- Multivariate analyses will be conducted through the use of general linear mixed models [5]. The models will include random subject effects to account for dependence among repeated measurements of subjects. This type of model is ideal when there are multiple measurements on subjects, such as when laboratory measurements are performed in triplicate. The dependent variables of interest will be the complement level measurements (log transformed, if necessary), while independent variables will include AMD status (case/control), factor H genotype (CC, CT, TT), smoking (current, former, never), and an interaction-term involving factor H genotype and smoking status. The interaction-term will help us to determine whether the impact of factor H genotype and smoking on serum complement levels is linear (additive), or non-linear (e.g., multiplicative). The model will also include adjustments for age, gender, and race, which may all affect complement factors. Thus, any differences among haplotypes will be adjusted (corrected) for effects that may be attributed to age, gender, or race. Different correlation structures will be examined for the random subject effects, and the investigators will use Akaike's Information Criterion to select the most appropriate model. Secondary analyses will involve excluding never smokers, to assess the nature of the association (if any) between pack/year histories. factor H genotypes. and their complement levels. An additional analysis (using conditional logistic regression) will be conducted to determine whether smoking interacts with a subject's factor H genotype with respect to the risk of AMD. Again, this model will be adjusted for age, gender, and race, and the results will be expressed as odds ratios associated with the risk of AMD.