  Genome-wide association studies (GWASs) aim to detect genetic risk factors
for complex human diseases by identifying disease-associated single-nucleotide
polymorphisms (SNPs). The traditional SNP-wise approach along with multiple
testing adjustment is over-conservative and lack of power in many GWASs. In
this article, we proposed a model-based clustering method that transforms the
challenging high-dimension-small-sample-size problem to
low-dimension-large-sample-size problem and borrows information across SNPs by
grouping SNPs into three clusters. We pre-specify the patterns of clusters by
minor allele frequencies of SNPs between cases and controls, and enforce the
patterns with prior distributions. In the simulation studies our proposed novel
model outperform traditional SNP-wise approach by showing better controls of
false discovery rate (FDR) and higher sensitivity. We re-analyzed two real
studies to identifying SNPs associated with severe bortezomib-induced
peripheral neuropathy (BiPN) in patients with multiple myeloma (MM). The
original analysis in the literature failed to identify SNPs after FDR
adjustment. Our proposed method not only detected the reported SNPs after FDR
adjustment but also discovered a novel BiPN-associated SNP rs4351714 that has
been reported to be related to MM in another study.
