Simulation studies were undertaken with POPGEN, a new population simulation program, to explore strategies for detecting loci underlying rare and common disorders in a small population that has been partially isolated for 10 generations. Haplotype-sharing analysis (HSA) and non-parametric linkage analysis (NPL) were applied to the simulated haplotype and pedigree data for 100 cases, 100 controls, and an average of 28 multiplex pedigrees from cases' families, for a 2-5 cM map of markers. When identity by descent (IBD) status was known (using unique founder marker allele designations assigned during simulation), a linkage disequilibrium (LD) signal could be detected under disease-generating models predicting relative risk to sibs of 11.8 (high-RR) or 2.67 (mod-RR). Detection was more difficult when marker alleles were down-coded to resemble microsatellites (heterozygosities 0.75-0.80). False-positive peaks on nondisease chromosomes were uncommon. NPL analysis was more powerful than HSA at this marker density using down-coded alleles and assuming availability of all affected relatives. LD mapping of common disorders is likely to require denser maps of highly polymorphic markers to approximate full IBD information. LD and linkage mapping provide independent information, and strategies that combine these two methods could be useful in studies of small isolated populations.