Identifying disease polymorphisms from case-control genetic association data.

In case-control association studies, it is typical to observe several associated polymorphisms in a gene region. Often the most significantly associated polymorphism is considered to be the disease polymorphism; however, it is not clear whether it is the disease polymorphism or there is more than one disease polymorphism in the gene region. Currently, there is no method that can handle these problems based on the linkage disequilibrium (LD) relationship between polymorphisms. To distinguish real disease polymorphisms from markers in LD, a method that can detect disease polymorphisms in a gene region has been developed. Relying on the LD between polymorphisms in controls, the proposed method utilizes model-based likelihood ratio tests to find disease polymorphisms. This method shows reliable Type I and Type II error rates when sample sizes are large enough, and works better with re-sequenced data. Applying this method to fine mapping using re-sequencing or dense genotyping data would provide important information regarding the genetic architecture of complex traits.