Abstract
The rapid increase in genetic dataset volume has demanded extensive adoption of biological knowledge to reduce the computational complexity, and the biological pathway is one well-known source of such knowledge. In this regard, we have introduced a novel statistical method that enables the pathway-based association study of large-scale genetic dataset-namely, PHARAOH. However, researcher-level application of the PHARAOH method has been limited by a lack of generally used file formats and the absence of various quality control options that are essential to practical analysis. In order to overcome these limitations, we introduce our integration of the PHARAOH method into our recently developed all-in-one workbench. The proposed new PHARAOH program not only supports various de facto standard genetic data formats but also provides many quality control measures and filters based on those measures. We expect that our updated PHARAOH provides advanced accessibility of the pathway-level analysis of large-scale genetic datasets to researchers.
