AGIDB: a versatile database for genotype imputation and variant decoding across species.

The high cost of large-scale, high-coverage whole-genome sequencing has limited its application in genomics and genetics research. The common approach has been to impute whole-genome sequence variants obtained from a few individuals for a larger population of interest individually genotyped using SNP chip. An alternative involves low-coverage whole-genome sequencing (lcWGS) of all individuals in the larger population, followed by imputation to sequence resolution. To overcome limitations of processing lcWGS data and meeting specific genotype imputation requirements, we developed AGIDB (https://agidb.pro), a website comprising tools and database with an unprecedented sample size and comprehensive variant decoding for animals. AGIDB integrates whole-genome sequencing and chip data from 17 360 and 174 945 individuals, respectively, across 89 species to identify over one billion variants, totaling a massive 688.57 TB of processed data. AGIDB focuses on integrating multiple genotype imputation scenarios. It also provides user-friendly searching and data analysis modules that enable comprehensive annotation of genetic variants for specific populations. To meet a wide range of research requirements, AGIDB offers downloadable reference panels for each species in addition to its extensive dataset, variant decoding and utility tools. We hope that AGIDB will become a key foundational resource in genetics and breeding, providing robust support to researchers.

Integrated analysis of genome-wide association studies and 3D epigenomic characteristics reveal the BMP2 gene regulating loin muscle depth in Yorkshire pigs.

BACKGROUND: The lack of integrated analysis of genome-wide association studies (GWAS) and 3D epigenomics restricts a deep understanding of the genetic mechanisms of meat-related traits. With the application of techniques as ChIP-seq and Hi-C, the annotations of cis-regulatory elements in the pig genome have been established, which offers a new opportunity to elucidate the genetic mechanisms and identify major genetic variants and candidate genes that are significantly associated with important economic traits. Among these traits, loin muscle depth (LMD) is an important one as it impacts the lean meat content. In this study, we integrated cis-regulatory elements and genome-wide association studies (GWAS) to identify candidate genes and genetic variants regulating LMD. RESULTS: Five single nucleotide polymorphisms (SNPs) located on porcine chromosome 17 were significantly associated with LMD in Yorkshire pigs. A 10 kb quantitative trait locus (QTL) was identified as a candidate functional genomic region through the integration of linkage disequilibrium and linkage analysis (LDLA) and high-throughput chromosome conformation capture (Hi-C) analysis. The BMP2 gene was identified as a candidate gene for LMD based on the integrated results of GWAS, Hi-C meta-analysis, and cis-regulatory element data. The identified QTL region was further verified through target region sequencing. Furthermore, through using dual-luciferase assays and electrophoretic mobility shift assays (EMSA), two SNPs, including SNP rs321846600, located in the enhancer region, and SNP rs1111440035, located in the promoter region, were identified as candidate SNPs that may be functionally related to the LMD. CONCLUSIONS: Based on the results of GWAS, Hi-C, and cis-regulatory elements, the BMP2 gene was identified as an important candidate gene regulating variation in LMD. The SNPs rs321846600 and rs1111440035 were identified as candidate SNPs that are functionally related to the LMD of Yorkshire pigs. Our results shed light on the advantages of integrating GWAS with 3D epigenomics in identifying candidate genes for quantitative traits. This study is a pioneering work for the identification of candidate genes and related genetic variants regulating one key production trait (LMD) in pigs by integrating genome-wide association studies and 3D epigenomics.

Coordination of zygotic genome activation entry and exit by H3K4me3 and H3K27me3 in porcine early embryos.

Histone modifications are critical epigenetic indicators of chromatin state associated with gene expression. Although the reprogramming patterns of H3K4me3 and H3K27me3 have been elucidated in mouse and human preimplantation embryos, the relationship between these marks and zygotic genome activation (ZGA) remains poorly understood. By ultra-low-input native chromatin immunoprecipitation and sequencing, we profiled global H3K4me3 and H3K27me3 in porcine oocytes and in vitro fertilized (IVF) embryos. We found that promoters of ZGA genes occupied sharp H3K4me3 peaks in oocytes, and these peaks became broader after fertilization, and reshaped into sharp again during ZGA. By simultaneous depletion of H3K4me3 demethylase KDM5B and KDM5C, we determined that broad H3K4me3 domain maintenance impaired ZGA gene expression, suggesting its function to prevent premature ZGA entry. By contrast, broad H3K27me3 domains underwent global removal upon fertilization, followed by a re-establishment for H3K4me3/H3K27me3 bivalency in morulae. We also found that bivalent marks were deposited at promoters of ZGA genes, and inhibiting this deposition was correlated with the activation of ZGA genes. It suggests that promoter bivalency contributes to ZGA exit in porcine embryos. Moreover, we demonstrated that aberrant reprogramming of H3K4me3 and H3K27me3 triggered ZGA dysregulation in somatic cell nuclear transfer (SCNT) embryos, whereas H3K27me3-mediated imprinting did not exist in porcine IVF and SCNT embryos. Our findings highlight two previously unknown epigenetic reprogramming modes coordinated with ZGA in porcine preimplantation embryos. Finally, the similarities observed between porcine and human histone modification dynamics suggest that the porcine embryo may also be a useful model for human embryo research.

Development and application of an efficient genomic mating method to maximize the production performances of three-way crossbred pigs.

Creating synthetic lines is the standard mating mode for commercial pig production. Traditional mating performance was evaluated through a strictly designed cross-combination test at the 'breed level' to maximize the benefits of production. The Duroc-Landrace-Yorkshire (DLY) three-way crossbred production system became the most widely used breeding scheme for pigs. Here, we proposed an 'individual level' genomic mating procedure that can be applied to commercial pig production with efficient algorithms for estimating marker effects and for allocating the appropriate boar-sow pairs, which can be freely accessed to public in our developed HIBLUP software at https://www.hiblup.com/tutorials#genomic-mating. A total of 875 Duroc boars, 350 Landrace-Yorkshire sows and 3573 DLY pigs were used to carry out the genomic mating to assess the production benefits theoretically. The results showed that genomic mating significantly improved the performances of progeny across different traits compared with random mating, such as the feed conversion rate, days from 30 to 120 kg and eye muscle area could be improved by -0.12, -4.64 d and 2.65 cm2, respectively, which were consistent with the real experimental validations. Overall, our findings indicated that genomic mating is an effective strategy to improve the performances of progeny by maximizing their total genetic merit with consideration of both additive and dominant effects. Also, a herd of boars from a richer genetic source will increase the effectiveness of genomic mating further.

HIBLUP: an integration of statistical models on the BLUP framework for efficient genetic evaluation using big genomic data.

Human diseases and agricultural traits can be predicted by modeling a genetic random polygenic effect in linear mixed models. To estimate variance components and predict random effects of the model efficiently with limited computational resources has always been of primary concern, especially when it involves increasing the genotype data scale in the current genomic era. Here, we thoroughly reviewed the development history of statistical algorithms used in genetic evaluation and theoretically compared their computational complexity and applicability for different data scenarios. Most importantly, we presented a computationally efficient, functionally enriched, multi-platform and user-friendly software package named 'HIBLUP' to address the challenges that are faced currently using big genomic data. Powered by advanced algorithms, elaborate design and efficient programming, HIBLUP computed fastest while using the lowest memory in analyses, and the greater the number of individuals that are genotyped, the greater the computational benefits from HIBLUP. We also demonstrated that HIBLUP is the only tool which can accomplish the analyses for a UK Biobank-scale dataset within 1 h using the proposed efficient 'HE + PCG' strategy. It is foreseeable that HIBLUP will facilitate genetic research for human, plants and animals. The HIBLUP software and user manual can be accessed freely at https://www.hiblup.com.

Both human diseases and agricultural traits can be predicted by incorporating phenotypic observations and a relationship matrix among individuals in a linear mixed model. Due to the great demand for processing massive data of genotyped individuals, the existing algorithms that require several repetitions of inverse computing on increasingly big dense matrices (e.g. the relationship matrix and the coefficient matrix of mixed model equations) have encountered a bottleneck. Here, we presented a software tool named 'HIBLUP' to address the challenges. Powered by our advanced algorithms (e.g. HE + PCG), elaborate design and efficient programming, HIBLUP can successfully avoid the inverse computing for any big matrix and compute fastest under the lowest memory, which makes it very promising for genetic evaluation using big genomic data.

IAnimal: a cross-species omics knowledgebase for animals.

With the exponential growth of multi-omics data, its integration and utilization have brought unprecedented opportunities for the interpretation of gene regulation mechanisms and the comprehensive analyses of biological systems. IAnimal (https://ianimal.pro/), a cross-species, multi-omics knowledgebase, was developed to improve the utilization of massive public data and simplify the integration of multi-omics information to mine the genetic mechanisms of objective traits. Currently, IAnimal provides 61 191 individual omics data of genome (WGS), transcriptome (RNA-Seq), epigenome (ChIP-Seq, ATAC-Seq) and genome annotation information for 21 species, such as mice, pigs, cattle, chickens, and macaques. The scale of its total clean data has reached 846.46 TB. To better understand the biological significance of omics information, a deep learning model for IAnimal was built based on BioBERT and AutoNER to mine 'gene' and 'trait' entities from 2 794 237 abstracts, which has practical significance for comprehending how each omics layer regulates genes to affect traits. By means of user-friendly web interfaces, flexible data application programming interfaces, and abundant functional modules, IAnimal enables users to easily query, mine, and visualize characteristics in various omics, and to infer how genes play biological roles under the influence of various omics layers.

Genetic basis of phenotypic convergence in pig terminal sires using pathway-based selection signature detection methods.

The primary purpose of genetic improvement in lean pig breeds is to enhance production performance. Owing to their similar breeding directions, Duroc and Pietrain pigs are ideal models for investigating the phenotypic convergence underlying artificial selection. However, most important economic traits are controlled by a polygenic basis, so traditional strategies for detecting selection signatures may not fully reveal the genetic basis of complex traits. The pathway-based gene network analysis method utilizes each pathway as a unit, overcoming the limitations of traditional strategies for detecting selection signatures by revealing the selection of complex biological processes. Here, we utilized 13 122 398 high-quality SNPs from whole-genome sequencing data of 48 Pietrain pigs, 156 Duroc pigs and 36 European wild boars to detect selective signatures. After calculating FST and iHS scores, we integrated the pathway information and utilized the r/bioconductor graphite and signet packages to construct gene networks, identify subnets and uncover candidate genes underlying selection. Using the traditional strategy, a total of 47 genomic regions exhibiting parallel selection were identified. The enriched genes, including INO80, FZR1, LEPR and FAF1, may be associated with reproduction, fat deposition and skeletal development. Using the pathway-based selection signatures detection method, we identified two significant biological pathways and eight potential candidate genes underlying parallel selection, such as VTN, FN1 and ITGAV. This study presents a novel strategy for investigating the genetic basis of complex traits and elucidating the phenotypic convergence underlying artificial selection, by integrating traditional selection signature methods with pathway-based gene network analysis.

H3K27ac modification and transcription characteristics of adipose and muscle tissues in Chuxiang Black pig.

The establishment of high-quality pork breeds for improving meat quality in the pig industry is needed. The Chuxiang Black (CX) pig is a new breed developed from Chinese local pigs and Western lean pigs that has a high proportion of lean meat and excellent meat quality. However, the characteristics of cis-regulatory elements in CX pigs are still unknown. In this study, cis-regulatory elements of muscle and adipose tissues in CX pigs were investigated using ChIP-seq and RNA sequencing. Compared with the reported cis-regulatory elements of muscle and adipose tissues, 1768 and 1012 highly activated enhancers and 433 and 275 highly activated promoters in CX muscle and adipose tissues were identified, respectively. Motif analysis showed that transcription factors, such as MEF2A and MEF2C, were core regulators of highly activated enhancers and promoters in muscle. Similarly, the transcription factors JUNB and CUX1 were identified as essential for highly activated enhancers and promoters in CX adipose tissue. These results enrich the resources for the analysis of cis-regulatory elements in the pig genome and provide new basic data for further meat quality improvement through breeding in pigs.

Long noncoding RNA lncMREF promotes myogenic differentiation and muscle regeneration by interacting with the Smarca5/p300 complex.

Long noncoding RNAs (lncRNAs) play important roles in the spatial and temporal regulation of muscle development and regeneration. Nevertheless, the determination of their biological functions and mechanisms underlying muscle regeneration remains challenging. Here, we identified a lncRNA named lncMREF (lncRNA muscle regeneration enhancement factor) as a conserved positive regulator of muscle regeneration among mice, pigs and humans. Functional studies demonstrated that lncMREF, which is mainly expressed in differentiated muscle satellite cells, promotes myogenic differentiation and muscle regeneration. Mechanistically, lncMREF interacts with Smarca5 to promote chromatin accessibility when muscle satellite cells are activated and start to differentiate, thereby facilitating genomic binding of p300/CBP/H3K27ac to upregulate the expression of myogenic regulators, such as MyoD and cell differentiation. Our results unravel a novel temporal-specific epigenetic regulation during muscle regeneration and reveal that lncMREF/Smarca5-mediated epigenetic programming is responsible for muscle cell differentiation, which provides new insights into the regulatory mechanism of muscle regeneration.

Lewis x-carrying O-glycans are candidate modulators for conceptus attachment in pigs†.

Successful attachment of conceptus to the uterine luminal epithelium (LE) is crucial for establishing a functional placenta in pigs. However, the underlying mechanisms are yet to be elucidated. The uterine LE-conceptus interface is enriched in various glycoconjugates essential to implantation. Using MALDI-MS profiling, we identified for the first time the O-glycan repertoire of pig endometrium during the conceptus attachment stage. The expression pattern of blood group A, O(H), Lewis x, y, a, b (Lex, Ley, Lea, and Leb), the sialylated and sulfated Lex antigens in the uterine LE-conceptus interface was assessed using immunofluorescence assays. Notably, the Lex-carrying O-glycans exhibited a temporal-spatial expression pattern. They were absent in the endometrium on estrous cycle days but strongly and spatially presented in the conceptus and uterine LE to which the conceptus apposes during the early conceptus attachment stage. In addition, Lex-carrying O-glycans were co-localized with secreted phosphoprotein 1 (SPP1), a well-characterized factor that plays a role in promoting conceptus attachment through interacting with integrin αVß3 and integrin αVß6. Meanwhile, the immunoprecipitation assays revealed an interaction between the Lex-carrying O-glycans and SPP1, integrin αV, and integrin ß6. Furthermore, we provided evidence that the ß1,4-galactosyltransferase 1 (B4GALT1) gene is a potential regulator for Lex antigen expression in the uterine LE-conceptus interface during the early conceptus attachment stage. In conclusion, our findings show that Lex-carrying O-glycans, presumably dependent on B4GALT1 gene expression, might modulate conceptus attachment by interacting with the SPP1-integrin receptor complex in pigs.

Genome-scale CRISPR screen identifies TMEM41B as a multi-function host factor required for coronavirus replication.

Emerging coronaviruses (CoVs) pose a severe threat to human and animal health worldwide. To identify host factors required for CoV infection, we used α-CoV transmissible gastroenteritis virus (TGEV) as a model for genome-scale CRISPR knockout (KO) screening. Transmembrane protein 41B (TMEM41B) was found to be a bona fide host factor involved in infection by CoV and three additional virus families. We found that TMEM41B is critical for the internalization and early-stage replication of TGEV. Notably, our results also showed that cells lacking TMEM41B are unable to form the double-membrane vesicles necessary for TGEV replication, indicating that TMEM41B contributes to the formation of CoV replication organelles. Lastly, our data from a mouse infection model showed that the KO of this factor can strongly inhibit viral infection and delay the progression of a CoV disease. Our study revealed that targeting TMEM41B is a highly promising approach for the development of broad-spectrum anti-viral therapeutics.

Genomic evaluation for two-way crossbred performance in cattle.

BACKGROUND: Dairy cattle production systems are mostly based on purebreds, but recently the use of crossbreeding has received increased interest. For genetic evaluations including crossbreds, several methods based on single-step genomic best linear unbiased prediction (ssGBLUP) have been proposed, including metafounder ssGBLUP (MF-ssGBLUP) and breed-specific ssGBLUP (BS-ssGBLUP). Ideally, models that account for breed effects should perform better than simple models, but knowledge on the performance of these methods is lacking for two-way crossbred cattle. In addition, the differences in the estimates of genetic parameters (such as the genetic variance component and heritability) between these methods have rarely been investigated. Therefore, the aims of this study were to (1) compare the estimates of genetic parameters for average daily gain (ADG) and feed conversion ratio (FCR) between these methods; and (2) evaluate the impact of these methods on the predictive ability for crossbred performance. METHODS: Bivariate models using standard ssGBLUP, MF-ssGBLUP and BS-ssGBLUP for the genetic evaluation of ADG and FCR were investigated. To measure the predictive ability of these three methods, we estimated four estimators, bias, dispersion, population accuracy and ratio of population accuracies, using the linear regression (LR) method. RESULTS: The results show that, for both ADG and FCR, the heritabilities were low with the three methods. For FCR, the differences in the estimated genetic parameters were small between the three methods, while for ADG, those estimated with BS-ssGBLUP deviated largely from those estimated with the other two methods. Bias and dispersion were similar across the three methods. Population accuracies for both ADG and FCR were always higher with MF-ssGBLUP than with ssGBLUP, while with BS-ssGBLUP the population accuracy was highest for FCR and lowest for ADG. CONCLUSIONS: Our results indicate that in the genetic evaluation for crossbred performance in a two-way crossbred cattle production system, the predictive ability of MF-ssGBLUP and BS-ssGBLUP is greater than that of ssGBLUP, when the estimated variance components are consistent across the three methods. Compared with BS-ssGBLUP, MF-ssGBLUP is more robust in its superiority over ssGBLUP.

E-GWAS: an ensemble-like GWAS strategy that provides effective control over false positive rates without decreasing true positives.

BACKGROUND: Genome-wide association studies (GWAS) are an effective way to explore genotype-phenotype associations in humans, animals, and plants. Various GWAS methods have been developed based on different genetic or statistical assumptions. However, no single method is optimal for all traits and, for many traits, the putative single nucleotide polymorphisms (SNPs) that are detected by the different methods do not entirely overlap due to the diversity of the genetic architecture of complex traits. Therefore, multi-tool-based GWAS strategies that combine different methods have been increasingly employed. To take this one step further, we propose an ensemble-like GWAS strategy (E-GWAS) that statistically integrates GWAS results from different single GWAS methods. RESULTS: E-GWAS was compared with various single GWAS methods using simulated phenotype traits with different genetic architectures. E-GWAS performed stably across traits with different genetic architectures and effectively controlled the number of false positive genetic variants detected without decreasing the number of true positive variants. In addition, its performance could be further improved by using a bin-merged strategy and the addition of more distinct single GWAS methods. Our results show that the numbers of true and false positive SNPs detected by the E-GWAS strategy slightly increased and decreased, respectively, with increasing bin size and when the number and the diversity of individual GWAS methods that were integrated in E-GWAS increased, the latter being more effective than the bin-merged strategy. The E-GWAS strategy was also applied to a real dataset to study backfat thickness in a pig population, and 10 candidate genes related to this trait and expressed in adipose-associated tissues were identified. CONCLUSIONS: Using both simulated and real datasets, we show that E-GWAS is a reliable and robust strategy that effectively integrates the GWAS results of different methods and reduces the number of false positive SNPs without decreasing that of true positive SNPs.

The relationship between AGR2 levels and intestinal barrier function in high-fat diet animal models.

This study aimed to observe the effect of anterior gradient protein 2 (AGR2) levels on intestinal barrier function in HFD animal models. For this purpose, thirty healthy male clean-grade C57BL/6 mice were randomly separated into a normal control group and a high-fat group. The normal control group was fed a normal diet, while the high-fat group was fed an HFD for a total of 8 weeks. It collected body weight changes before and after modeling of two groups of rats and serum samples and detected fasting blood glucose, total cholesterol, triglycerides, AGR2, and diamine oxidase (DAO) concentrations. It collected the expression levels of AGR2 in the colon of rats after modeling, evaluated the permeability of the colon and small intestine barrier by Ussing chamber and Evan's blue (EB) methods, and analyzed the correlation between AGR2 levels and intestinal barrier function using Pearson correlation. Results showed that when the two groups of mice were fed for 8 weeks, their body weight, fasting blood glucose, total cholesterol, and triglycerides all met the characteristics of an HFD mouse model, and the model was successfully established. When the two groups of mice were fed for 8 weeks, the serum AGR2 concentration, relative expression of AGR2 in colon tissue, Gt, and EB content of the high-fat group mice were higher than those of the normal control group, and the difference was significant (P<0.05); Meanwhile, the serum DAO concentration and Isc of the high-fat group mice were lower than those of the normal control group, with statistically significant differences (P<0.05); The relative expression levels of serum AFR2 and colon AGR2 were negatively correlated with Isc (r=-0.503, -0.623, P<0.05), and positively correlated with Gt (r=0.461, 0.560, P<0.05). There was a homogeneous distribution characteristic between the relative expression levels of serum AFR2 and serum DAO, colon AGR2, and Isc variables. It was concluded that HFD could upregulate the expression of AGR2 in mice, downregulate the level of DAO, and damage the intestinal barrier function of mice. Both serum AGR2 concentration and colonic AGR2 relative expression can participate in the regulation of colonic intestinal barrier function and can serve as potential indicators for evaluating intestinal barrier damage.

Cognitive status and its risk factors in patients with hypertension and diabetes in a low-income rural area of China: A cross-sectional study.

OBJECTIVES: The proportion of older people with dementia in China is gradually increasing with the increase in the aging population over recent years. Hypertension and diabetes are common non-communicable diseases among rural populations in China. However, it remains unclear whether these conditions affect the occurrence and development of cognitive impairment as there is limited research on cognitive status and its risk factors among residents of rural areas. METHODS: A multi-stage stratified cluster random sampling method was used to select 5400 participants from rural permanent residents. A self-designed structured questionnaire was used to investigate demographic data of the participants. Cognitive function was assessed using the Montreal Cognitive Function Assessment Scale (MoCA). The results were analyzed using chi-square test, ANOVA and multiple linear regression analysis. RESULTS: A total of 5028 participants returned the survey, giving a response rate of 93.1%. Higher education (odds ratio (OR) = 3.2, 95% confidence interval (CI) 2.87-3.54, p < 0.001), higher income (OR = 1.61, 95% CI 1.16-2.07, p < 0.001), and dietary control (OR = 0.66, 95%CI 0.34-0.98, p < 0.001) were protective factors. A visual representation of the relationship between annual income and MoCA score showed an inverted U-curve, the group with an annual income of 6000-7999 RMB had a maximum OR of 1.93 (95%CI 0.12-2.74, p < 0.001). While difficulty in maintaining sleep were risk factors for cognitive impairment (OR = -2.28, 95% CI-4.18-0.39, p = 0.018). CONCLUSIONS: Participants with middle incomes had better cognitive status than those with the highest incomes. Higher education, proper diet control and good sleep are beneficial to the cognitive status of residents in rural areas.

Cathepsin C promotes colorectal cancer metastasis by regulating immune escape through upregulating CSF1.

Since metastasis remains the primary reason for colorectal cancer (CRC) associated death, a better understanding of the molecular mechanism underlying CRC metastasis is urgently needed. Here, we elucidated the role of Cathepsin C (CTSC) in promoting CRC metastasis. The expression of CTSC was detected by real-time PCR and immunohistochemistry in the human CRC cohort. The metastatic capacities of CTSC-mediated metastasis were analyzed by in vivo metastasis model. Elevated CSTC expression was positively associated with tumor differentiation, tumor invasion, lymph node metastasis, and AJCC stage and indicated poor prognosis in human CRC. CTSC overexpression in CRC cells promoted myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs) recruitment by the CSF1/CSF1R axis. In contrast, the knockdown of CSF1 reduced CTSC-mediated MDSCs and TAMs infiltration and CRC metastasis. Depletion of either MDSCs or TAMs decreased CTSC-mediated CRC metastasis. In human CRC tissues, CTSC expression was positively associated with intratumoral MDSCs and TAMs infiltration. Furthermore, the combination of CTSC inhibitor AZD7986 and anti-PD-L1 antibody blocked CTSC-induced CRC metastasis. CTSC overexpression promoted MDSCs and TAMs infiltration by CSF1/CSF1R axis. Interruption of this oncogenic loop may provide a promising treatment strategy for inhibiting CTSC-driven CRC metastasis.

Pleiotropic architectures of porcine immune and growth trait pairs revealed by a self-product-based transcriptome method.

Pleiotropy is an important biological phenomenon with complicated genetic architectures for multiple traits. To date, pleiotropy has been mainly identified by multi-trait genome-wide association studies, but this method has its disadvantages, and new developments for pleiotropy detection methods are needed. Here we define a novel metric, self-product, to measure individual-level co-variation of two traits, and develop a novel self-product-based transcriptome method to detect pleiotropic genes (PGs). Our method was tested using four immune-growth trait pairs and four immune-immune trait pairs in pigs. Comparative transcriptome analyses identified hundreds of candidate PGs related to eight trait pairs from two tails of self-product distribution. Gene Ontology enrichment analysis indicated that most of identified PGs were involved in immune- or growth-related biological processes. We established PG interaction networks to exhibit core genes shared by eight trait pairs, of which CCL5 and IL-10 genes were the hub genes. Genetic association analyses showed that SmaI-polymorphisms of CCL5 and IL-10 genes had significant associations with phenotypic co-variations of multiple trait pairs, indicating that the variants in pleiotropic genes were also pleiotropic variants. Taken together, the validity of our proposed method was preliminarily verified, and our findings provide new insights into the genetic basis of pleiotropic architectures of immune and growth trait pairs in pigs.

Enhancer-promoter interaction maps provide insights into skeletal muscle-related traits in pig genome.

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.

Gene expression and chromatin conformation differs between worker bees performing different tasks.

BACKGROUND: Revealing the effect of transcriptomic regulation on behavioral differences is a fundamental goal in biology, but the relationship between gene regulatory networks and individual behavior differences remains largely unknown. Honey bees are considered as good models for studying the mechanisms underlying gene expression changes and behavioral differences since they exhibit strong and obvious differences in tasks between individuals. The cis-regulatory regions usually contain the binding sites of diverse transcription factor (TFs) influencing bee behavior. Thus, the identification of cis-regulatory elements in the brains across different behavioral states is important for understanding how genomic and transcriptomic variations affect different tasks in honeybees. METHODS: In this study, we employed transcriptome and genome-wide chromatin accessibility assays to analyze brain tissues of honey bees in different behavioral states for examining the relationship between individual behavior differences and brain gene expression changes. We also used the obtained open chromatin regions to identify cis-motifs associating differentially expressed TFs and genes in order to reveal the transcriptional regulatory mechanism related to the different tasks. RESULTS: We identified genetic regulatory modules regulating different tasks that contained key TFs (CTCF, Trl and schlank) associated with open chromatin regions enriched for DNA sequence motifs belonging to the family of the corresponding TFs. The most prominent transcriptomic changes, which correlated with chromatin accessibility modifications within their proximal promoter regions, occurred in nervous system development, and were associated with behavior switch. CONCLUSIONS: Our results revealed the regulatory landscape among three behavioral stages in honeybees and identified interactive molecular networks regulating different tasks. These results provide a comprehensive insight into behavioral differences of honeybees, which offers reference for future study.

Long-range interaction within the chromatin domain determines regulatory patterns in porcine skeletal muscle.

Spatial chromatin structure is crucial for understanding the early growth and development of porcine skeletal muscle. However, its characteristic of 3D architecture and elaborate regulation of gene transcription remains unclear. In this study, ChIA-PET method is used to study the changes of early chromatin three-dimensional structure in skeletal muscle of lean type Yorkshire pig and fat type Meishan pig. Integrating the in situ Hi-C data revealed the 3D architecture and long-range interaction of the porcine muscle. The results showed the CTCF/RNAPII mediated long-range interaction shapes the different chromatin architecture and dominates the unique regulation of enhancers. In addition, the results revealed that key myogenic genes like ssc-mir-1 had a unique enhancer regulation function in myogenesis. Interestingly, the FGF6 gene is of breed-specific regulation, implying the difference between two breeds in skeletal muscle development. Our research thus may provide a clue for the porcine genetic improvement of skeletal muscle.