Power to detect linkage based on multiple sets of data in the presence of locus heterogeneity: Comparative evaluation of model-based linkage methods for affected sib pair data

The development of rigorous methods for evaluating the overall strength of evidence for genetic linkage based on multiple sets of data is becoming inc reasingly important in connection with genomic screens for complex disorder s. We consider here what happens when we attempt to increase power to detec t linkage by pooling multiple independently collected sets of families unde r conditions of variable levels of locus heterogeneity across samples. We s how that power can be substantially reduced in pooled samples when compared to the most informative constituent subsamples considered alone, in spite of the increased sample size afforded by pooling. We demonstrate that for a ffected sib pair data, a simple adaptation of the lod score (which we call the compound lod), which allows for intersample admixture differences can a fford appreciably higher power than the ordinary heterogeneity led; and als o, that a statistic we have proposed elsewhere, the posterior probability o f linkage, performs at least as well as the compound lod while having consi derable computational advantages. The companion paper (this issue, pp 217-2 25) shows further that in application to multiple data sets, familiar model -free methods are in some sense equivalent to ordinary lod scores based on data pooling, and that they therefore will also suffer dramatic losses in p ower for pooled data in the presence of locus heterogeneity and other compl icating factors. Copyright (C) 2001 S. Karger AG, Basel.