Project description:A GWAS study was then performed in 52 non-adhesive and 68 strong adhesive pigs for F4ab/ac ETEC originating from 5 Belgian farms. A new refined candidate region (chr13: 144,810,100-144,993,222) for F4ac ETEC susceptibility was identified with MUC13 adjacent to the distal part of the region.
Project description:A GWAS study was then performed in 52 non-adhesive and 68 strong adhesive pigs for F4ab/ac ETEC originating from 5 Belgian farms. A new refined candidate region (chr13: 144,810,100-144,993,222) for F4ac ETEC susceptibility was identified with MUC13 adjacent to the distal part of the region. All pigs were phenotyped for the presence of the F4ab/ac receptor (F4ab/acR) using the in vitro villous adhesion assay with 4×108 F4ac E. coli (strain GIS26, serotype O149:K91, F4ac+) or F4ab E. coli (strain G7, serotype O8:K87, F4ab+) . A total of 120 F4ab/acR phenotyped pigs were genotyped using the Porcine SNP60 BeadChip (Illumina) containing 62,163 SNPs, according to the manufacturer’s protocol. The position of the SNPs was based on the current pig genome assembly (Sscrofa10.2).
Project description:Comparison of gene expression in the lungs of pigs classified as Resistant (RES) or Susceptible (SUS) to influenza pathology based on clinical lesion scores. The aim of the experiment is to identify genes whose expression is associated with resistance/susceptibility to influenza pathology. These are a subset of animals selected from a larger experiment that investigated the effect of low (LBW) or high (HBW) litter birth weight phenotype on influenza pathology.
Project description:Contemporary high dimensional biological assays, such as mRNA expression microarrays, regularly involve multiple data processing steps, such as experimental processing, computational processing, sample selection, or feature selection (i.e. gene selection), prior to deriving any biological conclusions. These steps can dramatically change the interpretation of an experiment. Evaluation of processing steps has received limited attention in the literature. It is not straightforward to evaluate different processing methods and investigators are often unsure of the best method. We present a simple statistical tool, Standardized WithIn class Sum of Squares (SWISS), that allows investigators to compare alternate data processing methods, such as different experimental methods, normalizations, or technologies, on a dataset in terms of how well they cluster a priori biological classes. SWISS uses Euclidean distance to determine which method does a better job of clustering the data elements based on a priori classifications. We apply SWISS to three different gene expression applications. The first application uses four different datasets to compare different experimental methods, normalizations, and gene sets. The second application, using data from the MicroArray Quality Control (MAQC) project, compares different microarray platforms. The third application compares different technologies: a single Agilent two-color microarray versus one lane of RNA-Seq. These applications give an indication of the variety of problems that SWISS can be helpful in solving. The SWISS analysis of one-color versus two-color microarrays provides investigators who use two-color arrays the opportunity to review their results in light of a single-channel analysis, with all of the associated benefits offered by this design. Analysis of the MACQ data shows differential intersite reproducibility by array platform. SWISS also shows that one lane of RNA-Seq clusters data by biological phenotypes as well as a single Agilent two-color microarray.
