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,

    Affiliated with

    • Paul Fisher2,

      Affiliated with

      • Jenny Hankinson3,

        Affiliated with

        • Leo Zeef4,

          Affiliated with

          • Steve Eyre5,

            Affiliated with

            • William E Ollier3,

              Affiliated with

              • John T McLaughlin1,

                Affiliated with

                • Andy Brass6,

                  Affiliated with

                  • Richard K Grencis7Email author and

                    Affiliated with

                    • Joanne L Pennock1Email author

                      Affiliated with

                      BMC Genomics201314:127

                      DOI: 10.1186/1471-2164-14-127

                      Received: 30 October 2012

                      Accepted: 14 February 2013

                      Published: 26 February 2013

                      Abstract

                      Background

                      Genetic susceptibility to colonic inflammation is poorly defined at the gene level. Although Genome Wide Association studies (GWAS) have identified loci in the human genome which confer susceptibility to Inflammatory Bowel Disease (Crohn’s and Ulcerative Colitis), it is not clear if precise loci exist which confer susceptibility to inflammation at specific locations within the gut e.g. small versus large intestine. Susceptibility loci for colitis in particular have been defined in the mouse, although specific candidate genes have not been identified to date. We have previously shown that infection with Trichuris muris (T. muris) induces chronic colitis in susceptible mouse strains with clinical, histological, and immunological homology to human colonic Crohn’s disease. We performed an integrative analysis of colitis susceptibility, using an F2 inter-cross of resistant (BALB/c) and susceptible (AKR) mice following T. muris infection. Quantitative Trait Loci (QTL), polymorphic and expression data were analysed alongside in silico workflow analyses to discover novel candidate genes central to the development and biology of chronic colitis.

                      Results

                      7 autosomal QTL regions were associated with the establishment of chronic colitis following infection. 144 QTL genes had parental strain SNPs and significant gene expression changes in chronic colitis (expression fold-change ≥ +/-1.4). The T. muris QTL on chromosome 3 (Tm3) mapped to published QTL in 3 unrelated experimental models of colitis and contained 33 significantly transcribed polymorphic genes. Phenotypic pathway analysis, text mining and time-course qPCR replication highlighted several potential cis-QTL candidate genes in colitis susceptibility, including FcgR1, Ptpn22, RORc, and Vav3.

                      Conclusion

                      Genetic susceptibility to induced colonic mucosal inflammation in the mouse is conserved at Tm3 and overlays Cdcs1.1. Genes central to the maintenance of intestinal homeostasis reside within this locus, implicating several candidates in susceptibility to colonic inflammation. Combined methodology incorporating genetic, transcriptional and pathway data allowed identification of biologically relevant candidate genes, with Vav3 newly implicated as a colitis susceptibility gene of functional relevance.

                      Keywords

                      Trichuris muris Colitis Genetic susceptibility Cdcs1 Crohn’s

                      Background

                      Many diseases result from the complex interaction of environmental and genetic factors (e.g. Crohn’s disease, diabetes mellitus) [1, 2]. Phenotypic expression is influenced by multiple genes, which individually may increase or decrease the probability of disease development. Gene variation and gene-gene interactions, additionally results in non-linear contributions to phenotypic variation. Discovering the genetic architecture of complex traits thus represents a true challenge [3] and requires collaborative multi-disciplinary investigation and a variety of experimental approaches [4, 5]. The exploration of new animal models of colitis with well-defined phenotypes and homology to human pathology, provide a comparative approach to refine biological discoveries for subsequent human translation.

                      Trichuris muris, a natural intestinal parasite of mice has been extensively studied as a model for human whipworm (Trichuris trichiura) infection. In dissecting the immune response to Trichuris infection, a paradigm of resistance and susceptibility to chronic colonic inflammation has emerged [6]. Following the ingestion of parasite ova, acute colitis develops in all mice, but it is the genetic composition of mouse strain which dictates the presence of colitis. BALB/c mice mount immune-mediated TH2 dependent parasite expulsion (IL4, IL5 and IL13 expression) [6, 7] with full resolution within 20 days. Conversely AKR mice sustain a chronic Trichuris infection, respond with a TH1 immune response (IFNγ, IL12), and subsequent establishment of colitis [8]. These polarized outcomes occur despite identical treatment and conditioning, and are almost certainly determined by host genetic variation. Importantly, we have recently characterised differences in colonic tissue transcription between susceptible and resistant mice and demonstrated phenotypic, immunological and biological pathway homology to human Crohn’s disease [9]. These data present T. muris infection not as an aetiological factor in the pathogenesis of Crohn’s disease, nor solely a model of infection but as a viable and relevant colitis model to investigate and study mucosal inflammation.

                      The multifactorial and complex nature of Crohn’s disease remains to be fully characterised, but it is evident that disease can be initiated anywhere along the digestive tract. It is likely that precise environmental triggers determine the site of initiation, but it is also possible that host genetics play a part. A variety of experimental models have been developed to study pathogenic mechanisms responsible for the induction and perpetuation of Crohn’s disease. Phenotypic and biological factors common between colonic Crohn’s disease and chronic T. muris induced colitis, present a novel opportunity to characterise the genetic architecture central to disease susceptibility in the colon. The aim of the current study was to identify genome wide genetic elements and mechanistic pathways which underpin the development and maintenance of such chronic inflammation.

                      Results

                      Systemic and colonic phenotyping of chronic T. muris colitis in an F2 population of resistant and susceptible mice

                      An F2 inter-cross of resistant (BALB/c) and susceptible (AKR) mice was phenotyped 35 days post-infection, a time-point when chronic inflammation is established in susceptible mice (Figure  1). Colonic worm burden was not normally distributed; the majority of animals were resistant, with the largest worm burdens harboured by a small number of individuals (Figure  1A). This pattern of worm load distribution is indicative of an out-bred cohort [10]. Serum parasite-specific IgG1 (TH2 specific) and IgG2a (TH1 specific) was measured. Many individuals had a combination of both serotypes. To determine the predominant phenotype expressed a serum IgG1:2a ratio was calculated. A highly significant difference was observed between the mean of IgG1:2a ratio in resistant and susceptible mice (Mann Whitney U test, p < 0.0001, Figure  1B). At day 35 post-infection, 83% (110/133) of individuals with persistent worm burden demonstrated serum antibody titre IgG1 < IgG2a, indicative of a polarised TH1 immune response. A dominant TH2 immune response (IgG1 > IgG2a) correlated with worm expulsion. Furthermore, females demonstrated more resistance compared to males; females had significantly fewer worms at D35 post infection (Additional file 1: Figure S1A) and significantly higher IgG1:2a ratio (p < 0.0001, Additional file 1: Figure S1B) indicative of a dominant Th2 immune response.
                      http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-14-127/MediaObjects/12864_2012_4891_Fig1_HTML.jpg
                      Figure 1

                      Phenotype data. A) Colonic worm burden across the F2 population with parental strains. B) Serum IgG1:IgG2a ratio was significantly different between resistant (0 worms) and susceptible (>0 worms) groups. C) Normal colonic histology in resistant mouse with predominant serum IgG1 (x100 magnification. H&E stain. Bar = 200μm). Histological mucosal and submucosal colonic inflammation, with crypt hyperplasia and elongation, seen in the vicinity of (D), and away from (E) T. muris colonic worms (arrow). F) Serum IgG1:IgG2a ratio, and G) colonic worm count correlate with histological inflammatory features.

                      Colonic histological assessment demonstrated persistent T. muris infection and large bowel inflammation. Mild-to-moderate inflammatory changes included: transmural tissue oedema and associated leukocytic infiltration (lymphocytes, macrophages, neutrophils); prominent mucosal and submucosal reactive lymphoid aggregates; colonic crypt hyperplasia and hypertrophy (Figure  1C-E). Significant correlation between histological parameters of inflammation (e.g. crypt length), immune response phenotype (Figure  1F: Spearman’s Rs = -0.54) and worm burden (Figure  1G: Spearman’s Rs = 0.84), were demonstrated. 98.5% of mice with persistent helminthosis demonstrated colonic inflammatory changes.

                      Whole genome Linkage analysis

                      In total 7 QTL demonstrated significant correlation between susceptibility phenotype and genotype. Chromosomal locus, LOD scoring, trait correlation and the number of genes found within each QTL were defined (Table  1). The majority of QTL were associated with both a TH1 pro-inflammatory immune response, as reflected in low IgG1:IgG2a ratio, and persistent worm burden. Tm10 however, was solely indicative of continued worm persistence. Tm17 demonstrated the most significant LOD score, overlying the major histocompatibility complex (MHC).
                      Table 1

                      Summary of Trichuris muris QTL ( Tm ) found across the genome

                      T. Muris QTL

                      Murine chrom

                      Left marker

                      Right marker

                      LODa

                      Trait

                      Dominance

                      Gene no. within QTL (Ensembl)

                      AKR vs BALB/c genes with SNPs

                      Tm1

                      1

                      D1Mit33

                      D1Mit36

                      2.39

                      Serum IgG’s Worm count

                      −8.3

                      90

                      68

                      Tm3

                      3

                      D3Mit156 92.4Mbp

                      D3Mit79 118.3Mbp

                      2.80

                      Serum IgG’s Worm count

                      8.4

                      342

                      270

                      Tm4

                      4

                      D4Mit166

                      D4Mit12

                      3.23

                      Serum IgG’s

                      −5.3

                      311

                      289

                      Tm10

                      10

                      D10Mit14

                      D10Mit35

                      4.10

                      Worm count

                      -

                      27

                      24

                      Tm11

                      11

                      D11Mit99

                      D11Mit61

                      4.14

                      Serum IgG’s Worm count

                      −0.3

                      265

                      182

                      Tm12

                      12

                      D12Mit285

                      D12Mit201

                      4.7

                      Serum IgG’s Worm count

                      −10.7

                      95

                      75

                      Tm17

                      17

                      D17Mit175

                      D17Mit176

                      8.04

                      Serum IgG’s Worm count

                      −5.7

                      287

                      251

                      aFor non-parametric traits, Kruskal Wallis values were converted to LOD according to published methods [13].

                      Of particular interest, Tm3 (92.4-118.3 Mbp, chromosome 3) demonstrated complete overlap with a susceptibility locus identified in three unrelated murine models of spontaneous experimental colitis: G-protein alpha inhibitory 2 chain knock-out (Gnai2 -/-) mice (Gpdc1 locus) [11]; C3H/HeJBir IL10-deficient mice (Cdcs1 locus) [12]; T-bet-/-Rag2-/- double-deficient mice that resemble ulcerative colitis (TRUC) (Cdcs1 locus) [13]. The Cdcs1 region has been shown to contain at least three distinct regions [14, 15]. Here, we show complete overlap of Tm3 with Cdcs1.1 (Figure  2), a region shown to contribute strongly to the severity of colitis in C3H/HeJBir mice [14, 15].
                      http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-14-127/MediaObjects/12864_2012_4891_Fig2_HTML.jpg
                      Figure 2

                      Tm3 overlays the colitic Cdcs1 QTL. Previous congenic analysis defined Cdcs1 between 87.1 and 131.1 Mbp (solid box), and overlays gpdc1 (dashed box), a mouse QTL which also correlates with spontaneous colitis. T. muris QTL Tm3 (broken line) lies between D3Mit156 (92 Mbp) and D3Mit79 (118 Mbp), outside the location of a previously defined candidate gene NfkB1 (135.1 Mbp). The threshold for suggestive correlation is shown for Tm3 at LOD 2.4.

                      Prioritization of QTL candidate genes via pathway-driven workflow analysis

                      Schematic representation and key stage data from each of the qtl_to_pathway[16] and refseq_ids_to_pathways[17] workflows are shown (Figure  3). In total, 1419 genes were identified within the 7 T. muris QTL. Genes attributed to one or more KEGG biological pathways were determined (Figure  3.1). Simultaneously, of 5476 genes with significant transcriptional differences during T. muris infection [9], 2504 genes displayed either an up-regulated or down-regulated change in expression of ≥ 1.4 fold (i.e. a 40% or greater change in expression over naïve controls, Figure  3.2). Biological KEGG pathways associated with significant gene expression data were determined. In total, 1158 of these genes (46%) were involved in 204 separate KEGG pathways (Figure  3.3).
                      http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-14-127/MediaObjects/12864_2012_4891_Fig3_HTML.jpg
                      Figure 3

                      Flow diagram showing unbiased identification of candidate genes in identified QTL. 3.1: Genes within QTL were identified and assigned biological pathways. 3.2: In parallel, genes in QTL with different relative expression between parental strains were assigned biological pathways. 3.3 &3.4: Genes within commonly identified pathways were ranked according to SNP number (AKR vs BALB/c http://​www.​sanger.​ac.​uk).

                      The cross correlation of functional pathways containing QTL genes and genes demonstrating significant expression were identified, linking genotype and phenotype trait interactions (Figure  3.3). Finally, polymorphic genes between parental AKR and BALB/c mice were identified within each locus (Figure  3.4).

                      As an example, 344 Ensembl gene ID’s were detected within Tm3. Of these, 97 (28%) were designated as functionally important within molecular interaction networks, as assigned by the KEGG pathway database. Significantly expressed microarray genes were similarly assigned biological pathways. For Tm3, the cross correlation of common pathway data and the exclusion of any gene which lacked SNPs between parental strains, identified 17 Quantitative Trait genes (Figure  3.4, Column D). In comparison, 61 KEGG-assigned polymorphic genes did not demonstrate any change in transcriptional activity (Column C). Of the genes yet to be allocated a KEGG pathway, 16 of 191 genes displayed significant transcription (Column B). The same process was undertaken for all 7 QTL (Figure  3).

                      Chromosome 3 candidates

                      Analysis of Tm3, revealed 33 polymorphic genes with significant transcriptional changes during infection. Candidate genes were analysed in two distinct subsets; those with and those without a designated KEGG pathway. Of the 33 genes, 17 demonstrated a central mechanistic role within one or more KEGG pathway (Table  2). Vav3 was associated with the highest number of pathways (n = 7). Candidate genes were ranked according to the number of SNPs that occurred between AKR and BALB/c strains. Vav3 was the gene with the highest number of SNP variants (n = 2047).
                      Table 2

                      Significantly expressed Tm3 genes possessing strain-specific SNPs and a designated biological (KEGG) pathway

                      Gene

                      KEGG designated pathway

                      SNP no.

                      Gene

                      KEGG designated pathway

                      SNP no.

                      Vav3

                      Chemokine signalling pathway

                      2047

                      Fcgr1

                      Fc gamma R-mediated phagocytosis

                      18

                       

                      Fc gamma R-mediated phagocytosis

                        

                      Hematopoietic cell lineage

                       
                       

                      Focal adhesion

                        

                      Leishmaniasis

                       
                       

                      Leukocyte transendothelial migration

                        

                      Phagosome

                       
                       

                      T cell receptor signalling pathway

                        

                      Systemic lupus erythematosus

                       
                       

                      B cell receptor signalling pathway

                       

                      Prpf38b

                      Spliceosome

                      9

                       

                      Regulation of actin cytoskeleton

                          

                      Hmgcs2

                      Butanoate metabolism

                      134

                      Vcam1

                      Cell adhesion moleculaes (CAMs)

                      8

                       

                      Metabolic pathways

                        

                      Leukocyte transendothelial migration

                       
                       

                      Synthesis and degradation of ketone

                        

                      Malaria

                       
                       

                      Terpenoid backbone biosynthesis

                       

                      Cdc14a

                      Cell cycle

                      7

                       

                      Valine, leuckine and isoleucine degradation

                          

                      Ctss

                      Antigen processing and presentation

                      120

                      Dbt

                      Metabolic pathways

                      2

                       

                      Lysosome

                        

                      Valine, leucine and isoleucine degradation

                       
                       

                      Phagosome

                          

                      Ap4b1

                      Lysosome

                      92

                      Gstm7

                      Drug metabolism – cytochrome P450

                      2

                      Rorc

                      Circadian rhythm

                      76

                       

                      Glutathione metabolism

                       
                          

                      Metabolism of xenobiotics by cytochrome p450

                       

                      Gstm3

                      Drug metabolism – cytochrome P450

                      73

                      Hsd3b6

                      Metabolic pathways

                      2

                       

                      Glutathione metabolism

                        

                      Steroid hormone biosynthesis

                       
                       

                      Metabolism of xenobiotcis by cytochrome P450

                          

                      Dpyd

                      Beta-Alanin metabolism

                      24

                      Fmo5

                      Drug metabolism – cytochrome p450

                      1

                       

                      Drug metabolism – other enzymes

                          
                       

                      Metabolic pathways

                       

                      Gstm6

                      Drug metabolism – cytochrome p450

                      1

                       

                      Pantothenate and CoA biosynthesis

                          
                       

                      Pyrimidine metabolism

                       

                      Hist2h2be

                      Systemic lupus erythematosus

                      1

                      The 16 genes with no KEGG pathway association were subsequently analysed using a workflow-based text-mining approach, to allow prioritization according to known biological roles in inflammation or gut immunology. Genes were ranked according to a cosine vector score (see Methods), estimating the significance of correlation between candidate gene and phenotype. SNP variation between parental strains was also considered. As a result, additional proposed candidates included: Ptpn22, S100a10, and Slc22a15 (Table  3).
                      Table 3

                      Significantly expressed genes possessing strain-specific SNPs but as of yet, undesignated a biological (KEGG) pathway

                      Gene

                      Text mining (cosine vector score)

                      SNP no.

                      S100a10

                      0.248

                      62

                      Slc22a150

                      0.0219

                      19

                      Selenbp1

                      0.0065

                      19

                      Mov10

                      0.0063

                      3

                      Ppm1j

                      0.0013

                      3

                      Pogz

                      0.0009

                      8

                      Extl2

                      0.0008

                      2

                      Igsf3

                      0.0007

                      177

                      Ptpn22

                      0.0006

                      276

                      Selenbp2

                      0.0004

                      21

                      Wdr

                      0.0004

                      1

                      Cd53

                      0.0002

                      92

                      Cttnbp2nl

                      0

                      55

                      Golph3l

                      0

                      34

                      4933421E11Rsk

                      0

                      19

                      Eps8l3

                      0

                      1

                      Genes ranked according to text-mining significance, and SNP number for each gene noted. (QTL Chromosome 3, TM3). The top candidate is highlighted in bold: S100a10 had the highest cosine vector score demonstrating maximum relevance in the literature.

                      Transcript profiling: Microarray data has been submitted to ArrayExpress. Accession number E-MEXP-3098.

                      Candidate gene validation

                      Quantitative PCR analysis was undertaken independently in infected parental strains (days 0, 7, 14, 21, and 35 post-infection) to validate microarray data (Tables  2 and 3).

                      For gene candidates, Vav3, Ptpn22, FcgR1 and S100a10, qPCR corroborated up-regulated expression found in susceptible AKR on day 35 post infection (Figure  4). Likewise, the down-regulation of Hmgcs2 was confirmed by qPCR. Microarray analysis of Ctss and RORc demonstrated down regulation of colonic gene expression in chronically affected individuals, which could not be replicated (data not shown for RORc).
                      http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-14-127/MediaObjects/12864_2012_4891_Fig4_HTML.jpg
                      Figure 4

                      Colonic Tm3 gene expression by independent qPCR. Results are displayed relative to naïve resistant BALB/c, following standardization and normalization of samples against housekeeper gene (β-actin). Shown are the top 3 candidates by pathway & SNP analysis (Table 1: Vav3, Hmgcs2 & CTSS), the current strongest candidate gene from the literature (Fcgr1), the candidate with the highest text mining score but no designated biological pathway (Table 3: S100a10) and the candidate with the most SNPs but as yet without a designated pathway (Table 3: Ptpn22). Open bars denote susceptible AKR, shaded bars denote resistant BALB/c.

                      Discussion

                      Trichuris muris-induced colitis represents a tractable murine model for understanding the patho-biological mechanisms of chronic intestinal inflammation [9, 19]. The use of Quantitative Trait Loci (QTL) mapping based on continuous phenotypic variation has proved a useful technique in many murine polygenic traits including intestinal inflammation [12, 20, 21]. Yet, of more than 2000 QTL documented within the mouse genome database [22] fewer than 1% of studies have actually been characterized at a gene or molecular level, due to the small effect size of the susceptibility locus in question (<10% penetrance), or the large interval size defined [22]. New multi-factorial approaches have been discussed in the literature [23] and demonstrate that understanding complex genetic traits requires an integrative analysis.

                      Specific steps were taken in our experimental design to consider recent reports concerning the QTL/microarray approach in the identification of QTL candidate genes [23]. First, QTL were defined with regards to experimental phenotype (pQTL), and correlated with transcriptional expression activity in parental strains. Second, the use of high density Affymetrix exon array, which targets approximately 40 exonic probes per gene, overcame any problem of potential allelic-biased probe binding. Third, a hypothesis-free pathway analysis, backed up by additional text-mining, was employed in the secondary filtering of potential candidate genes to reduce bias. Fourth, any genes lacking polymorphisms (coding and non-coding) between parental strains were excluded from analyses, and lastly, positional overlap with a previously replicated major colitis susceptibility quantitative trait locus (Cdcs1) prioritised Tm3 for targeted analysis.

                      With regards to this shared locus, Cdcs1 on chromosome 3 was first noted in a QTL study of spontaneous colitis using IL10 deficient mice [12]. This locus has been shown to contain at least 3 distinct regions (Cdcs1.1, 1.2 & 1.3) that contribute to a severe colitic phenotype [14, 15]. Interestingly, all three regions contribute to caecal and proximal colonic inflammation strongly suggesting that this locus is a colitis ‘hotspot’ for susceptibility and/or regulation. Here we show complete overlap of Tm3 with Cdcs1.1 (Figure  2). Although NF-kB1 has been suggested as a candidate gene for the Cdcs1 locus, it is clear that it is not responsible entirely for the severe pathology observed [15]. To date, FcgR1 remains the key candidate gene described in the Cdcs1.1 region [14, 15] and is corroborated by our findings. Additional association with colitis susceptibility in Gnai2-/- mice [11] suggests that this locus may govern key inflammatory pathways in disease development, irrespective of trigger. QTL mapping specifically highlighted the Cdcs1.3 region in the spontaneous colitis and colorectal cancer development of TRUC mice [13]. However, more distal colonic disease (distal third of the colon) or the potential for malignant transformation may not be represented at this sub-locus.

                      We have shown that at least 6 biologically significant and polymorphic candidate genes lie within the Cdcs1.1 autosomal region. Importantly, 4 of these candidate genes are key in pathways relevant in the context of human Crohn’s disease (FcgR1, Vav3, Vcam1 and Ctss), a disease with highly similar pathology to both the IL10 deficient and T. muris models of colonic inflammation [9, 24]. The remaining 2 genes are highly polymorphic and known to be important in inflammation (RORc, Ptpn22). As individual candidate genes, each demonstrates interesting biological functionality that could play a role in mucosal inflammation. For instance, FcgR1 codes for a high affinity IgG receptor, key to IgG2a-induced phagocytosis and antigen specific immune responses. In the mouse, FcgR1 has been associated with autoimmune disorders such as rheumatoid arthritis and bacterial infection [25]. In humans the closely related FcgR2a and FcgR3 have been associated with IBD [26]. The protein tyrosine phosphatase gene (Ptpn22) is of particular interest, as in humans a mis-sense SNP (C1858T) has already demonstrated strong correlation with rheumatoid arthritis [27], type-1 diabetes mellitus [28], and other autoimmune disease [29]. Interestingly, the C1858T gene variant is not associated with the establishment of human Crohn’s disease [30] and may even represent protection [31]. In our study, Ptpn22 demonstrated progressively increased expression within the colonic tissue of susceptible mice following the establishment of colitis.

                      The unbiased approach we have used to select candidate genes has also highlighted a gene whose currently assigned pathway (circadian rhythm) does not overtly relate to mucosal inflammation. The Retinoic acid-related orphan receptor-C (RORc/RORγ) gene encodes for RORγt (RORγ2), a lineage-specific transcription factor of CD4+ TH17 cell differentiation [32]. Excessive TH17 cell activity has been implicated in both autoimmune [33] and inflammatory bowel diseases [34].

                      Finally, Vav3 was the primary candidate revealed by integrative pathway and SNP analysis and is of particular interest, as in six week old Vav1/2/3 triple knockout mice altered gut enterocyte differentiation and morphology has been shown, along with spontaneous colitis and ulceration in the caecum and ascending colon [35]. Vav3 is also involved in at least 7 known biological pathways, all of which could play a role in mucosal homeostasis and regulation. Some of these pathways involve other candidate genes in this region, for instance FcgR1 (Fc-gamma receptor mediated phagocytosis), Vcam1 (leukocyte transendothelial migration, focal adhesion) and Ptpn22 (negative regulation of T-cell receptor signalling) [36, 37]. We hypothesise therefore that Cdcs1 is in fact a ‘colitis hotspot’ containing several genes which if dysregulated through genetic variation, could adversely affect gut inflammation. It is possible that the specific candidate genes for each colitis model are not the same. However, the biological interaction between genes at this locus, demonstrates the importance of Cdcs1 and why this region appears in unrelated models of gut inflammation.

                      Interestingly, Tm3 (Cdcs1) does not correlate with any known nematode infection susceptibility QTL, but instead appears exclusive to colonic inflammatory disease.

                      For instance, expulsion and resistance to the small intestinal nematode Heligmosomoides bakeri in mice has been characterized at murine chromosome 1 and 17 [38] corresponding to Tm1 and Tm17. Similarly, a study of Trichinella spiralis infection in rats, which causes acute and transient small bowel inflammation, identified a single significant QTL region homologous to the murine chromosome 1 locus (Tm1) [39]. Lastly, resistance to small bowel and abomasum/gastric nematode infections of sheep, have highlighted a number of suggestive QTL [4042], homologous to Tm17, and downstream of Tm10. All studies demonstrated that resistance/susceptibility to GI nematode infection is under multi-genetic control, with MHC and non-MHC loci important in outcome [43]. However, these studies also highlight the importance of the Cdcs1 locus with the establishment of a large bowel inflammatory phenotype, separate to precise anti-parasitic mechanisms.

                      In conclusion, we have corroborated three previously published studies which associate the locus Cdcs1 with colonic mucosal inflammation in the mouse. Furthermore, we have shown that in the AKR and BALB/c, genetic variation in this region has the potential to affect mucosal homeostasis through several different pathways. Most importantly, we have demonstrated that an unbiased integrative analysis can be beneficial in candidate gene identification and prioritization, particularly cis-regulated genes, even in large regions. This approach is particularly useful for hypothesis generation, and has positionally implicated Vav3 as a biologically relevant gene candidate in colitis.

                      Methods

                      Animals

                      Mice were housed with free access to food and water under specific pathogen free conditions. All experiments were performed under regulations of The UK Home Office Animals (Scientific Procedures) Act of 1986.

                      For QTL analysis, AKR/OlaHsd (susceptible, hereafter referred to AKR) and BALB/cOlaHsd (resistant, hereafter referred to BALB/c) mice (Harlan Olac, UK) were interbred. To generate an F1 population of mice, equal numbers of AKR males vs BALB/c females (F1a offspring), and AKR females vs BALB/c males (F1b offspring) were mated. At least 50 breeding-pairs of F1-mice were then interbred. All F1 vs F1 breeding was performed over the same time-period. To maintain genetic balance, F1a males were bred with F1a and F1b females; and, F1b males with F1a and F1b females. A single generation of 307 F2 mice (male and female) was created for study. All F2 mice were infected at the same time with T. muris ova at 6-8 weeks of age.

                      Parasites

                      Trichuris muris parasites were harvested and ova collected and maintained as previously described [18]. All infected mice received 300 T. muris ova in distilled water (200 μl) by oral gavage.

                      QTL phenotyping

                      Phenotypic analysis was performed for all 307 F2 mice. Day 35 post-infection, serum samples and intestines were taken at autopsy. Resistance (0 worm load) and susceptibility (>0 worms) were defined. All worm counts were performed by a single investigator over 1 week from caeca frozen at autopsy. This method of storage and counting is used routinely for large experiments and does not affect quantification. Parasite-specific antibody ELISA was performed as described previously [44], using in-house T. muris excretory-secretory (ES) protein. T. muris specific IgG1 (TH2 specific, driven by IL4) and IgG2a (TH1 specific, driven by IFNγ) optical density (OD) was measured simultaneously for all samples. All 307 serum ELISAs were performed in one run. For histology, 0.5cm of whole colonic segments from the proximal ascending colon was snap-frozen, thawed in 4% paraformaldehyde, paraffin embedded, and 5μm transverse tissue sections stained with Haematoxylin and Eosin (H&E) simultaneously. 50 randomly assigned colonic specimens were assessed. Proximal colonic specimens were scored according to colonic crypt length (μm), immune cell infiltration and tissue inflammation by a single investigator. Colonic crypt length per individual was taken as a mean across at least 20 crypt units and 3 separate sections. Crypt units were measured using Image-J software [45]. Spearman’s rank correlation coefficient was performed to measure the statistical dependence of (a) worm count and colonic crypt length variables, and (b) IgG1:IgG2a ratio and crypt length variables.

                      DNA extraction

                      DNA was isolated (Promega Wizard DNA isolation kit) from tail snips digested in proteinase K digestion buffer (20 mg/ml). DNA concentration was determined by Nanodrop spectrophotometer and then stored at -80°C until analysis.

                      Linkage Map

                      165 polymorphic murine microsatellite markers distinguishing between AKR and BALB/c were selected [46]. Whole genome coverage was 85% and median inter-marker distance 12.3 cM. Conversion of marker positions from recombination fraction (cM) to physical position (Mb) was achieved using the Ensembl database [47].

                      Microsatellite amplification and genotype analysis

                      Forward polymerase chain reaction (PCR) primers were fluorescently labelled with 6-FAM, HEX or NED (MWG Biotec, Applied Biosystems). 25 ng of genomic DNA was used for each marker. Semi-automated analysis of genotypes on pooled panels of PCR products was performed using an Applied Biosystems 3100 Capillary sequencer with Genescan analysis and Genotyper software.

                      QTL analysis

                      IgG1:IgG2a ratios were log10 transformed to achieve parametric distribution. Median, mean and kurtosis values were calculated using QStat, Windows QTL Cartographer 2.5. Normalised data were analysed using multiple interval mapping to optimize and refine QTL positions. A genome-wide permutation test (1000 repeats) determined thresholds for significance; a logarithm of odds (LOD) score of 4.0 or a p value of <5.2×10-5 was considered significant. A LOD score of 2.5 or p value of 1.6×10-3 was considered suggestive of linkage according to published guidelines [48]. All significant LOD scores were confirmed by 1-way ANOVA with pairwise comparison, using the Bonferroni correction method. Kruskal-Wallis analysis was used for worm burden and IgG2a data, and converted to an LOD score [49].

                      Genome-wide colonic transcriptional activity of parental murine strains

                      Naïve and infected 6-to-8 week old male AKR and BALB/c mice (Harlan Olac, UK) were monitored through to day 35 post-infection (n = 6, 3 experimental replicates for each conditional cohort) as described previously [9]. 3 replicate pooled samples of colonic RNA (ascending colon) were generated for each experimental group. Whole transcriptome microarray expression analysis (Affymetrix Genechip Mouse Exon 1.0 ST Array®) and bioinformatic analysis was performed. The entire genome-wide expression dataset was used for subsequent analysis [9, 50].

                      The in-silico prioritization of QTL candidate genes

                      The use of workflows in the analysis of large-scale genomic data provides a systematic and un-biased mechanism for hypothesis generation [51]. Previously constructed workflows were re-used for the analysis of QTL and gene expression data, to identify biological pathways which correlated with Trichuris muris infection. The identification of candidate genes underlying each QTL was carried out by firstly determining the precise co-ordinates of each genetic marker (Mbp) (Table  1). Each QTL was subsequently entered into the workflow qtl_to_pathway (Additional file 1: Figure S2 [16]). Genes located within each QTL were annotated with additional accession number identifiers (including UniProt ID and Entrez Gene IDs), in order to cross-reference Ensembl database identifiers to KEGG (Kyoto Encyclopaedia of Genes and Genomes) [52] pathway identifiers. As a result, annotated biological pathways were extracted from the KEGG database for inclusion in further analysis.

                      In parallel, differentially expressed genes identified from the T. muris microarray study [9] were analysed using the refseq_ids_to_pathways workflow (Additional file 1: Figure S3 [17]). This workflow required preliminary analysis of the gene expression data [9] (Partek Genomic Solution version 6.5, 2009, Partek, USA) and conversion of Affymetrix probe-set identification markers to their recognised NCBI RefSeq identification code (refseq ids). An identical process to that of the qtl_to_pathway workflow for gene annotation was then carried out.

                      The mapping of gene expression data to KEGG highlighted biological pathway activity in the pathogenesis of colonic disease. All genes with significant transcriptional differences between resistant and susceptible strains, in naïve and infected states (ANOVA, factor interaction, p <0.05), were included for analysis. To identify cis-QTL genes of biological relevance to phenotype, those genes with a higher degree of over/under expression (Fold Change ≥ +/-1.4 over naïve levels) during chronic T. muris intestinal inflammation, were used in the workflow analysis (see Figure  3).

                      The workflow common_pathways (Additional file 1: Figure S4 [53]) was used to identify candidate pathways containing differentially expressed genes within a QTL, in order to obtain an overall view of the mechanisms which may be influencing the expression of the phenotype.

                      Additional text mining was used to prevent potential candidate genes which lacked KEGG pathway annotation from being discarded. Transcribed QTL genes were analysed using a text mining workflow (Additional file 1: Figure S5 [54]). Briefly, published abstracts were identified from a PubMed search using the term “(“Colitis” AND “Inflammation”) AND (“Human” OR “Mouse”)”. All scientifically relevant keywords contained within individual abstracts were extracted, constructing a phenotype concept profile and allowing the calculation of inverse document frequency (IDF) scores ie a score relating the number of resulting documents which contained the keywords in question. In parallel, abstracts pertaining to selected genes were similarly recorded. The identification of phenotype keywords within individual gene abstracts allowed for the generation of a cosine vector score for each gene ranging from +1 to -1 (+1 = causation of phenotype; 0 = unknown association with phenotype; -1 = preventative of phenotype). Ranked by their cosine vector score, the association with phenotype of a particular gene was displayed. Similarly, individual phenotype keywords were also ranked according to the IDF scores, identifying possible correlations between each gene and the phenotype. All data regarding text mining and workflow approaches are published online [55].

                      Only QTL genes known to possess SNP variation between parental AKR and BALB/c [56] were subject to further analysis.

                      Independent replication of candidate gene expression by qPCR (Tm3)

                      Infected parental strains AKR and BALB/c (Harlan Olac, UK) received 300 T. muris ova by oral gavage. Mice were culled days 0 (naive), 7, 14, 21 and 35 post-infection for analysis (n = 3 for each cohort). mRNA was extracted from 0.5 cm of whole colonic tissue segments, from the ascending colon, according to manufacturer’s instruction (TRIZOL®, Invitrogen). cDNA was synthesised. A full list of gene primers (Eurofins-MWG-Operon, Germany) and their sequences are provided (Additional file 1: Table S1). Samples were quantitatively analysed using KAPA SYBR FAST qPCR Master Mix (Kapa Biosystems Inc., USA) and a Bio-Rad MyIQ™ PCR detection system (Bio-Rad IQ5 optical system software, version 2; Bio-Rad Laboratories Inc.,©). Three replicate cDNA samples were run at a 1:20, a 1:100, and a 1:500 dilutions for each time-point. Threshold cycles were calculated; gene detection within the three serially diluted samples was standardized, and then normalized against housekeeping gene beta-actin (Act-b). Relative fold change in gene quantity was calculated using naïve resistant mice as a reference.

                      Authors’ information

                      Richard K Grencis and Joanne L Pennock co-senior author.

                      Declarations

                      Acknowledgements

                      The authors acknowledge the Wellcome Trust (RKG, JLP) and MRC (SL Clinical Fellowship) for funding this work. Also the staff in the BSF of the University of Manchester for technical assistance and support.

                      Authors’ Affiliations

                      (1)
                      Gastrointestinal Sciences, Institute of Inflammation and Repair, Faculty of Medicine and Human Sciences, University of Manchester
                      (2)
                      Bioinformatics Scientist, Oncology, AstraZeneca
                      (3)
                      Centre for Integrated Genomic Medical Research, Institute of Population Health, Faculty of Medicine and Human Sciences, University of Manchester
                      (4)
                      Bioinformatics, Faculty of Life Sciences, University of Manchester
                      (5)
                      Arthritis Research UK Epidemiology Unit, Institute of Inflammation and Repair, Faculty of Medicine and Human Sciences, University of Manchester
                      (6)
                      School of Computer Sciences, University of Manchester
                      (7)
                      Faculty of Life Sciences, University of Manchester

                      References

                      1. Wellcome Trust Case Control Consortium: Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared control. Nature 2007, 447:661–678.View Article
                      2. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ionnidis JP, Hirschhorn JN: Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 2008, 9:356–369.PubMedView Article
                      3. Glazier AM, Nadeau JH, Altman TJ: Finding genes that underlie complex traits. Science 2002, 298:2345–2349.PubMedView Article
                      4. Stylianou IM, Affouritt JP, Shockley KR, Wilpan RY, Abdi FA, Bhardwaj S, Rollins J, Churchill GA, Paigen B: Applying gene expression, proteomics and single-nucleotide polymorphism analysis for complex trait gene identification. Genetics 2008, 178:1795–1805.PubMedView Article
                      5. Olofsson P, Holmberg J, Tordsson J, Lu S, Akerstrom B, Holmdahl R: Positional identification of Ncf1 as a gene that regulates arthritis severity in rats. Nat Genet 2003, 33:25–32.PubMedView Article
                      6. Cliffe LJ, Grencis RK: The Trichuris muris system: a paradigm of resistance and susceptibility to intestinal nematode infection. Adv Parasitol 2004, 57:255–307.PubMedView Article
                      7. Bancroft AJ, McKenzie AN, Grencis RK: A critical role for IL-13 in resistance to intestinal nematode infection. J Immunol 1998, 160:3463–3461.
                      8. Else KJ, Finkelman FD, Malizewski CR, Grencis RK: Cytokine mediated regulation of chronic intestinal helminth infection. J Exp Med 1994, 179:347–351.PubMedView Article
                      9. Levison SE, McLaughlin JT, Zeef LA, Fisher P, Grencis RK, Pennock JL: Colonic transcriptional profiling in resistance and susceptibility to trichuriasis: phenotyping a chronic colitis and lessons for iatrogenic helminthosis. Inflamm Bowel Dis 2010, 16:2065–2079.PubMedView Article
                      10. Awasthi S, Bundy DA, Savioli L: Helminthic infections. BMJ 2003, 327:431–433.PubMedView Article
                      11. Borm ME, He J, Kelsall B, Pena AS, Strober W, Bouma G: A major quantitative trait locus on mouse chromosome 3 is involved in disease susceptibility in different colitis models. Gastroenterology 2005, 128:74–85.PubMedView Article
                      12. Farmer MA, Sundberg JP, Bristol IJ, Churchill GA, Elson CO, Leiter EH: A major quantitative locus on chromosome 3 controls colitis severity in IL-10-deficient mice. Proc Natl Acad Sci USA 2001, 98:13820–13825.PubMedView Article
                      13. Ermann J, Garrett WS, Kuchroo J, Rorida K, Glickman JN, BLeich A, Glimcher LH: Severity of innate immune-mediated colitis is controlled by the cytokine deficiency-induced colitis susceptibility-1 (Cdcs1) locus . Proc Natl Acad Sci U S A 2011, 108:7137–7141.PubMedView Article
                      14. Bleich A, Buchler G, Beckwith J, Petell LM, Affourtit JP, King BL, Shaffer DJ, Roopenian DC, Hedrich HJ, Sundberg JP, Leiter EH: Cdcs1 a major colitis susceptibility locus in mice; subcongenic analysis reveals genetic complexity. Inflamm Bowel Dis 2010, 16:765–775.PubMedView Article
                      15. Beckwith J, Cong Y, Sunderberg JP: Cdcs1, a major colitogenic locus in mice, regulates innate and adaptive immune responses to enteric bacterial antigens. Gastroenterology 2005, 129:1473–1484.PubMedView Article
                      16. Fisher P: Pathways and Gene annotations for QTL region. http://​www.​myexperment.​org/​workflows/​1661.​html.
                      17. Fisher P: Pathways and Gene annotations for RefSeq ids. http://​www.​myexperiment.​org/​workflows/​1662.​html
                      18. Wakelin D: Acquired immunity to Trichuris muris in the albino laboratory mouse. Parasitology 1967, 57:515–524.PubMedView Article
                      19. Artis D, Grencis RK: The intestinal epithelium: sensors to effectors in nematode infection. Mucosal Immunol 2008, 1:252–264.PubMedView Article
                      20. Kowaiwa K, Sugawara K, Smith MF Jr, Carl V, Yamschikov V, Belyea B, McEwen SB, Moskaluk CA, Pizarro TT, Cominelli F, McDuffie M: Identification of a quantitative trait locus for ileitis in a spontaneous mouse model of crohn’s disease: SAMP/YitFc. Gastroenterology 2003, 125:477–490.View Article
                      21. Flint J, Valdar W, Shifman S, Mott R: Strategies for mapping and cloning quantitative trait genes in rodent. Nat Rev Genetics 2005, 6:271–286.View Article
                      22. Mouse Phenome Database http://​www.​informatics.​jax.​org
                      23. Verdugo RA, Farber CR, Warden CH, Medrano JF: Serious limitations of the QTL/Microarray approach for QTL gene discovery. BMC Biol 2010, 8:96.PubMedView Article
                      24. Bristol IJ, Farmer MA, Cong Y, Zheng XX, Strom TB, Elson CO, Sundberg JP, Leiter EH: Heritable susceptibility for colitis in mice induced by IL-10 deficiency. Inflamm Bowel Dis 2000, 6:290–302.PubMed
                      25. Iaon-Facsinay A, de Kimpe SJ, van Lent PL, Hofhuis FM, Van Ojik HH, Sedlik C, da Silveira SA, Gerver J, de Jong YF, Roozendaal R, Aarden LA, van den Berg WB, Saito T, Mosser D, Amigorena S, Izui S, van Ommen GJ, van Vugt M, van de Winkel JG, Verbeek JS: Fc-gamma-R1 (CD64) contributes substantially to severity of arthritis, hypersensitivity responses, and protection from bacterial infection. Immunity 2002, 16:391–402.View Article
                      26. Weersma RK, Crusius JB, Roberts RL, Koeleman BP, Palomino-Morales R, Wolkamp S, Hollis-Moffatt JE, Festen EA, Meisneris S, Heijmans R, Noble CL, Gearry RB, Barclay ML, Gomez-Garcia M, Lopez-Nevot MA, Nieto A, Rodrigo L, Radstake TR, van Bodegraven AA, Wijmenga C, Merriman TR, Stokkers PC, Pena AS, Martin J, Alizadeh BZ: Association of FcgR2a, but not FcgR3a, with inflammatory bowel disease across three Caucasian populations. Inflamm Bowel Dis 2010, 16:2080–2089.PubMedView Article
                      27. Begovich AB, Carlton VE, Honigberg LA, Schrodi SJ, CHokkalingam AP, Alexander HC, Ardlie KG, Huang Q, Smith AM, Spoerke JM, Conn MT, Chang M, Chang SY, Siki RK, Catanese JJ, Leong DU, Garcia VE, McAllister LB, Jeffery DA, Lee AT, Batliwalla F, Remmers E, Criswell LA, Seldin MF, Kastner DL, Amos CI, Sninsky JJ, Gregersen PK: A missense single-nucleotide polymorphism in a gene encoding a protein tyrosine phosphatase (PTPN22) is associated with rheumatoid arthritis. Am J Hum Genet 2004, 75:330–337.PubMedView Article
                      28. Bottini N, Musumeci L, Alonso A, Rahmouni S, Nika K, Rostamkhani M, MacMurray J, Meloni GH, Lucarelli P, Pellecchia M, Eisenbarth GS, Comings D, Mustelin T: A functional variant in lymphoid tyrosine phosphatase is associated with type I diabetes. Nat Genet 2004, 36:337–338.PubMedView Article
                      29. Lee YH, Rho YH, Choi SJ, Ji JD, Song GG, Nath SK, Harley JB: The Ptpn22 C1858T functional polymorphism and autoimmune disease – a meta-analysis. Rheumatology 2007, 46:49–56.PubMedView Article
                      30. van Oene M, Wintle RF, Lui X, Yazdanpanah M, Gu X, Newman B, Kwan A, Johnsn B, Owen J, Greer W, Mosher D, Maksymowych W, Keystone E, Rubin LA, Amos CI, Siminovitch KA: Association of the lymphoid tyrosine phosphatase R620W variant with rheumatoid arthritis, but not Crohn’s disease, in Canadian populations. Arthritis Rheum 2005, 52:1993–1998.PubMedView Article
                      31. Barrett JC, Hansoul S, Nicolae DL, Cho JH, Duerr R, Rioux JD, Brant SR, Silverberg MS, Raylor D, Barmada MM, Bitton A, Dasopoulos T, Datta LW, Green T, Riffiths AM, Kistner EO, Murtha MT, Reguieiro MD, Rotter JI, Schumm LP, Steinhart AH, Targan SR, Xavier RJ, Libioulle C, Sandor C, Lthrop M, Belaiche J, Dewit O, Gut I, NIHHKIBD Genetics Consortium: Genome-wide association defines more than 30 distinct susceptibility loci for Crohn’s disease. Nat Genet 2008, 40:955–962.PubMedView Article
                      32. Ivanov I, McKenzie BS, Zhou L, Tadokoro CE, Lepelley A, Lafaille JJ, Cua DJ, Littman DR: The orphan nuclear receptor RORγt directs the differentiation program of proinflammatory IL-17 + T-helper cells. Cell 2006, 126:1121–1133.PubMedView Article
                      33. Martinez GJ, Nurieva RI, Yang XO, Dong C: Regulation and function of proinflammatory TH17 cells. Ann N Y Acad Sci 2008, 1143:188–211.PubMedView Article
                      34. Sarra M, Pallone F, MacDonald TT, Monteleone G: I23/IL17 axis in IBD. Inflamm Bowel Dis 2010, 16:1808–1013.PubMedView Article
                      35. Liu JY, Seno H, Miletic AV, Mills JC, Swat W, Stappenbeck TS: Vav proteins are necessary for correct differentiation of mouse caecal and colonic enterocytes. J Cell Sci 2009, 122:324–334.PubMedView Article
                      36. Zeng L, Sachdev P, Yan L, Chan JL, Trengle T, McClelland M, Welsh J, Wang LH: Vav3 mediates receptor protein tyrosine kinase signalling, regulates GTPase activity, modulates cell morphology and induces cell transformation. Mol Cell Biol 2000, 20:9212–9224.PubMedView Article
                      37. Tybulewicz VLJ: Vav-family proteins in T-cell signalling. Curr Opin Immunol 2005, 17:267–274.PubMedView Article
                      38. Behnke JM, Iraqi FA, Mugambi JM, Clifford S, Nagda S, Wakelin D, Kemp SJ, Baker RL, Gibson JP: High resolution mapping of chromosomal regions controlling resistance to gastrointestinal nematode infections in an advanced intercross line of mice. Mamm Genome 2006, 17:584–597.PubMedView Article
                      39. Suzuki T, Ishih A, Kino H, Muregi FW, Takabayashi S, Nishikawa T, Takagi H, Terada M: Chromosomal mapping of host resistance to Trichinella spiralis nematode infection in rats. Immunogenetics 2006, 58:26–30.PubMedView Article
                      40. Beh KJ, Hulme DJ, Callaghan MJ, Leish Z, Lenane I, Windon RG, Maddox JF: A genome scan for quantitative trait loci affecting resistance to Trichostrongylus colubriformis in sheep. Anim Genet 2002, 33:97–106.PubMedView Article
                      41. Dominik S: Quantitative trait loci for internal nematode resistance in sheep: a review. Genet Sel Evol 2005,37(Suppl.1):S83-S96.PubMedView Article
                      42. Stear MJ, Boag B, Cattadori I, Murphy L: Genetic variation in resistance to mixed, predominantly Teladorsagia circumcincta nematode infection of sheep: from heritabilities to gene identification. Parasite Immunol 2009, 31:274–282.PubMedView Article
                      43. Behnke JM, Menge DM, Noyes H: Heligmosomoides bakeri : a model for exploring the biology and genetics of resistance to chronic gastrointestinal nematode infections. Parasitology 2009, 136:1565–1580.PubMedView Article
                      44. Else KJ, Entwistle GM, Grencis RK: Correlation between worm burden and markers of Th1 and Th2 cell subset induction in an inbred strain of mouse infected with Trichuris muris . Parasite Immunol 1993, 15:595–600.PubMed
                      45. Image-J Software http://​rsbweb.​nih.​gov/​ij
                      46. Broad Institute http://​www.​broad.​mit.​edu/​mouse
                      47. Ensembl Database: Ensembl Database. http://​www.​ensembl.​org] Ensembl 64 assembly accessed September 2011
                      48. Lander E, Kruglyak L: Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet 1995, 11:241–247.PubMedView Article
                      49. Broman KW: Mapping quantitative trait loci in the case of a spike in the phenotype distribution. Genetics 2003, 163:1169–1175.PubMed
                      50. Genome-wide array data: Genome-wide array data. available at: [http://​www.​ebi.​ac.​uk/​arrayexpress/​experiments/​E-MEXP-3098] available at:
                      51. Fisher P, Hedeler C, Wolstencroft K, Hulme H, Noyes H, Kemp S, Stevens R, Brass A: A systematic strategy for large-scale analysis of genotype–phenotype correlations: identification of candidate genes involved in African trypanosomiasis. Nucleic Acids Res 2007, 35:5625–5633.PubMedView Article
                      52. Kanehisa M, Goto S: KEGG: Kyoto encyclopaedia of genes and genome. Nucleic Acid Res 2000, 28:27–30.PubMedView Article
                      53. Fisher P: KEGG pathways common to both QTL and microarray based investigations. http://​www.​myexperiment.​org/​workflows/​1663
                      54. Fisher P: Pathway and Gene to PubMed; a text mining workflow. http://​www.​myexperiment.​org/​workflows/​1846
                      55. Fisher Phttp://​www.​myexperiment.​org/​packs/​169
                      56. Sanger Mouse SNP Repository http://​www.​sanger.​ac.​uk/​cgi-bin/​modelorgs/​mousegenomes/​snps/​pl

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                      This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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