Our wave adjustment approach shares some similarities with the improved version of the GIM algorithm (6) and with the algorithm proposed by Nannya et al. (7), since these approaches all incorporated GC content information into the model building. The improved GIM algorithm takes into account of the GC percentage of 40 kb of sequence surrounding each SNP, and then uses a robust regression to determine the optimal degree of polynomials in place of least-squares regression. The Nannya et al. algorithm applies an empirical formula: the regression model contains both the length and the GC content of the PCR fragment that contains SNP, as well as the squares of these two measures, into a quadratic regression form. Our algorithm has several distinct differences: first, we used linear least-squares regression, due to the simplicity and elegance of the model, and due to the linear relationships observed in Figure 2. Second, we used GC content of 1 Mb window around each marker in the regression model, since this long-range GC content appears to be better correlated with the variation of signal intensities. Third,
