Abstract
Complex disease mapping usually involves a combination of linkage and association techniques. Linkage analysis can scan the entire genome in a few hundred tests. Association tests may involve an even greater number of tests. However, association tests can localize the susceptibility genes more accurately. Using a recently developed combined linkage and association strategy, we analyzed a subset of the Collaborative Study on the Genetics of Alcoholism (COGA) data for the Genetic Analysis Workshop 14 (GAW14). In this analysis, we first employed linkage analysis based on frailty models that take into account age of onset information to establish which regions along the chromosome are likely to harbor disease susceptibility genes for alcohol dependence. Second, we used an association analysis by exploiting linkage disequilibrium to narrow down the peak regions. We also compare the methods with mean identity-by-descent tests and transmission/disequilibrium tests that do not use age of onset information.
Highlights
The Collaborative Study on the Genetics of Alcoholism (COGA) is a large, multi-site genetic study to identify susceptibility genes for alcohol dependence and related phenotypes [1]
We analyzed the COGA data using genetic methods based on additive genetic gamma frailty models to account for age of onset or covariate information [3,4]
Alcohol dependence is more common in males than females
Summary
The Collaborative Study on the Genetics of Alcoholism (COGA) is a large, multi-site genetic study to identify susceptibility genes for alcohol dependence and related phenotypes [1]. The COGA data have been analyzed using nonparametric sib-pair methods with the two-point linkage program and multipoint linkage program for affected sib pairs [2]. Age of onset data are often collected in studies designed to map a complex disease. If age at onset is genetically mediated, it may carry useful linkage information. Genetic analysis that incorporates variable age of onset may improve the ability to map genes for complex diseases. We analyzed the COGA data using genetic methods based on additive genetic gamma frailty models to account for age of onset or covariate information [3,4]
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