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One of the most challenging points in studying human common complex diseases is to search for both strong and weak susceptibility single-nucleotide polymorphisms (SNPs) and identify forms of genetic disease models. Currently, a number of methods have been proposed for this purpose. Many of them have not been validated through applications into various genome datasets, so their abilities are not clear in real practice. In this paper, we present a novel SNP association study method based on probability theory, called ProbSNP. The method firstly detects SNPs by evaluating their joint probabilities in combining with disease status and selects those with the lowest joint probabilities as susceptibility ones, and then identifies some forms of genetic disease models through testing multiple-locus interactions among the selected SNPs. The joint probabilities of combined SNPs are estimated by establishing Gaussian distribution probability density functions, in which the related parameters (i.e., mean value and standard deviation) are evaluated based on allele and haplotype frequencies. Finally, we test and validate the method using various genome datasets. We find that ProbSNP has shown remarkable success in the applications to both simulated genome data and real genome-wide data.