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As genetic maps become more highly dense, the ability to sufficiently localize putative disease loci becomes an achievable goal. This has prompted an increased interest in methods for constructing confidence intervals for the location of variants that contribute to a trait. Such intervals are important because, by reducing the number of candidate loci, they can help in the design of cost-effective and time-efficient follow-up studies. We introduce a new approach that can be used in whole-genome scans to obtain a confidence set of loci that contribute at least a predetermined percentage  h  to the overall genetic variation of a quantitative phenotype. The method is developed in the framework of generalized linear mixed models and can accommodate families of arbitrary size and structure. We apply our method to the Genetic Analysis Workshop 17 simulated data where we scan chromosomes 6, 15, 20, 21, and 22 to uncover loci regulating the simulated phenotype Q2. For the analyses we had prior knowledge of the simulation model used to generate the phenotype.

Confidence set of putative quantitative trait loci in whole genome scans with application to the Genetic Analysis Workshop 17 simulated data