Many methods have been proposed to detect disease association with sequence variants in candidate genomic regions. However, the literature lacks a comparison of these methods in terms of their ability to localize or fine-map the causal risk variants lying within the candidate region. We extend a previous comparison of the detection abilities of these methods to a comparison of their localization abilities. In contrast to previous work, cases and controls are sampled from a diploid (i.e., two-parent) rather than a haploid (one-parent) population. We simulated 200 sequencing datasets of a 2-million base-pair candidate genomic region for 50 cases and 50 controls. Risk variants were in a middle subregion. We present a case study of one simulated dataset to illustrate the methods and describe simulation results to score which method best localizes the risk subregion. Our results lend support to the potential of genealogy-based methods for genetic fine-mapping of disease.