By contrast, GWPA maximally utilizes the available genomic data. As a result, this approach can more readily detect unexpected relationships, including those across diseases operating in different body systems [9]. However, GWPA is computationally intensive, requiring more stringent corrections for multiple comparisons and making procedures like imputation more challenging. While strategies to reduce the dimensionality of genome-wide data for pathway analysis are in active development [10, 11], they will need to be evaluated further ahead of widespread use. Finally, GWPA benefits from systematic follow-up to deal with the often high overlap of genes across multiple pathways and to evaluate results in view of prior knowledge.
