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Gene-Environment Interactions and Disease
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Gene-Environment Interactions and Disease

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: closed (30 July 2017) | Viewed by 23391

Special Issue Editors

Prof. Dr. Bhramar Mukherjee

E-Mail Website
Guest Editor
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109-2029, USA
Interests: bayesian methods; gene-environment interactions; outcome dependent sampling; data fusion; study design
Dr. Sung Kyun Park

E-Mail Website
Guest Editor
Department of Epidemiology/Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109-2029, USA
Interests: epidemiology; environmental health; methods for pollution mixtures; gene-environment interaction
Dr. Jennifer A. Smith

E-Mail Website
Guest Editor
Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109-2029, USA
Interests: epidemiology; genomics; epigenomics; gene–environment interaction; cardiovascular disease; cognitive function; social factors

Special Issue Information

Dear Colleagues,

Nearly all human diseases are influenced by complex interactions between inherent genetic susceptibility and exposure to environmental agents. Traditional approaches for investigating disease aetiology that focus on evaluating genetic and environmental factors independently of one another may fail to identify important context-dependent risk factors for disease. In light of this, integrative approaches combining genetic and environmental exposure data will help identify people that are particularly susceptible to disease as a result of environmental insult. This insight may lead to improved prevention, intervention, and treatment of complex human diseases.

For this Special Issue, we focus on cutting-edge research papers investigating the interplay between genetic/genomic variation and a broad range of environmental exposures (physical, chemical, infectious, nutritional, and behavioural) on development of human disease, innovative statistical techniques for investigating gene-environment interactions, and the incorporation of genomic and other types of high-dimensional “-omics” data (transcriptomics, epigenomics, metabolomics) into studies of environmental effects on disease.

We are specifically interested in new and emerging approaches to gene–environment interaction (G×E) research, including (but not limited to):

  • Life course exposure and windows of susceptibility studies for G×E
  • Incorporating functional information into G×E analysis
  • Strengthening G×E analysis through studies of multiple phenotypes
  • Summarizing multiple markers (multi-G) or exposures (multi-E) simultaneously
  • Meta-analysis and/or replication studies of G×E
  • G×E with Omics data (genome × exposome)
  • Public health relevance of G×E findings and translational value
  • Role of G×E in precision medicine and precision public health

Prof. Dr. Bhramar Mukherjee
Dr. Sung Kyun Park
Dr. Jennifer A. Smith
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • gene-environment interaction
  • single nucleotide polymorphisms (SNPs)
  • genomics
  • epigenomics
  • transcriptomics
  • metabolomics
  • precision medicine
  • life course
  • biostatistics
  • epidemiology

Published Papers (5 papers)

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Research

590 KiB  
Article
Gene-by-Psychosocial Factor Interactions Influence Diastolic Blood Pressure in European and African Ancestry Populations: Meta-Analysis of Four Cohort Studies
by Jennifer A. Smith, Wei Zhao, Kalyn Yasutake, Carmella August, Scott M. Ratliff, Jessica D. Faul, Eric Boerwinkle, Aravinda Chakravarti, Ana V. Diez Roux, Yan Gao, Michael E. Griswold, Gerardo Heiss, Sharon L. R. Kardia, Alanna C. Morrison, Solomon K. Musani, Stanford Mwasongwe, Kari E. North, Kathryn M. Rose, Mario Sims, Yan V. Sun, David R. Weir and Belinda L. Needhamadd Show full author list remove Hide full author list
Int. J. Environ. Res. Public Health 2017, 14(12), 1596; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph14121596 - 18 Dec 2017
Cited by 4 | Viewed by 4888
Abstract
Inter-individual variability in blood pressure (BP) is influenced by both genetic and non-genetic factors including socioeconomic and psychosocial stressors. A deeper understanding of the gene-by-socioeconomic/psychosocial factor interactions on BP may help to identify individuals that are genetically susceptible to high BP in specific [...] Read more.
Inter-individual variability in blood pressure (BP) is influenced by both genetic and non-genetic factors including socioeconomic and psychosocial stressors. A deeper understanding of the gene-by-socioeconomic/psychosocial factor interactions on BP may help to identify individuals that are genetically susceptible to high BP in specific social contexts. In this study, we used a genomic region-based method for longitudinal analysis, Longitudinal Gene-Environment-Wide Interaction Studies (LGEWIS), to evaluate the effects of interactions between known socioeconomic/psychosocial and genetic risk factors on systolic and diastolic BP in four large epidemiologic cohorts of European and/or African ancestry. After correction for multiple testing, two interactions were significantly associated with diastolic BP. In European ancestry participants, outward/trait anger score had a significant interaction with the C10orf107 genomic region (p = 0.0019). In African ancestry participants, depressive symptom score had a significant interaction with the HFE genomic region (p = 0.0048). This study provides a foundation for using genomic region-based longitudinal analysis to identify subgroups of the population that may be at greater risk of elevated BP due to the combined influence of genetic and socioeconomic/psychosocial risk factors. Full article
(This article belongs to the Special Issue Gene-Environment Interactions and Disease)
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2259 KiB  
Article
Identification of Genetic Interaction with Risk Factors Using a Time-To-Event Model
by Mariza De Andrade, Sebastian M. Armasu, Bryan M. McCauley, Tanya M. Petterson and John A. Heit
Int. J. Environ. Res. Public Health 2017, 14(10), 1228; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph14101228 - 15 Oct 2017
Cited by 1 | Viewed by 3777
Abstract
Background: Certain diseases can occur with and without a trigger. We use Venous Thromboembolism (VTE) as our example to identify genetic interaction with pregnancy in women with VTE during pre- or postpartum. Pregnancy is one of the major risk factors for VTE as [...] Read more.
Background: Certain diseases can occur with and without a trigger. We use Venous Thromboembolism (VTE) as our example to identify genetic interaction with pregnancy in women with VTE during pre- or postpartum. Pregnancy is one of the major risk factors for VTE as it accounts for 10% of maternal deaths. Methods: We performed a whole genome association analysis using the Cox Proportional Hazard (CoxPH) model adjusted for covariates to identify genetic variants associated with the time-to-event of VTE related to pre- or postpartum during the childbearing age of 18–45 years using a case-only design in a cohort of women with VTE. Women with a VTE event after 45 years of age were censored and contributed only follow-up time. Results: We identified two intragenic single nucleotide polymorphisms (SNPs) at genome-wide significance in the PURB gene located on chromosome 7, and two additional intragenic SNPs, one in the LINGO2 gene on chromosome 9 and one in RDXP2 on chromosome X. Conclusions: We showed that the time-to-event model is a useful approach for identifying potential hazard-modification of the genetic variants when the event of interest (VTE) occurs due to a risk factor (pre- or post-partum). Full article
(This article belongs to the Special Issue Gene-Environment Interactions and Disease)
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1242 KiB  
Article
Interaction between Social/Psychosocial Factors and Genetic Variants on Body Mass Index: A Gene-Environment Interaction Analysis in a Longitudinal Setting
by Wei Zhao, Erin B. Ware, Zihuai He, Sharon L. R. Kardia, Jessica D. Faul and Jennifer A. Smith
Int. J. Environ. Res. Public Health 2017, 14(10), 1153; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph14101153 - 29 Sep 2017
Cited by 10 | Viewed by 6066
Abstract
Obesity, which develops over time, is one of the leading causes of chronic diseases such as cardiovascular disease. However, hundreds of BMI (body mass index)-associated genetic loci identified through large-scale genome-wide association studies (GWAS) only explain about 2.7% of BMI variation. Most common [...] Read more.
Obesity, which develops over time, is one of the leading causes of chronic diseases such as cardiovascular disease. However, hundreds of BMI (body mass index)-associated genetic loci identified through large-scale genome-wide association studies (GWAS) only explain about 2.7% of BMI variation. Most common human traits are believed to be influenced by both genetic and environmental factors. Past studies suggest a variety of environmental features that are associated with obesity, including socioeconomic status and psychosocial factors. This study combines both gene/regions and environmental factors to explore whether social/psychosocial factors (childhood and adult socioeconomic status, social support, anger, chronic burden, stressful life events, and depressive symptoms) modify the effect of sets of genetic variants on BMI in European American and African American participants in the Health and Retirement Study (HRS). In order to incorporate longitudinal phenotype data collected in the HRS and investigate entire sets of single nucleotide polymorphisms (SNPs) within gene/region simultaneously, we applied a novel set-based test for gene-environment interaction in longitudinal studies (LGEWIS). Childhood socioeconomic status (parental education) was found to modify the genetic effect in the gene/region around SNP rs9540493 on BMI in European Americans in the HRS. The most significant SNP (rs9540488) by childhood socioeconomic status interaction within the rs9540493 gene/region was suggestively replicated in the Multi-Ethnic Study of Atherosclerosis (MESA) (p = 0.07). Full article
(This article belongs to the Special Issue Gene-Environment Interactions and Disease)
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530 KiB  
Article
Multiple Gene-Environment Interactions on the Angiogenesis Gene-Pathway Impact Rectal Cancer Risk and Survival
by Noha Sharafeldin, Martha L. Slattery, Qi Liu, Conrado Franco-Villalobos, Bette J. Caan, John D. Potter and Yutaka Yasui
Int. J. Environ. Res. Public Health 2017, 14(10), 1146; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph14101146 - 28 Sep 2017
Cited by 6 | Viewed by 4262
Abstract
Characterization of gene-environment interactions (GEIs) in cancer is limited. We aimed at identifying GEIs in rectal cancer focusing on a relevant biologic process involving the angiogenesis pathway and relevant environmental exposures: cigarette smoking, alcohol consumption, and animal protein intake. We analyzed data from [...] Read more.
Characterization of gene-environment interactions (GEIs) in cancer is limited. We aimed at identifying GEIs in rectal cancer focusing on a relevant biologic process involving the angiogenesis pathway and relevant environmental exposures: cigarette smoking, alcohol consumption, and animal protein intake. We analyzed data from 747 rectal cancer cases and 956 controls from the Diet, Activity and Lifestyle as a Risk Factor for Rectal Cancer study. We applied a 3-step analysis approach: first, we searched for interactions among single nucleotide polymorphisms on the pathway genes; second, we searched for interactions among the genes, both steps using Logic regression; third, we examined the GEIs significant at the 5% level using logistic regression for cancer risk and Cox proportional hazards models for survival. Permutation-based test was used for multiple testing adjustment. We identified 8 significant GEIs associated with risk among 6 genes adjusting for multiple testing: TNF (OR = 1.85, 95% CI: 1.10, 3.11), TLR4 (OR = 2.34, 95% CI: 1.38, 3.98), and EGR2 (OR = 2.23, 95% CI: 1.04, 4.78) with smoking; IGF1R (OR = 1.69, 95% CI: 1.04, 2.72), TLR4 (OR = 2.10, 95% CI: 1.22, 3.60) and EGR2 (OR = 2.12, 95% CI: 1.01, 4.46) with alcohol; and PDGFB (OR = 1.75, 95% CI: 1.04, 2.92) and MMP1 (OR = 2.44, 95% CI: 1.24, 4.81) with protein. Five GEIs were associated with survival at the 5% significance level but not after multiple testing adjustment: CXCR1 (HR = 2.06, 95% CI: 1.13, 3.75) with smoking; and KDR (HR = 4.36, 95% CI: 1.62, 11.73), TLR2 (HR = 9.06, 95% CI: 1.14, 72.11), EGR2 (HR = 2.45, 95% CI: 1.42, 4.22), and EGFR (HR = 6.33, 95% CI: 1.95, 20.54) with protein. GEIs between angiogenesis genes and smoking, alcohol, and animal protein impact rectal cancer risk. Our results support the importance of considering the biologic hypothesis to characterize GEIs associated with cancer outcomes. Full article
(This article belongs to the Special Issue Gene-Environment Interactions and Disease)
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488 KiB  
Article
An Efficient Test for Gene-Environment Interaction in Generalized Linear Mixed Models with Family Data
by Mauricio A. Mazo Lopera, Brandon J. Coombes and Mariza De Andrade
Int. J. Environ. Res. Public Health 2017, 14(10), 1134; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph14101134 - 27 Sep 2017
Cited by 5 | Viewed by 3822
Abstract
Gene-environment (GE) interaction has important implications in the etiology of complex diseases that are caused by a combination of genetic factors and environment variables. Several authors have developed GE analysis in the context of independent subjects or longitudinal data using a gene-set. In [...] Read more.
Gene-environment (GE) interaction has important implications in the etiology of complex diseases that are caused by a combination of genetic factors and environment variables. Several authors have developed GE analysis in the context of independent subjects or longitudinal data using a gene-set. In this paper, we propose to analyze GE interaction for discrete and continuous phenotypes in family studies by incorporating the relatedness among the relatives for each family into a generalized linear mixed model (GLMM) and by using a gene-based variance component test. In addition, we deal with collinearity problems arising from linkage disequilibrium among single nucleotide polymorphisms (SNPs) by considering their coefficients as random effects under the null model estimation. We show that the best linear unbiased predictor (BLUP) of such random effects in the GLMM is equivalent to the ridge regression estimator. This equivalence provides a simple method to estimate the ridge penalty parameter in comparison to other computationally-demanding estimation approaches based on cross-validation schemes. We evaluated the proposed test using simulation studies and applied it to real data from the Baependi Heart Study consisting of 76 families. Using our approach, we identified an interaction between BMI and the Peroxisome Proliferator Activated Receptor Gamma (PPARG) gene associated with diabetes. Full article
(This article belongs to the Special Issue Gene-Environment Interactions and Disease)
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