A recent approach for gene mapping based on confidence set inference (CSI) promises several advantages, including avoidance of corrections for multiple tests, availability of confidence intervals with known statistical properties, and sufficient localizations of disease genes. This paper proposes an extended CSI procedure that can handle markers with incomplete polymorphism, thereby increasing the applicability of the set of CSI methods in practical situations. Simulation studies show that the new procedure retains the main advantages of the original CSI. Although it generally requires more data to achieve a similar power, this increase is moderate for markers with 80% heterozygosity or higher. We also investigate the effects of relative risk estimates and disease models. Our analyses show that perturbation from actual relative risks or multilocus disease models generally leads to reduction in power or inflation in type I error, as expected. Nevertheless, for certain classes of two-locus disease models, CSI can still perform well, with reasonably high actual coverage probabilities for at least one of the disease loci. Application of CSI to the data provided by the Genetic Analysis Workshop 13 yields encouraging results, as they compare favorably to those obtained from GENEHUNTER using its NPL sib-pair method.
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