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Background In the pig production industry, artificial insemination (AI) plays an important role in enlarging the beneficial impact of elite boars. Understanding the genetic architecture and detecting genetic markers associated with semen traits can help in improving genetic selection for such traits and accelerate genetic progress. In this study, we utilized a weighted single-step genome-wide association study (wssGWAS) procedure to detect genetic regions and further candidate genes associated with semen traits in a Duroc boar population. Overall, the full pedigree consists of 5284 pigs (12 generations), of which 2693 boars have semen data (143,113 ejaculations) and 1733 pigs were genotyped with 50 K single nucleotide polymorphism (SNP) array. Results Results show that the most significant genetic regions (0.4 Mb windows) explained approximately 2%~ 6% of the total genetic variances for the studied traits. Totally, the identified significant windows (windows explaining more than 1% of total genetic variances) explained 28.29, 35.31, 41.98, and 20.60% of genetic variances (not phenotypic variance) for number of sperm cells, sperm motility, sperm progressive motility, and total morphological abnormalities, respectively. Several genes that have been previously reported to be associated with mammal spermiogenesis, testes functioning, and male fertility were detected and treated as candidate genes for the traits of interest: Number of sperm cells, TDRD5 , QSOX1 , BLK , TIMP3 , THRA , CSF3 , and ZPBP1 ; Sperm motility, PPP2R2B , NEK2 , NDRG , ADAM7 , SKP2 , and RNASET2 ; Sperm progressive motility, SH2B1 , BLK , LAMB1 , VPS4A , SPAG9 , LCN2, and DNM1 ; Total morphological abnormalities, GHR , SELENOP , SLC16A5 , SLC9A3R1 , and DNAI2 . Conclusions In conclusion, candidate genes associated with Duroc boars’ semen traits, including the number of sperm cells, sperm motility, sperm progressive motility, and total morphological abnormalities, were identified using wssGWAS. KEGG and GO enrichment analysis indicate that the identified candidate genes were enriched in biological processes and functional terms may be involved into spermiogenesis, testes functioning, and male fertility.

Background Gene expression programs are intimately linked to the interplay of active cis regulatory elements mediated by chromatin contacts and associated RNAs. Genome-wide association studies (GWAS) have identified many variants in these regulatory elements that can contribute to phenotypic diversity. However, the functional interpretation of these variants remains nontrivial due to the lack of chromatin contact information or limited contact resolution. Furthermore, the distribution and role of chromatin-associated RNAs in gene expression and chromatin conformation remain poorly understood. To address this, we first present a comprehensive interaction map of nuclear dynamics of 3D chromatin-chromatin interactions (H3K27ac BL-HiChIP) and RNA-chromatin interactions (GRID-seq) to reveal genomic variants that contribute to complex skeletal muscle traits. Results In a genome-wide scan, we provide systematic fine mapping and gene prioritization from GWAS leading signals that underlie phenotypic variability of growth rate, meat quality, and carcass performance. A set of candidate functional variants and 54 target genes previously not detected were identified, with 71% of these candidate functional variants choosing to skip over their nearest gene to regulate the target gene in a long-range manner. The effects of three functional variants regulating KLF6 (related to days to 100 kg), MXRA8 (related to lean meat percentage), and TAF11 (related to loin muscle depth) were observed in two pig populations. Moreover, we find that this multi-omics interaction map consists of functional communities that are enriched in specific biological functions, and GWAS target genes can serve as core genes for exploring peripheral trait-relevant genes. Conclusions Our results provide a valuable resource of candidate functional variants for complex skeletal muscle-related traits and establish an integrated approach to complement existing 3D genomics by exploiting RNA-chromatin and chromatin-chromatin interactions for future association studies.

Genomic selection has become a widely adopted and effective breeding technology for modern genetic improvements in pigs. However, the core model currently used in genetic evaluation is primarily based on a linear mixed model, which accounts for only additive genetic effects. Non-additive effects and complex nonlinear interactions among genes or loci are often neglected, leaving substantial potential for improving the predictive ability of traits. To address this limitation, we here propose a Multi-omics Annotation and Residual Split strategy-based deep learning model (MARS). Through comprehensive comparisons and evaluations against various linear and nonlinear models across multiple pig traits, we demonstrate that the residual split indirect strategy effectively mitigates overfitting and underfitting issues commonly observed in deep learning models, thereby enhancing predictive accuracy for complex traits. Moreover, by incorporating multi-omics annotation information within a hierarchical feature selection procedure, our results show that it improves computational efficiency without significant sacrifices in prediction performance. It is foreseeable that our developed MARS would facilitate the application of artificial intelligence technology and the publicly available big omics data in the coming future of pig breeding.

The analyses of multi-omics data have revealed candidate genes for objective traits. However, they are integrated poorly, especially in non-model organisms, and they pose a great challenge for prioritizing candidate genes for follow-up experimental verification. Here, we present a general convolutional neural network model that integrates multi-omics information to prioritize the candidate genes of objective traits. By applying this model to Sus scrofa , which is a non-model organism, but one of the most important livestock animals, the model precision was 72.9%, recall 73.5%, and F1-Measure 73.4%, demonstrating a good prediction performance compared with previous studies in Arabidopsis thaliana and Oryza sativa . Additionally, to facilitate the use of the model, we present ISwine ( http://iswine.iomics.pro/ ), which is an online comprehensive knowledgebase in which we incorporated almost all the published swine multi-omics data. Overall, the results suggest that the deep learning strategy will greatly facilitate analyses of multi-omics integration in the future. Yuhua Fu et al. develop a CNN model that integrates multi-omics information to prioritize candidate genes of objective traits. Their model performs well when applied to important livestock non-model animals like Sus scrofa . Finally, the authors present ISwine, an online comprehensive knowledgebase which includes all published swine omics data to facilitate the integration of heterogeneous data.

Abstract Pig leg weakness not only causes huge economic losses for producers but also affects animal welfare. However, genes with large effects on pig leg weakness have not been identified and suitable methods to study porcine leg weakness are urgently needed. Bone mineral density (BMD) is an important indicator for determining leg soundness in pigs. Increasing pig BMD is likely to improve pig leg soundness. In this study, porcine BMD was measured using an ultrasound bone densitometer in a population with 212 Danish Landrace pigs and 537 Danish Yorkshires. After genotyping all the individuals using GeneSeek Porcine 50K SNP chip, genetic parameter estimation was performed to evaluate the heritability of BMD. Genome-wide association study and haplotype analysis were also performed to identify the variants and candidate genes associated with porcine BMD. The results showed that the heritability of BMD was 0.21 in Landrace and 0.31 in Yorkshire. Five single-nucleotide polymorphisms on chromosome 6 identified were associated with porcine BMD at suggestive significance level. Two candidate quantitative trait loci (74.47 to 75.33 Mb; 80.20 to 83.83 Mb) and three potential candidate genes (ZBTB40, CNR2, and Lin28a) of porcine BMD were detected in this study.

Genetic and breeding studies require diverse simulations, which include structured populations with phenotypes in varying genetic complexity. Here, we introduce SIMER, which simulates data accurately and assists in the design of breeding programs. SIMER implements numerous individual selection methods and reproduction modes to generate genotype data for humans, animals, plants, and microorganisms. SIMER simulates phenotypes following linear mixed model theory, including multiple covariates, fixed and random effects, as well as their interactions. It also enables breeders to design a program, which includes the optimal selection indexes construction, and to choose individuals for genotype and phenotype testing to maximize genetic progress.

Background A large number of pig breeds are distributed around the world, their features and characteristics vary among breeds, and they are valuable resources. Understanding the underlying genetic mechanisms that explain across-breed variation can help breeders develop improved pig breeds. Results In this study, we performed GWAS using a standard mixed linear model with three types of genome variants (SNP, InDel, and CNV) that were identified from public, whole-genome, sequencing data sets. We used 469 pigs of 57 breeds, and we identified and analyzed approximately 19 million SNPs, 1.8 million InDels, and 18,016 CNVs. We defined six biological phenotypes by the characteristics of breed features to identify the associated genome variants and candidate genes, which included coat color, ear shape, gradient zone, body weight, body length, and body height. A total of 37 candidate genes was identified, which included 27 that were reported previously (e.g., PLAG1 for body weight), but the other 10 were newly detected candidate genes (e.g., ADAMTS9 for coat color). Conclusion Our study indicated that using GWAS across a modest number of breeds with high density genome variants provided efficient mapping of complex traits.

Gene expression programs are intimately linked to the interplay of active cis regulatory elements mediated by chromatin contacts and associated RNAs. Genome-wide association studies (GWAS) have identified many variants in these regulatory elements that can contribute to phenotypic diversity. However, the functional interpretation of these variants remains nontrivial due to the lack of chromatin contact information or limited contact resolution. Furthermore, the distribution and role of chromatin-associated RNAs in gene expression and chromatin conformation remain poorly understood. To address this, we first present a comprehensive interaction map of nuclear dynamics of 3D chromatin-chromatin interactions (H3K27ac BL-HiChIP) and RNA-chromatin interactions (GRID-seq) to reveal genomic variants that contribute to complex skeletal muscle traits. In a genome-wide scan, we provide systematic fine mapping and gene prioritization from GWAS leading signals that underlie phenotypic variability of growth rate, meat quality, and carcass performance. A set of candidate functional variants and 54 target genes previously not detected were identified, with 71% of these candidate functional variants choosing to skip over their nearest gene to regulate the target gene in a long-range manner. The effects of three functional variants regulating KLF6 (related to days to 100 kg), MXRA8 (related to lean meat percentage), and TAF11 (related to loin muscle depth) were observed in two pig populations. Moreover, we find that this multi-omics interaction map consists of functional communities that are enriched in specific biological functions, and GWAS target genes can serve as core genes for exploring peripheral trait-relevant genes. Our results provide a valuable resource of candidate functional variants for complex skeletal muscle-related traits and establish an integrated approach to complement existing 3D genomics by exploiting RNA-chromatin and chromatin-chromatin interactions for future association studies.

In the pig production industry, artificial insemination (AI) plays an important role in enlarging the beneficial impact of elite boars. Understanding the genetic architecture and detecting genetic markers associated with semen traits can help in improving genetic selection for such traits and accelerate genetic progress. In this study, we utilized a weighted single-step genome-wide association study (wssGWAS) procedure to detect genetic regions and further candidate genes associated with semen traits in a Duroc boar population. Overall, the full pedigree consists of 5284 pigs (12 generations), of which 2693 boars have semen data (143,113 ejaculations) and 1733 pigs were genotyped with 50 K single nucleotide polymorphism (SNP) array. In conclusion, candidate genes associated with Duroc boars' semen traits, including the number of sperm cells, sperm motility, sperm progressive motility, and total morphological abnormalities, were identified using wssGWAS. KEGG and GO enrichment analysis indicate that the identified candidate genes were enriched in biological processes and functional terms may be involved into spermiogenesis, testes functioning, and male fertility.

Background:A large number of pig breeds are distributed around the world, their features and characteristics vary among breeds, and they are valuable resources. Understanding the underlying genetic mechanisms that explain across-breed variation can help breeders develop improved pig breeds. Results:In this study, we performed GWAS using a standard mixed linear model with three types of genome variants (SNP, InDel, and CNV) that were identified from public, whole-genome, sequencing data sets. We used 469 pigs of 57 breeds, and we identified and analyzed approximately 19 million SNPs, 1.8 million InDels, and 18,016 CNVs. We defined six biological phenotypes by the characteristics of breed features to identify the associated genome variants and candidate genes, which included coat color, ear shape, gradient zone, body weight, body length, and body height. A total of 37 candidate genes was identified, which included 27 that were reported previously (e.g., PLAG1 for body weight), but the other 10 were newly detected candidate genes (e.g., ADAMTS9 for coat color). Conclusion:Our study indicated that using GWAS across a modest number of breeds with high density genome variants provided efficient mapping of complex traits.