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Iberian pigs and its crosses are produced to obtain high-quality meat products. The objective of this work was to evaluate a wide panel of DNA markers, selected by biological and functional criteria, for association with traits related to muscle growth, fatness, meat quality and metabolism. We used 18 crossbred Iberian pigs with divergent postnatal growth patterns for whole genome sequencing and SNP discovery, with over 13 million variants being detected. We selected 1023 missense SNPs located on annotated genes and showing different allele frequencies between pigs with makerdly different growth patterns. We complemented this panel with 192 candidate SNPs obtained from literature mining and from muscle RNAseq data. The selected markers were genotyped in 480 Iberian × Duroc pigs from a commercial population, in which phenotypes were obtained, and an association study was performed for the 1005 successfully genotyped SNPs showing segregation. The results confirmed the effects of several known SNPs in candidate genes (such as LEPR, ACACA, FTO, LIPE or SCD on fatness, growth and fatty acid composition) and also disclosed interesting effects of new SNPs in less known genes such as LRIG3, DENND1B, SOWAHB, EPHX1 or NFE2L2 affecting body weight, average daily gain and adiposity at different ages, or KRT10, NLE1, KCNH2 or AHNAK affecting fatness and FA composition. The results provide a valuable basis for future implementation of marker-assisted selection strategies in swine and contribute to a better understanding of the genetic architecture of relevant traits.

Biodiversity is more than just the diversity of living species. It includes genetic and phenotypic diversity of organisms, functional diversity, interactions and the diversity of populations and whole ecosystems. The recent Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) report confirmed that our planet experiences a rapid loss of biodiversity with dramatic consequences for mankind. Answers to scientifically and societal relevant questions can only be found based on the availability of data integrated from multiple sources and building on different knowledge domains in science and society. The FAIR data principles provide a framework towards such integration. To foster access to interoperable data, NFDI4BioDiversity focuses on the following six objectives: 1) Promoting research data management as an integral part of biodiversity research, 2) Enabling FAIRness of data, 3) Consolidating FAIRness with quality, 4) Embedding NFDI4BioDiversity into the (inter-)national landscape, 5) Addressing NFDI-wide cross-cutting topics and 6) Promoting collaborative governance, viability and sustainability of data infrastructure services and science. These objectives are implemented by the NFDI4BioDiversity work programme and its five task areas: Task Area 1 - Community engagement (2involve): This task area will integrate across different knowledge holders and take care of research data management requests related to biodiversity data. A large part of all species data is collected by volunteers in natural history societies, NGOs, museums and citizen science projects as well as collected by different authorities This task area includes 23 use cases with community-driven topics and cross-domain user groups. Task Area 2 - National and international networking (2connect): This task area ensures that any cross-domain activities and developments of NFDI4BioDiversity are coordinated and in-line with the overall development of the NFDI as well as inte

Biodiversity is more than just the diversity of living species. It includes genetic and phenotypic diversity of organisms, functional diversity, interactions and the diversity of populations and whole ecosystems. The recent Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) report confirmed that our planet experiences a rapid loss of biodiversity with dramatic consequences for mankind. Answers to scientifically and societal relevant questions can only be found based on the availability of data integrated from multiple sources and building on different knowledge domains in science and society. The FAIR data principles provide a framework towards such integration. To foster access to interoperable data, NFDI4BioDiversity focuses on the following six objectives: 1) Promoting research data management as an integral part of biodiversity research, 2) Enabling FAIRness of data, 3) Consolidating FAIRness with quality, 4) Embedding NFDI4BioDiversity into the (inter-)national landscape, 5) Addressing NFDI-wide cross-cutting topics and 6) Promoting collaborative governance, viability and sustainability of data infrastructure services and science. These objectives are implemented by the NFDI4BioDiversity work programme and its five task areas: Task Area 1 - Community engagement (2involve): This task area will integrate across different knowledge holders and take care of research data management requests related to biodiversity data. A large part of all species data is collected by volunteers in natural history societies, NGOs, museums and citizen science projects as well as collected by different authorities This task area includes 23 use cases with community-driven topics and cross-domain user groups. Task Area 2 - National and international networking (2connect): This task area ensures that any cross-domain activities and developments of NFDI4BioDiversity are coordinated and in-line with the overall development of the NFDI as well as inte

The aim of this research was to screen polymorphism and to perform association study of porcine AMBP (alpha-1-microglobulin/bikunin precursor), GC (group-specific component protein) and PPP1R3B (protein phosphatase 1, regulatory (inhibitor) subunit 3B) genes with meat quality traits as well as to unravel the transcriptional regulation of these genes by expression QTL (eQTL) study. For this purpose, Duroc × Pietrain F2 resource population (DuPi; n = 313) and a commercial breed Pietrain (Pi; n = 110) were used for association and only DuPi for expression and eQTL study. A SNP was identified in the genes AMBP (g.22229C>T), GC (g.398C>T) and PPP1R3B (c.479A>G), respectively. In DuPi SNP of AMBP was associated (P < 0.05) with meat colour, pH1L, pH24L, pH24H and conductivity24L; SNP of GC showed tendency to association (P < 0.10) with pH24H, conductivity1L and thawing loss, and SNP of PPP1R3B was associated (P < 0.05) with meat colour, pH1L, pH24L, pH24H and shear force. In Pi SNPs of AMBP and GC was associated with pH24H and PPP1R3B SNP was associated with pH24L. The mRNA levels in Longissimus dorsi muscle tissue of these three genes were evaluated by using qRT-PCR to identify association between gene expression and meat quality traits as well as to analyse eQTL. The mRNA expression of PPP1R3B associated with pH24L (P < 0.05). Expression of these three genes was higher in animals with low pH of muscle. Linkage analysis using QTL Express revealed ten trans-regulated eQTL on seven porcine autosomes. Suggestive eQTL [P < 0.05, CW (chromosome-wide)] were found for PPP1R3B on SSC3 and 13. These results revealed that genetic variation and gene expression of these genes are associated with the meat quality traits. These three genes could influence meat quality and could be potential positional, physiological and functional candidate gene for meat quality traits in pigs. However, the analysis of eQTL also suggested that we need to consider additional genes encoding for transcrip

The aim of the present study was to detect quantitative trait loci (QTL) for the serum levels of cytokines and Toll-like receptors as traits related to innate immunity in pig. For this purpose, serum concentration of interleukin 2 (IL2), interleukin 10 (IL10), interferon-gamma (IFNG), Toll-like receptor 2 (TLR2) and Toll-like receptor 9 (TLR9) were measured in blood samples obtained from F2 piglets (n = 334) of a Duroc × Piétrain resource population (DUPI) after Mycoplasma hypopneumoniae (Mh), tetanus toxoid (TT) and Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) vaccination at 6, 9 and 15 weeks of age. Animals were genotyped at 82 genetic markers covering all autosomes. QTL analysis was performed under the line cross F2 model using QTL Express and 33 single QTL were detected on almost all porcine autosomes. Among the single QTL, eight, twelve and thirteen QTL were identified for innate immune traits in response to Mh, TT and PRRSV vaccine, respectively. Besides single QTL, six QTL were identified by a two-QTL model, of which two for TLR9_TT were in coupling phase and one for IL10_PRRSV was in repulsion phase. All QTL were significant at 5% chromosome-wide level including one and seven at 5% genome- and 1% chromosome-wide level significance. All innate immune traits are influenced by multiple chromosomal regions implying multiple gene action. Some of the identified QTL coincided with previously reported QTL for immune response and disease resistance, and the newly identified QTL are potentially involved in the immune function. The immune traits were also influenced by environmental factors like year of birth, age, parity and litter size. The results of this work shed new light on the genetic background of innate immune response and these findings will be helpful to identify candidate genes in these QTL regions related to immune competence and disease resistance in pigs.

The research aimed to screen for polymorphism, expression of Tenascin C (TNC) and association with meat and carcass quality traits. Three single nucleotide polymorphisms were detected. In a Duroc × Pietrain F2 cross (DuPi) population, g.44488 C > T was associated with meat color and ham weight; g.68794A > G was associated with pH at 24 h post mortem in ham (pH24H) and muscle area but g.68841 C > T was not statistically associated. Genotyping in a commercial Pietrain (Pi) population showed that g.44488 C > T was associated with pH24H, whereas g.68794A > G was associated with conductivity at 45 min post mortem in loin and backfat thickness. Diplotypes showed significant effects on pH24H in both populations. The expression was associated with pH at 45 min post mortem in loin and cooking loss. TNC was significantly higher in animals with higher muscle pH. Linkage analysis revealed four trans-regulated eQTL on four autosomes. These results suggest that TNC could be a potential candidate gene for meat quality traits in pigs.

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.

The aim of this research was to screen for polymorphism and to perform an association study of IFI6 with meat and carcass quality traits. A SNP (g.370A>G) was detected which was associated (P < 0.05) with meat colour, pH 24 h post mortem (p.m.) in ham, conductivity 45 min p.m. in loin and conductivity 24 h p.m. in ham, drip loss and carcass length in Duroc × Pietrain and with meat colour, muscle area and ham percentage in the Pietrain population. Highest expression of IFI6 mRNA was detected in skeletal muscle (longissimus dorsi) by qRT-PCR comparing different tissues. Both qRT-PCR and western blot revealed that the IFI6 gene and protein expressions were significantly (P < 0.05) higher in skeletal muscle with low drip loss compared to that of high drip loss. IFI6 protein was localized in the myocytes membrane. Results suggested that IFI6 might play roles in meat and carcass quality and is a potential positional, physiological and functional candidate gene for improving meat quality traits in pigs.

The aim of the present study was to detect quantitative trait loci (QTL) for innate and adaptive immunity in pigs. For this purpose, a Duroc × Pietrain F2 resource population (DUPI) with 319 offspring was used to map QTL for the immune traits blood antibodies and interferon-gamma using 122 microsatellites covering all autosomes. Antibodies response to Mycoplasma hyopneumoniae and tetanus toxoid vaccine and the interferon-gamma (IFNG) serum concentration were measured at three different time points and were used as phenotypes. The differences of antibodies and interferon concentration between different time points were also used for the linkage mapping. Line-cross and imprinting QTL analysis, including two-QTL, were performed using QTL Express. A total of 30 QTL (12, 6, and 12 for mycoplasma, tetanus antibody, and IFNG, respectively) were identified at the 5% chromosome-wide-level significant, of which 28 were detected by line-cross and 2 by imprinting model. In addition, two QTL were identified on chromosome 5 using the two-QTL approach where both loci were in repulsion phase. Most QTL were detected on pig chromosomes 2, 5, 11, and 18. Antibodies were increased over time and immune traits were found to be affected by sex, litter size, parity, and month of birth. The results demonstrated that antibody and IFNG concentration are influenced by multiple chromosomal areas. The flanking markers of the QTL identified for IFNG on SSC5 did incorporate the position of the porcine IFNG gene. The detected QTL will allow further research in these QTL regions for candidate genes and their utilization in selection to improve the immune response and disease resistance in pig.