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This article is part of the supplement: Genetic Analysis Workshop 14: Microsatellite and single-nucleotide polymorphism

Open Access Proceedings

Investigation of altering single-nucleotide polymorphism density on the power to detect trait loci and frequency of false positive in nonparametric linkage analyses of qualitative traits

Alison P Klein12*, Ya-Yu Tsai3, Priya Duggal1, Elizabeth M Gillanders1, Michael Barnhart3, Rasika A Mathias1, Ian P Dusenberry1, Amy Turiff1, Peter S Chines4, Janet Goldstein3, Robert Wojciechowski1, Wayne Hening5, Elizabeth W Pugh3 and Joan E Bailey-Wilson1

Author Affiliations

1 Inherited Disease Research Branch, NHGRI/NIH, Baltimore, MD, USA

2 Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA

3 CIDR, Johns Hopkins Medical School, Baltimore, MD, USA

4 Genome Technology Branch, NHGRI/NIH, Bethesda, MD, USA

5 Department of Neurology, UMDNJ-RW Johnson Medical School, New Brunswick, NJ, USA

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BMC Genetics 2005, 6(Suppl 1):S20  doi:10.1186/1471-2156-6-S1-S20

Published: 30 December 2005


Genome-wide linkage analysis using microsatellite markers has been successful in the identification of numerous Mendelian and complex disease loci. The recent availability of high-density single-nucleotide polymorphism (SNP) maps provides a potentially more powerful option. Using the simulated and Collaborative Study on the Genetics of Alcoholism (COGA) datasets from the Genetics Analysis Workshop 14 (GAW14), we examined how altering the density of SNP marker sets impacted the overall information content, the power to detect trait loci, and the number of false positive results. For the simulated data we used SNP maps with density of 0.3 cM, 1 cM, 2 cM, and 3 cM. For the COGA data we combined the marker sets from Illumina and Affymetrix to create a map with average density of 0.25 cM and then, using a sub-sample of these markers, created maps with density of 0.3 cM, 0.6 cM, 1 cM, 2 cM, and 3 cM. For each marker set, multipoint linkage analysis using MERLIN was performed for both dominant and recessive traits derived from marker loci. Our results showed that information content increased with increased map density. For the homogeneous, completely penetrant traits we created, there was only a modest difference in ability to detect trait loci. Additionally, as map density increased there was only a slight increase in the number of false positive results when there was linkage disequilibrium (LD) between markers. The presence of LD between markers may have led to an increased number of false positive regions but no clear relationship between regions of high LD and locations of false positive linkage signals was observed.