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Genetic screens steadily reveal more loci that show robust associations to complex human diseases, including multiple sclerosis ( MS). Although some of the identified genetic variants are easily interpreted into a biological function, most of the genetic associations are frequently challenging to interpret. Underlying these difficulties is the fact that chip-based assays typically detect single nucleotide polymorphisms ( SNPs) representative of a stretch of DNA containing many genomic variants in linkage disequilibrium. Furthermore, a large proportion of the SNPs with strongest association to MS are located in regions of the DNA that do not directly code for proteins. Here we discuss challenges faced by MS researchers to follow up the large-scale genetic screens that have been published over the past years in search of functional consequences of the identified MS-associated SNPs. We discuss experimental design, tools and methods that may provide the much-needed biological insights in both disease etiology and disease manifestations. [ABSTRACT FROM AUTHOR]

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]

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 F2 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 F2 phenotypic variance. Our analysis confirms that variation of clinical–chemical traits is associated with multiple chromosomal regions. [ABSTRACT FROM AUTHOR]