Search for Risk Haplotype Segments with GWAS Data by Use of Finite Mixture Models

The region-based association analysis has been proposed to capture the

involves a list of unphased multiple-locus genotypes with

To tackle the problem of the sparse distribution, a two-stage approach was proposed in literature: In the first stage, haplotypes are computationally inferred from genotypes, followed by a haplotype co-classification. In the second stage, the association analysis is performed on the inferred haplotype groups. If a haplotype is unevenly distributed between the case and control samples, this

false haplotypes which hamper the detection of rare but true

risk genotypes based on a finite mixture model. In Stage 2, we infer risk haplotypes from risk genotypes inferred from the

To estimate the finite mixture model, we propose an EM algorithm with a novel data partition-based initialization.

simulation studies and a real data analysis. Compared to the existing

multiple Z-test procedure, we find that the power of genome-wide association studies can be increased by using the proposed procedure.