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

Abstract

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.

RNA-Chrom: a manually-curated analytical database of RNA–chromatin interactome

“…Another group of methods is designed to search for all possible DNA and RNA contacts in a cell (MARGI [12], GRID-seq [13, 14, 15], ChAR-seq [16], iMARGI [17, 18], RADICL-seq [19], Red-C [20]). We will call this group of methods “all RNAs to all DNA loci”, or “all-to-all”.…”

Integrating Transcriptomic and ChIP-Seq Reveals Important Regulatory Regions Modulating Gene Expression in Myometrium during Implantation in Pigs

“…In humans and other animals, a large number of cis-regulatory elements have been determined using the ChIP-seq technique. Many cis-regulatory elements significantly contribute to phenotypic variation in complex traits [48,83,84]. We then examined the elements that may regulate the differential expression of the ADRA2A and some immune response genes identified in the study.…”

“…Our previous study has identified the genomic localization of H3K4me3 and H3K27ac in pig placentas by chromatin immunoprecipitation sequencing (ChIP-seq) [43]. Recently, many studies investigated the architecture of chromatin in multiple tissues from different pig breeds and characterized the function of the cis-regulatory elements marked by H3K4me3 and H3K27ac in the pig genome [44][45][46][47][48]. A recent study revealed that the myometrium undergoes a cascade of epigenetic events during active labor and postpartum in the mouse model, indicating that histone modifications contribute to regulating gene expression in the myometrium [49].…”

Advances in the discovery of genetic elements underlying longissimus dorsi muscle growth and development in the pig

“…In addition, integrative analyses of GWAS and epigenomics provide important insights and resources for understanding both methylation and histone epigenetic regulation, as well as their interaction and roles in complex trait genetics (Li et al, 2022). Integration of GWAS with others omics data, including metabolomics (Wang & Kadarmideen, 2020), proteomics and the microbiome (Power et al, 2017), has been applied to identify relevant SNPs of potential importance.…”

“…However, a large proportion of those genetic loci do not affect protein‐coding regions directly, and remain mostly intergenic or unannotated, which hinders the further interpretation of the molecular basis of muscle development. To bridge this gap between genetic variation and muscle phenotype, many studies have been designed to profile a diversity of molecular processes affected by non‐coding variants (Li et al, 2022), which reveals thousands of genetic variants impacting gene expression, mRNA splicing, DNA methylation, histone acetylation, RNA editing and protein levels (Figure 2). Joint analysis of expression QTL (eQTL) with phenotypic QTL (pQTL) segregation in pig longissimus dorsi muscle revealed many candidate genes significantly associated with pQTL for growth traits (Cheng et al, 2019; Velez‐Irizarry et al, 2019).…”