Participants at the Think Tank noted every GxE study must address challenges in measuring, assessing and modeling both genetic and environmental factors. For GWAS, directly genotyped common SNPs are often analyzed as if measured without error. However, a closer examination of cluster plots may demonstrate uncertainty in genotype measurement. The assumption of no error is less safe for imputed genotypes [Jiao, et al. 2011; Sinnott and Kraft 2012], For rare variants, and sequencing studies, variant calls are often made with less accuracy and confidence, particularly for low-coverage designs [Li, et al. 2011]. Additionally, error may be introduced in the choice of genetic model (i.e. assuming a log-additive model when the true effect is dominant or recessive [Prentice 2011]). The environment is dynamic, changes over time and individual’s lifespans, and is fraught with measurement error [Spiegelman 2010]. Participants stressed the importance of valid, efficient, computationally feasible methodology for measuring “E” in GxE studies, since misclassification can be a major source of bias and loss of power [Aschard, et al. 2012; Lindstrom, et al. 2009].
