Volume 6 Supplement 1
A study of genetic association with electrophysiological measures related to alcoholism: GAW14 data
© Yuan et al; licensee BioMed Central Ltd 2005
Published: 30 December 2005
Recently, alcohol-related traits have been shown to have a genetic component. Here, we study the association of specific genetic measures in one of the three sets of electrophysiological measures in families with alcoholism distributed as part of the Genetic Analysis Workshop 14 data, the NTTH (non-target case of Visual Oddball experiment for 4 electrode placements) phenotypes: ntth1, ntth2, ntth3, and ntth4. We focused on the analysis of the 786 Affymetrix markers on chromosome 4. Our desire was to find at least a partial answer to the question of whether ntth1, ntth2, ntth3, and ntth4 are separately or jointly genetically controlled, so we studied the principal components that explain most of the covariation of the four quantitative traits. The first principal component, which explains 70% of the covariation, showed association but not genetic linkage to two markers: tsc0272102 and tsc0560854. On the other hand, ntth1 appeared to be the trait driving the variation in the second principal component, which showed association and genetic linkage at markers in four regions: tsc0045058, tsc1213381, tsc0055068, and tsc0051777 at map distances 53.26, 85.42, 89.31, and 172.86, respectively. These results show that the partial answer to our starting question for this brief analysis is that the NTTH phenotypes are not jointly genetically controlled. The component ntth1 displays marked genetic linkage.
Recently, evidence has been found to relate alcoholism to genetic factors [1–4]. The Collaborative Study on the Genetics of Alcoholism (COGA) is a program to study this phenomenon extensively. For the Genetic Analysis Workshop 14 (GAW14), an expanded dataset was released for analysis. It contains multiple phenotypes and genome-wide scans. We chose to study genetic association of electrophysiological measures related to alcoholism focusing on the NTTH phenotypes and the 786 Affymetrix single-nucleotide polymorphisms (SNPs) on chromosome 4. This chromosome has been shown to be involved in NTTH phenotypes in some previous studies.
There are four NTTH phenotypes: ntth1, ntth2, ntth3, and ntth4. For the four correlated traits a natural question is, are they mediated by the same set of genes, or, is each separately genetically controlled? Here we attempt to find a partial answer to these questions.
In view of our desire to determine whether ntth1, ntth2, ntth3, and ntth4 are separately or jointly genetically controlled, we shall study the principal components that explain most of the covariation of the four quantitative traits. We shall then analyze the promising components separately.
For the association and linkage analyses, we used our Genetic Epidemiology Models (GEMs) package, which has routines for the regressive models for quantitative traits [5, 6]. Typically for this problem, one may perform a linkage analysis to pinpoint the highly spurious region, but this is computationally intensive and time consuming. In this dataset, the number of SNPs is large. For chromosome 4 alone there are 786 SNPs. We did a two-stage analysis. The first stage is an association analysis in which we regressed the trait on age, sex, and the SNPs, one at a time, across all the 786 SNPs on chromosome 4. Strong statistical association between the phenotypes and the SNPs provided us the basis to select the phenotypes/SNPs for the next stage of analysis. In the second stage, a formal linkage analysis was performed on the SNPs selected from the first stage.
The proportion of the total variance explained by the first, second, third and fourth components are: 0.698, 0.195, 0.084, and 0.024, respectively.
Factor loadings for first two principal components
0.338ntth1 + 0.515ntth2 + 0.569ntth3 + 0.545ntth4.
Instead of analyzing the second principal component, which, explains 20% of the covariation, we analyzed ntth1, which is the driving factor. The third and fourth principal components account for little covariation so they were not analyzed. However, we also performed univariate linkage analysis on ntth2, ntth3, and ntth4 for comparison.
The association analysis results for ntth1 alone are displayed in Figure 1B. We see ten SNPs significant at the 10-8 level. This is a far stronger result than those using the principal component combinations. Although Figures 1A and 1B show many peaks, the remarkable difference is that the markers around position 300 are prominent in ntth1 but not in the first principal component.
Linkage results on chromosome 4 for the first principal component of ntth
Linkage results on chromosome 4 for ntth1
Linkage results on chromosome 4 for ntth2-ntth4
Our analysis of the four NTTH phenotypes, although brief, is revealing. The first principal component, which explains 70% of the covariation, showed association but not linkage to two markers: tsc0272102 and tsc0560854. On the other hand, ntth1, which was the trait driving the variation in the second principal component, showed association and linkage at markers in four regions: tsc0045058, tsc1213381, tsc0055068, and tsc0051777 at map distances 53.26, 85.42, 89.31, and 172.86 respectively.
Collaborative Study on the Genetics of Alcoholism
Genetic Analysis Workshop 14
The research was supported in part by public health research grants from the National Institutes of Health (AG16996, AA014643), and National Center for Research Resources (2G12RR003048).
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