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Complex traits result from an interplay between genes and environment. A better understanding of their joint effects can help refine understanding of the epidemiology of the trait. Various tests have been proposed to assess the statistical interaction between genes and the environment ( G × E ) in case-parent trio data. However, these tests can lose power when the form of  G × E  departs from that for which the test was developed. To address this limitation, we propose a data-smoothing approach to estimate and test  G × E  between a single nucleotide polymorphism and a continuous environmental covariate. For estimating  G × E , we fit a generalized additive model using penalized likelihood. The resulting point- and interval-estimates of  G × E  lead to a graphical display, which can serve as a visualization tool for exploring the form of interaction. For testing  G × E , we propose a permutation approach, which accounts for the extra uncertainty introduced by the smoothing process. We investigate the statistical properties of the proposed methods through simulation. We also illustrate the use of the approach with an example data set. We conclude that the approach is useful for exploring novel interactions in data-rich settings.

A data-smoothing approach to explore and test gene-environment interaction in case-parent trios