Association of Cancer Susceptibility Variants with Risk of Multiple Primary Cancers: The Population Architecture using Genomics and Epidemiology Study

Multiple primary cancers account for ~16% of all incident cancers in the U.S.. While genome-wide association studies (GWAS) have identified many common genetic variants associated with various cancer sites, no study has examined the association of these genetic variants with risk of multiple primary cancers (MPC)

Association of the FTO Obesity Risk Variant rs8050136 With Percentage of Energy Intake From Fat in Multiple Racial/Ethnic Populations

Common obesity risk variants have been associated with macronutrient intake; however, these associations' generalizability across populations has not been demonstrated. We investigated the associations between 6 obesity risk variants in (or near) the NEGR1, TMEM18, BDNF, FTO, MC4R, and KCTD15 genes and macronutrient intake (carbohydrate, protein, ethanol, and fat) in 3 Population Architecture using Genomics and Epidemiology (PAGE) studies: the Multiethnic Cohort Study (1993–2006) (n = 19,529), the Atherosclerosis Risk in Communities Study (1987–1989) (n = 11,114), and the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) Study, which accesses data from the Third National Health and Nutrition Examination Survey (1991–1994) (n = 6,347). We used linear regression, with adjustment for age, sex, and ethnicity, to estimate the associations between obesity risk genotypes and macronutrient intake. A fixed-effects meta-analysis model showed that the FTO rs8050136 A allele (n = 36,973) was positively associated with percentage of calories derived from fat (βmeta = 0.2244 (standard error, 0.0548); P = 4 × 10−5) and inversely associated with percentage of calories derived from carbohydrate (βmeta = −0.2796 (standard error, 0.0709); P = 8 × 10−5). In the Multiethnic Cohort Study, percentage of calories from fat assessed at baseline was a partial mediator of the rs8050136 effect on body mass index (weight (kg)/height (m)2) obtained at 10 years of follow-up (mediation of effect = 0.0823 kg/m2, 95% confidence interval: 0.0559, 0.1128). Our data provide additional evidence that the association of FTO with obesity is partially mediated by dietary intake

Pleiotropic Associations of Risk Variants Identified for Other Cancers With Lung Cancer Risk: The PAGE and TRICL Consortia

BackgroundGenome-wide association studies have identified hundreds of genetic variants associated with specific cancers. A few of these risk regions have been associated with more than one cancer site; however, a systematic evaluation of the associations between risk variants for other cancers and lung cancer risk has yet to be performed.MethodsWe included 18023 patients with lung cancer and 60543 control subjects from two consortia, Population Architecture using Genomics and Epidemiology (PAGE) and Transdisciplinary Research in Cancer of the Lung (TRICL). We examined 165 single-nucleotide polymorphisms (SNPs) that were previously associated with at least one of 16 non–lung cancer sites. Study-specific logistic regression results underwent meta-analysis, and associations were also examined by race/ethnicity, histological cell type, sex, and smoking status. A Bonferroni-corrected P value of 2.5×10–5 was used to assign statistical significance.ResultsThe breast cancer SNP LSP1 rs3817198 was associated with an increased risk of lung cancer (odds ratio [OR] = 1.10; 95% confidence interval [CI] = 1.05 to 1.14; P = 2.8×10–6). This association was strongest for women with adenocarcinoma (P = 1.2×10–4) and not statistically significant in men (P = .14) with this cell type (P het by sex = .10). Two glioma risk variants, TERT rs2853676 and CDKN2BAS1 rs4977756, which are located in regions previously associated with lung cancer, were associated with increased risk of adenocarcinoma (OR = 1.16; 95% CI = 1.10 to 1.22; P = 1.1×10–8) and squamous cell carcinoma (OR = 1.13; CI = 1.07 to 1.19; P = 2.5×10–5), respectively.ConclusionsOur findings demonstrate a novel pleiotropic association between the breast cancer LSP1 risk region marked by variant rs3817198 and lung cancer risk

Genetic analyses of diverse populations improves discovery for complex traits

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry1–3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific4–10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States—where minority populations have a disproportionately higher burden of chronic conditions13—the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities. © 2019, The Author(s), under exclusive licence to Springer Nature Limited

Association of Cancer Susceptibility Variants with Risk of Multiple Primary Cancers: The Population Architecture using Genomics and Epidemiology Study

BACKGROUND: Multiple primary cancers account for ~16% of all incident cancers in the U.S.. While genome-wide association studies (GWAS) have identified many common genetic variants associated with various cancer sites, no study has examined the association of these genetic variants with risk of multiple primary cancers (MPC). METHODS: As part of the NHGRI Population Architecture using Genomics and Epidemiology (PAGE) study, we used data from the Multiethnic Cohort and Women’s Health Initiative. Incident MPC (IMPC) cases (n=1,385) were defined as participants diagnosed with >1 incident cancers after cohort entry. Participants diagnosed with only one incident cancer after cohort entry with follow-up equal to or longer than IMPC cases served as controls (single-index cancer controls; n= 9,626). Fixed-effects meta-analyses of unconditional logistic regression analyses were used to evaluate the association between cancer risk variants and IMPC risk. To account for multiple comparisons, we used the false positive report probability (FPRP) to determine statistical significance. RESULTS: A nicotine dependence-associated and lung cancer variant, CHRNA3 rs578776 (OR=1.16, 95% CI=1.05–1.26; p=0.004) and two breast cancer variants, EMBP1 rs11249433 and TOX3 rs3803662 (OR=1.16, 95% CI=1.04–1.28; p=0.005 and OR=1.13, 95% CI=1.03–1.23; p=0.006) were significantly associated with risk of IMPC. The associations for rs578776 and rs11249433 remained (p<0.05) after removing subjects who had lung or breast cancers, respectively (p-values≤0.046). These associations did not show significant heterogeneity by smoking status (p-heterogeneity≥0.53). CONCLUSIONS: Our study has identified rs578776 and rs11249433 as risk variants for IMPC. IMPACT: These findings may help to identify genetic regions associated with IMPC risk

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

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

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