Human Genetics

, Volume 133, Issue 2, pp 125–138 | Cite as

Network-assisted analysis to prioritize GWAS results: principles, methods and perspectives

  • Peilin Jia
  • Zhongming ZhaoEmail author
Review Paper


Genome-wide association studies (GWAS) have rapidly become a powerful tool in genetic studies of complex diseases and traits. Traditionally, single marker-based tests have been used prevalently in GWAS and have uncovered tens of thousands of disease-associated SNPs. Network-assisted analysis (NAA) of GWAS data is an emerging area in which network-related approaches are developed and utilized to perform advanced analyses of GWAS data in order to study various human diseases or traits. Progress has been made in both methodology development and applications of NAA in GWAS data, and it has already been demonstrated that NAA results may enhance our interpretation and prioritization of candidate genes and markers. Inspired by the strong interest in and high demand for advanced GWAS data analysis, in this review article, we discuss the methodologies and strategies that have been reported for the NAA of GWAS data. Many NAA approaches search for subnetworks and assess the combined effects of multiple genes participating in the resultant subnetworks through a gene set analysis. With no restriction to pre-defined canonical pathways, NAA has the advantage of defining subnetworks with the guidance of the GWAS data under investigation. In addition, some NAA methods prioritize genes from GWAS data based on their interconnections in the reference network. Here, we summarize NAA applications to various diseases and discuss the available options and potential caveats related to their practical usage. Additionally, we provide perspectives regarding this rapidly growing research area.


Obstructive Sleep Apnea Differentially Express Node Weight GWAS Data Gene Prioritization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was partially supported by National Institutes of Health grants (R01LM011177, R21HG006037, R03CA167695, and R03DE022093) and a 2010 NARSAD Young Investigator Award (to P.J.). We thank Ms. Rebecca H. Posey and Dr. Qingguo Wang for their valuable proofreading assistance.

Conflict of interest

The authors declare that they have no competing interests.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Biomedical InformaticsVanderbilt University School of MedicineNashvilleUSA
  2. 2.Department of PsychiatryVanderbilt University School of MedicineNashvilleUSA
  3. 3.Department of Cancer BiologyVanderbilt University School of MedicineNashvilleUSA

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