Journal
NUCLEIC ACIDS RESEARCH
Volume 46, Issue 10, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/nar/gky175
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Funding
- National Research Foundation (NRF) of Korea, Genomics Program [2014M3C9A3068555]
- Korean government, Basic Science Research Program (NRF) [2017R1E1A1A030 70107, 2015R1A1A3A04001269]
- NRF [2014M3C9A3068555]
- National Research Foundation of Korea [2015R1A1A3A04001269] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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 subnet-works for further studies.
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