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N. Chatterjee 179the Kaplan–Meyer estimators of risk for the first-degree relative of carriersand those for non-carriers.I attempted to formulate the problem in terms of a composite likelihoodframework (Chatterjee and Wacholder, 2001) so that the resulting estimatorhas desirable statistical properties such as monotonicity of the age-specificcumulative risk curve and are robust to strong assumptions about residualfamilial correlation of disease among family members. The likelihood-basedframework was quite attractive due to its flexibility for performing variousadditional analyses and I was happy that I was able to make a quick methodologiccontribution, even being fairly novice to the field. However, the actualapplication of the method to WAS hardly changed the estimate of penetrancefor BRCA1/2 mutation compared to the method of moment estimates previouslyavailable.In retrospect, I learned several lessons from my first post-doctoral project.First, it is often hard to beat a simple but sensible statistical method. Althoughthis may be obvious to many seasoned applied statisticians, this first-handexperience was an important lesson for me, fresh out of graduate school wheremy PhD thesis involved understanding of semi-parametric efficient estimationmethodology, the purpose of which is to get the last drop of informationfrom the data with minimal assumption about “nuisance” parameters. Second,although the substantive contribution of my first project was modest, it wasan invaluable exercise for me as it opened my gateway to the whole new areaof statistical genetics. To get a solid grasp of the problem without having anyknowledge of genetics apriori, I had to teach myself concepts of populationas well as molecular genetics. Self-teaching and my related struggles were aninvaluable experience for me that help me to date to think about each problemin my own way.17.3 Gene-environment interaction: Bridginggenetics and theory of case-control studiesAs I was wrapping up my work on kin-cohort studies, one day Sholom askedme for some help to analyze data from a case-control study of Ovarian Cancerto assess interaction between BRCA1/2 mutations and certain reproductivefactors, such as oral contraceptive use, which are known to reduce the risk forthe disease in the general population (Modan et al., 2001). Because BRCA1/2mutations are very rare in the general population, standard logistic regressionanalysis of interaction would have been very imprecise. Instead, the investigatorsin this study were pursuing an alternative method that uses a log-linearmodeling approach that can incorporate the reasonable assumption that reproductivefactors are distributed independently of genetic mutation statusin the general population. Earlier work has shown that incorporation of the