategies for multi-breed cattle populations. Further research is warranted to optimize GRM construction and to explore alternative models for genomic prediction across breeds.

Genomic selection (GS) plays a crucial role in livestock breeding. However, its implementation in Chinese beef cattle breeding is constrained by a limited reference population and incomplete data records. To address these challenges, this study aimed to identify more effective models for multi-population genomic selection. We simulated five different beef cattle populations and selected three populations with varying levels of kinship to investigate the impact of population relationships on genomic prediction. Utilizing results from a genome-wide association study (GWAS), we preselected different proportions of single nucleotide polymorphism (SNP). Subsequently, we employed three models—genomic best linear unbiased prediction (GBLUP), multi-genomic best linear unbiased prediction (MGBLUP), and weighted multi-genomic best linear unbiased prediction (WMGBLUP)—for within-population and multi-population genomic prediction. Our results showed that increasing the size of the training set improved within-population prediction accuracy. Furthermore, both MGBLUP and WMGBLUP outperformed GBLUP in terms of prediction accuracy for both within-population and multi-population analyses. Among the models evaluated, the WMGBLUP model, which utilized the top 5% of preselected SNPs based on GWAS findings, demonstrated superior performance, yielding an improvement of up to 11.1% in within-population prediction and 16.5% in multi-population prediction. In summary, both WMGBLUP and MGBLUP models exhibit enhanced efficacy in improving genomic prediction accuracy, and the incorporation of GWAS results can further optimize their performance.

Background: Various methods have been widely utilized to estimate the genomic breeding values (GEBVs) for genomic prediction. Traditional approaches often relied on the assumption of linear regression models, which struggle to effectively capture the nonlinear relationships between limited phenotypic data and high-dimensional genotypic data. Deep learning (DL) provided a powerful solution for addressing nonlinear problems. Herein, we proposed a novel deep learning method, named residual attention genomic prediction (ReaGP), which was characterized by two main features. It employed residual units to mitigate gradient instability and network degradation issues, while leveraging attention mechanisms to enhance the mining of critical feature information. Moreover, genomic data processed with frequency encoding was integrated into ReaGP to achieve a richer feature representation. Results: When assessing the predictive accuracy across three animal datasets and two plant datasets covering 15 traits with varying heritabilities, ReaGP improved predictive performance by 14.41% and 7.78% over linear models specifically genomic best linear unbiased prediction (GBLUP) and BayesB, and by 34.41% and 10.09% over kernel methods namely support vector regression (SVR) and reproducing kernel Hilbert space (RKHS), respectively. ReaGP achieved a 4.35% enhancement on average compared to deep neural network genomic prediction (DNNGP). Furthermore, while ReaGP has more trainable parameters than DNNGP, it requires only half the number of floating-point operations. Conclusions: We introduced a novel deep learning method for genomic prediction, which integrates residual units, attention mechanisms and frequency-encoded genomic data. Comprehensive evaluation on pig, dairy cow, Huaxi cattle, wheat and rice datasets demonstrated that ReaGP was a promising tool for most traits. Thus, ReaGP could be considered as an efficient deep learning method for genomic prediction in farm animals and crops. The source code in this study is available at https://github.com/LiJing5467/ReaGP .