Genome-wide association studies are popular for identifying genetic variants, such as single nucleotide polymorphisms, which are associated with disease susceptibility. Statistical tests are commonly performed for an assumed mode of inheritance in the recessive, additive, or dominant genetic model. This approach tends to result in inadequate power when the employed model is deviated from the underlying genetic model. To gain power and avoid the restrictive assumption about the mode of inheritance, we propose using generalized linear models to allow specifications of the three common genetic models. We make an order-restricted assumption that the effect of the heterozygous genotype falls between the effects of the two homozygous genotypes. We adopt a score-based resampling procedure to approximate the null distributions of the maximum of the likelihood ratio statistics. Simulation results show that our methods provide good control of the Type I error rate and achieve adequate power when compared to the power of the true genetic model. The new methods are applied to a case-control study in associating a candidate genetic variant to the susceptibility of Parkinson's disease.