Identification of Risk Pathways and Functional Modules for Coronary Artery Disease Based on Genome-wide SNP Data

Coronary artery disease （CAD） is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism （SNP） profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium （WTCCC） SNP datasets of CAD and control samples were used to assess the joint effect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene-gene interactions involved in these susceptible pathways with their protein protein interaction （PPI） knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer＇s disease, non-alcoholic fatty liver disease, and Huntington＇s disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer＇s disease. These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer＇s disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases.

Pathway-based Analysis Tools for Complex Diseases: A Review

Genetic studies are traditionally based on single-gene analysis. The use of these analyses can pose tremendous challenges for elucidating complicated genetic interplays involved in complex human diseases. Modern pathway-based analysis provides a technique, which allows a comprehen- sive understanding of the molecular mechanisms underlying complex diseases. Extensive studies uti- lizing the methods and applications for pathway-based analysis have significantly advanced our capacity to explore large-scale omics data, which has rapidly accumulated in biomedical fields. This article is a comprehensive review of the pathway-based analysis methods the powerful methods with the potential to uncover the biological depths of the complex diseases. The general concepts and procedures for the pathway-based analysis methods are introduced and then, a comprehensive review of the major approaches for this analysis is presented. In addition, a list of available path- way-based analysis software and databases is provided. Finally, future directions and challenges for the methodological development and applications of pathway-based analysis techniques are dis- cussed. This review will provide a useful guide to dissect complex diseases.

Pathway-based Analysis of the Hidden Genetic Heterogeneities in Cancers

Many cancers apparently showing similar phenotypes are actually distinct at the molecular level,leading to very different responses to the same treatment.It has been recently demonstrated that pathway-based approaches are robust and reliable for genetic analysis of cancers.Nevertheless,it remains unclear whether such function-based approaches are useful in deciphering molecular heterogeneities in cancers.Therefore,we aimed to test this possibility in the present study.First,we used a NCI60 dataset to validate the ability of pathways to correctly partition samples.Next,we applied the proposed method to identify the hidden subtypes in diffuse large B-cell lymphoma （DLBCL）.Finally,the clinical significance of the identified subtypes was verified using survival analysis.For the NCI60 dataset,we achieved highly accurate partitions that best fit the clinical cancer phenotypes.Subsequently,for a DLBCL dataset,we identified three hidden subtypes that showed very different 10-year overall survival rates （90％,46％ and 20％） and were highly significantly （P =0.008） correlated with the clinical survival rate.This study demonstrated that the pathwaybased approach is promising for unveiling genetic heterogeneities in complex human diseases.