The haplotype 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. It starts with inferring haplotypes from genotypes followed by a haplotype co-classification and marginal screening for disease-associated haplotypes. Unfortunately, phasing uncertainty may have a strong effects on the haplotype co-classification and therefore on the accuracy of predicting risk haplotypes. Here, to address the issue, we propose an alternative approach: In Stage 1, we select potential risk genotypes instead of co-classification of the inferred haplotypes. In Stage 2, we infer risk haplotypes from the genotypes inferred from the previous stage. 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. This research was supported by a grant from the Iraq Government.