Gene-environment interactions occur when the effect of a genetic variant differs in multiple environments. Representing an intermediate between the “nature and nurture” sides of genetic variation, these interactions are important contributors to the development of complex phenotypes. There have been many studies performed on humans and model organisms that have attempted to observe the effects of these interactions, most of them involving the use of techniques such as genome-wide association studies (GWAS) and candidate gene association studies (CGAS). These methods, however, have proven ineffective in elucidating the molecular mechanisms of gene-environment interactions.
Expression quantitative trait loci (eQTL) mapping is a powerful extension of standard quantitative mapping techniques that has shown promise in studying these interactions. As its name suggests, eQTL mapping involves the association of variances in gene expression and genetic polymorphisms. With modern technology, thousands of genetic transcripts can be simultaneously measured under varying genetic and environmental conditions. This allows for the application of eQTL mapping techniques when observing difficult-to-study phenomena such as gene-environment interactions.
Based on existing conclusions surrounding gene-environment interactions in different organisms, Smith and Kruglyak intended to characterize the overall genetic architecture of the gene-environment interactions in yeast. They particularly focused on the metabolism of two sugar sources: glucose and ethanol. These conditions provide an interesting environmental contrast, as yeast metabolizes both compounds through reverse metabolic pathways (fermentation for glucose and anaerobic respiration for ethanol). The researchers also narrowed down the genetic component by focusing on two particular strains: BY and RM. Due to the molecular differences between the conditions and the strains tested, most of the measured variance was hypothesized to be due to strain and condition effects. However, based on the literature evidence for gene-environment interactions, they also expected to observe a significant contribution of this component in phenotypic variance.
The two parental strains BY and RM, as well as 109 segregant strains derived from a cross between the parental strains, were grown in glucose and ethanol. Each strain was expression profiled by using DNA microarrays to hybridize mRNA that was extracted from the cells. A hybridization standard was created by mixing equal amounts of mRNA from both parents grown in both conditions.
Smith and Kruglyak’s experiments produced two major conclusions. First, from their analyses of variance (ANOVA), they determined that yeast transcripts are influenced by genetic components, environmental components, and by the interactions between both of these components (Figure 1). The presence of all three components demonstrates that even though genetic and environmental contributions to the phenotypic variance are dominant, the interactions between the two are still significant. Another major conclusion from this experiment was the characterization of local and distant linkages. Local linkages were described as being more stable and less dependent on the environment. They also usually affected both environmental conditions in the same manner. On the other hand, distant linkages were described as being more volatile and environmentally-dependent. In addition, distant linkages usually affected only one condition. In general, the majority of the interactions between genes and the environment occurred at distant linkages.
The results of this experiment highlight the importance of considering gene-environment interactions when performing linkage studies, as ignoring this component produces bias towards certain loci and compromises the data’s validity. More specifically, even though gene-environment interactions “play a dominant role in a minority of traits” (Smith & Kruglyak, 2008), these traits have the potential to play a significant role in determining the overall phenotypic variance. This is particularly relevant for creating lifestyle choices in relation to human diseases that have already been proven to possess a significant contribution from these interactions, like in the cases of heart disease, depression, and cancer.
Smith, E. N., & Kruglyak, L. (2008). Gene-Environment Interaction in Yeast Gene Expression. PLoS Biology, 6(4), 0812-0824.