Social scientists have been slow to incorporate the work of genetic epidemiologists and quantitative geneticists into their work, and genetic researchers have been slow to incorporate the work of sociologists. If anything is clear from this study, it is that each discipline needs to consider the large body of findings from the other. For example, these findings and the trend-based perspective on genetic influences are highly relevant to genetic epidemiologists in the recent push to identify single nucleotide polymorphisms (SNPs) that predict smoking (Li 2008). Estimates from genome-wide association studies (Lange et al. 2003) may be subject to periodic highs and lows in the genetic influences on a particular trait; if a genome-wide study on regular smoking were conducted on a national cohort of U.S. adults born in 1942, the researchers would have a very difficult time identifying SNPs that differentiate smokers from non-smokers. The current methods certainly consider this factor (e.g., the population prevalence is a key component of the estimation techniques), but they do not necessarily consider that any sample is drawn from a specific historical moment in a larger cycle with predictable ebbs and flows.
