Microsatellite mapping of quantitative trait loci affecting meat quality, stress hormones and production traits in Duroc × Large White F2 pigs

Biosecurity and Lairage Time versus Pork Meat Quality Traits in a Farm-Abattoir Continuum

The modern pig production chain is increasingly focused on biosecurity, quality, and safety of meat and is associated with many challenges impacting world meat markets, such as animal disease outbreaks and sanitary restrictions, trade regulations and quality requirements. To overcome such challenges and assure more consistent pork meat quality (and safety), there is a need to develop an effective and reliable monitoring system in a farm-abattoir continuum that can be based on selected biomarkers. This study assessed interrelations of selected stress and inflammation biomarkers (acute phase proteins (APP)) between farm biosecurity score versus pork meat quality traits after two different lairage periods. Briefly, the maximum recorded levels of stress hormones (436.2 and 241.2 ng/mL, for cortisol and Chromogranin A (CgA), respectively) and APP (389.4 and 400.9 μg/mL, Pig Major Acute Proteins (MAP) and Haptoglobin (Hp), respectively) at four commercial farms were within the recommended threshold values. Cortisol and APP were negatively correlated to the internal and total biosecurity scores of farms. The increase of level of both sets of biomarkers was found at bleeding (after transportation and lairage period), but with lower values after long (18-20 h) versus short (1-3 h) lairage lay-over time. In general, negative correlation was confirmed between stress and inflammation biomarkers and carcass/meat quality traits. The farm total biosecurity level significantly affected chilling yield, meat temperature, and a* value. Pig-MAP emerged as a good biomarker with a promising potential for assessment and anticipation of broad aspects in the pork meat chain. It can be used for detection of failures in the pig production system and might be incorporated in certification programs for the pork meat industry.

Identify known and novel candidate genes associated with backfat thickness in Duroc pigs by large-scale genome-wide association analysis

Backfat thickness (BFT) is complex and economically important traits in the pig industry, since it reflects fat deposition and can be used to measure the carcass lean meat percentage in pigs. In this study, all 6,550 pigs were genotyped using the Geneseek Porcine 50K SNP Chip to identify SNPs related to BFT and to search for candidate genes through genome-wide association analysis in two Duroc populations. In total, 80 SNPs, including 39 significant and 41 suggestive SNPs, and 6 QTLs were identified significantly associated with the BFT. In addition, 9 candidate genes, including a proven major gene MC4R, 3 important candidate genes (RYR1, HMGA1, and NUDT3) which were previously described as related to BFT, and 5 novel candidate genes (SIRT2, NKAIN2, AMH, SORCS1, and SORCS3) were found based on their potential functional roles in BFT. The functions of candidate genes and gene set enrichment analysis indicate that most important pathways are related to energy homeostasis and adipogenesis. Finally, our data suggest that most of the candidate genes can be directly used for genetic improvement through molecular markers, except that the MC4R gene has an antagonistic effect on growth rate and carcass lean meat percentage in breeding. Our results will advance our understanding of the complex genetic architecture of BFT traits and laid the foundation for additional genetic studies to increase carcass lean meat percentage of pig through marker-assisted selection and/or genomic selection.

Omics Application in Animal Science-A Special Emphasis on Stress Response and Damaging Behaviour in Pigs

Increasing stress resilience of livestock is important for ethical and profitable meat and dairy production. Susceptibility to stress can entail damaging behaviours, a common problem in pig production. Breeding animals with increased stress resilience is difficult for various reasons. First, studies on neuroendocrine and behavioural stress responses in farm animals are scarce, as it is difficult to record adequate phenotypes under field conditions. Second, damaging behaviours and stress susceptibility are complex traits, and their biology is not yet well understood. Dissecting complex traits into biologically better defined, heritable and easily measurable proxy traits and developing biomarkers will facilitate recording these traits in large numbers. High-throughput molecular technologies (“omics”) study the entirety of molecules and their interactions in a single analysis step. They can help to decipher the contributions of different physiological systems and identify candidate molecules that are representative of different physiological pathways. Here, we provide a general overview of different omics approaches and we give examples of how these techniques could be applied to discover biomarkers. We discuss the genetic dissection of the stress response by different omics techniques and we provide examples and outline potential applications of omics tools to understand and prevent outbreaks of damaging behaviours.

Emerging Role of Corticosteroid-Binding Globulin in Glucocorticoid-Driven Metabolic Disorders

Glucocorticoid hormones (GCs) are critical for survival since they ensure the energy supply necessary to the body in an ever challenging environment. GCs are known to act on appetite, glucose metabolism, fatty acid metabolism, and storage. However, to be beneficial to the body, GC levels should be maintained in an optimal window of concentrations. Not surprisingly, conditions of GC excess or deficiency, e.g., Cushing's syndrome or Addison's disease, are associated with severe alterations of energy metabolism. Corticosteroid-binding globulin (CBG), through its high specific affinity for GCs, plays a critical role in regulating plasma GC levels and their access to target cells. Genetic studies in various species including humans have revealed that CBG is the major factor influencing interindividual genetic variability of plasma GC levels, both in basal and stress conditions. Some, but not all, of these genetic studies have also provided data linking CBG levels to body composition and insulin levels. The examination of CBG-deficient mice submitted to hyperlipidic diets unveiled specific roles for CBG in lipid storage and metabolism. An influence of CBG on appetite has not been reported but remains to be more finely analyzed. Finally, only male mice have been examined under high-fat diet, while obesity is affecting women even more than men. Overall, a role of CBG in GC-driven metabolic disorders is emerging in recent studies. Although subtle, the influence of CBG in these diseases could open the way to new therapeutic interventions since CBG is easily accessible in the blood.

Analysis of non-synonymous SNPs of the porcine SERPINA6 gene as potential causal variants for a QTL affecting plasma cortisol levels on SSC7

Recently, the SERPINA6 gene encoding corticosteroid-binding globulin (CBG) has been proposed as a candidate gene for a quantitative trait locus (QTL) affecting cortisol level on pig chromosome 7. The QTL was repeatedly detected in different lines, including a Piétrain × (German Landrace × German Large White) cross (PiF1) and purebred German Landrace (LR). In this study, we investigated whether the known non-synonymous polymorphisms c.44G>T, c.622C>T, c.770C>T, c.793G>A, c.832G>A and c.919G>A of SERPINA6 are sufficient to explain the QTL in these two populations. Our investigations revealed that SNPs c.44G>T, c.622C>T, c.793G>A and c.919G>A are associated with cortisol level in PiF1 (P < 0.01). Haplotype analysis showed that these associations are largely attributable to differences between a major haplotype carrying SNPs c.793G>A and c.919G>A and a haplotype carrying SNPs c.44G>T and c.622C>T. Furthermore, some SNPs, particularly c.44G>T and c.622C>T and the carrier haplotype, showed association with meat quality traits including pH and conductivity (P < 0.05). In LR, the non-synonymous SNPs segregate at very low frequency (<5%) and/or show only weak association with cortisol level (SNPs c.832G>A and c.919G>A; P < 0.05). These findings suggest that the non-synonymous SNPs are not sufficient to explain the QTL across different breeds. Therefore, we examined whether the expression of SERPINA6 is affected by cis-regulatory polymorphisms in liver, the major organ for CBG production. We found allelic expression imbalance of SERPINA6, which suggests that its expression is indeed affected by genetic variation in cis-acting elements. This represents candidate causal variation for future studies of the molecular background of the QTL.

Joint analysis of quantitative trait loci and major-effect causative mutations affecting meat quality and carcass composition traits in pigs

Background

Detection of quantitative trait loci (QTLs) affecting meat quality traits in pigs is crucial for the design of efficient marker-assisted selection programs and to initiate efforts toward the identification of underlying polymorphisms. The RYR1 and PRKAG3 causative mutations, originally identified from major effects on meat characteristics, can be used both as controls for an overall QTL detection strategy for diversely affected traits and as a scale for detected QTL effects. We report on a microsatellite-based QTL detection scan including all autosomes for pig meat quality and carcass composition traits in an F2 population of 1,000 females and barrows resulting from an intercross between a Pietrain and a Large White-Hampshire-Duroc synthetic sire line. Our QTL detection design allowed side-by-side comparison of the RYR1 and PRKAG3 mutation effects seen as QTLs when segregating at low frequencies (0.03-0.08), with independent QTL effects detected from most of the same population, excluding any carrier of these mutations.

Results

Large QTL effects were detected in the absence of the RYR1 and PRKGA3 mutations, accounting for 12.7% of phenotypic variation in loin colour redness CIE-a* on SSC6 and 15% of phenotypic variation in glycolytic potential on SSC1. We detected 8 significant QTLs with effects on meat quality traits and 20 significant QTLs for carcass composition and growth traits under these conditions. In control analyses including mutation carriers, RYR1 and PRKAG3 mutations were detected as QTLs, from highly significant to suggestive, and explained 53% to 5% of the phenotypic variance according to the trait.

Conclusions

Our results suggest that part of muscle development and backfat thickness effects commonly attributed to the RYR1 mutation may be a consequence of linkage with independent QTLs affecting those traits. The proportion of variation explained by the most significant QTLs detected in this work is close to the influence of major-effect mutations on the least affected traits, but is one order of magnitude lower than effect on variance of traits primarily affected by these causative mutations. This suggests that uncovering physiological traits directly affected by genetic polymorphisms would be an appropriate approach for further characterization of QTLs.