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BACKGROUND: Obesity is a leading risk factor for chronic diseases, potentially related to excess abdominal adiposity. Phthalates are environmental chemicals that have been suggested to act as obesogens, driving obesity risk. For the associations between phthalates and adiposity, prior studies have focused primarily on body mass index. We hypothesize that more refined measures of adiposity and fat distribution may provide greater insights into these associations given the role of central adiposity in chronic disease risk. OBJECTIVES: To evaluate associations between urinary phthalate biomarkers and both visceral and subcutaneous adipose tissue (VAT and SAT) among postmenopausal women enrolled in the Womens Health Initiative (WHI). METHODS: We included 1125 WHI participants with available, coincident measurements of urinary phthalate biomarkers (baseline, year 3) and VAT and SAT (baseline, year 3, year 6). VAT and SAT measurements were estimated from DXA scans. Multilevel mixed-effects models estimated the prospective associations between urinary phthalate biomarkers at baseline and VAT and SAT three years later. RESULTS: In multivariable adjusted models, we observed positive associations between some phthalate biomarkers, including the sum of di-isobutyl phthalate (ΣDiBP) biomarkers, MCNP, and ΣDEHP, with VAT three years later. For example, we observed positive associations between concentrations of ΣDiBP and VAT (Q4 vs Q1 β = 7.15, 95% CI -1.76-16.06; Q3 vs Q1 β = 10.94, 95% CI 3.55-18.33). Associations were generally attenuated but remained significant after additional adjustment for SAT. MBzP was positively associated with SAT. Other phthalate biomarkers investigated (MEP, MCOP, MCPP, ΣDBP) were not significantly associated with VAT or SAT. DISCUSSION: Based on robust measures of adiposity, this study provides supportive evidence that higher urinary concentrations of select phthalate compounds were associated with higher VAT levels over time in postmenopausal women. Efforts to replicate these findings are needed.

BACKGROUND: Phthalates are endocrine-disrupting chemicals linked to a higher risk of numerous chronic health outcomes. Diet is a primary source of exposure, but prior studies exploring associations between dietary patterns and phthalate exposure are limited. OBJECTIVES: We evaluated the associations between dietary patterns and urinary phthalate biomarkers among a subset of postmenopausal women participating in the Womens Health Initiative (WHI). METHODS: We included WHI participants selected for a nested case-control study of phthalates and breast cancer (N = 1240). Dietary intake was measured via self-administered food frequency questionnaires at baseline and year-3. We used these data to calculate scores for alignment with the Dietary Approach to Stop Hypertension (DASH), alternative Mediterranean (aMed), and Dietary Inflammatory Index (DII) diets. We measured 13 phthalate metabolites and creatinine in 2-3 urine samples per participant collected over 3-years when all participants were cancer-free. We fit multivariable generalized estimating equation models to estimate the cross-sectional associations. RESULTS: DASH and aMed dietary scores were inversely associated with the sum of di(2-Ethylhexyl) phthalate (-6.48%, 95% CI -9.84, -3.00; -5.23%, 95% CI -8.73, -1.60) and DII score was positively associated (9.00%, 95% CI 5.04, 13.11). DASH and aMed scores were also inversely associated with mono benzyl phthalate and mono-3-carboxypropyl phthalate. DII scores were positively associated with mono benzyl phthalate and the sum of di-n-butyl phthalate. DISCUSSION: Higher dietary alignment with DASH and aMed dietary patterns were significantly associated with lower concentrations of certain phthalate biomarkers, while an inflammatory diet pattern was associated with higher phthalate biomarker concentrations. These findings suggest that dietary patterns high in fruits, vegetables, and low-fat foods and low in processed foods may be useful in avoiding exposure to phthalates.

BackgroundGenome-wide studies of gene-environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer.MethodsAnalyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene-environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs.ResultsAssuming a 1 × 10-5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability

The objective of this study was to determine the association between birthweight and risk of thyroid and autoimmune conditions in a large sample of postmenopausal women. Baseline data from the Women's Health Initiative (n = 80,806) were used to examine the associations between birthweight category (

BackgroundRecent experimental work has shown that phthalates may increase inflammation. Prior research has not examined the role of exposure to phthalates in relation to inflammatory status among postmenopausal women who are at higher risk of developing inflammation-related chronic disorders.ObjectivesWe aimed to examine the associations of urinary phthalate biomarker concentrations with circulating levels of c-reactive protein [CRP] and interleukin-6 [IL-6] among 443 postmenopausal women selected into a breast cancer case-control study nested within the Women's Health Initiative (WHI).MethodsA total of 13 phthalate metabolites were measured in urine samples provided at WHI enrollment from 1993 to 1998. We also measured baseline levels of CRP and IL-6 in these women's serum or plasma samples. Multivariable linear models were used to investigate the role of each phthalate biomarker in relation to CRP and IL-6, adjusting for potential confounding factors and specifically evaluating the role of BMI.ResultsIn adjusted models we observed positive associations of monocarboxynonyl phthalate (MCNP) with CRP (β = 0.092; 95% CI 0.026, 0.158) and IL-6 (β = 0.108; 95% CI 0.013, 0.204). These positive associations were attenuated and non-significant, however, after further adjustment for body mass index (BMI). In contrast, we observed inverse associations of monoethyl phthalate (MEP) (β = -0.019; 95% CI -0.036, -0.001) and monobenzyl phthalate (MBzP) (β = -0.034; 95% CI -0.058, -0.010) with CRP levels only after adjustment for BMI. Other phthalate biomarkers examined were not significantly associated with either CRP or IL-6 levels.ConclusionsOverall, these results do not suggest an important role for phthalates in promoting an inflammatory response. Future prospective studies are warranted to improve understanding of these associations, particularly in clarifying the role of BMI.

BackgroundPrevious gene-environment interaction studies of breast cancer risk have provided sparse evidence of interactions. Using the largest available dataset to date, we performed a comprehensive assessment of potential effect modification of 205 common susceptibility variants by 13 established breast cancer risk factors, including replication of previously reported interactions.MethodsAnalyses were performed using 28 176 cases and 32 209 controls genotyped with iCOGS array and 44 109 cases and 48 145 controls genotyped using OncoArray from the Breast Cancer Association Consortium (BCAC). Gene-environment interactions were assessed using unconditional logistic regression and likelihood ratio tests for breast cancer risk overall and by estrogen-receptor (ER) status. Bayesian false discovery probability was used to assess the noteworthiness of the meta-analysed array-specific interactions.ResultsNoteworthy evidence of interaction at ≤1% prior probability was observed for three single nucleotide polymorphism (SNP)-risk factor pairs. SNP rs4442975 was associated with a greater reduction of risk of ER-positive breast cancer [odds ratio (OR)int = 0.85 (0.78-0.93), Pint = 2.8 x 10-4] and overall breast cancer [ORint = 0.85 (0.78-0.92), Pint = 7.4 x 10-5) in current users of estrogen-progesterone therapy compared with non-users. This finding was supported by replication using OncoArray data of the previously reported interaction between rs13387042 (r2 = 0.93 with rs4442975) and current estrogen-progesterone therapy for overall disease (Pint = 0.004). The two other interactions suggested stronger associations between SNP rs6596100 and ER-negative breast cancer with increasing parity and younger age at first birth.ConclusionsOverall, our study does not suggest strong effect modification of common breast cancer susceptibility variants by established risk factors.

BackgroundPrior studies evaluating psychotropic medications in relation to breast cancer risk are inconsistent and have not separately evaluated invasive and in situ disease.MethodsWe estimated hazard ratios (HR) and 95% confidence intervals (CI) for the association of psychotropic medication use (any, typical antipsychotics, atypical antipsychotics, and lithium) with invasive and in situ breast cancer risk among Women's Health Initiative participants (N = 155,737).ResultsPrevalence of psychotropic medication use was low (n = 642; 0.4%). During an average 14.8 (SD, 6.5) years of follow-up, 10,067 invasive and 2,285 in situ breast tissues were diagnosed. Any psychotropic medication use was not associated with invasive breast cancer risk compared with nonusers (HR, 0.82; 95% CI, 0.57-1.18). In situ breast cancer risk was higher among "typical" antipsychotic medication users compared with nonusers (HR, 2.05; 95% CI, 0.97-4.30).ConclusionsThese findings do not support an association of psychotropic medication use with invasive breast cancer risk. The possible elevation in in situ breast cancer risk associated with "typical" antipsychotics could not be explained by differences in screening mammography utilization and merits further study.ImpactOur findings contribute to knowledge of the safety profile of psychotropic medications and may be useful to clinicians and patients considering use of these medications.

BackgroundWe examined the associations between germline variants and breast cancer mortality using a large meta-analysis of women of European ancestry.MethodsMeta-analyses included summary estimates based on Cox models of twelve datasets using ~10.4 million variants for 96,661 women with breast cancer and 7697 events (breast cancer-specific deaths). Oestrogen receptor (ER)-specific analyses were based on 64,171 ER-positive (4116) and 16,172 ER-negative (2125) patients. We evaluated the probability of a signal to be a true positive using the Bayesian false discovery probability (BFDP).ResultsWe did not find any variant associated with breast cancer-specific mortality at P

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.

PurposeAccurate risk assessment is necessary for decision-making around breast cancer prevention. We aimed to develop a breast cancer prediction model for postmenopausal women that would take into account their individualized competing risk of non-breast cancer death.MethodsWe included 73,066 women who completed the 2004 Nurses' Health Study (NHS) questionnaire (all ≥57 years) and followed participants until May 2014. We considered 17 breast cancer risk factors (health behaviors, demographics, family history, reproductive factors) and 7 risk factors for non-breast cancer death (comorbidities, functional dependency) and mammography use. We used competing risk regression to identify factors independently associated with breast cancer. We validated the final model by examining calibration (expected-to-observed ratio of breast cancer incidence, E/O) and discrimination (c-statistic) using 74,887 subjects from the Women's Health Initiative Extension Study (WHI-ES; all were ≥55 years and followed for 5 years).ResultsWithin 5 years, 1.8 % of NHS participants were diagnosed with breast cancer (vs. 2.0 % in WHI-ES, p = 0.02), and 6.6 % experienced non-breast cancer death (vs. 5.2 % in WHI-ES, p

Context Psychological distress has been linked to diabetes risk. Few population-based, epidemiologic studies have investigated the potential molecular mechanisms (eg, metabolic dysregulation) underlying this association. Objective To evaluate the association between a metabolomic signature for psychological distress and diabetes risk. Methods We conducted a nested case-control study of plasma metabolomics and diabetes risk in the Nurses' Health Study, including 728 women (mean age: 55.2 years) with incident diabetes and 728 matched controls. Blood samples were collected between 1989 and 1990 and incident diabetes was diagnosed between 1992 and 2008. Based on our prior work, we calculated a weighted plasma metabolite-based distress score (MDS) comprised of 19 metabolites. We used conditional logistic regression accounting for matching factors and other diabetes risk factors to estimate odds ratios (OR) and 95% confidence intervals (CI) for diabetes risk according to MDS. Results After adjusting for sociodemographic factors, family history of diabetes, and health behaviors, the OR (95% CI) for diabetes risk across quintiles of the MDS was 1.00 (reference) for Q1, 1.16 (0.77, 1.73) for Q2, 1.30 (0.88, 1.91) for Q3, 1.99 (1.36, 2.92) for Q4, and 2.47 (1.66, 3.67) for Q5. Each SD increase in MDS was associated with 36% higher diabetes risk (95% CI: 1.21, 1.54; P -trend <.0001). This association was moderately attenuated after additional adjustment for body mass index (comparable OR: 1.17; 95% CI: 1.02, 1.35; P -trend =.02). The MDS explained 17.6% of the association between self-reported psychological distress (defined as presence of depression or anxiety symptoms) and diabetes risk (P =.04). Conclusion MDS was significantly associated with diabetes risk in women. These results suggest that differences in multiple lipid and amino acid metabolites may underlie the observed association between psychological distress and diabetes risk. [ABSTRACT FROM AUTHOR]

BackgroundThe Breast Cancer Risk Assessment Tool (BCRAT, "Gail model") is commonly used for breast cancer prediction; however, it has not been validated for women age 75 years and older.MethodsWe used Nurses' Health Study (NHS) data beginning in 2004 and Women's Health Initiative (WHI) data beginning in 2005 to compare BCRAT's performance among women age 75 years and older with that in women age 55 to 74 years in predicting five-year breast cancer incidence. BCRAT risk factors include: age, race/ethnicity, age at menarche, age at first birth, family history, history of benign breast biopsy, and atypia. We examined BCRAT's calibration by age by comparing expected/observed (E/O) ratios of breast cancer incidence. We examined discrimination by computing c-statistics for the model by age. All statistical tests were two-sided.ResultsSeventy-three thousand seventy-two NHS and 97 081 WHI women participated. NHS participants were more likely to be non-Hispanic white (96.2% vs 84.7% in WHI, P < .001) and were less likely to develop breast cancer (1.8% vs 2.0%, P = .02). E/O ratios by age in NHS were 1.16 (95% confidence interval [CI] = 1.09 to 1.23, age 57-74 years) and 1.31 (95% CI = 1.18 to 1.45, age ≥ 75 years, P = .02), and in WHI 1.03 (95% CI = 0.97 to 1.09, age 55-74 years) and 1.10 (95% CI = 1.00 to 1.21, age ≥ 75 years, P = .21). E/O ratio 95% confidence intervals crossed one among women age 75 years and older when samples were limited to women who underwent mammography and were without significant illness. C-statistics ranged between 0.56 and 0.58 in both cohorts regardless of age.ConclusionsBCRAT accurately predicted breast cancer for women age 75 years and older who underwent mammography and were without significant illness but had modest discrimination. Models that consider individual competing risks of non-breast cancer death may improve breast cancer risk prediction for older women.

Leptin is an adipocyte-secreted hormone, the circulating levels of which correlate closely with overall adiposity. Although rare mutations in the leptin (LEP) gene are well known to cause leptin deficiency and severe obesity, no common loci regulating circulating leptin levels have been uncovered. Therefore, we performed a genome-wide association study (GWAS) of circulating leptin levels from 32,161 individuals and followed up loci reaching P

Obesity is well established as a cause of postmenopausal breast cancer incidence and mortality. In contrast, adiposity in early life reduces breast cancer incidence. However, whether short-term weight change influences breast cancer risk is not well known. We followed a cohort of 77,232 women from 1980 to 2006 (1,445,578 person-years), with routinely updated risk factor information, documenting 4196 incident cases of invasive breast cancer. ER and PR status were obtained from pathology reports and medical records yielding a total of 2033 ER+/PR+ tumors, 595 ER-/PR- tumors, 512 ER+/PR- tumors. The log incidence breast cancer model was used to assess the association of short-term weight gain (over past 4 years) while controlling for average BMI before and after menopause. Short-term weight change was significantly associated with breast cancer risk (RR 1.20; 95 % CI 1.09-1.33) for a 4-year weight gain of ≥15 lbs versus no change (≤5 lbs) (P_trend < 0.001). The association was stronger for premenopausal women (RR 1.38; 95 % CI 1.13-1.69) (P_trend = 0.004) than for postmenopausal women (RR 1.10; 95 % CI 0.97-1.25) (P_trend = 0.063). Short-term weight gain during premenopause had a stronger association for ER+/PR- (RR per 25 lb weight gain = 2.19; 95 % CI 1.33-3.61, P = 0.002) and ER-/PR- breast cancer (RR per 25 lb weight gain = 1.61; 95 % CI 1.09-2.38, P = 0.016) than for ER+/PR+ breast cancer (RR per 25 lb weight gain = 1.13; 95 % CI 0.89-1.43, P = 0.32). There are deleterious effects of short-term weight gain, particularly during pre-menopause, even after controlling for average BMI before and after menopause. The association was stronger for ER+/PR- and ER-/PR- than for ER+/PR+ breast cancer.

Analyses of genome-wide association study (GWAS) data have revealed that detectable genetic mosaicism involving large (>2 Mb) structural autosomal alterations occurs in a fraction of individuals. We present results for a set of 24,849 genotyped individuals (total GWAS set II [TGSII]) in whom 341 large autosomal abnormalities were observed in 168 (0.68%) individuals. Merging data from the new TGSII set with data from two prior reports (the Gene-Environment Association Studies and the total GWAS set I) generated a large dataset of 127,179 individuals; we then conducted a meta-analysis to investigate the patterns of detectable autosomal mosaicism (n = 1,315 events in 925 [0.73%] individuals). Restricting to events >2 Mb in size, we observed an increase in event frequency as event size decreased. The combined results underscore that the rate of detectable mosaicism increases with age (p value = 5.5 × 10(-31)) and is higher in men (p value = 0.002) but lower in participants of African ancestry (p value = 0.003). In a subset of 47 individuals from whom serial samples were collected up to 6 years apart, complex changes were noted over time and showed an overall increase in the proportion of mosaic cells as age increased. Our large combined sample allowed for a unique ability to characterize detectable genetic mosaicism involving large structural events and strengthens the emerging evidence of non-random erosion of the genome in the aging population.

Early menopause (EM) affects up to 10% of the female population, reducing reproductive lifespan considerably. Currently, it constitutes the leading cause of infertility in the western world, affecting mainly those women who postpone their first pregnancy beyond the age of 30 years. The genetic aetiology of EM is largely unknown in the majority of cases. We have undertaken a meta-analysis of genome-wide association studies (GWASs) in 3493 EM cases and 13 598 controls from 10 independent studies. No novel genetic variants were discovered, but the 17 variants previously associated with normal age at natural menopause as a quantitative trait (QT) were also associated with EM and primary ovarian insufficiency (POI). Thus, EM has a genetic aetiology which overlaps variation in normal age at menopause and is at least partly explained by the additive effects of the same polygenic variants. The combined effect of the common variants captured by the single nucleotide polymorphism arrays was estimated to account for ∼30% of the variance in EM. The association between the combined 17 variants and the risk of EM was greater than the best validated non-genetic risk factor, smoking.

Menopausal hormone therapy (MHT) is associated with an elevated risk of breast cancer in postmenopausal women. To identify genetic loci that modify breast cancer risk related to MHT use in postmenopausal women, we conducted a two-stage genome-wide association study (GWAS) with replication. In stage I, we performed a case-only GWAS in 731 invasive breast cancer cases from the German case-control study Mammary Carcinoma Risk Factor Investigation (MARIE). The 1,200 single nucleotide polymorphisms (SNPs) showing the lowest P values for interaction with current MHT use (within 6 months prior to breast cancer diagnosis), were carried forward to stage II, involving pooled case-control analyses including additional MARIE subjects (1,375 cases, 1,974 controls) as well as 795 cases and 764 controls of a Swedish case-control study. A joint P value was calculated for a combined analysis of stages I and II. Replication of the most significant interaction of the combined stage I and II was performed using 5,795 cases and 5,390 controls from nine studies of the Breast Cancer Association Consortium (BCAC). The combined stage I and II yielded five SNPs on chromosomes 2, 7, and 18 with joint P values

PurposeThe evidence of a relation between folate intake and one-carbon metabolism (OCM) with pancreatic cancer (PanCa) is inconsistent. In this study, the association between genes and single-nucleotide polymorphisms (SNPs) related to OCM and PanCa was assessed.MethodsUsing biochemical knowledge of the OCM pathway, we identified thirty-seven genes and 834 SNPs to examine in association with PanCa. Our study included 1,408 cases and 1,463 controls nested within twelve cohorts (PanScan). The ten SNPs and five genes with lowest p values (

Genome-wide association studies (GWAS) of breast cancer defined by hormone receptor status have revealed loci contributing to susceptibility of estrogen receptor (ER)-negative subtypes. To identify additional genetic variants for ER-negative breast cancer, we conducted the largest meta-analysis of ER-negative disease to date, comprising 4754 ER-negative cases and 31 663 controls from three GWAS: NCI Breast and Prostate Cancer Cohort Consortium (BPC3) (2188 ER-negative cases; 25 519 controls of European ancestry), Triple Negative Breast Cancer Consortium (TNBCC) (1562 triple negative cases; 3399 controls of European ancestry) and African American Breast Cancer Consortium (AABC) (1004 ER-negative cases; 2745 controls). We performed in silico replication of 86 SNPs at P ≤ 1 × 10(-5) in an additional 11 209 breast cancer cases (946 with ER-negative disease) and 16 057 controls of Japanese, Latino and European ancestry. We identified two novel loci for breast cancer at 20q11 and 6q14. SNP rs2284378 at 20q11 was associated with ER-negative breast cancer (combined two-stage OR = 1.16; P = 1.1 × 10(-8)) but showed a weaker association with overall breast cancer (OR = 1.08, P = 1.3 × 10(-6)) based on 17 869 cases and 43 745 controls and no association with ER-positive disease (OR = 1.01, P = 0.67) based on 9965 cases and 22 902 controls. Similarly, rs17530068 at 6q14 was associated with breast cancer (OR = 1.12; P = 1.1 × 10(-9)), and with both ER-positive (OR = 1.09; P = 1.5 × 10(-5)) and ER-negative (OR = 1.16, P = 2.5 × 10(-7)) disease. We also confirmed three known loci associated with ER-negative (19p13) and both ER-negative and ER-positive breast cancer (6q25 and 12p11). Our results highlight the value of large-scale collaborative studies to identify novel breast cancer risk loci.

Four loci have been associated with pancreatic cancer through genome-wide association studies (GWAS). Pathway-based analysis of GWAS data is a complementary approach to identify groups of genes or biological pathways enriched with disease-associated single-nucleotide polymorphisms (SNPs) whose individual effect sizes may be too small to be detected by standard single-locus methods. We used the adaptive rank truncated product method in a pathway-based analysis of GWAS data from 3851 pancreatic cancer cases and 3934 control participants pooled from 12 cohort studies and 8 case-control studies (PanScan). We compiled 23 biological pathways hypothesized to be relevant to pancreatic cancer and observed a nominal association between pancreatic cancer and five pathways (P < 0.05), i.e. pancreatic development, Helicobacter pylori lacto/neolacto, hedgehog, Th1/Th2 immune response and apoptosis (P = 2.0 × 10(-6), 1.6 × 10(-5), 0.0019, 0.019 and 0.023, respectively). After excluding previously identified genes from the original GWAS in three pathways (NR5A2, ABO and SHH), the pancreatic development pathway remained significant (P = 8.3 × 10(-5)), whereas the others did not. The most significant genes (P < 0.01) in the five pathways were NR5A2, HNF1A, HNF4G and PDX1 for pancreatic development; ABO for H.pylori lacto/neolacto; SHH for hedgehog; TGFBR2 and CCL18 for Th1/Th2 immune response and MAPK8 and BCL2L11 for apoptosis. Our results provide a link between inherited variation in genes important for pancreatic development and cancer and show that pathway-based approaches to analysis of GWAS data can yield important insights into the collective role of genetic risk variants in cancer.

Sex hormone-binding globulin (SHBG) is a glycoprotein responsible for the transport and biologic availability of sex steroid hormones, primarily testosterone and estradiol. SHBG has been associated with chronic diseases including type 2 diabetes (T2D) and with hormone-sensitive cancers such as breast and prostate cancer. We performed a genome-wide association study (GWAS) meta-analysis of 21,791 individuals from 10 epidemiologic studies and validated these findings in 7,046 individuals in an additional six studies. We identified twelve genomic regions (SNPs) associated with circulating SHBG concentrations. Loci near the identified SNPs included SHBG (rs12150660, 17p13.1, p = 1.8 × 10(-106)), PRMT6 (rs17496332, 1p13.3, p = 1.4 × 10(-11)), GCKR (rs780093, 2p23.3, p = 2.2 × 10(-16)), ZBTB10 (rs440837, 8q21.13, p = 3.4 × 10(-09)), JMJD1C (rs7910927, 10q21.3, p = 6.1 × 10(-35)), SLCO1B1 (rs4149056, 12p12.1, p = 1.9 × 10(-08)), NR2F2 (rs8023580, 15q26.2, p = 8.3 × 10(-12)), ZNF652 (rs2411984, 17q21.32, p = 3.5 × 10(-14)), TDGF3 (rs1573036, Xq22.3, p = 4.1 × 10(-14)), LHCGR (rs10454142, 2p16.3, p = 1.3 × 10(-07)), BAIAP2L1 (rs3779195, 7q21.3, p = 2.7 × 10(-08)), and UGT2B15 (rs293428, 4q13.2, p = 5.5 × 10(-06)). These genes encompass multiple biologic pathways, including hepatic function, lipid metabolism, carbohydrate metabolism and T2D, androgen and estrogen receptor function, epigenetic effects, and the biology of sex steroid hormone-responsive cancers including breast and prostate cancer. We found evidence of sex-differentiated genetic influences on SHBG. In a sex-specific GWAS, the loci 4q13.2-UGT2B15 was significant in men only (men p = 2.5 × 10(-08), women p = 0.66, heterogeneity p = 0.003). Additionally, three loci showed strong sex-differentiated effects: 17p13.1-SHBG and Xq22.3-TDGF3 were stronger in men, whereas 8q21.12-ZBTB10 was stronger in women. Conditional analyses identified additional signals at the SHBG gene that together almost double the proportion of variance explained at the locus. Using an independent study of 1,129 individuals, all SNPs identified in the overall or sex-differentiated or conditional analyses explained ~15.6% and ~8.4% of the genetic variation of SHBG concentrations in men and women, respectively. The evidence for sex-differentiated effects and allelic heterogeneity highlight the importance of considering these features when estimating complex trait variance.

BackgroundSubjects with non-O ABO blood group alleles have increased risk of pancreatic cancer. Glycosyltransferase activity is greater for the A(1) versus A(2) variant, whereas O01 and O02 variants are nonfunctioning. We hypothesized: 1) A(1) allele would confer greater risk than A(2) allele, 2) protective effect of the O allele would be equivalent for O01 and O02 variants, 3) secretor phenotype would modify the association with risk.MethodsWe determined ABO variants and secretor phenotype from single nucleotide polymorphisms in ABO and FUT2 genes in 1,533 cases and 1,582 controls from 12 prospective cohort studies. Adjusted odds ratios (OR) for pancreatic cancer were calculated using logistic regression.ResultsAn increased risk was observed in participants with A(1) but not A(2) alleles. Compared with subjects with genotype O/O, genotypes A(2)/O, A(2)/A(1), A(1)/O, and A(1)/A(1) had ORs of 0.96 (95% CI, 0.72-1.26), 1.46 (95% CI, 0.98-2.17), 1.48 (95% CI, 1.23-1.78), and 1.71 (95% CI, 1.18-2.47). Risk was similar for O01 and O02 variant O alleles. Compared with O01/O01, the ORs for each additional allele of O02, A(1), and A(2) were 1.00 (95% CI, 0.87-1.14), 1.38 (95% CI, 1.20-1.58), and 0.96 (95% CI, 0.77-1.20); P, O01 versus O02 = 0.94, A(1) versus A(2) = 0.004. Secretor phenotype was not an effect modifier (P-interaction = 0.63).ConclusionsAmong participants in a large prospective cohort consortium, ABO allele subtypes corresponding to increased glycosyltransferase activity were associated with increased pancreatic cancer risk.ImpactThese data support the hypothesis that ABO glycosyltransferase activity influences pancreatic cancer risk rather than actions of other nearby genes on chromosome 9q34.