The eQTL colocalization and transcriptome-wide association study identify potentially causal genes responsible for economic traits in Simmental beef cattle.

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.

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.

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.

Multiple association analysis of loci and candidate genes that regulate body size at three growth stages in Simmental beef cattle.

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, including SOX2, SNRPD1, RASGEF1B, EFNA5, PTBP1, SNX9, SV2C, PKDCC, SYNDIG1, AKR1E2, and PRIM2 identified by single-trait analysis; SLC37A1, LAP3, PCDH7, MANEA, and LHCGR identified by multi-trait analysis; and P2RY1, MPZL1, LINGO2, CMIP, and WSCD1 identified 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.

Genome-wide association studies using binned genotypes.

Linear mixed models (LMM) that tests trait association one marker at a time have been the most popular methods for genome-wide association studies. However, this approach has potential pitfalls: over conservativeness after Bonferroni correction, ignorance of linkage disequilibrium (LD) between neighboring markers, and power reduction due to overfitting SNP effects. So, multiple locus models that can simultaneously estimate and test all markers in the genome are more appropriate. Based on the multiple locus models, we proposed a bin model that combines markers into bins based on their LD relationships. A bin is treated as a new synthetic marker and we detect the associations between bins and traits. Since the number of bins can be substantially smaller than the number of markers, a penalized multiple regression method can be adopted by fitting all bins to a single model. We developed an innovative method to bin the neighboring markers and used the least absolute shrinkage and selection operator (LASSO) method. We compared BIN-Lasso with SNP-Lasso and Q + K-LMM in a simulation experiment, and showed that the new method is more powerful with less Type I error than the other two methods. We also applied the bin model to a Chinese Simmental beef cattle population for bone weight association study. The new method identified more significant associations than the classical LMM. The bin model is a new dimension reduction technique that takes advantage of biological information (i.e., LD). The new method will be a significant breakthrough in associative genomics in the big data era.

Identification of muscle-specific candidate genes in Simmental beef cattle using imputed next generation sequencing.

Genome-wide association studies (GWAS) have commonly been used to identify candidate genes that control economically important traits in livestock. Our objective was to detect potential candidate genes associated mainly with muscle development traits related to dimension of hindquarter in cattle. A next generation sequencing (NGS) dataset to imputed to 12 million single nucleotide polymorphisms (SNPs) (from 1252 Simmental beef cattle) were used to search for genes affecting hindquarter traits using a linear, mixed model approach. We also used haplotype and linkage disequilibrium blocks to further support our identifications. We identified 202 significant SNPs in the bovine BTA4 chromosome region associated with width of hind leg, based on a stringent statistical threshold (p = 0.05/ effective number of SNPs identified). After exploring the region around these SNPs, we found candidate genes that were potentially related to the associated markers. More importantly, we identified a region of approximately 280 Kb on the BTA4 chromosome that harbored several muscle specific candidate genes, genes to be in a potential region for muscle development. However, we also found candidate gene SLC13A1 on BTA4, which seems to be associated with bone disorders (such as chondrodysplasia) in Simmental beef cattle.

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.

Evaluation of GBLUP, BayesB and elastic net for genomic prediction in Chinese Simmental beef cattle.

Chinese Simmental beef cattle are the most economically important cattle breed in China. Estimated breeding values for growth, carcass, and meat quality traits are commonly used as selection criteria in animal breeding. The objective of this study was to evaluate the accuracy of alternative statistical methods for the estimation of genomic breeding values. Analyses of the accuracy of genomic best linear unbiased prediction (GBLUP), BayesB, and elastic net (EN) were performed with an Illumina BovineHD BeadChip on 1,217 animals by applying 5-fold cross-validation. Overall, the accuracies ranged from 0.17 to 0.296 for ten traits, and the heritability estimates ranged from 0.36 to 0.63. The EN (alpha = 0.001) model provided the most accurate prediction, which was also slightly higher (0.2-2%) than that of GBLUP for most traits, such as average daily weight gain (ADG) and carcass weight (CW). BayesB was less accurate for each trait than were EN (alpha = 0.001) and GBLUP. These findings indicate the importance of using an appropriate variable selection method for the genomic selection of traits and suggest the influence of the genetic architecture of the traits we analyzed.

Genome-wide association study identifies loci and candidate genes for internal organ weights in Simmental beef cattle.

Cattle internal organs as accessible raw materials have a long history of being widely used in beef processing, feed and pharmaceutical industry. These traits not only are of economic interest to breeders, but they are intrinsically linked to many valuable traits, such as growth, health, and productivity. Using the Illumina Bovine HD 770K SNP array, we performed a genome-wide association study for heart weight, liver weight, spleen weight, lung weight, and kidney weight in 1,217 Simmental cattle. In our research, 38 significant single nucleotide polymorphisms (SNPs) ( P < 1.49 × 10-6) were identified for five internal organ weight traits. These SNPs are within or near 13 genes, and some of them have been reported previously, including NDUFAF4, LCORL, BT.94996, SLIT2, FAM184B, LAP3, BBS12, MECOM, CD300LF, HSD17B3, TLR4, MXI1, and MB21D2. In addition, we detected four haplotype blocks on BTA6 containing 18 significant SNPs associated with spleen weight. Our results offer worthy insights into understanding the genetic mechanisms of internal organs' development, with potential application in breeding programs of Simmental beef cattle.

Searching for new loci and candidate genes for economically important traits through gene-based association analysis of Simmental cattle.

Single-marker genome-wide association study (GWAS) is a convenient strategy of genetic analysis that has been successful in detecting the association of a number of single-nucleotide polymorphisms (SNPs) with quantitative traits. However, analysis of individual SNPs can only account for a small proportion of genetic variation and offers only limited knowledge of complex traits. This inadequacy may be overcome by employing a gene-based GWAS analytic approach, which can be considered complementary to the single-SNP association analysis. Here we performed an initial single-SNP GWAS for bone weight (BW) and meat pH value with a total of 770,000 SNPs in 1141 Simmental cattle. Additionally, 21836 cattle genes collected from the Ensembl Genes 83 database were analyzed to find supplementary evidence to support the importance of gene-based association study. Results of the single SNP-based association study showed that there were 11 SNPs significantly associated with bone weight (BW) and two SNPs associated with meat pH value. Interestingly, all of these SNPs were located in genes detected by the gene-based association study.