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

collective behavior of sets of variants by testing the association of each set instead of individual variants with the disease. Such an analysis typically

involves a list of unphased multiple-locus genotypes with

potentially sparse frequencies in cases and controls.

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

haplotype is labeled as a risk haplotype. Unfortunately, the in-silico reconstruction of haplotypes might produce a proportion of

false haplotypes which hamper the detection of rare but true

haplotypes. Here, to address the issue, we propose an alternative approach: In Stage 1, we cluster genotypes instead of inferred haplotypes and estimate the

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

previous stage.

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

The performance of the proposed procedure is assessed by

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.