title: 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.
abstract: The development of rigorous methods for evaluating the overall strength of evidence for genetic linkage based on multiple sets of data is becoming increasingly important in connection with genomic screens for complex disorders. We consider here what happens when we attempt to increase power to detect linkage by pooling multiple independently collected sets of families under conditions of variable levels of locus heterogeneity across samples. We show 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 affected sib pair data, a simple adaptation of the lod score (which we call the compound lod), which allows for intersample admixture differences can afford appreciably higher power than the ordinary heterogeneity lod; and also, that a statistic we have proposed elsewhere, the posterior probability of linkage, performs at least as well as the compound lod while having considerable computational advantages. The companion paper (this issue, pp 217-225) 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 power for pooled data in the presence of locus heterogeneity and other complicating factors.
authors: Department of Biostatistics, University of Iowa, Iowa City, Iowa 52242, USA. veronica-vieland@uiowa.edu
authors: V J
authors: VJ
authors: Vieland
authors: K
authors: K
authors: Wang
authors: J
authors: J
authors: Huang
mesh_terms: D002874:Chromosome Mapping; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D018740:Genetic Heterogeneity; D008040:Genetic Linkage; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009679:Nuclear Family