By Topic

Network-assisted causal gene detection in genome-wide association studies: an improved module search algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Peilin Jia ; Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA ; Zhongming Zhao

The recent success of genome-wide association (GWA) studies has greatly expanded our understanding of many complex diseases by delivering previously unknown loci and genes. A large number of GWAS datasets have already been made available, with more being generated. To explore the underlying moderate and weak signals, we recently developed a network-based dense module search (DMS) method for identification of disease candidate genes from GWAS datasets, leveraging on the joint effect of multiple genes. DMS is designed to dynamically search for the best nodes in a step-wise fashion and, thus, could overcome the limitation of pre-defined gene sets. Here, we propose an improved version of DMS, the topologically-adjusted DMS, to facilitate the analysis of complex diseases. Building on the previous version of DMS, we improved the randomization process by taking into account the topological character, aiming to adjust the bias potentially caused by high-degree nodes in the whole network. We demonstrated the topologically-adjusted DMS algorithm in a GWAS dataset for schizophrenia. We found the improved DMS strategy could effectively identify candidate genes while reducing the burden of high-degree nodes. In our evaluation, we found more candidate genes identified by the topologically-adjusted DMS algorithm have been reported in the previous association studies, suggesting this new algorithm has better performance than the unweighted DMS algorithm. Finally, our functional analysis of the top module genes revealed that they are enriched in immune-related pathways.

Published in:

2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

Date of Conference:

4-6 Dec. 2011