Dataset Information

Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk: Candidate Genes, Obesity, and Hormone-Related Risk Factors.

ABSTRACT: BACKGROUND:Many epithelial ovarian cancer (EOC) risk factors relate to hormone exposure and elevated estrogen levels are associated with obesity in postmenopausal women. Therefore, we hypothesized that gene-environment interactions related to hormone-related risk factors could differ between obese and non-obese women. METHODS:We considered interactions between 11,441 SNPs within 80 candidate genes related to hormone biosynthesis and metabolism and insulin-like growth factors with six hormone-related factors (oral contraceptive use, parity, endometriosis, tubal ligation, hormone replacement therapy, and estrogen use) and assessed whether these interactions differed between obese and non-obese women. Interactions were assessed using logistic regression models and data from 14 case-control studies (6,247 cases; 10,379 controls). Histotype-specific analyses were also completed. RESULTS:SNPs in the following candidate genes showed notable interaction: IGF1R (rs41497346, estrogen plus progesterone hormone therapy, histology = all, P = 4.9 × 10(-6)) and ESR1 (rs12661437, endometriosis, histology = all, P = 1.5 × 10(-5)). The most notable obesity-gene-hormone risk factor interaction was within INSR (rs113759408, parity, histology = endometrioid, P = 8.8 × 10(-6)). CONCLUSIONS:We have demonstrated the feasibility of assessing multifactor interactions in large genetic epidemiology studies. Follow-up studies are necessary to assess the robustness of our findings for ESR1, CYP11A1, IGF1R, CYP11B1, INSR, and IGFBP2 Future work is needed to develop powerful statistical methods able to detect these complex interactions. IMPACT:Assessment of multifactor interaction is feasible, and, here, suggests that the relationship between genetic variants within candidate genes and hormone-related risk factors may vary EOC susceptibility. Cancer Epidemiol Biomarkers Prev; 25(5); 780-90. ©2016 AACR.

SUBMITTER: Usset JL

PROVIDER: S-EPMC4873330 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk: Candidate Genes, Obesity, and Hormone-Related Risk Factors.

Usset Joseph L JL Raghavan Rama R Tyrer Jonathan P JP McGuire Valerie V Sieh Weiva W Webb Penelope P Chang-Claude Jenny J Rudolph Anja A Anton-Culver Hoda H Berchuck Andrew A Brinton Louise L Cunningham Julie M JM DeFazio Anna A Doherty Jennifer A JA Edwards Robert P RP Gayther Simon A SA Gentry-Maharaj Aleksandra A Goodman Marc T MT Høgdall Estrid E Jensen Allan A Johnatty Sharon E SE Kiemeney Lambertus A LA Kjaer Susanne K SK Larson Melissa C MC Lurie Galina G Massuger Leon L Menon Usha U Modugno Francesmary F Moysich Kirsten B KB Ness Roberta B RB Pike Malcolm C MC Ramus Susan J SJ Rossing Mary Anne MA Rothstein Joseph J Song Honglin H Thompson Pamela J PJ van den Berg David J DJ Vierkant Robert A RA Wang-Gohrke Shan S Wentzensen Nicolas N Whittemore Alice S AS Wilkens Lynne R LR Wu Anna H AH Yang Hannah H Pearce Celeste Leigh CL Schildkraut Joellen M JM Pharoah Paul P Goode Ellen L EL Fridley Brooke L BL

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20160314 5

<h4>Background</h4>Many epithelial ovarian cancer (EOC) risk factors relate to hormone exposure and elevated estrogen levels are associated with obesity in postmenopausal women. Therefore, we hypothesized that gene-environment interactions related to hormone-related risk factors could differ between obese and non-obese women.<h4>Methods</h4>We considered interactions between 11,441 SNPs within 80 candidate genes related to hormone biosynthesis and metabolism and insulin-like growth factors with ...[more]

PMID: 26976855

Similar Datasets

Project description:BackgroundThe abuse of psychogenic drugs can lead to multiple health-related problems. Genetic and environmental vulnerabilities are factors in the emergence of substance use disorders. Empirical evidence regarding the gene-environment interaction in substance use is mixed. Summaries of the latest findings from a candidate gene approach will be useful for revealing the significance of particular gene contributions. Thus, we aim to identify different gene-environment interactions in patterns of substance use and investigate whether any effects trend notably across different genders and races.MethodsWe reviewed published studies, until March 1, 2022, on substance use for candidate gene-environment interaction. Basic demographics of the included studies, target genes, environmental factors, main findings, patterns of gene-environment interaction, and other relevant information were collected and summarized.ResultsAmong a total of 44 studies, 38 demonstrated at least one significant interaction effect. About 61.5% of studies on the 5-HTTLPR gene, 100% on the MAOA gene, 42.9% on the DRD2 gene, 50% on the DRD4 gene, 50% on the DAT gene, 80% on the CRHR1 gene, 100% on the OPRM1 gene, 100% on the GABRA1 gene, and 50% on the CHRNA gene had a significant gene-environment interaction effect. The diathesis-stress model represents a dominant interaction pattern (89.5%) in the studies with a significant interaction effect; the remaining significant effect on substance use is found in the differential susceptibility model. The social push and swing model were not reported in the included studies.ConclusionThe gene-environment interaction research on substance use behavior is methodologically multidimensional, which causes difficulty in conducting pooled analysis, or stated differently-making it hard to identify single sources of significant influence over maladaptive patterns of drug taking. In decreasing the heterogeneity and facilitating future pooled analysis, researchers must (1) replicate the existing studies with consistent study designs and measures, (2) conduct power calculations to report gene-environment correlations, (3) control for covariates, and (4) generate theory-based hypotheses with factorial based experiments when designing future studies.

Dataset Information

Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk: Candidate Genes, Obesity, and Hormone-Related Risk Factors.

Publications

Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk: Candidate Genes, Obesity, and Hormone-Related Risk Factors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets