# Exhaustive Variant Interaction Analysis Using Multifactor Dimensionality Reduction
## 4 Discussion  
Even though some studies have approached the analysis of variant interaction and its relation with complex disorders, it still represents a challenge. This is a consequence of the vast computational power needed to perform an extensive search across all the possible interactions at a genome-wide level. Thanks to technological advances, the combination of HPC methodologies with low-cost methods, such as the ones used in our study, is beginning to allow the analysis of the simplest interaction case: pairwise interactions. However, these low-cost methods have diverse limitations that can result in low statistical power for detecting significant pairs in real genomic datasets [5]. In this study, we confirmed that the use of more powerful methods, such as MDR, is key to detecting these significant pairwise interactions [51; 52]. However, even after developing an optimized HPC architecture with some of the state-of-the-art technologies, processing a whole genomic dataset is still out of the picture, thus limiting the analysis to a reduced dataset. For this reason, in this study, we ensured some degree of association between the pairs and the disease using a chi-square method to filter and reduce the input dataset (\(p\)-value \(<1\times 10^{-6}\)). Importantly, in contrast with other studies based on stricter filters [21; 53], and despite the reduction of the dataset to a feasible size, our proposed \(p\)-value threshold facilitated the inclusion in the study of potentially significant pairs. Our implementation of the MDR to detect pairwise variant interactions was implemented in a Singularity container, allowing the easy reproduction of the methodology in any environment. It is a plug-and-play framework, does not need the installation of any additional software other than Singularity itself, and can run on any operating system (OS) without root access. In comparison with traditional virtual machine environments, it also reduces the number of resources needed, since no OS image is needed [32]. While the use of a multi-node setup is encouraged to reduce the computational time, the model can also run in a single-node environment. Although it can be argued that other possible lines of work or hardware implementations may reduce the computational time, such as the use of GPUs or FPGAs [54], these technologies are not typically available and may restrict the method's access to other research groups. However, some interesting future work could focus on optimizing the method by further exploring different technologies. In terms of applicability, the use of our MDR approach on a subset of 1,883,192 pairs has enhanced the discovery of 104 pairs of variants associated with type 2 diabetes (T2D). Importantly, 93% of these associations correspond to already known T2D independent signals, and, according to previous research, 2 of the 104 pairs have some functional connection with the disease [45; 46; 47; 48; 49; 50]. These findings suggest gene expression and gene pathways as one of the putative underlying molecular mechanisms to mediate the link between these pairs and T2D and, therefore, possibly enhance the development of the disease. However, for the 7% of novel associations with T2D found in this study, further experimental validation will be necessary to confirm the relations between these pairs and their association with the disease as well as to improve the understanding of disease pathophysiology. In addition to this, further efforts will be necessary to translate the resultsinto clinical practice--involving, for example, the integration of the outcomes with current predictors and even the use of other cohorts to ensure their generalization across different populations. The advances in that direction will pave the way towards the early detection of the disease and the generation of new prevention and treatment protocols.