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Abstract A joint analysis of five paternal half-sib Holstein families that were part of two different granddaughter designs (ADR- or Inra-design) was carried out for five milk production traits and somatic cell score in order to conduct a QTL confirmation study and to increase the experimental power. Data were exchanged in a coded and standardised form. The combined data set (JOINT-design) consisted of on average 231 sires per grandsire. Genetic maps were calculated for 133 markers distributed over nine chromosomes. QTL analyses were performed separately for each design and each trait. The results revealed QTL for milk production on chromosome 14, for milk yield on chromosome 5, and for fat content on chromosome 19 in both the ADR- and the Inra-design (confirmed within this study). Some QTL could only be mapped in either the ADR- or in the Inra-design (not confirmed within this study). Additional QTL previously undetected in the single designs were mapped in the JOINT-design for fat yield (chromosome 19 and 26), protein yield (chromosome 26), protein content (chromosome 5), and somatic cell score (chromosome 2 and 19) with genomewide significance. This study demonstrated the potential benefits of a combined analysis of data from different granddaughter designs.

Abstract The porcine RN locus affects muscle glycogen content and meat quality. We previously mapped the RN locus to chromosome 15. This study describes the identification of polymorphisms for four class I and four class II markers located in the RN region. Resource families were genotyped with F-SSCP markers (fluorescent single strand conformation polymorphism) and microsatellite markers. Subsequent multipoint linkage analysis revealed the order FN1-IGFBP5-S1000-S1001-IL8RB-VIL1-RN-Sw936-Sw906. The gene order is identical to the previously reported porcine RH map of the same region. The described map will facilitate positional cloning of the RN gene.

Abstract Background As an in vitro model porcine peripheral blood mononuclear cells (PBMCs) is frequently used as for immunogenetic research with the stimulation of bacterial antigens. To investigate the immunocompetence of PBMCs for recognition of Gram-positive and Gram-negative bacteria and in order to dissect the pathogenesis of diseases, gene expression assay is most commonly used. The gene expressions are required to normalize for reference genes which have tremendous effect on the results of expression study. The reference genes should be stably expressed between different cells under a variety of experimental conditions, but recent influx of data showed that expression stability of reference genes are varied under different experimental conditions. But data regarding the expression stability of reference genes in porcine PBMCs are limited. Therefore, this study was aimed to know whether the expression stability of commonly used reference genes in PBMCs is affected by various bacterial antigens under different experimental conditions in pigs. Results The mRNA expression stability of nine commonly used reference genes (B2M, BLM, GAPDH, HPRT1, PPIA, RPL4, SDHA, TBP and YWHAZ) was determined by RT-qPCR in PBMCs that were stimulated by LPS and LTA in vitro as well as cells un-stimulated control and non-cultured were also consider for this experiment. mRNA expression levels of all genes were found to be affected by the type of stimulation and duration of the stimulation (P Conclusion There was discrepancy in the ranking order of reference genes obtained by different analysing algorithms (geNorm and NormFinder). In conclusion, the geometric mean of the RPL4, B2M and PPIA seemed to be the most appropriate combination of reference genes for accurate normalization of gene expression data in porcine PBMCs without knowing the type of bacterial pathogenic status of the animals and in the case of mixed infection with Gram-negative and Gram-positive bacteria. In case of PBMCs without any stimulation, PPIA, BLM and GAPDH could be suggested as suitable reference genes.

Abstract Background To obtain reliable quantitative real-time PCR data, normalization relative to stable housekeeping genes (HKGs) is required. However, in practice, expression levels of 'typical' housekeeping genes have been found to vary between tissues and under different experimental conditions. To date, validation studies of reference genes in pigs are relatively rare and have never been performed in porcine alveolar macrophages (AMs). In this study, expression stability of putative housekeeping genes were identified in the porcine AMs in response to the stimulation with two pathogen-associated molecular patterns (PAMPs) lipopolysaccharide (LPS) and lipoteichoic acid (LTA). Three different algorithms (geNorm, Normfinder and BestKeeper) were applied to assess the stability of HKGs. Results The mRNA expression stability of nine commonly used reference genes (B2M, BLM, GAPDH, HPRT1, PPIA, RPL4, SDHA, TBP and YWHAZ) was determined by qRT-PCR in AMs that were stimulated by LPS and LTA in vitro. mRNA expression levels of all genes were found to be affected by the type of stimulation and duration of the stimulation (P < 0.0001). geNorm software revealed that SDHA, B2M and RPL4 showed a high expression stability in the irrespective to the stimulation group, while SDHA, YWHAZ and RPL4 showed high stability in non-stimulated control group. In all cases, GAPDH showed the least stability in geNorm. NormFinder revealed that SDHA was the most stable gene in all the groups. Moreover, geNorm software suggested that the geometric mean of the three most stable genes would be the suitable combination for accurate normalization of gene expression study. Conclusions There was discrepancy in the ranking order of reference genes obtained by different analysing algorithms. In conclusion, the geometric mean of the SDHA, YWHAZ and RPL4 seemed to be the most appropriate combination of HKGs for accurate normalization of gene expression data in porcine AMs without knowing the type of bacterial pathogenic status of the animals.

Abstract Background Gene expression analysis using real-time RT-PCR (qRT-PCR) is increasingly important in biological research due to the high-throughput and accuracy of qRT-PCR. For accurate and reliable gene expression analysis, normalization of gene expression data against housekeeping genes or internal control genes is required. The stability of reference genes has a tremendous effect on the results of relative quantification of gene expression by qRT-PCR. The expression stability of reference genes could vary according to tissues, age of individuals and experimental conditions. In the pig however, very little information is available on the expression stability of reference genes. The aim of this research was therefore to develop a new set of reference genes which can be used for normalization of mRNA expression data of genes expressed in varieties of porcine tissues at different ages. Results The mRNA expression stability of nine commonly used reference genes (B2M, BLM, GAPDH, HPRT1, PPIA, RPL4, SDHA, TBP and YWHAZ) was determined in varieties of tissues collected from newborn, young and adult pigs. geNorm, NormFinder and BestKeeper software were used to rank the genes according to their stability. geNorm software revealed that RPL4, PPIA and YWHAZ showed high stability in newborn and adult pigs, while B2M, YWHAZ and SDHA showed high stability in young pigs. In all cases, GAPDH showed the least stability in geNorm. NormFinder revealed that TBP was the most stable gene in newborn and young pigs, while PPIA was most stable in adult pigs. Moreover, geNorm software suggested that the geometric mean of three most stable gene would be the suitable combination for accurate normalization of gene expression study. Conclusions Although, there was discrepancy in the ranking order of reference genes obtained by different analysing software methods, the geometric mean of the RPL4, PPIA and YWHAZ seems to be the most appropriate combination of housekeeping genes for accurate normalization of gene expression data in different porcine tissues at different ages.

Abstract Background Serum lipids are associated with many serious cardiovascular diseases and obesity problems. Many quantitative trait loci (QTL) have been reported in the pig mostly for performance traits but very few for the serum lipid traits. In contrast, remarkable numbers of QTL are mapped for serum lipids in humans and mice. Therefore, the objective of this research was to investigate the chromosomal regions influencing the serum level of the total cholesterol (CT), triglyceride (TG), high density protein cholesterol (HDL) and low density protein cholesterol (LDL) in pigs. For this purpose, a total of 330 animals from a Duroc × Pietrain F2 resource population were phenotyped for serum lipids using ELISA and were genotyped by using 122 microsatellite markers covering all porcine autosomes for QTL study in QTL Express. Blood sampling was performed at approximately 175 days before slaughter of the pig. Results Most of the traits were correlated with each other and were influenced by average daily gain, slaughter date and age. A total of 18 QTL including three QTL with imprinting effect were identified on 11 different porcine autosomes. Most of the QTL reached to 5% chromosome-wide (CW) level significance including a QTL at 5% experiment-wide (GW) and a QTL at 1% GW level significance. Of these QTL four were identified for both the CT and LDL and two QTL were identified for both the TG and LDL. Moreover, three chromosomal regions were detected for the HDL/LDL ratio in this study. One QTL for HDL on SSC2 and two QTL for TG on SSC11 and 17 were detected with imprinting effect. The highly significant QTL (1% GW) was detected for LDL at 82 cM on SSC1, whereas significant QTL (5% GW) was identified for HDL/LDL on SSC1 at 87 cM. Chromosomal regions with pleiotropic effects were detected for correlated traits on SSC1, 7 and 12. Most of the QTL identified for serum lipid traits correspond with the previously reported QTL for similar traits in other mammals. Two novel QTL on SSC16 for HDL and HDL/LDL ratio and an imprinted QTL on SSS17 for TG were detected in the pig for the first time. Conclusion The newly identified QTL are potentially involved in lipid metabolism. The results of this work shed new light on the genetic background of serum lipid concentrations and these findings will be helpful to identify candidate genes in these QTL regions related to lipid metabolism and serum lipid concentrations in pigs.

Abstract Background Leg weakness issues are a great concern for the pig breeding industry, especially with regard to animal welfare. Traits associated with leg weakness are partly influenced by the genetic background of the animals but the genetic basis of these traits is not yet fully understood. The aim of this study was to identify quantitative trait loci (QTL) affecting leg weakness in pigs. Methods Three hundred and ten F2 pigs from a Duroc × Pietrain resource population were genotyped using 82 genetic markers. Front and rear legs and feet scores were based on the standard scoring system. Osteochondrosis lesions were examined histologically at the head and the condylus medialis of the left femur and humerus. Bone mineral density, bone mineral content and bone mineral area were measured in the whole ulna and radius bones using dual energy X-ray absorptiometry. A line-cross model was applied to determine QTL regions associated with leg weakness using the QTL Express software. Results Eleven QTL affecting leg weakness were identified on eight autosomes. All QTL reached the 5% chromosome-wide significance level. Three QTL were associated with osteochondrosis on the humerus end, two with the fore feet score and two with the rear leg score. QTL on SSC2 and SSC3 influencing bone mineral content and bone mineral density, respectively, reached the 5% genome-wide significance level. Conclusions Our results confirm previous studies and provide information on new QTL associated with leg weakness in pigs. These results contribute towards a better understanding of the genetic background of leg weakness in pigs.

Abstract Background 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. Methods Five hundred and eighty five F2 pigs from a Duroc × Pietrain resource population were genotyped using 131 genetic markers (microsatellites and SNP) spread over 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. Results A number of interacting QTL was observed for different traits, leading to the identification of a variety of networks among chromosomal regions throughout the porcine genome. We distinguished 17 epistatic QTL pairs for carcass composition and 39 for meat quality traits. These interacting QTL pairs explained up to 8% of the phenotypic variance. Conclusions Our findings demonstrate the significance of epistasis in pigs. We have revealed evidence for epistatic relationships between different chromosomal regions, confirmed known QTL loci and connected regions reported in other studies. Considering interactions between loci allowed us to identify several novel QTL and trait-specific relationships of loci within and across chromosomes.

The experimental power of a granddaughter design to detect quantitative trait loci (QTL) in dairy cattle is often limited by the availability of progeny-tested sires, by the ignoring of already identified QTL in the statistical analysis, and by the application of stringent experimentwise significance levels. This study describes an experiment that addressed these points. A large granddaughter design was set up that included sires from two countries (Germany and France), resulting in almost 2000 sires, The animals were genotyped for markers on nine different chromosomes. The QTL analysis was done for six traits separately using a multimarker regression that included putative QTL on other chromosomes as cofactors in the model. Different variants of the false discovery rate (FDR) were applied. Two of them accounted for the proportion of truly null hypotheses, which were estimated to be 0.28 and 0.3, respectively, and were therefore tailored to the experiment. A total of 25 QTL could be mapped when cofactors were included in the model--7 more than without cofactors. Controlling the FDR at 0.05 revealed 31 QTL for the two FDR methods that accounted for the proportion of truly null hypotheses. The relatively high power of this study can be attributed to the size of the experiment, to the QTL analysis with cofactors, and to the application of an appropriate FDR.