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.

Genomic sequencing analysis reveals copy number variations and their associations with economically important traits in beef cattle.

Copy number variation (CNV) represents a major source of genetic variation, which may have potentially large effects, including alternating gene regulation and dosage, as well as contributing to gene expression and risk for normal phenotypic variability. We carried out a comprehensive analysis of CNV based on whole genome sequencing in Chinese Simmental beef cattle. Totally, we found 9313 deletion and 234 duplication events, covering 147.5 Mb autosomal regions. Within them, 257 deletion events of high frequency overlapped with 193 known RefGenes. Among these genes, we observed several genes were related to economically important traits, like residual feed intake, immune responding, pregnancy rate and muscle differentiation. Using a locus-based analysis, we identified 11 deletions and 1 duplication, which were significantly associated with three traits including carcass weight, tenderloin and longissimus muscle area. Our sequencing-based study provided important insights into investigating the association of CNVs with important traits in beef cattle.

Genome-Wide Assessment of Runs of Homozygosity in Chinese Wagyu Beef Cattle.

Runs of homozygosity (ROH) are continuous homozygous regions that generally exist in the DNA sequence of diploid organisms. Identifications of ROH leading to reduction in performance can provide valuable insight into the genetic architecture of complex traits. Here, we evaluated genome-wide patterns of homozygosity and their association with important traits in Chinese Wagyu beef cattle. We identified a total of 29,271 ROH segments from 462 animals. Within each animal, an average number of ROH was 63.36 while an average length was 62.19 Mb. To evaluate the enrichment of ROH across genomes, we initially identified 280 ROH regions by merging ROH events across all individuals. Of these, nine regions containing 154 candidate genes, were significantly associated with six traits (body height, chest circumference, fat coverage, backfat thickness, ribeye area, and carcass length; p < 0.01). Moreover, we found 26 consensus ROH regions with frequencies exceeding 10%, and several regions overlapped with QTLs, which are associated with body weight, calving ease, and stillbirth. Among them, we observed 41 candidate genes, including BCKDHB, MAB21L1, SLC2A13, FGFR3, FGFRL1, CPLX1, CTNNA1, CORT, CTNNBIP1, and NMNAT1, which have been previously reported to be related to body conformation, meat quality, susceptibility, and reproductive traits. In summary, we assessed genome-wide autozygosity patterns and inbreeding levels in Chinese Wagyu beef cattle. Our study identified many candidate regions and genes overlapped with ROH for several important traits, which could be unitized to assist the design of a selection mating strategy in beef cattle.

Genome-wide assessment of genetic diversity and population structure insights into admixture and introgression in Chinese indigenous cattle.

BACKGROUND: China exhibits a great diversity of ecosystems and abundant cattle resources, with nearly 30 million cattle from 53 indigenous breeds reared in specific geographic regions. To explore the genetic diversity and population structure of Chinese indigenous cattle, a population genetic analysis at both the individual and population levels was conducted and the admixture analysis was performed. We genotyped 572 samples from 20 Chinese indigenous cattle breeds using GeneSeek Genomic Profiler Bovine LD (GGP-LD, 30 K) and downloaded the published data of 77 samples from 4 worldwide commercial breeds genotyped with Illumina BovineSNP50 Beadchip (SNP50, 50 K). RESULTS: In principal component analysis (PCA) and neighbour-joining (NJ) tree analysis, samples of the same breeds were grouped together, leading to clear separation from other breeds. And Chinese indigenous cattle were clustered into two groups of southern and northern breeds, originated from Asian Bos indicus lineage and Eurasian Bos taurus lineage, respectively. In STRUCTURE K = 2, a clear transition occurred from the northern breeds to the southern breeds. Additionally, the northern breeds contained a smaller Eurasian taurine (62.5%) descent proportion than that reported previously (more than 90%). In STRUCTURE K = 3, a distinct descent was detected in the southern Chinese breeds, which could reflect a long-term selection history of Chinese indigenous cattle. The results from TreeMix and f3 statistic provided the evidence of an admixture history between southern breeds and northern breeds. CONCLUSIONS: Consistent with the observed geographical distributions, Chinese indigenous cattle were divided into two genetic clusters, northern indigenous cattle and southern indigenous cattle. Three improved breeds in the northern area also exhibited northern indigenous ancestry. We found that the breeds distributed in the northern China showed more southern lineage introgression than previously reported. Central-located populations appeared to the admixture between southern and northern lineages, and introgression events from European cattle were observed in Luxi Cattle, Qinchuan Cattle and Jinnan Cattle. The study revealed the population structures and levels of admixture pattern among Chinese indigenous cattle, shedding light on the origin and evolutionary history of these breeds.

Genetic assessment of inbred chicken lines indicates genomic signatures of resistance to Marek's disease.

BACKGROUND: Marek's disease (MD) is a highly contagious pathogenic and oncogenic disease primarily affecting chickens. However, the mechanisms of genetic resistance for MD are complex and not fully understood. MD-resistant line 63 and MD-susceptible line 72 are two highly inbred progenitor lines of White Leghorn. Recombinant Congenic Strains (RCS) were developed from these two lines, which show varied susceptibility to MD. RESULTS: We investigated genetic structure and genomic signatures across the genome, including the line 63 and line 72, six RCSs, and two reciprocally crossed flocks between the lines 63 and 72 (F1 63 × 72 and F1 72 × 63) using Affymetrix® Axiom® HD 600 K genotyping array. We observed 18 chickens from RCS lines were specifically clustered into resistance sub-groups distributed around line 63. Additionally, homozygosity analysis was employed to explore potential genetic components related to MD resistance, while runs of homozygosity (ROH) are regions of the genome where the identical haplotypes are inherited from each parent. We found several genes including SIK, SOX1, LIG4, SIK1 and TNFSF13B were contained in ROH region identified in resistant group (line 63 and RCS), and these genes have been reported that are contribute to immunology and survival. Based on FST based population differential analysis, we also identified important genes related to cell death and anti-apoptosis, including AKT1, API5, CDH13, CFDP and USP15, which could be involved in divergent selection during inbreeding process. CONCLUSIONS: Our findings offer valuable insights for understanding the genetic mechanism of resistance to MD and the identified genes could be considered as candidate biomarkers in further evaluation.

Fast genomic prediction of breeding values using parallel Markov chain Monte Carlo with convergence diagnosis.

BACKGROUND: Running multiple-chain Markov Chain Monte Carlo (MCMC) provides an efficient parallel computing method for complex Bayesian models, although the efficiency of the approach critically depends on the length of the non-parallelizable burn-in period, for which all simulated data are discarded. In practice, this burn-in period is set arbitrarily and often leads to the performance of far more iterations than required. In addition, the accuracy of genomic predictions does not improve after the MCMC reaches equilibrium. RESULTS: Automatic tuning of the burn-in length for running multiple-chain MCMC was proposed in the context of genomic predictions using BayesA and BayesCπ models. The performance of parallel computing versus sequential computing and tunable burn-in MCMC versus fixed burn-in MCMC was assessed using simulation data sets as well by applying these methods to genomic predictions of a Chinese Simmental beef cattle population. The results showed that tunable burn-in parallel MCMC had greater speedups than fixed burn-in parallel MCMC, and both had greater speedups relative to sequential (single-chain) MCMC. Nevertheless, genomic estimated breeding values (GEBVs) and genomic prediction accuracies were highly comparable between the various computing approaches. When applied to the genomic predictions of four quantitative traits in a Chinese Simmental population of 1217 beef cattle genotyped by an Illumina Bovine 770 K SNP BeadChip, tunable burn-in multiple-chain BayesCπ (TBM-BayesCπ) outperformed tunable burn-in multiple-chain BayesCπ (TBM-BayesA) and Genomic Best Linear Unbiased Prediction (GBLUP) in terms of the prediction accuracy, although the differences were not necessarily caused by computational factors and could have been intrinsic to the statistical models per se. CONCLUSIONS: Automatically tunable burn-in multiple-chain MCMC provides an accurate and cost-effective tool for high-performance computing of Bayesian genomic prediction models, and this algorithm is generally applicable to high-performance computing of any complex Bayesian statistical model.

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.