Search

For multiple sclerosis, genome wide association studies and follow up studies have identified susceptibility single nucleotide polymorphisms located in or near CLEC16A at chromosome 16p13.13, encompassing among others CIITA, DEXI and SOCS1 in addition to CLEC16A. These genetic variants are located in intronic or intergenic regions and display strong linkage disequilibrium with each other, complicating the understanding of their functional contribution and the identification of the direct causal variant(s). Previous studies have shown that multiple sclerosis-associated risk variants in CLEC16A act as expression quantitative trait loci for CLEC16A itself in human pancreatic β-cells, for DEXI and SOCS1 in thymic tissue samples, and for DEXI in monocytes and lymphoblastoid cell lines. Since T cells are major players in multiple sclerosis pathogenesis, we have performed expression analyses of the CIITA-DEXI-CLEC16A-SOCS1 gene cluster in CD4+ and CD8+ T cells isolated from multiple sclerosis patients and healthy controls. We observed a higher expression of SOCS1 and CLEC16A in CD4+ T cells in samples homozygous for the risk allele of CLEC16A rs12927355. Pair-wise linear regression analysis revealed high correlation in gene expression in peripheral T cells of CIITA, DEXI, CLEC16A and SOCS1. Our data imply a possible regulatory role for the multiple sclerosis-associated rs12927355 in CLEC16A.

Behavioural indices in vertebrates are under genetic control at least to some extent. In spite of significant behavioural problems in farm animals, information on the genetic background of behaviour is sparse. The aim of this study was to map QTL for behavioural indices in swine under healthy conditions and after infection with Sarcocystis miescheriana, as behaviour can be significantly influenced by disease. This well-described parasite model subsequently leads to acute (day 14 p.i.), subclinical (day 28 p.i.) and chronic disease (day 42 p.i.), allowing the study and comparison of the behaviour of pigs under four different states of health or disease. The study was based on a well-described Pietrain/Meishan F(2) family that has recently allowed the detection of QTL for disease resistance. We have mapped six genome-wide significant and 24 chromosome-wide significant QTL for six behavioural indices in swine. Six of these QTL (i.e. 20% of total QTL) showed effects on behavioural traits of the healthy pigs (day 0). Some of them (QTL on SSC11 and 18) lost influence on behavioural activities during disease, while the effects of others (QTL on SSC5, SSC8) partly remained during the whole experiment, although with different effects on the distinct behavioural indices. The disease model has been of high relevance to detect effects of gene loci on behavioural indices. Considering the importance of segregating alleles and environmental conditions that allow the identification of the phenotype, we conclude that there are indeed QTL with interesting effects on behavioural indices in swine.

Clinical-chemical traits are diagnostic parameters essential for characterization of health and disease in veterinary practice. The traits show significant variability and are under genetic control, but little is known about the fundamental genetic architecture of this variability, especially in swine. We have identified QTL for alkaline phosphatase (ALP), lactate (LAC), bilirubin (BIL), creatinine (CRE) and ionized sodium (Na(+)), potassium (K(+)) and calcium (Ca(++)) from the serum of 139 F(2) pigs from a Meishan/Pietrain family before and after challenge with Sarcocystis miescheriana, a protozoan parasite of muscle. After infection, the pigs passed through three stages representing acute disease, subclinical disease and chronic disease. Forty-two QTL influencing clinical-chemical traits during these different stages were identified on 15 chromosomes. Eleven of the QTL were significant on a genome-wide level; 31 QTL were chromosome-wide significant. QTL showed specific health/disease patterns with respect to the baseline values of the traits as well as the values obtained through the different stages of disease. QTL influencing different traits at different times were found primarily on chromosomes 1, 3, 7 and 14. The most prominent QTL for the investigated clinical-chemical traits mapped to SSC3 and 7. Baseline traits of ALP, LAC, BIL, Ca(++) and K(+) were influenced by QTL regions on SSC3, 6, 7, 8 and 13. Single QTL explained up to 21.7% of F(2) phenotypic variance. Our analysis confirms that variation of clinical-chemical traits is associated with multiple chromosomal regions.

Differential white blood cell counts are essential diagnostic parameters in veterinary practice but knowledge on the genetic architecture controlling variability of leucocyte numbers and relationships is sparse, especially in swine. Total leucocyte numbers (Leu) and the differential leucocyte counts, i.e. the fractions of lymphocytes (Lym), polymorphonuclear leucocytes [neutrophils (Neu), eosinophils (Eos) and basophils (Bas)] and monocytes (Mon) were measured in 139 F(2) pigs from a Meishan/Pietrain family, before and after challenge with the protozoan pathogen Sarcocystis miescheriana for genome-wide quantitative trait loci (QTL) analysis. After infection, the pigs passed through three stages representing acute disease, reconvalescence and chronic disease. Nine genome-wide significant and 29 putative, single QTL controlling leucocyte traits were identified on 15 chromosomes. Because leucocyte traits varied with health and disease status, QTL influencing the leucocyte phenotypes showed specific health/disease patterns. Regions on SSC1, 8 and 12 contained QTL for baseline leucocyte traits. Other QTL regions reached control on leucocyte traits only at distinct stages of the disease model. Two-thirds of the QTL have not been described before. Single QTL explained up to 19% of the phenotypic variance in the F(2) animals. Related traits were partly under common genetic influence. Our analysis confirms that leucocyte trait variation is associated with multiple chromosomal regions.

Haematological traits are essential diagnostic parameters in veterinary practice but knowledge on the genetic architecture controlling variability of erythroid traits is sparse, especially in swine. To identify QTL for erythroid traits in the pig, haematocrit (HCT), haemoglobin (HB), erythrocyte counts (RBC) and mean corpuscular haemoglobin content (MCHC) were measured in 139 F(2) pigs from a Meishan/Pietrain family, before and after challenge with the protozoan pathogen Sarcocystis miescheriana. The pigs passed through three stages representing acute disease, reconvalescence and chronic disease. Forty-three single QTL controlling erythroid traits were identified on 16 chromosomes. Twelve of the QTL were significant at the genome-wide level while 31 were significant at a chromosome-wide level. Because erythroid traits varied with health and disease status, QTL influencing the erythroid phenotypes showed specific health/disease patterns. Regions on SSC5, 7, 8, 12 and 13 contained QTL for baseline erythroid traits, while the other QTL regions affected distinct stages of the disease model. Single QTL explained 9-17% of the phenotypic variance in the F(2) animals. Related traits were partly under common genetic influence. Our analysis confirms that erythroid trait variation differs between Meishan and Pietrain breeds and that this variation is associated with multiple chromosomal regions.

The outcome of infectious diseases in vertebrates is under genetic control at least to some extent. In swine, e.g., marked differences in resistance/susceptibility to Sarcocystis miescheriana have been shown between Chinese Meishan and European Pietrain pigs, and these differences are associated with high heritabilities. A first step toward the identification of genes and polymorphisms causal for these differences may be the mapping of quantitative trait loci (QTLs). Considering clinical, immunological, and parasitological traits in the above model system, this survey represents the first QTL study on parasite resistance in pigs. QTL mapping was performed in 139 F(2) pigs of a Meishan/Pietrain family infected with S. miescheriana. Fourteen genome-wide significant QTLs were mapped to several chromosomal areas. Among others, major QTLs were identified for bradyzoite numbers in skeletal muscles (F = 17.4; p < 0.001) and for S. miescheriana-specific plasma IgG(2) levels determined 42 days p.i. (F = 20.9; p < 0.001). The QTLs were mapped to different regions of chromosome 7, i.e., to the region of the major histocompatibility complex (bradyzoites) and to an immunoglobulin heavy chain cluster, respectively. These results provide evidence for a direct and causal role for gene variants within these gene clusters (cis-acting) in differences in resistance to S. miescheriana.

Maintaining pH and blood gases in a narrow range is essential to sustain normal biochemical reactions. Decreased oxygenation, poor tissue perfusion, disturbance to CO2 expiration, and shortage of HCO3− can lead to metabolic acidosis. This is a common situation in swine, and originates from a broad range of medical conditions. pH and blood gases appear to be under genetic control, and populations with physiological traits closer to the pathological thresholds may be more susceptible to developing pathological conditions. However, little is known about the genetic basis of such traits. We have therefore estimated phenotypic and genetic variability and identified quantitative trait loci (QTL) for pH and blood gases in blood samples from 139 F2 pigs from the Meishan/Pietrain family. Samples were taken before and after challenge with Sarcocystis miescheriana, a protozoan parasite of muscle. Twenty-seven QTL influencing pH and blood gases were identified on nine chromosomes. Five of the QTL were significant on a genome-wide level; 22 QTL were significant on a chromosome-wide level. QTL for pH-associated traits have been mapped to SSC3, 18 and X. QTL associated with CO2 have been detected on SSC6, 7, 8 and 9, and QTL associated with O2 on SSC2 and SSC8. QTL showed specific health/disease patterns that were related to the physiological state of the pigs from day 0, to acute disease (day 14), convalescence (day 28) and chronic disease (day 42). The results demonstrate that pH and blood gases are influenced by multiple chromosomal areas, each with relatively small effects. [ABSTRACT FROM AUTHOR]