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

Content Provider	IEEE Xplore Digital Library
Author	Peilin Jia Zhongming Zhao
Copyright Year	2011
Description	Author affiliation: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA (Peilin Jia; Zhongming Zhao)
Abstract	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.
Starting Page	131
Ending Page	134
File Size	449450
Page Count	4
File Format	PDF
ISBN	9781467304917
ISSN	21503001
e-ISBN	9781467304900
e-ISBN	9781467304894
DOI	10.1109/GENSiPS.2011.6169462
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis dense module search GWAS Genomics Humans schizophrenia dmGWAS Diseases network Proteins gene set enrichment analysis Bioinformatics Cancer
Content Type	Text
Resource Type	Article

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