Some pitfalls in application of functional data analysis approach to association studies.

One of the most effective methods for gene-based mapping employs functional data analysis, which smoothes data using standard basis functions. The full functional linear model includes a functional representation of genotypes and their effects, while the beta-smooth only model smoothes the genotype effects only. Benefits and limitations of the beta-smooth only model should be studied before using it in practice. Here we analytically compare the full and beta-smooth only models under various scenarios. We show that when the full model employs two sets of basis functions equal in type and number, genotypes smoothing is eliminated from the model and it becomes analytically equivalent to the beta-smooth only model. If the basis functions differ only in type, genotypes smoothing is also eliminated from the full model, but the type of basis functions used for smoothing genotype effects becomes redefined. This leads to misinterpretation of the results and may reduce statistical power. When basis functions differ in number, no analytical comparison of the full and beta-smooth only models is possible. However, we show that the numbers of basis functions set unequal can become equal during the analysis, and the full model becomes disadvantageous.