Recent advancements of the next-generation sequencing technology have enabled the direct sequencing of rare genetic variants in both case and control individuals. Although there have been a few statistical methods for uncovering potential associations between multiple rare variants and human inherited diseases, most of these methods require computational approaches to filter out non-functional variants for the purpose of maximising the statistical power. To tackle this problem, we formulate the detection of genetic variants that are associated with a specific type of disease from the perspective of one-class novelty learning. We focus on a typical type of genetic variants called Single Amino Acid Polymorphisms (SAAPs), and we take advantages of a feature selection mechanism and two one-class learning methods to prioritise candidate SAAPs. Systematic validation demonstrates that the proposed model is effective in recovering disease-associated SAAPs.