Machine learning (ML) is perhaps the most useful tool for the interpretation of large genomic datasets. However, the performance of a single machine learning method in genomic selection (GS) is currently unsatisfactory. To improve the genomic predictions, we constructed a stacking ensemble learning framework (SELF), integrating three machine learning methods, to predict genomic estimated breeding values (GEBVs). The present study evaluated the prediction ability of SELF by analyzing three real datasets, with different genetic architecture; comparing the prediction accuracy of SELF, base learners, genomic best linear unbiased prediction (GBLUP) and BayesB. For each trait, SELF performed better than base learners, which included support vector regression (SVR), kernel ridge regression (KRR) and elastic net (ENET). The prediction accuracy of SELF was, on average, 7.70% higher than GBLUP in three datasets. Except for the milk fat percentage (MFP) traits, of the German Holstein dairy cattle dataset, SELF was more robust than BayesB in all remaining traits. Therefore, we believed that SEFL has the potential to be promoted to estimate GEBVs in other animals and plants.

Background: Body size traits as one of the main breeding selection criteria was widely used to monitor cattle growth and to evaluate the selection response. In this study, body size was defined as body height (BH), body length (BL), hip height (HH), heart size (HS), abdominal size (AS), and cannon bone size (CS). We performed genome-wide association studies (GWAS) of these traits over the course of three growth stages (6, 12 and 18 months after birth) using three statistical models, single-trait GWAS, multi-trait GWAS and LONG-GWAS. The Illumina Bovine HD 770 K BeadChip was used to identify genomic single nucleotide polymorphisms (SNPs) in 1217 individuals.Results: In total, 19, 29, and 10 significant SNPs were identified by the three models, respectively. Among these, 21 genes were promising candidate genes, includingSOX2, SNRPD1, RASGEF1B, EFNA5, PTBP1, SNX9, SV2C, PKDCC, SYNDIG1, AKR1E2,andPRIM2identified by single-trait analysis;SLC37A1, LAP3, PCDH7, MANEA,andLHCGRidentified by multi-trait analysis; andP2RY1, MPZL1, LINGO2, CMIP,andWSCD1identified by LONG-GWAS.Conclusions: Multiple association analysis was performed for six growth traits at each growth stage. These findings offer valuable insights for the further investigation of potential genetic mechanism of growth traits in Simmental beef cattle.

The objective of the present study was to perform a genome-wide association study (GWAS) for growth curve parameters using nonlinear models that fit original weight–age records. In this study, data from 808 Chinese Simmental beef cattle that were weighed at 0, 6, 12, and 18 months of age were used to fit the growth curve. The Gompertz model showed the highest coefficient of determination (R² = 0.954). The parameters’ mature body weight (A), time-scale parameter (b), and maturity rate (K) were treated as phenotypes for single-trait GWAS and multi-trait GWAS. In total, 9, 49, and 7 significant SNPs associated with A, b, and K were identified by single-trait GWAS; 22 significant single nucleotide polymorphisms (SNPs) were identified by multi-trait GWAS. Among them, we observed several candidate genes, including PLIN3, KCNS3, TMCO1, PRKAG3, ANGPTL2, IGF-1, SHISA9, and STK3, which were previously reported to associate with growth and development. Further research for these candidate genes may be useful for exploring the full genetic architecture underlying growth and development traits in livestock.

Improving the genetic process of growth traits is one of the major goals in the beef cattle industry, as it can increase meat production and reduce the cost of raising animals. Although several quantitative trait loci affecting growth traits in beef cattle have been identified, the genetic architecture of these economically important traits remains elusive. This study aims to map single nucleotide polymorphisms (SNPs) and genes associated with birth weight (BW), yearling weight (YW), average daily gain from birth to yearling (BYADG), and body weight at the age of 18 months (18MW) in a Chinese Simmental beef cattle population using a weighted, single-step, genome-wide association study (wssGWAS). Phenotypic and pedigree data from 6022 animals and genotypes from 744 animals (596,297 SNPs) were used for an association analysis. The results showed that 66 genomic windows explained 1.01–20.15% of the genetic variance for the four examined traits, together with the genes near the top SNP within each window. Furthermore, the identified genomic windows (>1%) explained 50.56%, 57.71%, 61.78%, and 37.82% of the genetic variances for BW, YW, BYADG, and 18MW, respectively. Genes with potential functions in muscle development and regulation of cell growth were highlighted as candidates for growth traits in Simmental cattle (SQOR and TBCB for BW, MYH10 for YW, RLF for BYADG, and ARHGAP31 for 18MW). Moreover, we found 40 SNPs that had not previously been identified as being associated with growth traits in cattle. These findings will further advance our understanding of the genetic basis for growth traits and will be useful for the molecular breeding of BW, YW, BYADG, and 18MW in the context of genomic selection 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.