A novel SNP-set analytical method without distinguishing common variants or rare variants in genome-wide association study

Abstract

Single nucleotide polymorphism (SNP)-set analysis in genome-wide association studies (GWASs) has become a hot topic. Most existing SNP-set analystic methods are designed and work well according to the different natures of common or rare variants and associated diseases. But the information that the disease associated variants are common or rare cannot be gained in advance. Therefore, in this research, we proposed a new and powerful weighted function method without distinguishing common or rare variants to select tagging SNP-set. We applied our selection method to sequence kernel association test (SKAT) and compared the power with some existing methods. The simulation results showed that our method has higher power not only than SKAT in un-weighted case, but also than SKAT in other weighted functions. Moreover, the power is improved significantly when the minor allele frequency (MAF) of causal SNP is relatively small.