Rebecca Hein

Gene–environment interactions for complex traits: definitions, methodological requirements and challenges

Genetic and environmental risk factors and their interactions contribute to the development of complex diseases. In this review, we discuss methodological issues involved in investigating gene–environment (G × E) interactions in genetic–epidemiological studies of complex diseases and their potential relevance for clinical application. Although there are some important examples of interactions and applications, the widespread use of the knowledge about G × E interaction for targeted intervention or personalized treatment (pharmacogenetics) is still beyond current means. This is due to the fact that convincing evidence and high predictive or discriminative power are necessary conditions for usefulness in clinical practice. We attempt to clarify conceptual differences of the term ‘interaction’ in the statistical and biological sciences, since precise definitions are important for the interpretation of results. We argue that the investigation of G × E interactions is more rewarding for the detailed characterization of identified disease genes (ie at advanced stages of genetic research) and the stratified analysis of environmental effects by genotype or vice versa. Advantages and disadvantages of different epidemiological study designs are given and sample size requirements are exemplified. These issues as well as a critical appraisal of common methodological concerns are finally discussed.

Obesity and risk of ovarian cancer subtypes: evidence from the Ovarian Cancer Association Consortium

Whilst previous studies have reported that higher BMI increases a womans risk of developing ovarian cancer, associations for the different histological subtypes have not been well defined. As the prevalence of obesity has increased dramatically, and classification of ovarian histology has improved in the last decade, we sought to examine the association in a pooled analysis of recent studies participating in the Ovarian Cancer Association Consortium. We evaluated the association between BMI (recent, maximum and in young adulthood) and ovarian cancer risk using original data from 15 case-control studies (13 548 cases and 17 913 controls). We combined study-specific adjusted odds ratios (ORs) using a random-effects model. We further examined the associations by histological subtype, menopausal status and post-menopausal hormone use. High BMI (all time-points) was associated with increased risk. This was most pronounced for borderline serous (recent BMI: pooled OR=1.24 per 5 kg/m(2); 95% CI 1.18-1.30), invasive endometrioid (1.17; 1.11-1.23) and invasive mucinous (1.19; 1.06-1.32) tumours. There was no association with serous invasive cancer overall (0.98; 0.94-1.02), but increased risks for low-grade serous invasive tumours (1.13, 1.03-1.25) and in pre-menopausal women (1.11; 1.04-1.18). Among post-menopausal women, the associations did not differ between hormone replacement therapy users and non-users. Whilst obesity appears to increase risk of the less common histological subtypes of ovarian cancer, it does not increase risk of high-grade invasive serous cancers, and reducing BMI is therefore unlikely to prevent the majority of ovarian cancer deaths. Other modifiable factors must be identified to control this disease.

Assessing interactions between the associations of common genetic susceptibility variants, reproductive history and body mass index with breast cancer risk in the breast cancer association consortium: a combined case-control study.

IntroductionSeveral common breast cancer genetic susceptibility variants have recently been identified. We aimed to determine how these variants combine with a subset of other known risk factors to influence breast cancer risk in white women of European ancestry using case-control studies participating in the Breast Cancer Association Consortium.MethodsWe evaluated two-way interactions between each of age at menarche, ever having had a live birth, number of live births, age at first birth and body mass index (BMI) and each of 12 single nucleotide polymorphisms (SNPs) (10q26-rs2981582 (FGFR2), 8q24-rs13281615, 11p15-rs3817198 (LSP1), 5q11-rs889312 (MAP3K1), 16q12-rs3803662 (TOX3), 2q35-rs13387042, 5p12-rs10941679 (MRPS30), 17q23-rs6504950 (COX11), 3p24-rs4973768 (SLC4A7), CASP8-rs17468277, TGFB1-rs1982073 and ESR1-rs3020314). Interactions were tested for by fitting logistic regression models including per-allele and linear trend main effects for SNPs and risk factors, respectively, and single-parameter interaction terms for linear departure from independent multiplicative effects.ResultsThese analyses were applied to data for up to 26,349 invasive breast cancer cases and up to 32,208 controls from 21 case-control studies. No statistical evidence of interaction was observed beyond that expected by chance. Analyses were repeated using data from 11 population-based studies, and results were very similar.ConclusionsThe relative risks for breast cancer associated with the common susceptibility variants identified to date do not appear to vary across women with different reproductive histories or body mass index (BMI). The assumption of multiplicative combined effects for these established genetic and other risk factors in risk prediction models appears justified.

Common Breast Cancer Susceptibility Loci Are Associated with Triple-Negative Breast Cancer

Triple-negative breast cancers are an aggressive subtype of breast cancer with poor survival, but there remains little known about the etiologic factors that promote its initiation and development. Commonly inherited breast cancer risk factors identified through genome-wide association studies display heterogeneity of effect among breast cancer subtypes as defined by the status of estrogen and progesterone receptors. In the Triple Negative Breast Cancer Consortium (TNBCC), 22 common breast cancer susceptibility variants were investigated in 2,980 Caucasian women with triple-negative breast cancer and 4,978 healthy controls. We identified six single-nucleotide polymorphisms, including rs2046210 (ESR1), rs12662670 (ESR1), rs3803662 (TOX3), rs999737 (RAD51L1), rs8170 (19p13.1), and rs8100241 (19p13.1), significantly associated with the risk of triple-negative breast cancer. Together, our results provide convincing evidence of genetic susceptibility for triple-negative breast cancer.

Serum 25-hydroxyvitamin D and postmenopausal breast cancer survival: a prospective patient cohort study

IntroductionVitamin D has been postulated to be involved in cancer prognosis. Thus far, only two studies reported on its association with recurrence and survival after breast cancer diagnosis yielding inconsistent results. Therefore, the aim of our study was to assess the effect of post-diagnostic serum 25-hydroxyvitamin D [25(OH)D] concentrations on overall survival and distant disease-free survival.MethodsWe conducted a prospective cohort study in Germany including 1,295 incident postmenopausal breast cancer patients aged 50-74 years. Patients were diagnosed between 2002 and 2005 and median follow-up was 5.8 years. Cox proportional hazards models were stratified by age at diagnosis and season of blood collection and adjusted for other prognostic factors. Fractional polynomials were used to assess the true dose-response relation for 25(OH)D.ResultsLower concentrations of 25(OH)D were linearly associated with higher risk of death (hazard ratio (HR) = 1.08 per 10 nmol/L decrement; 95% confidence interval (CI), 1.00 to 1.17) and significantly higher risk of distant recurrence (HR = 1.14 per 10 nmol/L decrement; 95%CI, 1.05 to 1.24). Compared with the highest tertile (≥ 55 nmol/L), patients within the lowest tertile (< 35 nmol/L) of 25(OH)D had a HR for overall survival of 1.55 (95%CI, 1.00 to 2.39) and a HR for distant disease-free survival of 2.09 (95%CI, 1.29 to 3.41). In addition, the association with overall survival was found to be statistically significant only for 25(OH)D levels of blood samples collected before start of chemotherapy but not for those of samples taken after start of chemotherapy (P for interaction = 0.06).ConclusionsIn conclusion, lower serum 25(OH)D concentrations may be associated with poorer overall survival and distant disease-free survival in postmenopausal breast cancer patients.

Population attributable risk of invasive postmenopausal breast cancer and breast cancer subtypes for modifiable and non-modifiable risk factors

BACKGROUND The population-level impact of modifiable postmenopausal breast cancer risk factors is incompletely understood, especially regarding potential heterogeneity by estrogen receptor (ER) and progesterone receptor (PR) status. METHODS Using data on 3074 cases and 6386 controls from a population-based case-control study of postmenopausal breast cancer conducted in Germany between 2002 and 2005, we calculated multivariable-adjusted odds ratios and population attributable risks (PARs) for modifiable and non-modifiable risk factors. We examined overall postmenopausal invasive breast cancer as well as tumor ER/PR subtypes. A bootstrap method provided estimates of 95% confidence intervals (95%CIs). RESULTS The summary PARs (95%CIs) for non-modifiable risk factors (age at menarche, age at menopause, parity, benign breast disease, and family history of breast cancer) were 37.2% (27.1-47.2%) regarding overall invasive tumors, 36.5% (23.3-47.6%) regarding ER+/PR+ tumors, 47.9% (26.4-64.4%) regarding ER+/PR- tumors, and 31.1% (4.0-51.9%) regarding ER-/PR- tumors. Of the modifiable risk factors (hormone therapy (HT) use, physical inactivity, BMI, alcohol consumption), HT use and physical inactivity had the highest impact with PARs of 19.4% (15.9-23.2%) and 12.8% (5.5-20.8%), respectively, regarding overall invasive tumors. For ER+/PR+ tumors, the corresponding PARs were 25.3% (20.9-29.7%) and 16.6% (7.0-26.0%). The summary PARs (95%CIs) for HT use and physical inactivity together were 29.8% (21.8-36.9%) and 37.9% (30.6-46.2%) regarding overall invasive and ER+/PR+ tumors, respectively. CONCLUSIONS The population-level impact of modifiable risk factors appears to be comparable to that of non-modifiable risk factors. Alterations in HT use and physical inactivity could potentially reduce postmenopausal invasive breast cancer incidence in Germany by nearly 30%, with the largest potential for reduction among ER+/PR+ tumors, the most frequently diagnosed subtype.

Association of genetic polymorphisms in ESR2, HSD17B1, ABCB1, and SHBG genes with colorectal cancer risk

The incidence rates and relative risks for colorectal cancer (CRC) are higher in men than in women. Sex steroids may play a role in this gender-associated difference in CRC risk. This study was conducted to explore the relationship of single nucleotide polymorphisms (SNPs) in steroid hormone signaling (ESR1, ESR2, PGR, NR1I2, and SHBG), phase I- and II-metabolizing enzyme (COMT, HSD17B1, CYP1A1, CYP17A1, CYP1A2, CYP1B1, CYP2C9, CYP3A4, CYP2C19, and GSTP1), and hormone transporter (ABCB1) genes with the risk of CRC in German women and men, separately. From the population-based DACHS study (South Germany), 47 putatively functional SNPs were genotyped in 1798 CRC cases (746 women and 1052 men) and 1810 controls (732 women and 1078 men). Significant allele dose-response associations were observed with ESR2_rs1255998, ESR2_rs928554, HSD17B1_rs605059, and ABCB1_rs2229109 in women (P trend=0.004, 0.05, 0.03, and 0.05 respectively) and with ABCB1_rs1045642, ABCB1_rs9282564, and SHBG_rs6259 in men (P trend=0.01, 0.03, and 0.02 respectively). The ESR2_rs1255998_G allele showed the most significant association with risk for CRC in women, with a per-allele odds ratio (OR) of 0.68 (95% confidence interval (CI) 0.52-0.88). This finding was replicated in an independent study from North Germany including 1076 female CRC cases and 1151 controls (OR=0.84, 95% CI 0.71-1.04), yielding a per-allele OR of 0.80 (95% CI 0.69-0.93, P trend=0.003) in the pooled sample. These findings implicate a role of ESR2 in the risk for developing CRC in women and suggest that HSD17B1, ABCB1, and SHBG genes may contribute to sex steroid-mediated effects on CRC development.

Sample size requirements for indirect association studies of gene–environment interactions (G × E)

Association studies accounting for gene–environment interactions (G × E) may be useful for detecting genetic effects. Although current technology enables very dense marker spacing in genetic association studies, the true disease variants may not be genotyped. Thus, causal genes are searched for by indirect association using genetic markers in linkage disequilibrium (LD) with the true disease variants. Sample sizes needed to detect G × E effects in indirect case–control association studies depend on the true genetic main effects, disease allele frequencies, whether marker and disease allele frequencies match, LD between loci, main effects and prevalence of environmental exposures, and the magnitude of interactions. We explored variables influencing sample sizes needed to detect G × E, compared these sample sizes with those required to detect genetic marginal effects, and provide an algorithm for power and sample size estimations. Required sample sizes may be heavily inflated if LD between marker and disease loci decreases. More than 10,000 case–control pairs may be required to detect G × E. However, given weak true genetic main effects, moderate prevalence of environmental exposures, as well as strong interactions, G × E effects may be detected with smaller sample sizes than those needed for the detection of genetic marginal effects. Moreover, in this scenario, rare disease variants may only be detectable when G × E is included in the analyses. Thus, the analysis of G × E appears to be an attractive option for the detection of weak genetic main effects of rare variants that may not be detectable in the analysis of genetic marginal effects only. Genet. Epidemiol. 2007.

A Genome-wide Association Study of Early-Onset Breast Cancer Identifies PFKM as a Novel Breast Cancer Gene and Supports a Common Genetic Spectrum for Breast Cancer at Any Age

Early-onset breast cancer (EOBC) causes substantial loss of life and productivity, creating a major burden among women worldwide. We analyzed 1,265,548 Hapmap3 single-nucleotide polymorphisms (SNP) among a discovery set of 3,523 EOBC incident cases and 2,702 population control women ages ≤ 51 years. The SNPs with smallest P values were examined in a replication set of 3,470 EOBC cases and 5,475 control women. We also tested EOBC association with 19,684 genes by annotating each gene with putative functional SNPs, and then combining their P values to obtain a gene-based P value. We examined the gene with smallest P value for replication in 1,145 breast cancer cases and 1,142 control women. The combined discovery and replication sets identified 72 new SNPs associated with EOBC (P < 4 × 10−8) located in six genomic regions previously reported to contain SNPs associated largely with later-onset breast cancer (LOBC). SNP rs2229882 and 10 other SNPs on chromosome 5q11.2 remained associated (P < 6 × 10−4) after adjustment for the strongest published SNPs in the region. Thirty-two of the 82 currently known LOBC SNPs were associated with EOBC (P < 0.05). Low power is likely responsible for the remaining 50 unassociated known LOBC SNPs. The gene-based analysis identified an association between breast cancer and the phosphofructokinase-muscle (PFKM) gene on chromosome 12q13.11 that met the genome-wide gene-based threshold of 2.5 × 10−6. In conclusion, EOBC and LOBC seem to have similar genetic etiologies; the 5q11.2 region may contain multiple distinct breast cancer loci; and the PFKM gene region is worthy of further investigation. These findings should enhance our understanding of the etiology of breast cancer. Cancer Epidemiol Biomarkers Prev; 23(4); 658–69. ©2014 AACR.

Combined and Interactive Effects of Environmental and GWAS-Identified Risk Factors in Ovarian Cancer

Background: There are several well-established environmental risk factors for ovarian cancer, and recent genome-wide association studies have also identified six variants that influence disease risk. However, the interplay between such risk factors and susceptibility loci has not been studied. Methods: Data from 14 ovarian cancer case–control studies were pooled, and stratified analyses by each environmental risk factor with tests for heterogeneity were conducted to determine the presence of interactions for all histologic subtypes. A genetic “risk score” was created to consider the effects of all six variants simultaneously. A multivariate model was fit to examine the association between all environmental risk factors and genetic risk score on ovarian cancer risk. Results: Among 7,374 controls and 5,566 cases, there was no statistical evidence of interaction between the six SNPs or genetic risk score and the environmental risk factors on ovarian cancer risk. In a main effects model, women in the highest genetic risk score quartile had a 65% increased risk of ovarian cancer compared with women in the lowest [95% confidence interval (CI), 1.48–1.84]. Analyses by histologic subtype yielded risk differences across subtype for endometriosis (Phet < 0.001), parity (Phet < 0.01), and tubal ligation (Phet = 0.041). Conclusions: The lack of interactions suggests that a multiplicative model is the best fit for these data. Under such a model, we provide a robust estimate of the effect of each risk factor that sets the stage for absolute risk prediction modeling that considers both environmental and genetic risk factors. Further research into the observed differences in risk across histologic subtype is warranted. Cancer Epidemiol Biomarkers Prev; 22(5); 880–90. ©2013 AACR.