Direct power comparisons between simple LOD scores and NPL scores for linkage analysis in complex diseases

Several methods have been proposed for linkage analysis of complex traits w ith unknown mode of inheritance. These methods include the LOD score maximi zed over disease models (MMLS) and the "nonparametric" linkage (NPL) statis tic. In previous work, we evaluated the increase of type I error when maxim izing over two or more genetic models, and we compared the power of MMLS to detect linkage, in a number of complex modes of inheritance, with analysis assuming the true model. In the present study, we compare MMLS and NPL dir ectly. We simulated 100 data sets with 20 families each, using 26 generatin g models: (1) 4 intermediate models (penetrance of heterozygote between tha t of the two homozygotes); (2) 6 two-locus additive models; and (3) 16 two- locus heterogeneity models (admixture alpha = 1.0, .7, .5, and .3; alpha = 1.0 replicates simple Mendelian models). For LOD scores, we assumed dominan t and recessive inheritance with 50% penetrance. We took the higher of the two maximum LOD scores and subtracted 0.3 to correct for multiple tests (MM LS-C). We compared expected maximum LOD scores and power, using MMLS-C and NPL as well as the true model. Since NPL uses only the affected family memb ers, we also performed an affecteds-only analysis using MMLS-C. The MMLS-C was both uniformly more powerful than NPL for most cases we examined, excep t when linkage information was low, and close to the results for the true m odel under locus heterogeneity. We still found better power for the MMLS-C compared with NPL in affecteds-only analysis. The results show that use of two simple modes of inheritance at a fixed penetrance can have more power t han NPL when the trait mode of inheritance is complex and when there is het erogeneity in the data set.