These results suggest that incorporating multiple covariates together may be more productive than the use of individual covariates in linkage analysis of complex diseases. Specifically, age at interview, sex, and smoking status (PS3) appear to be important covariates that can be used to account for heterogeneity associated with alcoholism. The inclusion of PS3 in the linkage analysis led to both the most significant overall p-value as well as the largest number of different markers yielding some significant evidence for linkage. The most significant individual markers were GATA193 (p = 0.0044) on chromosome 17, D2S200, D6S477, and D15S644 (all three with p = 0.0078). In the logistic regression for PS3 (Table 2), the smoking variable resulted in the greatest odds ratios (OR of 5.33 ± 1.02) among any of the covariates used in defining a PS, and for PS1, the sex variable resulted in one of the lowest ORs (0.108 ± 0.019) of any covariates. For PS2 and PS5, which identified the smallest number of markers with significant linkage, approximately one-third of the ttth1 covariate data was missing. Thus, examining the
