Gene–environment-wide association studies: emerging approaches

Abstract

Despite the yield of recent genome-wide association (GWA) studies, the identified variants explain only a small proportion of the heritability of most complex diseases. This unexplained heritability could be partly due to gene-environment (G×E) interactions or more complex pathways involving multiple genes and exposures. This article provides a tutorial on the available epidemiological designs and statistical analysis approaches for studying specific G×E interactions and choosing the most appropriate methods. I discuss the approaches that are being developed to study entire pathways and available techniques for mining interactions in GWA data. I also explore approaches to marrying hypothesis-driven pathway-based approaches with “agnostic” GWA studies.

Gene-by-environment experiments: a new approach to finding the missing heritability

“…A tutorial on the current study designs for mapping G×E interactions is provided by Duncan Thomas (Gene -environment-wide association studies: emerging approaches. Nature Reviews Genetics 11, 259-272 (2010)) 1 . Some 2 have argued that G×E interactions will contribute little to the missing heritability 3 if G×E effects are small or if correlations between gene and environment (rGE) mask genotype-by-genotype (G×G) effects.…”

Lung function, race and ethnicity: a conundrum

“…Race, ethnicity and ancestral categories falsely suggest genetic homogeneity within and heterogenity between groups; they ignore the genetic variability within groups, gene-environment interactions [13] and differences due to socially mediated mechanisms. Therefore, many scientists advocate abolishing such categorisation in research [4], versus those who believe there is still a role for the continued use of self-identified race and ethnicity in biomedical and genetic research [14][15][16].…”

NEW: Network-Enabled Wisdom in Biology, Medicine, and Health Care

“…To unearth the 85 to 90% of CCD risk that is not explained by current analyses of GWAS data sets, it will be important to address contexts defined by macro-and microenvironmental factors (20). Macroenvironmental factors-mainly those that affect the individual through exposure to toxins, food intake, and other life-style factors-vary over time and alter the microenvironments within distinct tissues and cell types (Fig.…”