Epistatic effects on carcass composition and meat quality in pigs

The analysis of epistasis is not yet a routine, but it has been shown by few studies in livestock animals that interaction effects contribute with considerable proportions to the phenotypic variance. Therefore the objective of this study was to evaluate the importance of epistatic effects in the Bonn Duroc × Pietrain resource population (DuPi) for carcass composition and meat quality traits. This population was investigated so far for single quantitative trait loci (QTL) considering additive, dominance and imprinting effects. In the first approach, 585 F2 pigs of DuPi were used to perform a two dimensional QTL scan. All animals were genotyped using 125 genetic markers (microsatellites and SNP) spread across the 18 pig autosomes. Phenotypic information for 26 carcass composition and meat quality traits was available for all F2 animals. Linkage analysis was performed in a two-step procedure using a maximum likelihood approach implemented in the QxPak program. A number of 56 interacting QTL was observed for 19 different traits. These interacting QTL pairs explained up to 8% of the phenotypic variance. Based on these results a variety of networks among chromosomal regions throughout the porcine genome were identified. Moreover, considering interactions between loci allowed to detect several novel QTL and trait-specific relationships of loci within and across chromosomes. In a second step the causes of an epistatic QTL pair between Sus scrofa chomsosome (SSC) 8 and 15 influencing pH value 1 h post mortem in M. long. dorsi were investigated. Gene expression data was obtained from loin tissue of 74 F2 which were selected from 585 animals. Gene expression profiles, genotypes and phenotypes of these pigs were investigated jointly applying three alternative models. Method A considered the phenotypic differences in pH values between groups of pigs with extreme values. Method B was based on differences between the genotype combinations of relevant epistatic QTL pairs between SSC8 and SSC15. Finally, method C was a linear model comprising the epistatic QTL genotypes as fixed effects. Overall method A, B and C revealed 1182, 480 and 1823 differentially expressed or associated genes, respectively. By means of a functional analysis it was possible to set up networks which contained mainly interactions between genes located within the specific regions on SSC8 and SSC15 and allowed a meaningful biological discussion. Expression QTL (eQTL) analyses were performed for functional and positional transcripts in order to assume regulations patterns. This approach showed that combining phenotype, genotype and transcriptome data helped to uncover the involved molecules of observed epistasis. In conclusion, this study revealed the importance of epistasis for the expression of complex traits. Furthermore, it was possible to uncover potential biological causes of observed epistatic QTL pairs applying different statistical models as well as bioinformatic tools.Epistatische Effekte auf die Schlachtkörperzusammensetzung und Fleischqualität beim Schwein Epistasie wird bisher nur selten in Untersuchungen komplexer Merkmale berücksichtigt. Dabei wurde bereits in einer Vielzahl von Studien gezeigt, dass die zu beobachtenden Variationen von quantitativen Merkmalen nicht alleine durch additive Effekte erklärt werden können. Daher war das Ziel dieser Studie, die Bedeutung von epistatischen Effekten auf Schlachtkörper- und Fleischqualitätsmerkmale innerhalb der Bonner Duroc × Piétain Ressourcenpopulation (DuPi) zu untersuchen. Bisherige Studien in der DuPi Population berücksichtigten nur einfache Quantitative Trait Loci (QTL), die additive, Dominanz oder Imprintingeffekte beinhalteten. In der ersten Analyse wurden 585 Schweine der F2-Generation verwendet um epistatische QTL Paare zu identifizieren. Diese Tiere sind mit 125 genetischen Markern genotypisiert worden, die sich gleichmäßig über alle 18 Autosomen verteilten. Als phänotypische Informationen wurden 26 verschiedene Schlachtkörper- und Fleischqualitätsmerkmale erfasst. Die Koppelungsanalyse wurde in einer zweistufigen Prozedur innerhalb des Programms Qxpak, basierend auf einem Maximum Likelihood Ansatzes, durchgeführt. Insgesamt konnten 56 interagierende QTL für 19 verschiedene Merkmale beobachtet werden. Für Schlachtkörpermerkmale konnten 17 und für Fleischqualitätsmerkmale 39 epistatische QTL Paare identifiziert werden. Diese interagierenden QTL Paare erklärten bis zu 8% der phänotypischen Varianz. Auf Grundlage dieser Ergebnisse konnten verschiedene Netzwerkstrukturen zwischen den verschiedenen Chromosomensegmenten identifiziert werden. Die Berücksichtigung der Beziehung zwischen zwei Genorten ermöglichte es einige neue QTL zu identifizieren, sowie merkmalsbezogene Beziehungen innerhalb eines Chromosoms und zwischen Chromosomen zu charakterisieren. In einer zweiten Untersuchung wurde versucht, die biologischen Gründe des epistatischen QTL Paares zwischen den porcinen Chromosomen (SSC) 8 und 15 aufzuklären. Für die Analyse standen die Muskeltranskriptionsprofile von 74 ausgewählten F2 Tieren der DuPi Population zur Verfügung. Die Interaktion zwischen SSC8 und 15 war assoziiert mit früh post mortalem pH Wert im M. long. dorsi. Genexpressionsprofile, Genotypen und Phänotypen dieser Tiere wurden mit drei verschiedenen statistischen Ansätzen und Modellen untersucht. Methode A berücksichtigte phänotypische Unterschiede des pH Wertes zwischen zwei Tiergruppen mit extremen Werten, Methode B basierte auf den Unterschieden zwischen den Genotypgruppen des relevanten epistatischen QTL Paares und Methode C berücksichtigte die Genotypen des epistatischen QTL Paares als fixen Effekt innerhalb eines linearen Modells. Insgesamt ließen sich mit Methode A, B und C 1182 und 480 unterschiedlich exprimierte Gene sowie 1823 linear assoziierte Gene identifizieren. Durch funktionale Analysen war es möglich Netzwerke zu erstellen, die nur Gene beinhalteten, die innerhalb der epistatischen Regionen lagen. Die daraus erzielten Ergebnisse erlaubten eine biologisch sinnvolle Diskussion möglicher Kandidatengene der epistatischen Regionen. Des Weiteren wurden Expressions-QTL Analysen durchgeführt um eine Aussage über die Genregulation zu treffen. Schlussfolgernd konnte gezeigt werden, dass Epistasie eine bedeutende Rolle bei der Ausprägung von komplexen Merkmalen beim Schwein hat. Es war des Weiteren möglich biologische Ursachen beobachteter epistatischer Beziehungen mit Hilfe verschiedener statistischer Methoden zu identifizieren

Epistatic QTL pairs associated with meat quality and carcass composition traits in a porcine Duroc × Pietrain population

Quantitative trait loci (QTL) analyses in pig have revealed numerous individual QTL affecting growth, carcass composition, reproduction and meat quality, indicating a complex genetic architecture. In general, statistical QTL models consider only additive and dominance effects and identification of epistatic effects in livestock is not yet widespread. The aim of this study was to identify and characterize epistatic effects between common and novel QTL regions for carcass composition and meat quality traits in pig

Molecular genetic analysis of boar taint

Boar taint is an unpleasant smell and taste of pork meat derived from some entire male pigs. The main causes of boar taint are the two compounds androstenone (5α-androst-16-en-3-one) and skatole (3-methylindole). It is crucial to understand the genetic mechanism of boar taint to select pigs for lower androstenone levels and thus reduce boar taint. The aim of this study was the identification of genes and pathways influencing boar taint and involved in androstenone and skatol metabolism. Therefore polymorphisms in relevant genes were identified and transcriptome analysis using Affymetrix-Chips and RNA-Seq in the two major organs involved in androstenone metabolism i.e the testis and the liver was performed. Differentially regulated genes in high androstenone testis and liver samples were involved in metabolic processes such as retinol metabolism, metabolism of xenobiotics by cytochrome P450 and fatty acid metabolism. Moreover, a number of genes encoding biosynthesis of steroids were highly expressed in high androstenone testis samples. Gene polymorphism analysis revealed potential mutations in HSP40, IGFBP1, CYP7A1 and FMO5 genes affecting androstenone levels. Further studies are required for verify the role of candidate genes to be used in genomic selection against boar taint in pig breeding programs. According to the results of association studies, FMO5, CYP21 and ESR1 turned out to be the most promising candidates for boar taint

Deciphering transcriptome profiles of peripheral blood mononuclear cells in response to PRRSV vaccination in pigs

List of DEGs in PBMCs of pigs at 6 hpv of PRRSV vaccination in pigs compared to control. (XLSX 57 kb

Transcriptome profile of lung dendritic cells after in vitro porcine reproductive and respiratory syndrome virus (PRRSV) infection

The porcine reproductive and respiratory syndrome (PRRS) is an infectious disease that leads to high financial and production losses in the global swine industry. The pathogenesis of this disease is dependent on a multitude of factors, and its control remains problematic. The immune system generally defends against infectious diseases, especially dendritic cells (DCs), which play a crucial role in the activation of the immune response after viral infections. However, the understanding of the immune response and the genetic impact on the immune response to PRRS virus (PRRSV) remains incomplete. In light of this, we investigated the regulation of the host immune response to PRRSV in porcine lung DCs using RNA-sequencing (RNA-Seq). Lung DCs from two different pig breeds (Pietrain and Duroc) were collected before (0 hours) and during various periods of infection (3, 6, 9, 12, and 24 hours post infection (hpi)). RNA-Seq analysis revealed a total of 20,396 predicted porcine genes, which included breed-specific differentially expressed immune genes. Pietrain and Duroc infected lung DCs showed opposite gene expression courses during the first time points post infection. Duroc lung DCs reacted more strongly and distinctly than Pietrain lung DCs during these periods (3, 6, 9, 12 hpi). Additionally, cluster analysis revealed time-dependent co-expressed groups of genes that were involved in immune-relevant pathways. Key clusters and pathways were identified, which help to explain the biological and functional background of lung DCs post PRRSV infection and suggest IL-1β1 as an important candidate gene. RNA-Seq was also used to characterize the viral replication of PRRSV for each breed. PRRSV was able to infect and to replicate differently in lung DCs between the two mentioned breeds. These results could be useful in investigations on immunity traits in pig breeding and enhancing the health of pigs

Livestock 2.0 – genome editing for fitter, healthier, and more productive farmed animals

Abstract The human population is growing, and as a result we need to produce more food whilst reducing the impact of farming on the environment. Selective breeding and genomic selection have had a transformational impact on livestock productivity, and now transgenic and genome-editing technologies offer exciting opportunities for the production of fitter, healthier and more-productive livestock. Here, we review recent progress in the application of genome editing to farmed animal species and discuss the potential impact on our ability to produce food

Comparison of the choice of animals for re-sequencing in two maternal pig lines

Next-generation sequencing is a promising approach for the detection of causal variants within previously identified quantitative trait loci. Because of the costs of re-sequencing experiments, this application is currently mainly restricted to subsets of animals from already genotyped populations. Imputation from a lower to a higher marker density could represent a useful complementary approach. An analysis of the literature shows that several strategies are available to select animals for re-sequencing. This study demonstrates an animal selection workflow under practical conditions. Our approach considers different data sources and limited resources such as budget and availability of sampling material. The workflow combines previously described approaches and makes use of genotype and pedigree information from a Landrace and Large White population. Genotypes were phased and haplotypes were accurately estimated with AlphaPhase. Then, AlphaSeqOpt was used to optimize selection of animals for re-sequencing, reflecting the existing diversity of haplotypes. AlphaSeqOpt and ENDOG were used to select individuals based on pedigree information and by taking into account key animals that represent the genetic diversity of the populations. After the best selection criteria were determined, a subset of 57 animals was selected for subsequent re-sequencing. In order to evaluate and assess the advantage of this procedure, imputation accuracy was assessed by setting a set of single nucleotide polymorphism (SNP) chip genotypes to missing. Accuracy values were compared to those of alternative selection scenarios and the results showed the clear benefits of a targeted selection within this practical-driven approach. Especially imputation of low-frequency markers benefits from the combined approach described here. Accuracy was increased by up to 12% compared to a randomized or exclusively haplotype-based selection of sequencing candidates

Different Statistical Approaches to Investigate Porcine Muscle Metabolome Profiles to Highlight New Biomarkers for Pork Quality Assessment.

The aim of this study was to elucidate the underlying biochemical processes to identify potential key molecules of meat quality traits drip loss, pH of meat 1 h post-mortem (pH1), pH in meat 24 h post-mortem (pH24) and meat color. An untargeted metabolomics approach detected the profiles of 393 annotated and 1,600 unknown metabolites in 97 Duroc × Pietrain pigs. Despite obvious differences regarding the statistical approaches, the four applied methods, namely correlation analysis, principal component analysis, weighted network analysis (WNA) and random forest regression (RFR), revealed mainly concordant results. Our findings lead to the conclusion that meat quality traits pH1, pH24 and color are strongly influenced by processes of post-mortem energy metabolism like glycolysis and pentose phosphate pathway, whereas drip loss is significantly associated with metabolites of lipid metabolism. In case of drip loss, RFR was the most suitable method to identify reliable biomarkers and to predict the phenotype based on metabolites. On the other hand, WNA provides the best parameters to investigate the metabolite interactions and to clarify the complex molecular background of meat quality traits. In summary, it was possible to attain findings on the interaction of meat quality traits and their underlying biochemical processes. The detected key metabolites might be better indicators of meat quality especially of drip loss than the measured phenotype itself and potentially might be used as bio indicators

Genome-wide association analyses for boar taint components and testicular traits revealed regions having pleiotropic effects

The aim of this study was to perform a genome-wide association analyses (GWAS) for androstenone, skatole and indole in different Pietrain sire lines and compare the results with previous findings in purebred populations. Furthermore, the genetic relationship of androstenone and skatole were investigated with respect to pleiotropy. In order to characterize the performance of intact boars, crossbred progenies of 136 Pietrain boars mated to crossbred sows from three different breeding companies were tested on four test stations. A total of 598 boars were performance tested according to the rules of stationary performance testing in Germany. Beside common fattening and carcass composition traits, the concentrations of the boar taint components and testicular size parameters were recorded. All boars were genotyped with the PorcineSNP60 Illumina BeadChip. The GWAS were performed using the whole data set as well as in sub groups according to the line of origin. Besides an univariate GWAS approach, principal component (PC) techniques were applied to identify common expression pattern affecting the biosynthesis and the metabolism of androstenone