This article is part of the supplement: Genetic Analysis Workshop 14: Microsatellite and single-nucleotide polymorphism
A genome-wide linkage analysis of alcoholism on microsatellite and single-nucleotide polymorphism data, using alcohol dependence phenotypes and electroencephalogram measures
1 Genomics Collaborations Department, Affymetrix, Santa Clara, CA, 95051, USA
2 Department of Algorithms and Data Analysis, Affymetrix, Santa Clara, CA, 95051, USA
3 Bioinformatics Department, Affymetrix, Emeryville, CA, 94608, USA
BMC Genetics 2005, 6(Suppl 1):S17 doi:10.1186/1471-2156-6-S1-S17Published: 30 December 2005
The Collaborative Study on the Genetics of Alcoholism (COGA) is a large-scale family study designed to identify genes that affect the risk for alcoholism and alcohol-related phenotypes. We performed genome-wide linkage analyses on the COGA data made available to participants in the Genetic Analysis Workshop 14 (GAW 14). The dataset comprised 1,350 participants from 143 families. The samples were analyzed on three technologies: microsatellites spaced at 10 cM, Affymetrix GeneChip® Human Mapping 10 K Array (HMA10K) and Illumina SNP-based Linkage III Panel. We used ALDX1 and ALDX2, the COGA definitions of alcohol dependence, as well as electrophysiological measures TTTH1 and ECB21 to detect alcoholism susceptibility loci. Many chromosomal regions were found to be significant for each of the phenotypes at a p-value of 0.05. The most significant region for ALDX1 is on chromosome 7, with a maximum LOD score of 2.25 for Affymetrix SNPs, 1.97 for Illumina SNPs, and 1.72 for microsatellites. The same regions on chromosome 7 (96–106 cM) and 10 (149–176 cM) were found to be significant for both ALDX1 and ALDX2. A region on chromosome 7 (112–153 cM) and a region on chromosome 6 (169–185 cM) were identified as the most significant regions for TTTH1 and ECB21, respectively. We also performed linkage analysis on denser maps of markers by combining the SNPs datasets from Affymetrix and Illumina. Adding the microsatellite data to the combined SNP dataset improved the results only marginally. The results indicated that SNPs outperform microsatellites with the densest marker sets performing the best.