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We applied three approaches for the identification of polymorphisms explaining the linkage evidence to the Genetic Analysis Workshop 14 simulated data: 1) the genotype-IBD sharing test (GIST); 2) an approach suggested by Horikawa and colleagues; and 3) the homozygote sharing test (HST). These tests were compared with a family-based association test. Two linked regions with highest nonparametric linkage scores were selected to apply these methods. In the first region, Horikawa's method identified the most SNPs within the region containing the disease susceptibility locus, while HST performed best in the second region. However, Horikawa's method also had the most type I errors. These methods show potential as additional tools to complement family-based association tests for the identification of disease susceptibility variants. Background Linkage analysis tends to identify broad regions of the genome that contain one or several disease susceptibility genes. However, going from a linkage peak to the actual functional polymorphisms is a daunting task. Methods that rely on linkage disequilibrium (LD), such as the transmission disequilibrium test (TDT), usually have a much better resolution for complex trait mapping. There has been recent interest in the literature for developing methods to identify polymorphisms that may be responsible for a linkage peak observed in a region. Here we apply two methods conditional on offspring genotypes [1,2] and one conditional on parental genotypes [3] to the Genetic Analysis Workshop (GAW14) simulated data for the identification of polymorphisms explaining the linkage evidence. The results are contrasted with the familybased association method implemented in TRANSMIT [4]. Methods To identify regions of the genome harboring susceptibility genes to Kofendred Personality Disorder (KPD), we performed nonparametric linkage (NPL) analysis, as implemented in GENEHUNTER [5], for a single replicate selected at random (replicate 71) for each population separately and for all 10 chromosomes provided. We selected the two regions with highest NPL scores (Karangar (KA) population on chromosome 9 and Danacaa (DA) population on chromosome 1), and requested the genotypes of additional single nucleotide polymorphisms (SNPs) located under these two linkage peaks. We then applied three methods, described briefly below, to identify polymorphisms that explain a linkage peak. The analyses were performed without knowledge of the true results. Horikawa method To assess whether a SNP is associated with the linkage evidence, Horikawa et al. [1] suggested computing the linkfrom Genetic Analysis Workshop 14: Microsatellite and single-nucleotide polymorphism Noordwijkerhout, The Netherlands, 7-10 September 2004 Published: 30 December 2005 BMC Genetics 2005, 6(Suppl 1):S88 doi:10.1186/1471-2156-6-S1-S88 Genetic Analysis Workshop 14: Microsatellite and single-nucleotide polymorphism Joan E Bailey-Wilson, Laura Almasy, Mariza de Andrade, Julia Bailey, Heike Bickeböller, Heather J Cordell, E Warwick Daw, Lynn Goldin, Ellen L Goode, Courtney GrayMcGuire, Wayne H ning, ail Jarvik, Brion S Maher, Nancy Mendell, Andrew D Paterson, John Rice, Glen Satten, Brian Suar z, Veronica Vieland, Marsha Wilcox, Heping Zhang, Andre s Ziegler and Jean W MacCluer Proceedings Page 1 of 6 (page number not for citation purposes) BMC Genetics 2005, 6:S88 Page 2 of 6 (page number not for citation purposes) Single SNP results for KA population on chromosome 9 (top) and DA population on chromosome 1 (bottom) Figure 1 Single SNP results for KA population on chromosome 9 (top) and DA population on chromosome 1 (bottom). The vertical dotted lines specify the haplotype region (HR). Significant SNPs are above the horizontal dotted line (-log100.05). -l o g 1 0 (p .v a lu e )