Genetic sensitivity to the environment: the case of the serotonin transporter gene and its implications for studying complex diseases and traits

Abstract

Evidence of marked variability in response among people exposed to the same environmental risk implies that individual differences in genetic susceptibility might be at work. The study of such Gene-by-Environment (GxE) interactions has gained momentum. In this article, the authors review research about one of the most extensive areas of inquiry: variation in the promoter region of the serotonin transporter gene (SLC6A4; also known as 5-HTT) and its contribution to stress sensitivity. Research in this area has both advanced basic science and generated broader lessons for studying complex diseases and traits. The authors evaluate four lines of evidence about the 5-HTT stress-sensitivity hypothesis: 1) observational studies about the serotonin transporter linked polymorphic region (5-HTTLPR), stress sensitivity, and depression in humans; 2) experimental neuroscience studies about the 5-HTTLPR and biological phenotypes relevant to the human stress response; 3) studies of 5-HTT variation and stress sensitivity in nonhuman primates; and 4) studies of stress sensitivity and genetically engineered 5-HTT mutations in rodents. The authors then dispel some misconceptions and offer recommendations for GxE research. The authors discuss how GxE interaction hypotheses can be tested with large and small samples, how GxE research can be carried out before as well as after replicated gene discovery, the uses of GxE research as a tool for gene discovery, the importance of construct validation in evaluating GxE research, and the contribution of GxE research to the public understanding of genetic science.

FIGURE 1

Role of 5-HTT Variation in Stress Sensitivity as Underscored by the Coherence of Findings From Hypothesis-Driven Studies in Multiple Species Employing Multiple Methodologies

FIGURE 2. How the 5-HTTLPR Affects Neural Circuitry for Responding to Environmental Threat and Stress

^a Implicated in humans and nonhuman primates. ^b Implicated in humans, nonhuman primates, and rodents.

FIGURE 3. How the Pow er to Detect G×E Interactions Depends on the Distributions of the Genotypes and Exposures in the Sample^a

^a The two rows of graphs demonstrate a key difference between interactions involving normally distributed continuous variables (top row) and those involving asymmetrically distributed categorical ones (middle row). If the product term A*B (i.e., the term that represents interaction in a multiple regression) is calculated from two normally distributed symmetrical variables A and B, it has a restricted variance (leptokurtic distribution) but is uncorrelated with the first-order predictors (i.e., the correlations between A and A*B [r_{A, A*B}] and between B and A*B [rB_{, A*B}] are zero). However, the product term G*E that represents two categorical variables (G: genotype with a minor allele frequency [MAF] of 25%; and E: categorical exposure in the population [P_EXP] of 25%) is strongly correlated with the first-order predictors (i.e., the correlations between G and G*E [r_{G, G*E}] and between E and G*E [r_{E, G*E}] are substantial). As a result, the residual variance of the product term (bottom of figure) after factoring out first-order predictors, and the power to detect interactions, declines rapidly as the rates of exposure and minor allele frequency depart from 50%. The full power for testing interactions between categorical variables is only preserved in the special case of minor allele frequency equal to 50% and exposure rate of 50% (the top segment in red). “Density” reflects the proportion of individuals falling within each narrow band of values of the variable on the x axis.

FIGURE 4. How the Frequency of an Environmental Exposure in a Sample Influences the Ability to Detect Genetic Effects and G×E Interactions^a

^a Panel A shows the influence of environmental exposure frequency on the ability to identify genetic effects, in two genotypes of equal prevalence. Genotype A shows no phenotypic response to the environmental exposure. Genotype B shows a response to the environmental exposure. What would happen if the association between genotype and phenotype were studied without knowledge of the environmental exposure and its frequency (shown from 10% to 90%)? A sample having many exposed subjects will report a genetic effect on the phenotype, whereas a sample having few exposed subjects will not, and if exposure is not ascertained, the source of nonreplication will remain a mystery. Panel B shows the influence of the rate of environmental exposure on statistical power to detect G×E interactions and main effects of genes. Each point is based on 10,000 simulations of samples of 1000 drawn from a population with equal distributions of two genotypes, with a continuous outcome generated as a moderately strong G×E (i.e., the difference in the environment-phenotype correlation between genetic strata =0.3), and no main effect. In samples with exposure frequency close to 0, there is no detectable interaction or main effect. For exposure frequency below 50%, there is greater power to detect a G×E interaction (blue line) than to detect a main effect of genes (red line). With rates of exposure exceeding 50%, the power of detecting a direct effect of genes (red line) increases above that of detecting an interaction, even though interaction is the data-generating mechanism. The probability of detecting a spurious main effect of genes (or environments) remains at the 2.5% chance level across the range of exposure frequency if the interaction term is retained in the equation.