The identification of genes differentially expressed in the skeletal muscle of pigs displaying distinct growth and fatness profiles might contribute to identify the genetic factors that influence the phenotypic variation of such traits. So far, the majority of porcine transcriptomic studies have investigated differences in gene expression at a global scale rather than at the mRNA isoform level. In the current work, we have investigated the differential expression of mRNA isoforms in the gluteus medius (GM) muscle of 52 Duroc HIGH (increased backfat thickness, intramuscular fat and saturated and monounsaturated fatty acids contents) and LOW pigs (opposite phenotype, with an increased polyunsaturated fatty acids content). Our analysis revealed that 10.9% of genes expressed in the GM muscle generate alternative mRNA isoforms, with an average of 2.9 transcripts per gene. By using two different pipelines, one based on the CLC Genomics Workbench and another one on the STAR, RSEM and DESeq2 softwares, we have identified 10 mRNA isoforms that both pipelines categorize as differentially expressed in HIGH vs LOW pigs (P-value < 0.01 and ±0.6 log2fold-change). Only five mRNA isoforms, produced by the ITGA5, SEMA4D, LITAF, TIMP1 and ANXA2 genes, remain significant after correction for multiple testing (q-value < 0.05 and ±0.6 log2fold-change), being upregulated in HIGH pigs. The increased levels of specific ITGA5, LITAF, TIMP1 and ANXA2 mRNA isoforms in HIGH pigs is consistent with reports indicating that the overexpression of these four genes is associated with obesity and metabolic disorders in humans. A broader knowledge about the functional attributes of these mRNA variants would be fundamental to elucidate the consequences of transcript diversity on the determinism of porcine phenotypes of economic interest.

We have used a RNA-seq approach to investigate differential expression in the skeletal muscle of swine (N = 52) with divergent lipid profiles i.e. HIGH (increased intramuscular fat and muscle saturated and monounsaturated fatty acid contents, higher serum lipid concentrations and fatness) and LOW pigs (leaner and with an increased muscle polyunsaturated fatty acid content). The number of mRNAs and non-coding RNAs (ncRNAs) expressed in the porcine gluteus medius muscle were 18,104 and 1,558, respectively. At the nominal level of significance (P-value ≤ 0.05), we detected 1,430 mRNA and 12 non-coding RNA (ncRNA) transcripts as differentially expressed (DE) in the gluteus medius muscle of HIGH vs LOW pigs. This smaller contribution of ncRNAs to differential expression may have biological and technical reasons. We performed a second analysis, that was more stringent (P-value ≤ 0.01 and fold-change ≥ 1.5), and only 96 and 0 mRNA-and ncRNA-encoding genes happened to be DE, respectively. The subset of DE mRNA genes was enriched in pathways related with lipid (lipogenesis and triacylglycerol degradation) and glucose metabolism. Moreover, HIGH pigs showed a more lipogenic profile than their LOW counterparts.

Background: Genetic pressure in animal breeding is sparking the interest of breeders for selecting elite boars with higher sperm quality to optimize ejaculate doses and fertility rates. However, the molecular basis of sperm quality is not yet fully understood. Our aim was to identify candidate genes, pathways and DNA variants associated to sperm quality in swine by analysing 25 sperm-related phenotypes and integrating genome-wide association studies (GWAS) and RNA-seq under a systems biology framework. Results: By GWAS, we identified 12 quantitative trait loci (QTL) associated to the percentage of head and neck abnormalities, abnormal acrosomes and motile spermatozoa. Candidate genes included CHD2 , KATNAL2 , SLC14A2 and ABCA1. By RNA-seq, we identified a wide repertoire of mRNAs (e.g. PRM1 , OAZ3 , DNAJB8 , TPPP2 and TNP1 ) and miRNAs (e.g. ssc-miR-30d, ssc-miR-34c, ssc-miR-30c-5p, ssc-miR-191, members of the let-7 family and ssc-miR-425-5p) with functions related to sperm biology. We detected 6128 significant correlations (P-value ≤ 0.05) between sperm traits and mRNA abundances. By expression (e)GWAS, we identified three trans-expression QTL involving the genes IQCJ , ACTR2 and HARS . Using the GWAS and RNA-seq data, we built a gene interaction network. We considered that the genes and interactions that were present in both the GWAS and RNA-seq networks had a higher probability of being actually involved in sperm quality and used them to build a robust gene interaction network. In addition, in the final network we included genes with RNA abundances correlated with more than four semen traits and miRNAs interacting with the genes on the network. The final network was enriched for genes involved in gamete generation and development, meiotic cell cycle, DNA repair or embryo implantation. Finally, we designed a panel of 73 SNPs based on the GWAS, eGWAS and final network data, that explains between 5% (for sperm cell concentration) and 36% (for percentage of neck abnormalities) of the phenotypic variance of the sperm traits. Conclusions: By applying a systems biology approach, we identified genes that potentially affect sperm quality and constructed a SNP panel that explains a substantial part of the phenotypic variance for semen quality in our study and that should be tested in other swine populations to evaluate its relevance for the pig breeding sector.

Feed efficiency (FE) has a major impact on the economic sustainability of pig production. We used a systems-based approach that integrates single nucleotide polymorphism (SNP) co-association and gene-expression data to identify candidate genes, biological pathways, and potential predictors of FE in a Duroc pig population. We applied an association weight matrix (AWM) approach to analyse the results from genome-wide association studies (GWAS) for nine FE associated and production traits using 31K SNPs by defining residual feed intake (RFI) as the target phenotype. The resulting co-association network was formed by 829 SNPs. Additive effects of this SNP panel explained 61% of the phenotypic variance of RFI, and the resulting phenotype prediction accuracy estimated by cross-validation was 0.65 (vs. 0.20 using pedigree-based best linear unbiased prediction and 0.12 using the 31K SNPs). Sixty-eight transcription factor (TF) genes were identified in the co-association network; based on the lossless approach, the putative main regulators were COPS5, GTF2H5, RUNX1, HDAC4, ESR1, USP16, SMARCA2 and GTF2F2. Furthermore, gene expression data of the gluteus medius muscle was explored through differential expression and multivariate analyses. A list of candidate genes showing functional and/or structural associations with FE was elaborated based on results from both AWM and gene expression analyses, and included the aforementioned TF genes and other ones that have key roles in metabolism, e.g. ESRRG, RXRG, PPARGC1A, TCF7L2, LHX4, MAML2, NFATC3, NFKBIZ, TCEA1, CDCA7L, LZTFL1 or CBFB. The most enriched biological pathways in this list were associated with behaviour, immunity, nervous system, and neurotransmitters, including melatonin, glutamate receptor, and gustation pathways. Finally, an expression GWAS allowed identifying 269 SNPs associated with the candidate genes’ expression (eSNPs). Addition of these eSNPs to the AWM panel of 829 SNPs did not improve the accuracy of genomic predictions. Candidate genes that have a direct or indirect effect on FE-related traits belong to various biological processes that are mainly related to immunity, behaviour, energy metabolism, and the nervous system. The pituitary gland, hypothalamus and thyroid axis, and estrogen signalling play fundamental roles in the regulation of FE in pigs. The 829 selected SNPs explained 61% of the phenotypic variance of RFI, which constitutes a promising perspective for applying genetic selection on FE relying on molecular-based prediction.

Meat quality traits have an increasing importance in the pig industry because of their strong impact on consumer acceptance. Herewith, we have combined phenotypic and microarray expression data to map loci with potential effects on five meat quality traits recorded in thelongissimus dorsi(LD) andgluteus medius(GM) muscles of 350 Duroc pigs,i.e.pH at 24 hours post-mortem (pH₂₄), electric conductivity (CE) and muscle redness (a*), lightness (L*) and yellowness (b*). We have found significant genome-wide associations for CE of LD on SSC4 (~104 Mb), SSC5 (~15 Mb) and SSC13 (~137 Mb), while several additional regions were significantly associated with meat quality traits at the chromosome-wide level. There was a low positional concordance between the associations found for LD and GM traits, a feature that reflects the existence of differences in the genetic determinism of meat quality phenotypes in these two muscles. The performance of an eQTL search for SNPs mapping to the regions associated with meat quality traits demonstrated that the GM a* SSC3 and pH₂₄SSC17 QTL display positional concordance with cis-eQTL regulating the expression of several genes with a potential role on muscle metabolism.

The genetic determinism of blood lipid concentrations, the main risk factor for atherosclerosis, is practically unknown in species other than human and mouse. Even in model organisms, little is known about how the genetic determinants of lipid traits are modulated by age-specific factors. To gain new insights into this issue, we have carried out a genome-wide association study (GWAS) for cholesterol (CHOL), triglyceride (TRIG) and low (LDL) and high (HDL) density lipoprotein concentrations measured in Duroc pigs at two time points (45 and 190 days). Analysis of data with mixed-model methods (EMMAX, GEMMA, GenABEL) and PLINK showed a low positional concordance between trait-associated regions (TARs) for serum lipids at 45 and 190 days. Besides, the proportion of phenotypic variance explained by SNPs at these two time points was also substantially different. The four analyses consistently detected two regions on SSC3 (124 Mb, CHOL and LDL at 190 days) and SSC6 (135 Mb, CHOL and TRIG at 190 days) with highly significant effects on the porcine blood lipid profile. Moreover, we have found that SNP variation within SSC3, SSC6, SSC10, SSC13 and SSC16 TARs is associated with the expression of several genes mapping to other chromosomes and related to lipid metabolism. Our data demonstrate that the effects of genomic determinants influencing lipid concentrations in pigs, as well as the amount of phenotypic variance they explain, are influenced by age-related factors.