The genome search meta-analysis (GSMA) method has been proposed as a valid and robust meta-analysis technique to combine the evidence for linkage across multiple linkage scans using a non-parametric ranking method (25, 26). Apart from the advantage of greater power in detecting small but consistent evidence for linkage, GSMA can combine linkage results from studies with different family structures, marker sets, and statistical analysis methods. While a unique genetic spectrum might characterize each specific smoking behavior, we hypothesize that some risk loci are shared across different assessments. Consequently, the aim of the current study is to identify potential risk loci which are independent of distinct smoking behavior assessments using the GSMA by pooling all independent genome scan results of smoking behavior. Because increased sample homogeneity can be helpful to reduce locus heterogeneity and therefore increase power to detect regions specific for a particularly defined sample set, subgroup GSMA based on FTND and MaxCigs24 was carried out. Samples incorporating subjects of mostly European ancestry were also analyzed separately.
