Pathway analysis of genomic data: concepts, methods, and prospects for future development

Abstract

Genome-wide data sets are increasingly being used to identify biological pathways and networks underlying complex diseases. In particular, analyzing genomic data through sets defined by functional pathways offers the potential of greater power for discovery and natural connections to biological mechanisms. With the burgeoning availability of next-generation sequencing, this is an opportune moment to revisit strategies for pathway-based analysis of genomic data. Here, we synthesize relevant concepts and extant methodologies to guide investigators in study design and execution. We also highlight ongoing challenges and proposed solutions. As relevant analytical strategies mature, pathways and networks will be ideally placed to integrate data from diverse -omics sources in order to harness the extensive, rich information related to disease and treatment mechanisms.

Bayesian nonparametric clustering and association studies for candidate SNP observations

“…Many statistical methods have been proposed based on those considerations, such as regularized regression models like lasso or ridge regression [1,2,3,4,5], gene-set enrichment analysis [4,6,7,8], pathway [9], and network analysis [10]. Those methods are helpful to analyze large-scale markers and their corresponding interactions in the same pathway or network, when the analytic genomic region is pre-defined.…”

The Complex Interaction of Mitochondrial Genetics and Mitochondrial Pathways in Psychiatric Disease

“…As such, GWAS looking for single-gene variants may struggle to detect small contributions from multiple genes. To address this limitation, a relatively new technique of analysis of highly complex diseases with a polygenic etiology is gene set analysis [57, 58]. Instead of looking for variation in particular SNPs or genes, in gene set analysis geneticists look for changes in biological pathway activity.…”

Compiling the Complement of Genes Implicated in Coronary Artery Disease

“…The statistical ways of handling pathway-based analyses, including the opportunities and the pitfalls, are beyond the scope of this editorial, but are outlined elsewhere. 22 Additional studies in model systems are also warranted to investigate the functional significance of genetic variants (including those in the complement pathway). In summary, the work by Xu et al 7 highlights the importance of the many complementary approaches for identifying the complement of genes and nongenic regions implicated in the pathogenesis of CAD.…”