We performed both unweighted and weighted GSMA analysis. In unweighted analysis, each study was assumed to contribute equally to the GSMA. The weighted GSMA takes into account the relative contribution from each study. The most appropriate weighting factor is not obvious; simulation studies have shown that the square root of the number of affected cases within each study performed well (26). Since most of the phenotypes we included in this GSMA were quantitative traits (FTND, MaxCigs24) and not binary outcomes, we used the square root of the number of genotyped subjects in each study as the primary weighting factor and the relevant results were reported in detail. To evaluate the influence of different weighting factors on the results, we also used an alternative weighting factor defined by the square root of number of pedigrees x number of markers used in each study (although we note that the latter is an imperfect approximation of information content in each study owing to varying information content from different markers, and diminishing information beyond a marker set that achieves genomewide coverage).
