As the number of individuals in genome-wide association (GWA) studies becomes larger and larger, some of the signals that emerge may turn out to reflect the action of modifiable (e.g. environmental or behavioral) exposures, rather than more direct biological effects. At present, what is likely to be required to understand these pathways is a two-step approach in which initial GWA study findings are interrogated further in studies in which detailed phenotype information is available. At present, this is not always possible—for example, a lack of smoking status information in the studies contributing to recent schizophrenia GWA studies means it is not possible to test the possible causal effect of smoking in a stratified analysis.
As large richly phenotyped cohort studies (e.g., UK Biobank) emerge, however, it should become increasingly possible to identify modifiable exposures from genetic data and to dissect those pathways within the same cohort. Here, “modifiable” can refer to substance use, but also to factors such as cholesterol or metabolite levels or blood pressure—i.e. some pathways might be directly influenced by lifestyle choices. A failure to appreciate this point will hamper our ability to translate the results of GWA studies into health benefits, by focusing attention on possible biological pathways, when, in fact, the target for intervention could be a modifiable environmental or behavioural exposure.
Authors [in attached publication] suggest the need to be cautious when using statistical adjustment to test whether or not a genetic variant operates entirely via the supposed intermediate behavioral pathway. Sometimes, the most prudent explanation (e.g. smoking causes lung cancer) is the best explanation.
PLoS Genet 2o16; 12: e1005765