A network approach to prioritizing susceptibility genes for genome‐wide association studies

Abstract

The heritability of complex diseases including cancer is often attributed to multiple interacting genetic alterations. Such a non‐linear, non‐additive gene–gene interaction effect, that is, epistasis, renders univariable analysis methods ineffective for genome‐wide association studies. In recent years, network science has seen increasing applications in modeling epistasis to characterize the complex relationships between a large number of genetic variations and the phenotypic outcome. In this study, by constructing a statistical epistasis network of colorectal cancer (CRC), we proposed to use multiple network measures to prioritize genes that influence the disease risk of CRC through synergistic interaction effects. We computed and analyzed several global and local properties of the large CRC epistasis network. We utilized topological properties of network vertices such as the edge strength, vertex centrality, and occurrence at different graphlets to identify genes that may be of potential biological relevance to CRC. We found 512 top‐ranked single‐nucleotide polymorphisms, among which COL22A1, RGS7, WWOX, and CELF2 were the four susceptibility genes prioritized by all described metrics as the most influential on CRC.

Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci

“…Studies using benchmarking comparisons with several models offer a form of standardization for selection, contributing to research transparency which is crucial for work justifying investment in functional study. Recent studies are more frequently incorporating benchmarking comparison showing the development of robust methodology in this field (Isakov et al, 2017;Kafaie et al, 2019;Vitsios and Petrovski, 2019).…”

“…These results were also supported by comparison with a combined framework using all models in prioritization, the stacking classifier, ensuring the highest reliability in the chosen classifier for each disease (Vitsios and Petrovski, 2019). Kafaie et al (2019) aimed to prioritize genes associated with colorectal cancer comparing various models (SVM, random forest, logistic regression with stochastic gradient descent, and K−nearest neighbors). They found that logistic regression was the highest performing ML model -emphasizing that a classification problem may require simpler solutions.…”

“…However, within benchmarking studies, SVM has not shown itself to be a top-performing model. For example, Vitsios and Petrovski (2019) found it had the lowest AUC (0.83, only slightly lower than the top-performing random forest at 0.85) of their seven models, while Kafaie et al (2019) found SVM performed better than random forest yet worse than logistic regression. The varying performance of SVM also highlights the importance of input data, as Kafaie et al (2019) were one of the only studies to focus on comparing feature selection methods as well as models.…”

“…For example, Vitsios and Petrovski (2019) found it had the lowest AUC (0.83, only slightly lower than the top-performing random forest at 0.85) of their seven models, while Kafaie et al (2019) found SVM performed better than random forest yet worse than logistic regression. The varying performance of SVM also highlights the importance of input data, as Kafaie et al (2019) were one of the only studies to focus on comparing feature selection methods as well as models. Kafaie et al (2019) found SVM performed well given certain features, whilst in comparison logistic regression had a more stable high performance regardless of external selection, emphasizing the value of logistic regression's internal feature selection via regularization.…”

“…The varying performance of SVM also highlights the importance of input data, as Kafaie et al (2019) were one of the only studies to focus on comparing feature selection methods as well as models. Kafaie et al (2019) found SVM performed well given certain features, whilst in comparison logistic regression had a more stable high performance regardless of external selection, emphasizing the value of logistic regression's internal feature selection via regularization. Deep learning has also been explored for prioritization, this method can increase sensitivity in larger datasets due to the methods ability to incrementally capture abstract representations of high-level information.…”