Dataset Information

Reverse gene-environment interaction approach to identify variants influencing body-mass index in humans.

ABSTRACT: Identifying gene-environment interactions (GxEs) contributing to human cardiometabolic disorders is challenging. Here we apply a reverse GxE candidate search by deriving candidate variants from promoter-enhancer interactions that respond to dietary fatty acid challenge through altered chromatin accessibility in human primary adipocytes. We then test all variants residing in the lipid-responsive open chromatin sites within adipocyte promoter-enhancer contacts for interaction effects between the genotype and dietary saturated fat intake on body mass index (BMI) in the UK Biobank. We discover 14 novel GxE variants in 12 lipid-responsive promoters, including well-known lipid genes (LIPE, CARM1, and PLIN2) and novel genes, such as LDB3, for which we provide further functional and integrative genomics evidence. We further identify 24 GxE variants in enhancers, totaling 38 new GxE variants for BMI in the UK Biobank, demonstrating that molecular genomics data produced in physiologically relevant contexts can discover new functional GxE mechanisms in humans.

SUBMITTER: Garske KM

PROVIDER: S-EPMC6752726 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Reverse gene-environment interaction approach to identify variants influencing body-mass index in humans.

Garske Kristina M KM Pan David Z DZ Miao Zong Z Bhagat Yash V YV Comenho Caroline C Robles Christopher R CR Benhammou Jihane N JN Alvarez Marcus M Ko Arthur A Ye Chun Jimmie CJ Pisegna Joseph R JR Mohlke Karen L KL Sinsheimer Janet S JS Laakso Markku M Pajukanta Päivi P

Nature metabolism 20190614 6

Identifying gene-environment interactions (GxEs) contributing to human cardiometabolic disorders is challenging. Here we apply a reverse GxE candidate search by deriving candidate variants from promoter-enhancer interactions that respond to dietary fatty acid challenge through altered chromatin accessibility in human primary adipocytes. We then test all variants residing in the lipid-responsive open chromatin sites within adipocyte promoter-enhancer contacts for interaction effects between the g ...[more]

PMID: 31538139

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Project description:In public health research, it has been well established that geographic location plays an important role in influencing health outcomes. In recent years, there has been an increased emphasis on the impact of neighborhood or contextual factors as potential risk factors for childhood obesity. Some neighborhood factors relevant to childhood obesity include access to food sources, access to recreational facilities, neighborhood safety, and socioeconomic status (SES) variables. It is common for neighborhood or area-level variables to be available at multiple spatial scales (SS) or geographic units, such as the census block group and census tract, and selection of the spatial scale for area-level variables can be considered as a model selection problem. In this paper, we model the variation in body mass index (BMI) in a study of pediatric patients of the Virginia Commonwealth University (VCU) Medical Center, while considering the selection of spatial scale for a set of neighborhood-level variables available at multiple spatial scales using four recently proposed spatial scale selection algorithms: SS forward stepwise regression, SS incremental forward stagewise regression, SS least angle regression (LARS), and SS lasso. For pediatric BMI, we found evidence of significant positive associations with visit age and black race at the individual level, percent Hispanic white at the census block group level, percent Hispanic black at the census tract level, and percent vacant housing at the census tract level. We also found significant negative associations with population density at the census tract level, median household income at the census tract level, percent renter at the census tract level, and exercise equipment expenditures at the census block group level. The SS algorithms selected covariates at different spatial scales, producing better goodness-of-fit in comparison to traditional models, where all area-level covariates were modeled at the same scale. These findings underscore the importance of considering spatial scale when performing model selection.

Dataset Information

Reverse gene-environment interaction approach to identify variants influencing body-mass index in humans.

Publications

Reverse gene-environment interaction approach to identify variants influencing body-mass index in humans.

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