Nonlinear tests for genome-wide association studies

As millions of single-nucleotide polymorphisms (SNPs) have been identified and high throughput genotyping technologies have been rapidly developed, large-scale genome-wide association studies are soon within reach. However, since a genome-wide association study involves a large number of SNPs and therefore it is nearly impossible to ensure a genome-wide significance level of 0.05 using the available statistics, although the multiple test problems can be alleviated, but not sufficiently, by the use of tagging SNPs. One strategy to circumvent the multiple test problem associated with genome-wide association tests is to develop novel test statistics with high power. In this report, we introduce several nonlinear tests, which are based on nonlinear transformation of allele or haplotype frequencies. We investigate the power of the nonlinear test statistics and demonstrate that under certain conditions, some nonlinear test statistics have much higher power than the standard test statistic. Type I error rates of the nonlinear tests are validated using simulation studies. We also show that a class of similarity measure-based test statistics is based on the quadratic function of allele or haplotype frequencies, thus they belong to nonlinear tests. To evaluate their performance, the nonlinear test statistics are also applied to three real datasets. Our study shows that nonlinear test statistics have great potential in association studies of complex diseases.

Key Words: association studies, complex diseases, genetic studies, linkage disequilibrium, nonlinear test

Online ISS 1091-6490
Copyright 2008 by the Genetics Society of America
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