Alcohol dependence has been shown to cluster in families. Multiple linkage analyses have been performed for phenotypes related to alcoholism, identifying phenotype-specific linkage evidence [1-5]. To increase the statistical power to detect linkage in the presence of heterogeneity, we explored the use of covariate-based linkage analysis based on a conditional logistic regression model [6,7]. Because one degree of freedom is added to the statistical test for each additional covariate analyzed, we incorporated a propensity score (PS) to collapse multiple covariates into one variable and showed in simulation studies it consistently improved the statistical power of the linkage test [[8,9], unpublished data, 2004]. Rosenbaum and Rubin [10] first described the PS in a causal inference analysis to control for multiple covariate effects that could potentially bias assessments of treatment effect outcomes when randomization experiments were not possible. In such a setting, the score is defined as the conditional probability of being assigned to a treatment group given the covariate data, and in practice, it can be estimated from the observed covariate data with a logistic model of the treatment group assignment
