Dataset Information

Systematic identification of interaction effects between genome- and environment-wide associations in type 2 diabetes mellitus.

ABSTRACT: Diseases such as type 2 diabetes (T2D) result from environmental and genetic factors, and risk varies considerably in the population. T2D-related genetic loci discovered to date explain only a small portion of the T2D heritability. Some heritability may be due to gene-environment interactions. However, documenting these interactions has been difficult due to low availability of concurrent genetic and environmental measures, selection bias, and challenges in controlling for multiple hypothesis testing. Through genome-wide association studies (GWAS), investigators have identified over 90 single nucleotide polymorphisms (SNPs) associated to T2D. Using a method analogous to GWAS [environment-wide association study (EWAS)], we found five environmental factors associated with the disease. By focusing on risk factors that emerge from GWAS and EWAS, it is possible to overcome difficulties in uncovering gene-environment interactions. Using data from the National Health and Nutrition Examination Survey (NHANES), we screened 18 SNPs and 5 serum-based environmental factors for interaction in association to T2D. We controlled for multiple hypotheses using false discovery rate (FDR) and Bonferroni correction and found four interactions with FDR <20 %. The interaction between rs13266634 (SLC30A8) and trans-β-carotene withstood Bonferroni correction (corrected p = 0.006, FDR <1.5 %). The per-risk-allele effect sizes in subjects with low levels of trans-β-carotene were 40 % greater than the marginal effect size [odds ratio (OR) 1.8, 95 % CI 1.3-2.6]. We hypothesize that impaired function driven by rs13266634 increases T2D risk when combined with serum levels of nutrients. Unbiased consideration of environmental and genetic factors may help identify larger and more relevant effect sizes for disease associations.

SUBMITTER: Patel CJ

PROVIDER: S-EPMC3625410 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundType 2 Diabetes (T2D) and other chronic diseases are caused by a complex combination of many genetic and environmental factors. Few methods are available to comprehensively associate specific physical environmental factors with disease. We conducted a pilot Environmental-Wide Association Study (EWAS), in which epidemiological data are comprehensively and systematically interpreted in a manner analogous to a Genome Wide Association Study (GWAS).Methods and findingsWe performed multiple cross-sectional analyses associating 266 unique environmental factors with clinical status for T2D defined by fasting blood sugar (FBG) concentration > or =126 mg/dL. We utilized available Centers for Disease Control (CDC) National Health and Nutrition Examination Survey (NHANES) cohorts from years 1999 to 2006. Within cohort sample numbers ranged from 503 to 3,318. Logistic regression models were adjusted for age, sex, body mass index (BMI), ethnicity, and an estimate of socioeconomic status (SES). As in GWAS, multiple comparisons were controlled and significant findings were validated with other cohorts. We discovered significant associations for the pesticide-derivative heptachlor epoxide (adjusted OR in three combined cohorts of 1.7 for a 1 SD change in exposure amount; p<0.001), and the vitamin gamma-tocopherol (adjusted OR 1.5; p<0.001). Higher concentrations of polychlorinated biphenyls (PCBs) such as PCB170 (adjusted OR 2.2; p<0.001) were also found. Protective factors associated with T2D included beta-carotenes (adjusted OR 0.6; p<0.001).Conclusions and significanceDespite difficulty in ascertaining causality, the potential for novel factors of large effect associated with T2D justify the use of EWAS to create hypotheses regarding the broad contribution of the environment to disease. Even in this study based on prior collected epidemiological measures, environmental factors can be found with effect sizes comparable to the best loci yet found by GWAS.

Dataset Information

Systematic identification of interaction effects between genome- and environment-wide associations in type 2 diabetes mellitus.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets