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Nam, Dougu
Statistical Genomics Lab
Research Interests
  • Gene network, pathway analysis, biclustering, disease classification

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Efficient pathway enrichment and network analysis of GWAS summary data using GSA-SNP2

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Title
Efficient pathway enrichment and network analysis of GWAS summary data using GSA-SNP2
Author
Yoon, SoraNguyen, Hai C. T.Yoo, Yun JKim, JinhwanBaik, BukyungKim, SounkouKim, JinKim, SangsooNam, Dougu
Issue Date
201803
Publisher
OXFORD UNIV PRESS
Citation
NUCLEIC ACIDS RESEARCH, v., no., pp. -
Abstract
Pathway-based analysis in genome-wide association study (GWAS) is being widely used to uncover novel multi-genic functional associations. Many of these pathway-based methods have been used to test the enrichment of the associated genes in the pathways, but exhibited low powers and were highly affected by free parameters. We present the novel method and software GSA-SNP2 for pathway enrichment analysis of GWAS P-value data. GSA-SNP2 provides high power, decent type I error control and fast computation by incorporating the random set model and SNP-count adjusted gene score. In a comparative study using simulated and real GWAS data, GSA-SNP2 exhibited high power and best prioritized gold standard positive pathways compared with six existing enrichment-based methods and two self-contained methods (alternative pathway analysis approach). Based on these results, the difference between pathway analysis approaches was investigated and the effects of the gene correlation structures on the pathway enrichment analysis were also discussed. In addition, GSA-SNP2 is able to visualize protein interaction networks within and across the significant pathways so that the user can prioritize the core subnetworks for further studies. GSA-SNP2 is freely available at https://sourceforge.net/projects/gsasnp2.
URI
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DOI
http://dx.doi.org/10.1093/nar/gky175
ISSN
0305-1048
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SLS_Journal Papers
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