Transcriptome Analysis of Compensatory Growth and Meat Quality Alteration after Varied Restricted Feeding Conditions in Beef Cattle.

Compensatory growth (CG) is a physiological response that accelerates growth following a period of nutrient limitation, with the potential to improve growth efficiency and meat quality in cattle. However, the underlying molecular mechanisms remain poorly understood. In this study, 60 Huaxi cattle were divided into one ad libitum feeding (ALF) group and two restricted feeding groups (75% restricted, RF75; 50% restricted, RF50) undergoing a short-term restriction period followed by evaluation of CG. Detailed comparisons of growth performance during the experimental period, as well as carcass and meat quality traits, were conducted, complemented by a comprehensive transcriptome analysis of the longissimus dorsi muscle using differential expression analysis, gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and weighted correlation network analysis (WGCNA). The results showed that irrespective of the restriction degree, the restricted animals exhibited CG, achieving final body weights comparable to the ALF group. Compensating animals showed differences in meat quality traits, such as pH, cooking loss, and fat content, compared to the ALF group. Transcriptomic analysis revealed 57 genes and 31 pathways differentially regulated during CG, covering immune response, acid-lipid metabolism, and protein synthesis. Notably, complement-coagulation-fibrinolytic system synergy was identified as potentially responsible for meat quality optimization in RF75. This study provides novel and valuable genetic insights into the regulatory mechanisms of CG in beef cattle.

MAK: a machine learning framework improved genomic prediction via multi-target ensemble regressor chains and automatic selection of assistant traits.

Incorporating the genotypic and phenotypic of the correlated traits into the multi-trait model can significantly improve the prediction accuracy of the target trait in animal and plant breeding, as well as human genetics. However, in most cases, the phenotypic information of the correlated and target trait of the individual to be evaluated was null simultaneously, particularly for the newborn. Therefore, we propose a machine learning framework, MAK, to improve the prediction accuracy of the target trait by constructing the multi-target ensemble regression chains and selecting the assistant trait automatically, which predicted the genomic estimated breeding values of the target trait using genotypic information only. The prediction ability of MAK was significantly more robust than the genomic best linear unbiased prediction, BayesB, BayesRR and the multi trait Bayesian method in the four real animal and plant datasets, and the computational efficiency of MAK was roughly 100 times faster than BayesB and BayesRR.

Improving Genomic Prediction with Machine Learning Incorporating TPE for Hyperparameters Optimization.

Depending on excellent prediction ability, machine learning has been considered the most powerful implement to analyze high-throughput sequencing genome data. However, the sophisticated process of tuning hyperparameters tremendously impedes the wider application of machine learning in animal and plant breeding programs. Therefore, we integrated an automatic tuning hyperparameters algorithm, tree-structured Parzen estimator (TPE), with machine learning to simplify the process of using machine learning for genomic prediction. In this study, we applied TPE to optimize the hyperparameters of Kernel ridge regression (KRR) and support vector regression (SVR). To evaluate the performance of TPE, we compared the prediction accuracy of KRR-TPE and SVR-TPE with the genomic best linear unbiased prediction (GBLUP) and KRR-RS, KRR-Grid, SVR-RS, and SVR-Grid, which tuned the hyperparameters of KRR and SVR by using random search (RS) and grid search (Gird) in a simulation dataset and the real datasets. The results indicated that KRR-TPE achieved the most powerful prediction ability considering all populations and was the most convenient. Especially for the Chinese Simmental beef cattle and Loblolly pine populations, the prediction accuracy of KRR-TPE had an 8.73% and 6.08% average improvement compared with GBLUP, respectively. Our study will greatly promote the application of machine learning in GP and further accelerate breeding progress.

Incorporating kernelized multi-omics data improves the accuracy of genomic prediction.

BACKGROUND: Genomic selection (GS) has revolutionized animal and plant breeding after the first implementation via early selection before measuring phenotypes. Besides genome, transcriptome and metabolome information are increasingly considered new sources for GS. Difficulties in building the model with multi-omics data for GS and the limit of specimen availability have both delayed the progress of investigating multi-omics. RESULTS: We utilized the Cosine kernel to map genomic and transcriptomic data as [Formula: see text] symmetric matrix (G matrix and T matrix), combined with the best linear unbiased prediction (BLUP) for GS. Here, we defined five kernel-based prediction models: genomic BLUP (GBLUP), transcriptome-BLUP (TBLUP), multi-omics BLUP (MBLUP, [Formula: see text]), multi-omics single-step BLUP (mssBLUP), and weighted multi-omics single-step BLUP (wmssBLUP) to integrate transcribed individuals and genotyped resource population. The predictive accuracy evaluations in four traits of the Chinese Simmental beef cattle population showed that (1) MBLUP was far preferred to GBLUP (ratio = 1.0), (2) the prediction accuracy of wmssBLUP and mssBLUP had 4.18% and 3.37% average improvement over GBLUP, (3) We also found the accuracy of wmssBLUP increased with the growing proportion of transcribed cattle in the whole resource population. CONCLUSIONS: We concluded that the inclusion of transcriptome data in GS had the potential to improve accuracy. Moreover, wmssBLUP is accepted to be a promising alternative for the present situation in which plenty of individuals are genotyped when fewer are transcribed.

Integrating genomics and transcriptomics to identify candidate genes for subcutaneous fat deposition in beef cattle.

Fat deposition is a complex economic trait regulated by polygenic genetic basis and environmental factors. Therefore, integrating multi-omics data to uncover its internal regulatory mechanism has attracted extensive attention. Here, we performed genomics and transcriptomics analysis to detect candidates affecting subcutaneous fat (SCF) deposition in beef cattle. The association of 770K SNPs with the backfat thickness captured nine significant SNPs within or near 11 genes. Additionally, 13 overlapping genes regarding fat deposition were determined via the analysis of differentially expressed genes and weighted gene co-expression network analysis (WGCNA). We then calculated the correlations of these genes with BFT and constructed their interaction network. Finally, seven biomarkers including ACACA, SCD, FASN, ACOX1, ELOVL5, HACD2, and HSD17B12 were screened. Notably, ACACA, identified by the integration of genomics and transcriptomics, was more likely to exert profound effects on SCF deposition. These findings provided novel insights into the regulation mechanism underlying bovine fat accumulation.

Transcriptomics and Lipid Metabolomics Analysis of Subcutaneous, Visceral, and Abdominal Adipose Tissues of Beef Cattle.

Fat deposition traits are influenced by genetics and environment, which affect meat quality, growth rate, and energy metabolism of domestic animals. However, at present, the molecular mechanism of fat deposition is not entirely understood in beef cattle. Therefore, the current study conducted transcriptomics and lipid metabolomics analysis of subcutaneous, visceral, and abdominal adipose tissue (SAT, VAT, and AAT) of Huaxi cattle to investigate the differences among these adipose tissues and systematically explore how candidate genes interact with metabolites to affect fat deposition. These results demonstrated that compared with SAT, the gene expression patterns and metabolite contents of VAT and AAT were more consistent. Particularly, SCD expression, monounsaturated fatty acid (MUFA) and triglyceride (TG) content were higher in SAT, whereas PCK1 expression and the contents of saturated fatty acid (SFA), diacylglycerol (DG), and lysoglycerophosphocholine (LPC) were higher in VAT. Notably, in contrast to PCK1, 10 candidates including SCD, ELOVL6, ACACA, and FABP7 were identified to affect fat deposition through positively regulating MUFA and TG, and negatively regulating SFA, DG, and LPC. These findings uncovered novel gene resources and offered a theoretical basis for future investigation of fat deposition in beef cattle.

Incorporating genome-wide and transcriptome-wide association studies to identify genetic elements of longissimus dorsi muscle in Huaxi cattle.

Locating the genetic variation of important livestock and poultry economic traits is essential for genetic improvement in breeding programs. Identifying the candidate genes for the productive ability of Huaxi cattle was one crucial element for practical breeding. Based on the genotype and phenotype data of 1,478 individuals and the RNA-seq data of 120 individuals contained in 1,478 individuals, we implemented genome-wide association studies (GWAS), transcriptome-wide association studies (TWAS), and Fisher's combined test (FCT) to identify the candidate genes for the carcass trait, the weight of longissimus dorsi muscle (LDM). The results indicated that GWAS, TWAS, and FCT identified seven candidate genes for LDM altogether: PENK was located by GWAS and FCT, PPAT was located by TWAS and FCT, and XKR4, MTMR3, FGFRL1, DHRS4, and LAP3 were only located by one of the methods. After functional analysis of these candidate genes and referring to the reported studies, we found that they were mainly functional in the progress of the development of the body and the growth of muscle cells. Combining advanced breeding techniques such as gene editing with our study will significantly accelerate the genetic improvement for the future breeding of Huaxi cattle.

Genome-Wide Association Study Based on Random Regression Model Reveals Candidate Genes Associated with Longitudinal Data in Chinese Simmental Beef Cattle.

Body weight (BW) is an important longitudinal trait that directly described the growth gain of bovine in production. However, previous genome-wide association study (GWAS) mainly focused on the single-record traits, with less attention paid to longitudinal traits. Compared with traditional GWAS models, the association studies based on the random regression model (GWAS-RRM) have better performance in the control of the false positive rate through considering time-stage effects. In this study, the BW trait data were collected from 808 Chinese Simmental beef cattle aged 0, 6, 12, and 18 months, then we performed a GWAS-RRM to fit the time-varied SNP effect. The results showed a total of 37 significant SNPs were associated with BW. Gene functional annotation and enrichment analysis indicated FGF4, ANGPT4, PLA2G4A, and ITGA5 were promising candidate genes for BW. Moreover, these genes were significantly enriched in the signaling transduction pathway and lipid metabolism. These findings will provide prior molecular information for bovine gene-based selection, as well as facilitate the extensive application of GWAS-RRM in domestic animals.

PacBio Single-Molecule Long-Read Sequencing Provides New Light on the Complexity of Full-Length Transcripts in Cattle.

Cattle (Bos taurus) is one of the most widely distributed livestock species in the world, and provides us with high-quality milk and meat which have a huge impact on the quality of human life. Therefore, accurate and complete transcriptome and genome annotation are of great value to the research of cattle breeding. In this study, we used error-corrected PacBio single-molecule real-time (SMRT) data to perform whole-transcriptome profiling in cattle. Then, 22.5 Gb of subreads was generated, including 381,423 circular consensus sequences (CCSs), among which 276,295 full-length non-chimeric (FLNC) sequences were identified. After correction by Illumina short reads, we obtained 22,353 error-corrected isoforms. A total of 305 alternative splicing (AS) events and 3,795 alternative polyadenylation (APA) sites were detected by transcriptome structural analysis. Furthermore, we identified 457 novel genes, 120 putative transcription factors (TFs), and 569 novel long non-coding RNAs (lncRNAs). Taken together, this research improves our understanding and provides new insights into the complexity of full-length transcripts in cattle.

Transcriptome profiling analysis of muscle tissue reveals potential candidate genes affecting water holding capacity in Chinese Simmental beef cattle.

Water holding capacity (WHC) is an important sensory attribute that greatly influences meat quality. However, the molecular mechanism that regulates the beef WHC remains to be elucidated. In this study, the longissimus dorsi (LD) muscles of 49 Chinese Simmental beef cattle were measured for meat quality traits and subjected to RNA sequencing. WHC had significant correlation with 35 kg water loss (r = - 0.99, p < 0.01) and IMF content (r = 0.31, p < 0.05), but not with SF (r = - 0.20, p = 0.18) and pH (r = 0.11, p = 0.44). Eight individuals with the highest WHC (H-WHC) and the lowest WHC (L-WHC) were selected for transcriptome analysis. A total of 865 genes were identified as differentially expressed genes (DEGs) between two groups, of which 633 genes were up-regulated and 232 genes were down-regulated. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment revealed that DEGs were significantly enriched in 15 GO terms and 96 pathways. Additionally, based on protein-protein interaction (PPI) network, animal QTL database (QTLdb), and relevant literature, the study not only confirmed seven genes (HSPA12A, HSPA13, PPARγ, MYL2, MYPN, TPI, and ATP2A1) influenced WHC in accordance with previous studies, but also identified ATP2B4, ACTN1, ITGAV, TGFBR1, THBS1, and TEK as the most promising novel candidate genes affecting the WHC. These findings could offer important insight for exploring the molecular mechanism underlying the WHC trait and facilitate the improvement of beef quality.

KCRR: a nonlinear machine learning with a modified genomic similarity matrix improved the genomic prediction efficiency.

Nowadays, advances in high-throughput sequencing benefit the increasing application of genomic prediction (GP) in breeding programs. In this research, we designed a Cosine kernel-based KRR named KCRR to perform GP. This paper assessed the prediction accuracies of 12 traits with various heritability and genetic architectures from four populations using the genomic best linear unbiased prediction (GBLUP), BayesB, support vector regression (SVR), and KCRR. On the whole, KCRR performed stably for all traits of multiple species, indicating that the hypothesis of KCRR had the potential to be adapted to a wide range of genetic architectures. Moreover, we defined a modified genomic similarity matrix named Cosine similarity matrix (CS matrix). The results indicated that the accuracies between GBLUP_kinship and GBLUP_CS almost unanimously for all traits, but the computing efficiency has increased by an average of 20 times. Our research will be a significant promising strategy in future GP.

A Stacking Ensemble Learning Framework for Genomic Prediction.

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.

Genome-Wide Association Analysis of Growth Curve Parameters in Chinese 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.

Application of ensemble learning to genomic selection in chinese simmental beef cattle.

Genomic selection (GS) using the whole-genome molecular makers to predict genomic estimated breeding values (GEBVs) is revolutionizing the livestock and plant breeding. Seeking out novel strategies with higher prediction accuracy for GS has been the ultimate goal of breeders. With the rapid development of artificial intelligence, machine learning algorithms were applied to estimate the GEBVs increasingly. Although some machine learning methods have better performance in phenotype prediction, there is still considerable room for improvement. In this study, we applied an ensemble-learning algorithm, Adaboost.RT, which integrated support vector regression (SVR), kernel ridge regression (KRR) and random forest (RF), to predict genomic breeding values of three economic traits (carcass weight, live weight, and eye muscle area) in Chinese Simmental beef cattle. Predictive accuracy measured as the Pearson correlation between the corrected phenotypes and predicted GEBVs. Moreover, we compared the reliability of SVR, KRR, RF, Adaboost.RT and GBLUP methods. The result showed that machine learning methods outperformed GBLUP, and the average improvement of four machine learning methods over the GBLUP was 12.8%, 14.9%, 5.4% and 14.4%, respectively. Among the four machine learning methods, the reliability of Adaboost.RT was comparable to KRR with higher stability. We therefore believe that the Adaboost.RT algorithm is a reliable and efficient method for GS.

A Fast and Powerful Empirical Bayes Method for Genome-Wide Association Studies.

Linear mixed model (LMM) is an efficient method for GWAS. There are numerous forms of LMM-based GWAS methods. However, improving statistical power and computing efficiency have always been the research hotspots of the LMM-based GWAS methods. Here, we proposed a fast empirical Bayes method, which is based on linear mixed models. We call it Fast-EB-LMM in short. The novelty of this method is that it uses a modified kinship matrix accounting for individual relatedness to avoid competition between the locus of interest and its counterpart in the polygene. This property has increased statistical power. We adopted two special algorithms to ease the computational burden: Eigenvalue decomposition and Woodbury matrix identity. Simulation studies showed that Fast-EB-LMM has significantly increased statistical power of marker detection and improved computational efficiency compared with two widely used GWAS methods, EMMA and EB. Real data analyses for two carcass traits in a Chinese Simmental beef cattle population showed that the significant single-nucleotide polymorphisms (SNPs) and candidate genes identified by Fast-EB-LMM are highly consistent with results of previous studies. We therefore believe that the Fast-EB-LMM method is a reliable and efficient method for GWAS.