Obesity Epidemiology

GENE-ENVIRONMENT INTERACTIONS AND OBESITY 463However, most genetic association studies lack detailed information on such interactionsor nongenetic exposures, especially diet—a factor that may account for substantialheterogeneity in findings, and, thus, lack of replication. 13 Failure to account for geneenvironmentinteractions may also decrease the power to identify disease susceptibilitygenes and lead to underestimation of effects conferred by genetic and environmentalfactors. Conversely, a better understanding of gene-environment interactions can helpto obtain more accurate estimates of the impact of environmental factors on geneticallysusceptible individuals and identify high-risk populations for targeted prevention andintervention.Statistical Models of Gene-Environment InteractionsIn general, the term interaction means interdependence of two or more variables in influencinga trait or phenotype. 14 Biological interactions occur when two or more factorsinteract physiologically or chemically in a causal pathway that involves the mechanismof a disease. Statistical interactions (also termed “effect modifications”) occur when thevalue of a second factor alters or modifies the association between an exposure variableand a trait. While biological interactions do not necessarily imply statistical interactions,the latter typically require biological interactions to explain underlying mechanisms.Gene-environment interaction can be defined as “a different effect of an environmentalexposure on disease risk in persons with different genotypes,” or, alternatively,“a different effect of a genotype on disease risk in persons with different environmentalexposures.” 14 The presence and extent of gene-environment interactions depends on thescale used to assess the effect. When a ratio measure is used, the interaction is assessedon the multiplicative scale; when a difference measure is used, the interaction is assessedon the additive scale. While multiplicative models often make useful contributions tothe understanding of disease etiology, departure from additivity may be more relevantfor public health concerns. 15 In most situations, the purpose of analyses is to discovernew genetic markers, most of which have no clinical utility in terms of genetic testing.Therefore, multiplicative models are more appropriate. However, in situations wherean interaction may have both public health and etiology implications, it is important toassess both multiplicative and additive models.Botto and Khoury 16 developed a simple 2 × 4 table to evaluate joint effects of agenotype and an exposure (both dichotomous) and gene-environment interactions(Table 22.1). The odds ratios (ORs) for the genotype and environmental exposure canbe independently assessed and a multiplicative or additive interaction demonstrated ifA(B × C) ≠ 1 or A – (B + C – 1) ≠ 0, respectively. In addition, a case-only OR (ag/ce) can be easily derived and used as a comparison with findings from case-only studies.The case-only design is an efficient and valid way to evaluate gene-environment interactions,if one assumes that the exposure and genetic factors occur independently, and thatthe disease is rare 17 (see later).One disadvantage of the 2 × 4 table is that it allows an assessment only of dichotomousgenotypes and environmental exposures. In addition, it does not allow for adjustmentsfor multiple covariates. Regression models are typically used to overcome theselimitations. In regression analyses, genotypes can be recoded to reflect dominant, recessive,and additive genetic influences (i.e., dominant model: AA = 1, Aa = 1, aa = 0;recessive: AA = 1, Aa = 0, aa = 0; additive: AA = 1, Aa = 0.5, aa = 0) dependingon the biological hypotheses. Multiplicative interactions (specifically, tests of departurefrom joint effects of exposed genotypes and environmental factors from the product of