Further evidence for the increased power of LOD scores compared with nonparametric methods

In genetic analysis of diseases in which the underlying model is unknown, " model free" methods-such as affected sib pair (ASP) tests-are often preferr ed over LOD-score methods, although LOD-score methods under the correct or even approximately correct model are more powerful than ASP tests. However, there might be circumstances in which nonparametric methods wi:ll outperfo rm LOD-score methods. Recently, Dizier et al. reported that, in some comple x two-locus (2L) models, LOD-score methods with segregation analysis-derive d parameters had less power to detect linkage than ASP tests. We investigat ed whether these particular models, in fact, represent a situation that ASP rests are more powerful than LOD scores. We simulated data according to th e parameters specified by Dizier et al. and analyzed the data by using a (a ) single locus (SL) LOD-score analysis performed twice, under a simple domi nant and a recessive mode of inheritance (MOI), (b) ASP methods, and (c) no nparametric linkage (NPL) analysis. We show that SL analysis performed twic e and corrected for the type I-error increase due to multiple testing yield s almost as much linkage information as does an analysis under the correct 2L model and is more powerful than either the ASP method or the NPL method. We demonstrate that, even for complex genetic models, the most important c ondition for linkage analysis is that the assumed MOI at the disease locus being tested is approximately correct, not that the inheritance of the dise ase per se is correctly specified. In the analysis by Dizier et al., segreg ation analysis led to estimates of dominance parameters that were grossly m isspecified for the locus tested in those models in which ASP tests appeare d to be more powerful than LOD-score analyses.