Characteristics of participating women.

*<p>Current smokers were excluded from the study. 12 women did not participate in the MRI studies.</p

Prediction of body fat content and distribution by anthropometry and biomarkers.

*<p>Model (2) included all biomarkers, age, ethnicity, and key covariates, including smoking status (never vs. former, pack-years of cigarette smoking), education, use of medications (estrogen, statins, aspirin) and dietary supplements, and number of children. Model (3) shows the top 5 predictors from Model (2).</p><p>Abbreviations: IGFBP1 (insulin-like growth factor binding protein 1); LAR (leptin to high-molecular-weight adiponectin ratio); PAI1 (plasminogen activator inhibitor-1); SHBG (sex hormone binding globulin); sLEPR (soluble leptin receptor).</p

Random Forest models for predicting adiposity.

<p>Total, abdominal (trunk-to-periphery fat ratio or TPFR), visceral and hepatic adiposity measurements were predicted to various extent by a number of blood biomarkers, as well as by demographic (age, ethnicity, education) and key lifestyle variables (smoking, medication use, supplement use, parity), without anthropometric variables. Predictors were ranked by the importance score, which was based on percent increase in mean square error upon random permutation of the given predictor. The figure shows the top 20 predictors for each adiposity measure. (Abbreviations: BMI [body mass index], %incMSE (percent increase in mean square error), RF [Random Forest]; see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043502#pone.0043502.s002" target="_blank">Table S1</a> for the full names of the biomarkers).</p

Pleiotropy of Cancer Susceptibility Variants on the Risk of Non-Hodgkin Lymphoma: The PAGE Consortium

<div><p>Background</p><p>Risk of non-Hodgkin lymphoma (NHL) is higher among individuals with a family history or a prior diagnosis of other cancers. Genome-wide association studies (GWAS) have suggested that some genetic susceptibility variants are associated with multiple complex traits (pleiotropy).</p><p>Objective</p><p>We investigated whether common risk variants identified in cancer GWAS may also increase the risk of developing NHL as the first primary cancer.</p><p>Methods</p><p>As part of the Population Architecture using Genomics and Epidemiology (PAGE) consortium, 113 cancer risk variants were analyzed in 1,441 NHL cases and 24,183 controls from three studies (BioVU, Multiethnic Cohort Study, Women's Health Initiative) for their association with the risk of overall NHL and common subtypes [diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), chronic lymphocytic leukemia or small lymphocytic lymphoma (CLL/SLL)] using an additive genetic model adjusted for age, sex and ethnicity. Study-specific results for each variant were meta-analyzed across studies.</p><p>Results</p><p>The analysis of NHL subtype-specific GWAS SNPs and overall NHL suggested a shared genetic susceptibility between FL and DLBCL, particularly involving variants in the major histocompatibility complex region (rs6457327 in 6p21.33: FL OR = 1.29, <i>p</i> = 0.013; DLBCL OR = 1.23, <i>p</i> = 0.013; NHL OR = 1.22, <i>p</i> = 5.9×E-05). In the pleiotropy analysis, six risk variants for other cancers were associated with NHL risk, including variants for lung (rs401681 in <i>TERT</i>: OR per C allele = 0.89, <i>p</i> = 3.7×E-03; rs4975616 in <i>TERT</i>: OR per A allele = 0.90, <i>p</i> = 0.01; rs3131379 in <i>MSH5</i>: OR per T allele = 1.16, <i>p</i> = 0.03), prostate (rs7679673 in <i>TET2</i>: OR per C allele = 0.89, <i>p</i> = 5.7×E-03; rs10993994 in <i>MSMB</i>: OR per T allele = 1.09, <i>p</i> = 0.04), and breast (rs3817198 in <i>LSP1</i>: OR per C allele = 1.12, <i>p</i> = 0.01) cancers, but none of these associations remained significant after multiple test correction.</p><p>Conclusion</p><p>This study does not support strong pleiotropic effects of non-NHL cancer risk variants in NHL etiology; however, larger studies are warranted.</p></div

Pleiotropic association of selected cancer susceptibility variants with the risk of overall non-Hodgkin lymphoma (NHL).

<p>* ORs and 95% CIs in individual studies were estimated in unconditional logistic regression models that were adjusted for age, sex (in BioVU and MEC) and ethnicity (ancestry informative markers). Summary ORs and 95% CIs were estimated in a meta-analysis of fixed-effects models.</p>†<p>The Bonferroni corrected <i>p-value</i> for 53 SNPs/tests is 4.4E-04.</p><p>Abbreviations: <i>p</i>-het. (<i>P</i>-values for heterogeneity across studies measured in Cochran's Q statistic); BioVU (the biorepository of the Vanderbilt University), MEC (the Multiethnic Cohort Study), WHI (the Women's Health Initiative).</p

Associations between a risk score (RS) for 53 GWAS-identified cancer risk variants and the overall and subtype-specific risks of NHL.

<p>* ORs and 95% CIs in individual studies were estimated per risk allele in unconditional logistic regression models that were adjusted for age, sex (in BioVU and MEC) and ethnicity. Summary odds ratios (ORs) and 95% confidence intervals (CIs) were estimated in a meta-analysis of fixed effects models.</p><p>Abbreviations: <i>p-het</i>. (<i>p-values</i> for heterogeneity across studies measured in Cochran's Q statistic); BioVU (the biorepository of Vanderbilt University), MEC (the Multiethnic Cohort Study), WHI (the Women's Health Initiative).</p

Characteristics of non-Hodgkin lymphoma (NHL) cases and controls in the PAGE studies.

<p>* Any prior cancer cases were excluded from the NHL cases and controls for the current analysis, based on self-report (BioVU, MEC, WHI), the SEER registry linkage (BioVU, MEC), and medical record reviews (BioVU, WHI).</p><p>Abbreviations: BioVU (the biorepository of the Vanderbilt University), MEC (the Multiethnic Cohort Study), WHI (the Women's Health Initiative); CLL/SLL (chronic lymphocytic leukemia/small lymphocytic lymphoma), DLBCL (diffuse large B-cell lymphoma), FL (follicular lymphoma), SEER (Surveillance, Epidemiology and End Results).</p

Forest plots for the association between a published follicular lymphoma risk variant (rs6457327) and the risk of follicular lymphoma or overall non-Hodgkin lymphoma (NHL) in the Multiethnic Cohort (MEC) and the Women's Health Initiative (WHI) in the PAGE consortium.

<p>(a) follicular lymphoma, (b) overall NHL, and (c) overall NHL among whites only.</p

Association between SNPs in the <i>FTO</i> region and BMI for all studies combined.

<p><b>a</b>SNPposition based on build 37.</p><p><b>b</b>Coding = coding allele, Base = baseline allele (risk estimates provide the log additive effect per copy of the coding allele).</p><p><b>c</b>CAF = coding allele frequency.</p><p><b>d</b>Rsq = measurement of imputation accuracy, ranging from 0 (low) to 1 (high).</p

Association results (p-values) and correlation structure for all SNPs in the 16q12.2/<i>FTO</i> region and lnBMI among African Americans using rs56137030 to calculate correlation among SNPs (LocusZoom plots).

<p>The top half of each figure has physical position along the x axis, and the −log₁₀ of the meta-analysis p-value on the y-axis. Each dot on the plot represents the p-value of the association for one SNP with lnBMI across all studies. The most significant SNP (rs56137030) is marked as a purple diamond. The color scheme represents the pairwise correlation (r²) for the SNPs across the 16q12.2/<i>FTO</i> region with the most significant SNP (rs56137030). Gray squares indicate that correlation was missing for this p-value because the variant was monomorphic in EA. The bottom half of the figure shows the position of the genes across the region. A and B show the same region and results. The only difference between A and B is that in A correlation with the most significant SNP (rs56137030) was calculated based on EAs, specifically based on data from 65 European Americans (Utah residents with Northern and Western European ancestry from the CEPH collection, CEU) sequenced as part of the 1000 Genomes Project and B correlation was based on 61 African Americans from the South-west (ASW) and sequenced as part of the 1000 Genomes Project.</p