Lei Zhou

The increasing availability of multi-omics data is promising in enhancing genomic prediction in breeding and human genetics. However, integrating multi-omi

Background: Multibreed genomic prediction (MBGP) is crucial for improving prediction accuracy for breeds with small populations, for which limited data are often available. Recent studies have demonstrated that partitioning the genome into nonoverlapping blocks to model heterogeneous genetic (co)variance in multitrait models can achieve higher joint prediction accuracy. However, the block partitioning method, a key factor influencing model performance, has not been extensively explored. Results: We introduce mbBayesABLD, a novel Bayesian MBGP model that partitions each chromosome into nonoverlapping blocks on the basis of linkage disequilibrium (LD) patterns. In this model, marker effects within each block are assumed to follow normal distributions with block-specific parameters. We employ simulated data as well as empirical datasets from pigs and beans to assess genomic prediction accuracy across different models using cross-validation. The results demonstrate that mbBayesABLD significantly outperforms conventional MBGP models, such as GBLUP and BayesR. For the meat marbling score trait in pigs, compared with GBLUP, which does not account for heterogeneous genetic (co)variance, mbBayesABLD improves the prediction accuracy for the small-population breed Landrace by 15.6%. Furthermore, our findings indicate that a moderate level of similarity in LD patterns between breeds (with an average correlation of 0.6) is sufficient to improve the prediction accuracy of the target breed. Conclusions: This study presents a novel LD block-based approach for multibreed genomic prediction. Our work provides a practical tool for livestock breeding programs and offers new insights into leveraging genetic diversity across breeds for improved genomic prediction.

Objective: The content of intramuscular fat (IMF) is closely linked to meat quality, and the mechanism of IMF deposition is complex. Despite numerous transcriptomic studies on IMF, variations in sample sizes and data analysis methods have produced inconsistent gene expression patterns and results. To identify the pivotal genes influencing pig IMF content, we performed a meta-analysis on 10 pig muscle transcriptome datasets with a total of eighty samples, forty with high and forty with low IMF samples.Methods: DESeq2 has been used to analyze the high and low IMF groups for 10 datasets each, resulting in the differentially expressed genes (DEGs) for each dataset. To identify key genes affecting IMF content, we performed a meta-analysis of the differential expression results from the 10 datasets using MetaVolcanoR. Subsequently, we conducted protein-protein interaction network analysis, Gene Ontology and Kyoto encyclopedia of genes and genomes functional enrichment analysis, and quantitative trait locus (QTL) analysis on the DEGs.Results: The meta-analysis identified 129 DEGs, comprising 71 upregulated and 58 downregulated DEGs in the high IMF group. The DEGs exhibited enrichment in processes associated with adipocyte differentiation and fat anabolism. QTL analysis demonstrated that five DEGs, including FASN and SCD, corresponded to six QTLs associated with IMF.Conclusion: The findings suggest that meta-analysis effectively integrates data from multiple datasets, resulting in more reliable outcomes. This approach enabled the identification of the core gene cluster comprising FASN, SCD, and PLIN1, LEP, and G0S2, which influence IMF content in pigs.

The design of breeding programs is crucial for maximizing economic gains. Simulation provides the most efficient measures to test these programs, as real-world trials are often costly and time-consuming. We developed GOplan, a comprehensive and user-friendly R package designed to develop animal breeding programs considering pure-bred populations and crossbreeding systems. Compared with other traditional simulators, it has mainstream crossbreeding frameworks that streamline modeling and use Gene Flow and Bayesian optimization methods to enhance breeding program efficiency. GOplan includes 3 key functions: runCore() to evaluate the effects of nucleus breeding programs, runWhole() to predict economic outcomes and the production performance of crossbreeding systems, and runOpt() to optimize crossbreeding structures for greater profitability. These functions support breeders in better planning and accelerating breeding goals. Additionally, the application of Bayesian optimization algorithms in this study provides valuable insights for developing new optimization algorithms in the future. The software is available at https://github.com/CAU-TeamLiuJF/GOplan.

Pigs are among the most essential sources of high-quality protein in human diets. Structural variants (SVs) are a major source of genetic variants associated with diverse traits and evolutionary events. However, the current linear reference genome of pigs limits the presentation of position information for SVs. In this study, we generated a pangenome of pigs and a genome variation map of 599 deep-sequenced genomes across Eurasia. Moreover, a section-wide gene repertoire was constructed, which indicated that core genes were more evolutionarily conserved than variable genes. Subsequently, we identified 546,137 SVs, their enrichment regions, and relationships with genomic features and found significant divergence across Eurasian pigs. More importantly, the pangenome-detected SVs could complement heritability estimates and genome-wide association studies based only on single nucleotide polymorphisms. Among the SVs shaped by selection, we identified an insertion in the promoter region of the TBX19 gene, which may be related to the development, growth, and timidity traits of Asian pigs and may affect the gene expression. Our constructed pig pangenome and the identified SVs provide rich resources for future functional genomic research on pigs.

Pigs are among the most essential sources of high-quality protein in human diets. Structural variants (SVs) are a major source of genetic variants associated with diverse traits and evolutionary events. However, the current linear reference genome of pigs limits the presentation of position information for SVs. In this study, we generated a pangenome of pigs and a genome variation map of 599 deep-sequenced genomes across Eurasia. Moreover, a section-wide gene repertoire was constructed, which indicated that core genes were more evolutionarily conserved than variable genes. Subsequently, we identified 546,137 SVs, their enrichment regions, and relationships with genomic features and found significant divergence across Eurasian pigs. More importantly, the pangenome-detected SVs could complement heritability estimates and genome-wide association studies based only on single nucleotide polymorphisms. Among the SVs shaped by selection, we identified an insertion in the promoter region of the TBX19 gene, which may be related to the development, growth, and timidity traits of Asian pigs and may affect the gene expression. Our constructed pig pangenome and the identified SVs provide rich resources for future functional genomic research on pigs.

Performing joint genomic predictions for multiple breeds (MBGP) to expand the reference size is a promising strategy for improving the prediction for limited population sizes or phenotypic records for a single breed. This study proposes an MBGP model—mbBayesAB, which treats the same traits of different breeds as potentially genetically related but different, and divides chromosomes into independent blocks to fit heterogeneous genetic (co)variances. Best practices of random effect (co)variance matrix priors in mbBayesAB were analyzed, and the prediction accuracies of mbBayesAB were compared with within-breed (WBGP) and other commonly used MBGP models. The results showed that assigning an inverse Wishart prior to the random effect and obtaining information on the scale of the inverse Wishart prior from the phenotype enabled mbBayesAB to achieve the highest accuracy. When combining two cattle breeds (Limousin and Angus) in reference, mbBayesAB achieved higher accuracy than the WBGP model for two weight traits. For the marbling score trait in pigs, MBGP of the Yorkshire and Landrace breeds led to a 6.27% increase in accuracy for Yorkshire validation using mbBayesAB compared to that using the WBGP model. Therefore, considering heterogeneous genetic (co)variance in MBGP is advantageous. However, determining appropriate priors for (co)variance and hyperparameters is crucial for MBGP.

Background: The pig (Sus Scrofa) is one of the oldest domesticated livestock species that has undergone extensive improvement through modern breeding. European breeds have advantages in lean meat development and highly-productive body type, whereas Asian breeds possess extraordinary fat deposition and reproductive performance. Consequently, Eurasian breeds have been extensively used to develop modern commercial breeds for fast-growing and high prolificacy. However, limited by the sequencing technology, the genome architecture of some nascent developed breeds and the human-mediated impact on their genomes are still unknown. Results: Through whole-genome analysis of 178 individuals from an Asian locally developed pig breed, Beijing Black pig, and its two ancestors from two different continents, we found the pervasive inconsistent gene trees and species trees across the genome of Beijing Black pig, which suggests its introgressive hybrid origin. Interestingly, we discovered that this developed breed has more genetic relationships with European pigs and an unexpected introgression from Asian pigs to this breed, which indicated that human-mediated introgression could form the porcine genome architecture in a completely different type compared to native introgression. We identified 554 genomic regions occupied 63.30 Mb with signals of introgression from the Asian ancestry to Beijing Black pig, and the genes in these regions enriched in pathways associated with meat quality, fertility, and disease-resistant. Additionally, a proportion of 7.77% of genomic regions were recognized as regions that have been under selection. Moreover, combined with the results of a genome-wide association study for meat quality traits in the 1537 Beijing Black pig population, two important candidate genes related to meat quality traits were identified. DNAJC6 is related to intramuscular fat content and fat deposition, and RUFY4 is related to meat pH and tenderness. Conclusions: Our research provides insight for analyzing the origins of nascent developed breeds and genome-wide selection remaining in the developed breeds mediated by humans during modern breeding.

Background: As warthogs (Phacochoerus africanus) have innate immunity against African swine fever (ASF), it is critical to understand the evolutionary novelty of warthogs to explain their specific ASF resistance. Methods: Here, we present two completed new genomes of one warthog and one Kenyan domestic pig as fundamental genomic references to elucidate the genetic mechanisms of ASF tolerance. Results: Multiple genomic variations, including gene losses, independent contraction, and the expansion of specific gene families, likely molded the warthog genome to adapt to the environment. Importantly, the analysis of the presence and absence of genomic sequences revealed that the DNA sequence of the warthog genome had an absence of the gene lactate dehydrogenase B (LDHB) on chromosome 2 compared with the reference genome. The overexpression and siRNA of LDHB inhibited the replication of the African swine fever virus. Combined with large-scale sequencing data from 42 pigs worldwide, the contraction and expansion of tripartite motif-containing (TRIM) gene families revealed that TRIM family genes in the warthog genome are potentially responsible for its tolerance to ASF. Conclusion: Our results will help improve the understanding of genetic resistance to ASF in pigs.