Summary of datasets used in this study.

<p>Summary of datasets used in this study.</p

Interactions within the MHC contribute to the genetic architecture of celiac disease

<div><p>Interaction analysis of GWAS can detect signal that would be ignored by single variant analysis, yet few robust interactions in humans have been detected. Recent work has highlighted interactions in the MHC region between known HLA risk haplotypes for various autoimmune diseases. To better understand the genetic interactions underlying celiac disease (CD), we have conducted exhaustive genome-wide scans for pairwise interactions in five independent CD case-control studies, using a rapid model-free approach to examine over 500 billion SNP pairs in total. We found 14 independent interaction signals within the MHC region that achieved stringent replication criteria across multiple studies and were independent of known CD risk HLA haplotypes. The strongest independent CD interaction signal corresponded to genes in the HLA class III region, in particular <i>PRRC2A</i> and <i>GPANK1/C6orf47</i>, which are known to contain variants for non-Hodgkin's lymphoma and early menopause, co-morbidities of celiac disease. Replicable evidence for statistical interaction outside the MHC was not observed. Both within and between European populations, we observed striking consistency of two-locus models and model distribution. Within the UK population, models of CD based on both interactions and additive single-SNP effects increased explained CD variance by approximately 1% over those of single SNPs. The interactions signal detected across the five cohorts indicates the presence of novel associations in the MHC region that cannot be detected using additive models. Our findings have implications for the determination of genetic architecture and, by extension, the use of human genetics for validation of therapeutic targets.</p></div

Independent validated interaction signals detected in UK1.

<p>Independent validated interaction signals detected in UK1.</p

Variation in two-locus models within and between populations.

<p>Distribution of two-locus models for VIPs in different studies as increasingly less significant SNP pairs are examined. Different colours represent a different subset of two-locus models. The “other” group represents the remaining set of models. Models have been simplified using the rules provided in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0172826#pone.0172826.ref024" target="_blank">24</a>].</p

Disease variance explained by models with additive and interacting genetic effects.

<p>Disease variance explained by models with additive and interacting genetic effects.</p

Summary of the number of significant SNP pairs detected and of those that also replicate in at least one cohort.

<p>Summary of the number of significant SNP pairs detected and of those that also replicate in at least one cohort.</p

Interactions and LD patterns within the extended MHC region.

<p>SNP pairs within 30KB of each other are shown as a single point on each heatmap. The colour of each point in the upper left half of the graph represents the most significant -log₁₀(P-value) returned by the GSS statistic for SNPs pairs within each point. The adjusted -log₁₀(P-value) is capped at 30 to increase contrast of lower values. The bottom right half of the graph shows the maximum r² obtained for any SNP pair within a given 30Kb block, demonstrating the strong LD patterns known to exist within this region.</p

Replication of interacting SNP pairs between populations.

<p>Overlap of significant pairs as a percentage between UK1 and remaining cohorts in order of decreasing GSS significance. Vertical dotted lines indicate the Bonferroni-adjusted significance for each study.</p

Enrichment of FRA1 in tumor cells at the invasive front of human CRCs.

<p>(A) Low power image of a representative colorectal carcinoma stained with an antibody detecting FRA1. The asterisk indicates the lumen, while the arrowheads indicate the deep invasive front. Scale bar represents 1 mm. (B and C) High power images of the tumor centre (TC) and invasive front (IF) shown in (A). Arrowheads indicate tumor buds. Scale bar represents 10 µM. (D) Relationship between the intensity of nuclear FRA1 expression and the tumor budding marker, cytokeratin AE1/AE3 in 25 CRC cases.</p

Characterization of EMT-related FRA1 transcriptional targets.

<p>(A) Heat map showing different functional groups of EMT-related genes bound and regulated by FRA1 (FRA1^EMT genes). Data from RNA-Seq analysis of two clones of BE shFRA1-A cells (n = 4 for each cell line) was normalised relative to shControl cells. Regions shown in red represent genes associated with an epithelial state that were upregulated upon FRA1 silencing (log fold-change&lt;−1, p&lt;0.05), while green regions represent mesenchymal-type genes repressed by FRA1 silencing (log fold-change&gt;1, p&lt;0.05). (B) Distribution of genomic FLAG-FRA1 binding sites identified by ChIP-Seq relative to a corresponding gene. The number of reads identified for each region is expressed as a percentage. (C) ChIP-qPCR analysis of FLAG-FRA1 binding to genomic regions in selected FRA1^EMT genes. Data represent relative enrichment compared to parental BE cells. A region of the miRNA-21 gene not bound by FLAG-FRA1 was used as negative control (<i>CTRL</i>). (D) qRT-PCR analysis of selected FRA1^EMT genes in BE cells stably transduced with one of two independent shRNAs targeting FRA1. Data are represented relative to expression levels in cells shNS cells. Student's t-test was used for all comparisons (^*p&lt;0.05, ^**p&lt;0.01, ^***p&lt;0.001). Error bars represent S.E.M. for 3 independent experiments. (E) FRA1 protein levels and (F) expression of epithelial and mesenchymal marker genes in a panel of CRC cell lines.</p