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Whole-genome variance components linkage analysis using single-nucleotide polymorphisms versus microsatellites on quantitative traits of derived phenotypes from factor analysis of electroencephalogram waves.

Yi Yu·Yan Meng·Qianli Ma·John Farrell·Lindsay A Farrer·Marsha A Wilcox
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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.

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Chromosome MappingElectroencephalographyFactor Analysis, StatisticalGenome, HumanHumansMicrosatellite RepeatsPhenotypePolymorphism, Single NucleotideQuantitative Trait, HeritableComparative StudyJournal Article

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Yi Yu, Yan Meng, Qianli Ma, John Farrell, Lindsay A Farrer, Marsha A Wilcox. 2005-12-30. Whole-genome variance components linkage analysis using single-nucleotide polymorphisms versus microsatellites on quantitative traits of derived phenotypes from factor analysis of electroencephalogram waves.. https://doi.org/10.1186/1471-2156-6-s1-s15

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