Abstract
Pathway analysis is a powerful method for data analysis in genomics, most often applied to gene expression analysis. It is also promising for single-nucleotide polymorphism (SNP) data analysis, such as genome-wide association study data, because it allows the interpretation of variants with respect to the biological processes in which the affected genes and proteins are involved. Such analyses support an interactive evaluation of the possible effects of variations on function, regulation or interaction of gene products. Current pathway analysis software often does not support data visualization of variants in pathways as an alternate method to interpret genetic association results, and specific statistical methods for pathway analysis of SNP data are not combined with these visualization features. In this review, we first describe the visualization options of the tools that were identified by a literature review, in order to provide insight for improvements in this developing field. Tool evaluation was performed using a computational epistatic dataset of gene–gene interactions for obesity risk. Next, we report the necessity to include in these tools statistical methods designed for the pathway-based analysis with SNP data, expressly aiming to define features for more comprehensive pathway-based analysis tools. We conclude by recognizing that pathway analysis of genetic variations data requires a sophisticated combination of the most useful and informative visual aspects of the various tools evaluated.
Highlights
Pathway Analysis for Genome-Wide Association Study DataToday, pathway analysis is routine with software or web services that accept and analyze different omics data, transcriptomics, proteomics with protein–protein interactions, and metabolomics
Pathway content provides the biological processes in which genomewide association studies (GWAS)-identified genes are known to be involved and shows other genes related by common function that may not pass GWAS significance thresholds
As indicated in Section “Results” and listed in Supplementary Table 1, some accepted statistical methods used for pathway analysis of GWAS data have been described
Summary
Pathway Analysis for Genome-Wide Association Study DataToday, pathway analysis is routine with software or web services that accept and analyze different omics data, transcriptomics, proteomics with protein–protein interactions, and metabolomics. A decade ago genetic variation data, such as single-nucleotide polymorphism (SNP) originating from analyses of array-based genomewide association studies (GWAS), began to be incorporated into pathway analysis (Wang et al, 2007). The method was applied to other types of studies involving SNPs such as: epigenome-wide association study (EWAS) (Shimada-Sugimoto et al, 2017) or sequencing-based GWAS (Guodong and Degui, 2013). Application of pathway analysis to SNP data is a valid approach to meet this challenge for different reasons: first, because of the polygenic nature of complex diseases, such an approach holds the promise to contextualize the SNP data better and to suggest novel interpretations of the results based on prior knowledge of genes and pathways (Wang et al, 2010). Examining the cumulative effects of numerous variants and visualize them at the pathway level, can empower detection of genetic risk factors for complex diseases (Manolio, 2013; Mooney and Wilmot, 2015)
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call