Investigations into gene-smoking interactions in birth defects have produced some insightful and interesting results; however, failure of replication and conflicting results are still major unresolved issues. Many of these studies, both positive and negative, are limited by a small sample size; consequently, comprehensive investigations of environment and gene-environment interactive effects with a larger sample size are needed. Multi-institutional collaborations such as the International Clearing-house for Birth Defects Surveillance and Research (Botto et al., 2006
), ECLAMC (Castilla and Orioli, 2004
), and CDC sponsored Birth Defects Prevention Study (Yoon and Rasmussen, 2001
) have already been successful in providing large, epidemiologic datasets for analysis, and in some settings have added DNA samples as well. Comprehensive collaborations between institutions and investigators can provide the large sample sizes required by studies of gene-environment interactions. The recently initiated Gene Association Information Network (GAIN) and Genes, Environment, and Health Initiative (GEI) are steps in the direction of promoting such collaborations that can also include genome wide association studies that can move past candidate gene analysis to search for common variants contributing to disease with no constricting, underlying models (Christensen and Murray, 2007
). GAIN is a public–private partnership established to investigate the genetics of complex disease through a series of whole genome association studies, using samples from existing case–control studies (Manolio et al., 2007
). GEI is a unique collaboration between geneticists and environmental scientists, which will take advantage of the innovative genetic technology as well as new instrument for measuring environmental factors to understand the genetic contributions and gene-environment interactions in common diseases. They provide new models of collaboration, data sharing, and intellectual protection. Such studies will also require accurate phenotyping and subphenotyping to maximize power, as has been demonstrated by several cleft studies (Rahimov et al., 2007
; Suzuki et al., 2007
In addition to adequate sample size, assessment of gene-environment interaction effects also depends upon the accurate and detailed measurement of exposures and the proper statistical evaluation. New methods of environmental variable measures that can be noninvasive and longitudinal will enhance detection efforts (Schwartz and Collins, 2007
). One important limitation that is common to previous studies is self-selection on smoking based in part on risk preferences and anticipated pregnancy outcomes which might result in biased estimates of the effects of smoking. Genetic instrumental variable models are being applied to address self-selection when estimating the average and interactive effects on smoking (Wehby et al., 2007
). These present a promising approach that can also be adopted for studying interactions between smoking and genetic factors.
Case–control is the traditional design for investigating gene-environment interactions. Interactions on either an additive or a multiplicative scale can be estimated using such a design; this design is however susceptible to population stratification. Case-only design is more powerful in detecting interaction on a multiplicative scale, but it cannot test for exposure main effects and its validity heavily relies on the assumption that gene and environment are independent in the control population. Case-parent design has the robustness to population stratification, but similar to case-only design it forfeits the ability to detect exposure main effects and it can only study interaction on a multiplicative scale. A hybrid design that can combine the advantages of the case–control and case-parent designs is desired. As samples and data become more available, the challenges will shift more towards identifying more efficient and robust statistical and analytical methods and approaches to interpret the results.
Interactions between genes, environments, and behaviors will continue to be a growing field to identify health risk factors that can be modified through policy or behavioral interventions. Studying the role of interactions with maternal smoking in adverse birth outcomes such as birth defects is one area with substantial successes and that can benefit significantly from further work.