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This article is part of the supplement: Genetic Analysis Workshop 13: Analysis of Longitudinal Family Data for Complex Diseases and Related Risk Factors

Open Access Proceedings

Detecting susceptibility genes in case-control studies using set association

Sung Kim1, Kui Zhang2 and Fengzhu Sun1*

Author Affiliations

1 Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, California, USA

2 Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, USA

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BMC Genetics 2003, 4(Suppl 1):S9  doi:10.1186/1471-2156-4-S1-S9

Published: 31 December 2003


Complex diseases are generally caused by intricate interactions of multiple genes and environmental factors. Most available linkage and association methods are developed to identify individual susceptibility genes assuming a simple disease model blind to any possible gene - gene and gene - environmental interactions. We used a set association method that uses single-nucleotide polymorphism markers to locate genetic variation responsible for complex diseases in which multiple genes are involved. Here we extended the set association method from bi-allelic to multiallelic markers. In addition, we studied the type I error rates and power for both approaches using simulations based on the coalescent process. Both bi-allelic set association (BSA) and multiallelic set association (MSA) tests have the correct type I error rates. In addition, BSA and MSA can have more power than individual marker analysis when multiple genes are involved in a complex disease. We applied the MSA approach to the simulated data sets from Genetic Analysis Workshop 13. High cholesterol level was used as the definitive phenotype for a disease. MSA failed to detect markers with significant linkage disequilibrium with genes responsible for cholesterol level. This is due to the wide spacing between the markers and the lack of association between the marker loci and the simulated phenotype.