Open Access Research article

Genetic analysis of the Trichuris muris-induced model of colitis reveals QTL overlap and a novel gene cluster for establishing colonic inflammation

Scott E Levison1, Paul Fisher2, Jenny Hankinson3, Leo Zeef4, Steve Eyre5, William E Ollier3, John T McLaughlin1, Andy Brass6, Richard K Grencis7 and Joanne L Pennock1*

Author Affiliations

1 Gastrointestinal Sciences, Institute of Inflammation and Repair, Faculty of Medicine and Human Sciences, University of Manchester, 4.004 AV Hill Building, Oxford Road, Manchester M13 9PT, UK

2 Bioinformatics Scientist, Oncology, AstraZeneca, Cheshire, UK

3 Centre for Integrated Genomic Medical Research, Institute of Population Health, Faculty of Medicine and Human Sciences, University of Manchester, Manchester, UK

4 Bioinformatics, Faculty of Life Sciences, University of Manchester, Manchester, UK

5 Arthritis Research UK Epidemiology Unit, Institute of Inflammation and Repair, Faculty of Medicine and Human Sciences, University of Manchester, Manchester, UK

6 School of Computer Sciences, University of Manchester, Manchester, UK

7 Faculty of Life Sciences, University of Manchester, Manchester, UK

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BMC Genomics 2013, 14:127  doi:10.1186/1471-2164-14-127

Published: 26 February 2013

Additional files

Additional file 1:

Contains Supplementary Figure S1 showing gender variation in phenotype. Also Supplementary Figures S2-S5 which detail stepwise representations of workflows mentioned in Methods (see legends below), and Table S1 showing primer sequences for qPCR. Table S1: Primer sequences for qPCR-amplified genes. Figure S1: Phenotype data for whole cohort, stratified by gender. A: Females showed significantly lower worm burden compared to males (Mann Whitney U test, p < 0.0001). B: Females showed significantly higher IgG1:2a antibody ratio (T test, p < 0.0001). Figure S2: Pathways and Gene annotations for QTL region. This workflow searches for genes which reside in a QTL (Quantitative Trait Loci) region in the mouse, Mus musculus. The workflow requires an input of: a chromosome name or number; a QTL start base pair position; QTL end base pair position. Data is then extracted from BioMart to annotate each of the genes found in this region. The Entrez and UniProt identifiers are then sent to KEGG to obtain KEGG gene identifiers. The KEGG gene identifiers are then used to search for pathways in the KEGG pathway database. (http://www.myexperiment.org/workflows/1661.html webcite). Figure S3: Pathways and Gene annotations for RefSeq ids. This workflow searches for Mus musculus genes found to be differentially expressed in a microarray study. The workflow requires an input of gene ref_seq identifiers. Data is then extracted from BioMart to annotate each of the genes found for each ref_seq id. The Entrez and UniProt identifiers are then sent to KEGG to obtain KEGG gene identifiers. The KEGG gene identifiers are then used to search for pathways in the KEGG pathway database. (http://www.myexperiment.org/workflows/1662.html webcite). Figure S4: KEGG pathways common to both QTL and microarray based investigations. This workflow takes in two lists of KEGG pathway ids. These are designed to come from pathways found from genes in a QTL (Quantitative Trait Loci) region, and from pathways found from genes differentially expressed in a microarray study. By identifying the intersecting pathways from both studies, a more informative picture is obtained of the candidate processes involved in the expression of a phenotype. (http://www.myexperiment.org/workflows/1663.html webcite). Figure S5: Pathway and Gene to Pubmed. This workflow takes in a list of gene names, KEGG pathway descriptions and phenotypes as keywords, and searches the PubMed database for corresponding articles. Retrieved abstracts are then used to calculate a cosine vector space between two sets of corpora (gene and phenotype, or pathway and phenotype). The workflow counts the number of articles in the PubMed database in which each term occurs, and identifies the total number of articles in the entire PubMed database so that a term enrichment score may be calculated. Scientiifc terms are then extracted from the abstract text and given a weighting according to the number of terms that appear in the document. The higher the value the better the score. This is given as: X (or Y) = log((a / b) / (c / d)) where: a = number of occurrences of individual terms in phenotype (or pathway) corpus, b = number of abstracts in entire phenotype (or pathway) corpus, c = number of occurrences of individual terms in entire PubMed, d = number of articles in entire PubMed. Once this has been created, the pathways obtained from the QTL and microarray pathway analysis workflows are analysed. The weighted terms are then given a link score X + Y. The higher the score the more “appropriate/interesting” the link between the pathway and the phenotype. This is calculated as: W= (X + Y). (http://www.myexperiment.org/workflows/1846.html webcite).

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