Association results for 17 SNPs from the current GWAS and the GWAS reported by Dubois et al. (REF) within the FRMD4B gene on 3p14.1.

<p>Association results for 17 SNPs from the current GWAS and the GWAS reported by Dubois et al. (REF) within the FRMD4B gene on 3p14.1.</p

Results of association analysis of microscopic colitis and dermatitis herpetiformis across celiac associated regions.

<p>Results of association analysis of microscopic colitis and dermatitis herpetiformis across celiac associated regions.</p

Gene Expression Differences in Prostate Cancers between Young and Old Men

<div><p>Prostate cancer incidence is increasing in younger men. We investigated whether men diagnosed with Gleason 7 (3+4) T2 prostate cancer at younger ages (≤ 45 years, young cohort) had different mRNA and miRNA expression profiles than men diagnosed at older ages (71–74 years, older cohort). We identified differentially expressed genes (DEGs) related to tumor-normal differences between the cohorts. Subsequent pathway analysis of DEGs revealed that the young cohort had significantly more pronounced inflammatory and immune responses to tumor development compared to the older cohort. Further supporting a role of inflammation-induced immune-suppression in the development of early-onset prostate cancer, we observed significant up-regulation of CTLA4 and IDO1/TDO2 pathways in tumors of the young cohort. Moreover, over-expression of <i>CTLA4</i> and <i>IDO1</i> was significantly associated with biochemical recurrence. Our results provide clues on the mechanisms of tumor development and point to potential biomarkers for early detection and treatment for prostate cancer in young men.</p></div

Four main age:tissue interaction patterns for genes that have significant differences in tumor-induced gene expression by age.

<p>Horizontal axis is tissue type and vertical axis is mean gene expression. For each interaction pattern, the trend of changes in expression from normal to tumor tissues for the older (dashed line) and young (solid line) cohorts were plotted. There was significantly increased expression in tumor tissue compared to corresponding normal tissue in the young cohort with insignificant change in expression in the older cohort (plot a), whereas in plot b, both cohorts showed increasing expression from normal to tumor with the larger change in the young cohort. In plot c, the young cohort had a significant decrease in expression in tumors compared to the normal tissue, with an insignificant change in the older cohort, whereas in plot d, there was a significant decrease in expression in the young cohort and a significant increase in the older cohort.</p

Top-five IPA results for the 121 up-regulated DEGs identified from the age:tissue interaction contrast.

<p>Top-five IPA results for the 121 up-regulated DEGs identified from the age:tissue interaction contrast.</p

Boxplots and dotplots of DASL data exhibiting outliers of expression in <i>IDO1</i>, <i>TDO2</i>, <i>ALOX15</i> and <i>DEFA6</i>.

<p>Patients with biochemical recurrence are shown with a pink color in the corresponding tumor samples.</p

Clinical characteristics of 49 patient samples.

<p>Clinical characteristics of 49 patient samples.</p

Additional file 2: of Male breast cancer in BRCA1 and BRCA2 mutation carriers: pathology data from the Consortium of Investigators of Modifiers of BRCA1/2

List of local ethics committees that granted approval for the access and use of the data in present study. (DOCX 23 kb

p-values of association (−log10 scale) with breast cancer risk in <i>BRCA2</i> carriers for genotyped and imputed SNPs in the <i>NEIL2</i> gene.

<p>SNP rs1466785 is indicated with a purple arrow and the best causal imputed SNPs, rs804276 and rs804271 are indicated with a red arrow. Colors represent the pariwise r². Plot generated with LocusZoom <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004256#pgen.1004256-Pruim1" target="_blank">[42]</a> (<a href="http://csg.sph.umich.edu/locuszoom/" target="_blank">http://csg.sph.umich.edu/locuszoom/</a>).</p

Associations with breast and ovarian cancer risk for SNPs observed at p-trend<0.05 in stage II of the experiment.

a<p>Hazard Ratio per allele (1 df) estimated from the retrospective likelihood analysis.</p>b<p>Hazard Ratio under the genotype specific models (2df) estimated from the retrospective likelihood analysis.</p>c<p>p-values were based on the score test.</p>d<p>HR per allele of 1.69 and p-trend of 1×10⁻⁴ for <i>BRCA2</i> mutation carriers in stage I of the study.</p>e<p>HR per allele of 1.43 and p-trend of 0.01 for <i>BRCA1</i> mutation carriers in stage I of the study.</p>f<p>HR per allele of 1.30 and p-trend of 0.03 for <i>BRCA1</i> mutation carriers in stage I of the study.</p>g<p>HR per allele of 0.64 and p-trend of 0.057 for <i>BRCA2</i> mutation carriers in stage I of the study.</p>h<p>HR per allele of 1.25 and p-trend of 0.04 for <i>BRCA1</i> mutation carriers in stage I of the study.</p>i<p>HR per allele of 1.25 and p-trend of 0.058 for <i>BRCA2</i> mutation carriers in stage I of the study.</p>j<p>rs3093926 did not yield results under the genotype specific model due to the low minor allele frequency.</p><p>Complete description of results from stage I are included in Supplementary <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004256#pgen.1004256.s002" target="_blank">Table S1</a>.</p><p>Highlighted in bold are those SNPs showing strongest associations with breast or ovarian cancer risk (p<0.01).</p