Abstract
Alcohol dependence is a serious public health problem. We studied data from families participating in the Collaborative Study on the Genetics of Alcoholism (COGA) and made available to participants in the Genetic Analysis Workshop 14 (GAW14) in order to search for genes predisposing to alcohol dependence. Using factor analysis, we identified four factors (F1, F2, F3, F4) related to the electroencephalogram traits. We conducted variance components linkage analysis with each of the factors. Our results using the Affymetrix single-nucleotide polymorphism dataset showed significant evidence for a novel linkage of F3 (factor comprised of the three midline channel EEG measures from the target case of the Visual Oddball experiment ttdt2, 3, 4) to chromosome 18 (LOD = 3.45). This finding was confirmed by analyses of the microsatellite data (LOD = 2.73) and Illumina SNP data (LOD = 3.30). We also demonstrated that, in a sample like the COGA data, a dense single-nucleotide polymorphism map provides better linkage signals than low-resolution microsatellite map with quantitative traits.
Highlights
Alcoholism is a complex disorder involving multiple genes likely interacting with one another and environmental factors
EGG data defined by different experimental designs were available to participants in Genetic Analysis Workshop 14 (GAW14)
Two alcoholism classifications were provided in the Collaborative Study on the Genetics of Alcoholism (COGA) data
Summary
Alcoholism is a complex disorder involving multiple genes likely interacting with one another and environmental factors. Quantitative endophenotypes, such as electroencephalogram (EEG) measurements, have been suggested as better indices of alcoholism susceptibility than the customary dichotomous affection status [1,2]. EGG data defined by different experimental designs were available to participants in Genetic Analysis Workshop 14 (GAW14). Since EEG phenotypes are correlated, it is likely that a smaller number of underlying dimensions contribute to the variance of these EEG phenotypes. Our aim was to identify the underlying factors for the EEG phenotypes and search for genes influencing the derived factors and increasing the risk of alcohol dependence
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