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Open Access Highly Accessed Review

Design and analysis issues in gene and environment studies

Chen-yu Liu12, Arnab Maity34, Xihong Lin3, Robert O Wright15 and David C Christiani16*

Author Affiliations

1 Environmental and Occupational Medicine and Epidemiology Program, Department of Environmental Health, Harvard School of Public Health, Boston, MA, USA

2 Institute of Environmental Health, College of Public Health, National Taiwan University, Taipei, Taiwan

3 Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA

4 Department of Statistics, North Carolina State University, Raleigh, NC, 27695, USA

5 Department of Preventive Medicine, Mount Sinai School of Medicine, New York, NY, USA

6 Department of Medicine, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA

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Environmental Health 2012, 11:93  doi:10.1186/1476-069X-11-93

Published: 19 December 2012

Abstract

Both nurture (environmental) and nature (genetic factors) play an important role in human disease etiology. Traditionally, these effects have been thought of as independent. This perspective is ill informed for non-mendelian complex disorders which result as an interaction between genetics and environment. To understand health and disease we must study how nature and nurture interact. Recent advances in human genomics and high-throughput biotechnology make it possible to study large numbers of genetic markers and gene products simultaneously to explore their interactions with environment. The purpose of this review is to discuss design and analytic issues for gene-environment interaction studies in the “-omics” era, with a focus on environmental and genetic epidemiological studies. We present an expanded environmental genomic disease paradigm. We discuss several study design issues for gene-environmental interaction studies, including confounding and selection bias, measurement of exposures and genotypes. We discuss statistical issues in studying gene-environment interactions in different study designs, such as choices of statistical models, assumptions regarding biological factors, and power and sample size considerations, especially in genome-wide gene-environment studies. Future research directions are also discussed.

Keywords:
Gene-environment; Interactions; Expanded environmental genomic disease paradigm; Critical developmental windows; Genome-wide; Epigenetics