| As more and more dense, yet cost-effective, genetic maps become increasingly available, the focus of linkage analysis is shifting from testing for linkage signals to sufficiently localizing putative disease loci before fine mapping begins. Currently, there exists only a limited number of methods that provide confidence regions for the locations of trait loci. Among them is the confidence set inference (CSI) procedure based on the mean IBD sharing statistic for data from affected sib-pair studies described by Lin (2002), that deduces such regions with known lower bound on their coverage. Although this method has many attractive features, including avoidance of multiplicity adjustment for the number of markers scanned, its formulation poses some restrictions that limit its usefulness on practical applications. First, it assumes that all markers are 100% polymorphic, so that the IBD state at each of them is inferred unequivocally, an assumption rarely met in reality. Second, when the genetic map available is sparse, it tends to produce intervals that overcover the trait locus. Finally, its application requires knowledge of the IBD sharing distribution at the trait locus by an affected sib-pair. These probabilities are estimated using population disease characteristics that can be obtained through epidemiological studies with reasonable accuracy. However, there is a number of issues that renders this method of estimating the IBD distribution impractical.; We propose several extensions that address some of the limitations of the CSI approach. First, we extend it to accommodate markers with incomplete polymorphism, thereby increasing its practical value. Next, we modify it so that it tests each location on the genome for its possibility to be the trait locus. This way, we obtain regions with known exact coverage probability, rather than placing a lower bound on it. Finally, a two-step application of the CSI approach promises to avoid using population disease characteristics, circumventing the issues associated with them. Through extensive simulations, theoretical results, and applications to real data we demonstrate that the new CSI versions are indeed effective tools for localizing genes, with increased capability, when compared to currently employed approaches. |
