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180 The road travelledgene-environment independence assumption can greatly enhance the efficiencyof interaction analysis in case-control studies (Piegorsch et al., 1994; Umbachand Weinberg, 1997).While I was attempting to analyze the study using the log-linear modelframework, I realized it is a bit of a cumbersome approach that requires creatingmassive contingency tables by categorizing all of the variables under study,carefully tracking different sets of parameters (e.g., parameters related to diseaseodds-ratios and exposure frequency distributions) and then constrainingspecific parameters to zero for incorporation of the gene-environment independenceassumption. I quickly realized that all of these details can be greatlysimplified by some of the techniques I had learned from my advisors NormanBreslow and Jon Wellner during my PhD thesis regarding semi-parametricanalysis of case-control and other types of studies that use complex outcomedependent sampling designs (Chatterjee et al., 2003). In particular, I was ableto derive a profile-likelihood technique that simplifies fitting of logistic regressionmodels to case-control data under the gene-environment independenceassumption(Chatterjee and Carroll, 2005). Early in the development, I toldRay Carroll, who has been a regular visitor at NCI for a long time, aboutsome of the results I have derived, and he got everyone excited because of hisown interest and earlier research in this area. Since then Ray and I have beenpartners in crime in many papers related to inference on gene-environmentinteractions from case-control studies.This project also taught me a number of important lessons. First, there istremendous value to understanding the theoretical underpinning of standardmethods that we routinely use in practice. Without the understanding of thefundamentals of semi-parametric inference for analysis of case-control datathat I developed during my graduate studies, I would never have made theconnection of this problem to profile-likelihood, which is essentially the backbonefor many standard methods, such as Cox’s partial likelihood analysis oflifetime data. The approach not only provided a simplified framework for exploitingthe gene-environment independence assumption for case-control studies,but also led to a series of natural extensions of practical importance so thatthe method is less sensitive to the violation of the critical gene-environment independenceassumption. My second lesson was that it is important not to losethe applied perspective even when one is deeply involved in the developmentof theory. Because we paid close attention to the practical limitation of theoriginal methods and cutting-edge developments in genetic epidemiology, mycollaborators, trainee and I (Ray Carroll, Yi-Hau Chen, Bhramar Mukherjee,Samsiddhi Bhattacharjee to name a few) were able to propose robustextensions of the methodology using conditional logistic regression, shrinkageestimation techniques and genomic control methods (Chatterjee et al., 2005;Mukherjee and Chatterjee, 2008; Bhattacharjee et al., 2010).